Question

How can you handle missing or corrupted data in a dataset? A.

Drop missing rows or columns B. Assign a unique category to missing
values C. Replace missing values with mean/median/mode D. All of
the above

Answer 1

To address missing or corrupted data, you can drop such data points, assign a unique category, or replace with mean/median/mode. When standard deviation is zero, all data values are identical. Examine data shape to choose the best measure of center.

Step-by-step explanation:

To handle missing or corrupted data in a dataset, there are several strategies:

• Drop missing rows or columns: If the missing data is not significant, you can simply remove the rows or columns containing missing values.
• Assign a unique category to missing values: For categorical data, a new category can be created for missing values.
• Replace missing values with mean/median/mode: You can fill in the missing values with the mean, median, or mode of the available data. This is particularly useful for numerical datasets with a normal distribution.

If the data set has a standard deviation equal to zero, this means that C. All of the data have the same value. This indicates no variability within your dataset.

Remember to examine the shape of the data to determine the most appropriate measure of center. Depending on whether the data is skewed or symmetric, you might find median or mean to be more representative.