Even Two Outliers Can Distort Your Data Analysis

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Let’s get to today’s post now.

How outliers distort correlation and regression fit

Many data scientists solely rely on the correlation matrix to study the association between variables.

But unknown to them, the obtained statistic can be heavily driven by outliers.

This is evident from the image below.

Adding just two outliers drastically changed the correlation coefficient and the regression fit.

Thus, plotting the data is highly important.

This can save you from drawing wrong conclusions, which you may have drawn otherwise by solely looking at the summary statistics.

One thing that I often do when using a correlation matrix is creating a PairPlot as well (shown below).

This lets me infer if the scatter plot of two variables and their corresponding correlation measure resonate with each other or not.

We covered 8 more pitfalls and cautionary measures in this article: 8 Fatal (Yet Non-obvious) Pitfalls and Cautionary Measures in Data Science.

👉 Over to you: What is the difference between regression coefficient and correlation coefficient?

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