Regression in R

The glm() function in R can be used to fit generalized linear models. This function uses the following syntax: glm(formula, family=gaussian, data,…

The Bayesian Information Criterion, often abbreviated BIC, is a metric that is used to compare the goodness of fit of different regression…

Whenever you perform linear regression in R, the output of your regression model will be displayed in the following format: Coefficients: Estimate…

The Akaike information criterion (AIC) is a metric that is used to compare the fit of several regression models. It is calculated…

In regression analysis, Mallows’ Cp is a metric that is used to pick the best regression model among several potential models. We…

Polynomial regression is a regression technique we use when the relationship between a predictor variable and a response variable is nonlinear. This…

Linear regression models are used to describe the relationship between one or more predictor variables and a response variable. However, once we’ve…

One of the key assumptions in linear regression is that there is no correlation between the residuals, e.g. the residuals are independent.…

Robust regression is a method we can use as an alternative to ordinary least squares regression when there are outliers or influential…

To fit a linear regression model in R, we can use the lm() function, which uses the following syntax: model We can…