This lesson introduces students to creating spreadsheets for statistical analysis.
This lesson introduces students to creating spreadsheets for statistical analysis.
This program focuses on the teamwork required to produce a successful mission and the importance of statistics in project design and management. Using the video and a hands-on lesson, students learn about statistical analysis and how people use statistics, such as mean, median, mode and range, to make decisions. Members of the Penske Racing Team and engineers from Pratt & Whitney Rocketdyne help students investigate the relationship between work, energy and power as they look at race car design, the space shuttle and the International Space Station.
Free statistical calculators online. Our basic statistical calculators will help you in common tasks you might encounter and deal mostly with simple distributions.
The Student Dust Counter is an instrument aboard the NASA New Horizons mission to Pluto, launched in 2006. As it travels to Pluto and beyond, SDC will provide information on the dust that strikes the spacecraft during its 14-year journey across the solar system. These observations will advance human understanding of the origin and evolution of our own solar system, as well as help scientists study planet formation in dust disks around other stars.
In this lesson, students learn the concepts of averages, standard deviation from the mean, and error analysis. Students explore the concept of standard deviation from the mean before using the Student Dust Counter data to determine the issues associated with taking data, including error and noise. Questions are deliberately open-ended to encourage exploration.
The Integrated Medical Model (IMM) is a Monte Carlo simulation-based tool designed to quantify the probability of the medical risks and potential consequences that astronauts could experience during a mission. In this activity, students will use Monte Carlo methods with a TI-Nspire™ to simulate and predict probabilities of CO2 headaches aboard the ISS.
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.
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 presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture overs the following: covariance patterns and generalized estimating equations (GEE).
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture overs the following: conditional logistic regression, conditional likelihood for matched pairs, the non-central hypergeometric, the conditional maximum likelihood estimator (CMLE), conditional confidence interval for odds ratios, and McNemar's statistic.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture overs the following: odds ratio, dependent proportion, marginal homogeneity, McNemar's Test, marginal homogeneity for greater than 2 levels, measures of agreement, and the kappa coefficient (weighted vs. unweighted).