Outliers
Channel: Mikko Rönkkö
Date: 24/07/2019

In this video, the focus is on the significance of outliers in data analysis, particularly in the context of regression analysis. Outliers, defined as observations that significantly differ from others, are not inherently problematic in regression, but their presence can sometimes necessitate removal. The video highlights the importance of understanding the nature of an outlier before deciding on its treatment.

The video uses the example of a prestige data set to illustrate how an outlier can influence a regression line, potentially skewing results. The outlier's impact on the regression line depends on its residual (the distance from the line) and its leverage (its position relative to other data points). The video emphasizes that outliers can arise from various sources, such as data entry errors, unique cases, or samples that do not belong to the population under study. It also outlines a three-step process for dealing with outliers: identification, analysis, and decision-making on whether to include or exclude them from the data set. This process is exemplified through Deephouse's article, demonstrating practical approaches to outlier management. The video concludes by recommending further reading on outlier identification and handling in different types of regression models, highlighting the varied nature of outliers and the importance of context-specific analysis.

Slides: https://osf.io/r76xp