This 1st edition of Statistics moves the curriculum in innovative ways while still looking relatively familiar. Statistics, 1e utilizes intuitive methods to introduce the fundamental idea of statistical inference. These intuitive methods are enabled through statistical software and are accessible at very early stages of a course. The text also includes the more traditional methods such as t-tests, chi-square tests, etc., but only after students have developed a strong intuitive understanding of inference through randomization methods. The text is designed for use in a one-semester introductory statistics course. The focus throughout is on data analysis and the primary goal is to enable students to effectively collect data, analyze data, and interpret conclusions drawn from data. The text is driven by real data and real applications. Students completing the course should be able to accurately interpret statistical results and to analyze straightforward data sets.
Unit A: Data
Chapter 1: Collecting Data
Chapter 2: Describing Data
Unit B: Understanding Inference
Chapter 3: Confidence Intervals
Chapter 4: Hypothesis Tests
Unit C: Inference for Means and Proportions
Chapter 5: Approximating with a Distribution
Chapter 6: Inference for Means and Proportions
Unit D: Inference for Multiple Parameters
Chapter 7: Chi-Square Tests for Categorical Variables
Chapter 8: ANOVA for Comparing Means
Chapter 9: Inference for Regression
Chapter 10: Multiple Regression
Chapter 11: Probability Basics
- Technology: A set of online interactive dynamic tools, called StatKey, is fully integrated with the text and is available on the web to illustrate key ideas and provide support for computer intensive procedures. Other than this optional user-friendly set of web tools, the text is not tied to any specific statistical software package. Output from a variety of different packages is regularly displayed so that students become comfortable reading output in different forms. Companion manuals (both print and online) are available to provide specific computing guidance for common statistical packages. The text uses many real datasets and all data is electronically available in multiple formats.
- Examples and Exercises: Applications in the text are drawn from a wide variety of disciplines, chosen primarily on the basis of perceived interest to students and instructors. Problems and exercises are plentiful and span a very wide range of difficulty levels, from very straightforward short answer problems to extended projects.
- Essential Synthesis: Integration of the parts into a coherent whole is also essential. To address this, sections called Essential Synthesis end each unit, in which students are asked to take a step back and look at the big picture. These integration sections will help to prepare students for the kind of statistical thinking they will most likely encounter after finishing the course.