Outliers and Erroneous Values

56 min
Block 5 — Data cleaning
Objective
separate an erroneous value from a legitimate outlier; master the univariate and multivariate detection methods together with their thresholds and their failure modes; quantify what a single extreme observation does to a mean, a standard deviation, a regression slope and an error metric; and choose a treatment that follows from the origin of the value rather than from its position in the distribution.
Estimated duration
55 minutes
Prerequisites
chapters 014 (descriptive statistics), 015 (visualization), 018 (missing values) and 020 (duplicates and data quality); the notion of a train/test split (chapter 026)
Associated quizzes
021.1-quiz-definitions-and-distinction.md to 021.7-quiz-train-test-scope.md

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This lesson is part of the “Cleaning the Data” module

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