Estimation Principles

  • This free online video program "lays out the parts of the confidence interval and gives an example of how it is used to measure the accuracy of long-term mean blood pressure. An example from politics and population surveys shows how margin of error and confidence levels are interpreted. The program also explains the use of a formula to convert the z* values into values on the sampling distribution curve. Finally, the concepts are applied to an issue of animal ethics."
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  • This webpage provides instructions for teaching confidence intervals using Sampling SIM software. It includes information regarding prerequisite knowledge, common misconceptions, and objectives, as well as links to an activity and a pre/post-test.
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  • This introductory tutorial for SPSS 10.1 and 11.0 for Windows explains how to enter and summarize data and groups of data and to generate graphs.
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  • This activity guides students through the process of checking the validity of data, performing summary analysis, constructing box plots, and determining whether significant differences exist. The data comes from a study of mineral levels in older adults and is available in Minitab, Excel, SAS, and text formats.
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  • 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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