# Essential Statistics, CourseSmart eTextbook

Published Date: Jan 10, 2013

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## Description

This book is ideal for a one-semester course in statistics, offering a streamlined presentation of Essential Statistics: Exploring the World through Data, by Gould and Ryan.

We live in a data-driven world, and this is a book about understanding and working with that data. In order to be informed citizens, authors Rob Gould and Colleen Ryan believe that learning statistics extends beyond the classroom to an essential life skill. They teach students of all math backgrounds how to think about data, how to reason using data, and how to make decisions based on data.

With a clear, unintimidating writing style and carefully chosen pedagogy, Essential Statistics: Exploring the World through Data makes data analysis accessible to all students. Guided Exercises support students by building their confidence as they learn to solve problems.  Snapshots summarize statistical procedures and concepts for convenient studying. While this text assumes the use of statistical software, formulas are presented as an aid to understanding the concepts rather than the focus of study. Check Your Tech features demonstrate how students will get the same numerical value by-hand as when using statistical software.

The robust ancillary package–including the authors’ teaching notes, instructor-to-instructor podcasts, lecture-ready PowerPoint® Slides, MyStatLab, and more-–helps any instructor to teach a modern course in statistics.

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

9.4 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