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