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I chose a pie chart to include in my data so that I can visually represent the percentage of each frequency of side effects as parts of a whole. Further, a pie chart was appropriate for my investigation because I am not exploring variables changing over time, but simply frequency.

By having each side affect represented as parts of a whole, the viewer can easily compare the size of each “slice” and therefore conclude which side effect was most frequent. Evidently, the largest “slice” is the other side effects and depression. The smallest slices” or least frequent side effect is a lack of energy.

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One of the variables I analyzed with regards to understanding the likelihood of having scoliosis was whether one of the parents had scoliosis. For the sample of subjects who currently have scoliosis, 16.67% of the sample have at least one parent who has scoliosis. For those who do not have scoliosis however, only 5.70% have one parent who has scoliosis. Therefore, the probability of one parent having scoliosis is higher within those individuals who have scoliosis.

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Sylvie Landman Data Presentation Graph #1

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Sylvie Landman Data Presentation Graph #1

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This graph shows that there is a strong positive correlation between the average of students in the United States who do not participate in at least one day of physical activity and the average of students in the United States who reported feeling sad or helpless. The correlation is linear and the data points are clustered towards the left side of the graph, meaning they are less than 2000. This indicates that the data points are more commonly between 0 - 2000 and the outliers, in this case meaning the states that have much more students on average are higher than 2000. The strong positive correlation conveys that there is a link between the number of students who don’t participate in physical activity and the number of students that feel sad or helpless in each state. I also noticed that the population of each state did not have an influence on these results. For example, California has a large population compared to other states and the data for California was lower compared to other states.

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This histogram presents the data in a way that allows for comparison between the most common times between both groups. On the x-axis, the farther right the bars go the longer the participants took to complete the snapping task. The higher the bar reaches on the y-axis, the more common that time slot was among the participants. As seen, the instrument players’ time to do the snapping motions correctly was most common between 0:00 and 0:30, while the non-instrument players’ time was most common around 5:01+. The histogram shows that the number of instrument-playing individuals decreases as the time slots progress, as most of them had completed the task quickly after the timer began. The non instrument-playing individuals were more varied, however the majority of them typically took over 5 minutes to complete the task.

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The graph above compared the expected distribution with the actual distribution in probability or decimal form for the electronic and physical decks. This shows that the physical deck and the electronic deck both achieved the expected value at least once, but the electronic deck did it twice, indicating it is closer to being more random and even across the four intervals. It again shows how much lower the third interval is and how much higher the fourth interval for the physical deck. The comparison against the expected value helps to make that clear, and it emphasizes the difference and questions the shuffling of the physical deck.

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This graph depicts the average shark detections per month on Cape Cod. This makes it clear that shark detections, on average, peak in August and are roughly normally distributed. They rise sharply from June to July, and July to August, and then fall more slowly in the fall months, eventually hitting almost 0 by December. It is interesting that there are essentially no detections from January-May, which is likely because of how much colder the water and air are in those months. This supports my original hypothesis, that temperature and shark detections have a positive correlation (which would make sense, because air temperatures increase dramatically in the summer months, which is when sharks are most commonly detected). I depicted this data using a line graph because it would most clearly show when the data peaks and falls.

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Box and Whisker plot of Average height of player in Women and Men in 2020 Olympics Volleyball (cm)

the min in the data is 167 and the max is 210 the range between the lights is very great and yet they compete against each other. the mediant height is 191 meaning the most players are around 190 cm but most players are above. the min is a lot further away from the median than the max again showing that most players are more closely together in height above 190 than not.

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Talia Caskey - Average Boulder vs. Average Days Climbed per Week

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Talia Caskey - Average Boulder vs. Average Days Climbed per Week

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I chose to create a scatter plot to represent the relationship between a climber's average boulder level - the level they can complete most comfortably - and how many days they climb per week. I wanted to see whether there would be any correlation throughout my data, and while still very weak, compared to my other variables, there is the largest correlation between average days climbed and boulder level. This scatter plot already begins to answer my question because I hypothesized that the more you practice the higher your level will be. This graph proves that there is some sort of a relationship between practicing and boulder level, further supporting my hypothesis.

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I chose to use a scatter plot so that I could show a correlation between my two variables in a more efficient way. The graph shows a weak correlation between the two variables which I was surprised to see because I thought that there would be a strong positive correlation in my data. The graph also shows that I have some outliers in my data which could be helpful in my future analysis of my data.

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This graph combines both the heights of the 43 swimmers and the times taken to swim 100m freestyle.  This graph is known as a scatter plot and is typically used to show to variables in a data set. In statistics, scatter plots are used to show the correlation between variables in a bi-modal distribution.They are used to show a correlation because it displays the strength, direction, and Linearity of the relationship between two variables. This scatter plot was used to help show if there was a correlation between height of a swimmer (y-axis) and time taken to swimm 100m (x-axis). The scatter plot shows a strong, linear, negative correlation between the two variables. What this graph is showing is that as time increases, height decreases. This is indicated by its negative direction. Furthermore, as shown by the histogram, there are more faster times and taller swimmers, as indicated by the majority of the points on the scatter plot being near the upper top left of the graph. These points reside in the area where the fastest times and tallest swimmers intersect. Also because the graph is liner, the negative dfirection is more easily identiguable. One noticing I have is that the r value is. 5 while the scatter plot shows a strong correlation. This is odd because with a point 5 r value, teh points should be more spread out than they are because it represents a moderate correlation. Aside from this, by using the scatters plot's strength, direction, adn linearity, I can conclude that the height of a wimmer does have an impact on the time taken to swimm 100m. This is supported by the scatter plot because the sammler the heights on the y-axis, the slower the time or the greater number on the x-axis.

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This graph is a pie chart that solely displays the range of different opinions on Roe v. Wade amongst people in New York. The purpose of this graph is to give an idea about the ratios of people in New York alone, without looking at the other factors that contribute to this data like age, political part, etc. This graphs makes it clear that the majority of people, specifically 62.7%, strongly disagree with Roe v. Wade being overturned. The graph also shows that the minority of people completely agree with the decision to overturn Roe v. Wade, with 3.9% of people voting for that option.

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Alexa Pepa - IA Presentation of Data Submission  - Sheet1

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I chose to use a scatterplot to represent the correlation between a teams value and their position in their league. The data  falls into line with my hypothesis that the greater a clubs value the higher they are on their table. However, it is interesting to see how the correlation is not actually as high as expected, and frequently clubs with lower value place much higher than clubs with higher values. This makes sense because a teams performance is not solely determined by their wealth, however typically a team with a higher value will obtain better players and as a result win more games.

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I chose to represent my data by a line graph because it most accurately represents the correlation between my variables by including a very useful and concise line of best fit which reveals which direction the line is moving toward is in. This connects to my hypothesis because we can clearly see from the line that as the income level goes up the rate of eating healthy food goes up and the line of best fit is linear and positive whereas the rate of eating unhealthy food goes down as income level goes down and the line of best fit is in linear and negative. 

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Correlation between income and food choices- Aleisha Tripathy

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Correlation between income and food choices- Aleisha Tripathy

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I chose a pie chart to include in my data so that I can represent the percentage of the frequency of names chosen that people think represent the "richest/ wealthiest Americans". A pie chart was appropriate for my investigation because I am not exploring variables changing over time, but rather the results of a poll that occurred one time. The viewer can gauge which result was most frequent amongst participants as well as how common the other results were.

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Frequency histograms are a way to display measures of central tendencies visually. The number of bars in the histograms can suggest that a certain pattern or characteristic is more or less frequent. From histograms, we are able to know the minimum, maximum, and median. We can also estimate a mean based on the distributions we see. Having individual histograms that have the same y-axis and x-axis scales for each swimming event is convenient because we are able to easily visualize the differences and similarities in the frequencies.

When comparing graphs, viewers are able to see which swimming events are the fastest on average based on how frequent values closest to zero occur. For example, viewers can infer that freestyle is the fastest stroke because the general pattern the histogram shows is that the highest frequencies occur in values closer to zero than they do for the other three swimming events. How close together or how evenly or unevenly distributed the bars on the histogram are can tell us how strong the patterns between the two variables are.

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I chose a pie chart to include in my data so that I can represent the percentage of the frequency of names chosen that people think represent the "richest/ wealthiest Americans". A pie chart was appropriate for my investigation because I am not exploring variables changing over time, but rather the results of a poll that occurred one time. The viewer can gauge which result was most frequent amongst participants as well as how common the other results were.

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Who was the Richest American_ (according to polls)

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Create a new document and edit with others at the same time -- from your computer, phone or tablet. Get stuff done with or without an internet connection. Use Docs to edit Word files. Free from Google.

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These statistical correlations present the data in a scatter plot that outlines the relationship between two of my variables and the end result of a trendline or “R” value, also known as the Pearson Product-Moment Correlation Coefficient. This coefficient is meant to calculate the linear strength of a relationship between two variables. This coefficient is not meant to calculate whether one of those variables causes the other, rather it calculates how much the two variables have a relationship to one another. In the graph “Correlation Between Average Cookie Spread and Each Flour's Protein Content”, I graphed the quantitative data against each other, which I hope to bring clarity to my hypothesis that the higher the protein content the smaller the spread, and the lower the protein content the larger the spread. In graphing this, the r value I found was -0.318, rounded to three significant figures. This r value shows a negative, weak, linear correlation between the two variables presented, which means that as the protein content increases, the spread may decrease, and vice versa.

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These two histograms help represent the correlation that is trying to be explored. It seems like in these graphs, they are bimodal distributions as well. It is surprising to see that the graphs drastically increase, showing how the places with higher data, meaning better physical activity and consumption of fruits and vegetables, to the places with lower data meaning worse data regarding the variables for the NYC neighborhoods. In addition, this indicates the places that have more access or better nutrition implementations. In the data found, Manhattan had higher physical activity and lower consumption of no fruits and vegetables percentages (as seen in the data table),  to the Bronx, Queens, and Brooklyn having relatively higher percentages for both variables. This allows for the frequencies to be higher or lower as well based off the percentages of those neighborhoods. Overall, the histograms help depict the correlation of healthy eating habits to physical activity.

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Graph Explanation & Interpretation: I chose to represent my data using a scatter plot, as I am trying to display the relationship between two numeric variables. Additionally, identification of correlational relationships are common with scatter plots, so I felt that representing my raw data in this way would be sufficient to highlight the relationship between the two variables. This scatter plot indicates that an extremely weak trend exists. All of the points seem to be relatively dispersed around the trend line, further highlighting this weak correlation. The trend line is horizontal, and is tilted slightly downwards, indicating a slightly negative correlation. Because there seems to be such a weak correlation, this means that the dependent variable (stress levels) is likely dependent on a number of different variables. While theater could play a part in influencing levels of stress, stress is highly subjective and subject to change due to a variety of reasons. If I were to conduct this survey again, I would need to find a way to somehow further isolate my variables to better display how theater can impact one’s life.

Pearson Correlation Coefficient Test -

R-value:  -0.0155949751 → -0.0156

Description & Interpretation: In looking at the r-value, it is clear that hours spent participating in theater per week and daily stress levels have a very weak, negative, non-linear relationship. This means that as the hours spent participating in theater per week increases, the stress levels of individuals vary. There is almost no correlation between the variables, which is likely because there are many factors that influence one's mental wellbeing and stress threshold. This supports the idea that correlation does not always imply causation, as theater can be a source of stress for many (or a source of stress relief), but there are many other factors in life that play into one’s ability to handle stress.

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Max Davis comparison of away and home game wins.

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Max Davis comparison of away and home game wins.

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For my raw data when it came to home and away wins and how it influenced win percentage, I thought it would make the most sense to compare both sets of data (home and away) by displaying scatter plots and correlation coefficients. Scatter plots are generally used when two variables need to be compared to each other mathematically and how they might influence one another. Additionally, scatter plots are able to display many collected data points and a general trend that the data points represent when compared to each other. This then leads to how the correlation coefficient connects to what scatter plots represent. Correlation coefficient represents the relationship between two variables being compared to one another. The result from a correlation coefficient in a scatter plot is the R value. The closer the R value is to 1 or -1 displays that the variables have a good or almost perfect relationship between one another.

When looking at the data I collected for home and away games and the correlation it shares with win percentage, both home and away games won and loss have a very significant impact on win percentage and neither one dominates the other in importance. This is proven when looking at the scatter plots as the R values are both very close to one another as when looking at the R value of teams home wins it’s 0.83 which displays that there's a good correlation between home games won. This is also the case when looking at away wins as the R value is very close to the data of home games which is 0.836.

This holds significance as it displays that home wins and away both have a very important impact on win percentage and they are not very mutually exclusive to one another when looking at NBA team’s both home and away games. A prime example of this is how the Suns finished with 32 home and away wins this season which led them to finishing in first place when it came to the NBA.

But even though it’s someone of a minor difference, from looking at certain data points in both graphs it’s displayed how home games and wins are highly valued than when it comes to away wins.

This is seen as when looking at the mean of home games won this season per each NBA team it comes out to 22.3. When looking at away wins it comes out to an average of 18.7 wins. It doesn’t seem like a huge difference in general and when looking at the scatter plots but when looking at the wider NBA context, a difference of 3.6 games is extremely substantial when it comes to standings and potential playoff placement.

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I made this pie chart to represent the survival rate for each individual class. When compared to the previous pie chart we can see that a large majority of the people in Class 2 survived, followed by Class 1 and lastly, in Class 3, 21.4% of the people survived. This allows us to look at the difference between this chart and the previous one as we can see that although the majority of the people who boarded were in Class 3, the majority of the people who survived were in Class 2 and in Class 3 the least amount of people survived. This further connects to my research through giving a clear visual to support that those who paid less for their fare, or those in Class 3 were less likely to survive even though they made up the majority of the passengers on board.

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I decided to use a scatter plot to represent my data because I wanted to understand how the points and penalty minutes of my data points compared with one another. This is a scatter plot graph that I created comparing each player’s career penalty minutes and their career points. I noticed that a lot of the data is spread out and does not fall on the line of best fit. I think the inconsistency in the points reflects the idea that players have different roles in hockey. Teams are not looking for one type of player, such as just a pure goal scorer, they are looking for players that instigate and bring energy to the game. I wonder how many of the points that are not on the line of best fit are considered outliers. The r value is the Pearson Correlation Coefficient, which represents the overall trends in the data. The r value that I found for this graph is 0.275, this represents a weak positive linear correlation between career points and penalty minutes. This further supports my hypothesis that players in the NHL have different kinds of roles and there is not one particular skill that NHL teams are looking to acquire over the rest. I also noticed through this graph that much of the data points are located towards the left bottom part of the graph, I think this just means that there aren't many players in the NHL that have played many games over 700, and I don't think this has an impact on the ultimate findings.

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This scatter plot shows the relationship between club value and the number of points the club gained in the 2021/2022 season of the Premier League. There is a trendline on the graph, showing the R-value or correlation between the two factors. The Pearson-Moment Correlation Coefficient is meant to show the relationship between two factors and how strong that relationship is. This graph has a very strong positive correlation, with an R-value of 0.909. As I predicted, this correlation is strong because the more money a club spends, the better the players they can afford so it is expected they have better results. Besides a few upsets, the club value and amount of points had a pretty strong correlation.

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I chose to represent my data using a histogram because I felt that it would illustrate the four different results of chair turns very clearly. Having my graph set up this way not only shows the results of which amount of chair turns is the most common for the top five finalists, but it also visualizes the frequency of each chair turn. Before conducting this research and collecting my data, I believed that the frequency would rise as the amount of chairs did, but that is not the case; the order of frequency from least to greatest is one chair: 12/105, three chairs: 22/105, four chairs: 32/105, two chairs: 39/105, which is clearly shown in this histogram. From collecting this data, I am able to start answering my research question because this has provided me with the frequency of each amount of chairs turned, allowing me to now find the correlation between a top five finalist, and how many chairs turned.

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This graph represents the goals per game scored by soccer players before and after getting transferred. It focuses on the top 21 biggest transfers in soccer history, and this graph is able to highlight the difference in performance. I choose to use a bar graph because it helps to show and highlight the difference in performance caused by a transfer. The red lines represent the year immediately after a transfer, while the blue is the year before.

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I decided to create a scatter plot to represent the relationship between the height of the competitors in the 2016 Rio Olympics Weightlifting Competition and their total score in kilograms. In my study, looking for a correlation between these two factors is very important because my study surrounds their relationship. In this graph, I notice that the correlation has an r-value of 0.74, meaning that there is a strong, positive correlation between the two factors. To be honest, I thought that the correlation would be even stronger, but it makes sense that there is some deviation away from the line of best fit, as weight does also play a role in this equation. Newton's second law could be stated as
F = ma, where F is force, m is mass, and a is accelaration. This means that more mass creates more force. Knowing this, the relationship between these two variables could actually be more of a correlation than a causation. In the future, I could look at the relationship between weight and total score.

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This scatter plot presents the correlation between the median household income and the percentage of students who met state standards on ELA state tests in NYC public schools in the 2018 to 2019 school year. This data was taken from 100 randomly selected zip codes. This scatter plot shows a moderate-strong, positive, linear correlation between the two variables. Many points are on or close to the line of best fit. A great amount of the points are plotted towards the lower y-values, illustrating that the household incomes of most zip codes in NYC are low, suggesting that many households are low income. When comparing the amount of points plotted on the lower range and higher range of the y values, the small amount of points plotted in the high income range suggests a wealth-gap. This scatter plot visually shows the data I collected, as well as the correlation between my two variables. The strong, positive, linear relationship that this scatter plot aids in answering my research question, and supports my hypothesis, and shows that as the median household income increases, the percentage of the proficiency of students on the ELA state tests increases.

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Who was/is the richest American? (With Current Data)

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Who was/is the richest American? (With Current Data)

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I chose a pie chart to include in my data so that I can represent the percentage of the frequency of names chosen that people think represent the "richest/ wealthiest Americans". A pie chart was appropriate for my investigation because I am not exploring variables changing over time, but rather the results of a poll that occurred one time. The viewer can gauge which result was most frequent amongst participants as well as how common the other results were.

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Who was_is the richest_wealthiest American

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I chose a pie chart for my representation of data in order to visually demonstrate the percentage of each relationship. This helps to visualize the differences in the amounts of people experiencing each issue and whether they are figure skaters or not. As seen from the pie chart, the largest ‘slice’ of the chart and therefore the largest percentage and probability is a non–figure skater experiencing body image and disordered eating altogether. The lowest percentage and, therefore, the probability is not being a figure skater and experiencing disordered eating. In all, the pie chart represents the relationships between figure skaters and nonfigure skaters having body image and disordered eating.

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I chose to use a scatterplot while including the trend line. The scatterplot was supposed to make it easier to interpret the correlation between books printed and CO2 emitted in that country. I made a graph for each country but I wasn't able to include the years in which each variable occurred in. I couldn't figure out a way to include 3 variables in a chart, so I thought that the year in which the variables occurred wasn't that relevant. I am glad that the trendline for Germany was clear in that the rise of books printed has a positive linear correlation with CO2 emitted into the atmosphere. South Korea, however, is a bit confusing because the trendline is pretty much a straight line. But while looking at the variables the X and Y axis doesn't increase very much. The points are pretty much around the same point. I think that this means that the correlation has almost stayed the same over the years.

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For this first presentation of data, I wanted to use a graph of vectors on a coordinate plane, because how much swimmers are moved off course as well as which direction they are skewed towards can be well represented by vectors. Vectors are line segments with magnitude and direction, and are used to measure how an object moves from one point to another. They all start at the origin to show their general direction, because there is no specific point where any of these vectors should start on the coordinate plane. The lines that are graphed above are vectors derived from all the vectors of wind direction and speed recorded throughout each day that I gathered data for. This means that the above vectors are the net wind speed and direction that a swimmer could have been pushed off course over the course of a day. I calculated this by creating different vectors, based on their cardinal direction and magnitude, for the wind direction that was recorded at 12 am, 6 am, 12pm, and 6 pm for each day, and adding them together.

Based on the graph above, I would say that the majority of the wind in the English Channel is blowing towards the southwest because the visual concentration of lines in the southwestern quadrant (Quadrant III) is very obvious. There is also very little southeastern wind, as there are only 3 vectors in Quadrant IV of the graph, which demonstrates that it would not usually be easy to swim across the English Channel, because that would be the most beneficial wind direction for someone swimming across the English Channel and it does not occur often.

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White and Blue Modern Health Tips Infographics

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Ruby Descoteaux Analysis & Approaches SL (3)