Ebook Applied Regression Analysis: A Second Course in Business and Economic Statistics (Book, CD-ROM & InfoTrac), by Terry E. Dielman
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Applied Regression Analysis: A Second Course in Business and Economic Statistics (Book, CD-ROM & InfoTrac), by Terry E. Dielman
Ebook Applied Regression Analysis: A Second Course in Business and Economic Statistics (Book, CD-ROM & InfoTrac), by Terry E. Dielman
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APPLIED REGRESSION ANALYSIS focuses on the application of regression to real data and examples while employing commercial statistical and spreadsheet software. Designed for both business/economics undergraduates and MBAs, this text provides all of the core regression topics as well as optional topics including ANOVA, Time Series Forecasting, and Discriminant Analysis. While only a prior introductory statistics course is required, a review of all necessary basic statistics is provided in chapter 2. The text emphasizes the importance of understanding the assumptions of the regression model, knowing how to validate a selected model for these assumptions, knowing when and how regression might be useful in a business setting, and understanding and interpreting output from statistical packages and spreadsheets.
- Sales Rank: #103903 in Books
- Brand: Brand: Brooks/Cole
- Published on: 2004-08-04
- Original language: English
- Number of items: 1
- Dimensions: 9.52" h x .99" w x 7.56" l, 2.09 pounds
- Binding: Hardcover
- 496 pages
- Used Book in Good Condition
Review
1. An Introduction to Regression Analysis. 2. Review of Basic Statistical Concepts. Introduction / Descriptive Statistics / Discrete Random Variables and Probability Distributions / The Normal Distribution / Populations, Samples, and Sampling Distributions / Estimating a Population Mean / Hypothesis Tests About a Population Mean / Estimating the Difference Between Two Population Means / Hypothesis Tests About the Difference Between Two Population Means. 3. Simple Regression Analysis. Using Simple Regression to Describe a Linear Relationship / Examples of Regression as a Descriptive Technique / Inferences from a Simple Regression Analysis / Assessing the Fit of the Regression Line / Prediction or Forecasting with a Simple Linear Regression Equation. Fitting a Linear Trend to Time-Series Data / Some Cautions in Interpreting Regression Results. 4. Multiple Regression Analysis. Using Multiple Regression to Describe a Linear Relationship / Inferences from a Multiple Regression Analysis / Assessing the Fit of the Regression Line / Comparing Two Regression Models / Prediction with a Multiple Regression Equation / Multicollinearity: A Potential Problem in Multiple Regression / Lagged Variables as Explanatory Variables in Time-Series Regression. 5. Fitting Curves to Data. Introduction / Fitting Curvilinear Relationships. 6. Assessing the Assumptions of the Regression Model. Introduction. Assumptions of the Multiple Linear Regression Model / The Regression Residuals / Assessing the Assumption That the Relationship is Linear / Assessing the Assumption That the Variance Around the Regression Line is Constant / Assessing the Assumption That the Disturbances are Normally Distributed / Influential observations / Assessing the Influence That the Disturbances are Independent. 7. Using Indicator and Interaction Variables. Using and Interpreting Indicator Variables / Interaction Variables / Seasonal Effects in Time-Series Regression. 8. Variable Selection. Introduction. All Possible Regressions. Other Variable Selection Techniques / Which Variable Selection Procedure is Best? 9. An Introduction to Analysis of Variance. One-Way Analysis of Variance. Analysis of Variance Using a Randomized Block Design / Two-Way Analysis of Variance / Analysis of Covariance. 10. Qualitative Dependent Variables: An Introduction to Discriminant Analysis and Logistic Regression. Introduction. Discriminant Analysis / Logistic Regression. 11. Forecasting Methods for Time-Series Data. Introduction / Naive Forecasts / Measuring Forecast Accuracy / Moving Averages / Exponential Smoothing / Decomposition. APPENDICES. A: Summation Notation. B: Statistical Tables. C: A Brief Introduction to MINITAB, Microsoft Excel, and SAS. D: Matrices and their Application to Regression Analysis. E: Solutions to Selected Odd-Numbered Exercises. References / Index.
About the Author
Terry Dielman is professor of Decision Sciences at Texas Christian University. Terry received his Ph.D. at the University of Michigan (Business Statistics), his M.S. at the University of Cincinnati (Mathematics) and his B.A. at Emporia State University (Mathematics). His recent research focuses on Regression Analysis, Time Series Forecasting, Robust Statistical Procedures and the Analysis of Pooled Cross-Sectional and Time Series Data. His recent publications include �Bootstrap versus Traditional Hypothesis Testing Procedures for Coefficients in Least Absolute Value Regression� in the JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION. He participates in the Editorial Board of the Journal of Business and Management, and consults for Forecasting Seminars and for various law firms.
Most helpful customer reviews
14 of 15 people found the following review helpful.
Excellent
By Peachbulb
For my class, the professor assigned Kleinbaum et al's textbook rather than this one. As I mentioned in my review for that book, it was so confusing and poorly organized. Luckily, I found this book in the library and used it instead and ended up a lot less confused in class than my classmates who were trying to understand the Kleinbaum book. Dielman's book is very well organized and laid out. It doesn't have colorful bells and whistles since it's a fairly upper level book, but it does have a very user-friendly layout. Furthermore, the formulas are never presented without an accompanying explanation in plain English and examples of how and when to use them. Another thing I really like about this book is that it gives thorough directions on how to do a lot of the analyses on some common statistical packages. Many of the instructions are accompanied by screenshots. They're at the end of each chapter rather than interspersed in the text, which makes them easy to find. This is actually becoming a great SAS manual for me.
For anyone struggling with the Kleinbaum book, or for any instructor considering using the Kleinbaum book, I would highly recommend this one instead.
10 of 10 people found the following review helpful.
A very enlightening book.
By G. Arora
excellent book: This is a successful attempt to make the complicated statistics much easier to grasp and use. includes output from various softwares like MINITAB, EXCEL, SAS...
only cautions are:
1. Doesn't go into Mathematically details involved.
2. One should consider that the title reads "a second course...".
10 of 12 people found the following review helpful.
A Cross-platform textbook
By Prophet
I have bought Dr. Dielman's Applied Regression 4e textbook in Taiwan) for preparing my MBA thesis regarding the interaction effects. Given the widespread use of commercially available packages, this book provided considerately coverage on ALL computer packages about the field of regression work.
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