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### Forecasting and Time Series - An Applied Approach (Bowerman)

 Author(s) Bruce L. Bowerman, Richard T. O'Connell Title Forecasting and Time Series - An Applied Approach Edition Third Edition Year 1993 Publisher Wadsworth, Inc. ISBN 0-534-93251-7 Website www.cengage.com  book link  link to book data sets (zip file)

Table of Contents

Part I: INTRODUCTION AND REVIEW OF BASIC STATISTICS.

1. An Introduction to Forecasting.
• Forecasting and Data.
• Forecasting Methods.
• Errors in Forecasting.
• Choosing a Forecasting Technique.
• An Overview of Quantitative Forecasting Techniques.

2. Basic Statistical Concepts.
• Populations.
• Probability.
• Random Samples and Sample Statistics.
• Continuous Probability Distributions.
• The Normal Probability Distribution.
• The t-Distribution, the F-Distribution, the Chi-Square Distribution.
• Confidence Intervals for a Population Mean.
• Hypothesis Testing for a Population Mean.
• Exercises.

Part II: REGRESSION ANALYSIS.

3. Simple Linear Regression.
• The Simple Linear Regression Model.
• The Least Squares Point Estimates. Point Estimates and Point Predictions.
• Model Assumptions and the Standard Error.
• Testing the Significance of the Slope and y Intercept.
• Confidence and Prediction Intervals.
• Simple Coefficients of Determination and Correlation.
• An F Test for the Model.
• Exercises.

4. Multiple Linear Regression.
• The Linear Regression Model.
• The Least Squares Estimates, and Point Estimation and Prediction.
• The Mean Square Error and the Standard Error.
• Model Utility: R2, Adjusted R2, and the Overall F Test.
• Testing the Significance of an Independent Variable.
• Confidence and Prediction Intervals.
• The Quadratic Regression Model.
• Interaction.
• Using Dummy Variables to Model Qualitative Independent Variables.
• The Partial F Test: Testing the Significance of a Portion of a Regression Model.
• Exercises.

5. Model Building and Residual Analysis.
• Model Building and the Effects of Multicollinearity.
• Residual Analysis in Simple Regression.
• Residual Analysis in Multiple Regression.
• Diagnostics for Detecting Outlying and Influential Observations.
• Exercises.

Part III: TIME SERIES REGRESSION, DECOMPOSITION METHODS, AND EXPONENTIAL SMOOTHING.

6. 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 Curves.
• Handling First-Order Autocorrelation.
• Exercises.

7. Decomposition Methods.
• Multiplicative Decomposition.
• Additive Decomposition.
• The X-12-ARIMA Seasonal Adjustment Method.
• Exercises.

8. Exponential Smoothing.
• Simple Exponential Smoothing.
• Tracking Signals.
• Holt's Trend Corrected Exponential Smoothing.
• Holt-Winters Methods.
• Damped Trends and Other Exponential Smoothing Methods.
• Models for Exponential Smoothing and Prediction Intervals.
• Exercises.

Part IV: THE BOX-JENKINS METHODOLOGY.

9. Nonseasonal Box-Jenkins Modeling 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.
• Exercises.

10. Estimation, Diagnostic Checking, and Forecasting for Nonseasonal Box-Jenkins Models.
• Estimation.
• Diagnostic Checking.
• Forecasting.
• A Case Study.
• Box-Jenkins Implementation of Exponential Smoothing.
• Exercises.

11. Box-Jenkins Seasonal Modeling.
• Transforming a Seasonal Time Series into a Stationary Time Series.
• Three Examples of Seasonal Modeling and Forecasting.
• Box-Jenkins Error Term Models in Time Series Regression.
• Exercises.

12. Advanced Box-Jenkins Modeling.
• The General Seasonal Model and Guidelines for Tentative Identification.
• Intervention Models.
• A Procedure for Building a Transfer Function Model.
• Exercises.

Appendix A: Statistical Tables

Appendix B:
• Matrix Algebra for Regression Calculations.
• Matrices and Vectors.
• The Transpose of a Matrix.
• Sums and Differences of Matrices.
• Matrix Multiplication.
• The Identity Matrix.
• Linear Dependence and Linear Independence.
• The Inverse of a Matrix.
• The Least Squares Point Esimates.
• The Unexplained Variation and Explained Variation.
• The Standard Error of the Estimate b.
• The Distance Value.
• Using Squared Terms.
• Using Interaction Terms.
• Using Dummy Variable.
• The Standard Error of the Estimate of a Linear Combination of Regression Parameters.
• Exercises.
Appendix C: References.

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