## Interactive Statistics, 1/e

Martha Aliaga, University of Michigan
Brenda Gunderson, University of Michigan

Published January, 1999 by Prentice Hall Engineering/Science/Mathematics

Spiral Bound
ISBN 0-13-231036-8

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Introductory Statistics-Algebra-Based-Statistics

This worktext encourages hands-on exploration of statistical concepts so that students take an active part in the learning process. With its strong emphasis on data analysis, this book seeks to make students better consumers of statistics and to give them the skills to design and execute experiments in an undergraduate research class. Statistical concepts are presented economically and immediately reinforced with activities—to be done in small groups or individually—that make the concepts clear and vivid. The TI graphing calculator, although not required, is integrated as an easy-to-use, portable tool that helps students to see statistical methods and models in action. A comprehensive Instructor's Resource Manual, developed by the authors, gives extensive support and guidance for teaching interactively.

NEW—Based on feedback from student and faculty users of the Preliminary Edition, Chapter One has been significantly revised. This unique chapter introduces students to the major ideas of themes of statistics they will use throughout the course and gives them a grounding in statistical reasoning early on. Changes include:

• The pace of the chapter has been slowed and definitions have been made clearer.
• The chapter has been more carefully divided to provide greater flexibility on the part of the instructor. Introductory material on p-values and n=2 have been separated into optional sections.
• More complete suggestions for teaching this chapter are provided in the Instructor's Resource Manual and interviews with the author and scenes of them teaching this chapter are provided on the Staff Development Video.
NEW—Chapter 10 has been reorganized to provide for a better, more logical flow of material. Many new Let's Do It! and Think About It activities have been included to make the chapter more interactive.
NEW—NEW - Three new chapters have been added to the book to flesh out the coverage and make it more appropriate for longer, more comprehensive courses. The new chapters are:
• Chapter 11 - “Comparing Two Treatments” which introduces inferences made for two samples.
• Chapter 12 - “Comparing Many Treatments” in which ANOVA is presented.
• Chapter 13 - “Analysis of Count Data” which covers Chi-Square analysis.
NEW—Many new exercises have been added to the book and a new Test Bank offers an abundance of additional problems from which to choose.
NEW—A comprehensive supplements package, including a web site, is now available to support the student and instructor. FEATURES
Interactive Exercises Built into the Text. Each chapter features many Let's Do It! activities and Think about It boxed questions that involve students in the text and reinforce statistical concepts.
• Let's Do It! activities are designed as individual or group projects to be completed in class. Let's Do It! activities reinforce the concepts just introduced or lead the students to discover the next statistical concept. By working with the Let's Do It! activities, students become engaged and active participants in the classroom. Students soon find themselves actually doing statistics—gathering data, analyzing the data they have collected, and discussing results with other members of their group.
• Think about It boxed questions ask the students to reflect on a concept or technique just presented. Think about It boxes encourage students to make the leap to the next related statistical concept. The questions help students retain information and lead to new discoveries. These boxes also are a device to show students how to apply their new knowledge practically, rather than relying on memorization.
Real Data in the Exercises and Examples. The authors pique interest in the examples and exercises by using data sets on current, timely topics that will engage students. The exercises and examples are drawn from newspapers, magazines, and journals that students will be familiar with, underscoring the practicality of statistics.
Early Coverage of Sampling and Experiment Design. Statistics instructors increasingly call for realistic treatment of sampling and experiments early in the course. The first three chapters deal with important issues in sampling such as the different sampling techniques, biases in the data, use of random samples, and the factors in planning statistically valid experiments like blinding and control groups.
Up-to-Date and Lucid Treatment of Probability. The authors have written a modern presentation of probability using examples and techniques that show how important probability is in understanding data and interpreting results. Chapter 1 introduces concepts from probability to familiarize students with these powerful ideas early.
Integrated Use of Graphing Calculator to Reinforce Concepts. The TI graphing calculator lets students enter, manipulate, and plot data quickly and conveniently. While a graphing calculator is not required, the authors rely on it in certain examples and exercises in which a calculator minimizes hand calculations and eases data plotting. Also, the authors have written TI Quick Steps at the ends of chapters to instruct students in how to use their calculators efficiently.
Comprehensive Instructor's Resource Manual. In the IRM, the authors show how to get the most out of an interactive class, how to set up effective student work groups, and how to incorporate the graphing calculator into instruction. Each chapter of the IRM gives learning goals, ideas for teaching, solutions to the Let's Do It! activities (including how long each activity takes, how to accomplish it, and its importance), solutions to the Think About It questions, extra examples, and solutions to all exercises.

(NOTE: Each chapter begins with an Introduction and ends with a Chapter Summary.)
1. How to Make a Decision with Statistics.

Introduction-Statistics and the Scientific Method. Decisions, Decisions. The Language of Statistical Decision-Making. What's in the Bag? Significant versus Important. Appendix 1.A - Selecting Two Vouchers.

2. Producing Data.

Why Sample? The Language of Sampling. Good Data? Simple Random Sample. Stratified Random Sampling. Systematic Sampling. Cluster Sampling. Multistage Sampling.

3. Observational Studies and Experiments.

Why Study Studies? The Language of Studies. Understanding Observational Studies. Understanding Experiments. Reading with a Critical Eye. What about Ethics?

4. Summarizing Data Graphically.

What Are We Summarizing? Displaying Distributions—Qualitative Variables. Displaying Distributions—Quantitative Variables. Guidelines for Plots, Graphs, and Pictures.

5. Summarizing Data Numerically.

Measuring Center. Measuring Variation or Spread. Linear Transformations and Standardization.

6. Using Models to Make Decisions.

Why Do We Need to Know Models? Modeling Continuous Variables. Modeling Discrete Variables.

7. Is There A Relationship?

Two Quantitative Variables. When Scatterplots Don't Work: Two Qualitative Variables.

8. How to Measure Uncertainty with Probability.

What Is Probability? Simulating Probabilities. The Language of Probability. Random Variables. Appendix 8.A - The Binomial Distribution.

9. Sampling Distributions: Measuring the Accuracy of Sample Results.

Sampling Distribution of a Sample Proportion. Bias and Variability. Sampling Distribution of a Sample Mean.

10. Making Decisions with Confidence.

Making Decisions about a Population Proportion. Making Decisions about a Population Mean. Confidence Interval Estimation: For a Proportion. Confidence Interval Estimation: For a Mean. Confidence Intervals and Hypothesis Testing.

11. Comparing Two Treatments.

Paired Samples versus Independent Samples. Paired Samples. Independent Samples: Comparing Means. Independent Samples: Comparing Proportions.

12. Comparing Many Treatments.

Analysis of Variance. The F-Test Statistic and the F-distribution. ANOVA: Letting the Computer Do the Work! How Do We Get the Mean Squares? What Does Reject H0 in ANOVA Mean? (And What Doesn't It Mean?).

13. Analysis of Count Data.

The Chi-Square Statistic. Test of Goodness of Fit. Test of Homogeneity. Test of Independence.

Appendix.