| Autore |
De Veaux Richard D.
|
| Edizione | [Global edition, Fourth edition.] |
| Pubbl/distr/stampa |
Harlow, England : , : Pearson, , [2015]
|
| Descrizione fisica |
1 online resource (991 pages) : illustrations (some color)
|
| Disciplina |
519.5
|
| Soggetto topico |
Statistics
Mathematical statistics
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| 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.
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| Record Nr. | UNINA-9910154945403321 |
Info
Intro stats / / Richard D. De Veaux, Paul F. Velleman, David E. Bock
