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Unit 14: Nonparametric Statistical Methods
Unit 14: Nonparametric Statistical Methods

... • Most methods studied so far have been based on the assumption of normally distributed data – Frequently this assumption is not valid – Sample size may be too small to verify it ...
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A Survey of Exact Inference for Contingency Tables Alan Agresti Statistical Science

... which the procedures can be applied. For some basic analyses of contingency tables, it is unnecessary to use large-sample approximations to sampling distributions when their adequacy is in doubt. This article surveys the current theoretical and computational developments of exact methods for conting ...
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manuscript.v7 - Royal Holloway, University of London

... others, only predicting the label of testing sample is not enough, the probability estimation, which measure our trust in the prediction, should also be provided since it provides important information for risk control. Probabilistic prediction methods can help solve the problem. Several classical p ...
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... Example: (Analogy from Cartoon Guide to Statistics) Consider an archer shooting at a target. Suppose she hits the bulls eye (a 10 cm radius) 95% of the time. In other words, she misses the bulls eye one out of 20 arrows. Sitting behind the target is another person who can't see the bull's eye. The a ...
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Chapt. 1 of the Text - HSU Users Web Pages

... observational because the group status (sex of the employee) was, obviously, not decided by the investigator. Causal Conclusions and Confounding Variables Can statistical analysis alone be used to establish causal relationships? The answer is simple and concise: Statistical inferences of cause-and-e ...
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Average Task Times in Usability Tests: What to Report?

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A Mixture of Generalized Negative Binomial Distribution with

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Resampling (statistics)

In statistics, resampling is any of a variety of methods for doing one of the following: Estimating the precision of sample statistics (medians, variances, percentiles) by using subsets of available data (jackknifing) or drawing randomly with replacement from a set of data points (bootstrapping) Exchanging labels on data points when performing significance tests (permutation tests, also called exact tests, randomization tests, or re-randomization tests) Validating models by using random subsets (bootstrapping, cross validation)Common resampling techniques include bootstrapping, jackknifing and permutation tests.
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