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 - Start

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To Accompany. Business Statistics: A Decision Making Approach, . 8th . Ed.. Chapter 16:. Analyzing and Forecasting Time-Series Data. By. Groebner, Shannon, Fry, & Smith. Prentice-Hall Publishing Company. ID: 426123 Download Presentation

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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

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, 2011

Slide2

Chapter 16 Minitab Examples

Trend Based Forecasting Taft Ice Cream CompanyNonlinear Trend Harrison Equipment CompanySeasonal Adjustment Big Mountain Ski ResortSingle Exponential Smoothing Dawson Graphic Designs

More Examples

Slide3

Chapter 16 Minitab Examples

Double Exponential Smoothing Billingsley Insurance Company

Slide4

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.MTW

Slide5

Open File

Taft.MTW

Trend Based Forecasting – Taft Ice Cream Company

Slide6

First click on

Graph, then Time Series Plot.

Trend Based Forecasting – Taft Ice Cream Company

Slide7

Select

Simple

Trend Based Forecasting – Taft Ice Cream Company

Slide8

Enter

data column to be graphed. Then click Time/Scale

Trend Based Forecasting – Taft Ice Cream Company

Slide9

Click on

Calendar and specify Year – then determine starting Year (1997)

Trend Based Forecasting – Taft Ice Cream Company

Slide10

A linear trend is evident in this time series plot.

Trend Based Forecasting – Taft Ice Cream Company

Slide11

To develop the trend line, click on

Stat, then Regression and Regression again

Trend Based Forecasting – Taft Ice Cream Company

Slide12

Identify the columns containing the

Time Series and also specify the dependent variable (t)

Trend Based Forecasting – Taft Ice Cream Company

Slide13

Linear Trend Model

Trend Based Forecasting – Taft Ice Cream Company

Slide14

A second method - select

Stat – Time Series – Trend Analysis

Trend Based Forecasting – Taft Ice Cream Company

Slide15

Specify time series variable and select

Linear model type

Trend Based Forecasting – Taft Ice Cream Company

Slide16

Measures of forecast accuracy

Trend Based Forecasting – Taft Ice Cream Company

Slide17

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 .MTW

Nonlinear Trend – Harrison Equipment Company

Slide18

Open File

Harrison.MTW

Nonlinear Trend – Harrison Equipment Company

Slide19

Select

Graph – then Time Series Plot

Nonlinear Trend – Harrison Equipment Company

Slide20

Select

Simple

Nonlinear Trend – Harrison Equipment Company

Slide21

Define Time Series variable (Repair Costs) and then select

Time/Scale

Nonlinear Trend – Harrison Equipment Company

Slide22

Select

Calendar and pick Quarter Year option

Nonlinear Trend – Harrison Equipment Company

Specify starting

Quarter

and

Year

Slide23

Nonlinear Trend – Harrison Equipment Company

Slide24

To develop a linear model, click on

Stat, then Time Series and finally Trend Analysis.

Nonlinear Trend – Harrison Equipment Company

Slide25

Specify time series variable (Repair Costs) and select

Linear

Nonlinear Trend – Harrison Equipment Company

Slide26

Linear Model Results

Nonlinear Trend – Harrison Equipment Company

Slide27

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

Calc, then Calculator.

Nonlinear Trend – Harrison Equipment Company

Slide28

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

OK

Nonlinear Trend – Harrison Equipment Company

Slide29

Click on

Stat, then Regression and Regression again.

Nonlinear Trend – Harrison Equipment Company

Slide30

Define the Response variable (

Repair Costs) Predictors (Qtr2) then click Storage.

Nonlinear Trend – Harrison Equipment Company

Slide31

Under Diagnostic Measures select Residuals, under Characteristics select Fits

. Click OK twice.

Nonlinear Trend – Harrison Equipment Company

Slide32

The Minitab output shows the regression model.

Nonlinear Trend – Harrison Equipment Company

Slide33

Seasonal Adjustment - Big Mountain Ski Resort

Issue: 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.MTW

Slide34

Open File

Big Mountain.MTW

Seasonal Adjustment – Big Mountain Ski Resort

Slide35

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 Resort

Slide37

The graph shows the actual and predicted values.

Seasonal Adjustment – Big Mountain Ski Resort

Slide38

This output shows the original data and other graphs.

Seasonal Adjustment – Big Mountain Ski Resort

Slide39

The

Trend Line Equation, the Seasonal Indices and MAPE, MAD and MSD are also given.

Seasonal Adjustment – Big Mountain Ski Resort

Slide40

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.MTW

Slide41

Open File

Dawson.MTW

Single Exponential Smoothing – Dawson Graphic Design

Slide42

Click on

Stat, then Time Series and finally Single Exponential Smoothing.

Single Exponential Smoothing – Dawson Graphic Design

Slide43

Identify the

Time Series Variable

. Either specify alpha or ask Minitab to optimize the forecasting model. Select Storage

Single Exponential Smoothing – Dawson Graphic Design

Slide44

Select

Fits

Single Exponential Smoothing – Dawson Graphic Design

Slide45

The graph shows the actual and forecast values. The

accuracy measures are also given.

Single Exponential Smoothing – Dawson Graphic Design

Slide46

To determine optimal alpha, Identify the

Time Series Variable

. Ask Minitab to optimize the forecasting model.

Single Exponential Smoothing – Dawson Graphic Design

Slide47

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.MTW

Double Exponential Smoothing Billingsley Insurance

Slide49

Open file

Billingsley.MTW

Double Exponential Smoothing – Billingsley Insurance

Slide50

Click on

Stat

then Time Series and finally Double Exponential Smoothing.

Double Exponential Smoothing – Billingsley Insurance

Slide51

Identify the

Time Series Variable

. Either specify

alpha and beta or ask Minitab to optimize the forecasting model.

Double Exponential Smoothing – Billingsley Insurance

Slide52

The graph shows the actual and forecast values. The

accuracy measures

are also given.

Double Exponential Smoothing – Billingsley Insurance

Slide53

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