# Guide to Using Excel 2007 For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 8th Ed. Chapter 15: Multiple.

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Guide to Using Excel 2007 For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 8th Ed. Chapter 15: Multiple Regression and Model Building By Groebner, Shannon, Fry, & Smith Prentice-Hall Publishing Company Copyright, 2011

Chapter 15 Excel Examples Multiple Regression Multiple Regression Multiple Regression Multiple Regression First City Real Estate Multiple Regression –Dummy Variable Multiple Regression –Dummy Variable Multiple Regression –Dummy Variable Multiple Regression –Dummy Variable First City Real Estate Curvilinear Regression Curvilinear Regression Curvilinear Regression Curvilinear Regression Ashley Investment Services Interaction Effects Interaction Effects Interaction Effects Interaction Effects Ashley Investment Services Ashley Investment Services More Examples

Chapter 15 Excel Examples Residual Analysis Residual Analysis Residual Analysis Residual Analysis First City Real Estate

Multiple Regression – First City Real Estate Issue: First City management wishes to build a model that can be used to predict sales prices for residential property. Objective: Use Excel 2007 to build a multiple regression model relating sales price to a set of measurable variables. Data file is FirstCity.xls

Multiple Regression – First City Real Estate Open the file FirstCity.xls, Sheet Homes-Sample 1 Select the Data Tab Select Data Analysis Select Correlation OK

Multiple Regression – First City Real Estate Input Range: A1:G320 Grouped by: Columns New Worksheet Ply: corr-1 OK

Multiple Regression – First City Real Estate This cell shows the correlation, 0.7477, between Price and Square Feet

Multiple Regression – First City Real Estate Return to Homes-Sample 1 Select the Data tab Select Data Analysis Select Regression OK

Multiple Regression – First City Real Estate Input Y Range: A1:A320 Input X Range: B1:F320 Select labels New Worksheet Ply: regress-1 OK

Multiple Regression – First City Real Estate Excel produces the Regression Model

Multiple Regression – First City Real Estate To determine if multicollinearity is a problem: Select the Add-Ins tab Select PHStat Select Regression Select Multiple Regression

Multiple Regression – First City Real Estate Y variable … : A1:A320 X variable … : B1:F320 Select First Cells in both … Select Regression Statistics … Select ANOVA and … Select Variance Inflation … OK

Multiple Regression – First City Real Estate PHStat will find Variance Inflation Factors for all independent variables. This slide shows the VIF for Garage # and all other X variables. Click on the sheet tabs X4,X3,X2,X1 to see the VIF for the other variables.

Issue: First City managers wish to improve the model by adding a location variable for the area. Objective: Use Excel 2007 to improve a regression model by adding a dummy variable for the area either foothills or flatland. Data file is First City.xls Multiple Regression - Dummy Variable -First City Real Estate

Multiple Regression- Dummy Variable – First City Real Estate Select the Home-Sample 2 sheet Select the Add-In tab Select PHStat Select Regression Select Multiple Regression

Multiple Regression- Dummy Variable – First City Real Estate Cut Bedrooms and Bathrooms columns and paste in cell G1. Delete columns C and D so that all data is in connected columns Select Data tab Select Regression Input Y Range: A1 – A320 Input X Range B1-E320 Select Labels New Worksheet: Regress-2 OK

Multiple Regression- Dummy Variable – First City Real Estate All variables are significant and have the expected sign

Curvilinear Relationships - Ashley Investment Services Issue: The director of personnel is trying to determine whether there is a relationship between employee burnout and time spent socializing with co-workers. Objective: Use Excel 2007 to determine whether the relationship between the two measures is statistically significant. Data file is Ashley.xls

Curvilinear Relationships – Ashley Investment Services Open the file Ashley.xls

Curvilinear Relationships – Ashley Investment Services Select A1:B21 Select the Insert Tab Select Scatter Plot Select the Scatter Plot desired

Curvilinear Relationships – Ashley Investment Services

Select the Data tab Select Data Analysis Select Regression

Curvilinear Relationships – Ashley Investment Services Y Range A1:A21 X Range B1:B21 Labels New Worksheet Ply: Ashley-1 OK

Curvilinear Relationships – Ashley Investment Services

To develop a nonlinear model, return to the data file. Create a new variable Socialization Squared

Curvilinear Relationships – Ashley Investment Services Select the Data tab Select Data Analysis Select Regression OK

Curvilinear Relationships – Ashley Investment Services Input Y Range: A1:A21 Input X Range: B1:C21 Select Labels New Worksheet Ply: Ashley-2

Curvilinear Relationships – Ashley Investment Services The output shows the R Square value and the Regression Coefficients.

Interaction Effects - Ashley Investment Services Interaction Effects - Ashley Investment Services Issue: The director of personnel is trying to determine whether the model can be improved by separating observations between those taken from men and women. Objective: Use Excel 2007 to determine whether the relationship between the measures can be improved. Data file is Ashley-2.xls

Interaction Effects – Ashley Investment Services Open file Ashley-2.xls Insert a new column C as (Socialization Squared) which is Column B squared Add Column E as Columns B * D Add Column F as Columns C * D

Interaction Effects – Ashley Investment Services Using The Insert tab and Chart tools set up a Scatter Plot for one gender

Interaction Effects – Ashley Investment Services Add the second gender Add exponential trend line for male and female

Interaction Effects – Ashley Investment Services The regression for the curvilinear model.

Issue: The company is interested in analyzing the residuals of the regression model to determine whether the assumptions of multiple regression are satisfied. Objective: Use Excel 2007 to analyze residuals from a regression model. Data file is First City-3.xls Residual Analysis - First City Real Estate Residual Analysis - First City Real Estate

Residual Analysis – First City Real Estate Open the file FirstCity3.xls Since Excel requires independent variables to be in adjacent columns – cut and paste these columns. We will be using: Price Sq. Feet Bedrooms Garage # Log of Lot Size Note: I will swap Lot Size and Log Lot Size to simplify the operation

Residual Analysis – First City Real Estate Select the Data tab Select Data Analysis Select Regression OK

Residual Analysis – First City Real Estate Define the range for X and Y variables. Excel gives several options for Residual Analysis but does not have as complete a set as Minitab

Residual Analysis – First City Real Estate This is the Residual Plot for Square Feet

Residual Analysis – First City Real Estate While Excel will not automatically generate a histogram of the standardized residuals, one can be created.

Residual Analysis – First City Real Estate Define Bin values for the Histogram

Residual Analysis – First City Real Estate Select the Data tab Select Data Analysis Select Histogram On the Histogram Chart identify the data and bins Select Chart output

Residual Analysis – First City Real Estate

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