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How to run R-style linear regressions in Python the easy way

The adjusted r-squared is helpful for multiple regression and corrects for erroneous regression, giving you a more accurate ...
JSTOR Daily

Linear Regression with Non-Constant, Unknown Error Variances: Sampling Experiments with Least Squares, Weighted Least Squares and Maximum Likelihood Estimators

John A. Jacquez, Frances J. Mather and Charles R. Crawford The theory of simple linear regression is extended to the case of non-uniform error variances for the ...
JSTOR Daily

SEMIPARAMETRIC EFFICIENCY FOR CENSORED LINEAR REGRESSION MODELS WITH HETEROSKEDASTIC ERRORS

Using a simplified approach developed by Severini and Tripathi (2001), we calculate the semiparametric efficiency bound for the finite-dimensional parameters of censored linear regression models with ...
Simon Fraser University

Lesson 10 Multiple Linear Regression

The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an extension of Lesson 9. I will start with a ...