Imputation
Imputation is a technique used in data science to fill in missing values in a dataset. It involves using statistical methods to estimate the missing values based on the values of other variables in the dataset. Imputation can be done using various methods such as mean imputation, median imputation, mode imputation, regression imputation, and k-nearest neighbor imputation. The choice of imputation method depends on the type of data and the nature of the missing values. Imputation is important because missing values can lead to biased or inefficient estimates and can reduce the power of statistical analyses. However, imputation should be done carefully and the assumptions underlying the imputation method should be checked to ensure that the imputed values are plausible and do not introduce bias into the analysis.
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