While examing your dataset, your analytics team found some missing data, which of the following is NOT true about how to handle your missing data?

If the probability of being missing is the same for all cases, then the data are said to be missing completely at random $($MCAR$)$

You can do list$-$wise $($complete case analysis$-$CCA$)$ deletion, pairwise $($available case analysis$-$ACA$)$ deletion, or simply dropping variables

You can do Mean$/$Mode imputation, Last Observation Carried Forward $($LOCF$),$ Next Observation Carried Backward $($NOCB$),$ Linear Interpolation, Common$-$Point Imputation, or Random Sampling Imputation

All of the above