Lecture 21: Correlation and normality
November 25th, 2024 (12th week of classes)Correlation, assessing normality and non-parametric statistical hypothesis testing
In this lecture, we cover Pearson’s correlation, a statistic used to measure the degree of covariation between two continuous variables. Following this, we explore a visual method for assessing normality: the QQ plot. The video below provides an excellent explanation of these plots and how they are used to evaluate normality.
Finally, we discuss an example of a non-parametric test, which can be used when the assumption of normality is violated. Non-parametric tests are a diverse group of methods, and a comprehensive discussion would require about half a semester. While traditional parametric tests, which assume normality, are the most widely used and are the focus of this course, it is crucial to be aware of these alternative methods.
In Lecture 22, we will delve further into these alternatives.
Normal Quantile-Quantile Plots, by JBStatistics.
“An introduction to normal quantile-quantile (QQ) plots (a graphical method for assessing whether a set of observations is approximately normally distributed). The author discusses the motivation for the plot, the construction of the plot, then look at several examples. In the examples they look at what a normal quantile-quantile plot looks like when sampling from various other distributions. They then illustrate what normal QQ plots look like when sampling from a normal distribution by simulating several samples, for two different sample sizes.”
Lecture