# Logistic Regression

In statistics, logistic regression is a type of regression analysis used for predicting the outcome of a categorical (a variable that can take on a limited number of categories) dependent variable based on one or more predictor variables. The probabilities describing the possible outcome of a single trial are modelled, as a function of explanatory variables, using a logistic function.

Logistic regression measures the relationship between a categorical dependent variable and usually a continuous independent variable (or several), by converting the dependent variable to probability scores.

### Other articles related to "regression, logistic regression, logistic":

Omnibus Test
... β1≠ β2≠...≠ βk in Multiple linear regression or in Logistic regression ... F Test in ANOVA with repeated measures F test for equality/inequality of the regression coefficients in Multiple Regression Chi-Square test for exploring significance differences ... overall F test in Analysis of Variance or F Test in Analysis of covariance or the F Test in Linear Regression, or Chi-Square in Logistic Regression) ...
Differential Item Functioning - Procedures For Detecting DIF - Logistic Regression
... Logistic regression approaches to DIF detection involve running a separate analysis for each item ... This set of variables can then be expressed by the following regression equation Y = β0 + β1M + β2G + β3MG where β0 corresponds to the intercept or the probability of a response when M and G are ... membership variable is denoted G and in the case of regression is represented through dummy coded variables ...
Multinomial Logit - Model - As A Latent-variable Model
... It is also possible to formulate multinomial logistic regression as a latent variable model, following the two-way latent variable model described for binary logistic regression ... models, and makes it easier to compare multinomial logistic regression to the related multinomial probit model, as well as to extend it to more complex models ... identically distributed extreme-value-distributed variables follows the logistic distribution, where the first parameter is unimportant ...
Logistic Regression - Model Accuracy
... A way to test for errors in models created by step-wise regression is to not rely on the model's F-statistic, significance, or multiple-r, but instead assess the ...
Multinomial Logit - Model - Setup
... The basic setup is the same as in logistic regression, the only difference being that the dependent variables are categorical rather than binary, i.e ... is somewhat shortened for more details, consult the logistic regression article ... The goal of multinomial logistic regression is to construct a model that explains the relationship between the explanatory variables and the outcome, so that the outcome of a new "experiment" can be correctly ...