When you have univariate data, that is, a single measure on a variety of units, the most common statistical graphic is a pie chart. But pie charts should not be used. Ever. When there are a lot of units, pie charts are unreadable. When there are only a few units, pie charts waste space. And research shows that, even with a moderate number of units, pie charts can distort the data (for example, using different colors leads to different estimates of the size of the wedges). Fortunately, there are better methods.
In a previous post, I dealt with some SAS code for scatterplots. Various problems can arise when using scatterplots. One of them is overplotting, where two or more data are the same point.
There are a variety of ways of dealing with this.
When you have two numeric variables and are interested in the relationship between them, the basic statistical graph is the scatterplot. These can be good, but there are ways to enhance them and there are also alternatives which can be better in some circumstances. In some circumstances, scatterplots can be problematic, and there are ways to deal with these problems. In this post, I show SAS code to create a basic scatterplot and some enhanced versions.
OK, there are lots of places where it’s written that using RUN statements makes code look cleaner, but that invocation of another PROC statement makes the previous PROC get submitted. So…. It sounds like that RUN statement is a sort of esthetic extra.
But it can bite you
Description of concordant and discordant in SAS PROC LOGISTIC
Part of the default output from PROC LOGISTIC is a table that has entries including`percent concordant’ and `percent discordant’. To me, this implies the percent that would correctly be assigned, based on the results of the logistic regression. But that is not what it is. It looks at all possible pairs of observations. A pair is concordant if the observation with the larger value of X also has the larger value of Y. A pair is discordant if the observation with the larger value of X has the smaller value of Y; here, X and Y are the predicted value and the actual value.