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Estimation Principles

  • Tips for helping students to take more effective notes during lecture.
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  • This section of ARTIST contains suggestions for implementing student journals, writing assignments, and minute papers in statistics classes. Links to general references for writing assessments are included.
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  • This online, interactive lesson on point estimation provides examples, exercises, and applets concerning estimators, method of moments, maximum likelihood, Bayes estimators, best unbiased estimators, and sufficient, complete and ancillary statistics.
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  • This online, interactive lesson on set estimation provides examples, exercises, and applets concerning estimation of the normal model, estimation in the Bernoulli Model, estimation in the two-sample normal model, and Bayesian set estimation.

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  • This set of pages describes software the author wrote to implement bootstrap and resampling procedures. It also contains an introduction to resampling and the bootstrap; and examples applying these procedures to the mean, the median, correlation between two groups, and analysis of variance.
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  • This set of pages is an introduction to Maximum Likelihood Estimation. It discusses the likelihood and log-likelihood functions and the process of optimizing.
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  • This page introduces the definition of sufficient statistics and gives two examples of the use of factorization to prove sufficiency.
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  • This page introduces the Cramer-Rao lower bound, discusses it's usefulness, and proves the inequality.
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  • This file applies the Cramer-Rao inequality to a binomial random variable to prove that the usual estimator of p is a minimum variance unbiased estimator.
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  • These slides address point estimation including unbiasedness and efficiency and the Cramer-Rao lower bound.
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