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Business Forecasting : International Edition

ISBN: 9780131073852 | 0131073850
Edition: 9th
Format: Hardcover
Publisher: Prentice Hall
Pub. Date: 1/1/2009

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SummaryTable of Contents
The eighth edition ofBusiness Forecastingpresents basic statistical techniques that are useful for preparing individual business forecasts and long-range plans. Written in a simple, straightforward style and making extensive use of practical business examples, the book includes fifty-three cases that provide readers with the necessary link between theoretical concepts and their real-world applications. Readers should have a basic knowledge of statistics and be familiar with computer applications such as word processing and spreadsheets.The book... MORE
Prefacexiii
Introduction to Forecasting
1(14)
The History of Forecasting
1(1)
... MORE
The Need for Forecasting
1(2)
Types of Forecasts
3(1)
Macroeconomic Forecasting Considerations
4(1)
Choosing a Forecasting Method
4(1)
Forecasting Steps
5(1)
Managing the Forecasting Process
6(1)
Computer Forecasting Packages
7(1)
Online Information
8(1)
Forecasting Example
8(1)
Summary
9(1)
Case 1-1: Mr. Tux
10(1)
Case 1-2: Consumer Credit Counseling
10(1)
Minitab Applications
11(1)
Excel Applications
12(1)
References
12(3)
A Review of Basic Statistical Concepts
15(42)
Describing Data with Numerical Summaries
15(4)
Displays of Numerical Information
19(3)
Probability Distributions
22(4)
Sampling Distributions
26(2)
Inference from a Sample
28(1)
Estimation
28(1)
Hypothesis Testing
29(3)
p-Value
31(1)
Correlation Analysis
32(3)
Scatter Diagrams
32(3)
Correlation Coefficient
35(2)
Fitting a Straight Line
37(2)
Assessing Normality
39(3)
Application to Management
42(1)
Glossary
43(1)
Key Formulas
43(2)
Problems
45(5)
Case 2-1: Alcom Electronics
50(1)
Case 2-2: Mr. Tux
51(1)
Case 2-3: Alomega Food Stores
52(1)
Minitab Applications
53(2)
Excel Applications
55(1)
References
56(1)
Exploring Data Patterns and Choosing a Forecasting Technique
57(44)
Exploring Time Series Data Patterns
58(2)
Exploring Data Patterns with Autocorrelation Analysis
60(14)
Are the Data Random?
65(2)
Do the Data Have a Trend?
67(2)
Are the Data Seasonal?
69(5)
Choosing a Forecasting Technique
74(4)
Forecasting Techniques for Stationary Data
75(1)
Forecasting Techniques for Data with a Trend
75(1)
Forecasting Techniques for Seasonal Data
76(1)
Forecasting Techniques for Cyclical Series
76(1)
Other Factors to Consider When Choosing a Forecasting Technique
77(1)
Empirical Evaluation of Forecasting Methods
77(1)
Measuring Forecasting Error
78(3)
Basic Forecasting Notation
79(2)
Determining the Adequacy of a Forecasting Technique
81(2)
Application to Management
83(1)
Glossary
84(1)
Key Formulas
84(1)
Problems
85(5)
Case 3-1A: Murphy Brothers Furniture
90(2)
Case 3-1B: Murphy Brothers Furniture
92(1)
Case 3-2: Mr. Tux
92(2)
Case 3-3: Consumer Credit Counseling
94(1)
Case 3-4: Alomega Food Stores
94(1)
Minitab Applications
95(3)
Excel Applications
98(2)
References
100(1)
Moving Averages and Smoothing Methods
101(56)
Naive Models
102(3)
Forecasting Methods Based on Averaging
105(9)
Simple Averages
105(2)
Moving Averages
107(3)
Double Moving Averages
110(4)
Exponential Smoothing Methods
114(16)
Exponential Smoothing Adjusted for Trend: Holt's Method
121(5)
Exponential Smoothing Adjusted for Trend and Seasonal Variation: Winters' Method
126(4)
Application to Management
130(1)
Glossary
131(1)
Key Formulas
131(2)
Problems
133(6)
Case 4-1: The Solar Alternative Company
139(1)
Case 4-2: Mr. Tux
140(1)
Case 4-3: Consumer Credit Counseling
141(1)
Case 4-4: Murphy Brothers Furniture
141(1)
Case 4-5: Five-Year Revenue Projection for Downtown Radiology
142(6)
Minitab Applications
148(2)
Excel Applications
150(1)
Excel Applications: CB Predictor
151(4)
References
155(2)
Time Series and Their Components
157(54)
Decomposition
158(2)
Trend
160(11)
Additional Trend Curves
164(2)
Forecasting Trend
166(1)
Seasonality
167(4)
Seasonally Adjusted Data
171(6)
Cyclical and Irregular Variations
172(5)
Forecasting a Seasonal Time Series
177(2)
The Census II Decomposition Method
179(2)
Application to Management
181(1)
Appendix: Price Index
182(2)
Glossary
184(1)
Key Formulas
184(1)
Problems
185(6)
Case 5-1: The Small Engine Doctor
191(1)
Case 5-2: Mr. Tux
192(4)
Case 5-3: Consumer Credit Counseling
196(1)
Case 5-4: Murphy Brothers Furniture
197(3)
Case 5-5: AAA Washington
200(2)
Case 5-6: Alomega Food Stores
202(1)
Minitab Applications
203(3)
Excel Applications
206(3)
References
209(2)
Simple Linear Regression
211(58)
Regression Line
212(4)
Standard Error of the Estimate
216(1)
Forecasting Y
217(3)
Decomposition of Variance
220(4)
Coefficient of Determination
224(2)
Hypothesis Testing
226(3)
Analysis of Residuals
229(2)
Computer Output
231(2)
Variable Transformations
233(4)
Growth Curves
237(5)
Application to Management
242(1)
Glossary
243(1)
Key Formulas
243(2)
Problems
245(9)
Case 6-1: Tiger Transport
254(2)
Case 6-2: Butcher Products, Inc.
256(1)
Case 6-3: Ace Manufacturing
257(1)
Case 6-4: Mr. Tux
258(1)
Case 6-5: Consumer Credit Counseling
259(1)
Case 6-6: AAA Washington
260(2)
Minitab Applications
262(3)
Excel Applications
265(2)
References
267(2)
Multiple Regression Analysis
269(58)
Several Predictor Variables
269(1)
Correlation Matrix
270(1)
Multiple Regression Model
271(2)
Statistical Model for Multiple Regression
271(2)
Interpreting Regression Coefficients
273(1)
Inference for Multiple Regression Models
274(6)
Standard Error of the Estimate
275(1)
Significance of the Regression
276(2)
Individual Predictor Variables
278(1)
Forecast of a Future Response
279(1)
Computer Output
280(1)
Dummy Variables
281(4)
Multicollinearity
285(3)
Selecting the ``Best'' Regression Equation
288(7)
All Possible Regressions
290(2)
Stepwise Regression
292(2)
Final Notes on Stepwise Regression
294(1)
Regression Diagnostics and Residual Analysis
295(2)
Forecasting Caveats
297(1)
Overfitting
297(1)
Useful Regressions, Large F Ratios
298(1)
Application to Management
298(2)
Glossary
300(1)
Key Formulas
300(1)
Problems
301(9)
Case 7-1: The Bond Market
310(3)
Case 7-2: AAA Washington
313(2)
Case 7-3: Fantasy Baseball (A)
315(5)
Case 7-4: Fantasy Baseball (B)
320(4)
Minitab Applications
324(2)
Excel Applications
326(1)
References
326(1)
Regression with Time Series Data
327(54)
Time Series Data and the Problem of Autocorrelation
327(4)
Durbin-Watson Test for Serial Correlation
331(3)
Solutions to Autocorrelation Problems
334(12)
Model Specification Error (Omitting a Variable)
335(2)
Regression with Differences
337(5)
Autocorrelated Errors and Generalized Differences
342(3)
Autoregressive Models
345(1)
Time Series Data and the Problem of Heteroscedasticity
346(3)
Using Regression to Forecast Seasonal Data
349(3)
Econometric Forecasting
352(1)
Application to Management
353(1)
Glossary
353(1)
Key Formulas
353(2)
Problems
355(7)
Case 8-1: Company of Your Choice
362(1)
Case 8-2: Business Activity Index for Spokane county
363(4)
Case 8-3: Restaurant Sales
367(2)
Case 8-4: Mr. Tux
369(2)
Case 8-5: Consumer Credit Counseling
371(1)
Case 8-6: AAA Washington
372(3)
Case 8-7: Alomega Food Stores
375(1)
Minitab Applications
376(1)
Excel Applications
377(2)
References
379(2)
The Box-Jenkins (ARIMA) Methodology
381(82)
Box-Jenkins Methodology
381(8)
Autoregressive Models
386(1)
Moving Average Models
387(1)
Autoregressive Moving Average Models
388(1)
Summary
389(1)
Implementing the Model-Building Strategy
389(39)
Step 1: Model Identification
389(2)
Step 2: Model Estimation
391(1)
Step 3: Model Checking
392(1)
Step 4: Forecasting with the Model
392(19)
Final Comments
411(1)
Model Selection Criteria
412(2)
Models for Seasonal Data
414(10)
Simple Exponential Smoothing and an ARIMA Model
424(2)
Advantages and Disadvantages of ARIMA Models
426(2)
Application to Management
428(1)
Glossary
429(1)
Key Formulas
429(1)
Problems
430(10)
Case 9-1: Restaurant Sales
440(2)
Case 9-2: Mr. Tux
442(2)
Case 9-3: Consumer Credit Counseling
444(1)
Case 9-4: The Lydia E. Pinkham Medicine Company
444(3)
Case 9-5: City of College Station
447(3)
Case 9-6: UPS Air Finance Division
450(3)
Case 9-7: AAA Washington
453(2)
Minitab Applications
455(2)
Excel Applications: CB Predictor
457(3)
References
460(3)
Judgmental Forecasting and Forecast Adjustments
463(22)
The Delphi Method
464(3)
Scenario Writing
467(1)
Combining Forecasts
468(2)
Forecasting and Neural Networks
470(2)
Summary of Judgmental Forecasting
472(1)
Other Tools Useful in Making Judgments About the Future
473(4)
Key Formulas
477(1)
Problems
478(1)
Case 10-1: Golden Gardens Restaurant
478(1)
Case 10-2: Alomega Food Stores
479(1)
Case 10-3: The Lydia E. Pinkham Medicine Company Revisited
480(2)
References
482(3)
Managing the Forecasting Process
485(18)
The Forecasting Process
485(1)
Monitoring Forecasts
486(5)
Forecasting Steps Reviewed
491(1)
Forecasting Responsibility
492(1)
Forecasting Costs
493(1)
Forecasting and the MIS System
493(1)
Selling Management on Forecasting
494(1)
The Future of Forecasting
494(1)
Problems
495(1)
Case 11-1: Boundary Electronics
495(1)
Case 11-2: Busby Associates
496(3)
Case 11-3: Consumer Credit Counseling
499(1)
Case 11-4: Mr. Tux
500(1)
Case 11-5: Alomega Food Stores
501(1)
References
501(2)
APPENDIX A Derivations
503(2)
Correlation Derivation
503(1)
Least Squares Derivation
503(1)
Partial Derivatives
503(2)
APPENDIX B Data for Case 7-1
505(2)
APPENDIX C Tables
507(10)
Table C-1 Individual Terms of the Binomial Distribution
507(2)
Table C-2 Table of Areas for Standard Normal Probability Distribution
509(1)
Table C-3 Critical Values of t
510(1)
Table C-4 Critical Values of Chi-Square
511(2)
Table C-5 Table of F Distribution
513(1)
Table C-6 Durbin-Watson Test Bounds
514(3)
APPENDIX D Data Sets and Databases
517(14)
Index531

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