# Guide to Using Minitab 14 For Basic Statistical Application

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## Guide to Using Minitab 14 For Basic Statistical Application

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Guide to Using Minitab 14 For Basic Statistical Applications

To AccompanyBusiness Statistics: A Decision Making Approach, 8th Ed.Chapter 15:Multiple Regression and Model BuildingByGroebner, Shannon, Fry, & SmithPrentice-Hall Publishing CompanyCopyright, 2011

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Chapter 15 Minitab Examples

Multiple Regression First City Real EstateMultiple Regression – Variance Inflation Factor First City Real EstateMultiple Regression – Dummy Variable First City Real EstateCurvilinear Regression Prediction Ashley Investment Services

More Examples

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Chapter 15 Minitab Examples (cont’d)

Second Order Model Ashley Investment ServicesStandard Stepwise Regression Lomgmont CorporationResidual Analysis First City Real Estate

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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 Minitab to build a multiple regression model relating sales price to a set of measurable variables. Data file is First City.MTW

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Open File

First City.MTW

Multiple Regression – First City Real Estate

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First click on

Stat, then Basic Statistics and finally on Correlation.

Multiple Regression – First City Real Estate

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Identify columns for

Variables. Click on OK

Multiple Regression – First City Real Estate

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The Minitab output shows the

correlation (r = -0.073) between Age and Square Feet.

Multiple Regression – First City Real Estate

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The

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

Multiple Regression – First City Real Estate

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Click on

Stat, then Regression and then Regression again.

Multiple Regression – First City Real Estate

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Define the columns containing the

Response (Price) and Predictor Variables

Multiple Regression – First City Real Estate

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The

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

Multiple Regression – First City Real Estate

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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

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Open file

First City.MTW.

Multiple Regression – Dummy Variable - First City

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Click on

Stat then Regression and then Regression again.

Multiple Regression – Dummy Variable - First City

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Select the columns containing the

Response and Predictor Variables.

Multiple Regression – Dummy Variable - First City

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

Multiple Regression – Dummy Variable - First City

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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

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Open File

Ashley.MTW

File contains values for 20 Investment Advisors.

Curvilinear Relationships – Ashley Investment Services

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To develop the scatter plot first click on

Graph button then select Scatterplot

Curvilinear Relationships – Ashley Investment Services

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Select

Simple

Curvilinear Relationships – Ashley Investment Services

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Identify the columns containing the

variables to be graphed.

Curvilinear Relationships – Ashley Investment Services

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

Curvilinear Relationships – Ashley Investment Services

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Click on

Stat then Regression and then Regression.

Curvilinear Relationships – Ashley Investment Services

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Identify the columns containing the

X and Y variables

. Then click OK.

Curvilinear Relationships – Ashley Investment Services

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To find a nonlinear model, click on

Stat

then Regression and select Fitted Line Plot.

Curvilinear Relationships – Ashley Investment Services

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Minitab gives the choice of three models, select

.

Curvilinear Relationships – Ashley Investment Services

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This gives the

Quadratic Regression Line. The Regression Equation and R-Square value are given.

Curvilinear Relationships – Ashley Investment Services

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This gives

Regression Equation and R-square value. The R-Square value is larger than that for the linear model.

Curvilinear Relationships – Ashley Investment Services

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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

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Interactive Effects – Ashley Investment Services

Open File

Ashley-2.MTW

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Interactive Effects – Ashley Investment Services

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

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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

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Interactive Effects

– Ashley Investment Services

Click on

Stat

then Regression and then Regression.

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Identify the columns containing the

X and Y variables

. Then click OK.

Interactive Effects

– Ashley Investment Services

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Interactive Effects – Ashley Investment Services

Regression Coefficients

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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

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Open file

First City-3.MTW

Residual Analysis – First City Real Estate

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Click on

Stat, then Regression and then Regression again.

Residual Analysis – First City Real Estate

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Identify the

x and y variables.

Residual Analysis – First City Real Estate

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R-square = 96.9%

Residual Analysis – First City Real Estate

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These are the options using the

Graphs button – Select Residuals versus fits.

Residual Analysis – First City Real Estate

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Residual Plot versus fitted y values

.

Residual Analysis – First City Real Estate

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Select

Histogram of residuals

Residual Analysis – First City Real Estate

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Residual Analysis – First City Real Estate

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