PART ⅠINTRODUCTION 1
CHAPTER 1AN INTRODUCTION TO FORECASTING 2
1.1 Introduction 2
1.2 Forecasting and Time Series 3
1.3 Forecasting Methods 8
1.4 Errors in Forecasting 12
1.5 Choosing a Forecasting Technique 17
1.6 An Overview of Quantitative Forecasting Techniques 19
1.7 Computer Packages:Minitab and SAS 23
Exercises 23
CHAPTER 2 BASIC STATISTICAL CONCEPTS 26
2.1 Populations 27
2.2 Probability 29
2.3 Random Samples and Sample Statistics 31
2.4 Continuous Probability Distributions 34
2.5 The Normal Probability Distribution 36
2.6 The t-Distribution,the F-Distribution,and the Chi-Square Distribution 45
2.7 Confidence Intervals for a Population Mean 48
2.8 Hypothesis Testing for a Population Mean 58
Exercises 72
PART Ⅱ FORECASTING BY USING REGRESSION ANALYSIS 76
CHAPTER 3 SIMPLE LINEAR REGRESSION 77
3.1 The Simple Linear Regression Model 78
3.2 The Least Squares Point Estimates 86
3.3 Point Estimates and Point Predictions 90
3.4 Model Assumptions,the Mean Square Error,and the Standard Error 93
3.5 Testing the Significance of the Independent Variable 97
3.6 A Confidence Interval for a Mean Value of the Dependent Variable and a Prediction Interval for an Individual Value of the Dependent Variable 104
3.7 Simple Coefficients of Determination and Correlation 112
3.8 An F-Test for the Simple Linear Regression Model 118
3.9 Using the Computer 121
Exercises 122
CHAPTER 4 MULTIPLE REGRESSION 131
4.1 The Linear Regression Model 132
4.2 The Least Squares Point Estimates 144
4.3 Point Estimates and Point Predictions 149
4.4 The Regression Assumptions and the Standard Error 153
4.5 Multiple Coefficients of Determination and Correlation 156
4.6 An F-Test for the Overall Model 159
4.7 Statistical Inference for βj and Multicollinearity 161
4.8 Confidence Intervals and Prediction Intervals 166
4.9 An Introduction to Model Building 172
4.10 Residual Analysis 179
4.11 Using the Computer 198
Exercises 200
CHAPTER 5 TOPICS IN REGRESSION ANALYSIS 214
5.1 Interaction 215
5.2 An F-Test for a Portion of a Model 226
5.3 Using Dummy Variables to Model Qualitative Independent Variables 230
5.4 Advanced Concepts of Multicollinearity 240
5.5 Advanced Model Comparison Methods 248
5.6 Stepwise Regression,Forward Selection,Backward Elimination,and Maximum R2 Improvement 255
5.7 Outlying and Influential Observations 260
5.8 Handling Unequal Variances 266
5.9 Using the Computer 270
Exercises 273
PART Ⅲ FORECASTING BY USING TIME SERIES REGRESSION,DECOMPOSITION METHODS,AND EXPONENTIAL SMOOTHING 289
CHAPTER 6 TIME SERIES REGRESSION 290
6.1 Modeling Trend by Using Polynomial Functions 291
6.2 Detecting Autocorrelation 301
6.3 Types of Seasonal Variation 308
6.4 Modeling Seasonal Variation by Using Dummy Variables and Trigonometric Functions 316
6.5 Growth Curve Models 325
6.6 Handling First-Order Autocorrelation 330
6.7 Using the Computer 337
Exercises 342
CHAPTER 7 DECOMPOSITION METHODS 354
7.1 Multiplicative Decomposition 355
7.2 Additive Decomposition 368
7.3 Shifting Seasonal Patterns 370
7.4 The Census II Decomposition Method and SAS PROC X11 373
7.5 Using the Computer 374
Exercises 375
CHAPTER 8 Exponential Smoothing 379
8.1 Simple Exponential Smoothing 380
8.2 Adaptive Control Procedures 386
8.3 Double Exponential Smoothing 389
8.4 Winters'Method 403
8.5 Exponential and Damped Trends 421
8.6 Prediction Intervals 427
8.7 Concluding Comments 430
8.8 Using the Computer 431
Exercises 431
PART Ⅳ FORECASTING BY USING BASIC TECHNIQUES OF THE BOX-JENKINS METHODOLOGY 435
CHAPTER 9 NONSEASONAL BOX-JENKINS MODELS AND THEIR TENTATIVE IDENTIFICATION 436
9.1 Stationary and Nonstationary Time Series 437
9.2 The Sample Autocorrelation and Partial Autocorrelation Functions:The SAC and SPAC 441
9.3 An Introduction to Nonseasonal Modeling and Forecasting 457
9.4 Tentative Identification of Nonseasonal Box-Jenkins Models 467
9.5 Using the Computer 477
Exercises 478
CHAPTER 10 ESTIMATION,DIAGNOSTIC CHECKING,AND FORECASTING FOR NONSEASONAL BOX-JENKINS MODELS 487
10.1 Estimation 488
10.2 Diagnostic Checking 496
10.3 Forecasting 502
10.4 A Case Study 504
10.5 Using the Computer 512
Exercises 514
CHAPTER 11 AN INTRODUCTION TO BOX-JENKINS SEASONAL MODELING 521
11.1 Transforming a Seasonal Time Series into a Stationary Time Series 521
11.2 Two Examples of Seasonal Modeling and Forecasting 533
11.3 Using the Computer 550
Exercises 552
PART Ⅴ FORECASTING BY USING ADVANCED TECHNIQUES OF THE BOX-JENKINS METHODOLOGY 566
CHAPTER 12 GENERAL BOX-JENKINS SEASONAL MODELING 567
12.1 The General Seasonal Model and Guidelines for Tentative Identification 568
12.2 Improving an Inadequate Seasonal Model 581
12.3 Using the Computer 595
Exercises 596
CHAPTER 13 USING THE BOX-JENKINS METHODOLOGY TO IMPROVE TIME SERIES REGRESSION MODELS AND TO IMPLEMENT EXPONENTIAL SMOOTHING 606
13.1 Box-Jenkins Error Term Models in Time Series Regression 607
13.2 Seasonal Intervention Models 618
13.3 Box-Jenkins Implementation of Exponential Smoothing 625
13.4 Using the Computer 639
Exercises 643
CHAPTER 14 TRANSFER FUNCTIONS AND INTERVENTION MODELS 657
14.1 A Three-Step Procedure for Building a Transfer Function Model 658
14.2 Intervention Models 677
14.3 Using the Computer 689
Exercises 694
APPENDIX A STATISTICAL TABLES 706
APPENDIX B REFERENCES 716
Index 719