We’ll show how to use the DID model to estimate the effect of hurricanes on house prices — In this article, we will study the Difference-In-Differences regression model. The DID model is a powerful and flexible regression technique that can be used to estimate the differential impact of a ‘Treatment’ on the treated group of individuals or things. Defining the terms: Treatment, treated group, control group

And how to interpret the regression coefficients of dummy variables — A dummy variable is a binary variable that takes a value of 0 or 1. One adds such variables to a regression model to represent factors which are of a binary nature i.e. they are either observed or not observed. Within this broad definition lie several interesting use cases. Here…

How to compare regression models that have all or some variables in common — Often, the regression modeler is faced with a choice among two or more competing models for the same problem. Goodness-of-fit measures such as R-squared (or pseudo-R-squared) and the F-test for regression analysis can be used to indicate how well an individual model fits the training data set, while AIC or…

Your model looses precision. We’ll explain why. — In the previous article, we saw how leaving out important variables causes the regression model’s coefficients to become biased. In this article, we’ll look at the converse of this situation namely, the damage caused to your regression model from stuffing it with variables that are entirely superfluous. What are irrelevant and superfluous variables?

We’ll understand what is Omitted Variable Bias and illustrate its calculation using a real-world data set — We‘ll study the consequences of failing to include important variables in a linear regression model. For illustration, we’ll base our discussion on a real world data set of automobile characteristics. Our goal will be to formulate a well-known result in statistical modeling called Omitted Variable Bias and to illustrate the…

And a tutorial on how to calculate them using a real-world data set — Conditional Variance and Conditional Covariance are concepts that are central to statistical modeling. In this article, we’ll learn what they are, and we’ll illustrate how to calculate them using a real-world data set. First, a quick refresher on what is variance and covariance. Variance of a random variable measures its…

A tutorial on how the variance-covariance matrices are constructed and when to be skeptical about the data they report — I am basing this article on a topic that I hope will be a joy to read for those of you who are mathematically inclined, and also for those of us who have wondered what standard errors are or where they come from. As a quick refresher of concepts: the…

An illustration of how an artifact that is fundamental to regression modeling is constructed, using a real-world data set — The variance-covariance matrix forms the keystone artifact of regression models. The variance-covariance matrix of the regression model’s errors is used to determine whether the model’s error terms are homoskedastic (constant variance) and uncorrelated. The variance-covariance matrix of the fitted regression model’s coefficients is used to derive the standard errors and…

And a Python tutorial to get you up and running with the RE model on a real world data set — The Random Effects regression model is used to estimate the effect of individual-specific characteristics such as grit or acumen that are inherently unmeasurable. Such individual-specific effects are often encountered in panel data studies. …