PART ⅠINTRODUCTION

CHAPTER 1AN INTRODUCTION TO FORECASTING

1.1 Introduction

1.2 Forecasting and Time Series

1.3 Forecasting Methods

1.4 Errors in Forecasting

1.5 Choosing a Forecasting Technique

1.6 An Overview of Quantitative Forecasting Techniques

1.7 Computer Packages:Minitab and SAS

Exercises

CHAPTER 2 BASIC STATISTICAL CONCEPTS

2.1 Populations

2.2 Probability

2.3 Random Samples and Sample Statistics

2.4 Continuous Probability Distributions

2.5 The Normal Probability Distribution

2.6 The t-Distribution,the F-Distribution,and the Chi-Square Distribution

2.7 Confidence Intervals for a Population Mean

2.8 Hypothesis Testing for a Population Mean

Exercises

PART Ⅱ FORECASTING BY USING REGRESSION ANALYSIS

CHAPTER 3 SIMPLE LINEAR REGRESSION

3.1 The Simple Linear Regression Model

3.2 The Least Squares Point Estimates

3.3 Point Estimates and Point Predictions

3.4 Model Assumptions,the Mean Square Error,and the Standard Error

3.5 Testing the Significance of the Independent Variable

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

3.7 Simple Coefficients of Determination and Correlation

3.8 An F-Test for the Simple Linear Regression Model

3.9 Using the Computer

Exercises

CHAPTER 4 MULTIPLE REGRESSION

4.1 The Linear Regression Model

4.2 The Least Squares Point Estimates

4.3 Point Estimates and Point Predictions

4.4 The Regression Assumptions and the Standard Error

4.5 Multiple Coefficients of Determination and Correlation

4.6 An F-Test for the Overall Model

4.7 Statistical Inference for βj and Multicollinearity

4.8 Confidence Intervals and Prediction Intervals

4.9 An Introduction to Model Building

4.10 Residual Analysis

4.11 Using the Computer

Exercises

CHAPTER 5 TOPICS IN REGRESSION ANALYSIS

5.1 Interaction

5.2 An F-Test for a Portion of a Model

5.3 Using Dummy Variables to Model Qualitative Independent Variables

5.4 Advanced Concepts of Multicollinearity

5.5 Advanced Model Comparison Methods

5.6 Stepwise Regression,Forward Selection,Backward Elimination,and Maximum R2 Improvement

5.7 Outlying and Influential Observations

5.8 Handling Unequal Variances

5.9 Using the Computer

Exercises

PART Ⅲ FORECASTING BY USING TIME SERIES REGRESSION,DECOMPOSITION METHODS,AND EXPONENTIAL SMOOTHING

CHAPTER 6 TIME SERIES REGRESSION

6.1 Modeling Trend by Using Polynomial Functions

6.2 Detecting Autocorrelation

6.3 Types of Seasonal Variation

6.4 Modeling Seasonal Variation by Using Dummy Variables and Trigonometric Functions

6.5 Growth Curve Models

6.6 Handling First-Order Autocorrelation

6.7 Using the Computer

Exercises

CHAPTER 7 DECOMPOSITION METHODS

7.1 Multiplicative Decomposition

7.2 Additive Decomposition

7.3 Shifting Seasonal Patterns

7.4 The Census II Decomposition Method and SAS PROC X11

7.5 Using the Computer

Exercises

CHAPTER 8 Exponential Smoothing

8.1 Simple Exponential Smoothing

8.2 Adaptive Control Procedures

8.3 Double Exponential Smoothing

8.4 Winters'Method

8.5 Exponential and Damped Trends

8.6 Prediction Intervals

8.7 Concluding Comments

8.8 Using the Computer

Exercises

PART Ⅳ FORECASTING BY USING BASIC TECHNIQUES OF THE BOX-JENKINS METHODOLOGY

CHAPTER 9 NONSEASONAL BOX-JENKINS MODELS AND THEIR TENTATIVE IDENTIFICATION

9.1 Stationary and Nonstationary Time Series

9.2 The Sample Autocorrelation and Partial Autocorrelation Functions:The SAC and SPAC

9.3 An Introduction to Nonseasonal Modeling and Forecasting

9.4 Tentative Identification of Nonseasonal Box-Jenkins Models

9.5 Using the Computer

Exercises

CHAPTER 10 ESTIMATION,DIAGNOSTIC CHECKING,AND FORECASTING FOR NONSEASONAL BOX-JENKINS MODELS

10.1 Estimation

10.2 Diagnostic Checking

10.3 Forecasting

10.4 A Case Study

10.5 Using the Computer

Exercises

CHAPTER 11 AN INTRODUCTION TO BOX-JENKINS SEASONAL MODELING

11.1 Transforming a Seasonal Time Series into a Stationary Time Series

11.2 Two Examples of Seasonal Modeling and Forecasting

11.3 Using the Computer

Exercises

PART Ⅴ FORECASTING BY USING ADVANCED TECHNIQUES OF THE BOX-JENKINS METHODOLOGY

CHAPTER 12 GENERAL BOX-JENKINS SEASONAL MODELING

12.1 The General Seasonal Model and Guidelines for Tentative Identification

12.2 Improving an Inadequate Seasonal Model

12.3 Using the Computer

Exercises

CHAPTER 13 USING THE BOX-JENKINS METHODOLOGY TO IMPROVE TIME SERIES REGRESSION MODELS AND TO IMPLEMENT EXPONENTIAL SMOOTHING

13.1 Box-Jenkins Error Term Models in Time Series Regression

13.2 Seasonal Intervention Models

13.3 Box-Jenkins Implementation of Exponential Smoothing

13.4 Using the Computer

Exercises

CHAPTER 14 TRANSFER FUNCTIONS AND INTERVENTION MODELS

14.1 A Three-Step Procedure for Building a Transfer Function Model

14.2 Intervention Models

14.3 Using the Computer

Exercises

APPENDIX A STATISTICAL TABLES

APPENDIX B REFERENCES

Index