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Regression Modeling


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Table of Contents

Review of Fundamentals of Statistics
Bivariate Linear Regression and Correlation
Misspecified Disturbance Terms
Nonparametric Regression
Logistic Regression. Bayesian Regression
Robust Regression. Fuzzy Regression
Random Coefficients Regression
L1 and q-Quantile Regression
Regression in a Spatial Domain
Multiple Regression
Normal Correlation Models
Ridge Regression
Indicator Variables
Polynomial Model Estimation
Semiparametric Regression
Nonlinear Regression
Issues in Time Series Modeling and Estimation

About the Author

Panik, Michael


! As an introductory text, it is mostly successful ! . One of the great strengths of the text is that the examples tend to be linked into a structure ! so that a student can more easily see how each procedure is connected to the concepts that preceded it. Another strength of the book is the detailed appendices at the end of each chapter. ! A unique feature of this book is that it contains many chapters on facets of regression which are not covered in typical introductory texts ! the book has great expository strength. It contains detailed verbal descriptions of the procedures used and the reasoning behind them, and these are always clear and linked to the previous descriptions. ! the book serves as an excellent conceptual aid to a professor who would prefer to emphasize statistical reasoning to students, rather than to just rely upon the formulaic structure. --The American Statistician, November 2010, Vol. 64, No. 4 In his book, Michael Panik takes up many aspects of modeling with a pedagogical approach, helping the reader to understand the process of the problem and proposed methods. The appendices enrich his process to [readers] who want to increase their knowledge. ! this book is a very good tool for students and teachers in statistics, but also for researchers wishing to improve their knowledge in statistical modeling to apply it in their expertise domain. --Christian Derquenne, Journal of Statistical Software, February 2010

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