Andrew Reid PhD

In a previous series of posts, I discussed simple and multiple linear regression (MLR) approaches, with the aid of interactive 2D and 3D plots and a bit of math. In this post, I am sharing a series of short videos aimed at psychology undergraduates, each explaining different aspects of MLR in more detail. The goal of these videos (which formed part of my second-year undergraduate module) is to give a little more depth to fundamental concepts that many students struggle with. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

In this post, I explore multiple linear regression, generalizing from the simple two-variable case to three- and many-variable cases. This includes an interactive 3D plot of a regression plane and a discussion of statistical inference and overfitting. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

In this post, I introduce the concept of simple linear regression, where we are evaluating the how well a linear model approximates a relationship between two variables of interest, and how to perform statistical inference on this model. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

This is the first of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology. In this post, I will try to provide an intuitive explanation of (1) the Pearson correlation coefficient, (2) confounding, and (3) how partial correlations can be used to address confounding.

One important caveat when working with large datasets is that you can almost always produce a statistically significant result when performing a null hypothesis test. This is why it is even more critical to evaluate the effect size than the p value in such an analysis. It is equally important to consider the distribution of your data, and its implications for statistical inference. In this blog post, I use simulated data in order to explore this caveat more intuitively, focusing on a pre-print article that was recently featured on BBC.