Please help with this probelem Tabulate the percent of observations in the SASHELP.HEART dataset that have non-missing values for all the predictor variables that you will use in later analyses: AgeAtStart, BP_Status, Chol_Status, Cholesterol, Diastolic, Height, MRW, Sex, Smoking, Smoking_Status, Systolic, Weight, and Weight_Status. ... View more

I'm having issues on the problem: Tabulate the percent of observations in the SASHELP.HEART dataset that have non-missing values for all the predictor variables that you will use in later analyses: AgeAtStart, BP_Status, Chol_Status, Cholesterol, Diastolic, Height, MRW, Sex, Smoking, Smoking_Status, Systolic, Weight, and Weight_Status.   Please help! ... View more

I'm analyzing the DeathCause and AgeAtDeath grouped by Status and the question is :      Are DeathCause and AgeAtDeath ever non-missing when Status=Alive? This is my response, am I looking at this correct?  I keep getting confused the "non-missing" verbiage DeathCause and AgeAtDeath are not relative to Status=Alive since the person is Alive so there would be no information to add to the Deathcause and AgeAtDeath categories.  DeathCause and AgeAtDeath would be non-missing at Status=Alive ... View more