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Confidence Intervals in Generalized Regression Models

A Cohesive Approach to Regression Models Confidence Intervals in Generalized Regression Models introduces a unified representation-the;

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Confidence Intervals in Generalized Regression Models

A Cohesive Approach to Regression Models Confidence Intervals in Generalized Regression Models introduces a unified representation-the;

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Simultaneous Inference in Regression

regression models. The final chapter demonstrates the use of simultaneous confidence bands in generalized linear regression models, such as logistic;

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Simultaneous Inference in Regression

regression models. The final chapter demonstrates the use of simultaneous confidence bands in generalized linear regression models, such as logistic;

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Mathematical Statistics

intervals. There's also a chapter on parametric statistical models featuring sections on non-iid observations, linear regression, logistic;

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Nonparametric Regression and Spline Smoothing

Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions;

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Nonparametric Regression and Spline Smoothing

Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions;

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Linear Model Theory

This book contains 296 exercises and solutions covering a wide variety of topics in linear model theory, including generalized inverses;

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Linear Model Theory

This book contains 296 exercises and solutions covering a wide variety of topics in linear model theory, including generalized inverses;

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Advanced Linear Models

prediction. The authors discuss the application of statistical theories and methodologies to various linear models such as the linear regression;

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Design and Analysis of Gauge R&R Studies

the methods easily. The book describes methods for constructing two types of confidence intervals: modified large-sample (MLS) and generalized;

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Multivariate Analysis for the Behavioral Sciences, Second Edition

book covers regression methods, including simple linear regression, multiple regression, locally weighted regression, generalized linear models;

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Multivariate Analysis for the Behavioral Sciences, Second Edition

covers regression methods, including simple linear regression, multiple regression, locally weighted regression, generalized linear models;

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Extending The Linear Model With R

expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and;

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Generalized Additive Models

of data types, Generalized Additive Models enhances this methodology even further by incorporating the flexibility of nonparametric regression;

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Categorical Data Analysis Using SAS

regression, weighted least squares modeling, repeated measurements analyses, loglinear models, generalized estimating equations, and bioassay;

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Applied Regression Analysis and Generalized Linear Models

Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the;

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Data Science Tools

intervals, normal distribution, T-Tests, linear regression, histograms, and geographic analysis using real data from Federal Government sources;

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Applied Regression Including Computing and Graphics

of regression models, including logistic regression and generalized linear models More than 300 figures, easily reproducible on the computer Numerous;

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Logistic Regression Using SAS

edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit;

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An Introduction to Generalized Linear Models

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive;

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Interpreting and Visualizing Regression Models Using Stata

Interpreting and Visualizing Regression Models Using Stata, Second Edition provides clear and simple examples illustrating how to interpret;

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Logistic Regression Using SAS

edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit;

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Regression Analysis and Linear Models

, generalized linear model, etc. This book presents the complex subject of regression analysis and linear models, in the most comprehensible and easy to;

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A Primer on Linear Models

of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact;

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A Primer on Linear Models

of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact;

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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data;

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