Online Statistics Education: A Multimedia Course of Study. Project Leader: David M. Lane, Rice University.

**Content**:

- Introduction
- Graphing Distributions
- Summarizing Distributions
- Describing Bivariate Data
- Probability
- Research Design
- Normal Distributions
- Advanced Graphs
- Sampling Distributions
- Estimation
- Logic of Hypothesis Testing
- Testing Means
- Power
- Regression
- ANOVA
- Transformations
- Chi Square
- Distribution Free Tests
- Effect Size
- Case Studies
- Calculators
- Glossary

*Guess what the correlation is: see here*

The (free) PDF version (660 pages) is available here.The online book also features various calculators (Gaussian distributions etc.) as well as the following simulations:

- Introduction: Sampling, Measurement
- Graphing Distributions: Box Plot
- Summarizing Distributions: Balance Scale, Absolute Difference, Squared Differences, Mean and Median,Variability Demo, Estimating Variance , Comparing Distributions
- Describing Bivariate Data: Guessing Correlations, Restriction of Range
- Probability: Conditional Probability, Gamblers Fallacy, Birthday, Binomial, Bayes' Theorem, Monty Hall Problem
- Normal Distributions: Varieties of Normal Distributions, Normal Approximation
- Sampling Distributions: Basic Demo, Sample Size Demo, Sampling Distributions, Central Limit Theorem
- Estimation: Confidence Interval
- Logic of Hypothesis Testing (none): Testing Means, t Distribution, Robustness , Correlated t Test
- Power: Power 1, Power 2
- Prediction: Linear Fit

ANOVA: One-Way, Power of Within-Subjects Designs - Chi Square: 2 x 2 Table, Testing Distributions

This resource is accessible here.

**Related resources:**

- Central Limit Theorem and Statistical Testing: Beyond the Basics
- Free book: Applied Stochastic Processes

**DSC Resources**

- Invitation to Join Data Science Central
- Free Book: Applied Stochastic Processes
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- Hire a Data Scientist | Search DSC | Classifieds | Find a Job
- Post a Blog | Forum Questions

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central