Guide to Using Minitab 14 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
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Groebner Business Statistic 8ed Ch15 Minitab Tutorial
Materi ini merupakan bahan ajar sebagai pelengkap e-materi mata kuliah statistika bisnis. Groebner, D. F., Shannon, P. W., Fry, P. C. & Smith, K. D. (2011). Business Statistics: A Decision Making Approach 8th Edition. pearson.
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Guide to Using Minitab 14 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, & SmithPrentice-Hall Publishing Company
Copyright, 2011
Chapter 15 Minitab Examples
Multiple Regression
First City Real Estate
Multiple Regression – Variance Inflation Factor
First City Real Estate
Multiple Regression – Dummy Variable
First City Real Estate
Curvilinear Regression Prediction
Ashley Investment Services
More Examples
Chapter 15 Minitab Examples (cont’d)
Second Order Model
Ashley Investment Services
Standard Stepwise Regression
Lomgmont Corporation
Residual Analysis
First City Real Estate
Multiple RegressionFirst 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 Minitab to build a multiple
regression model relating sales price to a set of
measurable variables.
Data file is First City.MTW
Open File First City.MTW
Multiple Regression – First City Real Estate
First click on Stat,then Basic Statisticsand finally on Correlation.
Multiple Regression – First City Real Estate
Identify columns for Variables. Click on OK
Multiple Regression – First City Real Estate
The Minitab output shows the correlation (r = -0.073)between Age and Square Feet.
Multiple Regression – First City Real Estate
The correlation between eachpredictor and Price is highly significant. Thus, each predictor will be inserted into the regression model.
Multiple Regression – First City Real Estate
Click on Stat, then Regression and then Regression again.
Multiple Regression – First City Real Estate
Define the columns containing the Response (Price)and Predictor Variables
Multiple Regression – First City Real Estate
The regression coefficients, R2, S, and sum of squares are all generated by the regression command.
Multiple Regression – First City Real Estate
Issue: First City managers wish to improve the
model by adding a location variable.
Objective: Use Minitab to improve a regression
model by adding a dummy variable.
Data file is First City.MTW
Multiple Regression –
Dummy Variable
First City Real Estate
Open file First City.MTW.
Multiple Regression – Dummy Variable - First City
Click on Stat then
Regression and then
Regression again.
Multiple Regression – Dummy Variable - First City
Select the columns containing the Response and Predictor Variables.
Multiple Regression – Dummy Variable - First City
The output shows an improved regression model with the variable, Area, included.
Multiple Regression – Dummy Variable - First City
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 Minitab to determine whether the
relationship between the two measures is statistically