Suppose that you have several data distributions that you want to compare. Questions you might ask include "Which variable has the largest spread?" and "Which variables exhibit skewness?" More generally, you might be interested in visualizing how the distribution of one variable differs from the distribution of other variables. The
Has anyone noticed that the REG procedure in SAS/STAT 12.1 produces heat maps instead of scatter plots for fit plots and residual plots when the regression involves more than 5,000 observations? I wasn't aware of the change until a colleague informed me, although the change is discussed in the "Details"
I've conducted a lot of univariate analyses in SAS, yet I'm always surprised when the best way to carry out the analysis uses a SAS regression procedure. I always think, "This is a univariate analysis! Why am I using a regression procedure? Doesn't a regression require at least two variables?"
At a recent conference, I talked with a SAS customer who told me that he was using an R package to create a three-panel visualization of a distribution. Unfortunately, he couldn't remember the name of the package, and he has not returned my e-mails, so the purpose of today's article
PROC UNIVARIATE has provided confidence intervals for standard percentiles (quartiles) for eons. However, in SAS 9.3M2 (featuring the 12.1 analytical procedures) you can use a new feature in PROC UNIVARIATE to compute confidence intervals for a specified list of percentiles. To be clear, percentiles and quantiles are essentially the same
I often see variations of the following question posted on statistical discussion forums: I want to bin the X variable into a small number of values. For each bin, I want to draw the quartiles of the Y variable for that bin. Then I want to connect the corresponding quartile
I was recently asked how to compute the difference between two density estimates in SAS. The person who asked the question sent me a link to a paper from The Review of Economics and Statistics that contains several examples of this technique (for example, see Figure 3 on p. 16
In statistics, distances between observations are used to form clusters, to identify outliers, and to estimate distributions. Distances are used in spatial statistics and in other application areas. There are many ways to define the distance between observations. I have previously written an article that explains Mahalanobis distance, which is
Someone recently asked a question on the SAS Support Communities about estimating parameters in ridge regression. I answered the question by pointing to a matrix formula in the SAS documentation. One of the advantages of the SAS/IML language is that you can implement matrix formulas in a natural way. The
Argh! I've just spilled coffee on output that shows the least squares coefficients for a regression model that I was investigating. Now the parameter estimate for the intercept is completely obscured, although I can still see the parameter estimates for the coefficients of the continuous explanatory variable. What can I
