PART I INTRODUCTION

An Introduction to Forecasting

Introduction

Forecasting and Time Series

Forecasting Methods

Errors in Forecasting

Choosing a Forecasting Techniques

An Overview of Quantitative Forecasting Techniques

Computer Packages: Minitab and SAS

Exercises

Basic Statistical Concepts

Populations

Probability

Random Samples and Sample Statistics

Continuous Probability Distributions

The Normal Probability Distribution

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

Confidence Intervals for a Population Mean

Hypothesis Testing for a Population Mean

Exercises

PART II FORECASTING BY USING REGRESSION ANALYSIS

Simple Linear Regression

The Simple Linear Regression Model

The Least Squares Point Estimates

Point Estimates and Point Predictions

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

Testing the Significance of the Independent Variable

A Confidence Interval for a Mean Value of the Dependent Variable and a Prediction Interval for an Individual Value of the Dependent Variable

Simple Coefficients of Determination and Correlation

An F-Test for the Simple Linear Regression Model

Using the Computer

Exercises

Multiple Regression

The Linear Regression Model

The Least Squares Point Estimates

Point Estimates and Point Predictions

The Regression Assumptions and the Standard Error

Multiple Coefficients of Determination and Correlation

An F-Test for the Overall Model

Statistical Inference for βj and Multicollinerity

Confidence Intervals and Prediction Intervals

An Introduction to Model Building

Residual Analysis

Using the Computer

Exercises

Topics in Regression Analysis

Interaction

An F-Test for a Portion of a Model

Using Dummy Variables to Model Qualitative Independent Variables

Advanced Concepts of Multicollinearity

Advanced Model Comparison Methods

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

Outlying and Influential Observations

Handling Unequal Variances

Using the Computer

Exercises

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

Time Series Regression

Modeling Trend by Using Polynomial Functions

Detecting Autocorrelation

Types of Seasonal Variation

Modeling Seasonal Variation by Using Dummy Variables and Trigonometric Functions

Growth Curve Models

Handling First-Order Autocorrelation

Using the Computer

Exercises

Decomposition Methods

Multiplicative Decomposition

Additive Decomposition

Shifting Seasonal Patterns

The Census II Decomposition Method and SAS PROC X11

Using the Computer

Exercises

Exponential Smoothing

Simple Exponential Smoothing

Adaptive Control Procedures

Double Exponential Smoothing

Winters' Method

Exponential and Damped Trends

Prediction Intervals

Concluding Comments

Using the Computer

Exercises

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

Nonseasonal Box-Jenkins Models and Their Tentative Identification

Stationary and Nonstationary Time Series

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

An Introduction to Nonseasonal Modeling and Forecasting

Tentative Identification of Nonseasonal Box-Jenkins Models

Using the Computer

Exercises

Estimation, Diagnostic Checking, and Forecasting For Nonseasonal Box-Jenkins Models

Estimation

Diagnostic Checking

Forecasting

A Case Study

Using the Computer

Exercises

An Introduction to Box-Jenkins Seasonal Modeling

Transforming a Seasonal Time Series into a Stationary Time Series

Two Examples of Seasonal Modeling and Forecasting

Using the Computer

Exercises

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

General Box-Jenkins Seasonal Modeling

The General Seasonal Model and Guidelines for Tentative Identification

Improving an Inadequate Seasonal Model

Using the Computer

Using the Computer

Exercises

Using the Box-Jenkins Methodology to Improve Time Series Regression Models and to Implement Exponential Smoothing

Box-Jenkins Error Term Models in Time Series Regression

Seasonal Intervention Models

Box-Jenkins Implementation of Exponential Smoothing

Using the Computer

Exercises

Transfer Functions and Intervention Models

A Three-Step Procedure for Building a Transfer Function Model

Intervention Models

Using the Computer

Exercises

Appendix A Statistical Tables

Appendix B References

Index