lesson
The 1.5 × IQR Rule for Detecting Outliers
Apply the 1.5 × IQR rule to formally identify outliers in a data set.
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This lesson is part of a track
Public learning track
Comparing Distributions and Analyzing Outliers
9 lessons
Goal
Learners will compare data sets by analyzing distribution shapes, identifying outliers using the 1.5 × IQR rule, and evaluating the impact of extreme values.
9 lessons
0 of 9 done
- Comparing Two Data Sets Using Center and SpreadUp next
- Identifying Distribution Shapes: Symmetric, Skewed, UniformNot started yet
- How Skewness Affects Mean vs. MedianNot started yet
- What Is an Outlier? Identifying Extreme ValuesNot started yet
- The 1.5 × IQR Rule for Detecting OutliersYou are here
- Effects of Outliers on the Mean and Standard DeviationNot started yet
- Effects of Outliers on the Median and IQRNot started yet
- Comparing Data Sets with Different Shapes and OutliersNot started yet
- Deciding Whether to Exclude Outliers from AnalysisNot started yet
