Groebner Business Statistic 8ed Ch16 Minitab Tutorial

Guide to Using Minitab 14 For Basic

Statistical Applications

To Accompany

Business Statistics: A Decision Making

Approach, 8th Ed.

Chapter 16:

Analyzing and Forecasting Time-Series

Data

By

Groebner, Shannon, Fry, & SmithPrentice-Hall Publishing Company

Copyright, 2011

Chapter 16 Minitab Examples

Trend Based Forecasting

Taft Ice Cream Company

Nonlinear Trend

Harrison Equipment Company

Seasonal Adjustment

Big Mountain Ski Resort

Single Exponential Smoothing

Dawson Graphic Designs

More Examples

Chapter 16 Minitab Examples

Double Exponential Smoothing

Billingsley Insurance Company

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

Open File Taft.MTW

Trend Based Forecasting – Taft Ice Cream Company

First click on

Graph, then Time

Series Plot.


Select Simple


Enter data column

to be graphed.

Then click

Time/Scale


Click on Calendar

and specify Year –

then determine

starting Year

(1997)


A linear trend is

evident in this time

series plot.


To develop the trend line, click on Stat,

then Regression and Regression again


Identify the

columns

containing the

Time Series

and also

specify the

dependent

variable (t)


Linear Trend

Model


A second method

- select Stat –

Time Series –

Trend Analysis


Specify time

series variable

and select

Linear model

type


Measures of

forecast

accuracy


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

Open File Harrison.MTW

Nonlinear Trend – Harrison Equipment Company

Select Graph – then Time

Series Plot


Select Simple


Define Time

Series variable

(Repair Costs)

and then select

Time/Scale


Select

Calendar

and pick

Quarter

Year option


Specify

starting

Quarter and

Year


To develop a

linear model,

click on Stat,

then Time

Series and

finally Trend

Analysis.


Specify time

series

variable

(Repair

Costs) and

select Linear


Linear

Model

Results


To develop a

model with time

squared as the

variable Click on

Calc, then

Calculator.


Identify column for

new variable, in

Expressions box enter

form of new variable.

Click OK


Click on

Stat, then

Regression

and

Regression

again.


Define the

Response

variable

(Repair

Costs)

Predictors

(Qtr2) then

click

Storage.


Under

Diagnostic

Measures

select

Residuals,

under

Characterist

ics select

Fits. Click

OK twice.


The Minitab

output shows

the regression

model.


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

Open File Big

Mountain.MTW

Seasonal Adjustment – Big Mountain Ski Resort


Click on Stat,

then Time

Series and then

select

Decomposition.

Define the

Variable, the

Model Type,

the Seasonal

length and

the Model

Components.


The graph shows

the actual and

predicted values.


This output

shows the

original data

and other

graphs.


The Trend

Line Equation,

the Seasonal

Indices and

MAPE, MAD

and MSD are

also given.


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

Open File Dawson.MTW

Single Exponential Smoothing – Dawson Graphic Design

Click on

Stat, then

Time Series

and finally

Single

Exponential

Smoothing.


Identify the Time

Series Variable.

Either specify

alpha or ask

Minitab to

optimize the

forecasting model.

Select Storage


Select Fits


The graph shows the actual

and forecast values. The

accuracy measures are also

given.


To determine

optimal

alpha,

Identify the

Time Series

Variable. Ask

Minitab to

optimize the

forecasting

model.



The graph shows the actual

and forecast values. The

accuracy measures and the

optimum alpha are also given.

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

Open file Billingsley.MTW

Double Exponential Smoothing – Billingsley Insurance

Click on

Stat then

Time Series

and finally

Double

Exponential

Smoothing.


Identify the

Time Series

Variable.

Either specify

alpha and

beta or ask

Minitab to

optimize the

forecasting

model.


The graph shows the actual and

forecast values. The accuracy

measures are also given.


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Groebner Business Statistic 8ed Ch16 Minitab Tutorial

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