Logistic Regression in R – Part Two

My previous post covered the basics of logistic regression. We must now examine the model to understand how well it fits the data and generalizes to other observations. The evaluation process involves the assessment of three distinct areas – goodness of fit, tests of individual predictors, and validation of predicted values – in order to Continue reading Logistic Regression in R – Part Two

Logistic Regression in R – Part One

Logistic regression is used to analyze the relationship between a dichotomous dependent variable and one or more categorical or continuous independent variables. It specifies the likelihood of the response variable as a function of various predictors. The model expressed as , where refers to the parameters and represents the independent variables. The , or log Continue reading Logistic Regression in R – Part One