When there are one or more explanatory variables that are categorical, one employs the technique of regression analysis with dummy variables. When the dependent variable is a categorical variable, the three models (referred to as probability models) that can be used are the linear probability model...
Regression Models for Categorical Dependent Variables Using Stata, 2nd Edition, by J. Scott Long and Jeremy Freese, shows how to fit and interpret regression models for categorical data with Stata. Nearly 50% longer than the previous edition, the book covers new topics for fitting and interpret...
This chapter discusses how logistic regression is designed to use a mix of continuous and categorical predictor variables to predict a nominal categorical dependent variable. Logistic regression does not directly predict the values of the dependent variable. The scale component is an optional modification...
A unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied. In addition, the author explains how...
Regression Models for Categorical Dependent Variables Using Stata (rev.).Regression Models for Categorical Dependent Variables Using Stata (rev.).Reviews the book "Regression Models for Categorical Dependent Variables Using Stata," by J. Scott Long and Jeremy Freese.EBSCO...
出版社:Stata Press 副标题:Revised Edition 出版年:2005-11-15 页数:527 定价:USD 93.95 装帧:Paperback ISBN:9781597180115 豆瓣评分 评价人数不足 评价: 写笔记 写书评 加入购书单 分享到 推荐 喜欢读"Regression Models for Categorical Dependent Variables Using Stata"的人也喜欢· ··· 应用STATA...
The third edition is divided into two parts. Part I begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fitting, and interpretation of models for categorical dependent variables. The book is thus accessible to new users of Stata and those...
The third edition is divided into two parts. Part I begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fitting, and interpretation of models for categorical dependent variables. The book is thus accessible to new users of Stata and those...
In general, a linear regression model can be a model of the form yi=β0+∑k=1Kβkfk(Xi1,Xi2,⋯,Xip)+εi, i=1,⋯,n, wheref(.) is a scalar-valued function of the independent variables,Xijs. The functions,f(X), might be in any form including nonlinear functions or polynom...
\left(\hat{\beta} - \hat{\beta}_c\right)^TX^TX\left(\hat{\beta}_c - \beta\right) = \hat{\lambda}_c^TA\left(X^TX\right)^{-1}X^TX\left(\hat{\beta}_c - \beta\right) = \hat{\lambda}_c^T\left(A\hat{\beta}_c - A\beta\right) = \hat{\lambda}_c^T\left(b -...