Gould R. Introductory Statistics. Exploring the World Through Data 4ed 2026

Textbook in PDF format Introductory Statistics: Exploring the World Through Data helps you learn to think critically with and about data, communicate your findings to others, and evaluate others’ arguments carefully. Crafted by authors who are active in the classroom and in the statistics education community, it combines clear, conversational writing with new and frequent opportunities to apply what you’ve learned. The 4th Edition fully revises end-of-chapter Data Projects, updates technology guides to match current hardware and software, adds hundreds of new exercises and updates previous exercises, and much more. Preface Introduction to Data Case study: Dangerous Habit? What Are Data? Classifying and Storing Data Investigating Data Organizing Categorical Data Collecting Data to Understand Causality Data project: Introduction: Importing Data Picturing Variation with Graphs Case study: Student-to-Teacher Ratio at Colleges Visualizing Variation in Numerical Data Summarizing Important Features of a Numerical Distribution Visualizing Variation in Categorical Variables Summarizing Categorical Distributions Interpreting Graphs Data project: The Bare Bones: Data Types Numerical Summaries of Center and Variation Case study: Living in a Risky World Summaries for Symmetric Distributions What’s Unusual? The Empirical Rule and z-Scores Summaries for Skewed Distributions Comparing Measures of Center Using Boxplots for Displaying Summaries Data project: The W’s and One H: Asking Interrogative Questions Regression Analysis: Exploring Associations between Variables Case study: Forecasting Home Prices Visualizing Variability with a Scatterplot Measuring Strength of Association with Correlation Modeling Linear Trends Evaluating the Linear Model Data project: Get it Sorted: Sorting Data Modeling Variation with Probability Case study: SIDS or Murder? What Is Randomness? Finding Theoretical Probabilities Associations in Categorical Variables Finding Empirical and Simulated Probabilities Data project: Ask Good Questions: Posing Statistical Investigative Questions Modeling Random Events: The Normal and Binomial Models Case study: You Sometimes Get More Than You Pay for Probability Distributions Are Models of Random Experiments The Normal Model The Binomial Model Data project: Transformations for a Better Analysis Survey Sampling and Inference Case study: Spring Break Fever: Just What the Doctors Ordered? Learning about the World through Surveys Measuring the Quality of a Survey The Central Limit Theorem for Sample Proportions Estimating the Population Proportion with Confidence Intervals Comparing Two Population Proportions with Confidence Data project: Make it Smaller: Subsetting Data Hypothesis Testing for Population Proportions Case study: Dodging the Question The Essential Ingredients of Hypothesis Testing Hypothesis Testing in Four Steps Hypothesis Tests in Detail Comparing Proportions from Two Populations Data project: On and Off, and Off and On: Working with Binary Variables Inferring Population Means Case study: You Look Sick! Are You Sick? Sample Means of Random Samples The Central Limit Theorem for Sample Means Answering Questions about the Mean of a Population Hypothesis Testing for Means Comparing Two Population Means Overview of Analyzing Means Data project: Non-binary Coding: Creating Multi-valued Categorical Variables Associations between Categorical Variables Case study: Popping Better Popcorn 506 The Basic Ingredients for Testing with Categorical Variables The Chi-Square Test for Goodness of Fit Chi-Square Tests for Associations between Categorical Variables Hypothesis Tests When Sample Sizes Are Small Data project: Dating Yourself: Creating New Variables from Dates Multiple Comparisons and Analysis of Variance Case study: Seeing Red Multiple Comparisons The Analysis of Variance The ANOVA Test Post Hoc Procedures Data project: What’s in a Name: Sp

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