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

  • Robert N. Gould, 
  • Colleen Ryan

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Real-world data can be complex and interesting. Introductory Statistics: Exploring the World Through Data teaches you how to analyze data to answer real-world problems.

Published by Pearson (July 15th 2020) - Copyright © 2020

ISBN-13: 9780136880882

Subject: Introductory Statistics

Category: Introductory Statistics: Exploring the World Through Data

Table of contents

Brief Contents

  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

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