An outlier is a data point that significantly deviates from the rest of the dataset, either being unusually high or low. It is an observation that lies far away from most other observations.
Imagine you're at a party where everyone is around the same height, but suddenly you see someone who is extremely tall or extremely short compared to everyone else. That person would be considered an outlier in terms of height.
Skewness: Skewness refers to the asymmetry or lack of symmetry in a distribution. It measures how much a distribution deviates from being perfectly symmetrical.
Quartiles: Quartiles divide a dataset into four equal parts, each containing 25% of the data. They help identify potential outliers by comparing values to these quartile boundaries.
Z-Score: A z-score measures how many standard deviations an observation is away from the mean. It helps determine if an individual data point can be classified as an outlier based on its distance from the mean.
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