Nonlinear statistical models, of which logistic regression is one example, are often used in applied demographic analysis as well as in many other social sciences. Presentation of the results of such models can be enhanced by the calculation of predicted probabilities, or expected values of the phenomenon of interest, yet such calculations can often be rather time-consuming. This paper describes the use of a computer program, INLOGIT, which can help the applied researcher interpret and display the results of a logistic regression model
We present R package mnlogit for training multinomial logistic regression models, particularly those...
Social and biological scientists widely use logit (logistic) regression to model binary dependent va...
textabstractIn his recent textbook "Primer of Biostatistics", S, A, Glantz refers to the nowadays gr...
The purpose of this paper is to describe a simple program for computing log-linear analysis based on...
Logistic regression is slowly gaining acceptance in the social sciences, and fills an important nich...
Logistic regression is slowly gaining acceptance in the social sciences, and fills an important nich...
The teaching of logit regression analysis is much neglected in statistics courses within sociology. ...
A Matlab based software for logistic regression is developed to enhance the process of teaching quan...
The purpose of an analysis using this method is the same as that of any technique in constructing mo...
[This document is a work in progress. Comments are welcome. Parts of this paper are adapted from the...
Social scientists and other users of large data sets often desire a model to predict the probability...
Since its release in 1976, Wingersky, Barton, and Lord’s (1982) LOGIST has been the most widely use...
Distributions and linear regressions are discussed. The section dealing with the former topic emphas...
This article is in the form of a short tutorial discussion, presenting the logistic (logit)regressio...
A large amount of data collected in the social sciences are counts crossclassified into categories. ...
We present R package mnlogit for training multinomial logistic regression models, particularly those...
Social and biological scientists widely use logit (logistic) regression to model binary dependent va...
textabstractIn his recent textbook "Primer of Biostatistics", S, A, Glantz refers to the nowadays gr...
The purpose of this paper is to describe a simple program for computing log-linear analysis based on...
Logistic regression is slowly gaining acceptance in the social sciences, and fills an important nich...
Logistic regression is slowly gaining acceptance in the social sciences, and fills an important nich...
The teaching of logit regression analysis is much neglected in statistics courses within sociology. ...
A Matlab based software for logistic regression is developed to enhance the process of teaching quan...
The purpose of an analysis using this method is the same as that of any technique in constructing mo...
[This document is a work in progress. Comments are welcome. Parts of this paper are adapted from the...
Social scientists and other users of large data sets often desire a model to predict the probability...
Since its release in 1976, Wingersky, Barton, and Lord’s (1982) LOGIST has been the most widely use...
Distributions and linear regressions are discussed. The section dealing with the former topic emphas...
This article is in the form of a short tutorial discussion, presenting the logistic (logit)regressio...
A large amount of data collected in the social sciences are counts crossclassified into categories. ...
We present R package mnlogit for training multinomial logistic regression models, particularly those...
Social and biological scientists widely use logit (logistic) regression to model binary dependent va...
textabstractIn his recent textbook "Primer of Biostatistics", S, A, Glantz refers to the nowadays gr...