Introductory Statistics: Exploring the World Through Data, 3rd edition

Robert N. Gould
, Rebecca Wong
, Colleen Ryan

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Data in the real world can be dynamic and sometimes messy. This complexity might intimidate students who are new to math and statistics, but it's also what makes statistics so interesting! Introductory Statistics: Exploring the World Through Data teaches you how to explore and analyze real data to answer real-world problems. Crafted by authors who are active in the classroom and in the statistics education community, the 3rd Edition pairs a clear, conversational writing style with new and frequent opportunities to apply statistical thinking. Its tone and learning aids are designed to equip all students to analyze, interpret and tell a story about modern data, regardless of mathematical background.

ISBN-13: 9780136880882

Subject: Introductory Statistics

Category:

1. Introduction to Data

1.1 What Are Data?
1.2 Classifying and Storing Data
1.3 Investigating Data
1.4 Organizing Categorical Data
1.5 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 Finding Empirical Probabilities

6. Modeling Rando 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 (Optional)

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
7.5 Comparing Two Population Proportions with Confidence

8. Hypothesis Testing for Population Proportions

8.1 The Essential Ingredients of Hypothesis Testing
8.2 Hypothesis Testing in Four Steps
8.3 Hypothesis Tests in Detail
8.4 Comparing Proportions from Two Populations

9. Inferring Population Means

9.1 Sample Means of Rando Samples
9.2 The Central Limit Theorem for Sample Means
9.3 Answering Questions about the Mean of a Population
9.4 Hypothesis Testing for Means
9.5 Comparing Two Population Means
9.6 Overview of Analyzing Means

10. Associations between Categorical Variables

10.1 The Basic Ingredients for Testing with Categorical Variables
10.2 The Chi-Square Test for Goodness of Fit
10.3 Chi-Square Tests for Associations between Categorical Variables
10.4 Hypothesis Tests When Sample Sizes Are Small

11. Multiple Comparisons and Analysis of Variance

11.1 Multiple Comparisons
11.2 The Analysis of Variance
11.3 The ANOVA Test
11.4 Post-Hoc Procedures

12. Experimental Design: Controlling Variation

12.1 Variation Out of Control
12.2 Controlling Variation in Surveys
12.3 Reading Research Papers

13. Inference without Normality

13.1 Transforming Data
13.2 The Sign Test for Paired Data
13.3 Mann-Whitney Test for Two Independent Groups
13.4 Randomization Tests

14. Inference for Regression

14.1 The Linear Regression Model
14.2 Using the Linear Model
14.3 Predicting Values and Estimating Means