In our previous seminar, we covered Generalized Linear Models (GLMs), partial correlation, and their implementation in Python. Today, we will incorporate categorical variables, such as male/female or patients/controls. As discussed in the lecture, selecting an appropriate coding scheme is essential for addressing these research questions. We will focus on the following four coding schemes from the lecture:
Dummy coding
Unweighted effects coding
Weighted effects coding
Contrast coding
After familiarizing yourself with these coding schemes, you will apply your newly learned skills to a dataset related to heart disease.
The Dataset¶
Backround: Alzheimer’s disease (AD) is a progressive disorder characterized by the degeneration and death of brain cells. One of the most extensively studied genetic risk factors for AD is a variant of the apolipoprotein E gene (APOE). Specifically, the APOE e4 allele has been associated with an increased risk of developing Alzheimer’s disease.
Research Aim: Predict figural working memory ability (WMf) based on genotype groups.