https://seeing-theory.brown.edu/index.html

### Chapter 1

This chapter is an introduction to the basic concepts of probability theory.

Chance Events Expectation Variance

This chapter discusses further concepts that lie at the core of probability theory.

Set Theory Counting Conditional Probability

A probability distribution specifies the relative likelihoods of all possible outcomes.

Go to Probability Distributions

Random Variables Discrete and Continuous Central Limit Theorem

Frequentist inference is the process of determining properties of an underlying distribution via the observation of data.

Point Estimation Interval Estimation The Bootstrap

Bayesian inference techniques specify how one should update one’s beliefs upon observing data.

Bayes' Theorem Likelihood Prior to Posterior

Regression Analysis is an approach for modeling the linear relationship between two variables.

Ordinary Least Square Correlation Analysis of Variance

Seeing Theory was created by Daniel Kunin while an undergraduate at Brown University. The goal of this website is to make statistics more accessible through interactive visualizations (designed using Mike Bostock’s JavaScript library D3.js).

The Team

Awards & Press

We are currently working on a textbook for Seeing Theory. Download a draft of our pdf below. You can provide feedback on our writing here.