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Intro Stats, Global Edition - Bock, David

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        Présentation Intro Stats, Global Edition de Bock, David Format Broché

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        Livre - Bock, David - 30/06/2024 - Broché - Langue : Anglais

        . .

      • Auteur(s) : Bock, David - Velleman, Paul - de Veaux, Richard
      • Editeur : Pearson Education Limited
      • Langue : Anglais
      • Parution : 30/06/2024
      • Format : Moyen, de 350g à 1kg
      • Nombre de pages : 848.0
      • Expédition : 1794
      • ISBN : 129247064X



      • Résumé :

        * Indicates optional section

        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
      • Scatterplots, Association, and Correlation
        • 6.1 Scatterplots
        • 6.2 Correlation
        • 6.3 Warning: Correlation Causation
        • 6.4 *Straightening Scatterplots
      • 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
      • 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
      • 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
      • 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
      • Experiments and Observational Studies
        • 11.1 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: FROM THE DATA AT HAND TO THE WORLD AT LARGE
      • From Randomness to Probability
        • 12.1 Random Phenomena
        • 12.2 Modeling Probability
        • 12.3 Formal Probability
        • 12.4 Conditional Probability and the General Multiplication Rule
        • 12.5 Independence
        • 12.6 Picturing Probability: Tables, Venn Diagrams, and Trees
        • 12.7 Reversing the Conditioning and Bayes' Rule
      • Sampling Distribu...

        Biographie:
        About our authors

        Richard D. DeVeaux is an internationally known educator and consultant. He has taught at the Wharton School and the Princeton University School of Engineering, where he won a Lifetime Award for Dedication and Excellence in Teaching. He is the C. Carlisle and M. Tippit Professor of Statistics at Williams College, where he has taught since 1994. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is a fellow of the American Statistical Association (ASA) and an elected member of the International Statistical Institute (ISI). In 2008, he was named Statistician of the Year by the Boston Chapter of the ASA, and was the 2018-2021 Vice-President of the ASA. Dick is also well known in industry, where for more than 30 years he has consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. Because he consulted with Mickey Hart on his book Planet Drum, he has also sometimes been called the Official Statistician for the Grateful Dead. His real-world experiences and anecdotes illustrate many of this book's chapters.

        Dick holds degrees from Princeton University in Civil Engineering (B.S.E.) and Mathematics (A.B.) and from Stanford University in Dance Education (M.A.) and Statistics (Ph.D.), where he studied dance with Inga Weiss and Statistics with Persi Diaconis. His research focuses on the analysis of large data sets and data mining in science and industry.

        In his spare time, he is an avid cyclist and swimmer. He also is the founder of the Diminished Faculty, an a cappella Doo-Wop quartet at Williams College, and sings bass in the college concert choir and with the Choeur Vittoria of Paris. Dick is the father of 4 children.

        Paul F. Velleman ...

        Sommaire:
        empowers you to reach every student. This flexible digital platform combines unrivaled content, online assessments, and customizable features so you can personalize learning and improve results. Learn more about MyLab Statistics.

        Pearson eText is an easy-to-use digital textbook available within MyLab Statistics that lets students read, highlight, take notes, and review key vocabulary all in one place. For instructors not using MyLab Statistics, Pearson eText can also be adopted on its own as the main course material. Learn more about Pearson eText.

        ...

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