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Multiple Regression Analysis

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Multiple Regression Analysis

Multiple regression analysis is one of the most common types of linear regression analysis – linking dependent and independent variables to find trends. In contrast to simple linear regression, multiple linear regression is the term used when two or more independent variables are used to influence the dependent variable. Multiple linear regression analysis is viewed as more reliable and accurate than simple linear regression because of its ability to use two or more independent variables. Linear regression, as a whole, is used to view future trends and to see how much the dependent variables would change in certain scenarios (when the independent variables are changed).
Companies will use multiple linear regression to enable them to predict future trends and to understand what causes changes to occur. For example, a university would like to find out why certain students perform better in exams than others. The independent variables in this study would be: number of lectures attended, amount of revision, and A-level grades, with the dependent variable being the exam mark. The research study found that students who attended the most lectures and did the most revision ended up with higher marks than the students who did less revision and went to less lectures. However, the researcher found no correlation between previous A-level results and the outcome of the exam. This research would allow the university to focus more on lectures and revision, than on previous test results.

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