So we have categorical data that can describe things not necessarily associated with a number, like cities or colors: red, blue, pink, etc.  But there are also data which might look categorical but you could still describe numerically like weight class where underweight < normal < overweight or smoking status never < ex-smoker < current smoker.  Which is stuff I see in the everyday data I analyze.  Anyway, we can call these data ordinal.  Now, can we make categorical variables into ordinal variables for say, imputing?  What? Did someone say … imputing?  Well, I guess we could by relating it to a continuous variable, say, the average annual temperature of a city.  So if we looked at the cities of Miami, Atlanta, and Philadelphia and ranged them by the average annual temperature, we would get Philadelphia < Atlanta < Miami.  Now, what’s even more fun than that?  Well, we can convert our transform our ordinal values to numeric values and do whatever we want to do with them, say, impute them, and then. But we’ll get more to that next time.  But anyway, have a great weekend, peeps, and remember that Sunday is Mother’s Day!


And you know what would be a great gift for mom?  Yeah, okay, I gotchya …