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Statistical Formulas For Programmers

evanmiller.org

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Re: Statistical Formulas For Programmers

#31

Post by vdm »

yankoff wrote: post #14
christopheraden wrote: post #12

Earlier quoted context omitted.

Yankoff, you might want to be more specific. Intro statistics in general, or for computer scientists, or scientists, or looking to learn R at the same time? I liked Freedman, Pisani, and Purves [1], and have TA'ed using McClave, Sincich, and Mendenhall [2]. You may want something a little more advanced than these, but they are pretty good for intro level. [1]: http://www.amazon.com/Statistics-4th-David-Freedman/dp/03…

Yeah, I meant something for computer scientists. I'm going over coursera ML course currently and wanted to learn at least basics of statistics in parallel. Thanks, I'll check out your links. Btw, what do you think of OpenIntro statistics? http://www.openintro.org/stat/down/OpenIntroStatSecond.pdf

That OpenIntro pdf looks great! Thanks for the link.

Re: Statistical Formulas For Programmers

#32

Post by esalman »

deanclatworthy wrote: post #27

A nice resource, however I'd expected to see some actual programming on this page. Perhaps some sample code in a few languages would be nice. You could put tabs on a code box to switch between a python and PHP implementation for example.

Same here. Theory, formula, assumptions etc. are available in a textbook, Wikipedia and elsewhere.

This could be useful- PHP stats functions: http://www.php.net/manual/en/ref.stats.php

Re: Statistical Formulas For Programmers

#34

Post by pornel »

I hoped for it to be more "for programmers", like this one:

http://gdr.geekhood.net/gdrwpl/metnum.php

For me formulas written in pseudocode are much easier to understand than classic mathematical notation.

For example I've learned bayesian classification, chi-square, etc. from Practical Common Lisp (http://www.gigamonkeys.com/book/practical-a-spam-filter.html) after failing to understand how to apply formulas from Wikipedia. It was easier for me to learn Lisp than to decipher abstract declarative mathematical notation (admittedly I get a brain freeze whenever I see ∑, even though I know what it means. I prefer `for(…) acc += …`).

Re: Statistical Formulas For Programmers

#35

Post by yummyfajitas »

scottedwards wrote: post #23
kevinalexbrown wrote: post #21

If you're looking for things to add big-picture-wise, it might be helpful to specify what assumptions go into various tests/methods. In my experience, this is the biggest hangup and mistake, because a) it's more difficult to understand and b) ignoring it gives the appearance of rigor even if the test used is inappropriate for the data. I should emphasize that this is not a nitpick or even a criticism, just a feature…

Great suggestion. I've been amazed to find out that many coders and amateur "data scientists" don't realize that testing the assumptions is an important part of conducting statistical analyses. Part of this may be due to the recent emphasis on machine learning techniques, which tend to be assumption-free (often just assuming independence of cases in the sample).

Part of this may be due to the recent emphasis on machine learning techniques, which tend to be assumption-free...

No statistical technique is assumption free, unless it is purely descriptive.

Some of them are free of explicit assumptions known by the practitioner, but that's not the same thing. In much the same way, my code is all bug-free.

Re: Statistical Formulas For Programmers

#36

Post by timr »

kevinalexbrown wrote: post #21

If you're looking for things to add big-picture-wise, it might be helpful to specify what assumptions go into various tests/methods. In my experience, this is the biggest hangup and mistake, because a) it's more difficult to understand and b) ignoring it gives the appearance of rigor even if the test used is inappropriate for the data. I should emphasize that this is not a nitpick or even a criticism, just a feature…

This guy has written up many of the most common statistical tests, their interpretations, and their assumptions in a very human-readable way:

http://udel.edu/~mcdonald/statintro.html

I point a lot of newbs to pages on that site so that they can develop a better intuition for the methods.

Re: Statistical Formulas For Programmers

#37

Post by mattrepl »

yankoff wrote: post #14
christopheraden wrote: post #12

Earlier quoted context omitted.

Yankoff, you might want to be more specific. Intro statistics in general, or for computer scientists, or scientists, or looking to learn R at the same time? I liked Freedman, Pisani, and Purves [1], and have TA'ed using McClave, Sincich, and Mendenhall [2]. You may want something a little more advanced than these, but they are pretty good for intro level. [1]: http://www.amazon.com/Statistics-4th-David-Freedman/dp/03…

Yeah, I meant something for computer scientists. I'm going over coursera ML course currently and wanted to learn at least basics of statistics in parallel. Thanks, I'll check out your links. Btw, what do you think of OpenIntro statistics? http://www.openintro.org/stat/down/OpenIntroStatSecond.pdf

You may like Wasserman's All of Statistics:

http://www.stat.cmu.edu/~larry/all-of-statistics/

It was written as an introduction to statistics for people in CS and related fields.

Re: Statistical Formulas For Programmers

#40

Post by bearmf »

I feel this would be quite confusing for an average programmer. It is more like a cheat sheet for people who have some statistical training but always have to look the formulas up because they don't use them frequently enough. For an average programmer, really understanding how linear regression works and some basic linear algebra would be a good start. A lot of programmers have trouble even with these "simple" topics.

Most of these formulas are very rarely used even by quantitative analysts. The most used are for standard deviation and regression. The more complicated ones are generally used as a part of statistical routines, say, in R. It is very rare that someone has to code them.

> From a statistical point of view, 5 events is > indistinguishable from 7 events. What is this supposed to mean? There is a concept of statistical significance but if an effect is not statistically significant it does not follow that it does not exist. Btw where is the Bayes formula? :)

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