Test 2 Notes - Section 3.3 Toward Statistical Inference...

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Section 3.3 Toward Statistical Inference Parameter - is a number that describes the population. Statistic - is a number the describes a sample Sampling Distribution - is the distribution of values taken by the statistic in all possible samples of the same size from the same population Bias - concerns the center of the sampling distribution. The reduce bias use random sampling. Variability - describes the spread of the sampling distribution To reduce variability use a larger sample Population Size doesn’t matter: The variability of a statistic from a random sample does not depend on the size of the population as long as the population is at least 100 times larger than the sample 4.1 Randomness *chance behavior is unpredictable in the short run but has a regular and predictable pattern in the long run Probability only describes what happens in the long run Probability is empirical in that it is based on observation rather than theorizing Random - only random is the outcomes are uncertain but there is nonetheless a regular distribution of outcomes in a larger number of repetitions 4.4 The sampling distribution of a sample mean Law of Large Numbers : Draw independent observations at random from any population with finite mean u. As the number of observations drawn increases, the mean (X bar) of the observed values gets closer and closer to the mean u of the population Mean of sample mean - the mean of the sampling distribution of xbar is u. (When we want information about the population
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This note was uploaded on 06/24/2011 for the course STA 309 taught by Professor Gemberling during the Spring '07 term at University of Texas at Austin.

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Test 2 Notes - Section 3.3 Toward Statistical Inference...

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