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An introduction to Bootstrap Methods Outline Monte Carlo
An introduction to Bootstrap Methods Outline Monte Carlo

Understanding Your Data Set
Understanding Your Data Set

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Power and Sample Size for the Two-sample t
Power and Sample Size for the Two-sample t

... Power is the probability that a study will reject the null hypothesis. The estimated probability is a function of sample size, variability, level of significance, and the difference between the null and alternative hypotheses. Similarly, the sample size required to ensure a pre-specified power for a ...
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... Means of Two Populations: Large-Sample Case Par, Inc. is a manufacturer of golf equipment and has developed a new golf ball that has been designed to provide “extra distance.” In a test of driving distance using a mechanical driving device, a sample of Par golf balls was compared with a sample of go ...
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... by the U.S. Food and Drug Administration (FDA), and the FDA requires the manufacturers to demonstrate through the use of animal studies and controlled clinical trials the safety and effectiveness of their product. These studies must be conducted using valid statistical methods. So any medical invest ...
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Exposure Assessment: Tolerance Limits, Confidence Intervals and

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%YAMGAST: Yet Another Macro to Generate a Summary Table

... There are many published macros that generate a summary table for clinical study report. All of these macros require users to define both the type of descriptive statistics and the method of statistical test. However for a fixed sample size, when the type of descriptive statistics is chosen, there i ...
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... sample, i.e., for all ‘missing’ data. (Note: For model-assisted design-based sampling, we make use of ‘auxiliary data.’ These same data used for strictly model-based or cutoff sampling are called ‘regressor data.’) For cutoff sampling, the idea is to use regression to estimate for data that could no ...
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Chapter 6: Confidence Intervals

Estimating and Finding Confidence Intervals - TI Education
Estimating and Finding Confidence Intervals - TI Education

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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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