The dataset presented in this article contains information on respiratory function and smoking. The data can be used to explore descriptive statistics, graphical analysis, regression, and observational studies. The data are in .dat format.
This article presents a dataset based on an industrial case study using design of experiments. It can be used to discuss sample size, power, statistical significance, interaction terms, Type I and Type II errors, the role and importance of the error term, design of experiments, and analysis of variance.
This article describes a dataset containing information for 25 brands of domestic cigarettes. The dataset can be used to illustrate multiple regression, outliers, and collinearity.
This article presents a dataset containing physical measurements for 507 physically active individuals. These data can be used to demonstrate simple descriptive statistics, least squares and multiple regression, or discriminant and classification analysis. The data are in .dat format.
This Department of Energy website provides weekly average gasoline prices for several regions, states and cities. The averages are produced from a weekly survey of around 800 retail gasoline stations. The site includes information on data collection methods, survey methodology and historical data.
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.
An independent, nonpartisan resource on trends in American public opinion. Gives examples of recent polls, margins of error, questions asked, and sample sizes.
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.
