Section3.3posted

Section3.3posted - STOR 155, Section 2 Thursday, February...

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STOR 155, Section 2 Thursday, February 25, 2010 Section 3.3
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3.3 Towards statistical inference Parameters and statistics Sampling variability Sampling distributions Bias and variability of a statistic Sampling from large populations Why randomize?
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3.3 Towards statistical inference: Parameters and statistics Example: A SRS of 1140 voters are asked if they think the President is doing a good job. 695 (61%) say ‘yes’ when asked if they think the President is doing a good job. The observed number 61% percent of the sample who say yes – is a statistic . The unknown percent of the population who would say yes is a parameter .
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Parameter : a number that reflects properties of a population. In practice, a parameter is an unknown constant . Statistic : a number that reflects properties of a sample. A statistic is known (computed from a sample), but variable – its value will be different when a different sample is taken. 3.3 Towards statistical inference: Parameters and statistics
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3.3 Towards statistical inference: Notation for parameters and statistics Proportions parameter: unknown proportion in the population who are in some category. Denoted p. statistic: observed proportion in the sample who are in that category. Denoted . Means parameter: unknown mean of some variable in the population. Denoted μ. statistic: observed mean of that variable in the sample. Denoted . Standard deviations
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This note was uploaded on 06/12/2010 for the course STOR 155 taught by Professor Andrewb.nobel during the Spring '08 term at UNC.

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Section3.3posted - STOR 155, Section 2 Thursday, February...

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