Stats data and models / / Richard D. De Veaux, Paul F. Velleman, David E. Bock

• Autore: De Veaux Richard D.
• Pubblicazione: Harlow, England : , : Pearson, , [2015]; ©2015
• Edizione: Global edition, Fourth edition.
• Descrizione fisica: 1 online resource (991 pages) : illustrations (some color)
• Disciplina: 519.5
• Soggetto topico: Statistics; Mathematical statistics
• Persona (resp. second.): VellemanPaul F. <1949->; BockDavid E.
• Note generali: Includes index.
• Nota di contenuto: Cover -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Supplements -- Acknowledgments -- Part I Exploring and Understanding Data -- Chapter 1 Stats Starts Here -- 1.1 What Is Statistics? -- 1.2 Data -- 1.3 Variables -- Chapter 2 Displaying and Describing Categorical Data -- 2.1 Summarizing and Displaying a Single Categorical Variable -- 2.2 Exploring the Relationship Between Two Categorical Variables -- Chapter 3 Displaying and Summarizing Quantitative Data -- 3.1 Displaying Quantitative Variables -- 3.2 Shape -- 3.3 Center -- 3.4 Spread -- 3.5 Boxplots and 5-Number Summaries -- 3.6 The Center of Symmetric Distributions: The Mean -- 3.7 The Spread of Symmetric Distributions: The Standard Deviation -- 3.8 Summary-What to Tell About a Quantitative Variable -- Chapter 4 Understanding and Comparing Distributions -- 4.1 Comparing Groups with Histograms -- 4.2 Comparing Groups with Boxplots -- 4.3 Outliers -- 4.4 Timeplots: Order, Please! -- 4.5 Re-Expressing Data: A First Look -- Chapter 5 The Standard Deviation as a Ruler and the Normal Model -- 5.1 Standardizing with z-Scores -- 5.2 Shifting and Scaling -- 5.3 Normal Models -- 5.4 Finding Normal Percentiles -- 5.5 Normal Probability Plots -- Part II Exploring Relationships Between Variables -- Chapter 6 Scatterplots, Association, and Correlation -- 6.1 Scatterplots -- 6.2 Correlation -- 6.3 Warning: Correlation fi Causation -- *6.4 Straightening Scatterplots -- Chapter 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 -- Chapter 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 -- Chapter 9 Re-expressing Data: Get It Straight! -- 9.1 Straightening Scatterplots - The Four Goals -- 9.2 Finding a Good Re-Expressio -- Part III Gathering Data -- Chapter 10 Understanding Randomness -- 10.1 What Is Randomness? -- 10.2 Simulating by Hand -- Chapter 11 Sample Surveys -- 11.1 The Three Big Ideas of Sampling -- 11.2 Populations and Parameters -- 11.3 Simple Random Samples -- 11.4 Other Sampling Designs -- 11.5 From the Population to the Sample: You Can't Always Get What You Want -- 11.6 The Valid Survey -- 11.7 Common Sampling Mistakes, or How to Sample Badly -- Chapter 12 Experiments and Observational Studies -- 12.1 Observational Studies -- 12.2 Randomized, Comparative Experiments -- 12.3 The Four Principles of Experimental Design -- 12.4 Control Treatments -- 12.5 Blocking -- 12.6 Confounding -- Part IV Randomness and Probability -- Chapter 13 From Randomness to Probability -- 13.1 Random Phenomena -- 13.2 Modeling Probability -- 13.3 Formal Probability -- Chapter 14 Probability Rules! -- 14.1 The General Addition Rule -- 14.2 Conditional Probability and the General Multiplication Rule -- 14.3 Independence -- 14.4 Picturing Probability: Tables, Venn Diagrams, and Trees -- 14.5 Reversing the Conditioning and Bayes' Rule -- Chapter 15 Random Variables -- 15.1 Center: The Expected Value -- 15.2 Spread: The Standard Deviation -- 15.3 Shifting and Combining Random Variables -- 15.4 Continuous Random Variables -- Chapter 16 Probability Models -- 16.1 Bernoulli Trials -- 16.2 The Geometric Model -- 16.3 The Binomial Model -- 16.4 Approximating the Binomial with a Normal Model -- 16.5 The Continuity Correction -- 16.6 The Poisson Model -- 16.7 Other Continuous Random Variables: The Uniform and the Exponential. ; Part V From the Data at Hand to the World at Large -- Chapter 17 Sampling Distribution Models -- 17.1 Sampling Distribution of a Proportion -- 17.2 When Does the Normal Model Work? Assumptions and Conditions -- 17.3 The Sampling Distribution of Other Statistics -- 17.4 The Central Limit Theorem: The Fundamental Theorem of Statistics -- 17.5 Sampling Distributions: A Summary -- Chapter 18 Confidence Intervals for Proportions -- 18.1 A Confidence Interval -- 18.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? -- 18.3 Margin of Error: Certainty vs. Precision -- 18.4 Assumptions and Conditions -- Chapter 19 Testing Hypotheses About Proportions -- 19.1 Hypotheses -- 19.2 P-Values -- 19.3 The Reasoning of Hypothesis Testing -- 19.4 Alternative Alternatives -- 19.5 P-Values and Decisions: What to Tell About a Hypothesis Test -- Chapter 20 Inferences About Means -- 20.1 Getting Started: The Central Limit Theorem (Again) -- 20.2 Gosset's t -- 20.3 Interpreting Confidence Intervals -- 20.4 A Hypothesis Test for the Mean -- 20.5 Choosing the Sample Size -- Chapter 21 More About Tests and Intervals -- 21.1 Choosing Hypotheses -- 21.2 How to Think About P-Values -- 21.3 Alpha Levels -- 21.4 Critical Values for Hypothesis Tests -- 21.5 Errors -- Part VI Accessing Associations Between Variables -- Chapter 22 Comparing Groups -- 22.1 The Standard Deviation of a Difference -- 22.2 Assumptions and Conditions for Comparing Proportions -- 22.3 A Confidence Interval for the Difference Between Two Proportions -- 22.4 The Two Sample z-Test: Testing for the Difference Between Proportions -- 22.5 A Confidence Interval for the Difference Between Two Means -- 22.6 The Two-Sample t-Test: Testing for the Difference Between Two Means -- 22.7 The Pooled t-Test: Everyone into the Pool? -- Chapter 23 Paired Samples and Blocks -- 23.1 Paired Data. ; 23.2 Assumptions and Conditions -- 23.3 Confidence Intervals for Matched Pairs -- 23.4 Blocking -- Chapter 24 Comparing Counts -- 24.1 Goodness-of-Fit Tests -- 24.2 Chi-Square Test of Homogeneity -- 24.3 Examining the Residuals -- 24.4 Chi-Square Test of Independence -- Chapter 25 Inferences for Regression -- 25.1 The Population and the Sample -- 25.2 Assumptions and Conditions -- 25.3 Intuition About Regression Inference -- 25.4 Regression Inference -- 25.5 Standard Errors for Predicted Values -- 25.6 Confidence Intervals for Predicted Values -- 25.7 Logistic Regression -- Part VII Inference When Variables Are Related -- Chapter *26 Analysis of Variance -- 26.1 Testing Whether the Means of Several Groups Are Equal -- 26.2 The ANOVA Table -- 26.3 Assumptions and Conditions -- 26.4 Comparing Means -- 26.5 ANOVA on Observational Data -- Chapter 27 Multifactor Analysis of Variance -- 27.1 A Two Factor ANOVA Model -- 27.2 Assumptions and Conditions -- 27.3 Interactions -- Chapter 28 Multiple Regression -- 28.1 What Is Multiple Regression? -- 28.2 Interpreting Multiple Regression Coefficients -- 28.3 The Multiple Regression Model-Assumptions and Conditions -- 28.4 Multiple Regression Inference -- 28.5 Comparing Multiple Regression Models -- Chapter 29 Multiple Regression Wisdom (available online) -- 29.1 Indicators -- 29.2 Diagnosing Regression Models: Looking at the Cases -- 29.3 Building Multiple Regression Models -- Appendixes -- A Answers -- B Photo Acknowledgments -- C Index -- D Tables and Selected Formulas.
• Sommario/riassunto: Richard De Veaux, Paul Velleman, and David Bock wrote Stats: Data and Models with the goal that students and instructors have as much fun reading it as they did writing it. Maintaining a conversational, humorous, and informal writing style, this new edition engages students from the first page. The authors focus on statistical thinking throughout the text and rely on technology for calculations. As a result, students can focus on developing their conceptual understanding. Innovative Think/Show/Tell examples give students a problem-solving framework and, more importantly, a way to think through any statistics problem and present their results. The Fourth Edition is updated with instructor podcasts, video lectures, and new examples to keep material fresh, current, and relevant to today's students. MyStatLab not included. Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. MyStatLab is an online homework, tutorial, and assessment product designed to personalize learning and improve results. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts.
• Titolo autorizzato: Stats data and models
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910154945403321
• Lo trovi qui: Univ. Federico II