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Zusatztext This highly readable book should be useful for students! lecturers! and practitioners alike as it covers most of the standard regression techniques and even some methods beyond.-Karsten Webel! Statistical Papers (2012) 53As 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. 4In 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 Informationen zum Autor Panik! Michael Klappentext Requiring only basic knowledge of statistics and calculus! this textbook explores the diversity of regression techniques. It first reviews random variables! probability distributions! and classical statistical interference. The book then presents the many varieties of regression analyses! including ordinary least squares methods! semiparametric regression! Bayesian methods! robust regression! random coefficients! fuzzy regression! L1 and q-Quantile regression! and regression in a spatial domain. SAS code is available for most of the methods presented. To avoid overwhelming students with calculations! proofs and derivations are provided only in the appendices. Zusammenfassung Presents an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. This title documents the SAS programs and explains the output produced by the programs. It also covers nonlinear and time series modeling. Inhaltsverzeichnis Preface. 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. L 1 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. Appendix. References. Index. ...
