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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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Applied Nonparametric Regression is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable. The computer and the development of interactive graphics programs have made curve estimation possible. This volume focuses on the applications and practical problems of two central aspects of curve smoothing: the choice of smoothing parameters and the construction of confidence bounds. Härdle argues that all smoothing methods are based on a local averaging mechanism and can be seen as essentially equivalent to kernel smoothing. To simplify the exposition, kernel smoothers are introduced and discussed in great detail. Building on this exposition, various other smoothing methods (among them splines and orthogonal polynomials) are presented and their merits discussed. All the methods presented can be understood on an intuitive level; however, exercises and supplemental materials are provided for those readers desiring a deeper understanding of the techniques. The methods covered in this text have numerous applications in many areas using statistical analysis. Examples are drawn from economics as well as from other disciplines including medicine and engineering.

AA / International- internationaal --- 305.971 --- Speciale gevallen in econometrische modelbouw. --- Nonparametric statistics --- Regression analysis --- 519.234 --- 681.3*G3 --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 519.234 Non-parametric methods --- Non-parametric methods --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric --- Mathematical statistics --- 519.536 --- Quantitative methods (economics) --- Regression Analysis --- Analyse de regression --- Statistique non-paramétrique --- Nonparametric statistics. --- Regression analysis. --- Statistique non-paramétrique --- Analyse de régression --- Speciale gevallen in econometrische modelbouw --- Statistique non paramétrique --- Business, Economy and Management --- Economics