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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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