The first step in conducting a regression-based study is to specify a model. In real applications, this is usually the most challenging step - deciding which variables “belong” in the model and which ...

In particular, principal components regression selects factors that explain as much predictor variation as possible, reduced rank regression selects factors that explain as much response variation as ...

Parametric versus Semi/nonparametric Regression Models Course Topics Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

In a logistic regression model, the coefficients (represented by β in the equation) represent the log odds of the outcome variable being 1 for each one-unit increase in a particular explanatory ...

Course TopicsStructural equation modeling (SEM) encompasses such diverse statistical techniques as path analysis, confirmatory factor analysis, causal modeling with latent variables, and even analysis ...

The Cox proportional hazards model restricts the log hazard ratio to be linear in the covariates. A smooth nonlinear covariate effect may go undetected in this model but can be well approximated by a ...