Essential Statistics, 3rd edition

  • Robert N. Gould, 
  • Rebecca Wong, 
  • Colleen Ryan

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With Essential Statistics you'll learn to think critically with and about data, report your findings, and evaluate arguments. The fundamentals of exploratory data analysis and inferential statistics are covered, along with additional methods, including estimating population means and analyzing categorical variables.

Published by Pearson (January 25th 2021) - Copyright © 2021

ISBN-13: 9780135964705

Subject: Statistics, Introductory

Category: Introductory Statistics

Table of contents

Index of Applications

1. Introduction to Data
Case Study: Deadly Cell Phones?
1.1 What Are Data?
1.2 Classifying and Storing Data
1.3 Organizing Categorical Data
1.4 Collecting Data to Understand Causality Data Project: How Are Data Stored?

2. Picturing Variation with Graphs
Case Study: Student-to-Teacher Ratio at Colleges
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 Data Project: Asking Questions

3. Numerical Summaries of Center and Variation
Case Study: Living in a Risky World
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 Data Project: The Statistical Investigation Cycle

4. Regression Analysis: Exploring Associations between Variables
Case Study: Forecasting Home Prices
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 Data Project: Data Moves

5. Modeling Variation with Probability
Case Study: SIDS or Murder?
5.1 What Is Randomness?
5.2 Finding Theoretical Probabilities
5.3 Associations in Categorical Variables
5.4 Finding Empirical Probabilities Data Project: Submitting Data

6. Modeling Random Events: The Normal and Binomial Models
Case Study: You Sometimes Get More Than You Pay For
6.1 Probability Distributions Are Models of Random Experiments
6.2 The Normal Model
6.3 The Binomial Model (optional) Data Project: Generating Random Numbers

7. Survey Sampling and Inference
Case Study: Spring Break Fever: Just What the Doctors Ordered?
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 Data Project: Population Proportions

8. Hypothesis Testing for Population Proportions
Case Study: Dodging the Question
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 Data Project: Dates as Data

9. Inferring Population Means
Case Study: You Look Sick! Are You Sick?
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 Hypothesis Testing for Means
9.5 Comparing Two Population Means
9.6 Overview of Analyzing Means Data Project: Data Structures

10. Analyzing Categorical Variables and Interpreting Research
Case Study: Popping Better Popcorn
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 Data Project: Think Small

Appendix A: Tables
Appendix B: Check Your Tech Answers
Appendix C: Credits Index

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