Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

A framework is developed for the interpretation of regression plots, including plots of the response against selected covariates, residual plots, added-variable plots, and detrended added-variable ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

Learn the difference between linear regression and multiple regression and how investors can use these types of statistical analysis.

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

Exploratory data analysis plays a central role in applied statistics and econometrics. In the popular regression-discontinuity (RD) design, the use of graphical analysis has been strongly advocated ...

Linear regression and feature selection are two such foundational topics. Linear regression is a powerful technique for predicting numbers from other data.