The use of general linear regression methods for the analysis of categorical data is recommended. The general linear model analysis of a 0,1 coded response variable produces estimates of the same response probabilities that might otherwise be estimated from frequencies in a multiway contingency table. When factors in the design are correlated, the regression analysis estimates the same response probabilities that would be estimated from the simple marginal frequencies in a balanced orthogonal design. The independent effects that are estimated by the regression analysis are the unweighted means of the response probabilities in various cells of a cross-classification design; however, it is not necessary that all cells in a compl...
Linear regression is a statistical procedure for calculating the value of a dependent variable from ...
Linear regression analysis is one of the most important statistical methods. Itexamines the linear r...
The analysis of binary response data commonly uses models linear in the logistic transform of probab...
The general linear model, which incorporates statistical analyses, such as ordinary least squares re...
This chapter deals with the very simple situation where the mean of a variable, the response variabl...
Hypotheses about the relationship among variables in a multiway contingency table may be tested by a...
The person-centered approach in categorical data analysis is introduced as a complementary approach ...
A linear regression model defines a linear relationship between two or more random variables. The ra...
This paper is an overview of a unified framework for analyzing designed experiments with univariate ...
In general linear models for categorical data analysis, goodness-of-fit statistics only provide a br...
When building models to investigate outcomes and variables of interest, researchers often want to ad...
One can imagine a possible loss of parameter estimation efficiency when response correlation is ign...
The present study evaluates the performance of four methods for estimating regression coefficients u...
This chapter deals with the multiple linear regression. That is we investigate the situation where t...
In categorical data analysis, log-linear models are widely used statistical tools for analyzing the ...
Linear regression is a statistical procedure for calculating the value of a dependent variable from ...
Linear regression analysis is one of the most important statistical methods. Itexamines the linear r...
The analysis of binary response data commonly uses models linear in the logistic transform of probab...
The general linear model, which incorporates statistical analyses, such as ordinary least squares re...
This chapter deals with the very simple situation where the mean of a variable, the response variabl...
Hypotheses about the relationship among variables in a multiway contingency table may be tested by a...
The person-centered approach in categorical data analysis is introduced as a complementary approach ...
A linear regression model defines a linear relationship between two or more random variables. The ra...
This paper is an overview of a unified framework for analyzing designed experiments with univariate ...
In general linear models for categorical data analysis, goodness-of-fit statistics only provide a br...
When building models to investigate outcomes and variables of interest, researchers often want to ad...
One can imagine a possible loss of parameter estimation efficiency when response correlation is ign...
The present study evaluates the performance of four methods for estimating regression coefficients u...
This chapter deals with the multiple linear regression. That is we investigate the situation where t...
In categorical data analysis, log-linear models are widely used statistical tools for analyzing the ...
Linear regression is a statistical procedure for calculating the value of a dependent variable from ...
Linear regression analysis is one of the most important statistical methods. Itexamines the linear r...
The analysis of binary response data commonly uses models linear in the logistic transform of probab...