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Negative Binomial Regression

At last - a book devoted to the negative binomial model and its many variations. Every model currently offered in commercial statistical;

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Statistical Regression Modeling with R

regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial;

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Statistical Regression Modeling with R

regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial;

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Multivariable Analysis

techniques including Poisson regression, negative binomial regression, multinomial logistic regression and proportional odds regression. As before;

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Regression With Social Data

response models* Poisson, negative binomial, and other regression models for event-count data* Survival analysis using multistate, multiepisode;

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Statistical Methods For Rates And Proportions

This work: includes a new chapter on logistic regression; discusses the design and analysis of random trials; explores the latest;

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Statistical Modelling in Higher Learning

used when the outcome variable is a dichotomous variable (has two values). Logistic Regression uses Binomial Probability Theory in which there;

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Logistic Regression Models

evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression, varieties;

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Logistic Regression Models

evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression, varieties;

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Cognitive Linguistics - The Quantitative Turn

methods and explains their implications for the field. The methods include chi-square, Fisher test, binomial test, ANOVA, correlation, regression;

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Applied Linear Regression 4th Ed

variance assumptions * Updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection;

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Understanding Regression Analysis

Understanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models;

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Analysis of Incidence Rates

negative binomial regression, methods for clustered data, and the analysis of longitudinal data. Also, reviews controversies and limitations;

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Analysis of Incidence Rates

extensions of count models for rates, including negative binomial regression, methods for clustered data, and the analysis of longitudinal data. Also;

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Regression Models for Categorical Dependent Variables Using Stata

-truncated negative binomial models, the hurdle model for counts, the stereotype logistic regression model, the rank-ordered logit model, and the;

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SPSS Step by Step

. Clearly written and with no mathematical formulae, it features a chapter on qualitative data analysis; multiple regression; survival analysis;

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SPSS Step By Step

. Clearly written and with no mathematical formulae, it features a chapter on qualitative data analysis; multiple regression; survival analysis;

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Regression Models for Categorical, Count, and Related Variables

statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The;

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The Art of Proving Binomial Identities

The Art of Proving Binomial Identities accomplishes two goals: (1) It provides a unified treatment of the binomial coefficients, and (2;

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The Art of Proving Binomial Identities

The Art of Proving Binomial Identities accomplishes two goals: (1) It provides a unified treatment of the binomial coefficients, and (2;

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An Introduction to Generalized Linear Models

focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models;

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Affective Aspects of Learning

-regression statistics reveal that positive emotions positively correlated with learning strategy use, whilst negative emotions negatively correlated;

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Bayesian Ideas And Data Analysis

, such as Markov chain Monte Carlo (MCMC) simulation. After discussing linear structures in regression, it presents binomial regression, normal;

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Binomial Ideals

This textbook provides an introduction to the combinatorial and statistical aspects of commutative algebra with an emphasis on binomial;

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Binomial Ideals

This textbook provides an introduction to the combinatorial and statistical aspects of commutative algebra with an emphasis on binomial;

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Mathematical Statistics with Applications

other realistic experimental situations motivate the theoretical foundation. Updated and expanded coverage includes: the negative binomial;

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Univariate Discrete Distributions

statistical fundamentals, the authors provide clear coverage of the key topics in the field, including: families of discrete distributions; binomial;

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