Spotting the Odd: A Beginner’s Quiz on Outlier Detection and Treatment Quiz

1. Identifying Outliers Using Standard Deviations

   Which technique identifies outliers in a dataset as points lying more than 3 standard deviations from the mean, such as a test score of 100 when the class average is 60 and standard deviation is 10?

   1. A. Z-Score Method
   2. B. Median Imputation
   3. C. Cluster Sampling
   4. D. K-means Outlier
   5. E. Linear Regression

2. Treatment by Capping (Winsorization)

   If high-income values above a certain threshold are replaced with the value at the 95th percentile, which outlier treatment technique is being used?

   1. A. Transformation
   2. B. Winsorization
   3. C. Bootstrapping
   4. D. Mean Centering
   5. E. Pruning

3. Visual Detection with Boxplots

   When a data analyst uses a boxplot to visually detect outliers, which characteristic typically reveals an outlier, such as a dot or asterisk beyond the 'whiskers'?

   1. A. Tall boxes
   2. B. Short whiskers
   3. C. Points outside the whiskers
   4. D. Colored bars
   5. E. Shaded quartiles

4. Using the IQR Rule

   Suppose a value in a dataset is below Q1 – 1.5×IQR or above Q3 + 1.5×IQR; which rule is being applied to flag outliers?

   1. A. Range Rule
   2. B. Variance Test
   3. C. Interquartile Range (IQR) Rule
   4. D. Correlation Check
   5. E. Gini Coefficient

5. Imputing Outliers with Median Values

   For a dataset containing an unusually high sensor reading due to an error, which treatment replaces this outlier by using the median value of the data?

   1. A. Median Imputation
   2. B. Mean Division
   3. C. Model Fitting
   4. D. K-Nearest Repair
   5. E. Interpolation