Statistical Inference & Techniques

  • 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.

    View Resource
    0
    No votes yet

  • 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.

    View Resource
    0
    No votes yet

  • 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.

    View Resource
    0
    No votes yet

  • 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.

    View Resource
    0
    No votes yet

  • 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). 

    View Resource
    0
    No votes yet

  • 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.

    View Resource
    0
    No votes yet

  • 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).

    View Resource
    0
    No votes yet

  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: sparse tables, sampling zeros, structural zeros, and log-linear model (and limitations).

    View Resource
    0
    No votes yet

  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: partial/conditional tables, confounding, types of independence (mutual, joint, marginal, and conditional), identifiability constraints, partial odds ratios, hierarchical log-linear model, pairwise interaction log-linear model, conditional independence log-linear model, goodness of fit, and model building.

    View Resource
    0
    No votes yet

  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: conditional independence, log-linear models for 2x2 tables, expected counts, logistic regression, odds ratio, parameters of interest for different designs and the MLEs, poisson log-linear model, double dichotomy, the multinomial, and the multinomial log-linear model.

    View Resource
    0
    No votes yet

Pages