Data Collection

  • This NASA-HANDBOOK is published by the National Aeronautics and Space Administration (NASA) to provide a Bayesian foundation for framing probabilistic problems and performing inference on these problems. It is aimed at scientists and engineers and provides an analytical structure for combining data and information from various sources to generate estimates of the parameters of uncertainty distributions used in risk and reliability models. The overall approach taken in this document is to give both a broad perspective on data analysis issues and a narrow focus on the methods required to implement a comprehensive database repository.

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  • Dr. Kuan-Man Xu from the NASA Langley Reserach Center writes, "A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. "

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  • This paper comes from researchers at the NASA Langley Research Center and College of William & Mary.  

    "The experience of retinex image processing has prompted us to reconsider fundamental aspects of imaging and image processing. Foremost is the idea that a good visual representation requires a non-linear transformation of the recorded (approximately linear) image data. Further, this transformation appears to converge on a specific distribution. Here we investigate the connection between numerical and visual phenomena. Specifically the questions explored are: (1) Is there a well-defined consistent statistical character associated with good visual representations? (2) Does there exist an ideal visual image? And (3) what are its statistical properties?"

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  • This article gives a brief overview of the role of a biostatistician at NASA.  It also provides names of those one can contact in this area.  

     

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  • This lesson introduces students to creating spreadsheets for statistical analysis.

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  • 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.

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  • 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 our 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 explore the SDC data interface to establish any trends in the dust distribution in the solar system. Students record the number of dust particles, "hits," recorded by the instrument and the average mass of the particles in a given region.

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Mantel-Haenszel estimator of common odds ratio, confounding in logistic regression, univariate/multivariate analysis, bias vs. variance, and simulations.

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Pearson's chi-square; the empirical logit; and prospective, case-control, and cross-sectional studies.

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  • This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Pearson's chi-square; the empirical logit; and prospective, case-control, and cross-sectional studies.

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