Forecasting, Time Series, and Regression

Name: Forecasting, Time Series, and Regression
Availability: InStock
ISBN: 9780534409777

ISBN-10: 0534409776

ISBN-13: 9780534409777

Edition: 4th 2005 (Revised)

Authors: Bruce L. Bowerman, Richard O'Connell, Anne Koehler

List price: $267.95

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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…

Book details

List price: $267.95
Edition: 4th
Copyright year: 2005
Publisher: Brooks/Cole
Publication date: 4/29/2004
Binding: Hardcover
Pages: 720
Size: 7.50" wide x 9.25" long x 1.10" tall
Weight: 3.498
Language: English

Richard T. O'Connell is an associate professor of decision sciences at Miami University in Oxford, Ohio. He has more than 32 years of experience teaching basic statistics, statistical quality control and process improvement, regression analysis, time series forecasting, and design of experiments to both undergraduate and graduate business students. In 2000 Professor Oï¿½Connell received an Effective Educator award from the Richard T. Farmer School of Business Administration. Together with Bruce L. Bowerman, he has written seven textbooks. These include Forecasting and Time Series: An Applied Approach and Linear Statistical Models: An Applied Approach. He is one of the first college…




Introduction and Review of Basic Statistics



An Introduction to Forecasting


Forecasting and Data


Forecasting Methods


Errors in Forecasting


Choosing a Forescasting Technique


An Overview of Quantitative Forecasting Techniques



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



Regression Analysis



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



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



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



Time Series Regression, Decomposition Methods, and Exponential Smoothing



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



Decomposition Methods


Multiplicative Decomposition


Additive Decomposition


The X-12-ARIMA Seasonal Adjustment Method


Exercises



Exponential Smoothing


Simple Exponential Smoothing


Tracking Signals


Holts Trend Corrected Exponential Smoothing


Holt-Winters Methods


Damped Trends and Other Exponential Smoothing Methods


Models for Exponential Smoothing and Prediction Intervals


Exercises



The Box-Jenkins Methodology



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



Estimation, Diagnostic Checking, and Forecasting for Nonseasonal Box-Jenkins Models


Estimation


Diagnostic Checking


Forecasting


A Case Study


Box-Jenkins Implementation of Exponential Smoothing


Exercises



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



Advanced Box-Jenkins Modeling


The General Seasonal Model and Guidelines for Tentative Identificatino


Intervention Models


A Procedure for Building a Transfer Function Model


Exercises



Statistical Tables



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



References.