OpenIntro Statistics 4th edition

Access is contingent on use of this textbook in the instructor's classroom.

• Chapter 1: Introduction to data
  • 1.1: Case study: using stents to prevent strokes
  • 1.2: Data basics
  • 1.3: Sampling principles and strategies
  • 1.4: Experiments
  • 1: Chapter exercises
  
  
• Chapter 2: Summarizing data
  • 2.1: Examining numerical data
  • 2.2: Considering categorical data
  • 2.3: Case study: malaria vaccine
  • 2: Chapter exercises
  • 2: Labs
  
  
• Chapter 3: Probability
  • 3.1: Defining probability
  • 3.2: Conditional probability
  • 3.3: Sampling from a small population
  • 3.4: Random variables
  • 3.5: Continuous distributions
  • 3: Chapter exercises
  • 3: Labs
  
  
• Chapter 4: Distributions of random variables
  • 4.1: Normal distribution
  • 4.2: Geometric distribution
  • 4.3: Binomial distribution
  • 4.4: Random variables
  • 4.5: Continuous distributions
  • 4: Chapter exercises
  • 4: Labs
  
  
• Chapter 5: Foundations for inference
  • 5.1: Point estimates and sampling variability
  • 5.2: Confidence intervals for a proportion
  • 5.3: Hypothesis testing for a proportion
  • 5: Chapter exercises
  • 5: Labs
  
  
• Chapter 6: Inference for categorical data
  • 6.1: Inference for a single proportion
  • 6.2: Difference of two proportions
  • 6.3: Testing for goodness of fit using chi-square
  • 6.4: Testing for independence in two-way tables
  • 6: Chapter exercises
  • 6: Labs
  
  
• Chapter 7: Inference for numerical data
  • 7.1: One-sample means with the t-distribution
  • 7.2: Paired data
  • 7.3: Difference of two means
  • 7.4: Power calculations for a difference of means
  • 7.5: Comparing many means with ANOVA
  • 7: Chapter exercises
  • 7: Labs
  
  
• Chapter 8: Introduction to linear regression
  • 8.1: Fitting a line, residuals, and correlation
  • 8.2: Least squares regression
  • 8.3: Types of outliers in linear regression
  • 8.4: Inference for linear regression
  • 8: Chapter exercises
  • 8: Labs
  
  
• Chapter 9: Multiple and logistic regression
  • 9.1: Introduction to multiple regression
  • 9.2: Model selection
  • 9.3: Checking model assumptions using graphs
  • 9.4: Multiple regression case study: Mario Kart
  • 9.5: Introduction to logistic regression
  • 9: Chapter exercises
  • 9: Labs
  
  
• Chapter PJT: Project
  • PJT.1: Project