The applets in this section of Statistical Java allow you to see how the Central Limit Theorem works. The main page gives the characteristics of five non-normal distributions (Bernoulli, Poisson, Exponential, U-shaped, and Uniform).

This resource consists of a Java applet and expository text. The applet is a simulation of the experiment that consists of tossing a coin and then rolling either a red die or a green die, depending on the outcome of the coin toss. The probability of heads and the distributions of the two dice can be specified. The applet illustrates a two-stage experiment.

This course covers the algorithmic and machine learning foundations of computational biology combining theory with practice. We cover both foundational topics in computational biology, and current research frontiers. We study fundamental techniques, recent advances in the field, and work directly with current large-scale biological datasets.

With the growing availability and lowering costs of genotyping and personal genome sequencing, the focus has shifted from the ability to obtain the sequence to the ability to make sense of the resulting information. This course is aimed at exploring the computational challenges associated with interpreting how sequence differences between individuals lead to phenotypic differences in gene expression, disease predisposition, or response to treatment.

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

This concept-building activity contains a set of sequenced simulations for investigating how atoms can be excited to give off radiation (photons). Students explore 3-dimensional models to learn about the nature of photons as "wave packets" of light, how photons are emitted, and the connection between an atom's electron configuration and how it absorbs light. Registered users are able to use free data capture tools to take snapshots, drag thumbnails, and submit responses. This item is part of the Concord Consortium, a nonprofit research and development organization dedicated to transforming education through technology.

This lesson combines conditional probability and combinations to determine the probability of picking a fair coin given that it flipped 4 out of 6 heads. [Probability playlist: Lesson 16 of 29]

The applets in this section allow you to see how different bivariate data look under different correlation structures. The Movie applet either creates data for a particular correlation or animates a multitude data sets ranging correlations from -1 to 1.

You can find statistics in every day life - this example explains how to calculate the chance of a member of a committee is receiving a bribe. This resource is from PUMAS - Practical Uses of Math and Science - a collection of brief examples created by scientists and engineers showing how math and science topics taught in K-12 classes have real world applications.

These games support student development of number sense and the concept of mathematical operations

Using a website simulation tool, students build on their understanding of random processes on networks to interact with the graph of a social network of individuals and simulate the spread of a disease. They decide which two individuals on the network are the best to vaccinate in an attempt to minimize the number of people infected and "curb the epidemic." Since the results are random, they run multiple simulations and compute the average number of infected individuals before analyzing the results and assessing the effectiveness of their vaccination strategies.

Using two different coins and recording the results of both coins helps students dispel this initial misconception as they analyze the graph results. Class discussion should focus on analyzing the data to determine if the game is fair or not. Directions and gameboard are included in the download.

These activities support students as they conceptually develop a sense of how probability affects the outcome of games. Students will find that applying their knowledge of probability will help them win some of the games

Simulate the original experiment that proved that electrons can behave as waves. Watch electrons diffract off a crystal of atoms, interfering with themselves to create peaks and troughs of probability.

This resource consists of a Java applet and expository text. The applet simulates the experiment of rolling a die and then tossing a coin the number of times shown on the die. The die distribution and the probability of heads can be specified. The applet illustrates a two-stage experiment.

A review activity for college probability and statistics. Topics include:~Plausible values for population mean based on a confidence interval~The effects of confidence level on the width/precision of the confidence interval~Notations for sample statistics and population parameters~Comparing p-values to a significance level to make a hypothesis test decision~Evaluating the strength of statistical evidence in a hypothesis test(Thanks to Dr. Justin Grieves, math professor at Charleston Southern University, for providing problems for the activity, the use of his name and picture.)

These activities help students use organized lists and systematic counting to solve combination problems. Map coloring and networks are also discrete math problems that students can relate to real-world applications.

Discrete stochastic processes are essentially probabilistic systems that evolve in time via random changes occurring at discrete fixed or random intervals. This course aims to help students acquire both the mathematical principles and the intuition necessary to create, analyze, and understand insightful models for a broad range of these processes. The range of areas for which discrete stochastic-process models are useful is constantly expanding, and includes many applications in engineering, physics, biology, operations research and finance.

Explore tunneling splitting in double well potentials. This classic problem describes many physical systems, including covalent bonds, Josephson junctions, and two-state systems such as spin 1/2 particles and ammonia molecules.