Understanding logistic regression analysis

Understanding logistic regression analysis

Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the associati...

Understanding logistic regression analysis in clinical reports: an introduction.

M of our understanding of biological effects and their determinants is gained through statistical regression analysis. Linear and nonlinear regression methods are often applied in the basic sciences. Clinical studies that evaluate the relative contribution of various factors to a single binary outcome, such as the presence or absence of death or disease, most often employ the method of logistic...

Logistic Regression Tree Analysis

This chapter describes a tree-structured extension and generalization of the logistic regression method for fitting models to a binary-valued response variable. The technique overcomes a significant disadvantage of logistic regression, which is interpretability of the model in the face of multicollinearity and Simpson’s paradox. Section 1 summarizes the statistical theory underlying the logisti...

Fuzzy Class Logistic Regression Analysis

Distribution mixtures are used as models to analyze grouped data. The estimation of parameters is an important step for mixture distributions. The latent class model is generally used as the analysis of mixture distributions for discrete data. In this paper, we consider the parameter estimation for a mixture of logistic regression models. We know that the expectation maximization (EM) algorithm...

Biostatistics 202: logistic regression analysis.