Stats: Data and Models, Global Edition -- MyLab Statistics with Pearson eText

Name: Stats: Data and Models, Global Edition -- MyLab Statistics with Pearson eText
Author: Richard D. De Veaux

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Onbekend, blz. | Engels

Pearson Education | 2021

ISBN13: 9781292362267

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Pearson Education e druk, 2021 9781292362267

€ 74,14

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ISBN13:9781292362267

Taal:Engels

Bindwijze:onbekend

Uitgever:Pearson Education

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<table class="MsoNormalTable" border="0" cellspacing="0" cellpadding="0" width="100%" style="width:100.0%;mso-cellspacing:0in;mso-yfti-tbllook:1184;mso-padding-alt: 0in 0in 0in 0in"> <tbody> <tr style="mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-yfti-lastrow:yes"> <td width="100%" valign="top" style="width:100.0%;padding:0in 7.5pt 7.5pt 7.5pt"> Preface <o:p></o:p> Index of Applications <o:p></o:p> <o:p>   </o:p> I: EXPLORING AND UNDERSTANDING DATA <o:p></o:p> <o:p>   </o:p> 1. Stats Starts Here  <o:p></o:p> 1.1 What Is Statistics?  1.2 Data  1.3 Variables  1.4 Models <o:p></o:p> <o:p>   </o:p> 2. Displaying and Describing Data <o:p></o:p> 2.1 Summarizing and Displaying a Categorical Variable  2.2 Displaying a Quantitative Variable  2.3 Shape  2.4 Center  2.5 Spread  <o:p></o:p> <o:p>   </o:p> 3. Relationships Between Categorical Variables–Contingency Tables <o:p></o:p> 3.1 Contingency Tables  3.2 Conditional Distributions  3.3 Displaying Contingency Tables  3.4 Three Categorical Variables <o:p></o:p> <o:p>   </o:p> 4. Understanding and Comparing Distributions <o:p></o:p> 4.1 Displays for Comparing Groups  4.2 Outliers  4.3 Re-Expressing Data: A First Look <o:p></o:p> <o:p>   </o:p> 5. The Standard Deviation as a Ruler and the Normal Model <o:p></o:p> 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 <o:p></o:p> <o:p>   </o:p> Review of Part I: Exploring and Understanding Data <o:p></o:p> <o:p>   </o:p> II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES <o:p></o:p> <o:p>   </o:p> 6. Scatterplots, Association, and Correlation <o:p></o:p> 6.1 Scatterplots 6.2 Correlation 6.3 Warning: Correlation ≠ Causation *6.4 Straightening Scatterplots <o:p></o:p> <o:p>   </o:p> 7. Linear Regression <o:p></o:p> 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 <o:p></o:p> <o:p>   </o:p> 8. Regression Wisdom <o:p></o:p> 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 <o:p></o:p> <o:p>   </o:p> 9. Multiple Regression <o:p></o:p> 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  <o:p></o:p> <o:p>   </o:p> Review of Part II: Exploring Relationships Between Variables  <o:p></o:p> <o:p>   </o:p> III. GATHERING DATA <o:p></o:p> <o:p>   </o:p> 10. Sample Surveys <o:p></o:p> 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 <o:p></o:p> <o:p>   </o:p> 11. Experiments and Observational Studies <o:p></o:p> 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 <o:p></o:p> <o:p>   </o:p> Review of Part III: Gathering Data <o:p></o:p> <o:p>   </o:p> IV. RANDOMNESS AND PROBABILITY  <o:p></o:p> <o:p>   </o:p> 12. From Randomness to Probability <o:p></o:p> 12.1 Random Phenomena  12.2 Modeling Probability  12.3 Formal Probability <o:p></o:p> <o:p>   </o:p> 13.Probability Rules! <o:p></o:p> 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 <o:p></o:p> <o:p>   </o:p> 14. Random Variables <o:p></o:p> 14.1 Center: The Expected Value  14.2 Spread: The Standard Deviation  14.3 Shifting and Combining Random Variables  14.4 Continuous Random Variables <o:p></o:p> <o:p>   </o:p> 15. Probability Models <o:p></o:p> 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 <o:p></o:p> <o:p>   </o:p> Review of Part IV: Randomness and Probability <o:p></o:p> <o:p>   </o:p> V. INFERENCE FOR ONE PARAMETER  <o:p></o:p> <o:p>   </o:p> 16. Sampling Distribution Models and Confidence Intervals for Proportions <o:p></o:p> 16.1 The Sampling Distribution Model for a Proportion  16.2 When Does the Normal Model Work? Assumptions and Conditions  16.3 A Confidence Interval for a Proportion  16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? 16.5 Margin of Error: Certainty vs. Precision  *16.6 Choosing the Sample Size <o:p></o:p> <o:p>   </o:p> 17. Confidence Intervals for Means <o:p></o:p> 17.1 The Central Limit Theorem  17.2 A Confidence Interval for the Mean  17.3 Interpreting Confidence Intervals  *17.4 Picking Our Interval up by Our Bootstraps  17.5 Thoughts About Confidence Intervals <o:p></o:p> <o:p>   </o:p> 18. Testing Hypotheses <o:p></o:p> 18.1 Hypotheses 18.2 P-Values  18.3 The Reasoning of Hypothesis Testing  18.4 A Hypothesis Test for the Mean  18.5 Intervals and Tests  18.6 P-Values and Decisions: What to Tell About a Hypothesis Test <o:p></o:p> <o:p>   </o:p> 19. More About Tests and Intervals <o:p></o:p> 19.1 Interpreting P-Values  19.2 Alpha Levels and Critical Values  19.3 Practical vs. Statistical Significance  19.4 Errors <o:p></o:p> <o:p>   </o:p> Review of Part V: Inference for One Parameter <o:p></o:p> <o:p>   </o:p> VI. INFERENCE FOR RELATIONSHIPS <o:p></o:p> <o:p>   </o:p> 20. Comparing Groups <o:p></o:p> 20.1 A Confidence Interval for the Difference Between Two Proportions  20.2 Assumptions and Conditions for Comparing Proportions  20.3 The Two-Sample z-Test: Testing for the Difference Between Proportions 20.4 A Confidence Interval for the Difference Between Two Means 20.5 The Two-Sample t-Test: Testing for the Difference Between Two Means *20.6 Randomization Tests and Confidence Intervals for Two Means *20.7 Pooling  *20.8 The Standard Deviation of a Difference  <o:p></o:p> <o:p>   </o:p> 21. Paired Samples and Blocks <o:p></o:p> 21.1 Paired Data  21.2 The Paired t-Test  21.3 Confidence Intervals for Matched Pairs  21.4 Blocking <o:p></o:p> <o:p>   </o:p> 22. Comparing Counts <o:p></o:p> 22.1 Goodness-of-Fit Tests  22.2 Chi-Square Test of Homogeneity  22.3 Examining the Residuals  22.4 Chi-Square Test of Independence  <o:p></o:p> <o:p>   </o:p> 23. Inferences for Regression <o:p></o:p> 23.1 The Regression Model  23.2 Assumptions and Conditions  23.3 Regression Inference and Intuition  23.4 The Regression Table  23.5 Multiple Regression Inference  23.6 Confidence and Prediction Intervals  *23.7 Logistic Regression  *23.8 More About Regression <o:p></o:p> <o:p>   </o:p> Review of Part VI: Inference for Relationships <o:p></o:p> <o:p>   </o:p> VII. INFERENCE WHEN VARIABLES ARE RELATED <o:p></o:p> <o:p>   </o:p> 24. Multiple Regression Wisdom <o:p></o:p> 24.1 Multiple Regression Inference  24.2 Comparing Multiple Regression Model  24.3 Indicators  24.4 Diagnosing Regression Models: Looking at the Cases  24.5 Building Multiple Regression Models <o:p></o:p> <o:p>   </o:p> 25. Analysis of Variance <o:p></o:p> 25.1 Testing Whether the Means of Several Groups Are Equal  25.2 The ANOVA Table  25.3 Assumptions and Conditions  25.4 Comparing Means  25.5 ANOVA on Observational Data <o:p></o:p> <o:p>   </o:p> 26. Multifactor Analysis of Variance <o:p></o:p> 26.1 A Two Factor ANOVA Model   26.2 Assumptions and Conditions  26.3 Interactions <o:p></o:p> <o:p>   </o:p> 27. Statistics and Data Science <o:p></o:p> 27.1 Introduction to Data Mining <o:p></o:p> <o:p>   </o:p> Review of Part VII: Inference When Variables Are Related <o:p></o:p> <o:p>   </o:p> Parts I—V Cumulative Review Exercises <o:p></o:p> <o:p>   </o:p> Appendixes: <o:p></o:p> A. Answers  <o:p></o:p> B. Credits  <o:p></o:p> C. Indexes  <o:p></o:p> D. Tables and Selected Formulas  <o:p></o:p> </td> </tr> </tbody> </table>

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