Stack #65663
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| a parameter is | a number that describes a population
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| a statisctic is | a number that can be computed from sample data without making use of any unknown parameterss
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| two most common statistics are | x (sample mean) and p (p hat)
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| statistics are used to | estimate parameters
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| sampling variablility | sicne differnt samples yield diffren vlaues of the statistic in question, the varaition is called the sampling variable
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| sampling distribution of a statistic | is the distribution of values taken by the statistics in all possible samples of the same size from the same population
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| standard devation of a sample statistic is called | the standard error of the statistic
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| sampling distribtions are described by examining | the sahpe of its graph, the center, its starndard dev, and outliers
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| a measurment porcess is biased if | it systematically overstrates or udnerstats the true value of the variable it attemps to estimate
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| a statistic used to estimate a parameter is unbiased | if the mean of the sampling distribution is equal to the true value of the parameter being estimated
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| the sampling distribution of p hat is | approximately normal and closer to a normal distribution when the sample size n is large
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| normal distributions are used to approximate the sampling distribution of p when both | np>10 and nq > 10
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| the mean of p hat is | p (uphat=p)
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| the satnadrd dev of p hat is | square root of pq/n
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| if a population has a normla distribution then xbar of n independent observations | is normally distributed, has a mean of mu, has a standard dev of sigma/ root n which is smasller than the population satndard dev
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| CLT | draw an srs of size n from an population with mean mu and finite standard deviation. when n is large the sampling sitruibtion of the sample mean x bar is approxiately normal with mean mu and satndard deviation sigma over root n
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Created by:
lilee256