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Answer: Mean imputation preserves the mean of the distribution but can distort the variance.
Mean imputation replaces missing values with the mean of the available data, which preserves the mean of the distribution but can distort the variance and introduce outliers if the data is not missing at random. Median imputation, on the other hand, replaces missing values with the median, which is less sensitive to outliers and can better preserve the shape of the distribution. However, it does not preserve the mean. The choice between mean and median imputation depends on the nature of the data and the assumptions about the missingness mechanism.
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Compare and contrast the use of mean imputation and median imputation for handling missing values in numerical data. Discuss the potential impacts on the distribution of the data and the performance of the model.
A
Mean imputation preserves the mean of the distribution but can distort the variance.
B
Median imputation preserves the median of the distribution but can distort the mean.
C
Both methods preserve the original distribution and have similar impacts on model performance.
D
Mean imputation is always superior to median imputation in terms of preserving the original distribution.