Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Statistical Formulas For Programmers

evanmiller.org

Post reply on HN
Print view View original

11–20 of 66 posts

Re: Statistical Formulas For Programmers

#11

Post by pseut »

yankoff wrote: post #10
christopheraden wrote: post #6

Hey Evan, from one statistics guy to another, thanks for fighting the good fight :). The formulas might benefit from examples, especially with some of the more complicated cases (KS test and onwards). The important part of statistics comes from knowing _when_ to apply something, rather than _how_ to (that part is just math/numerical analysis). A mention of the assumptions of each of these intervals would be good, too…

While you guys are here, can you recommend a good intro book for statistics?

Very intro (like, Stats 101 intro): Purves, Pisani, and Freedman's is the best book I've seen. I'd combine that with something like Tufte's or Wainer's statistical graphics books to try to get some sophistication (for lack of a better word).

Re: Statistical Formulas For Programmers

#12

Post by christopheraden »

yankoff wrote: post #10
christopheraden wrote: post #6

Hey Evan, from one statistics guy to another, thanks for fighting the good fight :). The formulas might benefit from examples, especially with some of the more complicated cases (KS test and onwards). The important part of statistics comes from knowing _when_ to apply something, rather than _how_ to (that part is just math/numerical analysis). A mention of the assumptions of each of these intervals would be good, too…

While you guys are here, can you recommend a good intro book for statistics?

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/03939... [2]: http://www.amazon.com/Statistics-11th-Edition-Book-CD/dp/013... [3]: http://stats.stackexchange.com/questions/421/what-book-would...

Re: Statistical Formulas For Programmers

#13

Post by pseut »

EvanMiller wrote: post #7
pseut wrote: post #4

The first example, "unbiased standard deviation" is mislabeled. The estimator of the variance is unbiased but the square root of an unbiased estimator is not itself unbiased. So it's not as nit-picky as it looks; it's either a brain fart or a hole in understanding (especially since the linked Wikipedia page discusses this issue)[1,2] Not to be a dick, but getting the first example wrong like that doesn't inspire conf…

Thanks! I have relabeled it. (In a previous draft the entry was for unbiased variance and the inaccuracy slipped in during the transition.)

Your post said, "draft," so I think you're covered for that sort of error. :)

Re: Statistical Formulas For Programmers

#14

Post by yankoff »

christopheraden wrote: post #12
yankoff wrote: post #10

Earlier quoted context omitted.

While you guys are here, can you recommend a good intro book for statistics?

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

Re: Statistical Formulas For Programmers

#15

Post by [deleted] »

christopheraden wrote: post #12
yankoff wrote: post #10

Earlier quoted context omitted.

While you guys are here, can you recommend a good intro book for statistics?

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…

[deleted]

Re: Statistical Formulas For Programmers

#16

Post by christopheraden »

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 will find the intro books don't talk much about parallel computing. Most of the general data sets in intro books will be no more than 30 observations. They are trying to teach classical methods moreso than useful computational techniques. As for parallel statistics, I don't have a good book recommendation. Most of my knowledge on the topic comes from papers and vignettes from the R community and not books. Maybe check out one of those O'Reilly books about big data techniques?

I haven't seen this OpenIntro statistics before. I'll check it out!

Re: Statistical Formulas For Programmers

#18

Post by wsh »

yankoff wrote: post #10
christopheraden wrote: post #6

Hey Evan, from one statistics guy to another, thanks for fighting the good fight :). The formulas might benefit from examples, especially with some of the more complicated cases (KS test and onwards). The important part of statistics comes from knowing _when_ to apply something, rather than _how_ to (that part is just math/numerical analysis). A mention of the assumptions of each of these intervals would be good, too…

While you guys are here, can you recommend a good intro book for statistics?

I like the NIST/SEMATECH e-Handbook of Statistical Methods (http://www.itl.nist.gov/div898/handbook/).

The e-Handbook won't make you an expert statistician, but as an engineer needing to understand and apply statistical methods, I've found it to be a good starting point.

Re: Statistical Formulas For Programmers

#20

Post by scottedwards »

Great effort, and I certainly hope more coders will get into statistics (most I know are only interested in machine learning). However, I think your definition of 1.3 "Confidence Interval around the Mean" could be improved. You state:

"A confidence interval reflects the set of statistical hypotheses that won't be rejected at a given significance level. So the confidence interval around the mean reflects all possible values of the mean that can't be rejected by the data."

That seems a bit vague and perhaps confusing. Might I suggest something more like this:

"The confidence interval specifies a range (+/- a multiple of the above standard error [SE]) around our estimate of the mean (x-bar) such that: if we repeated our sampling process an infinite number of times (i.e. with the same sample size and forming a new x-bar and SE each time [and therefore, a new confidence interval]), Confidence_Level% of those intervals would contain the population (true) mean."

In addition, I think in this case, at least, there are no assumptions about the data to worry about, given a sufficiently moderate sample size due to the Central Limit Theorem (I'm confident about that in the case of the mean (x-bar), but I'll leave it up to others to correct me if I'm wrong about this applying to the standard error (SE)).

Post reply on HN