I was reading a statistics book when I encountered a histogram that caught my eye. The histogram looked similar to the one at the left. It contained a normal density estimate overlaid on a histogram, but the height of the density curve seemed too short when compared to the heights
A SAS programmer asked for a list of SAS/IML functions that operate on the columns of an n x p matrix and return a 1 x p row vector of results. The functions that behave this way tend to compute univariate descriptive statistics such as the mean, median, standard deviation, and quantiles. The following
I previously wrote about the best way to suppress output from SAS procedures. Suppressing output is necessary in simulation and bootstrap analyses, and it is useful in other contexts as well. In my previous article, I wrote, "many programmers use ODS _ALL_ CLOSE as a way to suppress output, but
SAS procedures can produce a lot of output, but you don't always want to see it all. In simulation and bootstrap studies, you might analyze 10,000 samples or resamples. Usually you are not interested in seeing the results of each analysis displayed on your computer screen. Instead, you want to
When I was an undergraduate physic major, my favorite professor would start each class with a joke or pun. One day he began class with a paraphrase of a famous quote from the movie Star Trek 4: The Voyage Home (the one with the whales). "Today," my professor said, imitating
North Carolina is a state that requires yearly inspections of motor vehicles. An inspection checks for safety features (lights, brakes, tires,....) as well as checking vehicle emissions to ensure that vehicles meet air pollution standards. I recently had a car inspected and noticed a pie chart on the inspection's summary
Perhaps you saw the headlines earlier this week about the fact that it has been nine years since the last major hurricane (category 3, 4, or 5) hit the US coast. According to a post on the GeoSpace blog, which is published by the American Geophysical Union (AGU), researchers ran
I always learn something new when I attend SAS Global Forum, and it often results from an informal conversations in The Quad or in a hallway. Last week a SAS customer described a scenario that he wanted to implement as part of an analysis of some genetic data. To oversimplify
A common task in data analysis is to locate observations that satisfy multiple criteria. For example, you might want to locate all zip codes in certain counties within specified states. The SAS DATA step contains the powerful WHERE statement, which enables you to extract a subset of data that satisfy
Last week I attended SAS Global Forum 2015 in Dallas. It was packed with almost 5,000 attendees. I learned many interesting things at the conference, including the fact that you need to arrive EARLY to the statistical presentations if you want to find an empty seat! It was gratifying to
