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Guide to Using Minitab 14 For Basic Statistical Application Guide to Using Minitab 14 For Basic Statistical Application

Guide to Using Minitab 14 For Basic Statistical Application - PowerPoint Presentation

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Guide to Using Minitab 14 For Basic Statistical Application - PPT Presentation

To Accompany Business Statistics A Decision Making Approach 8th Ed Chapter 16 Analyzing and Forecasting TimeSeries Data By Groebner Shannon Fry amp Smith PrenticeHall Publishing Company ID: 426123

company trend time forecasting trend company forecasting time smoothing harrison exponential equipment model series nonlinear taft ice based cream

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Slide1

Guide to Using Minitab 14 For Basic Statistical Applications

To AccompanyBusiness Statistics: A Decision Making Approach, 8th Ed.Chapter 16:Analyzing and Forecasting Time-Series DataByGroebner, Shannon, Fry, & SmithPrentice-Hall Publishing CompanyCopyright, 2011Slide2

Chapter 16 Minitab ExamplesTrend Based Forecasting

Taft Ice Cream CompanyNonlinear Trend Harrison Equipment CompanySeasonal Adjustment Big Mountain Ski ResortSingle Exponential Smoothing Dawson Graphic DesignsMore ExamplesSlide3

Chapter 16 Minitab ExamplesDouble Exponential Smoothing

Billingsley Insurance CompanySlide4

Trend Based Forecasting - Taft Ice Cream Company

Issue: The owners of Taft Ice Cream Company considering expanding their manufacturing facilities. The bank requires a forecast of future sales. Objective: Use Minitab to build a forecasting model based on 10 years of data. Data file is Taft.MTWSlide5

Open File

Taft.MTWTrend Based Forecasting – Taft Ice Cream CompanySlide6

First click on

Graph, then Time Series Plot.Trend Based Forecasting – Taft Ice Cream CompanySlide7

Select

SimpleTrend Based Forecasting – Taft Ice Cream CompanySlide8

Enter

data column to be graphed. Then click Time/ScaleTrend Based Forecasting – Taft Ice Cream CompanySlide9

Click on

Calendar and specify Year – then determine starting Year (1997)Trend Based Forecasting – Taft Ice Cream CompanySlide10

A linear trend is evident in this time series plot.

Trend Based Forecasting – Taft Ice Cream CompanySlide11

To develop the trend line, click on

Stat, then Regression and Regression againTrend Based Forecasting – Taft Ice Cream CompanySlide12

Identify the columns containing the

Time Series and also specify the dependent variable (t) Trend Based Forecasting – Taft Ice Cream CompanySlide13

Linear Trend Model

Trend Based Forecasting – Taft Ice Cream CompanySlide14

A second method - select

Stat – Time Series – Trend AnalysisTrend Based Forecasting – Taft Ice Cream CompanySlide15

Specify time series variable and select

Linear model typeTrend Based Forecasting – Taft Ice Cream CompanySlide16

Measures of forecast accuracy

Trend Based Forecasting – Taft Ice Cream CompanySlide17

Issue:

Harrison Equipment is interested in forecasting future repair costs for a crawler tractor it leases to contractors. Objective: Use Minitab to develop a nonlinear forecasting model. Data file is Harrison .MTWNonlinear Trend – Harrison Equipment CompanySlide18

Open File

Harrison.MTWNonlinear Trend – Harrison Equipment CompanySlide19

Select

Graph – then Time Series PlotNonlinear Trend – Harrison Equipment CompanySlide20

Select

SimpleNonlinear Trend – Harrison Equipment CompanySlide21

Define Time Series variable (Repair Costs) and then select

Time/ScaleNonlinear Trend – Harrison Equipment CompanySlide22

Select

Calendar and pick Quarter Year optionNonlinear Trend – Harrison Equipment CompanySpecify starting

Quarter

and

YearSlide23

Nonlinear Trend – Harrison Equipment CompanySlide24

To develop a linear model, click on

Stat, then Time Series and finally Trend Analysis.Nonlinear Trend – Harrison Equipment CompanySlide25

Specify time series variable (Repair Costs) and select

LinearNonlinear Trend – Harrison Equipment CompanySlide26

Linear Model Results

Nonlinear Trend – Harrison Equipment CompanySlide27

To develop a model with time squared as the variable Click on

Calc, then Calculator.Nonlinear Trend – Harrison Equipment CompanySlide28

Identify column for new variable, in Expressions box enter form of new variable. Click

OKNonlinear Trend – Harrison Equipment CompanySlide29

Click on

Stat, then Regression and Regression again.Nonlinear Trend – Harrison Equipment CompanySlide30

Define the Response variable (

Repair Costs) Predictors (Qtr2) then click Storage.Nonlinear Trend – Harrison Equipment CompanySlide31

Under Diagnostic Measures select Residuals, under Characteristics select Fits

. Click OK twice.Nonlinear Trend – Harrison Equipment CompanySlide32

The Minitab output shows the regression model.

Nonlinear Trend – Harrison Equipment CompanySlide33

Seasonal Adjustment -

Big Mountain Ski ResortIssue: The resort wants to build a forecasting model from data that has a definite seasonal component.Objective: Use Minitab to develop a forecasting model adjusting for seasonal data. Data file is Big Mountain.MTWSlide34

Open File

Big Mountain.MTWSeasonal Adjustment – Big Mountain Ski ResortSlide35

Seasonal Adjustment – Big Mountain Ski Resort

Click on Stat

,

then

Time Series

and then select

Decomposition

. Slide36

Define the

Variable, the Model Type, the Seasonal length and the Model Components.

Seasonal Adjustment – Big Mountain Ski ResortSlide37

The graph shows the actual and predicted values.

Seasonal Adjustment – Big Mountain Ski ResortSlide38

This output shows the original data and other graphs.

Seasonal Adjustment – Big Mountain Ski ResortSlide39

The

Trend Line Equation, the Seasonal Indices and MAPE, MAD and MSD are also given.Seasonal Adjustment – Big Mountain Ski ResortSlide40

Single Exponential Smoothing Dawson Graphic Design

Issue: The company needs to develop a forecasting model to forecast incoming customer calls so they are able to make informed future staffing decisions. Because the time series appears to be relatively stable, a relatively small smoothing constant will be used.Objective: Use Minitab to develop a single exponential smoothing forecasting model. Data file is Dawson.MTWSlide41

Open File

Dawson.MTWSingle Exponential Smoothing – Dawson Graphic DesignSlide42

Click on

Stat, then Time Series and finally Single Exponential Smoothing.Single Exponential Smoothing – Dawson Graphic DesignSlide43

Identify the

Time Series Variable. Either specify alpha or ask Minitab to optimize the forecasting model. Select StorageSingle Exponential Smoothing – Dawson Graphic DesignSlide44

Select

FitsSingle Exponential Smoothing – Dawson Graphic DesignSlide45

The graph shows the actual and forecast values. The

accuracy measures are also given.Single Exponential Smoothing – Dawson Graphic DesignSlide46

To determine optimal alpha, Identify the

Time Series Variable. Ask Minitab to optimize the forecasting model.Single Exponential Smoothing – Dawson Graphic DesignSlide47

Single Exponential Smoothing – Dawson Graphic Design

The graph shows the actual and forecast values. The accuracy measures and the optimum alpha are also given.Slide48

Issue:

The claims manager has data for 12 months and wants to forecast claims for month 13. But the time series contains a strong upward trend Objective: Use Minitab to develop a double exponential smoothing model. Data file is Billingsley.MTWDouble Exponential Smoothing Billingsley InsuranceSlide49

Open file

Billingsley.MTWDouble Exponential Smoothing – Billingsley InsuranceSlide50

Click on

Stat then Time Series and finally Double Exponential Smoothing. Double Exponential Smoothing – Billingsley InsuranceSlide51

Identify the

Time Series Variable. Either specify alpha and beta or ask Minitab to optimize the forecasting model.Double Exponential Smoothing – Billingsley InsuranceSlide52

The graph shows the actual and forecast values. The

accuracy measures are also given.Double Exponential Smoothing – Billingsley Insurance