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Correlation, simple linear, and Multiple Regression Analysis

This assignment is planned to be solved via EXCEL- So, please plan to use Excel ONLY for this assignment (also make sure you have installed the add in as advised in the previous posting).

Below is the grading rubrics- I have attempted to tie in the rubrics with the assignment objective and what needs to be specifically done for more clarity.

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?

Below is the grading rubrics- I have attempted to tie in the rubrics with the assignment objective and what needs to be specifically done for more clarity.

This posting will explain to run "simple" and "multiple" regression via excel as well as interpret the results. This posting will also explain the objectives of the assignment and ensure you also understand the grading rubrics for this assignment. 

Analyzed and predicted what sign each variable will be before running the regression, and explained why they made that prediction. - Before you conduct regressions, analyze and include commentary on how the Microsoft sales will correlate with each of the 3 variables you pick. In the commentary, mention if the correlation for sales and say Operating Income is positive and explain why.  

Interpreted the simple linear and multiple regression after running the regression.- You will conduct a total of 4 Regressions- 3 simple and 1 multiple.

Conduct Simple Regression between Sales (y) and first predictor variable, Sales and second predictor variable and then sales and third variable separately. 

Then conduct Multiple regression once with Sales as y (criterion) variable and all 3 variables together.

 For each of the above Regressions, look at the excel regression output and determine the correlation and look for significance from the output as well

 

Analyzed and explained if the regression fits the data well.

Look at the R squared value for each of the Excel regression outputs and determine the fit. 

Analyzed and explained if each predictor plays a significant role in explainning the significance of the multiple regression. 

Only look at the Excel output for multiple regression and look at the p values and determine significance for each of the 3 variables you picked 

Analyzed and explained if some predictors are not useful.

Considered removing the predictors that are not useful and rerunning the regression.

Analyzed and explained if the predictors are related too significantly to one another (correlation).