PROC SGPLOT shows a histogram of the data and overlays a vertical line at the location of the geometric mean.Ģ0.2 for these data, estimates the "center" of the data. To demonstrate this, the following DATA step simulates 100 random observations from a lognormal distribution. The TTEST procedure is the easiest way to compute the geometric mean (GM) and geometric CV (GCV) of positive data. Lastly, the SAS file that accompanies this article contains a SAS/IML function (geoStats) that makes it easy to compute the statistics and their confidence intervals.įor an introduction to the geometric mean, see "What is a geometric mean." For information about the (arithmetic) coefficient of variation (CV) and its applications, see the article "What is the coefficient of variation?" Compute the geometric mean and geometric CV in SASĪs discussed in my previous article, the geometric mean arises naturally when positive numbers are being multiplied and you want to find the average multiplier.Īlthough the geometric mean can be used to estimate the "center" of any set of positive numbers, it is frequently used to estimate average values in a set of ratios or to compute an average growth rate. It then shows how to compute several geometric statistics in the SAS/IML language. It first shows how to use PROC TTEST to compute the geometric mean and the geometric coefficient of variation. This article shows how to compute the geometric mean, the geometric standard deviation, and the geometric coefficient of variation in SAS. In addition, some published papers and web sites that claim to show how to calculate the geometric mean in SAS contain wrong or misleading information. Unfortunately, the answers to these questions are sometimes confusing or even wrong. I frequently see questions on SAS discussion forums about how to compute the geometric mean and related quantities in SAS.
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