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"The text does an outstanding job of providing the necessary mechanics and theory of the S language. I will use this book in every such course that I teach from this point on." 						 JEFF GILL, University of Florida, Gainesville "The book provides a valuable supplement to texts on regression analysis and linear models by showing readers how to put into practice the strategies and techniques involved in modern statistical methodology. It explains clearly the use of a very sophisticated and powerful statistical software system. And, while the examples and objectives are focused closely on regression and related techniques, the discussion successfully conveys general advice and principles for statistical computing with the S system." 					 WILLIAM JACOBY, University of South Carolina Features: Various facilities of S are introduced as needed within the context of detailed examples Cumulative examples: later examples often depend upon earlier ones, but examples in separate chapters are always independent of each other Asterisks note more demanding material that may be skipped without loss of continuity Boxes explain and highlight the many small differences between S-PLUS and R, and between S3 and S4, so that the reader can quickly locate the material that is relevant to their version of S or skip that information A companion Web site, featuring: instructions for downloading, installing, and using the Windows version of R; add-on packages; versions of the car (companion to applied regression) library that includes the software and data sets described in the book; an appendix for various extensions of regression analysis not covered in the text; and downloadable scripts for all of the examples in the text "If you want to keep up with the latest developments in statistics, you need to use the computer language S. If you want to learn S, there isn't a better way to get started than Fox's An R and S-Plus Companion to Applied Regressio

Computer. Automation --- Mathematical statistics --- regressie-analyse --- wiskundige statistiek --- Regression analysis --- R (Computer program language) --- -R (Computer program language) --- 519.536 --- GNU-S (Computer program language) --- Domain-specific programming languages --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Data processing. --- Data processing --- S-Plus. --- SPlus --- R (Computer program language).

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"The goal of Regression Models for Categorical Dependent Variables Using Stata, Third Edition is to make it easier to carry out the computations necessary to fully interpret regression models for categorical outcomes by using Stata's margins command. Because the models are nonlinear, they are more complex to interpret. Most software packages that fit these models do not provide options that make it simple to compute the quantities useful for interpretation. In this book, the authors briefly describe the statistical issues involved in interpretation, and then they show how you can use Stata to perform these computations."--Back cover.

Programming --- Quantitative methods in social research --- Mathematical statistics --- Regression Analysis --- Social sciences --- Statistical methods --- Data processing --- Stata --- Regression analysis. --- Data processing. --- #SBIB:303H4 --- 519.246 --- 301.085 --- 311 --- 681.333 --- 681.33 --- Regression analysis --- -519.536 --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- 519.246 Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- Informatica in de sociale wetenschappen --- Kwantitatieve sociologische onderzoeksmethoden --- Statistiekwetenschap --- Softwarepakket --- Programmering --- -Data processing --- Stata. --- 519.536 --- Statistical methods&delete& --- Social sciences - Statistical methods - Data processing

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Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

Mathematical statistics --- Multilevel models (Statistics) --- 519.536 --- Hierarchical linear models (Statistics) --- Mixed effects models (Statistics) --- Random coefficient models (Statistics) --- Variance component models (Statistics) --- Multilevel models (Statistics). --- Regression analysis. --- Methoden en technieken --- statistiek. --- wiskundige statistiek --- regressie-analyse --- #SBIB:303H520 --- 519.2 --- Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Mathematical models --- Multivariate analysis --- Structural equation modeling

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Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a doctor, auto accidents or job hiring. This analysis provides a comprehensive account of models and methods to interpret such data. The authors have conducted research in the field for nearly fifteen years and in this work combine theory and practice to make sophisticated methods of analysis accessible to practitioners working with widely different types of data and software. The treatment will be useful to researchers in areas such as applied statistics, econometrics, operations research, actuarial studies, demography, biostatistics, quantitatively-oriented sociology and political science. The book may be used as a reference work on count models or by students seeking an authoritative overview. The analysis is complemented by template programs available on the Internet through the authors' homepages.

Regression Analysis --- Econometrics --- Business, Economy and Management --- Economics --- Mathematical statistics --- Regression analysis. --- Econometrics. --- Analyse de régression --- Econométrie --- Economics, Mathematical --- Statistics --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Regression analysis --- 519.536 --- 303.5 --- AA / International- internationaal --- 330.115 --- 519.235 --- 330.115 Econometrie --- Econometrie --- 519.235 Statistics of dependent variables. Contingency tables --- Statistics of dependent variables. Contingency tables --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek) --- Wiskundige statistiek --- #SBIB:303H520 --- #SBIB:303H522 --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Methoden sociale wetenschappen: handboeken statistische analyse

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From the reviews of the First Edition."An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."-Choice"Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."-Contemporary Sociology"An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."-The StatisticianIn this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.

Mathematical statistics --- QA 278.2 .H67 2000 Regression analysis. Correlation analysis --- Regression analysis --- Regression Analysis --- Méthode statistique --- Statistical methods --- Analyse de données --- Data analysis --- 519.23 --- #SBIB:303H520 --- Statistische analyse 519.23 --- Economische gedragsmodellen 519.865 --- 519.233.5 --- AA / International- internationaal --- 303.5 --- 519.536 --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Statistical analysis. Inference methods --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Correlation analysis. Regression analysis --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek). --- Regression analysis. --- Basic Sciences. Statistics --- Correlation and Regression Analysis --- Correlation and Regression Analysis. --- 519.233.5 Correlation analysis. Regression analysis --- 519.23 Statistical analysis. Inference methods --- Theorie van correlatie en regressie. (OLS, adjusted LS, weighted LS, restricted LS, GLS, SLS, LIML, FIML, maximum likelihood). Parametric and non-parametric methods and theory (wiskundige statistiek) --- Statistique mathematique --- Regression