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• Chapter 1: Statistics: The Art and Science of Learning from Data
  • 1.1: How Can You Investigate Using Data? (4)
  • 1.2: We Learn about Populations Using Samples (4)
  • 1.3: What Role do Computers Play in Statistics? (2)
  • 1: Chapter Problems (2)
  
  
• Chapter 2: Exploring Data with Graphs and Numerical Summaries
  • 2.1: What Are the Types of Data? (5)
  • 2.2: How Can We Describe Data Using Graphical Summaries? (7)
  • 2.3: How Can We Describe the Center of Quantitative Data? (7)
  • 2.4: How Can We Describe the Spread of Quantitative Data? (6)
  • 2.5: How Can Measures of Position Describe Spread? (8)
  • 2.6: How Are Descriptive Summaries Misused? (2)
  • 2: Chapter Problems (10)
  
  
• Chapter 3: Association: Contingency, Correlation, and Regression
  • 3.1: How Can We Explore the Association between Two Categorical Variables? (5)
  • 3.2: How Can We Explore the Association between Two Quantitative Variables? (6)
  • 3.3: How Can We Predict the Outcome of a Variable? (5)
  • 3.4: What are Some Cautions in Analyzing Associations? (6)
  • 3: Chapter Problems (10)
  
  
• Chapter 4: Gathering Data
  • 4.1: Should We Experiment of Should We Merely Observe? (5)
  • 4.2: What Are Good Ways and Poor Ways to Sample? (6)
  • 4.3: What Are Good Ways and Poor Ways to Experiment? (3)
  • 4.4: What Are Other Ways to Perform Experimental and Observational Studies? (4)
  • 4: Chapter Problems (11)
  
  
• Chapter 5: Probability in Our Daily Lives
  • 5.1: How Can Probability Quantify Randomness? (3)
  • 5.2: How Can We Find Probabilities? (6)
  • 5.3: Conditional Probability: What's the Probability of A, Given B? (9)
  • 5.4: Applying the Probability Rules (8)
  • 5: Chapter Problems (6)
  
  
• Chapter 6: Probability Distributions
  • 6.1: How Can We Summarize Possible Outcomes and Their Probabilities? (5)
  • 6.2: How Can We Find Probabilites for Bell-Shaped Distributions? (8)
  • 6: How Can We Find Probabilities When Each Observation Has Two Possible Outcomes? (6)
  • 6.4: How Likely Are the possible Values of a Statistic? The Sampling Distribution (5)
  • 6.5: How Close Are Sample Means to Population Means? (3)
  • 6.6: How Can We Make Inferences About a Population? (2)
  • 6: Chapter Problems (9)
  
  
• Chapter 7: Statistical Inference: Confidence Intervals
  • 7.1: What Are Point and Interval Estimates of Population Parameters? (4)
  • 7.2: how Can We Construct a Confidence Interval to Estimate a Population Proportion? (5)
  • 7.3: How Can We Construct a Confidence Interval to Estimate a Population Mean? (6)
  • 7.4: How Do We Choose the Sample Size for a Study? (5)
  • 7.5: How Do computers Make New Estimation Methods Possible? (1)
  • 7: Chapter Problems
  
  
• Chapter 8: Statistical Inference: Significance Tests About Hypotheses
  • 8.1: What Are the Steps for Performing a Signigicance Test? (2)
  • 8.2: Significance Tests About Proportions (4)
  • 8.3: Significance Tests About Means (2)
  • 8.4: Decisions and Types of Errors in Significance Tests (2)
  • 8.5: Limitations of Significance Tests (2)
  • 8.6: How Likely Is a Type II Error (Not Rejecting Ho, Even though it's False)? (1)
  • 8: Chapter Problems
  
  
• Chapter 9: Comparing Two Groups
  • 9.1: Categorical Response: How Can We Compare Two Proportions? (6)
  • 9.2: Quantitative Response: How Can We Compare Two Means? (6)
  • 9.3: Other Ways of Comparing Means and Comparing Proportions (5)
  • 9.4: How Can We Analyze Dependent Samples? (2)
  • 9.5: How Can We Adjust for Effects of Other Variables? (2)
  • 9: Chapter Problems
  
  
• Chapter 10: Analyzing the Association Between Categorical Variables
  • 10.1: What Is Independence and What Is Association?
  • 10.2: How Can We Test Whether Categorical Variables are Independent?
  • 10.3: How Strong Is the Association?
  • 10.4: How Can Residuals Reveal the Pattern of Association?
  • 10.5; What is the Sample Size is Small? Fisher's Exact Test
  • 10: Chapter Problems
  
  
• Chapter 11: Analyzing Association Between Quantitative Variables: Regression Analysis
  • 11.1: How Can We "Model" How Two Variables Are Related?
  • 11.2: How Can We Describe Strength of Association?
  • 11.3: How Can We Make Inferences About the Association?
  • 11.4: What Do We Learn from How the Data Vary Around the Regression Line?
  • 11.5: Exponential Regression: A Model for Nonlinearity
  • 11: Chapter Problems
  
  
• Chapter 12: Multiple Regression
  • 12.1: How Can We Use Several Variables to Predict a Response?
  • 12.2: Extending the Correlation and R-Squared for Multiple Regression
  • 12.3: How Can We use multiple Regression to Make Inferences?
  • 12.4: Checking a Regression Model Using Residual Plots
  • 12.5: How Can Regression Include Categorical Predictors?
  • 12.6: How Can We Model a Categorical Response?
  • 12: Chapter Problems
  
  
• Chapter 13: Comparing Groups: Analysis of Variance Methods
  • 13.1: How Can We Compare Several Means? One-Way ANOVA
  • 13.2: How Should We Follow Up and ANOVA F Test?
  • 13.3: What if There Are Two Factors? Two Way ANOVA
  • 13: Chapter Problems
  
  
• Chapter 14:Nonparametric Statistics
  • 14.1: How Can We Compare Two Groups by Ranking?
  • 14.2: Nonparametric Methods for Several Groups and for Matched Pairs
  • 14: Chapter Problems