Intro Stats

<p>Preface </p> <p>Index of Applications</p> <p>&nbsp;</p> <p>Part I. Exploring and Understanding Data</p> <p>&nbsp;</p> <p>1. Stats Starts Here!</p> <p>1.1 What Is Statistics?</p> <p>1.2 Data</p> <p>1.3 Variables</p> <p>&nbsp;</p> <p>2. Displaying and Describing Categorical Data </p> <p>2.1 Summarizing and Displaying a Single Categorical Variable</p> <p>2.2 Exploring the Relationship Between Two Categorical Variables</p> <p>&nbsp;</p> <p>3. Displaying and Summarizing Quantitative Data</p> <p>3.1 Displaying Quantitative Variables</p> <p>3.2 Shape</p> <p>3.3 Center</p> <p>3.4 Spread</p> <p>3.5 Boxplots and 5-Number Summaries</p> <p>3.6 The Center of Symmetric Distributions: The Mean</p> <p>3.7 The Spread of Symmetric Distributions: The Standard Deviation</p> <p>3.8 Summary—What to Tell About a Quantitative Variable</p> <p>&nbsp;</p> <p>4. Understanding and Comparing Distributions</p> <p>4.1 Comparing Groups with Histograms</p> <p>4.2 Comparing Groups with Boxplots</p> <p>4.3 Outliers</p> <p>4.4 Timeplots: Order, Please!</p> <p>4.5 Re-expressing Data: A First Look</p> <p>&nbsp;</p> <p>5. The Standard Deviation as a Ruler and the Normal Model</p> <p>5.1 Standardizing with z-Scores</p> <p>5.2 Shifting and Scaling </p> <p>5.3 Normal Models</p> <p>5.4 Finding Normal Percentiles</p> <p>5.5 Normal Probability Plots</p> <p>&nbsp;</p> <p>Review of Part I: Exploring and Understanding Data</p> <p>&nbsp;</p> <p>Part II. Exploring Relationships Between Variables</p> <p>&nbsp;</p> <p>6. Scatterplots, Association, and Correlation</p> <p>6.1 Scatterplots</p> <p>6.2 Correlation</p> <p>6.3 Warning: Correlation ≠ Causation</p> <p>6.4 Straightening Scatterplots </p> <p>&nbsp;</p> <p>7. Linear Regression</p> <p>7.1 Least Squares: The Line of "Best Fit"</p> <p>7.2 The Linear Model</p> <p>7.3 Finding the Least Squares Line</p> <p>7.4 Regression to the Mean</p> <p>7.5 Examining the Residuals</p> <p>7.6 R2—The Variation Accounted for by the Model</p> <p>7.7 Regression Assumptions and Conditions</p> <p>&nbsp;</p> <p>8. Regression Wisdom</p> <p>8.1 Examining Residuals</p> <p>8.2 Extrapolation: Reaching Beyond the Data</p> <p>8.3 Outliers, Leverage, and Influence</p> <p>8.4 Lurking Variables and Causation</p> <p>8.5 Working with Summary Values</p> <p>&nbsp;</p> <p>Review of Part II: Exploring Relationships Between Variables</p> <p>&nbsp;</p> <p>Part III. Gathering Data</p> <p>&nbsp;</p> <p>9. Understanding Randomness</p> <p>9.1 What is Randomness?</p> <p>9.2 Simulating By Hand</p> <p>&nbsp;</p> <p>10. Sample Surveys</p> <p>10.1 The Three Big Ideas of Sampling</p> <p>10.2 Populations and Parameters</p> <p>10.3 Simple Random Samples</p> <p>10.4 Other Sampling Designs</p> <p>10.5 From the Population to the Sample: You Can't Always Get What You Want</p> <p>10.6 The Valid Survey</p> <p>10.7 Common Sampling Mistakes, or How to Sample Badly</p> <p>&nbsp;</p> <p>11. Experiments and Observational Studies</p> <p>11.1 Observational Studies</p> <p>11.2 Randomized, Comparative Experiments</p> <p>11.3 The Four Principles of Experimental Design</p> <p>11.4 Control Treatments</p> <p>11.5 Blocking</p> <p>11.6 Confounding</p> <p>&nbsp;</p> <p>Review of Part III: Gathering Data</p> <p>&nbsp;</p> <p>Part IV. Randomness and Probability</p> <p>&nbsp;</p> <p>12. From Randomness to Probability</p> <p>12.1 Random Phenomena</p> <p>12.2 Modeling Probability</p> <p>12.3 Formal Probability</p> <p>&nbsp;</p> <p>13. Probability Rules!</p> <p>13.1 The General Addition Rule</p> <p>13.2&nbsp;&nbsp; Conditional Probability and the General Multiplication Rule</p> <p>13.3 Independence</p> <p>13.4 Picturing Probability: Tables, Venn Diagrams and Trees</p> <p>13.5 Reversing the Conditioning and Bayes' Rule</p> <p>&nbsp;</p> <p>14. Random Variables and Probability Models</p> <p>14.1 Expected Value: Center</p> <p>14.2 Standard Deviation</p> <p>14.3 Combining Random Variables</p> <p>14.4 The Binomial Model</p> <p>14.5 Modelin</p>