Activity-Based Statistics helps build real statistical understanding through a set of innovative hands-on activities that can be used each day in conjunction with other texts. The second edition continues to emphasize discovery by motivating readers to apply the skills they have learned to discover the everyday relevance of statistics. There are over 45 activities and five long-term projects that have been updated and extended to encourage readers to experience statistics in context. While the second edition includes updated technology extensions for Fathom, technology is used throughout the book to extend activities and is not required to complete them.

Dr. Richard L Sceaffer's research and scholarly interests are in statistical education and sampling theory/practice. He is currently a professor in the department of statistics at the University of Florida.
B.A. 1962 Lycoming college; M.A. 1964 Bucknell University; Ph.D. 1968 Florida State University.

Preface	p. x
To the Student	p. xi
Acknowledgments	p. xii
Exploring Data
A Living Histogram	p. 2
How to build a histogram out of people
Getting to Know the Class	p. 4
Surveying the class
Exploratory data analysis
Displaying categorical and quantitative data
A Living Box Plot	p. 8
How to construct a box-and-whisker plot with people
Shape, center, and spread
V Is for Variation: How Far Are You from the Mean?	p. 10
Measuring variability in data
Matching Plots to Variables	p. 14
Connecting our knowledge of real-life distributions to their graphs
Matching Statistics to Plots	p. 19
Matching summary statistics to graphs of distributions
How the mean can differ from the median
Variation in Measurement	p. 23
Collecting measurement data and looking at its distribution
Measurement Bias	p. 27
Experiencing measurement bias
Let Us Count	p. 35
Variation due to the process of measurement
Matching Descriptions to Scatter Plots	p. 39
Making the correspondence between scatter plots and statistics (regression line and r)
The Regression Effect	p. 43
Find out about the regression effect
Contents Leonardo's Model Bodies	p. 48
Looking at correlation between the sizes of different body parts
Relating to Correlation	p. 54
How the correlation coefficient from a sample varies about the true population coefficient
Models, Models, Models . . .	p. 58
Modeling time series data with lines
Breaking the time series into two pieces
Predictable Pairs	p. 63
Relationships between categorical variables
Association in two-way tables
Ratings and Ranks	p. 73
The relationship between ratings and ranks
Planning a Study
Random Rectangles	p. 82
Sampling bias
The importance of random sampling
The Rating Game	p. 89
Analyzing ratings that are results from a questionnaire you design
Stringing Students Along	p. 92
Learning about selection bias
Gummy Bears in Space	p. 95
Factorial designs and interaction
Controlling variables
Funnel Swirling	p. 101
Experimental design, particularly factorial design
Variability in an experiment
Jumping Frogs	p. 104
Experiments in a factorial situation
Estimating the effects of each factor and of the interaction
How to Ask Questions: Designing a Survey	p. 111
How the way a question is worded can affect the outcome of a survey
Anticipating Patterns
What Is Random Behavior?	p. 116
Gambler's fallacy
It's hard to predict short-term random behavior
The Law of Averages	p. 119
What is the law of averages? How probability helps us predict in the long term
Streaky Behavior	p. 125
Runs in Bernoulli trials
Randomness is streakier than we think
Counting Successes	p. 129
How to create simulations to study problems about the number of successes in repetitions of an event with a known probability
Waiting for Sammy Sosa	p. 136
The geometric, or waiting-time, distribution
The Lazy Student	p. 140
What happens to the spread when you add random variables
What's the Chance?	p. 143
Dependent and independent trials
Spinning Pennies	p. 147
Sampling distributions
Distribution of sample proportions where p 0.5
Cents and the Central Limit Theorem	p. 151
How the sampling distribution of the mean of a nonnormal distribution looks normal
Sampling Error and Estimation	p. 154
How an estimate (for example, of a mean) based on a sample differs from the population value
How Accurate Are the Polls?	p. 157
How an estimate of a proportion differs from the population value
How the spread of sampling distributions defines a margin of sampling error
Statistical Inference
How Many Tanks?	p. 164
Estimating a population from serial numbers
Unbiased estimators
Estimating the Total of a Res
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