Solution Manual for Business Forecasting, 9th Edition

Name: Solution Manual for Business Forecasting, 9th Edition
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CONTENTS
Preface
Chapter 2 A Review of Basic Statistical Concepts 1
Problems 1
Cases:
Alcam Electronics 8
Mr. Tux 8
Alomega Food Stores 8
Chapter 3 Exploring Data Patterns and Choosing a Forecasting Technique 9
Problems 9
Cases:
Murphy Brothers Furniture 20
Mr. Tux 20
Consumer Credit Counseling 21
Alomega Food Stores 23
Surtido Cookies 23
Chapter 4 Moving Averages and Smoothing Methods 25
Problems 25
Cases:
The Solar Alternative Company 43
Mr. Tux 45
Consumer Credit Counseling 46
Murphy Brothers Furniture 47
Five-year Revenue Projection for Downtown Radiology 49
Web Retailer 49
Southwest Medical Center 52
Surtido Cookies 54
Chapter 5 Time Series And Their Components 56
Problems 56
Cases:
The Small Engine Doctor 76
Mr. Tux 79
Consumer Credit Counseling 80
Murphy Brothers Furniture 83
AAA Washington 84
Alomega Food Stores 87
Surtido Cookies 88
Southwest Medical Center 90
Chapter 6 Regression Analysis 94

Problems 94
Cases:
Tiger Transport Company 113
Butcher Products, Inc. 113
Ace Manufacturing 114
Mr. Tux 114
Consumer Credit Counseling 115
AAA Washington 117
Chapter 7 Multiple Regression 120
Problems 120
Cases:
The Bond Market 140
AAA Washington 141
Fantasy Baseball (A) 143
Fantasy Baseball (B) 145
Chapter 8 Regression With Time Series Data 146
Problems 146
Cases:
Business Activity Index for Spokane County 164
Restaurant Sales 165
Mr. Tux 165
Consumer Credit Counseling 166
AAA Washington 168
Alomega Food Stores 169
Surtido Cookies 169
Southwest Medical Center 171
Chapter 9 Box-Jenkins (ARIMA) Methodology 173
Problems 173
Cases:
Restaurant Sales 202
Mr. Tux 203
Consumer Credit Counseling 205
The Lydia E. Pinkham Medicine Company 207
City of College Station 209
UPS Air Finance Division 210
AAA Washington 212
Web Retailer 213
Surtido Cookies 215
Southwest Medical Center 218
Chapter 10 Judgmental Elements in Forecasting 223
Problems 223

PREFACE
The goal of the ninth edition of Business Forecasting remains the same as that
of the previous editions: To present the basic statistical techniques that are useful for
preparing individual business forecasts and long-range plans. This instructor’s manual
contains answers to chapter-end problems and comments on the case studies that
appear at the end of every chapter.
Our work in forecasting over many years has taught us that intuition and good
judgment are essential components of a good forecasting process, a point we stress in
Chapters 1 and 11. This is a difficult concept to put across in the remaining chapters,
which deal with important forecasting techniques involving data analysis. We hope
that the instructor can bring a measure of real-world common sense to the study of
forecasting to supplement the quantitative material with which this instructor’s manual
is concerned.
We also hope that students can gain practical experience through hands on use
of computer programs in their study of forecasting. Our solutions to problems and
cases here rely heavily on Minitab software. Forecasters have just begun to tap the
potential offered by resources on the Internet.
Data sets that appear in the text are available in several formats on the CD
included with the book and on our website maintained by Prentice Hall at
www.prenhall.com/Hanke.
Finally, we hope you find the ninth edition of Business Forecasting useful.
Comments for improvement are welcome. We can be reached at the following email
addresses:
John Hanke – john_hanke@msn.com
Dean Wichern – d-wichern@tamu.edu

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5
12. a.
b. Positive linear relationship
c. Y = 2312 Y2 = 515,878 X = 53.7
X2 = 282.55 XY = 12,029.3 r = .95Yˆ
= 32.5 + 36.4XYˆ
= 32.5 + 36.4(5.2) = 222
13. This is a good population for showing how random samples are taken. If three-digit
random numbers are generated from Minitab as demonstrated in Problem 10, the selected
items for the sample can be easily found. In this population,  = 0.06 so most
students will get a sample correlation coefficient r close to 0. The least squares line will,
in
most cases, have a slope coefficient close to 0, and students will not be able to reject the
null hypothesis H0: β1 = 0 (or, equivalently, ρ = 0) if they carry out the hypothesis test.
14. a.

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16
d. MAPE =31
2227.11 = .3620 or 36.2%
e. MPE =31
1988.2− = -.0709
21. a. Time series plot follows
b. The sales time series appears to vary about a fixed level so it is stationary.
c. The sample autocorrelation function for the sales series follows:
The sample autocorrelations die out rapidly. This behavior is consistent with a
stationary series. Note that the sales data are not random. Sales in adjacent
weeks tend to be positively correlated.

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17
22. a. The residualsYYe tt −= are listed below
b. The residual autocorrelations follow
Since, in this case, the residuals differ from the original observations by the
constant05.2460=Y , the residual autocorrelations will be the same as the
autocorrelations for the sales numbers. There is significant residual
autocorrelation at lag 1 and the autocorrelations die out in an exponential fashion.
The random model is not adequate for these data.
23. a. & b. Time series plot follows.

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18
Since this series is trending upward, it is nonstationary. There is also a seasonal
pattern since 2nd and 3rd quarter earnings tend to be relatively large and 1st and 4th
quarter earnings tend to be relatively small.
c. The autocorrelation function for the first 10 lags follows.
The autocorrelations are consistent with choice in part b. The autocorrelations fail
to die out rapidly consistent with nonstationary behavior. In addition, there are
relatively large autocorrelations at lags 4 and 8, indicating a quarterly seasonal
pattern.

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