## Introduction to Linear Regression Analysis: How to do Linear Regression Analysis in R in Six Steps

In this article we describe how to perform linear regression. We go over some linear…

## Linear Regression: Log Transforming Response

There are several reasons to log transform the response. The obvious one is to fix…

## Linear Regression: Log Transformation of Features

In linear regression, you fit the model (1)   However, often the relationship between your…

## Linear Regression Plots: Residuals vs Leverage

In this post we analyze the residuals vs leverage plot. This can help detect outliers…

## The Scale Location Plot: Interpretation in R

In this post we describe how to analyze a scale location plot. You may also…

## The QQ Plot in Linear Regression

In this post we describe how to interpret a QQ plot, including how the comparison…

## Linear Regression Plots: Fitted vs Residuals

In this post we describe the fitted vs residuals plot, which allows us to detect…

## Causality: Basics, Potential Outcomes, and Counterfactuals

Note: this is loosely based on Coursera’s A Crash Course on Causality: Inferring Causal Effects…

## Observational vs Experimental Data: Linear Regression, Exogeneity, and Endogeneity

Background Classical statistics was developed to study how to collect and analyze data in the…

## When Should You Use Non-parametric Tests?

You should use non-parametric tests when the most naive distributional assumptions of a parametric test…