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With inferential Statistics, a researcher can make inferences about the population but the entire population may be unavailable so a representative sample of the population will do.
Estimation statistics is where inferences are made on a population from sample data. The 2 types of estimation statistics are confidence intervals and parameter estimation.

Hypothesis Testing is a brand of statistics that is used to make inferences about whether or not data support a particular hypothesis. Hypotheses are expressed in terms of the research variable and a hypothesis test is to determine whether there is sufficient evidence to make a generalisation whether that stated in the hypothesis is true to the population.

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This unit dealt with Data Analysis, the first session was on Qualitative data analysis. Qualitative data analysis is the non-numerical examination and interpretation of observations to discover underlying meanings and patterns (Babbie, 2014). It can be a tedious task to analysis qualitative data given the large amount of text data involved. In order for the researcher to understand all that qualitative data entails, they must use John Seidel simple model of the data analysis process. The model includes noticing things, collecting things and thinking about things.
There is a process involve in analysing qualitative data, the researcher must transcripe the data then organise the data followed by familiarising themselve with the data collected then code the data and finally find the themes of the data.
Computer-assisted data analysis is available for use to researchers when conduction qualitative research. These computer software will assist in the data storage and management, data searching and retrieval, coding, developing and testing theory and writing reports. As the researcher, you are required to do the thinking, reflection and analysis of the data as the computer software is unable to do this.

Session 8.2 deals with Quantitative data analysis, this is the opposite of qualitative data analysis. It is the systematic analysis of numerical data collected or the analysis of data that is converted to numerical form and quantitative data can be analysed using statistic which is the study of numerical data, that is, the collection, analysis and interpretation of numerical data.
In Statistic there are 2 branches, they are inferential and descriptive. The former is used to make inferences or draw conclusions about a population and the latter is used to describe, aggregate and present the features and characteristics of data collected.
Descriptive data analysis can be done by using either univariate analyis or bivariate analysis. The former is the study of the characteristics of one variable at a time while the latter is the study of the relationship between 2 variables.

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Used to display large amounts of data

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After preparing the data for analysis, the researcher must engage in in exploratory data analysis to visualise the data. This can be done using various tables and charts such as frequency tables, bar charts, pie charts, histograms, line graphs, stem and leaf. Frequency tables can be used to present numerical data, the data is easy to read and organized in a table format in a form of ascending order. In Addition, it is useful in showing data where no observation occur within the range that cannot be shown in a pie chart or a bar chart.
Bar Charts are used to display number or frequency for discrete data and are useful for ungrouped data, it is not to be used for grouped data. Histogram, on the other hand are used to display continuous random data and it allows the researcher to easily indentify the most popular class in the data along with the simple patterns in the data.
Pie Charts are used to display grouped or categorical data and represent the total distribution of data within each category. Line graphs are used to highlight how one or more variables vary over a continuous time. Whereas, scatter plots are used to display the relationship between 2 variables and can be used to determine if a systemtic or causal relationship exists. Lastly, a stem and leaf display is used to display large data sets graphically and it is useful in presenting both continuous and discrete data.

It is important to understand the uses of the charts and graphs to facilitate preliminary data analysis and use in reports. Charts and graphs can be used in research reports as a way of displaying data during the presentation state of the report as they can be used as a way to summarise results. The researcher must ensure that the charts and graphs they choose to use in their reports are designed thoroughly so that it would be understood by persons who may not have an understanding in the topic or charts and graphs.

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This unit focuses on Data Preparation and Presentation, Session 7.1 deals with Edition and Coding of data. After a researcher is done with collecting data, he or she must make sense of that data which was collected as fieldwork usually produces data with errors which must be removed in order for the integrity of the data collected be compromise. This leads the researcher to data editing, that is the process used to check data for omissions, consistency and legibility in preparation of coding. Coding is next step in data preparation as it is done to group data into categories by assigning a numerical number or a symbol to edited data. In qualitative research, codes are not numerical, instead of using numbers, words and phrases that are representative of themes are used. The codebook contains the details on the all the codes used in the research and can be used for more accurate and efficient data entry and analysis. The codebook describes data files which are the files used to conduct analysis. In recent times, software has been created for the coding of data as computer files contains data, one such program is SPSS which is a data editor software.
A database must be created, which allows for the data to be stored and organized for ease of manipulation and analysis and it is structured based on fields, records and files. A data dictionary is mainly for the clarification of data for the benefit of persons involved in the analytic process. Once coding of the data is complete, the data must be inputted into the database.
Data Cleaning is a step that must be taken before the data is processed and it is a means of quality control. It allows the researcher to verify all the collected values are correct and in conjunction with certain rules.
Data modification is when data is altered to create broader categories, create an index score by adding data used to code responses and to rearrange the numerical order of categories.

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This unit dealt with Questionnaires, the design aspect along with the administration and errors that may be created and the way to minimise said errors.
The administration of questionnaires is important as it assist the Respondents with the interpretation of the questions. In Addition, the design of the questionnaire is extremely important as it determines the type of data that the Researcher is seeking with the questionnaire. The collection of relevant data is also dependent on the questionnaire design.
A questionnaire can be administered 2 ways, it can be by an interviewer or self administered, which is  when the Respondent records their own responses on the questionnaire.
There are 2 types of questions that should be used in a questionnaire, the open-ended and closed questions. The former allows the Respondent more freedom in answering the  questions while the latter only allows the Respondent to choose an answer from the fixed set of options. In Addition, the construction of the questions are important, once they are constructed according to the guidelines, they can reduce some of the errors associated with questionnaires and can increase the accuracy and relevance of the questions.
The errors associated with Questionnaire are interviewer error and Respondent error. The former is created by the interviewer, which can be caused when the interviewer cheats or by influencing the responses from the Respondents. The latter is created when a Respondent is not trueful when answering the questions. These errors can be minimise by proper questionnaire design

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This Unit introduced Primary and Secondary data, the former is original data that the researcher collects for the sole purpose of his/her study and the latter is data that was not originally collected by the current researcher with the purpose of the original researcher.
Sources of primary data are interviews, diaries, letters, personal journals and surveys along with others. There are 2 types of secondary sources, internal and external. The former is where research was created within the organisation and the latter is where research on a organisation is used by another organisation; for example, policy reports, presented papers, search engines, scholarly books and others.
When collecting primary data, the researcher can collect additional data if needed and the researcher has greater control over the quality of data collected. However, primary data is costly to collect and it tends to be more time consuming. On the other hand, when collecting secondary data, it saves more time as the data is readily available and it saves money since it is cheaper to collect secondary data than primary data but with secondary data, the data can be outdated and the accuracy of the data may not be known.
Primary data is collected at first hand while secondary data is collected by third parties.
Field workers are trained by researchers to carry out specific roles in order to gather the necessary data that is necessary for the research.

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Research proposal was used as a method of communicating the research plan to Manager or Head of Companies. The Research proposal is used to highlight a problem and a solution to that particular problem. A researcher can be hired internally or externally and must have adequate knowledge of the company. There are basic questions that a Researcher must answer in the creation of a research proposal as different companies may remove or require some components based on the size of the study (UWIOC 2015).
The Research report highlights what was done and the outcome of the reserch that was carried out. The report differs from the proposal as the proposal states what would be done and the report highlights what was done during the research.

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Research proposal is a systematic way to interpret data to managers or head of departments. It includes a 6 step process, i.e. problem definition, Research Design, Measurement, Data collection, Data Analysis and Results & Findings.
Problem definition does not mean that the business has a problem but it can be that the business wants to make an improvement. There is a 6 step process is identify key symptoms, identify problems from the symptoms, write management decision statement & research objectives, determine unit of analysis, determine relevan variables and write research questions and or hypotheses.
After the problem is defined, the next step is for the researcher to prepare a literature review.
The literature reveiw can be defined as a summary of the important points of an article bascially using information that already existed and this information can be from a primary or secondary source and putting it into context.

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Reasons for Conducting business research

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Reasons for Conducting business research

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Here are several reason for conduction business research

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In this first section of Unit 1, Research was defined as the process by which you collect, analyze, interpret and use data (Mertens, 2015).

Then, business research was defined as the objective and systematic inquiry into a specify business problem (UWI Open Campus, 2015). This is important as it benefits the business as they will learn about the market, market and other businesses products so that they can maintain a competitive edge.

There are 3 main types of business research, they are exploratory which is qualitative in nature; descriptive and causal research which are both quantitative.