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Keyword: ‘%21x’

How to read the %21x format, part 2

In my previous posting last week, I explained how computers store binary floating-point numbers, how Stata’s %21x display format displays with fidelity those binary floating-point numbers, how %21x can help you uncover bugs, and how %21x can help you understand behaviors that are not bugs even though they are surpising to us base-10 thinkers. The point is, it is sometimes useful to think in binary, and with %21x, thinking in binary is not difficult.

This week, I want to discuss double versus float precision. Read more…

How to read the %21x format

%21x is a Stata display format, just as are %f, %g, %9.2f, %td, and so on. You could put %21x on any variable in your dataset, but that is not its purpose. Rather, %21x is for use with Stata’s display command for those wanting to better understand the accuracy of the calculations they make. We use %21x frequently in developing Stata. Read more…

Using Stata’s random-number generators, part 1

I want to start a series on using Stata’s random-number function. Stata in fact has ten random-number functions: Read more…

The Penultimate Guide to Precision

There have recently been occasional questions on precision and storage types on Statalist despite all that I have written on the subject, much of it posted in this blog. I take that as evidence that I have yet to produce a useful, readable piece that addresses all the questions researchers have.

So I want to try again. This time I’ll try to write the ultimate piece on the subject, making it as short and snappy as possible, and addressing every popular question of which I am aware—including some I haven’t addressed before—and doing all that without making you wade with me into all the messy details, which I know I have a tendency to do. Read more…

Merging data, part 1: Merges gone bad

Merging concerns combining datasets on the same observations to produce a result with more variables. We will call the datasets one.dta and two.dta.

When it comes to combining datasets, the alternative to merging is appending, which is combining datasets on the same variables to produce a result with more observations. Appending datasets is not the subject for today. But just to fix ideas, appending looks like this: Read more…

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