Regression in Python

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

Cubic regression is a type of regression we can use to quantify the relationship between a predictor variable and a response variable…

One of the key assumptions of linear regression is that the residuals are distributed with equal variance at each level of the…

You can use the following basic syntax to extract the regression coefficients from a regression model built with scikit-learn in Python: pd.DataFrame(zip(X.columns,…

The Goldfeld-Quandt test is used to determine if heteroscedasticity is present in a regression model. Heteroscedasticity refers to the unequal scatter of…

In regression analysis, multicollinearity occurs when two or more predictor variables are highly correlated with each other, such that they do not…

You can use the following basic syntax to use a regression model fit using the statsmodels module in Python to make predictions…

Ordinary least squares (OLS) regression is a method that allows us to find a line that best describes the relationship between one…

You can use the following methods to extract p-values for the coefficients in a linear regression model fit using the statsmodels module…

When fitting machine learning models to datasets, we often split the dataset into two sets: 1. Training Set: Used to train the…