In recent columns we showed how linear regression can be used to predict a continuous dependent variable given other independent variables 1,2. When the dependent variable is categorical, a common ...

This family includes models for categorical data such as logistic, probit, and complementary log-log regression for binomial data and Poisson regression for count data, as well as continuous models ...

This pattern is an example of positive autocorrelation. Time series regression usually involves independent variables other than a time-trend. However, the simple time-trend model is convenient for ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Compared to standard linear ...

This approach allows fast computation speed, natural extension to data sets with categorical variables, and direct detection of local twovariable interactions. Previous algorithms are not unbiased and ...

Course TopicsOrdinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be discrete (e.g ...

Based on a statistic proposed by Cook (1977) for the detection of influential observations, we propose a measure for the influence of variables in linear-regression models, and we introduce the ...