# Resource Library

#### Statistical Topic

Advanced Search | Displaying 121 - 130 of 591
• ### Analysis Tool: The BUGS Project

The BUGS (Bayesian inference Using Gibbs Sampling) project is concerned with flexible software for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods. This site is primarily concerned with the stand-alone WinBUGS 1.4.1 package, which has a graphical user interface and on-line monitoring and convergence diagnostics. This program can be downloaded for free from the site.

• ### Analysis Tool: Confidence Interval Simulation

This Java applet demonstrates confidence intervals for the mean. It allows the user to alter sample size, samples taken, intervals, and the option of standard error. The applet displays sample values, such as average, standard deviation, and percent covered.

• ### Webinar: Putting Your Spotlight on CAUSEweb

Submitting your spotlight presentation from USCOTS 2005 to CAUSEweb is an easy process, and you are in a prime position to submit your work! What better way to have your work showcased than in a peer-reviewed repository of contributions to statistics education? This Webinar from January 2006 provided an opportunity to talk about how to prepare your USCOTS spotlight for submission to CAUSEweb and to discuss the benefits of submission.

• ### Poisson Distribution Calculator

This page calculates probabilities for a Poisson distribution.

• ### Analysis Tool: Binomial Distribution Applet

This applet simulates drawing samples from a binomial distribution. Users set the population proportion of success (pi), sample size (n), and number of samples. By clicking "Draw Samples," the applet will draw a sample and display the corresponding sample histogram. Each new sample drawn is added to the previous ones unless the user clicks "Reset" between samples. Users can choose to display the number and proportion of successes above or below a certain value (tail probabilities) by entering a value in the "Num Successes" box and clicking "Count." The portion of the distribution that meets the condition is highlighted in red, and the proportion of success is given at the bottom of the page. Clicking the inequality sign changes its direction. Clicking "Theo Values" displays the theoretical distribution in green on top of the empirical. Instructions and an activity for this applet can be found in the textbook "Investigating Statistical Concepts, Applications, and Methods" (ISCAM) in Lesson 3.2.2 on page 205.

• ### Analysis Tool: F Distribution Tables

This page provides a table of F distribution probabilities for alpha = 0.10, 0.05, 0.025, and 0.01.

• ### Analysis Tool: Normal Distribution Table

This page provides a z-table with alpha levels from .00 to .09.

• ### Analysis Tool: T-Distribution Table

This page provides a t-table with degrees of freedom 1-30, 60, 120, and infinity and seven levels of alpha from .1 to .0005.

• ### Data Visualization: Plotly

This online software allows you to load data and make professional-looking graphs with it. Graph types are basic (scatterplot, line plot, bar charts, etc.), statistical (histograms, box plots), scientific (error bars, heat map, contour), 3D charts, and financial (e.g. time series). Other graphs are available with the paid pro version. Log in is required, which allows you to upload data and save it for next use.

• ### Being Warren Buffett: a classroom simulation of financial risk (includes Dataset)

March 24, 2009 Activity webinar presented by Nicholas Horton, Smith College, and hosted by Leigh Slauson, Otterbein College. Students have a hard time making the connection between variance and risk. To convey the connection, Foster and Stine (Being Warren Buffett: A Classroom Simulation of Risk and Wealth when Investing in the Stock Market; The American Statistician, 2006, 60:53-60) developed a classroom simulation. In the simulation, groups of students roll three colored dice that determine the success of three "investments". The simulated investments behave quite differently. The value of one remains almost constant, another drifts slowly upward, and the third climbs to extremes or plummets. As the simulation proceeds, some groups have great success with this last investment--they become the "Warren Buffetts" of the class. For most groups, however, this last investment leads to ruin because of variance in its returns. The marked difference in outcomes shows students how hard it is to separate luck from skill. The simulation also demonstrates how portfolios, weighted combinations of investments, reduce the variance. In the simulation, a mixture of two poor investments is surprisingly good. In this webinar, the activity is demonstrated along with a discussion of goals, context, background materials, class handouts, and references (extra materials available for download free of charge)