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Table of Contents

Part I: Collecting and Exploring Data

Chapter 1 Picturing Distributions with Graphs

Individuals and variables

Identifying categorical and quantitative variables

Categorical variables: pie charts and bar graphs

Quantitative variables: histograms

Interpreting histograms

Quantitative variables: dotplots

Time plots

Discussion: (Mis)adventures in data entry

Measures of center: median, mean

Measures of spread: percentiles, standard deviation

Graphical displays of numerical summaries

Spotting suspected outliers*

Discussion: Dealing with outliers

Organizing a statistical problem

Explanatory and response variables

Relationship between two quantitative variables: scatterplots

Adding categorical variables to scatterplots

Measuring linear association: correlation

The least-squares regression line

Facts about least-squares regression

Outliers and influential observations

Working with logarithm transformations*

Cautions about correlation and regression

Association does not imply causation

Marginal distributions

Conditional distributions

Simpson's paradox

Observation versus experiment

Sampling

Sampling designs

Sample surveys

Cohorts and case-control studies

Designing experiments

Randomized comparative experiments

Common experimental designs

Cautions about experimentation

Ethics in experimentation

Discussion: The Tuskegee syphilis study

Part I Summary

Comprehensive Review Exercises

Large Dataset Exercises

Online Data Sources

EESEE Case Studies

The idea of probability

Probability models

Probability rules

Discrete versus continuous probability models

Random variables

Risk and odds*

Relationships among several events

Conditional probability

General probability rules

Tree diagrams

Bayes's theorem

Discussion: Making sense of conditional probabilities in diagnostic tests

Normal distributions

The 68-95-99.7 rule

The standard Normal distribution

Finding Normal probabilities

Finding percentiles

Using the standard Normal table*

Normal quantile plots*

The binomial setting and binomial distributions

Binomial probabilities

Binomial mean and standard deviation

The Normal approximation to binomial distributions

The Poisson distributions

Poisson probabilities

Parameters and statistics

Statistical estimation and sampling distributions

The sampling distribution of

Authors

Brigitte Baldi

Brigitte Baldi is a graduate of France’s Ecole Normale Supérieure in Paris. In her academic studies, she combined a love of math and quantitative analysis with wide interests in the life sciences. She studied math and biology in a double major and obtained a Masters in molecular biology and biochemistry and a Masters in cognitive sciences. She earned her Ph.D. in neuroscience from the Université Paris VI studying multisensory integration in the brain and used computer simulations to study patterns of brain reorganization after lesion as a post-doctoral fellow at the California Institute of Technology. She then worked as a management consultant advising corporations before returning to academia to teach statistics. Dr. Baldi is currently a lecturer in the Department of Statistics at the University of California, Irvine. She is actively involved in statistical education. She was a local and later national advisor in the development of the statistics telecourse Statistically Speaking, replacing David Moore’s earlier telecourse Against All Odds. She developed UCI’s first online statistics courses and is interested in ways to integrate new technologies in the classroom to enhance participation and learning. She is currently serving as an elected member to the Executive Committee At Large of the section on Statistical Education of the American Statistical Association.

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David S. Moore

David S. Moore is Shanti S. Gupta Distinguished Professor of Statistics, Emeritus, at Purdue University and was 1998 president of the American Statistical Association. He received his AB from Princeton and his PhD from Cornell, both in mathematics. He has written many research papers in statistical theory and served on the editorial boards of several major journals. Professor Moore is an elected fellow of the American Statistical Association and of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. He has served as program director for statistics and probability at the National Science Foundation.

In recent years, Professor Moore has devoted his attention to the teaching of statistics. He was the content developer for the Annenberg/Corporation for Public Broadcasting college-level telecourse Against All Odds: Inside Statistics and for the series of video modules Statistics: Decisions through Data, intended to aid the teaching of statistics in schools. He is the author of influential articles on statistics education and of several leading texts. Professor Moore has served as president of the International Association for Statistical Education and has received the Mathematical Association of America’s national award for distinguished college or university teaching of mathematics.

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