Stats: Data and Models, Global Edition (häftad)
Häftad (Paperback)
Antal sidor
De Veaux, Richard / Velleman, Paul / Bock, David
32 x 275 x 215 mm
1970 g
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Stats: Data and Models, Global Edition

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Häftad,  Engelska, 2021-04-21
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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 Preview sample pages from Stats: Data and Models, Global Edition
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Preface Index of Applications I: EXPLORING AND UNDERSTANDING DATA 1. Stats Starts Here 1.1 What Is Statistics? 1.2 Data 1.3 Variables 1.4 Models 2. Displaying and Describing Data 2.1 Summarizing and Displaying a Categorical Variable 2.2 Displaying a Quantitative Variable 2.3 Shape 2.4 Center 2.5 Spread 3. Relationships Between Categorical Variables-Contingency Tables 3.1 Contingency Tables 3.2 Conditional Distributions 3.3 Displaying Contingency Tables 3.4 Three Categorical Variables 4. Understanding and Comparing Distributions 4.1 Displays for Comparing Groups 4.2 Outliers 4.3 Re-Expressing Data: A First Look 5. The Standard Deviation as a Ruler and the Normal Model 5.1 Using the Standard Deviation to Standardize Values 5.2 Shifting and Scaling 5.3 Normal Models 5.4 Working with Normal Percentiles 5.5 Normal Probability Plots Review of Part I: Exploring and Understanding Data II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES 6. Scatterplots, Association, and Correlation 6.1 Scatterplots 6.2 Correlation 6.3 Warning: Correlation Causation *6.4 Straightening Scatterplots 7. Linear Regression 7.1 Least Squares: The Line of "Best Fit" 7.2 The Linear Model 7.3 Finding the Least Squares Line 7.4 Regression to the Mean 7.5 Examining the Residuals 7.6 R2-The Variation Accounted for by the Model 7.7 Regression Assumptions and Conditions 8. Regression Wisdom 8.1 Examining Residuals 8.2 Extrapolation: Reaching Beyond the Data 8.3 Outliers, Leverage, and Influence 8.4 Lurking Variables and Causation 8.5 Working with Summary Values *8.6 Straightening Scatterplots-The Three Goals *8.7 Finding a Good Re-Expression 9. Multiple Regression 9.1 What Is Multiple Regression? 9.2 Interpreting Multiple Regression Coefficients 9.3 The Multiple Regression Model-Assumptions and Conditions 9.4 Partial Regression Plots *9.5 Indicator Variables Review of Part II: Exploring Relationships Between Variables III. GATHERING DATA 10. Sample Surveys 10.1 The Three Big Ideas of Sampling 10.2 Populations and Parameters 10.3 Simple Random Samples 10.4 Other Sampling Designs 10.5 From the Population to the Sample: You Can't Always Get What You Want 10.6 The Valid Survey 10.7 Common Sampling Mistakes, or How to Sample Badly 11. Experiments and Observational Studies11.1 Observational Studies 11.2 Randomized, Comparative Experiments 11.3 The Four Principles of Experimental Design 11.4 Control Groups 11.5 Blocking 11.6 Confounding Review of Part III: Gathering Data IV. RANDOMNESS AND PROBABILITY 12. From Randomness to Probability 12.1 Random Phenomena 12.2 Modeling Probability 12.3 Formal Probability 13.Probability Rules! 13.1 The General Addition Rule 13.2 Conditional Probability and the General Multiplication Rule 13.3 Independence 13.4 Picturing Probability: Tables, Venn Diagrams, and Trees 13.5 Reversing the Conditioning and Bayes' Rule 14. Random Variables 14.1 Center: The Expected Value 14.2 Spread: The Standard Deviation 14.3 Shifting and Combining Random Variables 14.4 Continuous Random Variables 15. Probability Models 15.1 Bernoulli Trials 15.2 The Geometric Model 15.3 The Binomial Model 15.4 Approximating the Binomial with a Normal Model 15.5 The Continuity Correction 15.6 The Poisson Model 15.7 Other Continuous Random Variables: The Uniform and the Exponential Review of Part IV: Randomness and Probability V. INFERENCE FOR ONE PARAMETER 16. Sampling Distribution Models and Confidence Intervals for Proportions 16.1 The Sampling Distribution Model for a Proportion 16.2 When Does the Normal Model