Correlation, simple linear, and Multiple Regression Analysis

Multiple regression analysis is widely used in business research in order to forecast and predict purposes. It is also used to determine what independent variables have an influence on dependent variables, such as sales.

Sales can be attributed to quality, customer service, and location. In multiple regression analysis, we can determine which independent variable contributes the most to sales; it could be quality or customer service or location.

Now, consider the following scenario. You have been assigned the task of creating a multiple regression equation of at least three variables that explains Microsoft’s annual sales.

Use a time series of data of at least 10 years. You can search for this data using the Internet.

• Before running the regression analysis, predict what sign each variable will be and explain why you made that prediction.

• Run three simple linear regressions by considering one independent variable at a time

• After running each of the three linear regressions, interpret the regression.

• Does the regression fit the data well?

• Run a multiple regression using all three independent variables.

• Interpret the multiple regressions. Does the regression fit the data well?

• Does each predictor play a significant role in explaining the significance of the regression?

• Are some predictors not useful?

• If so, did you consider removing those and rerunning the regression?


• Are the predictors related too significantly to one another? What is the coefficient of correlation “r”? Do you think this “r” value suggests a strong correlation among the predictors (the independent variables?

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