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We can apply the Pearson r correlation when our monthly electricity watt consumption goes higher, our monthly electricity bill will follow as well. Just like what the Pearson r meaning, it is a measure to determine the relationship (instead of difference) between two quantitative variables (interval/ratio) and the degree to which the two variables coincide with one another.

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When we want to determine if there is a difference between two variables, we can apply the Spearman correlation. For example, data was obtained from a group of students to explore the relationship between their shoes and size.

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When we want to determine if there is a difference between two variables, we can apply the Spearman correlation. For example, data was obtained from a group of students to explore the relationship between their shoes and size.

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BSA 1C
Pearson's correlation is utilized when you have two quantitative variables and you wish to see if there is a linear relationship between those variables. Your research hypothesis would represent that by stating that one score affects the other in a certain way. The correlation is affected by the size and sign of the r.

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As a result, the -0.1792 correlation value suggests a very weak negative linear association. You may also use this to see if your employees are capable of handling the tasks or positions that have been allotted to them. In this example, I utilize the pearson correlation coefficient to find out the relationship between the employee's competency rating and the amount of seminars given.

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Employee performance is one of the most important measures highlighted by top management, and employees are more concerned about their own productivity and conscious of the fast obsolescence of knowledge and skills in their unpredictable environment. Employees will become more aligned for career progress if they are properly trained and developed, according to the literature—career potential boosts personal drive. Employee discomfort and conflict can result from poor performance reviews caused by insufficient work training. Although there is no direct correlation between training and work satisfaction in the literature.

For clarity, there is no significant relationship between the proficiency rating of an employee and the number of seminars he/she attended.

https://journals.sagepub.com/doi/10.1177/2158244011433338

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The Spearman Rank is a method of determining the correlation between the two variables. It can also be used to sort the results of a student's regular intervals evaluation.

Based on their results, Spearman rank will identify and arrange the students' ranks.

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Prado Lady Neneth T.BSED SCIENCE A1
The relationship between the distance  traveled by a jogger and the calories they burned.When a jogger runs it can lower his calories. Because running is a type of exercise that reduces calories.

https://www.bing.com/search?&pc=HWMB&form=HWMBFC&q=there+is+any+significant+relationship+between+the+jogger+and+the+calories+they+burned

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Yes there is a significant relationship between jogging and burning calories. Since jogging is one of an example of physical activity or exercise." When losing weight, more physical activity increases the number of calories your body uses for energy or “burns off.” The burning of calories through physical activity, combined with reducing the number of calories you eat, creates a “calorie deficit” that results in weight loss.
Most weight loss occurs because of decreased caloric intake. However, evidence shows the only way to maintain weight loss is to be engaged in regular physical activity.
Most importantly, physical activity reduces risks of cardiovascular disease and diabetes beyond that produced by weight reduction alone." The far the distance that jogger do, it increase the level of burning calories.

https://www.cdc.gov/healthyweight/physical_activity/index.html

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Fajardo, Erika S.
BSA 1A

To test if there was a significant association between an employee's proficiency rating and the number of seminars they attended, I used the Spearman Rank correlation. Because I believe the Spearman Rank correlation is the best to use.  And I got a score of - 0.18, suggesting that there is no correlation between the number of seminars attended by an employee and their proficiency rating.

According to Statistics solutions, a correlation coefficient of 0 indicates that the variables have no correlation. On the other hand, correlation coefficients such as Spearman and Pearson are based on a linear relationship between variables.

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In this example, I use pearson correlation coefficient to determine the relation between the number of seminars attended by the employee and the proficiency rating.  As the result, the correlation coefficient is -0.20 indicates a very week negative linear relationship. And by using this  you can also monitor whether your employees can handle the tasks or position assigned to them.

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I used Spearman rank correlation to determine how strong the relationship between the seminars attended by the employee and their proficiency rating, because the proficiency rank can be categorized as ordinal data. I came up with the answer of -0.1792 and by this result we can conclude that the association between the two variables would not be considered statistically significant, we can say that it has a very weak negative linear relationship between the seminars attended by the employee and their proficiency rating.

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I used Spearman rank correlation to determine how strong or weak the relationship between the variable a)Work proficiency rating b)numbers of seminars attended for the reason that the variables fall into the ordinals scale. The computed result I’ve got is -0.1792 which indicates clearly that the two variables have a very weak linear relationship.

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BSED  Sci 1A
Simple linear regression is the most applicable data analysis for the given example. Simple linear regression is used to know the relation of an dependent and independent variable, distance (km) as the independent variable and calories as the dependent variable. The longer the distance of the runner/jogger the higher the calories will burned.

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For me, the most appropriate to use is the pearson r or the pearson correlation because the given variables are interval or ratio scales. Accordingto what I’ve read so far and examples I’ve seen, pearson r is the mkst appropriate for measurements taken from interval scale. Using the said method, I came up with the answer 0.9846 and it means that the linear relationship between the two variables is strong.

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Daguinod, Maverick Charles E. BSED Science 1A

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Daguinod, Maverick Charles E. BSED Science 1A

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I believe the most appropriate to use is .the spearman rank correlation. The value i obtained using spearman is 0.50357 or 0.5 It is between +41 - +.60 meaning the two variables has moderately posotive correlation. The significant relation between the variables is not the strong or weak, it's because the number of seminars attended by a worker do affects his proficiency rating. As he gains more knowledge and experience while attending seminars

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Bataan Dyhan O.
BAC-1A
In this case, I used the following strategy.To determine the Spearman Rank

Attendance at Seminars as well as a Work Proficiency Score I used the Spearman Rank.

due to the fact that the information provided is fictitious ordinal data is a type of data that isn't grouped into categories.

type of statistics data with numbers In my opinion, as a result of computation,

is -0.1792, and it is possible to conclude

that the information that

Seminar Attendance and Attendance at Seminars Work Proficiency and Attention

The score is quite low.The relationship is a negative linear one

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There is a significant relationship between the distance traveled by a jogger and the calories they burn. According to Healthline, "Additional studies have found that setting distance-based goals instead of time-based goals may be more effective for realizing these benefits. For example, working up to walking 10 miles per day could be better than working up to 2 hours of walking per day in terms of progressive goal setting." It means that jogging for a very large distance is a better option to burn more calories. The larger the distance, the larger the amount of calories we burn.There is a significant relationship between the distance traveled by a jogger and the calories they burn. According to Healthline, "Additional studies have found that setting distance-based goals instead of time-based goals may be more effective for realizing these benefits. For example, working up to walking 10 miles per day could be better than working up to 2 hours of walking per day in terms of progressive goal setting." It means that jogging for a very large distance is a better option to burn more calories. The larger the distance, the larger the amount of calories we burn.

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Spearman correlation is applicable to compute and analyze the result of the data. It should be arranged by their rank, because the data is in order form (arrange from 1-20) and to determine the relation of the grades in ESP and Math, with the help of spearman rank correlation is 0.1577 and it indicates the interpretation of the data as "very weak" positive linear relationships between the Subject ESP and Math.

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Basing upon the computed result shown for the grade of Grade 9 students in Math and ESP, which it has a very weak linear relationship that is determine with the used of Spearman rank. I can conclude that there is no significant relationship between the grades of Grade 9 students in Math and ESP, because each of this subject contains different lesson, offer different pattern to provide knowledge, and has different level of difficulties. Also, students has differ skills and comprehension in different subjects.

https://www.jstor.org/stable/1162880?fbclid=IwAR1Pahp_U6-1dhD2ddTwXeKrxmSKdolqEA7nRIHL8HJLxxrWE7Iel-iSY4s

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Bataan dyhan O.
BAC-1A

In the analysis, I applied Pearson r

to evaluate the circumstance described above the relationship between the two.

I believe base on data. the most appropriate upon it yielded a value of 0 as a result of the computation 0. 9846 with this, we can conclude that the a straight line between the distance traveled and calories consumed.

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BAC -1A

When we want to know how the season influences the income of tourist spots, we can use Pearson r in this circumstance.

Incomes will either succeed or fail.

or falls in value

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I used the pearson r for the reason i think is there is an independent and dependent variable in this situation. The calories burned up depends on the distance traveled. The value I got is between ±.60 - ±1.00 meaning there is a strong positive correlation between the distance traveled by the jogger and the calories he/she burned. There is a significant relation between the distance travelled and the calories burned up not just because of the obvious results, but also because the longer you run the more sweat you produced and sweat means your burning fat, using your energy.

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Spearman Rank  is used to measure the degree of association between two variables, it can be use on arranging the scores of the students in their periodical examination. Spearman rank will determine and arrange the rank of the students base on their scores.

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There is a significant relationship between the distance traveled by a jogger and the calories burned.

Running is a more intense activity and therefore consumes more calories per minute than walking. However,  walking   takes longer than running because it is slow to walk. Many factors affect calorie consumption in both activities (age, weight, fitness level, pace, surface ...). However, when comparing calories burned at the same distance, the most important factor is speed. When a certain running speed is reached, any further increase in calorie consumption per minute is minimized. The biggest difference in calories burned can be seen when comparing a slow walk to a km  run. if you run at high intensity, you can take advantage of the extra calories burned by the after burn effect. Suitable if you want to burn more calories in less time.

https://www.runtastic.com/blog/en/calories-burned-walking-vs-running/

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Do You Burn More Calories Running or Walking the Same Distance?

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Running vs. walking the same distance - what burns more calories? Compare the weight loss benefits.

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Ntf-elcac and Brgy beneficiaries

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Infrastructure and debt.
More infrastructure possible the debt will be added

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As a college student,sleep is essential to maintain a good health and be productive when it come to school activities.The more I sleep the more productive I get.
Pearson r can be applied because sleep and productivity are two connected variables which shows the independent and dependent one.

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In products for example is the food we purchase and intake, the price and the nutrient content; Price as the dependent variable because the value of its ingredients affects this variable, while the nutrient content as the independent variable.

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Spearman correlation, it is used to evaluate relationships involving ordinal variables. Example situation: One employee is completing test exercise that relates to the number of months he/she have been employed.

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The two related characteristics are for example: The music albums and the number of people who purchasing the albums. These two related characteristics, it determined the capacity or how much the album sales to gain with how many people are actually purchasing. It relates how these characteristics affects with each other. The dependent variable is the album sales while the independent variable is the number of people who purchased the album.

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Because the data is in order, it must be rated in order to evaluate the association between ESP and Math grades. The Spearman rank correlation can be used to calculate and assess the outcome of a given set of data.

According to my calculations, the result shows 0.1557, which can be said as follows. This means that the linear relationship of the given data is very weak.

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Since Pearson r, is all about the measurement of relationship or the association of two variables. There is situation that can be applied. For example, you are studying about the income of every individual according to their age and it shows how people with age of 20 to 40 years old is more capable to produce or has the opportunity to gain wealth or income than the younger or older.

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In terms of significant relationship between of Math and E.S.P. it illustrates the importance and its similarities with each other. As for my own perception Math and E.S.P subjects, they are a lot in common especially in terms of analyzing, observing, determining any situations and most importantly the problem solving. These factors are affecting the students grades. It can be referred to how much they interested to the subjects or the applied languages. According to the (Times of India, 2015) with their study of the "Importance of Math in everyday life". They stated that Mathematics is a methodical application of matter. Mathematics makes our life orderly and prevent chaos. Certain qualities that are nurtured by Mathematics are power reasoning, creativity, abstract or spatial thinking, critical thinking, problem solving ability and even effective communication skills.

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As the oldest daughter of the family, I am assigned to do the groceries and for budget for the whole month, and for me to do so, I have to choose the product by their prices (as low as possible), I have to choose wisely between them but of course i must consider its quality and its nutrient content.

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DUMARAOS,JUDIDIAH DAWN V
BAC 1A

SPEARMAN correlation is indicate in two variables which is arranged in rank order and its also use in ranking of individual students on two diff, subjects

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Pearson r is used to analyze between the relationship of two variables, and we can used pearson r when we are conducting a research. As long as the variables being evaluated are both quantitative, it should ALWAYS be utilized to test an associative hypothesis of the study.

A simple situation for this is our height, As a youngster grows older, his or her height will continue to rise. And of course, His or her development is influenced by a number of factors.

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Pearson's r is a bivariate statistical model that analyzes two variables. Pearson's correlation may always be used to test an associative research hypothesis as long as the variables being analyzed are both quantitative.

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Fajardo, Erika S.
BSA 1A

A Spearman correlation coefficient is also referred to as Spearman rank correlation or Spearman’s rho, it measures the strength and direction of association between two ranked variables.

A situation where Spearman Rank can be used as the statistical analysis in conducting other aspects of problematic use of social media such as the relationship between social media addiction and students' academic performance, anxiety, stress, disturbed sleep, self-esteem, and socialism. By using Spearman Rank, you will find out which one has a highest rank or effect of being irresponsible social media user. It's either it will affect you academic performance, your mental health, and so on.

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Fajardo, Erika S.
BSA 1A

The two related characteristics are social media addiction and depression. The social media addiction will be the independent variable and depression will be the dependent variable. These two can be analyzed using simple linear progression as social media addiction is one of the effects why humans have a depression, that means social media addiction has a positive correlation with depression. In other words, the higher the student addiction level, the greater his/her depression level is.

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Pontiga klarence Mhae M. Bsed Science 1A

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Pontiga klarence Mhae M. Bsed Science 1A

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The greater the gap between the skill required to perform a task and your actual ability to do it, the greater the lack of job satisfaction. An individual's work proficiency does not always depend on the seminars he or she has attended. Work proficiency can also be influenced by an employee's credibility, skills, and knowledge. According to the website BohatALA, when employees are satisfied with the organization, they will be more productive.I can say that there is no significant relationship between an employee's proficiency rating and the number of seminars that he/she attended because it is possible that he/she already has knowledge before entering the company, but if he/she is already skilled enough, I can say that there is a significant relationship.

https://journals.sagepub.com/doi/10.1177/2158244011433338

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Abstract

It is incumbent on training and development professionals to design, implement, and evaluate the effectiveness of their programs in reducing disputes in workplace performance. This study explores the relationships between training experiences and attitudes and attitudes about perceived job proficiency. In a sample of 237 full-time salaried/exempt and hourly/nonexempt employees from one academic institution and three businesses in the states of Maryland, Delaware, and Arizona, the author finds a direct relationship between one's positive training experiences and attitudes and one's proficiency. In this study, 86.8% of those who had updated training had the most positive attitudes toward training (γ = .293, p < .05). Furthermore, 80% of those who had negative training attitudes also had negative views on their proficiency (γ = .465, p < .000).

Any institution of higher learning or business whose goals are to survive and prosper in this present day diverse and regressed economy has found it imperative to invest in ongoing training and development to improve proficiencies in production as well as to acquire the greatest return in investment of human capital ( Knoke & Kalleberg, 1994). Although this area of training effectiveness seems paramount, and although training is an integral part of the employer-employee relationship, Knoke and Kalleberg (1994) suggest direct evidence about company training practices based on representative samples of diverse employing organizations is almost non-existent. Furthermore, several authors have suggested that training is most extensive only in establishments which operate in complex market environments ( Rowden & Conine, 2005; Sahinidis & Bouris, 2008). In addition, Rowden and Conine (2005) indicate that there is limited research on human resource development in small and midsized businesses. According to these authors, most people believe that small businesses do little, if any, development of their workers. Moreover, Rowden and Conine cite Training Magazine, which annually conducts research on the training industry in the United States, as not even attempting to contact businesses with fewer than 100 employees. In addition, in their annual research sample, only 16% consisted of companies between 100 and 500 workers. In general, little human resource development occurs in small businesses (Hill & Stewart, 1999). In summary, a slowly growing number of authors are currently doing more research in the areas of training and development and its effects on employees that we have not seen in past literature.

Months of research failed to locate objective evidence in support of connecting training and attitude, its impact on job proficiencies, and the employee's perception that lack of meaningful training directly affects proficiencies and attitude. The objective of this study is to gather and provide empirical data for training relationships that are not currently available.

Cheng and Ho (2001) also discuss the importance of training and its impact on job performance:

While employee performance is one of the crucial measures emphasized by the top management, employees are more concerned about their own productivity and are increasingly aware of the accelerated obsolescence of knowledge and skills in their turbulent environment. As the literature suggests, by effectively training and developing employees, they will become more aligned for career growth-career potential enhances personal motivation. (p. 22)

To illustrate Cheng and Ho's (2001) position, Constantino and Merchant (1996) comment that "both training and education are necessary components for a successful conflict management system " (p. 22). One might clearly imagine how failure to provide training and education by the organization could result in conflict between employer and employee. Such conflict could potentially lead to any number of complicated scenarios, including but not limited to formal complaints by the employee which eventually become actual law suits against the organization, all of which could cost the industry time, energy, and money. When universities or businesses withhold opportunities for training and development, they also fail to demonstrate an understanding of how to identify organizational conflict. According to these same authors, all organizations should have a conflict management system in place ( Constantino & Merchant, 1996). When conflict is manifested in the organization by clusters of employees who are dissatisfied, this group dissatisfaction state of mind can result in frenzied chaos, dissatisfaction, grievances, and turmoil in the organization. Again, having a conflict management system in place would potentially be able to short circuit such a disruptive process. Clearly, it is extremely important that employees benefit from ongoing employer provided training. "One of the most frequently encountered human capital development interventions is training" ( Campbell & Kuncel, 2001, p. 278). To enhance job performance, training skills and behaviors have to be transferred to the workplace, maintained over time, and generalized across contexts ( Holton & Baldwin, 2000). Consequently, specific job training is a complicated matter and has been the focus of much of the training literature ( Chiaburu & Teklab, 2005). More specifically, in addition to the exact nature of job training, training is seen as relevant to fostering a positive relationship between learning satisfaction and the effectiveness of applied learning ( Liu, 2002; Wang, 2001). As an aside, even though authors have suggested that training programs are vital to organizations ( Knoke & Kalleberg, 1994; Liu, 2002; Wang, 2001), training programs are often the first to go ( Young, 2008). This trend to cut training programs during poor economic times seems shortsighted, if in fact, training does affect job proficiency and relieves workplace conflict. The definition of results and empirical data included in this study demonstrates that employee do, in fact, perceive that training directly effects job proficiency is an indicator which many employers interested in sound business management and growth in the marketplace should subscribe to initiating and maintaining offerings of soft skill (leadership, effective communications, and coaching) and technical proficiency training.

Moreover, several authors have also written about the importance of staff development. Both formal and informal training opportunities are thought to provide a forum for the development of talent. When talent is fostered and nurtured, competitive advantages in performance are untainted ( Becker & Gerhard, 1996; Bowling, 2007; Davenport, 2006; Peters & Waterman, 1982). Furthermore, the organizational commitment or "the relative strength of an individual's identification and involvement in a particular organization" ( Pool & Pool, 2007, p. 353) depends on effective training and development programs. According to these authors, organizations demonstrating keen insight make provisions for satisfying the training needs of their current workforce. Cheng and Ho (2001) indicate that adequate training produces marked improvements in employee communication and proficiency of performances as well as extending retention time. Moreover, when programs target communication skills with coworkers, there are significant increases in profit as well as a greater number of reported positive working relationships that are formed. Employees with good communication skills gather more information concerning procedures and technologies related to job performances, thus assuming greater accountability and subsequent responsibility, both of which effect improve proficiency ( Adams, 1989; Gordon, 1977). Also, training and education have been shown to have a significant positive effect on job involvement, job satisfaction, and organizational commitment ( Karia & Asaari, 2006).

Similarly, Ahmad and Karia (n.d.) have emphasized the relationship between training and attitude as it relates to working with others. Employees with positive attitudes become stakeholders in the process and job accomplishment is of a higher priority. Furthermore, successful organizations achieve a partnership between workers and management. The partnership includes participation in teamwork activities and continuous learning application. The reported findings further suggest that an employee's participation in decision making and problem solving develops organizational trust ( Anschutz, 1995).

Corporations and businesses need to grow and innovate continuously, pursue sustained development, and cope with rapid changes in their external environments as well as increasingly competitive international markets. Hence, organizations need to strengthen or expand the knowledge base, skills, and abilities of their employees. For this purpose, education and training must be incorporated into a systematic and formal system if the goals of employees and corporation are to be attained ( Liu, 2002; McGehee & Thayer, 1961).

Training, as defined in the present study "is the planned intervention that is designed to enhance the determinants of individual job performance" ( Chiaburu &Teklab, 2005). Training is related to the skills an employee must acquire to improve the probability of achieving the organization's overall business and academic goals and objectives. Positive training offered to employees may assist with reduction of anxiety or frustration, which most employees have experienced on more than one occasion during their employment careers ( Cheng & Ho, 2001).

According to Tsai, Yen, Huang, and Huang (2007), employees who are committed to learning showed a higher level of job satisfaction that has a positive effect on their performance. Moreover, Locke defined job satisfaction as a pleasurable or positive emotional state resulting from a positive appraisal of the job or job experiences ( Locke, 1976). The literature suggests that commitment results from adequate training and development for successful job completion and an increase in job performance ( Tsai et al., 2007).

In addition, the larger the gap between the skills required to perform a task and the actual skills available for performing a task, the greater the lack of job satisfaction and the greater the increase in employee turnover within the organization. Conversely then, not having the skills to perform a job correctly can set up employees for failure and put the business at a less-than-competitive disadvantage. The resulting high turnover would predict the need for even more training that would then have a direct impact on the bottom line of any business.

Moreover, poor performance reviews due to inadequate job training can produce employee dissatisfaction and conflict. Although there is no direct link in the literature between training and job satisfaction, Rowden and Shamsuddin (2000) and Rowden and Conine (2005) argue that the most thoroughly trained employees will better satisfy the needs of their customers and employees.

In summary, although the literature strongly suggests a direct relationship between job training and job performance as well as job training and employee attitudes, there is little empirical support for this suggestion. Clearly, there is a need to provide such empirical support. The purpose of the present study was to explore the relationship between training and development and its perceived impact on employee attitudes and perceived job performance proficiencies. The empirical data suggest a perceived gap by the employees between training attitudes and job proficiencies. This gap could then result in conflict between the employee and employer relationship. The present study elaborates and extends previous research by exploring participant attitudes in an academic institution as well as in three businesses, one of which is a small-to-moderate size business that is usually overlooked in other research studies. The hypotheses in this study specifically address the relationship between employees with training experiences who have positive attitudes about training (training attitudes), and those positive training attitudes are perceived to improve job proficiency.

Items and Reliability of Survey Instruments Items a .b.

Demographic of Participants Exempt/Salaried and Nonexempt/Hourly

Updated Training by Training Attitudes

Training Attitudes/Proficiency Cross-Tabulation

Analysis of gender by training attitudes, work status by proficiency, and business by proficiency were computed. In Table 6, gender by training attitudes resulted in the finding that women (73.6%) had significantly greater positive training attitudes than did men (60.0%) with χ 2 = 6.519, df = 2, p < .05. The results of the work status by proficiency (see Table 7) resulted in the findings that 25.6% of nonexempt/hourly employees disagreed that they received adequate job training compared with 12.4% of the exempt/salaried employees (χ 2 = 6.310, df = 2, p < .05). The results of the business by proficiency (see Table 8) showed that the Delaware Division (71.9%) compared with Arizona Corporation (31.2%) and the Maryland Corporation (53.3%) had significantly more participants who often agreed that training provided enhanced job proficiency (χ 2 = 20.757, df = 6, p < .005).

Gamma tests assessing the strength of the association of the cross-tabulated data between the following eight comparisons were measured: updated training by training attitudes, updated training by job proficiency, effective coaching by job proficiency, meaningful coaching by job proficiency, meaningful coaching by training attitudes, solicits input by job proficiency, solicits input training attitudes cross-tabulation, and training attitudes proficiency cross-tabulations.

Table 4 shows the relationship between updated training and training attitudes of those who were fully updated on training, 86.8%, had positive training attitudes compared to only 65.06% of those who disagreed with being up-to-date on training. Gamma = .293, p < .05 reveals that there is a moderate, significant relationship between the variables.

The cross-tab of updated training by proficiency shows (see Table 9) a significant and high strength of association between updated training and proficiency. In all, 83.3% fully disagreed that they had updated training and disagreed that they are proficient, compared with the 7.7% that fully agreed to having updated training and disagreed to being proficient. This shows a significant and strong relationship between updated training and proficiency (γ = .592, p < .000).

Table 10 shows that 71.4% of those who fully agree that they had effective coaching also fully agree that they feel proficient compared with 13.0% who fully disagreed that they had effective coaching. This shows a high and significant correlation between effective coaching variables and effective coaching (γ = .619, p < .000).

The cross-tab between meaningful coaching by proficiency (see Table 11) reveals that 88.9% fully agree they had meaningful coaching and also fully agree that they felt proficient compared with 6.7% who fully disagreed that they had meaningful coaching and fully agreed that they were job proficient. This shows a very strong significant relationship between meaningful coaching and perceived job proficiency (γ = .728, p < .000).

Table 12 shows that those who fully agreed that they had meaningful coaching (94.1%) also revealed experienced positive training attitudes compared with 64.9% of those who disagreed to experiencing meaningful coaching, yet had positive training attitudes, which reveals a moderate and significant relationship ( y = .252, p < .10).

Table 13 of solicits input by proficiency demonstrates that 82.4% fully disagreed that their supervisor solicits input from them which lead to less agreement (disagree) on proficiency compared with 1.6% who fully agreed that supervisors solicited their input and disagree that it positively impacted on proficiency. This shows a significant and very strong relationship between bosses who do not solicit input and employees who do not feel proficient (γ = .845, p < .000).

The cross-tab of solicits input and training attitudes (see Table 14) reveals that 83.1% fully agreed that having supervisors solicit input effects training attitudes. Moreover, 52.9% who fully disagreed with solicits input, however, fully agreed that it impacts on training attitudes. This shows a moderate and significant relationship between employers who solicit input and employee attitude about training (γ = .344, p < .005).

The training attitudes and perceived proficiency cross-tab (see Table 5) showed that 80.0% disagreed that they feel positive about training and also disagreed that they feel proficient compared with 13.9% who fully agreed that training was positive and disagreed that they felt proficient. The relationship between training attitude and perceived proficiency showed a moderate and significant correlation (γ = .465, p < .000; see Table 5).

The final analysis analyzed the responses to the open-ended question that solicited requests for potential training topics. A total of 87 participants (22.89% of the total sample) answered the open-ended question: "I would like to have the following training offered." Common themes were soft skill (i.e., leadership, effective communication, mentoring, etc.) and technological training. Out of 87 responses, 47 participants (54%) requested more soft skill training such as leadership, effective communications, and mentoring. A total of 36 participants (41.3%) requested more technological training such as computer programming and software applications. Two participants (2.29%) responded as not having the available time for training due to budget cuts/constraints. One participant (1.14%) requested "any form of training," and 1 (1.14%) participant indicated the need for more "real-world" training. Some participants provided more than one response and others did not indicate a preference, so the percentages will not add up to 100%.

The purpose of the present study was to investigate the first hypothesis, those employees with training experiences have positive attitudes about training (training attitudes), and the second hypothesis, positive training attitudes are perceived to improve job proficiency. The results of this study support the hypotheses. The gamma calculations found strong significant associations between those employees who fully agreed that they had updated training and subsequent positive training attitudes, as well a feeling of increased job proficiency. The results also showed that those employees who fully agreed that they received effective coaching and those who fully agreed they received meaningful coaching felt they demonstrated an increase in job proficiency.

These results agree with those of Becker and Gerhard, (1996), Bowling (2007), and Davenport (2006), who also suggest that the quality of training has a lot to do with employee's feelings of importance concerning the job.

The results of the questionnaire demonstrated a strong and significant relationship between bosses. This finding also supports the literature that discusses the impact of job training on psychological variables such as motivation and commitment ( Cheng & Ho, 2001; Liu, 2002; Pool & Pool, 2007; Wang, 2001), organizational trust ( Anschutz, 1995), and willingness to go above and beyond to meet their job requirements ( Rowden & Conine, 2005).

Although this study did not directly assess organizational "conflict," it seems clear that because training is seen to be so important in forming positive attitudes that it may further lead to job proficiency, and not receiving adequate training is associated with a feeling of reduced competency, it seems very reasonable to predict that poor training could result in poor attitudes about performance, which then could lead to poor performance. This finding opens up employees to an entire set of potential consequences; one such consequence is conflict in the workplace environment. Other consequences could include poor performance review, lack of organizational trust (because the employee feels unfairly evaluated due to not receiving adequate training for job functions), and the beginning of a downhill spiral leading to possible termination and subsequent loss of resources for the organization.

Clearly, where job training is concerned, poor fits between employee and the job/organization may result in eventual performance gaps ( Harrison, 2005) emphasizing the need for conflict management systems to be in place at all levels of the organization.

Surprisingly, the research did not find significant differences in attitudes between the younger and older employees. However, the research did find significant differences between men and women with respect to overall positive training attitudes. Women, significantly more often than men, indicated positive attitudes toward training. It was not known if these results suggest that women felt that training is more beneficial because of the technical nature of current job demands being placed on women(who may not feel prepared for these demands), or if women are more supportive in general of extra educational offerings than are men. Future research may address this gender-related disparity when developing future training programs.

Although exempt/salary and the nonexempt/hourly employees fully agreed that training was related to job proficiency, when they disagreed, they significantly disagreed. Specifically, nonexempt/hourly employees disagreed more strongly that attitudes were related to job proficiency. Although an interesting finding, more interesting is the finding that both groups fully agreed at equal levels that attitudes were related to job proficiency, again supporting the original hypotheses.

The finding that the Delaware Division significantly more often agreed that attitude leads to proficiency than did the other three organizations may be explained by the fact that those employees had just completed a job training, which may have affected their position.

Interestingly, in Table 8, the highest fully agreed ratings that attitudes affect proficiency come from the university in Maryland and the Arizona Corporation. This author was told that in the Arizona Corporation, job training occurs very regularly, and for the first three probationary years, employees are expected to pass proficiency tests following each training session to stay employed with the company. An excellent study for future research might investigate whether job training requiring passing proficiency tests as a condition of employment is more valuable than training without the conditional testing criteria.

In summary, this author researched the hypotheses that adequate job training is related to positive attitudes about job proficiency and that having adequate job training is related to positive attitudes about job training. The results have supported the hypotheses. However, given the nature of the research, there are several limitations to this study.

Survey research has many problems associated with its use. Surveys are self-reported instruments and, as such, may not be completely valid or reliable. However, it can be reported that a strong internal consistency of the instrument was confirmed by using both Cronbach's alpha and Guttman's split-half coefficient. Both of these measures provide reliability information when instruments use formats that generate ordinal data.

A major limitation to the interpretation of the results has to do with the instrument (see Appendix B). A close examination of Appendix B shows the direction provided between Questions 5 and 6, "If you have not had training, please skip Questions 6-12." This directive probably should not have been included on this questionnaire because it may have eliminated all those respondents who have never been trained, leaving only respondents who have had some training, effective and ineffective, meaningful and nonmeaningful. Given that industry trains very often, and given that the return rate for the three for-profit organizations was 100%, the missing data came from the Maryland university sample. Only 130 questionnaires out of 380 were returned from the Maryland University. Omitting this directive may have extended the sample from those who had some type of training to include a category for those who had never received training, making the comparison more meaningful. Based on the present finding, one may never know how those "never having training" felt about their competence.

Moreover, it may have been better to have used a true 7-point Likert-type scale, allowing the author to make finer discriminations in the findings.

In addition, the survey could have included more useful demographic information questions as well as questions related to conflict. Specifically, respondents could have been asked if they felt they received the right amount of training, and if that having the right amount of training ever resulted in a negative performance consequences. Respondents could also have been asked questions related to how conflict is mediated in their workplace and how important it was to them to have an actual conflict management system in place in their place of work.

Even though the study could have been methodologically improved in several ways, it is still important to state that this study is one of only a few attempting empirically to answer questions related to the importance of job training. Furthermore, this study is also a first attempt at trying to assess the real importance of job training to the employee and the far-reaching complications of the ensuing conflict in our present day work environments where the employee perceives that meaningful job training is unavailable. Chris Young of Rainmaker Group, Inc. has noted that the "training and development budget is often the first to go and the last to come back" ( Young, 2008). Organizations undergoing fiscal challenges may target employee training as the first cost-cutting measure. If we follow the collective wisdom of successful businesses, conflict management system professionals, and the results of this empirical research, one must conclude that effective and ongoing training and development should be the final cost-cutting measure. Training and its positive effects on employee attitude and proficiencies create long-lasting stakeholders and could serve as the binding force for business success during trouble times.

When understanding the connection between employee attitudes about competence and potential organizational conflict, a conflict management system may be the first line of defense to assess and assist in the analysis of the fits and gaps when dealing with the organizational culture of attitude and training.

Cheng and Ho (2001) claim that as a result, "ungraded" employees look for improving their job performance as well as enhancing their future career prospects. Obviously, when employees transfer their positive learning content to their job, a win-win solution for organizations and employees can be accomplished. Therefore, when employees are provided better and more intensive training, the result is an increase in self-worth and greater job performance proficiency. If the employee's perception is one of having sufficient training and development, it may also serve as a catalyst for enhancing the employee teaming effect.

Future research could build on this study's results by measuring quantitatively the actual increases in job performance as well as the actual changes in job proficiency attitudes. This could be accomplished in a simple pretest-posttest design using a control group not having had the training as a comparison group. It would be valuable to connect perception to actual changes in job performance that can be quantified and measured. Attitude studies like this one are limited to representing what the person would be feeling should he or she be in that position, or try to recall what he or she felt when she actually was in that position. More actual behavioral data of this nature might convince upper level management to invest in employee development programs. It is important for employers to provide relevant and meaningful training for the employees, which supports and enhances employee attitude. When training is perceived by the employee to be relevant and meaningful, the results may be demonstrated through improved proficiencies.

When examining the open-ended question on the survey that asked about preferred training programs to be offered, most responders requested training on soft skills, such as leadership, values, effective communications, and so on, and technology training such as, computer literacy, computer updated software programs, and so on. These results of the open-ended question strengthen our position that employees require and desire training that will make them competitive in this highly demanding market.

The present survey was conducted to assess a relationship between attitudes about training and perceived subsequent job proficiency. The data that were collected supported our hypotheses that job training is correlated with positive feelings about job training and that positive job training predicts positive attitudes about job proficiency. Limitations to this study have been considered and suggestions for future research have been provided. Clearly, empirical research in the area of expected job training and resulted job proficiency and potential organizational conflict is only in its infancy. This study has been an important first step in this line of research.

Table

References

Ahmad, Z. A., Karia, N. (n.d.). Quality practices that pay: Empowerment and teamwork (School of Management). Malaysian Management Review.
Google Scholar

Chiaburu, D. S., Teklab, A. G. (2005). Individual and contextual influences on multiple dimensions of training effectiveness. Journal of European Industrial Training, 20, 282-290. Retrieved from http://www.jstor.org
Google Scholar

Learning to stay the course? Journal of European Industrial Training, 23, 286-299. doi:10. 1108/03090599910284669 |
Google Scholar Crossref

Locke, E. A. (1976). The nature and causes of job satisfaction. In Dunnette, M. D. (Ed.), Handbook of industrial and organizational psychology (Vol. 1, pp. 1297-1343). Chicago, IL: Rand McNally.
Google Scholar

Rowden, R. W., Conine, C. T. (2005). The impact of workplace learning on job satisfaction in small US commercial banks. Journal of Workplace Learning, 17, 216-230. doi:10.1108/13665620510597176 |
Google Scholar Crossref

Rowden, R. W., Shamsuddin, A. (2000). The relationship between workplace learning and job satisfaction in small to midsize business in Malaysia. Human Resource Development, 3, 307-322. doi:10.1080113678860050128492 |
Google Scholar Crossref

Young, C. (2008). Five tips for improving employee training and development during a recession or economic downturn, maximize possibility blog.
Google Scholar

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The Effect of Training and Development on Employee Attitude as it Relates to Training and Work Proficiency - Debra L. Truitt, 2011

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Abstract It is incumbent on training and development professionals to design, implement, and evaluate the effectiveness of their programs in reducing disputes in workplace performance. This study explores the relationships between training experiences and attitudes and attitudes about perceived job proficiency.

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Pontiga,Klarence Mhae M. Bsed-Science 1A

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Pontiga,Klarence Mhae M. Bsed-Science 1A

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The distance traveled and the calories burned have a significant relationship. The distance traveled and the calories burned have a strong relationship, according to the results of the simple linear regression. Increasing the distance of your workout, whether walking or running, provides benefits and burns more calories. Adding 10-15 minutes to a regular workout also gradually increases your walking or running time. This demonstrates that the two variables, distance jogged and calories burned, are strongly related.

https://www.runtastic.com/blog/en/calories-burned-walking-vs-running/

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Students’ conceptions and attitudes regarding math and social studies were different. Consistent with expectations, pupils characterized positive and negative experiences in math in regard to their success or ability to do the work while social studies experiences were evaluated more in terms of whether they were interesting or boring.

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For me no, According to the  www.runnerworlds.com, “The faster someone runs, the more calories they will burn per minute. However, by distance,there is a relatively steady amount of calories burned .” For example, in 30 minutes of running at 6 miles per hour (that's a 10-minute mile pace), a 155-pound person will burn 372 calories.

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pontiga,Klarence Mhae M. BESD-Science 1A

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pontiga,Klarence Mhae M. BESD-Science 1A

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In this case, I used Spearman rank because I believe it is the most appropriate to use to identify the association of the given data. And the computed value is -0.1792 to determine how strong the relationship is between the number of seminars attended by employees and their proficiency rating. As a result, we can conclude that there is a very weak negative linear relationship between employee seminar attendance and proficiency rating.

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Fajardo, Erika S.
BSA 1A

Pearson R is used when there are two quantitative variables, it calculates the effect of change in one variable when the other variable changes.

A situation where Pearson R can be used as the statistival analysis is about the relationship between social media addiction and depression. We can expect that the findings will be a neither positive or negative correlation between social media addicition and depression. As most of us, especially those young adult users who spend the most time online on social media have a higher possibility to being mentally unstable. But, there’s also a possibility that most of us have being happy because of those social media platforms as we have a chance to befriend and to communicate with other people who give us a comfort whenever we feel lonely or sad.

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Pontiga, Klarence Mhae M. BSSED-SCIENCE 1a

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Pontiga, Klarence Mhae M. BSSED-SCIENCE 1a

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According to Statistic Solutions, the Spearman rank correlation test makes no assumptions about the distribution of the data and is the appropriate correlation analysis when the variables are measured on a scale that is at least ordinal. Additionally, there is no significant relationship between Grade 9 students' Math grades and ESP. This is due to the fact that students' abilities in one subject differ from their abilities in another.
This is supported by a study that was conducted to find out what students thought about learning Math and Social Studies (Stodolsky; Salk; Glaessner, 1991). According to the findings of this study, students' performance in the two subjects differs because it is influenced by their prior experiences and perceptions of how difficult a subject area is.

https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php

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This guide will tell you when you should use Spearman's rank-order correlation to analyse your data, what assumptions you have to satisfy, how to calculate it, and how to report it. If you want to know how to run a Spearman correlation in SPSS Statistics, go to our Spearman's correlation in SPSS Statistics guide.

When should you use the Spearman's rank-order correlation?

The Spearman's rank-order correlation is the nonparametric version of the Pearson product-moment correlation. Spearman's correlation coefficient, (ρ, also signified by r s) measures the strength and direction of association between two ranked variables.

What are the assumptions of the test?

You need two variables that are either ordinal, interval or ratio (see our Types of Variable guide if you need clarification). Although you would normally hope to use a Pearson product-moment correlation on interval or ratio data, the Spearman correlation can be used when the assumptions of the Pearson correlation are markedly violated. However, Spearman's correlation determines the strength and direction of the monotonic relationship between your two variables rather than the strength and direction of the linear relationship between your two variables, which is what Pearson's correlation determines.

What is a monotonic relationship?

A monotonic relationship is a relationship that does one of the following: (1) as the value of one variable increases, so does the value of the other variable; or (2) as the value of one variable increases, the other variable value decreases. Examples of monotonic and non-monotonic relationships are presented in the diagram below:

Why is a monotonic relationship important to Spearman's correlation?

Spearman's correlation measures the strength and direction of monotonic association between two variables. Monotonicity is "less restrictive" than that of a linear relationship. For example, the middle image above shows a relationship that is monotonic, but not linear.

A monotonic relationship is not strictly an assumption of Spearman's correlation. That is, you can run a Spearman's correlation on a non-monotonic relationship to determine if there is a monotonic component to the association. However, you would normally pick a measure of association, such as Spearman's correlation, that fits the pattern of the observed data. That is, if a scatterplot shows that the relationship between your two variables looks monotonic you would run a Spearman's correlation because this will then measure the strength and direction of this monotonic relationship. On the other hand if, for example, the relationship appears linear (assessed via scatterplot) you would run a Pearson's correlation because this will measure the strength and direction of any linear relationship. You will not always be able to visually check whether you have a monotonic relationship, so in this case, you might run a Spearman's correlation anyway.

How to rank data?

In some cases your data might already be ranked, but often you will find that you need to rank the data yourself (or use SPSS Statistics to do it for you). Thankfully, ranking data is not a difficult task and is easily achieved by working through your data in a table. Let us consider the following example data regarding the marks achieved in a maths and English exam:

Marks

English

56

75

45

71

61

64

58

80

76

61

Maths

66

70

40

60

65

56

59

77

67

63

The procedure for ranking these scores is as follows:

First, create a table with four columns and label them as below:

English (mark)

Maths (mark)

Rank (English)

Rank (maths)

61
65

6.5

5

61
63

6.5

6

You need to rank the scores for maths and English separately. The score with the highest value should be labelled "1" and the lowest score should be labelled "10" (if your data set has more than 10 cases then the lowest score will be how many cases you have). Look carefully at the two individuals that scored 61 in the English exam (highlighted in bold). Notice their joint rank of 6.5. This is because when you have two identical values in the data (called a "tie"), you need to take the average of the ranks that they would have otherwise occupied. We do this because, in this example, we have no way of knowing which score should be put in rank 6 and which score should be ranked 7. Therefore, you will notice that the ranks of 6 and 7 do not exist for English. These two ranks have been averaged ((6 + 7)/2 = 6.5) and assigned to each of these "tied" scores.

What is the definition of Spearman's rank-order correlation?

There are two methods to calculate Spearman's correlation depending on whether: (1) your data does not have tied ranks or (2) your data has tied ranks. The formula for when there are no tied ranks is:

where d i = difference in paired ranks and n = number of cases. The formula to use when there are tied ranks is:

where i = paired score.

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This guide will tell you when you should use Spearman's rank-order correlation to analyse your data, what assumptions you have to satisfy, how to calculate it, and how to report it. If you want to know how to run a Spearman correlation in SPSS Statistics, go to our Spearman's correlation in SPSS Statistics guide.

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When I compute the relationship between the number of seminars attended and the employees' work proficiency rating, I get -0.18, which is a very weak negative linear regression because it falls between 0.00-0.20. I used spearman rank order correlation to solve this problem because it shows the relationship between two variables that are arranged in rank order.

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Pontiga Klarence Mhae M. BSED-SCIENCE 1A

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Pontiga Klarence Mhae M. BSED-SCIENCE 1A

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I believe it is the most applicable is Pearson r to identify the relationship of the given data in the above situation. As they are continuous data, determine the relationships between the two related variables. The computation yielded 0.9846 or 0.98 when rounded off. In this case, we can conclude that the linear relationship between distance and calories burned is very strong.is considered to be a very strong linear relationship between a jogger's distance traveled and the calories burned.

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The Spearman rank is based on the ranked values for each variable rather than the raw data. It helps us to determine which variable got the higher rank. For example, it can be used in conducting a presidential survey and the results would helps us to determine has the higher chance of winning and who is the lowest by based on the results of the survey.

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Pontiga,Klarence Mhae M. Bsed-Science 1A

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Pontiga,Klarence Mhae M. Bsed-Science 1A

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In this case, I used Spearman Rank because it shows the relationship between the two variables, which are the students' grades in ESP and Math. The data or grades, on the other hand, show that it is an ordinal data, which is a statistical type, and it is quantitative data. The result of my computation shows that I obtained the correlation coefficient of 0.17. It ranges from 0.00 to 0.20, indicating a very weak positive linear relationship.

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Pearson R can be used as statistical analysis when creating a business plan. It would help us to determine the feasibility of the product with a help of another product or services, for example, you are planning to establish a telecommunication company, since most of the people nowadays uses mobile phones , computer and other gadgets that needs an access to an internet there is a higher possibility that the business will succeed.

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Pearson's correlation can be used when working with two quantitative variables in a population. As per the research hypotheses, the variables could have a positive linear relationship, a negative linear relationship, or no linear relationship at all. Correlation cannot be used to test an attributive research hypothesis, but it can be used to test a causal hypothesis if the experiment is effectively carried out. Correlation can be used at any moment to test an associative hypothesis.

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There is no significant relationship between the two subject, while modern academics agree that we do not all learn in the same way, they also believe that our interests and preferences have a greater impact on how we recall new information than an inborn learning style.https://www.efrontlearning.com/blog/2014/12/learn-way.html

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A couple of decades ago it was inconceivable for educators to tailor their materials and teaching approach towards the individual student. The different learning styles as a concept started gaining pace ever since the term and idea behind it was first introduced in the mid 70's.

We Learn Best in Different Ways

Today, it is only too obvious that we don't all share the same learning mechanisms, and while it may be impractical to apply this to the everyday classroom environment, it has certainly influenced the direction, in which online learning is developing.

You don't need to look further than yourself and your immediate circle of friends to spot the differences in your learning styles. While you may feel comfortable reading long pages of solid material, some of your friends may find it completely indigestible. While some people remember close to everything of what they hear, others' attention wanders off as soon as they hear a narrator's monotonous drone. While some need to isolate themselves in the deepest, darkest corner of the library or office to concentrate, others need to talk through new material with a partner or a group.

The 7 Basic Styles of Learning

Supporters of the learning styles theory have been able to isolate 7 main modes of learning that supposedly ease the instruction process for students if the right approach is applied.

1. Visual (Spatial) - learners prefer to use images and spatial orientation to acquire new material.

These people respond best to images, mind maps, flow charts and other methods for visualizing new concepts.

2. Aural (Auditory and Musical) - these people are positively affected by sounds and often resort to mnemonics or background music to retain new information.

3. Verbal (Linguistic) - students are strongly reliant on words and need to listen, speak and read to digest new material.

They benefit from recording notes and lectures for later listening, read aloud and put ideas into words.

4. Physical (Kinaesthetic) - the body, movements and tactile impressions work best for people with this learning style.

Physical activities don't need to be limited to jumping and dancing, but also include drawing diagrams and writing down key information.

5. Solitary (Intrapersonal) - these learners perform better when they are left to their own devices when faced with new material.

They benefit from self-paced courses and are very focused on their internal feelings and personal associations.

6. Social (Interpersonal) - on the contrary, these people reach their peak performance in learning through group discussions, role plays and activities.

7. Logical (Mathematical) - using logical reasoning and drawing associations works best for these people.

They need to see the connections and dive deeper into the relationship between ideas.

Obviously, it is impossible to categorize one person as a strictly physical or social learner.

We definitely all share some degree of preference for how we are introduced new concepts and how we deal with retaining them.

Opposition to the Learning Styles Theory

Criticism of learning styles being the main obstacle to achieving better learning outcomes, however, is not scarce. Contemporary researchers agree that we don't all learn in the same way, but at the same time maintain that our interests and preferences have more to do with how we retain new information than an inborn learning style.

For example, if a person enjoys the piano and is interested in developing his or her skills in this area, there is a higher chance that the person will learn more effectively than others who don't share the interest. On the other hand, a student's background and abilities are also a factor in achieving better results.

Critics point out that there is no conclusive evidence suggesting that we learn better through a single method and that matching a learning style of a student to the mode of instruction improves outcomes.

In any case, the buzz around learning styles has allowed e-learning course developers to expand their approach to teaching to incorporate more instructional techniques. In the online environment it is much easier to include visual, aural or logical elements to course materials, as well as encourage both solitary and group work.

Final Thoughts

True or not, the idea that we have different learning styles may not have turned course instruction on its head, but has certainly raised the issue that more than a few teaching modes should be explored.

This comes in handy when designing a new e-learning program by bringing diversity and versatility to teaching techniques and creating richer, more memorable learning experiences.

What do you think? Do you have your own learning style? What strategies work best for you in understanding and remembering new information?
The article received a lot of comments on social media and on our blog, countering some arguments, so we decided to do an update on the blog post:
I wouldn't take what a cognitive neuroscientist has to say about learning styles as the gospel. Especially since it's an article in a popular (not scientific) magazine, and a very one-sided one at that. From 40+ years of scholarly studies on learning styles, he only links to a few papers that counter the idea.

First of all,there are several approaches and disciplines that contribute to learning science, and it's reductionistic to think that neuroscience is the be all end all in our understanding of the learning process (though, of course, many neuroscientists do).

A complex psychological, social and cultural phenomenon such as learning is not easily reduced to one discipline, however fashionable it might be.

Second, I wouldn't take recent papers at face value either. Papers in peer reviewed journals are a dime-a-dozen. Most of them, in thorough meta-studies have been found to be non-reproducible and, well, plain wrong. Besides, for each peer reviewed paper there's one or several peer reviewed papers that attack it.

Only after a subject has been studied for a while, several studies have been made pro and against, and the dust has settled down, we can have an understanding of a certain subject that we can depend upon.

Else we'll be like like popular science outlets, which come up every week with alternating "X is good for you", "X is bad for you" headlines, based on some recently published paper or another (this is especially common with medicine / nutrition research).

The author is also writing a book titled "Great Myths of the Brain". I find this genre of "myth-busting" popular science books doing more harm than good. For obvious and agreed upon myths they are OK (e.g. about "only using 10% of our brain", or stuff like creationism).

But for controversial topics, with scientists on both sides of the debate those books should not take one side and label the other as "myth" based on their authors prejudices. It would be like a neo-liberal economist labelling Keynesian economics a "myth". That's for the scientific consensus to decide.

With all that said, and with all the caveats about recent(ish) scientific papers that we mentioned, here is a recent study that goes against the conclusions of the papers in the article, finding measurable (via fMRI) evidence for learning styles:
http://www.jneurosci.org/content/29/12/3792.short
And here's a paper from a behavioral scientist (and the project leader of the MINT learning center at the ETH Zurich, Institute for Behavioral Sciences), regarding neuroscience and pedagogy:
http://www.educ.ethz.ch/mint/pp/ralphs/The_Brain_Is_Not_Enough.pdf

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Do We All Learn the Same Way? [Updated] - eFront Blog

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A couple of decades ago it was inconceivable for educators to tailor their materials and teaching approach towards the individual student. The different learning styles as a concept started gaining pace ever since the term and idea behind it was first introduced in the mid 70's.

# data[].attributes.headline
Pontiga, Klarence Mhae M. BSED - science 1A

# data[].attributes.subject
Pontiga, Klarence Mhae M. BSED - science 1A

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The i give two related charectheristic is independent variable is brand of paper towel and then the dependent variable is  amount of liquid absorbed by the paper towel and to compare different brands of paper towels to see which one holds the most liquid

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Bacoy, Peter Marchy Bac 1 A. Weight of the person and  how much the person eats. The independent variable is the amount of food eaten and dependent variable is  weight, in this case we want to predict the value of dependent variable based on independent variable therefore simple regression is used.

# data[].attributes.subject
Bacoy, Peter Marchy Bac 1 A. Weight of the person and  how much the person eats. The independent variable is the amount of food eaten and dependent variable is  weight, in this case we want to predict the value of dependent variable based on independent variable therefore simple regression is used.

# data[].attributes.headline
Daguinod, Maverick Charles E. BSED SCIENCE 1A

# data[].attributes.subject
Daguinod, Maverick Charles E. BSED SCIENCE 1A

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I've been playing so much mobile games lately and the two related characteristics that i think of is the times i'm playing a mobile game and the rank that i will reach on that specific game. The times that I'm playing will be the independent characteristic and my rank in that game will be the dependent characateristic. The amount of time I'm playing that game will be the one to predict the rank i will reach.

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Yere, Geraldine D.
BSA 1C

Independent Variable: Poverty level
Dependent Variable: Teen birth rate

We can use linear regression here because As the poverty level increases, the birth rate for teen females tends to increase as well. This shows a generally linear relationship.

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Yere, Geraldine D.
BSA 1C

You can use spearman rank correlation when you have two ranked variables and want to see if they covary (that is, if when one variable increases, the other variable tends to increase or decrease)

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Yere, Geraldine D.
BSA 1C

Pearson r or pearson correlation is defined as the statistic used to measure the relationship between two variables and their association with each other. Thus, an example to this would be our age ang height. Because as our age increases, our height also does.

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Pearson r is a metric that measures the closeness of a relationship between two or more variables, and it can be influenced by a variety of factors. For example, calculate a Pearson correlation coefficient for childs age and child shoe size.

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Because proficiency rank is ordinal data, I utilized Spearman rank correlation to determine how strong the relationship is between the seminars attended by the employee and their proficiency rating.

I came up with a result of -0.1792, and we can assume from this that the link between the two variables is not statistically significant; we can claim that there is a very weak negative linear relationship between the employee's attendance at seminars and their proficiency rating.

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A positive correlation signifies that if variable A goes up, then B will also go up, whereas if the value of the correlation is negative, then if A increases, B decreases, it is according to Science Direct.

Because of this I can say that there is a significant relationship between the distance of jogging and the way a person burns calories. The farther the distance a person runs, the more calories a person reduces.

https://www.sciencedirect.com/topics/computer-science/pearson-correlation

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The Spearman rank correlation is only appropriate if the relationship between your variables is monotonic, which means that if one variable increases, the other tends to either increase or decrease (NOT BOTH), according to Statistics Online Support.

My calculations for grades in MATH and ESP for grade 9 students demonstrate that there is a very weak linear relationship. For the simple fact that not all pupils have the same abilities in all fields. As a result, there is no significant relationship between grade 9 students' MATH and ESP grades.

http://sites.utexas.edu/sos/guided/inferential/numeric/bivariate/rankcor/

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You're a research assistant for the FBI, you're investigating the relationship between a person's attempt at deception and percentage changes in their pupil size. You ask subject series question, some of which they must answer dishonesty, at the 0.5 level of significance, what is the correlation coefficient? Subj 1 2 3 4 5 Deception 87 63 95 50 43 Pupil size 10 6 11 7 0.

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The Spearman rank correlation can be used to measure the degree of correlation between a person's or a family's monthly income and the amount of money they spend on needs. Because we may group the variable monthly income into low income, moderate income, and high income, it is an ordinal data. As a result, the circumstance is able to use spearman rank correlation as a statistical tool.

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Even though it is Christmas season, our weather is warm these days. Pearson R examines the link between the two variables, much as how the higher the scale on a thermometer, the warmer the weather, and vice versa.

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I used pearson r to determine if there was a link between a jogger's distance traveled and the number of calories burned, and the result reveals that there is, based on my calculation, a fairly strong association. I believe there is a strong link between the jogger's distance traveled and the number of calories expended. Based on a qualitative interpretation, 0.81-1.00 is a fairly strong linear relationship. The Pearson correlation approach is the most often used method for numerical variables, according to David Nettleton (2014). When two variables have a correlation value of 0.7, it means there is a significant and positive association between them. A positive correlation means that as variable A rises, so does variable B, whereas a negative correlation means that as A rises, so does B.

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Sweetzel Rebong
BSA-1C

A nonparametric measure of rank correlation is the Spearman's rank coefficient of correlation (statistical dependence of ranking between two variables). It assesses the magnitude and direction of the relationship between two ranking variables. It can be used to analyze students' exam and quiz results. It may be determined who received the highest and lowest score using the Spearman coefficient.

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Sweetzel Rebong
BSA-1C

The Pearson coefficient is a form of correlation coefficient that shows how two variables measured on the same interval or ratio scale are related. Let's say you were examining the relationship between your participants' age and their stated level of income. The Pearson coefficient is a measure of the ability of the association between two continuous variables. You want to know if there's a positive or negative correlation between someone's age and their income level. Your Pearson correlation coefficient value is positive after you've completed the exam. As a result, the two variables have a slightly positive correlation, and the strength of the link is similarly positive and regarded strong.

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One of example is a child height and age, a child height will keep increasing as a child ages increases. But there's possibilities that growth depends upon various factors, like genes, location, diet and lifestyle. (QPS 2020)

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For the example above, simple linear regression appears to be the most appropriate data analysis. Simple linear regression is used to determine the relationship between an independent and dependent variable. In this case, the independent variable is distance, and the dependent variable is calories. The data points are close to the straight line, indicating that there is a high correlation between the two variables, indicating a strong association.

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To get the grade result of the students in ESP and MATH, I used the spearman rank correlation and I came up with the answer 0.1577 by normal standards, the association between the two variables would not be considered statistically significant. This means that the relationship between ESP and MATH grades of students is very weak in terms of linear relationship.

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The easiest approach to calculate how many calories are expended by a jogger who jogs for 6,7, and 8 km is to use the Pearson r, and when I computed the result was 0.98 ( this is the round off) by this computation that is positive. I can n claim that the association between these two distances is strong.

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This can be utilized in situations when a product's pricing is linked to the location or location where it is offered.

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The association between the joggers' distance traveled and the calories they released was determined using Pearson correlation. According to my answer, the total of my computation is 0.9846, which is 0.98 when rounded to the nearest hundredths. This is a strong positive correlation, which says that high X variable scores correlate with high Y variable scores (and vice versa).

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Yes, there is, because as a general rule, a person running at a moderate (conversational) pace on a flattish surface burns around 60 calories every km (100 calories per mile). This proves that the longer the distance that a jogger will travel, the higher the calories he may burn. The distance affect the calories burned, and the amount of calories that a jogger will burn also depends on the distance that a jogger will travel.
Pearson, C. (n.d.). Endurance Bike and Run. Https://Www.Goendurancebikeandrun.Com. https://www.google.com/amp/s/endurancebikeandrun.com/blog/2020/4/15/how-many-calories-does-running-burn%3fformat=amp

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In this case, I utilized spearman rank order correlation to solve the problem; my result was 0.1577, which is in the middle of the range of 0.00-0.20, indicating that the grades of grde9 students in the sIbject of Math and Esp have a very weak linear relationship.

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Yes, there is because as a general rule, a person running at a moderate (conversational) pace on a flattish surface burns around 60 calories every km (100 calories per mile). This proves that the longer the distance that a jogger will travel, the higher the calories he may burn. The distance affect the calories that will burn

Pearson, C. (n.d.). Endurance Bike and Run. Https://Www.Goendurancebikeandrun.Com. https://www.google.com/amp/s/endurancebikeandrun.com/blog/2020/4/15/how-many-calories-does-running-burn?format=amp

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Balatibat, Queen Jherylle H. BSEd-Science1A

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Balatibat, Queen Jherylle H. BSEd-Science1A

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There is no significant relationship between the grades of Grade 9 students in Math and ESP because the data shows that some students are good at Math and some are not. on the other hand, some students are good at ESP and some are not. It can be said that some students can solve the Math problem easily and their ability in learning in ESP is quite not good enough and students who are good in ESP are struggling in Math. We have different level of understanding of the topics in different subjects. Our IQ can improve with our hardwork if you work hard enough, you'll be surprised that you will be good in subject that you think that you can't be good.
( Miles Kimball, 2013)
https://www.google.com/amp/s/qz.com/139453/theres-one-key-difference-between-kids-who-excel-at-math-and-those-who-dont/amp/

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Daguinod, Maverick Charles E. BSED SCIENCE 1A

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Daguinod, Maverick Charles E. BSED SCIENCE 1A

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Since spearman rank correlation determines the strength and direction between two ranked variables. It can be used to compare student's scores in exams or quizzes to know if there is a certain relationship between their scores.

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Ballesteros, Janica May, M.  BSED-Science 1a

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Ballesteros, Janica May, M.  BSED-Science 1a

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When we sleep, for example, the independent variable is the amount of time you spend sleeping, whereas the dependent variable is the items you missed due to your extended slumber. This two-variable relationship is represented by linear regression.

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Daguinod, Maverick Charles E. BSED SCI 1A

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Daguinod, Maverick Charles E. BSED SCI 1A

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One example is the relationship between supply and demand. As the supply rise the demand decreases. It's a negative correlation

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I utilized the Spearman Rank correlation to examine if there was a link between an employee's competency rating and the number of seminars they attended.

Because I believe the Spearman Rank correlation is the most appropriate, and I received a result of - 0.18, implying that an employee's number of seminars attended and their competency rating have no link.

A correlation value of 0 shows that there is no relationship between the variables, according to Statistics Solutions.

On the other hand, Spearman and Pearson correlation coefficients are predicated on a linear relationship between variables.

A non-linear relationship can exist even if the correlation coefficient is zero.

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REA MARY ANN I.
BSED-SCIENCE

Weather Forecasts. Statistics are very important for observation, analysis and mathematical prediction models.

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I compute the person r correlation based on the current scenario and get a value of 0.98, indicating that there is a significant relationship between the jogger's distance traveled and the calories burned.

Because the estimated result was positive, there is a strong positive linear relationship between these two variables.

The distance covered in this table is used to calculate the number of calories expended by a jogger.

The longer the distance traveled, the more calories expended.

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Because the data or number of cases given is between 25 and 30, I utilized the spearman rank correlation to compute.

Because the result, or total, is 0.1577 or 0.16, I conclude that students' grades in ESP and Math have a weak linear relationship.

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The two related characteristics that can be analyzed using simple linear regression is the time of work which is the independent variable then the salary is the dependent variable. The salary varies depending on how many days or time you go to work. Salary relying on the time you go to work. It cannot stand all alone it needs a independent variable which is the time of work.

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Spearman rank can be used to determine the number of months the employee being employed. In this statistical test we found out when there is an increase or decrease in the ranking on how they prolonged in a company.

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The temperature of a person and the mercury inside the thermometer as the temperature of a person increase the mercury inside the thermometer also increase

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The temperature of a person and the mercury inside the thermometer as the temperature of a person increase the mercury inside the thermometer also increase

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Pearson r can be used in financial analysis and to support decision making. It is more often in quantitative so that's why you can used in a business like sari-sari store for example you need to determine the expenses, sales and the total sales of your business. You can come up to the solution or you may know if your store are profitable by using this statistical test.

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Basing on the computed results shown, I can clearly conclude that there is a significant relationship between the distance traveled by a jogger and the calories they burned. People who weigh more will burn more calories during either exercise, while those who weigh less will burn fewer calories. (Beth Sissons, 2020). Physical activities like jogging increases the burn offs of the calories in our body that leads to weight loss or fit and health body. Clearly there is a relationship between the given data.

SOURCE: https://www.medicalnewstoday.com/articles/biking-vs-running#bone-health

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BSA - 1A

From the computation, I obtained  - 0.18 which entails that there is a negative correlation because one variable increases, the other tends to decrease. Which also indicates that there is no significant relationship between the proficiency rating of an employee and the number of seminars he/she attended because according to SimplyPsychology, a p-value that is higher or greater than 0.05 is not significant because it shows a strong evidence for the null hypothesis. Since the answer is higher than 0.05, there is no significant in this situation and we will reject the alternative hypothesis.

Retrieved from:
https://www.simplypsychology.org/p-value.html

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What a p-value tells you about statistical significance

By Saul McLeod, published 2019

When you perform a statistical test a p-value helps you determine the significance of your results in relation to the null hypothesis.

The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other). It states the results are due to chance and are not significant in terms of supporting the idea being investigated. Thus, the null hypothesis assumes that whatever you are trying to prove did not happen.

The alternative hypothesis is the one you would believe if the null hypothesis is concluded to be untrue.

The alternative hypothesis states that the independent variable did affect the dependent variable, and the results are significant in terms of supporting the theory being investigated (i.e. not due to chance).

How do you know if a p-value is statistically significant?

The level of statistical significance is often expressed as a p-value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.

A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the alternative hypothesis.
However, this does not mean that there is a 95% probability that the research hypothesis is true. The p-value is conditional upon the null hypothesis being true is unrelated to the truth or falsity of the research hypothesis.

A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This means we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis, we can only reject the null or fail to reject it.
A statistically significant result cannot prove that a research hypothesis is correct (as this implies 100% certainty).

Instead, we may state our results "provide support for" or "give evidence for" our research hypothesis (as there is still a slight probability that the results occurred by chance and the null hypothesis was correct - e.g. less than 5%).

How to report a p-value APA style

The 6th edition of the APA style manual (American Psychological Association, 2010) states the following on the topic of reporting p-values:

"When reporting p values, report exact p values (e.g., p = .031) to two or three decimal places. However, report p values less than .001 as p < .001. The tradition of reporting p values in the form p < .10, p < .05, p < .01, and so forth, was appropriate in a time when only limited tables of critical values were available." (p. 114)

Note:

Do not use 0 before the decimal point for the statistical value p as it cannot equal 1, in other words, write p = .001 instead of p = 0.001.

Please pay attention to issues of italics (p is always italicized) and spacing (either side of the = sign).

p = .000 (as outputted by some statistical packages such as SPSS) is impossible and should be written as p < .001.

The opposite of significant is "nonsignificant", not "insignficant".

Why the p-value is not enough

A lower p-value is sometimes interpreted as meaning there is a stronger relationship between two variables. However, statistical significance means that it is unlikely that the null hypothesis is true (less than 5%).

To understand the strength of the difference between two groups (control vs. experimental) a researcher needs to calculate the effect size.

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P-Values and Statistical Significance | Simply Psychology

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By Saul McLeod, published 2019 When you perform a statistical test a p-value helps you determine the significance of your results in relation to the null hypothesis. The null hypothesis states that there is no relationship between the two variables being studied (one variable does not affect the other).

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BSA - 1A

Spearman Rank - Order Correlation is the statistical tool that is applicable for this situation. Because the given data is ordinal type, and the number of cases is less than 25.  From the computation using the formula of Spearman Rank, I obtained the value of - 0.18 which entails that there is a negative correlation because one variable increases, the other tends to decrease. The relationship between the numbers of seminars attended by the employee and their proficiency rating is very weak.

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BSA - 1A

From the computation using the simple linear regression formula, I obtained the value of 0.0303 and 0.3060 which is close to the straight line. Thus, showing a strong relationship between these two variables. This also indicated that increasing in distance variable, increases also in calories burned.

According to Jim, the sign of a regression coefficient tells you whether there is a positive or negative correlation between each independent variable and the dependent variable. A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase.

Retrieved from:
https://statisticsbyjim.com/regression/interpret-coefficients-p-values-regression/

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By Jim Frost

P-values and coefficients in regression analysis work together to tell you which relationships in your model are statistically significant and the nature of those relationships. The coefficients describe the mathematical relationship between each independent variable and the dependent variable. The p-values for the coefficients indicate whether these relationships are statistically significant.

After fitting a regression model, check the residual plots first to be sure that you have unbiased estimates. After that, it's time to interpret the statistical output. Linear regression analysis can produce a lot of results, which I'll help you navigate. In this post, I cover interpreting the p-values and coefficients for the independent variables.

Related posts: When Should I Use Regression Analysis? and How to Perform Regression Analysis Using Excel

Interpreting P-Values for Variables in a Regression Model

Regression analysis is a form of inferential statistics. The p-values help determine whether the relationships that you observe in your sample also exist in the larger population. The p-value for each independent variable tests the null hypothesis that the variable has no correlation with the dependent variable. If there is no correlation, there is no association between the changes in the independent variable and the shifts in the dependent variable. In other words, there is insufficient evidence to conclude that there is an effect at the population level.

If the p-value for a variable is less than your significance level, your sample data provide enough evidence to reject the null hypothesis for the entire population. Your data favor the hypothesis that there is a non-zero correlation. Changes in the independent variable are associated with changes in the dependent variable at the population level. This variable is statistically significant and probably a worthwhile addition to your regression model.

On the other hand, a p-value that is greater than the significance level indicates that there is insufficient evidence in your sample to conclude that a non-zero correlation exists.

The regression output example below shows that the South and North predictor variables are statistically significant because their p-values equal 0.000. On the other hand, East is not statistically significant because its p-value (0.092) is greater than the usual significance level of 0.05.

It is standard practice to use the coefficient p-values to decide whether to include variables in the final model. For the results above, we would consider removing East. Keeping variables that are not statistically significant can reduce the model's precision.

Related posts: F-test of overall significance in regression and What are Independent and Dependent Variables?

The sign of a regression coefficient tells you whether there is a positive or negative correlation between each independent variable and the dependent variable. A positive coefficient indicates that as the value of the independent variable increases, the mean of the dependent variable also tends to increase. A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease.

The coefficient value signifies how much the mean of the dependent variable changes given a one-unit shift in the independent variable while holding other variables in the model constant. This property of holding the other variables constant is crucial because it allows you to assess the effect of each variable in isolation from the others.

The coefficients in your statistical output are estimates of the actual population parameters. To obtain unbiased coefficient estimates that have the minimum variance, and to be able to trust the p-values, your model must satisfy the seven classical assumptions of OLS linear regression.

Statisticians consider regression coefficients to be an unstandardized effect size because they indicate the strength of the relationship between variables using values that retain the natural units of the dependent variable. Effect sizes help you understand how important the findings are in a practical sense. To learn more about unstandardized and standardized effect sizes, read my post about Effect Sizes in Statistics.

Graphical Representation of Regression Coefficients

A simple way to grasp regression coefficients is to picture them as linear slopes. The fitted line plot illustrates this by graphing the relationship between a person's height (IV) and weight (DV). The numeric output and the graph display information from the same model.

The height coefficient in the regression equation is 106.5. This coefficient represents the mean increase of weight in kilograms for every additional one meter in height. If your height increases by 1 meter, the average weight increases by 106.5 kilograms.

The regression line on the graph visually displays the same information. If you move to the right along the x-axis by one meter, the line increases by 106.5 kilograms. Keep in mind that it is only safe to interpret regression results within the observation space of your data. In this case, the height and weight data were collected from middle-school girls and range from 1.3 m to 1.7 m. Consequently, we can't shift along the line by a full meter for these data.

Let's suppose that the regression line was flat, which corresponds to a coefficient of zero. For this scenario, the mean weight wouldn't change no matter how far along the line you move. That's why a near zero coefficient suggests there is no effect-and you'd see a high (insignificant) p-value to go along with it.

The plot really brings this to life. However, plots can display only results from simple regression-one predictor and the response. For multiple linear regression, the interpretation remains the same.

Contour plots can graph two independent variables and the dependent variable. For more information, read my post Contour Plots: Using, Examples, and Interpreting.

Use Polynomial Terms to Model Curvature in Linear Models

The previous linear relationship is relatively straightforward to understand. A linear relationship indicates that the change remains the same throughout the regression line. Now, let's move on to interpreting the coefficients for a curvilinear relationship, where the effect depends on your location on the curve. The interpretation of the coefficients for a curvilinear relationship is less intuitive than linear relationships.

As a refresher, in linear regression, you can use polynomial terms model curves in your data. It is important to keep in mind that we're still using linear regression to model curvature rather than nonlinear regression. That's why I refer to curvilinear relationships in this post rather than nonlinear relationships. Nonlinear has a very specialized meaning in statistics. To read about this distinction, read my post: The Difference between Linear and Nonlinear Regression Models.

This regression example uses a quadratic (squared) term to model curvature in the data set. You can see that the p-values are statistically significant for both the linear and quadratic terms. But, what the heck do the coefficients mean?

Graphing the Data for Regression with Polynomial Terms

Graphing the data really helps you visualize the curvature and understand the regression model.

The chart shows how the effect of machine setting on mean energy usage depends on where you are on the regression curve. On the x-axis, if you begin with a setting of 12 and increase it by 1, energy consumption should decrease. On the other hand, if you start at 25 and increase the setting by 1, you should experience an increased energy usage. Near 20 and you wouldn't expect much change.

Regression analysis that uses polynomials to model curvature can make interpreting the results trickier. Unlike a linear relationship, the effect of the independent variable changes based on its value. Looking at the coefficients won't make the picture any clearer. Instead, graph the data to truly understand the relationship. Expert knowledge of the study area can also help you make sense of the results.

Related post: Curve Fitting using Linear and Nonlinear Regression

Regression Coefficients and Relationships Between Variables

Regression analysis is all about determining how changes in the independent variables are associated with changes in the dependent variable. Coefficients tell you about these changes and p-values tell you if these coefficients are significantly different from zero.

All of the effects in this post have been main effects, which is the direct relationship between an independent variable and a dependent variable. However, sometimes the relationship between an IV and a DV changes based on another variable. This condition is an interaction effect. Learn more about these effects in my post: Understanding Interaction Effects in Statistics.

In this post, I didn't cover the constant term. Be sure to read my post about how to interpret the constant!

The statistics I cover in the post tell you how to interpret the regression equation, but they don't tell you how well your model fits the data. For that, you should also assess R-squared.

If you're learning regression and like the approach I use in my blog, check out my eBook!

Note: I wrote a different version of this post that appeared elsewhere. I've completely rewritten and updated it for my blog site.

Related

Filed Under: Tagged With: Regression analysis example, conceptual, interpreting results

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How to Interpret P-values and Coefficients in Regression Analysis

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By Jim Frost P-values and coefficients in regression analysis work together to tell you which relationships in your model are statistically significant and the nature of those relationships. The coefficients describe the mathematical relationship between each independent variable and the dependent variable. The p-values for the coefficients indicate whether these relationships are statistically significant.

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I used Spearman rank in this situation because I think that this is the most applicable to used to identity the association of the given data. Upon computation it resulted -0.1792 or -0.18 when rounded off.  In this we can conclude that the linear relationship between number of seminars attended and work proficiency rating is very weak.

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BSA - 1A

For identifying the relationship between the distance the joggers jog and calories they burned, the applicable statistical tool is the simple linear regression because this situation has independent and dependent variable. It can determine that as the far the distance the joggers jog the more calories burned. From the computation using the simple linear regression formula, I obtained the value of 0.0303 and 0.3060 which is close to the straight line. Thus, showing a strong relationship between these two variables.

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I used Pearson r in the situation above to identify the relationship of the given data because I think this is the most applicable. Upon computation it resulted 0.9846 or 0.98 when rounded off.  In this we can conclude that the linear relationship between the distance and the calories burned is very strong.

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BSA - 1A

From the computation using Pearson r formula, I obtained r= 0.0494 or 0.05 (rounded off), which indicates that there is a  weak relationship between the students' grades in ESP and Math. In this note, this simply means that there is no significant relationship between the grades of Grade 9 students in ESP and Math because the value of r is very near to 0.

According to Statiscticshowto, the correlation coefficient is used to determine the strength of a relationship between data. A score of 1 means there is a strong positive relationship, a result of -1 means there is a strong negative link, and a result of 0 means there is no association at all.

Retrieved from:
https://www.statisticshowto.com/probability-and-statistics/correlation-coefficient

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Correlation coefficients are used to measure how strong a relationship is between two variables. There are several types of correlation coefficient, but the most popular is Pearson's. Pearson's correlation (also called Pearson's R) is a correlation coefficient commonly used in linear regression. If you're starting out in statistics, you'll probably learn about Pearson's R first. In fact, when anyone refers to the correlation coefficient, they are usually talking about Pearson's.

Watch the video for an overview of the correlation coefficient, or read on below:

Can't see the video? Click here.
Contents:

What is a correlation coefficient?

What is Pearson Correlation? How to Calculate:

Cramer's V Correlation

Where did the Correlation Coefficient Come From?

Correlation Coefficient Hypothesis Test.

Relationship to cosine

More Articles / Correlation Coefficients

Correlation coefficient formulas are used to find how strong a relationship is between data. The formulas return a value between -1 and 1, where:

1 indicates a strong positive relationship.

-1 indicates a strong negative relationship.

A result of zero indicates no relationship at all.

Meaning

A correlation coefficient of 1 means that for every positive increase in one variable, there is a positive increase of a fixed proportion in the other. For example, shoe sizes go up in (almost) perfect correlation with foot length.

A correlation coefficient of -1 means that for every positive increase in one variable, there is a negative decrease of a fixed proportion in the other. For example, the amount of gas in a tank decreases in (almost) perfect correlation with speed.

Zero means that for every increase, there isn't a positive or negative increase. The two just aren't related.

The absolute value of the correlation coefficient gives us the relationship strength. The larger the number, the stronger the relationship. For example, |-.75| = .75, which has a stronger relationship than .65.

Like the explanation? Check out the Practically Cheating Statistics Handbook, which has hundreds of step-by-step, worked out problems!

Types of correlation coefficient formulas.

There are several types of correlation coefficient formulas.

One of the most commonly used formulas is Pearson's correlation coefficient formula. If you're taking a basic stats class, this is the one you'll probably use:

Two other formulas are commonly used: the sample correlation coefficient and the population correlation coefficient.

Sample correlation coefficient

S x and s y are the sample standard deviations, and s xy is the sample covariance.

Population correlation coefficient

The population correlation coefficient uses σ x and σ y as the population standard deviations, and σ xy as the population covariance.

Check out my Youtube channel for more tips and help with statistics!

Correlation between sets of data is a measure of how well they are related. The most common measure of correlation in stats is the Pearson Correlation. The full name is the Pearson Product Moment Correlation (PPMC). It shows the linear relationship between two sets of data. In simple terms, it answers the question, Can I draw a line graph to represent the data? Two letters are used to represent the Pearson correlation: Greek letter rho (ρ) for a population and the letter "r" for a sample.

Potential problems with Pearson correlation.

The PPMC is not able to tell the difference between dependent variables and independent variables. For example, if you are trying to find the correlation between a high calorie diet and diabetes, you might find a high correlation of .8. However, you could also get the same result with the variables switched around. In other words, you could say that diabetes causes a high calorie diet. That obviously makes no sense. Therefore, as a researcher you have to be aware of the data you are plugging in. In addition, the PPMC will not give you any information about the slope of the line; it only tells you whether there is a relationship.
Real Life Example
Pearson correlation is used in thousands of real life situations. For example, scientists in China wanted to know if there was a relationship between how weedy rice populations are different genetically. The goal was to find out the evolutionary potential of the rice. Pearson's correlation between the two groups was analyzed. It showed a positive Pearson Product Moment correlation of between 0.783 and 0.895 for weedy rice populations. This figure is quite high, which suggested a fairly strong relationship.

If you're interested in seeing more examples of PPMC, you can find several studies on the National Institute of Health's Openi website, which shows result on studies as varied as breast cyst imaging to the role that carbohydrates play in weight loss.
Back to Top

Watch the video to learn how to find PPMC by hand.

Can't see the video? Click here.

Example question: Find the value of the correlation coefficient from the following table:

Make a chart. Use the given data, and add three more columns: xy, x 2, and y 2.

Multiply x and y together to fill the xy column. For example, row 1 would be 43 × 99 = 4,257.

Take the square of the numbers in the x column, and put the result in the x2 column.

Take the square of the numbers in the y column, and put the result in the y2 column.

Add up all of the numbers in the columns and put the result at the bottom of the column. The Greek letter sigma (Σ) is a short way of saying "sum of" or summation.

Step 6: Use the following correlation coefficient formula.

The answer is: 2868 / 5413.27 = 0.529809
Click here if you want easy, step-by-step instructions for solving this formula.
From our table:

The correlation coefficient =

The range of the correlation coefficient is from -1 to 1. Our result is 0.5298 or 52.98%, which means the variables have a moderate positive correlation.
Back to Top.

Like the explanation? Check out the Practically Cheating Statistics Handbook, which has hundreds more step-by-step explanations, just like this one!

If you're taking AP Statistics, you won't actually have to work the correlation formula by hand. You'll use your graphing calculator. Here's how to find r on a TI83.

Step 1: Type your data into a list and make a scatter plot to ensure your variables are roughly correlated. In other words, look for a straight line. Not sure how to do this? See: TI 83 Scatter plot.

Step 2: Press the STAT button.

Step 3: Scroll right to the CALC menu.

Step 4: Scroll down to 4:LinReg(ax+b), then press ENTER. The output will show "r" at the very bottom of the list.

Tip: If you don't see r, turn Diagnostic ON, then perform the steps again.

Watch the video:

Can't see the video? Click here.

Step 1: Type your data into two columns in Excel. For example, type your "x" data into column A and your "y" data into column B.

Step 2: Select any empty cell.

Step 3: Click the function button on the ribbon.

Step 4: Type "correlation" into the 'Search for a function' box.

Step 5: Click "Go." CORREL will be highlighted.

Step 7: Type the location of your data into the "Array 1" and "Array 2" boxes. For this example, type "A2:A10" into the Array 1 box and then type "B2:B10" into the Array 2 box.

Step 8: Click "OK." The result will appear in the cell you selected in Step 2. For this particular data set, the correlation coefficient(r) is -0.1316.

Caution: The results for this test can be misleading unless you have made a scatter plot first to ensure your data roughly fits a straight line. The correlation coefficient in Excel 2007 will always return a value, even if your data is something other than linear (i.e. the data fits an exponential model).

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Watch the video for the steps:

Can't see the video? Click here.

Step 3: Click the "Pearson" check box if it isn't already checked. Then click either a "one-tailed" or "two-tailed" test radio button. If you aren't sure if your test is one-tailed or two-tailed, see: Is it a a one-tailed test or two-tailed test?

Tip #1: It's always a good idea to make an SPSS scatter plot of your data set before you perform this test. That's because SPSS will always give you some kind of answer and will assume that the data is linearly related. If you have data that might be better suited to another correlation (for example, exponentially related data) then SPSS will still run Pearson's for you and you might get misleading results.
Tip #2: Click on the "Options" button in the Bivariate Correlations window if you want to include descriptive statistics like the mean and standard deviation.
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Watch this video on how to calculate the correlation coefficient in Minitab:

Can't see the video? Click here.

The Minitab correlation coefficient will return a value for r from -1 to 1.

Example question: Find the Minitab correlation coefficient based on age vs. glucose level from the following table from a pre-diabetic study of 6 participants:

Step 1: Type your data into a Minitab worksheet. I entered this sample data into three columns.

Step 2: Click "Stat", then click "Basic Statistics" and then click "Correlation."

Step 4: (Optional) Check the "P-Value" box if you want to display a P-Value for r.

Step 5: Click "OK". The Minitab correlation coefficient will be displayed in the Session Window. If you don't see the results, click "Window" and then click "Tile." The Session window should appear.

Tip: Give your columns meaningful names (in the first row of the column, right under C1, C2 etc.). That way, when it comes to choosing variable names in Step 3, you'll easily see what it is you are trying to choose. This becomes especially important when you have dozens of columns of variables in a data sheet!

Pearson's Correlation Coefficient is a linear correlation coefficient that returns a value of between -1 and +1. A -1 means there is a strong negative correlation and +1 means that there is a strong positive correlation. A 0 means that there is no correlation (this is also called zero correlation).

This can initially be a little hard to wrap your head around (who likes to deal with negative numbers?). The Political Science Department at Quinnipiac University posted this useful list of the meaning of Pearson's Correlation coefficients. They note that these are " crude estimates " for interpreting strengths of correlations using Pearson's Correlation:

+.70 or higher

Very strong positive relationship

+.40 to +.69

Strong positive relationship

+.30 to +.39

Moderate positive relationship

+.20 to +.29

weak positive relationship

+.01 to +.19

No or negligible relationship

0

No relationship [zero correlation]

-.01 to -.19

No or negligible relationship

-.20 to -.29

weak negative relationship

-.30 to -.39

Moderate negative relationship

-.40 to -.69

Strong negative relationship

-.70 or higher

Very strong negative relationship

It may be helpful to see graphically what these correlations look like:

The images show that a strong negative correlation means that the graph has a downward slope from left to right: as the x-values increase, the y-values get smaller. A strong positive correlation means that the graph has an upward slope from left to right: as the x-values increase, the y-values get larger.
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Cramer's V Correlation is similar to the Pearson Correlation coefficient. While the Pearson correlation is used to test the strength of linear relationships, Cramer's V is used to calculate correlation in tables with more than 2 x 2 columns and rows. Cramer's V correlation varies between 0 and 1. A value close to 0 means that there is very little association between the variables. A Cramer's V of close to 1 indicates a very strong association.

A correlation coefficient gives you an idea of how well data fits a line or curve. Pearson wasn't the original inventor of the term correlation but his use of it became one of the most popular ways to measure correlation.

Francis Galton (who was also involved with the development of the interquartile range) was the first person to measure correlation, originally termed "co-relation," which actually makes sense considering you're studying the relationship between a couple of different variables. In Co-Relations and Their Measurement, he said

"The statures of kinsmen are co-related variables; thus, the stature of the father is correlated to that of the adult son,..and so on; but the index of co-relation ... is different in the different cases."

It's worth noting though that Galton mentioned in his paper that he had borrowed the term from biology, where "Co-relation and correlation of structure" was being used but until the time of his paper it hadn't been properly defined.

In 1892, British statistician Francis Ysidro Edgeworth published a paper called "Correlated Averages," Philosophical Magazine, 5th Series, 34, 190-204 where he used the term "Coefficient of Correlation." It wasn't until 1896 that British mathematician Karl Pearson used "Coefficient of Correlation" in two papers: Contributions to the Mathematical Theory of Evolution and Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity and Panmixia. It was the second paper that introduced the Pearson product-moment correlation formula for estimating correlation.

If you can read a table - you can test for correlation coefficient. Note that correlations should only be calculated for an entire range of data. If you restrict the range, r will be weakened.

Sample problem: test the significance of the correlation coefficient r = 0.565 using the critical values for PPMC table. Test at α = 0.01 for a sample size of 9.

Subtract two from the sample size to get df, degrees of freedom.
9 - 7 = 2

Look the values up in the PPMC Table. With df = 7 and α = 0.01, the table value is = 0.798

Step 3: Draw a graph, so you can more easily see the relationship.

r = 0.565 does not fall into the rejection region (above 0.798), so there isn't enough evidence to state a strong linear relationship exists in the data.

It's rare to use trigonometry in statistics (you'll never need to find the derivative of tan(x) for example!), but the relationship between correlation and cosine is an exception. Correlation can be expressed in terms of angles:

Positive correlation = acute angle <45°,

Negative correlation = obtuse angle >45°,

Uncorrelated = orthogonal (right angle).

More specifically, correlation is the cosine of an angle between two vectors defined as follows (Knill, 2011):

If X, Y are two random variables with zero mean, then the covariance Cov[XY] = E[X · Y] is the dot product of X and Y. The standard deviation of X is the length of X.

References

Acton, F. S. Analysis of Straight-Line Data. New York: Dover, 1966.
Edwards, A. L. "The Correlation Coefficient." Ch. 4 in An Introduction to Linear Regression and Correlation. San Francisco, CA: W. H. Freeman, pp. 33-46, 1976.
Gonick, L. and Smith, W. "Regression." Ch. 11 in The Cartoon Guide to Statistics. New York: Harper Perennial, pp. 187-210, 1993.
Knill, O. (2011). Lecture 12: Correlation. Retrieved April 16, 2021 from: http://people.math.harvard.edu/~knill/teaching/math19b_2011/handouts/lecture12.pdf

Other similar formulas you might come across that involve correlation ( click for article):

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Correlation Coefficient: Simple Definition, Formula, Easy Steps

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Correlation coefficients are used to measure how strong a relationship is between two variables. There are several types of correlation coefficient, but the most popular is Pearson's. Pearson's correlation (also called Pearson's R) is a correlation coefficient commonly used in linear regression. If you're starting out in statistics, you'll probably learn about Pearson's R first.

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BSA - 1A

In this situation, the applicable statistical tool is Pearson r. This statistical tool will determine if there is a correlation between students' grades in Math and ESP. From the computation using the Pearson r formula, I obtained a value of r= 0.0494 or 0.05 (rounded off), indicating a modest or weak linear association between the ESP and Math subjects.