Yearly Archives: 2013

New Book

My new book, Understanding Statistics: Basic Theory and Practice, is now available. You can get it here.

This is an introductory text, assumes no prior knowledge of statistics, and doesn’t require calculus. Those of you who already practice statistics will find it rather elementary. Those of you who have always wanted to learn something about the topic but never really got around to it — enjoy!

UPDATE:

Commenters requested a table of contents, which is given below. I’ve also added the table of contents to the “preview” (which includes the TOC and the first 10 pages of chapter 1).

Someone mentioned getting it to brush up on time series analysis. That is not covered — this is really “Statistics 101.” I do have an elementary introduction to time series available here. Don’t let the title put you off, its focus is astronomical data but the methods are quite general and you don’t need to know anything about astronomy. It’s not your usual time series text, it’s geared to physical science data and doesn’t really cover topics like autocorrelation or ARIMA models, but I think it’s an excellent intro to studying time series data, especially for those who’ve never done it before. I’m also working on a graduate-level text about time series (which will include the usual stuff covered in such texts, and more to boot).

And, I have more books in the works, including an Introduction to Fourier Analysis, and a Brief Introduction to Bayesian Statistics.

There are requests for an e-reader version. I haven’t quite figured out how to do that yet, but I’m looking into it.

Contents:

Introduction
Preface: the Value of Data

I. Basic Theory

1. What Is Statistics?
2. Average
3. Histograms
4. Densities and Distributions
5. Probability
6. Dispersion
7. Box and Whiskers
8. Expected Value
9. Expected Values
10. A Miracle Happens
11. Normal
12. Uncertainty
13. Hypothesis Testing
14. Descriptive Statistics

II. Basic Practice

15. Binomial Distribution
16. Uniform Distribution
17. Chi-Square Distributions
18. Multinomial Distribution
19. Student’s t Distribution
20. F Distribution
21. ANOVA
22. Covariance and Correlation
23. Regression
24. Smoothing
25. Testing the Distribution
26. Outliers
27. Non-Parametric Statistics
28. Case Studies
29. Linear Models
30. Bayesian Statistics

StatCoverSmall

Advertisements

Welcome to the Jungle

A reader recently commented that he had moved to the coutnry and taken steps to arrange an independent and sustainable life. His reason: that he expects, when the climate shit hits the fan, that people may place their own survival above the rules of ethics, that we may attempt to scratch and claw our way to the top of the heap at the expense of others, that life for most people will become a “law of the jungle.” He was providing for his own escape from what he viewed as a future not unlike the post-apocalyptic nightmare of a Mad Max movie.

I sometimes have similar thoughts.

Continue reading

Spreading like Wildfire

Twice we’ve examined the fakery from George Will about the danger of wildfire in the U.S. and its relation to global warming. It’s hard to imagine being more ignorant about this issue than George Will. Or, is he simply willing to mislead people deliberately, to expose our nation and our people to extreme danger, just to push a political agenda? Or … both?

This much is certain: George Will is completely wrong about wildfire risk in the U.S. But he is oh so right about propaganda! To create doubt about the scientific truth of increased wildfire risk, all you need to do is say whatever you want. No matter how wrong, it will be repeated by those who push your same political agenda. Doubt is their product.

Continue reading

Just say NO to Keystone XL Pipeline

From Natural Resources Defense Council.

Willful Ignorance

In the last post we saw how ignorant George Will is about global warming and its consequences — yet nonetheless Will is willing to spew his ignorance in order to influence public policy.

Continue reading

Where there’s a Will … theres a way to distort the truth

And George Will will.

Continue reading

Extreme Weather: When Worlds Collide

Real data — temperature, for instance — are almost always the combination of signal and noise, which we could also refer to as trend and fluctuation. Fluctuations are ubiquitous, they happen all the time. Sometimes they go up, sometimes down, sometimes a little and sometimes a lot, but the one thing they don’t do is stop.
Continue reading