Foundations of Linear and Generalized Linear Models

Foundations of Linear and Generalized Linear Models By Agresti, Alan Published by WILEY Publication Date: 2015-04-03 Subject: Mathematics Probability & Statistics General, Mathematics Probability & Statistics - General, Mathematics, Probability & Statistics, Probability And Statistics, Hc Mathematik Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik, Mathematics Probability & Statistics General, Mathematics, Probability & Statistics, General, Mathematics, Hc Mathematik Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik, Hc, Mathematik, Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik, Applied Probability & Statistics - Models, Data Analysis, Applied Probability & Statistics, Linear Models; Generalized Linear Models; R Software; Count Data; Bayesian Modeling; High-Dimensional Problems; Statistical Models; Binary Data; Least Squares Theory; Statistical Inference; Biostatistics; Multinomial Response Models; Quasi―Likelihood Methods; Model Fitting; Inference; Data Analysis; Applied Probability; Applied Statistics; Categorical Data; Correlated Multi-Response Data, Linear Models; Generalized Linear Models; R Software; Count Data; Bayesian Modeling; High-Dimensional Problems; Statistical Models; Binary Data; Least Squares Theory; Statistical Inference; Biostatistics; Multinomial Response Models; Quasi―Likelihood Methods; Model Fitting; Inference; Data Analysis; Applied Probability; Applied Statistics; Categorical Data; Correlated Multi-Response Data Subject Keywords: Linear models; generalized linear models; R software; count data; Bayesian modeling; high-dimensional problems; statistical models; binary data; least squares theory; statistical inference; biostatistics; multinomial response models; quasi―likelihood methods; model fitting; inference; data analysis; applied probability; applied statistics; categorical data; correlated multi-response data, Linear models; generalized linear models; R software; count data; Bayesian modeling; high-dimensional problems; statistical models; binary data; least squares theory; statistical inference; biostatistics; multinomial response models; model fitting; inference; data analysis; applied probability; applied statistics; categorical data; correlated multi-response data; quasi—likelihood methods Genre: Mathematics, Probability & Statistics, General, Mathematics, Hc, Mathematik, Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik Target Audience: Professional and scholarly