Written by: Philip B. Stark (University of California, Berkeley)

Written for a terminal introductory statistics class for students not taking a science or math curriculum where more than a single semester of statistics will suffice. In other words, it is a general education course that does not require advanced mathematics courses, such as calculus. In writing his text, Philip B. Stark, Department of Statistics, University of California, Berkeley; hopes to help students “think logically about quantitative evidence and to translate real-world situations into mathematical questions; and to expose students to a few important statistical and probabilistic concepts and to some of the difficulties, subjective decisions and pitfalls, in analyzing data and making inferences from numbers.”

While calculus is not a prerequisite for this introductory statistics textbook, students should be familiar with percentages, exponentiation, square roots and “scientific notation” (numbers times powers of ten). Students can review the math needed by reviewing Assignment 0 and taking the accompanying quiz.

I really like a number of things Stark has done here. He has embedded within the text JavaScript-based tools where students can enter datasets, highlight a range of values and read off the probability – Yay! – no need for finding, downloading and installing the professor’s statistics program of choice.

The second thing I like about the presentation of this introductory statistics textbook is that it uses frames to great advantage by splitting the page horizontally. The top section contains the chapter, the bottom the glossary – meaning students can look at the definition and the section of the content where it is used at the same time. And, unlike those pop-up definition boxes some authors are using, the glossary information can be easily copied and pasted into the student’s notes.

Thirdly, this textbook offers something I’ve not seen anywhere else – dynamic content. According to the preface, many of the examples, self-test exercises and assignments change when the page is reloaded or change depending upon student input.

Contents of SticiGui – Introductory Statistics Textbook

Chapter 0: Preface
Chapter 1: Introduction.
Chapter 2: Reasoning and Fallacies.
Chapter 3: Statistics.
Chapter 4: Measures of Location and Spread.
Chapter 5: Multivariate Data and Scatterplots.
Chapter 6: Association.
Chapter 7: Correlation and Association.
Chapter 8: Computing the Correlation Coefficient.
Chapter 9: Regression.
Chapter 10: Regression Diagnostics.
Chapter 11: Errors in Regression.
Chapter 12: Counting.
Chapter 13: The Meaning of Probability: Theories of probability.
Chapter 14: Set Theory: The Language of Probability.
Chapter 15: Categorical Logic.
Chapter 16: Propositional Logic.
Chapter 17: Probability: Axioms and Fundaments.
Chapter 18: The “Let’s Make a Deal” (Monty Hall) Problem.
Chapter 19: Probability Meets Data.
Chapter 20: Random Variables and Discrete Distributions.
Chapter 21: The Long Run and the Expected Value.
Chapter 22: Standard Error.
Chapter 23: The Normal Curve, the Central Limit Theorem, and Markov’s and Chebychev’s Inequalities for Random Variables.
Chapter 24: Sampling.
Chapter 25: Estimating Parameters from Simple Random Samples.
Chapter 26: Confidence Intervals.
Chapter 27: Hypothesis Testing: Does Chance explain the Results?.
Chapter 28: Does Treatment Have an Effect?.
Chapter 29: Testing Equality of Two Percentages.
Chapter 30: Approximate Hypothesis Tests: the z Test and the t Test.
Chapter 31: The Multinomial Distribution and the Chi-Squared Test for Goodness of Fit.
Chapter 32: A Case Study in Natural Resource Legislation: California Abalone Fisheries.
Chapter 33: A Case Study in Risk Assessment: Bovine Spongiform Encephalopathy and Import Restrictions.

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