Comprehensive and Detailed In-Depth Explanation:
In data analysis, handling outliers is crucial to ensure the accuracy and reliability of the dataset.Outliers can significantly skew statistical analyses and lead to misleading conclusions. One common method to address outliers isimputation, which involves replacing missing or anomalous data with substituted values based on other available information.
Option A:Recode
* Rationale:Recoding involves changing the values of a variable to a different set of values, often to simplify categories or to correct data entry errors. While useful, recoding is not specifically aimed at addressing outliers.
Option B:Impute
* Rationale:Imputation is the process of replacing missing or anomalous data points with substituted values, often derived from the dataset's statistical properties, such as the mean, median, or mode. This technique helps maintain the dataset's integrity by ensuring that analyses are not biased by missing or extreme values.