OPRE 6301 TEST 2 EXAM WITH CORRECT
ACTUAL QUESTIONS AND CORRECTLY
WELL DEFINED ANSWERS LATEST
ALREADY GRADED A+
sampling distribution - ANSWERS-created by, as the name
suggests, sampling
distribution of the sample statistic - like sample mean,
proportion
*central limit theorem - ANSWERS-the larger the sample
size, the more closely the sampling distribution of x-bar will
resemble a normal distribution
if the population is normal, then x-bar is normally
distributed for all values of n
,if the population is non-normal, then x-bar is approximately
normal only for larger values of n
In most practical situations, a sample size of 30 may be
sufficiently large to allow us to use the normal distribution
as an approximation for the sampling distribution of x-bar.
the definition of "sufficiently large" depends on the extent
of nonnormality of x (e.g. heavily skewed; multimodal)
treat any population that is at least 20 times larger than the
sample size as large
*sample mean (x-bar) - ANSWERS-mean of x-bar = mean
stdev of x-bar (aka std error) = stdev/sqrt(n)
stdev^2 of x-bar = stdev^2/n
*sample proportion p̂ - ANSWERS-estimator of a population
proportion of successes
,X is the number of successes, n is the sample size
p̂ mean number of successes = X/n
mean = n
stdev = sqrt((p(1-p))/n)
expected value = p
variance = (p(1-p))/n
x-bar +- z*stdev
*difference between two sample means - ANSWERS-
requires independent random samples be drawn from each
of two normal populations. If this condition is met, then the
sampling distribution of the difference between the two
sample means, i.e.
will be normally distributed.
, if the two populations are not both normally distributed,
but the sample sizes are "large" (>30), the distribution of x-
bar1 - x-bar2 is approximately normal
mean = mean1 - mean2
stdev = sqrt((stdev^2/n1) + stdev^2/n2))
statistical inference - ANSWERS-process by which we
acquire information and draw conclusions about
populations from samples
data >> stats >> information
estimation - ANSWERS-objective is to determine the
approximate value of a population parameter on the basis
of a sample statistic
point estimators and interval estimators
qualities desirable in estimators include unbiasedness,
consistency, and relative efficiency
ACTUAL QUESTIONS AND CORRECTLY
WELL DEFINED ANSWERS LATEST
ALREADY GRADED A+
sampling distribution - ANSWERS-created by, as the name
suggests, sampling
distribution of the sample statistic - like sample mean,
proportion
*central limit theorem - ANSWERS-the larger the sample
size, the more closely the sampling distribution of x-bar will
resemble a normal distribution
if the population is normal, then x-bar is normally
distributed for all values of n
,if the population is non-normal, then x-bar is approximately
normal only for larger values of n
In most practical situations, a sample size of 30 may be
sufficiently large to allow us to use the normal distribution
as an approximation for the sampling distribution of x-bar.
the definition of "sufficiently large" depends on the extent
of nonnormality of x (e.g. heavily skewed; multimodal)
treat any population that is at least 20 times larger than the
sample size as large
*sample mean (x-bar) - ANSWERS-mean of x-bar = mean
stdev of x-bar (aka std error) = stdev/sqrt(n)
stdev^2 of x-bar = stdev^2/n
*sample proportion p̂ - ANSWERS-estimator of a population
proportion of successes
,X is the number of successes, n is the sample size
p̂ mean number of successes = X/n
mean = n
stdev = sqrt((p(1-p))/n)
expected value = p
variance = (p(1-p))/n
x-bar +- z*stdev
*difference between two sample means - ANSWERS-
requires independent random samples be drawn from each
of two normal populations. If this condition is met, then the
sampling distribution of the difference between the two
sample means, i.e.
will be normally distributed.
, if the two populations are not both normally distributed,
but the sample sizes are "large" (>30), the distribution of x-
bar1 - x-bar2 is approximately normal
mean = mean1 - mean2
stdev = sqrt((stdev^2/n1) + stdev^2/n2))
statistical inference - ANSWERS-process by which we
acquire information and draw conclusions about
populations from samples
data >> stats >> information
estimation - ANSWERS-objective is to determine the
approximate value of a population parameter on the basis
of a sample statistic
point estimators and interval estimators
qualities desirable in estimators include unbiasedness,
consistency, and relative efficiency
