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   DIR Return to: Probability and Statistics
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       #Post#: 77--------------------------------------------------
       Statistics Glossary
   DIR By: Dragon Hellfire
       Date: March 26, 2019, 1:44 am
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       Bootstrapping: Taking repeated Simple Random Samples With
       Replacement from the sample.
       Census: A sample that consists of each element of the population
       once.
       Central Limit Theorem: Estimators follow an approximately normal
       distribution
       Confidence Interval: Were the procedure repeated on multiple
       samples, the calculated confidence interval will contain the
       true parameter (1-alpha)% of the time.
       Finite Population Correction: Typically (N-n)/N; A measure of
       precision corresponding to taking a high proportion of samples.
       Estimator: A method of calculating a statistic.
       Parameter: A quantity that characterizes a population.
       Post-Stratified Sample: A Sampling Design such that sampling
       units are stratified after being collected via Simple Random
       Sampling.
       Sampling Design: A function P(S) that gives the probability of
       drawing a sample.
       Sampling Distribution: The distribution of values a sample
       statistic can take.
       Sampling Unit: An element that can be parsed by the sampling
       design.
       Simple Random Sample: A Sampling Design such that each sample
       has an equal probability of being selected/drawn.
       - With Replacement: Each sampling unit can be drawn more than
       once.
       - Without Replacement: Each sampling unit can only be drawn
       once.
       Standard Error: The square root of the variance of a statistic.
       The Standard Deviation of the Sampling Distribution.
       Stratified Random Sample: A Sampling Design where the sampling
       units are divided into strata and each stratum is independently
       sampled using Simple Random Sampling.
       Systematic Sample: A Sampling Design such that a random starting
       location determines the entire sample.
       Unbiased Estimator: An estimator such that the Expected Value of
       the estimator is its corresponding parameter.
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