Stats: Data & Models 5/e [De Veaux] 9781292362212

Encourages statistical thinking using technology, innovative methods, and a sense of humour Inspired by the 2016 GAISE Report revision, Stats: Data and Models, 5th Edition by De Veaux, Velleman, and Bock uses innovative strategies to help students think critically about data, while maintaining the book's core concepts, coverage, and most importantly, readability.
The authors make it easier for instructors to teach and for students to understand more complicated statistical concepts later in the course (such as the Central Limit Theorem). In addition, students get more exposure to large data sets and multivariate thinking, which better prepares them to be critical consumers of statistics in the 21st century. The 5th Edition’s approach to teaching Stats: Data and Models is revolutionary, yet it retains the book's lively tone and hallmark pedagogical features such as its Think/Show/Tell Step-by-Step Examples.

頁數：1024
版次：第5版
年份：2021年
規格：平裝/彩色
ISBN：9781292362212

Part I: Exploring and Understanding Data
1. Stats Starts Here
2. Displaying and Describing Data
3. Relationships Between Categorical Variables—Contingency Tables
4. Understanding and Comparing Distributions
5. The Standard Deviation as a Ruler and the Normal Model
Part II: Exploring Relationships Between Variables
6. Scatterplots, Association, and Correlation
7. Linear Regression
8. Regression Wisdom
9. Multiple Regression
Part III: Gathering Data
10. Sample Surveys
11. Experiments and Observational Studies
Part IV: Randomness and Probability
12. From Randomness to Probability
13. Probability Rules!
14. Random Variables
15. Probability Models
Part V: Inference for One Parameter
16. Sampling Distribution Models and Confidence Intervals for Proportions
17. Confidence Intervals for Means
18. Testing Hypotheses
19. More About Tests and Intervals
Part VI: Inference for Relationships
20. Comparing Groups
21. Paired Samples and Blocks
22. Comparing Counts
23. Inferences for Regression
Part VII: Inference When Variables Are Related
24. Multiple Regression Wisdom
25. Analysis of Variance
26. Multifactor Analysis of Variance
27. Introduction to Statistical Learning and Data Science