Lecture 19: Regression (part 1)

November 15th, 2024 (10th week of classes)

Simple linear regression

Regression is one of the most essential statistical frameworks in biological research. Although it may not be immediately apparent in BIOL322, regression shares the same foundational principles as ANOVA, but with a focus on the relationship between two continuous variables. Regressions empower biologists to address a wide range of research questions.

In this first lecture on regression, we explore several biological research examples where regression was instrumental in generating evidence to support compelling and significant hypotheses. Additionally, we examine the “anatomy” of a regression model, including its key components, mathematical formulation, and the interpretation of regression coefficients.

In part two of this lecture (next week), we will build on this introduction by delving into more complex applications of regression models for statistical hypothesis testing. We will also discuss diagnostic tools to evaluate the reliability of regression models for prediction and examine the assumptions underlying these models to determine when they can be trusted.

Lecture

This lecture was delivered by Fonya Irvine, a PhD candidate in Biology at Concordia, as part of their pedagogical training. My version of the lecture, which incorporates the vast majority of the elements Fonya presented, is also available below.