Stats: Data and Models - 5th Edition - by DeVeaux - ISBN 9780135164013

Stats: Data and Models
5th Edition
DeVeaux
Publisher: PEARSON CO
ISBN: 9780135164013

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Name: Stats: Data and Models
Author: DeVeaux
ISBN: 9780135164013

Chapter 1 - Stats Starts Here Chapter 1.3 - Variables Chapter 2 - Displaying And Describing Data Chapter 2.3 - Shape Chapter 2.5 - Spread Chapter 3 - Relationships Between Categorical Variables—contingency Tables Chapter 3.2 - Conditional Distributions Chapter 4 - Understanding And Comparing Distributions Chapter 4.1 - Displays For Comparing Groups Chapter 5 - The Standard Deviation As A Ruler And The Normal Model

Chapter 5.1 - Using The Standard Deviation To Standardize Values Chapter 5.2 - Shifting And Scaling Chapter 5.3 - Normal Models Chapter 6 - Scatterplots, Association, And Correlation Chapter 6.2 - Correlation Chapter 7 - Linear Regression Chapter 7.4 - Regression To The Mean Chapter 7.6 - R2—the Variation Accounted For By The Model Chapter 8 - Regression Wisdom Chapter 8.3 - Outliers, Leverage, And Influence Chapter 8.7 - Finding A Good Re-expression Chapter 9 - Multiple Regression Chapter 9.4 - Partial Regression Plots Chapter 10 - Sample Surveys Chapter 10.2 - Populations And Parameters Chapter 10.4 - Other Sampling Designs Chapter 11 - Experiments And Observational Studies Chapter 11.3 - The Four Principles Of Experimental Design Chapter 11.4 - Control Groups Chapter 12 - From Randomness To Probability Chapter 12.1 - Random Phenomena Chapter 12.3 - Formal Probability Chapter 13 - Probability Rules!Chapter 13.1 - The General Addition Rule Chapter 13.3 - Independence Chapter 13.4 - Picturing Probability: Tables, Venn Diagrams, And Trees Chapter 14 - Random Variables Chapter 14.1 - Center: The Expected Value Chapter 14.3 - Shifting And Combining Random Variables Chapter 15 - Probability Models Chapter 15.6 - The Poisson Model Chapter 16 - Sampling Distribution Models And Confidence Intervals For Proportions Chapter 16.2 - When Does The Normal Model Work? Assumptions And Conditions Chapter 16.3 - A Confidence Interval For A Proportion Chapter 16.5 - Margin Of Error: Certainty Vs. Precision Chapter 17 - Confidence Intervals For Means Chapter 17.2 - A Confidence Interval For The Mean Chapter 17.3 - Interpreting Confidence Intervals Chapter 18 - Testing Hypotheses Chapter 18.2 - P-values Chapter 18.4 - A Hypothesis Test For The Mean Chapter 18.6 - P-values And Decisions: What To Tell About A Hypothesis Test Chapter 19 - More About Tests And Intervals Chapter 19.2 - Alpha Levels And Critical Values Chapter 19.3 - Practical Vs. Statistical Significance Chapter 19.4 - Errors Chapter 20 - Comparing Groups Chapter 20.2 - Assumptions And Conditions For Comparing Proportions Chapter 20.3 - The Two-sample Z-test: Testing For The Difference Between Proportions Chapter 20.4 - A Confidence Interval For The Difference Between Two Means Chapter 20.5 - The Two-sample T-test: Testing For The Difference Between Two Means Chapter 21 - Paired Samples And Blocks Chapter 21.4 - Blocking Chapter 22 - Comparing Counts Chapter 22.3 - Examining The Residuals Chapter 22.4 - Chi-square Test Of Independence Chapter 23 - Inferences For Regression Chapter 23.5 - Multiple Regression Inference Chapter 24 - Multiple Regression Wisdom Chapter 24.3 - Building Multiple Regression Models Chapter 25 - Analysis Of Variance Chapter 25.2 - The An Ova Table Chapter 26 - Multifactor Analysis Of Variance Chapter 26.3 - Interactions Chapter 27 - Introduction To Statistical Learning And Data Science Chapter 27.1 - Data Science And Big Data Chapter 27.2 - The Data Mining Process Chapter 27.3 - Data Mining Algorithms: A Sample Chapter 27.4 - Models Built From Combining Other Models Chapter R - Review Exercises Chapter CR - Cumulative Review Exercises

Book Details

For courses in Introductory Statistics.

Encourages statistical thinking using technology, innovative methods, and a sense of humor

Inspired by the 2016 GAISE Report revision, Stats: Data and Models, 5th Edition by De Veaux/Velleman/Bock uses innovative strategies to help students think critically about data -- while maintaining the book’s core concepts, coverage, and most importantly, readability.

By using technology and simulations to demonstrate variability at critical points throughout the course, 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.