This website is a collection of analysis tools commonly used in statistics and mathematics. These tools are divided into 7 categories: 1) Summarizing Data 2)Computational Probability 3)Requirements for most tests and computations 4) One population and one variable 5)One population and two or more variables 6)Two or three populations and one variable 7) Several populations and one or more variables
This resource defines a pie chart. It also allows the user to input values to create their own graphs. The user has control over the title, up to 15 slices, the color of each slice, and can choose a 3-D option.
A collection of several applets related to probability.
This resource provides two sets of detailed notes on the Bernoulli and Binomial distributions. Additional readings, examples, exercises, and links to applets illustrating the respective distributions are also given.
The sixth chapter of an online Introduction to Biostatistics course. Three sets of lecture notes are provided (only the first one works). Additionally, links for additional reading and exercises with solutions are provided.
The fifth chapter of an online Introduction to Biostatistics course. Three sets of lecture notes are provided. Additionally, links for additional reading and exercises with solutions are provided. A link is also given for a related statistical applet on the normal distribution.
The third chapter of an online Introduction to Biostatistics course. Lecture notes are provided. Additionally, links for additional reading and exercises with solutions are provided.
As quoted on the site, "This website ... offers the possibility to download true random numbers generated using a quantum random number generator upon demand. Users can specify the length of the random number sequence and their upper boundary."
This online, interactive lesson on probability spaces provides examples, exercises, and applets that cover conditional probability, independence, and several modes of convergence that are appropriate for random variables. This section also covers probability space, the paradigm of a random experiment and its mathematical model as well as sample spaces, events, random variables, and probability measures.
This online, interactive lesson on set estimation provides examples, exercises, and applets concerning estimation of the normal model, estimation in the Bernoulli Model, estimation in the two-sample normal model, and Bayesian set estimation.