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Awarded Outstanding Academic Book by CHOICE magazine in its first edition, FORECASTING, TIME SERIES, AND REGRESSION: AN APPLIED APPROACH now appears in a fourth edition that illustrates the vital importance of forecasting and the various statistical techniques that can be used to produce them. With an emphasis on applications, this book provides both the conceptual development and practical motivation students need to effectively implement forecasts of their own. Bruce Bowerman, Richard O'Connell, and Anne Koehler clearly demonstrate the necessity of using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management. In addition, new technology coverage makes the latest edition the most applied text available on the market.
- New modernized, high-resolution computer graphics and output reflect the latest software releases and illustrate practical tools and applications.
- The exercises have been expanded and updated to include recent data and realistic scenarios.
- Regression is more thoroughly addressed with expanded sections on the topic as well as an optional appendix on matrix algebra.
- The totally re-written exponential smoothing chapter emphasizes modern techniques, models, and spreadsheets.
- Clearer discussion of Box-Jenkins models enables students to better understand this important topic.
- New balanced organization allows the book to be used in a variety of course syllabi. Whether you wish to focus exclusively on Forecasting, Time Series or even Regression, or cover some of each, this text provides all the topics you'll need in one book.
1. An Introduction to Forecasting.
Forecasting and Data. Forecasting Methods. Errors in Forecasting. Choosing a Forescasting 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 Identificatino. 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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