chp 7 stat - workers within a company. 2. A random sample...

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Kyle Daniele Measure Statistic Parameter Mean x bar mu Variance s squared sigma squared Standard Deviation s sigma Proportion p hat p The principles of inferences are to estimate the value of a population parameter and to formulate a decision about the value of a population parameter. Theorem 7.1: Describes the distribution of a particular statistic: namely, the distribution of sample means (x bar). The standard deviation of a statistic is referred to as the standard error of that statistic. The Central Limit Theorem: If x possesses any distribution with mean mu and standard deviation sigma, then the sample mean x bar based on a random sample of size n will have a distribution that approaches the distribution of a normal random variable with mean mu and standard deviation sigma divided by the square root of n as n increases without limit. Pg. 341 1. A population is a set of measurements, either existing or conceptual. Three examples are students in a school, people who’ve climbed Mt. Everest, and
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Unformatted text preview: workers within a company. 2. A random sample is n measurements taken from a population. 3. A population parameter is the numerical descriptive measure. Three examples are the population mean, the population variance, and the population standard deviation. 4. A sample statistic is a numerical descriptive measure that is usually random. Three examples are the sample mean, sample variance, and sample standard deviation. 5. To create a developed guess about the entire populations data. Population parameter inferences are to estimate the value of a population parameter and to formulate a decision about the value of a population parameter. Pg. 348 3. a: No, it is too small. b: Yes, it will be a normal distribution. -1.71<z<.86 = .7615 5. a: x<74.5= z< -.63= .2643 b: x bar < 74.5 = z< -2.79 = .0026 c: No, if the mean of 20 cars were less than 74.5 it slipped out of adjustment....
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This note was uploaded on 12/14/2009 for the course ENG 101 taught by Professor Chang during the Spring '09 term at 東京国際大学.

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