Practice Outlier Detection & Removal (5.7) - Data Cleaning and Preprocessing

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9 questions

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6 questions · about 4 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define what an outlier is.
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Think of data points that stand out from the average.
Question 2.
What does IQR stand for?
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It's a measure related to quartiles in a dataset.
3Question 3.
What is an outlier?
- A typical data point
- A data point that differs significantly
- A data point that is missing
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Consider what makes a data point stand out.
4Question 4.
True or False: A Z-Score above 3 indicates an outlier.
- True
- False
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Think about standard deviations from the mean.
Question 5.
You have a dataset of test scores: [55, 60, 70, 75, 80, 100, 200]. Apply both the IQR and Z-Score methods to detect outliers.
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Calculate Q1 and Q3 for IQR, and Z-Scores for the outliers.
Question 6.
Consider a dataset of monthly incomes: [3500, 3600, 3700, 4000, 4200, 10000]. Discuss how you would handle the outlier at 10000.
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Consider business logic for whether the outlier is valid.

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