Comments on: What statistics texts and other analytics books should we recommend to people?

By: Joe

Joe — Fri, 05 Jun 2009 20:15:19 +0000

I just finished a class in quantitative analysis and the book that was used was:

http://www.amazon.com/Second-Course-Statistics-Regression-Analysis/dp/0130223239/ref=sr_1_3?ie=UTF8&s=books&qid=1244232799&sr=8-3

It’s a college textbook so it’s a little intimating at first, but the author does a good job of making it readable.

By: John M. Wildenthal

John M. Wildenthal — Thu, 04 Jun 2009 17:32:48 +0000

I would recommend “Practical Nonparametric Statistics, Third Edition” by WJ Conover. People without training will often use parametric statistics that are not suited for the data they are analyzing. Using nonparametric tests solves that issue. Inside the front cover is a table of what tests are available for each kind of data (nominal, ordinal, interval) and where to find them in the book.

Required math level is pre-calculus.

Emphasis on “Practical”.

By: Hans

Hans — Thu, 04 Jun 2009 15:11:15 +0000

My experience is that it is incredibly rare to find someone who, years out of school and working in a non-mathematical field, will spend the time on their own to learn enough stats to be useful in a job.

I usually try to point people to books with zero math, or recommend that they take a grad class or two.

I just don’t see it being realistic to point someone who doesn’t work with math regularly (and I don’t mean “I’m a programmer, and logic comes from math” kind of thing), to a stats textbook, and have them come back in a few months with a good understanding of the material. Or at least, I have never seen it happen.

I have seen someone get a bit of good insight out of a summary of regression that used no math, but explained a few basic concepts like least squares. That’s about as far as I would expect without enrolling in a class.

Maybe I’m being too cynical…

By: Curt Monash

Curt Monash — Thu, 04 Jun 2009 01:12:16 +0000

It might be helpful to indicate what level of math is needed. Calculus of a single variable? Measure theory?

If it’s the latter, we’re talking about a specialized audience. I even botched the measure theory in the first chapter of my dissertation, re-proving a version the Kolmogorov Extension Theorem because neither I nor my advisor knew such a thing existed.

By: Joseph A. di Paolantonio

Joseph A. di Paolantonio — Wed, 03 Jun 2009 21:41:48 +0000

I should have noted that the link to Task Views from The Comprehensive R Archive Network, http://cran.cnr.Berkeley.edu/index.html (or other local mirror), is very helpful in locating the right package among the 1828 available packages

By: Joseph A. di Paolantonio

Joseph A. di Paolantonio — Wed, 03 Jun 2009 21:28:12 +0000

Perhaps a better approach might be to download R from http://r-project.org/ and check out the demos and available packages. Several support statistics books at various levels. Find a demo/package/book combo that suits the need and go from there.

– twitter/JAdP

By: Hans

Hans — Wed, 03 Jun 2009 21:05:28 +0000

As an intro to pattern analysis and learning, I like Pattern Classification by Duda, Hart, Stork. It’s the 2nd edition of a 30 year old classic. I am not sure that a beginner would get anywhere with most of it, but chapter 1 is really great with no math, and it can probably be had for free on Google books. I would recommend that everyone who works with data read it.

By: Curt Monash

Curt Monash — Wed, 03 Jun 2009 20:46:12 +0000

Thanks. Those all sound like excellent suggestions!

By: Bob Zurek

Bob Zurek — Wed, 03 Jun 2009 20:25:58 +0000

One of my favorite stats books is
“Statistics Hacks – Tips & Tools For Measuring the World and Beating the Odds” by Bruce Frey.
I like it as statistics can be lots of fun and Bruce does an excellent job putting statistics to fun and interesting uses. No need to get bored to death with this book. Enjoy.
Bob

By: David Kanter

David Kanter — Wed, 03 Jun 2009 19:37:33 +0000

I learned basic statistics in high school, studied math in college and took a very good econometrics course in my 4th year at the University of Chicago.

For those not familiar with the distinction between econometrics and statistics, the difference is subtle. In economics, it is rarely possible to conduct controlled experiments (hey – what do you think the relationship between inflation does to unemployment is? Let’s do a controlled experiment with the UK and France!). Econometrics is an evolution of statistics to deal with observational data and a lack of controlled studies.

Our econometrics class didn’t necessarily use a book per-se, it was mostly notes and problem sets written by the professor.

However, there are two good books on econometrics that I would recommend:
http://www.amazon.com/Course-Econometrics-Arthur-S-Goldberger/dp/0674175441

http://www.amazon.com/Econometric-Analysis-William-H-Greene/dp/0135132452/ref=sr_1_1?ie=UTF8&s=books&qid=1244057439&sr=1-1

Both books cover the theoretical and practical basics, including linear regressions, time series, etc.

They also address how to deal with situations when the underlying assumptions of these models do not hold true (e.g. heteroskedacity – when your observational errors are not independently distributed).

MIT’s OpenCourseWare also has a class on econometrics that uses the goldberger book:
http://ocw.mit.edu/OcwWeb/Economics/14-32Spring-2007/Readings/index.htm