Correlation simple linear and Multiple Regression Analysis Multiple regression

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 timeAfter 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?Submit your answers in a two- to three-page Word document.On a separate page cite all sources using the APA guidelines with in-text citations and no wiki websites.Assignment 2 Grading Criteria Maximum PointsAnalyzed and predicted what sign each variable will be before running the regression and explained why they made that prediction. 10Interpreted the simple linear and multiple regression after running the regression. 10Analyzed and explained if the regression fits the data well. 5Analyzed and explained if each predictor plays a significant role in explaining the significance of the multiple regression.5Analyzed and explained if some predictors are not useful. 5Considered removing the predictors that are not useful and rerunning the regression. 5Analyzed and explained if the predictors are related too significantly to one another (correlation). 5Used correct spelling grammar and professional vocabulary. Cited all sources using the APA guidelines. 5Total 50