a number that describes a population

a number that can be computed from sample data without making use of any unknown parameterss

sampling variablility

sicne differnt samples yield diffren vlaues of the statistic in question, the varaition is called the sampling variable

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

standard devation of a sample statistic is called

the standard error of the statistic

sampling distribtions are described by examining

the sahpe of its graph, the center, its starndard dev, and outliers

a measurment porcess is biased if

it systematically overstrates or udnerstats the true value of the variable it attemps to estimate

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

the sampling distribution of p hat is

approximately normal and closer to a normal distribution when the sample size n is large

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

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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Stack #65663

Question	Answer
a parameter is	a number that describes a population
a statisctic is	a number that can be computed from sample data without making use of any unknown parameterss
two most common statistics are	x (sample mean) and p (p hat)
statistics are used to	estimate parameters
sampling variablility	sicne differnt samples yield diffren vlaues of the statistic in question, the varaition is called the sampling variable
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
standard devation of a sample statistic is called	the standard error of the statistic
sampling distribtions are described by examining	the sahpe of its graph, the center, its starndard dev, and outliers
a measurment porcess is biased if	it systematically overstrates or udnerstats the true value of the variable it attemps to estimate
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
the sampling distribution of p hat is	approximately normal and closer to a normal distribution when the sample size n is large
normal distributions are used to approximate the sampling distribution of p when both	np>10 and nq > 10
the mean of p hat is	p (uphat=p)
the satnadrd dev of p hat is	square root of pq/n
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
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

Created by: lilee256

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