# Introductory Statistics: A Student-Centered Approach

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## First EditionAnn Cannon; Daren Starnes; Josh Tabor

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Learn More## Table of Contents

**Chapter 1 Collecting Data**

Section 1.1 Introduction to Data Collection

Section 1.2 Sampling: Good and Bad

Section 1.3 Simple Random Sampling

Section 1.4 Other Sampling Methods

Section 1.5 Observational Studies and Experiments

Section 1.6 Completely Randomized Designs

Section 1.7 Blocking

Section 1.8 Data Ethics and the Scope of Inference

**Chapter 2 Displaying Data with Graphs**

Section 2.1 Displaying Categorical Data

Section 2.2 Displaying Relationships Between Two Categorical Variables

Section 2.3 Displaying Quantitative Data: Dotplots

Section 2.4 Displaying Quantitative Data: Stemplots

Section 2.5 Displaying Quantitative Data: Histograms

Section 2.6 Displaying Relationships Between Two Quantitative Variables

**Chapter 3 Numerical Summaries for Quantitative Data**

Section 3.1 Measuring Center

Section 3.2 Measuring Variability

Section 3.3 Boxplots and Outliers

Section 3.4 Measuring Location in a Distribution

Section 3.5 Relationships Between Two Variables: Correlation

Section 3.6 More About Correlation

**Chapter 4 Probability**

Section 4.1 Randomness, Probability, and Simulation

Section 4.2 Basic Probability Rules

Section 4.3 Two-Way Tables and Venn Diagrams

Section 4.4 Conditional Probability and Independence

Section 4.5 The General Multiplication Rule and Bayes’ Theorem

Section 4.6 The Multiplication Rule for Independent Events

Section 4.7 The Multiplication Counting Principle and Permutations

Section 4.8 Combinations and Probability

**Chapter 5 Discrete Random Variables**

Section 5.1 Introduction to Random Variables

Section 5.2 Analyzing Discrete Random Variables

Section 5.3 Binomial Random Variables

Section 5.4 Analyzing Binomial Random Variables

Section 5.5 Poisson Random Variables

**Chapter 6 Normal Distributions and Sampling Distributions**

Section 6.1 Continuous Random Variables

Section 6.2 Normal Distributions: Finding Areas from Values

Section 6.3 Normal Distributions: Finding Values from Areas

Section 6.4 Normal Approximation to the Binomial Distribution and Assessing Normality

Section 6.5 Sampling Distributions

Section 6.6 Sampling Distributions: Bias and Variability

Section 6.7 Sampling Distribution of the Sample Proportion

Section 6.8 Sampling Distribution of the Sample Mean and the Central Limit Theorem

**Chapter 7 Estimating a Parameter**

Section 7.1 The Idea of a Confidence Interval

Section 7.2 Factors That Affect the Margin of Error

Section 7.3 Estimating a Population Proportion

Section 7.4 Confidence Intervals for a Population Proportion

Section 7.5 Estimating a Population Mean

Section 7.6 Confidence Intervals for a Population Mean

Section 7.7 Estimating a Population Standard Deviation or Variance

Section 7.8 Confidence Intervals for a Population Standard Deviation or Variance

**Chapter 8 Testing a Claim**

Section 8.1 The Idea of a Significance Test

Section 8.2 Significance Tests and Decision Making

Section 8.3 Testing a Claim About a Population Proportion

Section 8.4 Significance Tests for a Population Proportion

Section 8.5 Testing a Claim About a Population Mean

Section 8.6 Significance Tests for a Population Mean

Section 8.7 Power of a Test

Section 8.8 Significance Tests for a Population Standard Deviation or Variance

**Chapter 9 Comparing Two Populations or Treatments**

Section 9.1 Confidence Intervals for a Difference Between Two Population Proportions

Section 9.2 Significance Tests for a Difference Between Two Population Proportions

Section 9.3 Confidence Intervals for a Difference Between Two Population Means

Section 9.4 Significance Tests for a Difference Between Two Population Means

Section 9.5 Analyzing Paired Data: Confidence Intervals for a Population Mean Difference

Section 9.6 Significance Tests for a Population Mean Difference

Section 9.7 Significance Tests for Two Population Standard Deviations or Variances

**Chapter 10 Chi-Square and Analysis of Variance (ANOVA)**

Section 10.1 Testing the Distribution of a Categorical Variable in a Population

Section 10.2 Chi-Square Tests for Goodness of Fit

Section 10.3 Testing the Relationship Between Two Categorical Variables in a Population

Section 10.4 Chi-Square Tests for Association

Section 10.5 Introduction to Analysis of Variance

Section 10.6 One-Way Analysis of Variance

**Chapter 11 Linear Regression**

Section 11.1 Regression Lines

Section 11.2 The Least-Squares Regression Line

Section 11.3 Assessing a Regression Model

Section 11.4 Confidence Intervals for the Slope of a Population Least-Squares Regression Line

Section 11.5 Significance Tests for the Slope of a Population Least-Squares Regression Line

Section 11.6 Confidence Intervals for a Mean Response and Prediction Intervals in Regression

**Chapter 12 Multiple Regression**

Section 12.1 Introduction to Multiple Regression

Section 12.2 Indicator Variables and Interaction

Section 12.3 Inference for Multiple Regression

**Chapter 13 Nonparametric Methods**

Section 13.1 The Sign Test

Section 13.2 The Wilcoxon Signed Rank Test

Section 13.3 The Wilcoxon Rank Sum Test

Section 13.4 The Kruskal-Wallis Test

Section 13.5 Randomization Tests

Section 13.6 Bootstrapping

Section 13.2 The Wilcoxon Signed Rank Test

Section 13.3 The Wilcoxon Rank Sum Test

Section 13.4 The Kruskal-Wallis Test

Section 13.5 Randomization Tests

Section 13.6 Bootstrapping