Now that you've got a good sense of how to "speak" R, let's use it with linear regression to make distinctive predictions. The R system has three components: a scripting language, an interactive ...

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

We describe how to conduct a regression analysis for competing risks data. The use of an add-on package for the R statistical software is described, which allows for the estimation of the ...

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and linear statistical models in particular. In this module, we will learn how to fit linear ...

I 'm a big fan of Python for data analysis, but even I get curious about what else is available. R has long been the go-to language for statistics, but the "Tidyverse" has given the language a serious ...

Last month we explored how to model a simple relationship between two variables, such as the dependence of weight on height 1. In the more realistic scenario of dependence on several variables, we can ...

Sliced inverse regression (SIR) and an associated chi-squared test for dimension have been introduced as a method for reducing the dimension of regression problems whose predictor variables are normal ...