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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Presentations text content in 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

Slide3

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

Quadratic

.

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

Identify the column headings

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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

Slide37

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

Slide38

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

Slide43

Residual Plot versus fitted y values

.

Residual Analysis – First City Real Estate

Slide44

Select

Histogram of residuals

Residual Analysis – First City Real Estate

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Residual Analysis – First City Real Estate

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