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.
In the first video (slides here), we will learn how relationships between two variables can be approximated by a line:
Next (slides), I explain how we can use the line equation as a model explaining the relationship between two variables. The interactive plot shown in this video is available here.
In the following video (slides), you will learn how to determine what line best fits your data, and how to quantify the goodness of that fit. This video covers concepts introduced in this blog post, which includes the interactive 2D plot.
Next (slides), you will learn how to extend simple linear regression, with one predictor variable, to multiple linear regression, with two or more predictor variables. These concepts, including the interactive 3D plot, are discussed in this blog post.
Linear regression models allow us to perform statistical inference. In this video (slides), you will learn how to define and test a null hypothesis about a linear regression model, based on null hypothesis testing:
There are a number of practical ways to approach MLR methods. Two main approaches to are simultaneous and hierarchical, which are introduced here (slides):
MLR inference depends on a number of important assumptions, which should be tested and, if necessary, addressed prior to concluding anything from your analysis. These are explained in this final video (slides):
That's the lot of them. Hopefully these are of use to anyone starting out or struggling with some of the basics in MLR approaches. Please feel free to get in touch or leave some comments below if you have questions or (eek) find any issues with the content here.