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Full Description
For courses in Introductory Statistics. 
 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.
 Samples Download the detailed table of contents
Contents
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
 II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES
 
 6. Scatterplots, Association, and Correlation
 7. Linear Regression
 8. Regression Wisdom
 9. Multiple Regression
 III. GATHERING DATA
 
 10. Sample Surveys
 11. Experiments and Observational Studies
 IV. RANDOMNESS AND PROBABILITY
 
 12. From Randomness to Probability
 13.Probability Rules!
 14. Random Variables
 15. Probability Models
 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
 VI. INFERENCE FOR RELATIONSHIPS
 
 20. Comparing Groups
 21. Paired Samples and Blocks
 22. Comparing Counts
 23. Inferences for Regression
 VII. INFERENCE WHEN VARIABLES ARE RELATED
 
 24. Multiple Regression Wisdom
 25. Analysis of Variance
 26. Multifactor Analysis of Variance
 27. Statistics and Data Science

              
              
              

