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Table of Contents

1. Introduction to Data

1.1 What Are Data?

1.2 Classifying and Storing Data

1.3 Organizing Categorical Data

1.4 Collecting Data to Understand Causality

 

2. Picturing Variation with Graphs

2.1 Visualizing Variation in Numerical Data

2.2 Summarizing Important Features of a Numerical Distribution

2.3 Visualizing Variation in Categorical Variables

2.4 Summarizing Categorical Distributions

2.5 Interpreting Graphs

 

3. Numerical Summaries of Center and Variation

3.1 Summaries for Symmetric Distributions

3.2 What's Unusual? The Empirical Rule and z-Scores

3.3 Summaries for Skewed Distributions

3.4 Comparing Measures of Center

3.5 Using Boxplots for Displaying Summaries

 

4. Regression Analysis: Exploring Associations between Variables

4.1 Visualizing Variability with a Scatterplot

4.2 Measuring Strength of Association with Correlation

4.3 Modeling Linear Trends

4.4 Evaluating the Linear Model

 

5. Modeling Variation with Probability

5.1 What is Randomness?

5.2 Finding Theoretical Probabilities

5.3 Associations in Categorical Variables

5.4 The Law of Large Numbers

 

6. Modeling Random Events: The Normal and Binomial Models

6.1 Probability Distributions Are Models of Random Experiments

6.2 The Normal Model

6.3 The Binomial Model

 

7. Survey Sampling and Inference

7.1 Learning about the World through Surveys

7.2 Measuring the Quality of a Survey

7.3 The Central Limit Theorem for Sample Proportions

7.4 Estimating the Population Proportion with Confidence Intervals

 

8. Hypothesis Testing for Population Proportions

8.1 The Main Ingredients of Hypothesis Testing

8.2 Characterizing p-values

8.3 Hypothesis Testing in Four Steps

8.4 Comparing Proportions from Two Populations

8.5 Understanding Hypothesis Testing

 

9. Inferring Population Means

9.1 Sample Means of Random Samples

9.2 The Central Limit Theorem for Sample Means

9.3 Answering Questions about the Mean of a Population

9.4 Comparing Two Population Means

9.5 Overview of Analyzing Means

 

10. Associations between Categorical Variables

10.1 The Basic Ingredients for Testing with Categorical Variables

10.2 Chi-Square Tests for Associations between Categorical Variables

10.3 Reading Research Papers

Get Essential Statistics (Subscription) by Robert Gould, University of California, Los Angeles Colleen N. Ryan, California Lutheran University

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