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

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

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

Curvilinear Relationships – Ashley Investment Services

Select Simple

Curvilinear Relationships – Ashley Investment Services

Identify the columns containing the variables to be graphed.

Curvilinear Relationships – Ashley Investment Services

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

Curvilinear Relationships – Ashley Investment Services

Click on Stat then Regressionand then Regression.

Curvilinear Relationships – Ashley Investment Services

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

Curvilinear Relationships – Ashley Investment Services

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

Curvilinear Relationships – Ashley Investment Services

Minitab gives the choice of three models, select Quadratic.

Curvilinear Relationships – Ashley Investment Services

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

Curvilinear Relationships – Ashley Investment Services

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

Curvilinear Relationships – Ashley Investment Services

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

Interactive Effects – Ashley Investment Services

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

Interactive Effects – Ashley Investment Services

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

Column C4 – Expression C2 * C2

Column C5 – Expression C2 * C1

Column C6 – Expression C4 * C3

Identify the column headings

Interactive Effects – Ashley Investment Services

Click on Stat then Regression and then Regression.

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

Interactive Effects – Ashley Investment Services

Interactive Effects – Ashley Investment Services

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 -

First City Real Estate

Open file First City-3.MTW

Residual Analysis – First City Real Estate

Click on Stat, then Regressionand then Regression again.

Residual Analysis – First City Real Estate

Identify the x and y variables.

Residual Analysis – First City Real Estate

R-square = 96.9%

Residual Analysis – First City Real Estate

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

Residual Analysis – First City Real Estate

Residual Plot versus fitted y values.

Residual Analysis – First City Real Estate

Select Histogram of residuals

Residual Analysis – First City Real Estate

Residual Analysis – First City Real Estate