Resource Library

This worksheet activity teaches random sampling and theoretical probabilities by simulating the effects of randomly assigning newborn babies to their mothers. Students will perform trials and keep track of results, then use the information to deduce properties of random sampling. The relation website is an applet that simulates the process automatically.

This is a collection of activities as Java applets that can be used to explore probability and statistics. Each activity is supplemented with background information, activity instructions, and a curriculum for the activity.

This site provides applets, lessons, and objectives for learning about conditional probability. The applet activity introduces multiple-outcomes events and computing probabilities.

This virtual applet simulates randomly drawing numbers from a box. You can choose which numbers you would like to choose from and the number of draws. The applet has the option to show theoretical probability and displays the results in histogram form.

This is a virtual applet, which models repeaded coin tossing by a random number generator. It allows you to change the number of tosses as well as runs and records your results.

This is a virtual spinner applet, which allows you to change spinner regions and the number of spins. It records the results and displays the data in a histogram.

An independent, nonpartisan resource on trends in American public opinion. Gives examples of recent polls, margins of error, questions asked, and sample sizes.

This lesson plan uses the Birthday Paradox to introduce basic concepts of probability. Students run a Monte Carlo simulation using the TI-83 graphing calculator to generate random dates, and then search for matching pairs. Students also perform a graphical analysis of the birthday-problem function. Key Words: Permutations; Explicit Function; Recursive Function; Modeling.

This page provides an example of pseudo random number generators (PRNG) creating spread spectrum broadcasts and signals for encryption and decryption of wireless transmissions.

Pseudo random number generators (PRNG) start with a seed value and will eventually repeat all the numbers they generate in exactly the same order. Putting in the same seed value will give precisely the same set of random numbers. On large scale Monte Carlo simulations (depends on generation of multiple random numbers), care has to be taken to make sure that the PRNG cycle is significantly longer than the quantity of random numbers needed or the pattern in the PRNG cycle can show up as an error producing pattern in the simulation results.