MIND ON STATISTICS is designed to help students learn statistical ideas by encouraging them to actively think about those ideas. The authors blend the spirit of statistical literacy (the conceptual development of statistical ideas) with the statistical methodology taught in the Introductory Statistics course. By focusing on ideas instead of mathematical formulation, this book generates student excitement, motivation, and understanding. Using a wide variety of applications, the book clearly demonstrates the relevance of statistics. As students master the material, they see how statistics influences their daily lives. The text also helps students develop statistical intuition and learn how to interpret the results of statistical studies.
Product dimensions: 8.60 (w) x 10.90 (h) x 1.20 (d)
Meet the Author
Jessica Utts is Professor of Statistics at the University of California at Irvine. She received her B.A. in math and psychology at SUNY Binghamton, and her M.A. and Ph.D. in statistics at Penn State University. Aside from MIND ON STATISTICS, she is the author of SEEING THROUGH STATISTICS and the co-author with Robert Heckard of STATISTICAL IDEAS AND METHODS both published by Cengage Learning. Jessica has been active in the Statistics Education community at the high school and college level. She served as a member and then chaired the Advanced Placement Statistics Development Committee for six years, and was a member of the American Statistical Association task force that produced the GAISE (Guidelines for Assessment and Instruction in Statistics Education) recommendations for Elementary Statistics courses. She is the recipient of the Academic Senate Distinguished Teaching Award and the Magnar Ronning Award for Teaching Excellence, both at the University of California at Davis. She is also a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. Beyond statistics education, Jessica's major contributions have been in applying statistics to a variety of disciplines, most notably to parapsychology, the laboratory study of psychic phenomena. She has appeared on numerous television shows, including LARRY KING LIVE, ABCNIGHTLINE, CNN MORNING NEWS, and 20/20, and most recently appears in a documentary included on the DVD with the movie SUSPECT ZERO.
Robert F. Heckard is a senior lecturer in statistics at the Pennsylvania State University, where he has taught for more than 30 years. He has taught introductory and intermediate applied statistics to more than 15,000 college students. Bob has been awarded several grants to develop multimedia and web-based instructional materials for teaching statistical concepts. Aside from MIND ON STATISTICS, he is the co-author of STATISTICAL IDEAS AND METHODS (first edition, 2006, Cengage Learning). As a consultant, he is active in the statistical analysis and design of highway safety research and has frequently been a consultant in cancer treatment clinical trials.
1. Statistics Success Stories and Cautionary Tales. What Is Statistics? Seven Statistical Stories with Morals. The Common Elements in the Seven Stories. Key Terms. Exercises. References. 2. Turning Data Into Information. Raw Data. Types of Data. Summarizing One or Two Categorical Variables. Finding Information in Quantitative Data. Pictures for Quantitative Data. Numerical Summaries of Quantitative Variables. Bell-Shaped Distributions of Numbers. Key Terms. Exercises. References. 3. Gathering Useful Data. Description of Decision? Using Data Wisely. Speaking the Language of Research Studies. Designing a Good Experiment. Designing a Good Observational Study. Difficulties and Disasters in Experiments and Observational Studies. Key Terms. Exercises. References. 4. Sampling: Surveys and How to Ask Questions. The Beauty of Sampling. Sampling Methods. Difficulties and Disasters in Sampling. How to Ask Survey Questions. Key Terms. Exercises. References. 5. Relationships Between Quantitative Variables. Looking for Patterns with Scatterplots. Describing Linear Patterns with a Regression Line. Measuring Strength and Direction with Correlation. Why Answers May Not Make Sense. Correlation Does Not Prove Causation. Keys Terms. Exercises. References. 6. Relationships Between Categorical Variables. Displaying Relationships between Categorical Variables. Risk, Relative Risk, Odds Ratio, and Increased Risk. Misleading Statistics about Risk. The Effect of a Third Variable and Simpson's Paradox. Assessing the Statistical Significance of a 2x2 Table. Key Terms. Exercises. References. 7. Probability. Random Circumstances. Interpretations of Probability. Probability Definitions and Relations. Basic Rules forFinding Probabilities. Strategies for Finding Complicated Probabilities. Using Simulation to Estimate Probabilities. Coincidences and Intuitive Judgments about Probability. 8. Random Variables. What is a Random Variable?. Discrete Random Variables. Expectations for Random Variables. Binomial Random Variables. Continuous Random Variables. Normal Random Variables. Approximating Binomial Distribution Probabilities. Sums, Differences, and Combinations of Random Variables. Key Terms. Exercises. References. 9. Means and Proportions as Random Variables. Understanding Dissimilarity among Samples. Sampling Distributions for Sample Proportions. What to Expect of Sample Means. What to Expect in Other Situations: Central Limit Theorem. Sampling Distribution for Any Statistic. Standardized Statistics. Student's t-distribution: Replacing s with s. Statistical Inference. Key Terms. Exercises. 10. Estimating Proportions with Confidence. The Language and Notation of Estimation. Margin of Error. Confidence Intervals. Calculating a Margin of Error for 95% Confidence. General Theory of Confidence Intervals for a Proportion. Choosing a Sample Size for a Survey. Using Confidence Intervals to Guide Decisions. Key Terms. Exercises. 11. Testing Hypotheses About Proprtions. Formulating Hypothesis Statements. The Logic of Hypothesis Testing: What if the Null is True?. Deciding Between the Two Hypotheses. Testing Hypotheses about a Proportion. The Role of Sample Size in Statistical Significance. Real Importance versus Statistical Significance. What Can Go Wrong: The Two Types of Errors. Key Terms. Exercises. 12. More About Confidence Intervals. Examples of Different Estimation Situations. Standard Errors. Approximate 95% Confidence Intervals. General Confidence Interval Procedures for One Mean or Paired Data. General Confidence Interval for the Difference in Two Means (Independent Samples). The Difference Between Two Proportions (Independent Samples). Understanding Any Confidence Interval. Key Terms. Summary. Exercises. t*multipliers. 13. More About Significance Tests. The General Ideas of Significance Testing. Testing Hypotheses about One Mean or Paired Data. Testing the Difference Between Two Means (Independent Samples). Testing the Difference Between Two Population Proportions. The Relationship Between Significance Test and Confidence Intervals. The Two Types of Errors and Their Probabilities. Evaluating Significance in Research Reports. Key Terms. Exercises. 14. More About Regression. The Sample and Population Regression Lines. Estimating the Standard Deviation from the Mean. Inference about the Linear Regression Relationship. Predicting the Value of Y for an Individual. Estimating the Mean Y at a Specified X. Checking Conditions for Using Regression Models for Inference. 15. More About Categorical Variables. The Chi-Square Test for Two-way Tables. Analyzing 2x2 Tables. Testing Hypotheses about One Categorical Variable: Goodness of Fit. Key Terms. Exercises. Chi-Square Table. 16. Analysis of Variance. Comparing Means with an ANOVA F-test. Details of One-way Analysis of Variance. Other Methods for Comparing Populations. Two-way Analysis of Variance. Key Terms. Exercises. 17. Turning Information Into Wisdom. Beyond the Data. Transforming Uncertainty into Wisdom. Making Personal Decisions. Control of Societal Risks. Understanding our World. Getting to Know You. Words to the Wise. Chapter Exercises.