Resource Library

The Neutral Buoyancy Laboratory allows astronauts an atmosphere resembling zero gravity (weightlessness) in order to train for missions involving spacewalks. In this activity, students will evaluate pressures experienced by astronauts and scuba divers who assist them while training in the NBL.  This lesson addresses correlation, regression, residuals, inerpreting graphs, and making predictions.

NASA's Math and Science @ Work project provides challenging supplemental problems for students in advanced science, technology, engineering and mathematics, or STEM classes including Physics, Calculus, Biology, Chemistry and Statistics, along with problems for advanced courses in U.S. History and Human Geography.

Math and Science @ Work presents an activity for high school AP Statistics students. In this activity, students will look at data from an uncalibrated radar and a calibrated radar and determine how statistically significant the error is between the two different data sets.

NASA's Math and Science @ Work project provides challenging supplemental problems for students in advanced science, technology, engineering and mathematics, or STEM classes including Physics, Calculus, Biology, Chemistry and Statistics, along with problems for advanced courses in U.S. History and Human Geography.

This page will perform basic multiple regression analysis for the case where there are several independent predictor variables, X1, X2, etc., and one dependent or criterion variable, Y. Requires import of data from a spreadsheet.

This activity focuses on basic ideas of linear regression. It covers creating scatterplots from data, describing the association between two variables, and correlation as a measure of linear association. After this activity students will have the knowledge to create output that yields R-square, the slope and intercept, as well as their interpretations. This activity also covers some of the basics about residual analysis and the fit of the linear regression model in certain settings. The corresponding data set for this activity, 'BAC data', can be found at the following web address: http://www.causeweb.org/repository/ACT/BAC.txt

This activity explains the important features of a distribution: shape, center, spread, and unusual features. It also covers how to determine the difference between mean and median, and their respective measures of spread, as well as when to apply them to a particular distribution. Graphical displays such as: histograms and boxplots are also introduced in this activity. The corresponding data set for this activity is found at the following web address: http://www.causeweb.org/repository/ACT/food.txt

This activity enables students to learn about confidence intervals and hypothesis tests for a population mean. It focuses on the t-distribution, the assumptions for using it, and graphical displays. The activity also focuses on how to interpretations a confidence interval, a p-value, and a hypothesis test.

This collection of case studies includes the following topics: Stock Prices; Breast Cancer Research; Effect of Fitness Program; Water Use in Los Angeles; Oral Hygiene in the ICS-II project; Brinks vs NYC; Effect of Exercise on Heart Disease; National Assessment of Educational Progress; The London Underground; Suicides of Women and Men; Temperature in San Francisco; Lead Intake; Voting for Johnson; Salaries of Yale Men; K-Mart Cookie Sales; Skeleton Differences between Tribes; Advertising for Detergents; Did Mendel Fudge his Data; Rainfall in the United Kingdom; Jury selection in Alameda County; Racial Bias in Jury Selection: Swain vs Alabama.; Gender Bias in Jury Selection: The Case of Dr. Spock.; The ELISA test for the AIDS Virus.; School Careers in the Netherlands in 1959.; The Northridge Earthquake of January 1994.; The Trial of the Pix.

November 23, 2010 Activity Webinar presented by Stacey Hancock, Reed College, Jennifer Noll, Portland State University, Sean Simpson, Westchester Community College, and Aaron Weinberg, Ithaca College, and hosted by Leigh Slauson, Capital University. Extra materials available for download free of charge. Many instructors ask students to demonstrate the frequentist notion of probability using a simulation early in an intro stats course. Typically, the simulation involves dice or coins, which give equal (and known) probabilities. How about a simulation involving an unknown probability? This webinar discusses an experiment involving rolling (unbalanced) pigs. Since the probabilities are not equal, this experiment also allows the instructor to have students think about the concept of fairness within games.

October 10, 2006 webinar presented By John Holcomb, Cleveland State University, and hosted by Jackie Miller, The Ohio State University. This webinar presents a quick overview of assessment methods related to student writing assignments and data analysis projects. Beginning with short writing assignments, Dr. Holcomb progresses through a range of different approaches to projects at the introductory course level. On-line resources containing existing project ideas will be shown along with ideas for creating one's own projects. The webinar also discusses several approaches to evaluating the range of assignments.

Many introductory Statistics courses consist of two main components: lecture sections and computer laboratory sections. In the computer labs, students often review fundamental course concepts, learn to analyze data using statistical software, and practice applying their knowledge to real world scenarios. Lab time could be better utilized if students arrived with 1) prior exposure to the core statistical ideas, and 2) a basic familiarity with the statistical software package. To achieve these objectives, PreLabs have been integrated into an introductory statistics course. A simple screen capture software (Jing) was used to create videos. The videos and a very short corresponding assignment together form a PreLab and are made available to students to access at appropriate times in the course. Some PreLabs were created to expose the students to statistical software details. Other PreLabs incorporate an available online learning resource or applet which allows students to gain a deeper understanding of a course concept through simulation and visualization. Not all on-line learning resources are ready to use 'as in' in a course. Some may be lacking a preface or description on how they are to be used; others may use slightly different notation or language than your students are accustomed to; a few may even contain an error or item that needs some clarification. One solution to such difficulties was to create a video wrapper so students can see how the applet works while receiving guidance from the instructor. In this webinar we will share the success story of how one introductory Statistics course integrated these video wrappers into the course and the discuss other possible applications.