The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. The inaugural effort is OpenIntro Statistics. Probability is optional, inference is key, and we feature real data whenever possible.
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- 1 Introduction to Data
- 1.1 Case study
- 1.2 Data basics
- 1.3 Overview of data collection principles
- 1.4 Observational studies and sampling strategies
- 1.5 Experiments
- 1.6 Examining numerical data
- 1.7 Considering categorical data
- 1.8 Case study: gender discrimination
- 2. Probability (special topic)
- 2.1 Defining probability
- 2.2 Conditional probability
- 2.3 Sampling from a small population
- 2.4 Random variables
- 2.5 Continuous distributions
- 3. Distributions of random variables
- 3.1 Normal distribution
- 3.2 Evaluating the normal approximation
- 3.3 Geometric distribution
- 3.4 Binomial distribution
- 3.5 More discrete distributions
- 4. Foundations for inference
- 4.1 Variability in estimates
- 4.2 Confidence intervals
- 4.3 Hypothesis testing
- 4.4 Examining the Central Limit Theorem
- 4.5 Inference for other estimators
- 4.6 Sample size and power
- 5. Inference for numerical data
- 5.1 Paired data
- 5.2 Difference of two means
- 5.3 One-sample means with the t distribution
- 5.4 The t distribution for the difference of two means
- 5.5 Comparing many means with ANOVA
- 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
- 6.5 Small sample hypothesis testing for a proportion
- 6.6 Hypothesis testing for two proportions
- 7. Introduction to linear regression
- 7.1 Line fitting, residuals, and correlation
- 7.2 Fitting a line by least squares regression
- 7.3 Types of outliers in linear regression
- 7.4 Inference for linear regression
- 8. Multiple and logistic regression
- 8.1 Introduction to multiple regression
- 8.2 Model selection
- 8.3 Checking model assumptions using graphs
- 8.4 Logistic regression
- MyOpenMath online homework. MyOpenMath is a free online homework system, built on the open source IMathAS assessment platform. It provides randomized, algorithmically generated homework with automated grading of numerical and algebraic answers, similar to WebAssign and MyMathLab. It also provides a course management system with gradebook, file posting, discussion forums, etc. Assessment sets have been created for this textbook, which may be available for self-study by students, or can copied as a starter course shell by faculty.
MyOpenMath use is a provided free by Lumen Learning. Community support may be available through faculty support forums. For direct support, course development and support service contracts are available from Lumen.
For Washington State faculty, the WAMAP.org site also mirrors this content.
- Videos. See the comments below for details.
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Notes: There are videos and slides for some sections and some R-based labs on their website.