Advanced Regression in Excel - The Excel Statistical Master
50 pages of complete step-by-step instructions and videos showing exactly how to perform a variety of advanced regression techniques and how to do them all in Excel. Some of these advanced regression techniques include nonlinear regression, logistic regression, and dummy-variable regression. This e-manual will also clearly explain all of the steps of a regression and how to quickly read the output of regression done in Excel. This e-manual is loaded with completed examples, screenshots, and videos of the advanced regression techniques all being performed in Excel. The instructions, screenshots, and videos are clear and easy-to-follow but at the graduate level. If you are currently taking a difficult graduate-level statistics course that covers advanced regression techniques such as nonlinear regression, logistic regression, or dummy-variable regression, you will find this e-manual to be an outstanding course supplement that will explain these advanced regression techniques much more clearly than your textbook does. If you are a business manager, you will really appreciate how easily and clearly this e-manual will show you how you can perform these advanced regression techniques in Excel to solve difficult statistical problems on your job. Nonlinear regression, logistic regression, and dummy-variable regression are extremely useful in business, but are not widely understood. For example, you can use logistic regression to accurately calculate the probability that your next customer will make a purchase. That use of logistic regression is covered in detail in this e-manual. A detailed model of dummy-variable regression is provided in the e-manual which shows how to calculate how important each attribute of your product is to your customers. Not many people have a working knowledge of these advanced and useful regression techniques. You will. This e-manual will make you an Excel Statistical Master of nonlinear regression, logistic regression, and dummy-variable regression.
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Add-Ins Adding New Input Adjusted R Square Ads Running)B1 Analysis in Excel B3 to B5 calculate Cell G13 cells B3 Constraints consumer’s degree Correlation Coefficient Crucial Step curve-fitting dependent variable Dummy Variable Regression example Excel Output Excel Regression Output Excel Solver Eyeballing the data Graphing the Data GRG Nonlinear Here’s Internet Connection Turned linear linked video shows Logistic Regression Low Correlation Mark Harmon marketer Nonlinear Regression Number of Ads number of sales optimal Output of Regression output variable P-Value Perform Conjoint Analysis Predicted Sales predictive equation predictors Prevent Collinearity previous prospects prior to running probability of purchasing Product Attributes product choices prospect’s age Quickly Read Regression Analysis regression equation Regression in Excel Remove Input Variables resulting P(X Run the Regression Running a Regression Running Correlation Analysis running the Solver scatterplot Significance of F Solver dialogue box Sound and Internet Step-By-Step Video Showing Steps of Regression understand the output Utilities Y-Intercept