Interactive StatisticsDescription |
For algebra-based Introductory Statistics courses. This text takes a hands-on approach to the introduction of basic statistical methods and concepts, using a highly interactive method that promotes active learning and true assimilation of key concepts. Students are taught to ask “why,” thinking like a statistician to find the logical solution. With its strong emphasis on data analysis, the book seeks to make students better consumers of statistics and to give them the skills to understand and interpret statistical results using real data from newspapers and research journals.  |
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Features |
· Unique introductory chapter - Provides an overview of the basic components of decision-making and using statistical ideas to assist students in mastering the language of statistics, understanding the importance of a p-value, and grasping the meaning of statistical significance. · “Let's do it!”exercises - Emphasize active learning with workbook-style problems that reinforce statistical concepts and lead students to discover the next concept. Gives students an opportunity to discuss their ideas by working with a partner, and draws them into gathering and analyzing data. · “Think About It”boxed questions - Ask students to reflect on a particular concept or technique, helping them make the leap to the next related statistical concept. · Real data in exercises and examples - Engages interest by using data sets on current, timely topics drawn from newspapers, magazines, and journals familiar to students. · Integrated use of graphing calculator - Includes chapter-ending TI Quick Steps to instruct students in using calculators to enter, manipulate, and plot data quickly and conveniently. · Early coverage of sampling and experiment design - Introduces students early to important issues such as sampling techniques, biases in data, use of random samples, and the factors in planning statistically valid experiments. · Up-to-date and lucid treatment of probability - Offers students a modern presentation of probability using examples and techniques that show the importance of probability in understanding data and interpreting results. |
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New to this Edition |
• Updated and revised content throughout - Includes expanded coverage of current topics and new coverage such as power of the test and effect size, bootstrap, and blocking designs in experiments • Examples are presented in a three-step consistent format: Problem, Solution, andWhat We've Learned - Assists students in navigating examples and thinking of multiple ways to address problems, while offering an excellent summary or reflection on the main point or feature of the example. • Highly diverse coverage - Uses examples from business, sports, economics, and more to address a wide range of student interests. • At least 15% updated and new exercises including Critical Thinking Exercises - Require a deeper level of critical thinking. • Caution boxes in the margins - Highlight critical concepts or things to watch out for. • Chapter outlines and objectives - Now featured at the beginning of each chapter for convenient reference. • Reformatted End-of-Chapter Summaries - Now bulleted to help students better digest and review the main ideas in each chapter. • Comprehensive Key Terms and List of Formulas - Include page numbers to facilitate student review. |
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Table of Contents |
Note: Each chapter ends with a Chapter Summary, Key Terms, and Exercises. Chapter 1: How to Make a Decision with Statistics Chapter 2: Producing Data Chapter 3: Observation Studies and Experiments Chapter 4 Summarizing Data Graphically Chapter 5 Summarizing Data Numerically Chapter 6 Using Models to Make Decisions Chapter 7: How to Measure Uncertainty with Probability Chapter 8: Sampling Distributions: Measuring the Accuracy of Sample Results Chapter 9: Making Decisions about a Population Proportion with Confidence Chapter 10: Making Decisions about a Population Mean with Confidence Chapter 11: Comparing Two Treatments Chapter 12: Comparing Many Treatments Chapter 13: Relationships Between Quantitative Variables Chapter 14: Analysis of Count Data Chapter 15: What if the Assumptions Don't Hold? |
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