GO TO
Business Forecasting, Ninth Edition
by
John E. Hanke; Dean W. Wichern
Publisher: Prentice Hall
Publishing Date: 2008/02/08
eText ISBN-10
0-13-604924-9
eText ISBN-13
978-0-13-604924-1
Print ISBN-10
0-13-230120-2
Print ISBN-13
978-0-13-230120-6
« Back to My CourseSmart
Business Forecasting, Ninth Edition
by
John E. Hanke; Dean W. Wichern
eTextbook $76.00
(180 day subscription)
Compare Online & Downloadable
Copyright, ii
Preface, xv
Chapter 1. Introduction t...
Chapter 2. A Review of Ba...
Chapter 3. Exploring Data...
Chapter 4. Moving Average...
Chapter 5. Time Series an...
Chapter 6. Simple Linear ...
Chapter 7. Multiple Regre...
Chapter 8. Regression wit...
Chapter 9. The Box-Jenkin...
Chapter 10. Judgmental Fo...
Chapter 11. Managing the ...
Appendix A. Data for Case...
Appendix B. Tables, 523
Appendix C. Data Sets and...
Index, 547
Table of Contents
Copyright, ii
Preface, xv
Chapter 1. Introduction to Forecasting, 1
The History of Forecasting, 1
Is Forecasting Necessary?, 2
Types of Forecasts, 2
Macroeconomic Forecasting Considerations, 3
Choosing a Forecasting Method, 4
Forecasting Steps, 4
Managing the Forecasting Process, 6
Forecasting Software, 6
Online Information, 7
Forecasting Examples, 7
Summary, 9
Case 1-1: Mr. Tux, 10
Case 1-2: Consumer Credit Counseling, 10
Minitab Applications, 11
Excel Applications, 12
References, 12
Chapter 2. A Review of Basic Statistical Concepts, 15
Describing Data with Numerical Summaries, 15
Displays of Numerical Information, 19
Probability Distributions, 22
Sampling Distributions, 26
Inference from a Sample, 29
Estimation, 29
Hypothesis Testing, 30
p
-Value, 32
Correlation Analysis, 34
Scatter Diagrams, 34
Correlation Coefficient, 37
Fitting a Straight Line, 39
Assessing Normality, 42
Application to Management, 44
Glossary, 44
Key Formulas, 45
Problems, 46
Case 2-1: Alcam Electronics, 53
Case 2-2: Mr. Tux, 54
Case 2-3: Alomega Food Stores, 56
Minitab Applications, 56
Excel Applications, 58
References, 60
Chapter 3. Exploring Data Patterns and an Introduction to Forecasting Techniques, 61
Exploring Time Series Data Patterns, 62
Exploring Data Patterns with Autocorrelation Analysis, 64
Are the Data Random?, 69
Do the Data Have a Trend?, 72
Are the Data Seasonal?, 76
Choosing a Forecasting Technique, 76
Forecasting Techniques for Stationary Data, 78
Forecasting Techniques for Data with a Trend, 78
Forecasting Techniques for Seasonal Data, 79
Forecasting Techniques for Cyclical Series, 79
Other Factors to Consider When Choosing a Forecasting Technique, 79
Empirical Evaluation of Forecasting Methods, 81
Measuring Forecast Error, 81
Determining the Adequacy of a Forecasting Technique, 84
Application to Management, 86
Glossary, 87
Key Formulas, 87
Problems, 88
Case 3-1A: Murphy Brothers Furniture, 94
Case 3-1B: Murphy Brothers Furniture, 96
Case 3-2: Mr. Tux, 97
Case 3-3: Consumer Credit Counseling, 98
Case 3-4: Alomega Food Stores, 99
Case 3-5: Surtido Cookies, 100
Minitab Applications, 101
Excel Applications, 103
References, 105
Chapter 4. Moving Averages and Smoothing Methods, 107
Naive Models, 108
Forecasting Methods Based on Averaging, 111
Simple Averages, 111
Moving Averages, 113
Double Moving Averages, 116
Exponential Smoothing Methods, 119
Exponential Smoothing Adjusted for Trend: Holt’s Method, 126
Exponential Smoothing Adjusted for Trend and Seasonal Variation: Winter’s Method, 130
Application to Management, 135
Glossary, 136
Key Formulas, 136
Problems, 138
Case 4-1: The Solar Alternative Company, 145
Case 4-2: Mr. Tux, 147
Case 4-3: Consumer Credit Counseling, 148
Case 4-4: Murphy Brothers Furniture, 148
Case 4-5: Five-Year Revenue Projection for Downtown Radiology, 149
Case 4-6: Web Retailer, 154
Case 4-7: Southwest Medical Center, 158
Case 4-8: Surtido Cookies, 159
Minitab Applications, 159
Excel Applications, 161
References, 163
Chapter 5. Time Series and Their Components, 165
Decomposition, 166
Trend, 168
Additional Trend Curves, 171
Forecasting Trend, 174
Seasonality, 175
Seasonally Adjusted Data, 179
Cyclical and Irregular Variations, 180
Summary Example, 180
Business Indicators, 184
Forecasting a Seasonal Time Series, 185
The Census II Decomposition Method, 187
Application to Management, 189
Appendix: Price Index, 190
Glossary, 192
Key Formulas, 192
Problems, 193
Case 5-1: The Small Engine Doctor, 201
Case 5-2: Mr. Tux, 202
Case 5-3: Consumer Credit Counseling, 206
Case 5-4: Murphy Brothers Furniture, 207
Case 5-5: AAA Washington, 210
Case 5-6: Alomega Food Stores, 212
Case 5-7: Surtido Cookies, 213
Case 5-8: Southwest Medical Center, 214
Minitab Applications, 214
Excel Applications, 217
References, 219
Chapter 6. Simple Linear Regression, 221
Regression Line, 222
Standard Error of the Estimate, 226
Forecasting
Y
, 227
Decomposition of Variance, 230
Coefficient of Determination, 234
Hypothesis Testing, 236
Analysis of Residuals, 239
Computer Output, 241
Variable Transformations, 243
Growth Curves, 246
Application to Management, 250
Glossary, 252
Key Formulas, 253
Problems, 254
Case 6-1: Tiger Transport, 266
Case 6-2: Butcher Products, Inc., 268
Case 6-3: Ace Manufacturing, 269
Case 6-4: Mr. Tux, 270
Case 6-5: Consumer Credit Counseling, 270
Case 6-6: AAA Washington, 271
Minitab Applications, 274
Excel Applications, 277
References, 279
Chapter 7. Multiple Regression Analysis, 281
Several Predictor Variables, 281
Correlation Matrix, 282
Multiple Regression Model, 283
Statistical Model for Multiple Regression, 283
Interpreting Regression Coefficients, 285
Inference for Multiple Regression Models, 286
Standard Error of the Estimate, 287
Significance of the Regression, 288
Individual Predictor Variables, 290
Forecast of a Future Response, 291
Computer Output, 292
Dummy Variables, 293
Multicollinearity, 297
Selecting the “Best” Regression Equation, 300
All Possible Regressions, 302
Stepwise Regression, 304
Final Notes on Stepwise Regression, 306
Regression Diagnostics and Residual Analysis, 307
Forecasting Caveats, 309
Overfitting, 309
Useful Regressions, Large
F
Ratios, 310
Application to Management, 310
Glossary, 312
Key Formulas, 312
Problems, 313
Case 7-1: The Bond Market, 324
Case 7-2: AAA Washington, 328
Case 7-3: Fantasy Baseball (A), 330
Case 7-4: Fantasy Baseball (B), 334
Minitab Applications, 336
Excel Applications, 337
References, 338
Chapter 8. Regression with Time Series Data, 339
Time Series Data and the Problem of Autocorrelation, 339
Autocorrelation and the Durbin-Watson Test, 343
Solutions to Autocorrelation Problems, 347
Model Specification Error (Omitting a Variable), 348
Regression with Differences, 350
Autocorrelated Errors and Generalized Differences, 354
Autoregressive Models, 357
Summary, 358
Time Series Data and the Problem of Heteroscedasticity, 358
Using Regression to Forecast Seasonal Data, 361
Econometric Forecasting, 364
Cointegrated Time Series, 365
Application to Management, 367
Glossary, 367
Key Formulas, 367
Problems, 369
Case 8-1: Company of Your Choice, 378
Case 8-2: Business Activity Index for Spokane County, 379
Case 8-3: Restaurant Sales, 383
Case 8-4: Mr. Tux, 385
Case 8-5: Consumer Credit Counseling, 388
Case 8-6: AAA Washington, 389
Case 8-7: Alomega Food Stores, 392
Case 8-8: Surtido Cookies, 393
Case 8-9: Southwest Medical Center, 394
Minitab Applications, 395
Excel Applications, 396
References, 398
Chapter 9. The Box-Jenkins (ARIMA) Methodology, 399
Box-Jenkins Methodology, 399
Autoregressive Models, 404
Moving Average Models, 405
Autoregressive Moving Average Models, 407
Summary, 407
Implementing the Model-Building Strategy, 407
Step 1: Model Identification, 407
Step 2: Model Estimation, 409
Step 3: Model Checking, 410
Step 4: Forecasting with the Model, 411
Model-Building Caveats, 430
Model Selection Criteria, 431
ARIMA Models for Seasonal Data, 432
Simple Exponential Smoothing and an ARIMA Model, 442
Advantages and Disadvantages of ARIMA Models, 443
Application to Management, 444
Glossary, 445
Key Formulas, 445
Problems, 446
Case 9-1: Restaurant Sales, 457
Case 9-2: Mr. Tux, 459
Case 9-3: Consumer Credit Counseling, 460
Case 9-4: The Lydia E. Pinkham Medicine Company, 461
Case 9-5: City of College Station, 463
Case 9-6: UPS Air Finance Division, 466
Case 9-7: AAA Washington, 469
Case 9-8: Web Retailer, 471
Case 9-9: Surtido Cookies, 474
Case 9-10: Southwest Medical Center, 476
Minitab Applications, 478
References, 480
Chapter 10. Judgmental Forecasting and Forecast Adjustments, 481
Judgmental Forecasting, 483
The Delphi Method, 483
Scenario Writing, 485
Combining Forecasts, 486
Forecasting and Neural Networks, 488
Summary of Judgmental Forecasting, 490
Other Tools Useful in Making Judgments About the Future, 491
Key Formulas, 496
Problems, 496
Case 10-1: Golden Gardens Restaurant, 497
Case 10-2: Alomega Food Stores, 497
Case 10-3: The Lydia E. Pinkham Medicine Company, 498
References, 501
Chapter 11. Managing the Forecasting Process, 503
The Forecasting Process, 503
Monitoring Forecasts, 504
Forecasting Steps Reviewed, 509
Forecasting Responsibility, 510
Forecasting Costs, 511
Forecasting and Management Information Systems, 511
Selling Management on Forecasting, 512
The Future of Forecasting, 512
Problems, 513
Case 11-1: Boundary Electronics, 513
Case 11-2: Busby Associates, 514
Case 11-3: Consumer Credit Counseling, 517
Case 11-4: Mr. Tux, 518
Case 11-5: Alomega Food Stores, 519
Case 11-6: Southwest Medical Center, 520
References, 520
Appendix A. Data for Case 7-1, 521
Appendix B. Tables, 523
Table B-1 Individual Terms of the Binomial Distribution, 523
Table B-2 Areas for Standard Normal Probability Distribution, 525
Table B-3 Critical Values of
t
, 526
Table B-4 Critical Values of Chi-Square, 527
Table B-5
F
Distribution, 529
Table B-6 Durbin-Watson Test Bounds, 530
Appendix C. Data Sets and Databases, 533
Index, 547
Please use the Print button in the CourseSmart Reader header.
