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Creating a multiple regression equation

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Reference no: EM132134611

Regression and Correlation 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 regression.

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?

Verified Expert

This task provides a clear working example of regression analysis. Simple linear regression was used to predict the dependent variable Annual Revenue using one independent variable. on the other hand, multiple regression analysis was used to predict the dependent variable Annual revenue using two or more independent variables

Reference no: EM132134611

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11/23/2018 1:37:33 AM

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