#1. Use the "Winsor" method to treat the outliers of variable X. In this Winsor method, the normal range of the variable is defined by [Q1-1.5*IQR, Q3+1.5*IQR]. Save the winsored X as X1. Use the summary() function to provide a statistical summary of X1. (Partial points can be given if students generate the correct normal range, you need to show those normal ranges to get the partial points)

#2. Use the "Winsor" method to treat the outliers of variable X. In this Winsor method, the normal range of the variable is defined by [1% percentile, 99% percentile]. Save the winsored X as X2. Use the summary() function to provide a statistical summary of X2. (Partial points can be given if students generate the correct normal range, you need to show those normal ranges to get the partial points)

#3 . Based on X1, perform a missing imputation through replacing the missing value by an unconditional mean of X1. Save this imputed X1 as X3. Use the summary() function to provide a statistical summary of X3. (Partial points can be given if students generate the correct unconditional mean, you need to show those normal ranges to get the partial points)

#4. Based on X1, perform a missing imputation through replacing the missing value by an condition mean of X1 on variable Z. Save this imputed X1 as X4. Use the summary() function to provide a statistical summary of X4. (Partial points can be given if students generate the correct conditional means, you need to show those normal ranges to get the partial points)