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  • NASA's Math and Science @ Work presents an activity focused on correlation coefficients, weighted averages and least squares. Students will analyze the data collected from a NASA experiment, use different approaches to estimate the metabolic rates of astronauts, and compare their own estimates to NASA's estimates.

    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.

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  • One health concern that arises when shifting from an environment with gravity to microgravity is the loss of bone mass density. This Math and Science @ Work advanced statistics activity has students analyze two different exercise countermeasures and construct null and alternative hypotheses to determine their relative effectiveness in maintaining bone mineral density.

    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.

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  • NASA's Math and Science @ Work presents a free-response-styled question for advanced high school statistics. Students will evaluate the data from an experiment about astronaut response time. They then will perform hypothesis tests to see if a difference in response times indicates whether one control panel display is preferable to another.

    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.

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  • 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.

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  • 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

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  • 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

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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