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Eli Anderson
Eli Anderson

Testing Statistical Hypotheses: Volume I (Sprin...



For all these reasons, formal hypothesis testing of competing forecast models has largely been neglected, and decisions on whether to make model changes are often made in ignorance of whether or not the differences are statistically significant. Guidance is needed on when simple hypothesis tests are appropriate to use and when and if more complex or computationally expensive tests are required.




Testing Statistical Hypotheses: Volume I (Sprin...


Download: https://www.google.com/url?q=https%3A%2F%2Fmiimms.com%2F2uiwxd&sa=D&sntz=1&usg=AOvVaw0b3VPkrdElb06TG3cJtxkX



Nonetheless, competing precipitation forecasts can be tested to evaluate whether improvements are statistically significant. A simple paired t test or Wilcoxon signed-rank test provides an estimate, but when evaluating threat scores these tests may be unduly sensitive to small changes in contingency table elements on dry days and are thus not recommended. The resampling technique operating on a vector of daily contingency table elements is preferred, since the methodology is insensitive to small changes in the contingency table population and is consistent with the way threat score statistics are calculated. For testing differences in RPSS, a simple Wilcoxon signed-rank test or t test is a worthy substitute for a resampling test, but the user should design the test to operate on the daily differences in sums of RPS rather than the daily differences in RPSS.


Electrical engineers study a fair amount of hypothesis testing (as well as estimation) at the graduate level, and there are many textbooks geared towards the applications (signal detection, signal parameter estimation, statistical signal processing) that interest EEs. Many of these books do exceed 300 pages in length (though not all the pages are devoted to hypothesis testing).


Introduction to statistical inference, including descriptive statistics, probability, sampling, estimation, hypothesis testing, and simple regression analysis. Instruction in the use of computer packages.


STAT 391 Quantitative Introductory Statistics for Data Science (4)The basic concepts of statistics, machine learning and data science, as well as their computational aspects. Statistical models, likelihood, maximum likelihood and Bayesian estimation, regression, classification, clustering, principal component analysis, model validation, statistical testing. Practical implementation and visualization in data analysis. Assumes knowledge of basic probability, mathematical maturity, and ability to program. Prerequisite: either CSE 312, or STAT 394/MATH 394 and STAT 395/MATH 395. Offered: Sp.View course details in MyPlan: STAT 391


STAT 441 Multivariate Statistical Methods (4) RSNIntroduces statistical methods for analysis of multidimensional data. Methods include tools for exploratory analysis of high-dimensional data, statistical modeling approaches to parameter estimation and hypothesis testing, and nonparametric methods for classification and clustering. Includes applications in R. Prerequisite: MATH 208; and either STAT 341, STAT 390/MATH 390, or STAT 391. Offered: W.View course details in MyPlan: STAT 441


A course emphasizing concepts and practice of statistical data analysis for health sciences. Basic techniques of descriptive and inferential statistical methods applied to health related surveys and designed experiments. Populations and samples, parameters and statistics; sampling distributions for hypothesis testing and confidence intervals for means and proportions involving one sample, paired samples and multiple independent samples; odds ratios, risk ratios, simple linear regression. Use of statistical software to facilitate the collection, manipulation, analysis and interpretation of health related data.(3 credit hours) Offered: Fall, Spring, Summer.Pr.: Junior standing and equivalent of college algebra or with instructor permission


Application, theory, and computational aspects of resampling methods. Topics include parametric and nonparametric bootstrap methods, the jackknife, and randomization/permutation methods; techniques for estimation, bias correction, confidence intervals, and hypothesis testing; applications to linear and nonlinear models; different test statistics for randomization inferences such as mean differences, rank based statistics, t-statistics, and moderated t-statistics for high-dimensional settings; implementation of methods using statistical software; simulation designs for comparing methods.(3 credit hours) Offered: Spring, even years.Pr.: STAT 713, 771.


Statistical methods for the analysis of large scale data. Data mining, supervised and unsupervised statistical learning techniques for prediction and pattern recognition. Methods for model selection, multiple testing control, and estimation in high-dimensions. Applications in various fields, including the sciences and engineering using computer software.(3 credit hours) Offered: Fall, even years.Pr.: STAT 713 and 771, plus one introductory course in statistical computing (e.g. STAT 726 or equivalent background).


Generalized linear models and generalized mixed models. Statistical models based on the exponential family of distributions. Applications to non-normal and discrete data, including binary, Poisson and gamma regression, and log-linear models. Topics include likelihood-based estimation and testing, model-fitting, residual analyses, over-dispersed models, quasi-likelihood, large sample properties, and the use of computer packages. Also, methods for longitudinal repeated measures data that will include inference for continuous and discrete data. Inferential objectives include prediction of response and estimation of correlation/covariance structures. Nonparametric and semiparametric methods covered as time permits.(3 credit hours) Offered: Fall, even years.Pr.: STAT 861, plus one introductory course in statistical computing (e.g. STAT 725 or 726 or equivalent background).


  • Course Objectives:Provide theoretical basis for multivariate statistical analysis and optimal statisticalhypothesis testing, point and interval estimation.

  • Learn about modern statistical methods of statistical analysis including nonlinear mod-els, data mining, and classification techniques.

  • Get experience in statistical solutions of real-life-high-volume problems, includingshape and image analysis, using statistical package R.

  • Preparation for a career in data analysis and statistical problem solutions.

  • Programming in R is required.



A similar success story is observed for cardiorespiratory fitness. For the cardiorespiratory system, we measured a 6% decrease in VO2peak from preflight to postflight while our previous study of ISS crewmembers found a 15% decrement in aerobic capacity postflight35. Together, these data suggest that current ISS exercise countermeasures (both standard of care and Sprint) provide considerably better protection of musculoskeletal and cardiorespiratory outcomes during long-duration spaceflight than did previous hardware and training protocols. These excellent outcomes allow us to consider, for the first time, whether the exercise countermeasures are sufficient or whether additional optimization is necessary. This raises interesting questions: Do we need to completely mitigate in-flight loss? If not, how much loss is acceptable? Does the amount of loss that would be accepted depend, at least in part, on the initial starting point for that individual or the mission tasks or landing scenarios that individual will be asked to perform? It is now time to shift the paradigm to consider these individual details and view astronauts as a tactical population akin to the military, police, and firefighters36. These operational professions have evolved from endeavoring to simply maintain an arbitrary fitness threshold to testing and preparing personnel to ensure that they are physiologically capable of meeting the demands of their job. In the last 5 years, both the Canadian Armed Forces and the US Army have completely overhauled their fitness for duty standards; both retired test batteries that largely featured tests of muscle and aerobic endurance and in their place adopted standards that inclusively evaluate aerobic fitness, anaerobic fitness, and muscle strength/power37,38. These changes were motivated by the inability of the previous fitness tests to predict performance in the field. In this new perspective that tightly links and subordinates testing and training to job performance, it is easy to envision a primary role for HIT in the preparation and training of soldiers and astronauts alike. Indeed, the developers of these new military fitness standards highlight the direct applicability of high intensity/low volume interval training (in contrast to legacy training that centered on low intensity/high volume exercise) to enhanced health and performance in military personnel39. 041b061a72


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