
Interactive Statistics, 1/e
Martha Aliaga, University of Michigan Published January, 1999 by Prentice Hall Engineering/Science/Mathematics
 
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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:
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.
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. UptoDate 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.)
IntroductionStatistics and the Scientific Method. Decisions, Decisions. The Language of Statistical DecisionMaking. 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 FTest Statistic and the Fdistribution. 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 ChiSquare Statistic. Test of Goodness of Fit. Test of Homogeneity. Test of Independence. Appendix. Answers to OddNumbered Exercises. Index.
