groebner business statistic 8ed ch15 minitab tutorial

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


Multiple Regression – Dummy Variable


Curvilinear Regression Prediction

Ashley Investment Services

More Examples

Chapter 15 Minitab Examples (cont’d)

Second Order Model


Standard Stepwise Regression

Lomgmont Corporation

Residual Analysis


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.


Identify columns for Variables. Click on OK


The Minitab output shows the correlation (r = -0.073)between Age and Square Feet.


The correlation between eachpredictor and Price is highly significant. Thus, each predictor will be inserted into the regression model.


Click on Stat, then Regression and then Regression again.


Define the columns containing the Response (Price)and Predictor Variables


The regression coefficients, R2, S, and sum of squares are all generated by the regression command.


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


Open file First City.MTW.

Multiple Regression – Dummy Variable - First City

Click on Stat then

Regression and then

Regression again.


Select the columns containing the Response and Predictor Variables.


The output shows an improved regression model with the variable, Area, included.


Curvilinear Relationships -


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

significant.

Data file is Ashley.MTW

Open File Ashley.MTW

File contains values for 20 Investment Advisors.

Curvilinear Relationships – Ashley Investment Services

To develop the scatter plot first click on Graphbutton then select Scatterplot


Select Simple


Identify the columns containing the variables to be graphed.


Relationship may be curvilinear – next, fit linear to see model results


Click on Stat then Regressionand then Regression.


Identify the columns containing the X and Y variables. Then click OK.


To find a nonlinear model, click on Stat then Regression and select Fitted Line Plot.


Minitab gives the choice of three models, select Quadratic.


This gives the Quadratic Regression Line. The Regression Equation and R-Squarevalue are given.


This gives Regression Equationand R-squarevalue. The R-Square value is larger than that for the linear model.


Interactive Effects - Ashley

Investment Services

Issue: The director of personnel is trying to determine

whether there are interactive effects in the relationship

between employee burnout and time spent socializing

with co-workers.

Objective: Use Minitab to determine whether interactive

effects between the two measures are statistically

significant.

Data file is Ashley-2.MTW

Interactive Effects – Ashley Investment Services

Open File Ashley-2.MTW


To simplify the next few steps, modify the names of Columns C2 and C3, adding X1 and X2


Using the Calculator tab, set up columns C4, C5 and C6 as:

Column C4 – Expression C2 * C2



Identify the column headings


Click on Stat then Regression and then Regression.

Identify the columns containing the X and Y variables. Then click OK.



Regression Coefficients

Issue: The company is interested in analyzing the

residuals of the regression model to determine whether

the assumptions are satisfied.

Objective: Use Minitab to analyze residuals from a

regression model.

Data file is First City-3.MTW

Residual Analysis -


Open file First City-3.MTW

Residual Analysis – First City Real Estate

Click on Stat, then Regressionand then Regression again.


Identify the x and y variables.


R-square = 96.9%


These are the options using the Graphsbutton – Select Residuals versus fits.


Residual Plot versus fitted y values.


Select Histogram of residuals


groebner business statistic 8ed ch15 minitab tutorial

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