Forecasting, Time Series, and Regression

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 strategic management. In addition, new technology coverage makes the latest edition the most applied text available on the market.
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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: 3.014
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 O�Connell 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 instructors in the United States to integrate statistical process control and process improvement methodology into his basic business statistics course. He (with Professor Bowerman) has written several articles advocating this approach. ..

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
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
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
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
Using Dummy Variables to Model Qualitative Independent Variables
The Partial F Test: Testing the Significance of a Portion of a Regression Model
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
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
Decomposition Methods
Multiplicative Decomposition
Additive Decomposition
The X-12-ARIMA Seasonal Adjustment Method
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
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
Estimation, Diagnostic Checking, and Forecasting for Nonseasonal Box-Jenkins Models
Diagnostic Checking
A Case Study
Box-Jenkins Implementation of Exponential Smoothing
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
Advanced Box-Jenkins Modeling
The General Seasonal Model and Guidelines for Tentative Identificatino
Intervention Models
A Procedure for Building a Transfer Function Model
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
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