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
Open Mind 