Table of Contents

a. Introduction
b. Quantitative Forecasting Has Become Widely Accepted
c. Forecasting in Business Today
d. Forecasting in the Public and Not-for-Profit Sectors
e. Forecasting and Supply Chain Management
f. Computer Use and Quantitative Forecasting
g. Subjective Forecasting Methods
h. New-Product Forecasting
i. Two Simple Na Models
j. Evaluating Forecasts
k. Sources of Data
l. Forecasting Domestic Car Sales
m. Overview of the Text
n. Integrative Case: Forecasting Sales of The Gap
o. Case Questions
p. Solutions to Case Questions
q. Case References
r. About ForecastX(TM): The Software on the CD with your Business Forecasting with Accompanying Excel-Based ForecastX(TM)Software Text
s. Getting Started: Procast(TM) Jump Starts Your Forecasting Process
t. Suggested Readings and Web Sites
u. Exercises

a. Introduction
b. The Forecast Process
c. Trend, Seasonal, and Cyclical Data Patterns
d. Data Patterns and Model Selection
e. A Statistical Review
f. Correlograms: An Alternative Method of Data Exploration
g. Domestic Car Sales: Exploratory Data Analysis and Model Selection
h. Integrative Case: The Gap
i. Case Questions
j. Solutions to Case Questions
k. Using ForecastX(TM) to Find Autocorrelation Functions
l. Suggested Readings
m. Exercises

a. Moving Averages
b. Simple Exponential Smoothing
c. Holt's Exponential Smoothing
d. Winters' Exponential Smoothing
e. Adaptive-Response-Rate Single Exponential Smoothing
f. Using Single, Holt's or ADRES Smoothing to Forecast a Seasonal Data Series
g. Event Modeling
h. Summary
i. Forecasting Domestic Car Sales with Exponential Smoothing
j. Integrative Case: The Gap
k. Case Questions
l. Solutions to Case Questions
m. Using ForecastX(TM) to Make Exponential Smoothing Forecasts
n. Suggested Readings
o. Exercises

a. The Bivariate Regression Model
b. Visualization of Data: An Important Step in Regression Analysis
c. A Process for Regression Forecasting
d. Forecasting with a Simple Linear Trend
e. Using a Causal Regression Model to Forecast
f. Retail Sales Forecast Based on Disposable Personal Income Per Capita
g. Retail Sales Forecast Based on Mortgage Rate
h. Heteroscedasticity
i. Forecasting Domestic Car Sales with Bivariate Regression
j. Integrative Case: The Gap
k. Case Questions
l. Solutions to Case Questions
m. Using ForecastX(TM) to Make Regression Forecasts
n. Further Comments on Regression Models
o. Suggested Readings
p. Exercises

a. The Multiple-Regression Model
b. Selecting Independent Variables
c. Forecasting with a Multiple-Regression Model
d. Statistical Evaluation of Multiple-Regression Models
e. Serial Correlation and the Omitted-Variable Problem
f. Accounting for Seasonality in a Multiple-Regression Model
g. Extensions of the Multiple-Regression Model
h. Advice on Using Multiple Regression in Forecasting
i. Forecasting Domestic Car Sales with Multiple Regression
j. Integrative Case: The Gap
k. Case Questions
l. Solutions to Case Questions
m. Using ForecastX(TM) to Make Multiple-Regression Forecasts
n. Suggested Readings
o. Exercises

a. The Basic Time-Series Decomposition Model
b. Deseasonalizing the Data and Finding Seasonal Indexes
c. Finding the Long-Term Trend
d. Measuring the Cyclical Component
e. The Time-Series Decomposition Forecast
f. Forecasting Domestic Car Sales by Using Time-Series Decomposition
g. Integrative Case: The Gap
h. Case Questions
i. Solutions to Case Questions
j. Using ForecastX(TM) to Make Time-Series Decomposition Forecasts
k. Suggested Reading
l. Exercises
m. Appendix 6.1 Components of the Composite Indexes

a. Introduction
b. The Philosophy of Box-Jenkins
c. Moving-Average Models
d. Autoregressive Models
e. Mixed Autoregressive and Moving-Average Models
f. Stationarity
g. The Box-Jenkins Identification Process
h. ARIMA: A Set of Numerical Examples
i. Forecasting Seasonal Time Series
j. Domestic Car Sales
k. Integrative Case: Forecasting Sales of The Gap
l. Case Questions
m. Solutions to Case Questions
n. Using ForecastX(TM) to Make ARIMA (Box-Jenkins) Forecasts
o. Suggested Readings
p. Exercises
q. Appendix Critical Values of Chi-Square

a. Introduction
b. Bias
c. An Example
d. What Kinds of Forecasts Can Be Combined?
e. Considerations in Choosing the Weights for Combined Forecasts
f. Three Techniques for Selecting Weights When Combining Forecast
g. Integrative Case: The Gap Part 8
h. Case Questions
i. Solutions to Case Questions
j. Using ForecastX(TM) to Combine Forecasts
k. Suggested Readings
l. Exercises

a. Keys to Obtaining Better Forecast
b. The Forecast Process
c. Choosing the Right Forecasting Techniques
d. New Product Forecasting
e. Artificial Intelligence and Forecasting
f. Summary
g. Using Procast(TM) in ForecastX(TM) to Make Forecasts
h. Suggested Readings
i. Exercises