What is the primary use of regression imputation in handling missing data?
- To delete missing data
- To estimate missing values based on relationships with other variables
- To replace missing data with mean values
- To replace missing data with median values
The primary use of regression imputation in handling missing data is to estimate missing values based on relationships with other variables. It uses the relationships between the variable with missing data and other variables to estimate what the missing value could be.
Loading...
Related Quiz
- In what scenario would a modified Z-score be beneficial to use for outlier detection?
- Under what conditions would the median be a better measure of central tendency than the mean?
- What is the impact on training time if missing data is incorrectly handled in a large dataset?
- The IQR method defines an outlier as any value below Q1 - _______ or above Q3 + _______.
- What information is needed to calculate a Z-score for a particular data point?