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Regression with Dummy Variables

qualitative (i.e., measured at only the nominal level), dummy variables will allow the researcher to represent information about group membership;

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Econometrics

hypothesis, t, F or chi-square test statistics and distributions, and interpret regression results. Dummy variables model qualitative data and Chow;

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Spline Regression Models

Spline Regression Models shows the nuts-and-bolts of using dummy variables to formulate and estimate various spline regression models. For;

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A Stata Companion To Political Analysis

descriptive statistics, cross-tabulation analysis, mean comparisons, linear correlation and regression (including dummy variables and interaction;

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An R Companion to Political Analysis

inference, linear correlation and regression, dummy variables and interaction effects, and logistic regression. The clear explanation and;

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Nonlinear Statistical Models

. The three major categories of statistical models that relate dependent variables to explanatory variables are covered: univariate regression;

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Logistic Regression

variables Shows readers how multilevel change models with logistic regression are different from multilevel growth curve models for continuous;

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Correlation and Regression Analysis

relations during inflation have been elucidated. The presentation and analysis of data along with detailed information about macroeconomic variables;

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QUANTILE REGRESSION - Applications on Experimental and Cross Section Data Using EVIEWS

between a dependent variable and one or more independent variables, where each equation estimation of a regression can give only a single;

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Who are the cigarette smokers in Arizona?

logistic regression model, even though overall, demographic variables cannot account for much of the variance in the smoking status of the people;

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Regression Diagnostics

With Regression Diagnostics, researchers now have an accessible explanation of the techniques needed for exploring problems that compromise;

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Gaussian Process Regression Analysis for Functional Data

and mixed covariates of functional and scalar variables. Covering the basics of Gaussian process regression, the first several chapters;

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Regression With Social Data

An accessible introduction to the use of regression analysis in the social sciences Regression with Social Data: Modeling Continuous and;

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Regression Diagnostics

Provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates;

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Regression Models for Categorical Dependent Variables Using Stata, Third Edition

Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression;

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Regression Analysis by Example

relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results;

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Multiple-Regression in Behavioral Research

of regression analysis to various research problems. He discusses major research studies and journal articles in conjunction with specific methods;

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Dummy Auxiliaries in First and Second Language Acquisition

of research on dummy auxiliaries with contributions covering English, Dutch, German, French, Cypriot-Greek, first and second language acquisition, and;

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Regression Models for Categorical Dependent Variables Using Stata

Although regression models for categorical dependent variables are common, few texts explain how to interpret such models. Regression;

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Sensitivity Analysis in Linear Regression

due to different variables. The robustness of the regression fit is assessed qualitatively and numerically.;

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Economic Applications of Quantile Regression

Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing;

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Dynamic Regression Models for Survival Data

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and;

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Dynamic Regression Models for Survival Data

This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and;

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Data Analysis Using Regression & Multi

instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and;

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Economic Applications of Quantile Regression

Quantile regression has emerged as an essential statistical tool of contemporary empirical economics and biostatistics. Complementing;

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Adaptive Regression for Modeling Nonlinear Relationships

when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for;

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Regression Models

introduced and other regression models for non-continuous dependent variables, such as the ordered probit. Concluding with an exploration of some;

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