Refer to the previous exercise for a description of the data set. 

a. Are there any missing values in the data set? If there are, which variables have missing values? Which observations have missing values? How many missing values are there in the data set? 

b. Use the omission strategy to handle missing values. How many observations are removed due to missing values? 

c. Return to the original data set. If there are missing values for Height or Weight, use the imputation strategy to replace the missing values with the mean of the variable. If there are missing values for Siblings, FamilySize, or Income, replace the missing values with the median of the variable. What are the imputed values of the variables with missing data?

Data from Exercises 33

The accompanying table contains a portion of data from the National Longitudinal Survey (NLS), which follows over 12,000 individuals in the United States over time. Variables in this analysis include the following information on individuals: Urban (1 if lives in urban area, 0 otherwise), Siblings (number of siblings), White (1 if white, 0 otherwise), Christian (1 if Christian, 0 otherwise), FamilySize, Height, Weight (in pounds), and Income (in $).

Transcribed Image Text:

Urban 1 1 ⠀ 1 Siblings 8 1 ⠀ 2 White 1 1 I 1 ... Income 0 40000 43000