Forecasting, Time Series, and Regression

Awarded Outstanding Academic Book by CHOICE magazine in its first edition, FORECASTING, TIME SERIES, AND REGRESSION: AN APPLIED APPROACH 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 you need to effectively implement forecasts of your own. You'll understand why using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management is so vital.

(Cengage Learning EMEA)

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Part I: INTRODUCTION AND REVIEW OF BASIC STATISTICS.
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. Forec...

(Cengage Learning EMEA)