Resource Library

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.

Using a parameter it's possible to represent a property of an entire population with a single number instead of millions of individual data points. There are a number of possible parameters to choose from such as the median, mode, or interquartile range. Each is calculated in a different manner and illuminates the data from a different point of view. The mean is one of the most useful and widely used and helps us understand populations. A population is simulated by generating 10,000 floating point random numbers between 0 and 10. Sample means are displayed in histograms and analyzed.

This page explores Benford's Law: For naturally occurring data, the digits 1 through 9 do not have equal probability of being the first significant digit in a number; the digit 1 has greater odds of being the first significant digit than the others. This law can be used to catch tax fraud because truly random numbers used by embezzlers do not meet this condition.

This is an example of "growing" a decision tree to analyze two possible outcomes. The tree's branches examine the two possible conditions of employee drug use with corresponding probabilities. This example looks at the final outcome probabilities of being correctly and incorrectly identified versus testing accuracy.

This lesson describes bootstrapping in the context of a statistics class for psychology students.

This short article discusses how the comparative ratios of the tails of normal distributions can result in bias in hiring practices. It contains a link to an applet that shows the comparative tail probability ratios.

This exercise uses descriptive statistics to analyze a data set about how rats respond to rock music vs. classical music.

This site is a tutorial that takes students through a mayoral election process while discussing the concept of randomness. Topics include margin of error and confidence levels.

This website provides data files, examples, guides that are referenced in David Howell's textbook published in 2013. There is also a student manual and links to other useful websites.