Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...

A logistic model for multivariate binary time series is proposed. First, we establish the equivalence between a log-linear model for the marginal distribution of a multivariate binary random vector ...

Kung-Yee Liang, Extended Mantel-Haenszel Estimating Procedure for Multivariate Logistic Regression Models, Biometrics, Vol. 43, No. 2 (Jun., 1987), pp. 289-299 ...

run; proc logistic data=Data1; model outcome=Gall Hyper / noint CLODDS=PL; run; Results from the two conditional logistic analyses are shown in Output 39.9.1 and Output 39.9.2. Note that there is only ...

Novel nomogram models predict early neurological deterioration and 90-day outcomes in AIS patients treated with IV thrombolysis, helping healthcare professionals personalize treatment strategies.

Who Uses Multivariate Models Multivariate models—like the Monte Carlo model—are popular statistical tools that use multiple variables to forecast possible outcomes.

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...