Chapter 7 LearnSmart

Random samples of size 400 are taken from a population whose population proportion is 0.25. The expected value of the sample proportion is A. 0.02 B. 400 C. 0,75 D. 0.25

D. 0.25

A population has a mean of 50 and a standard deviation of 10. A random sample of 144 is selected. The expected value of x-bar is equal to _________. A. 144 B. 10 C. 0.625 D. 50

D. 50

The variance of the sample mean is represented by:

sigma^2/n

The expected value of the sample mean is represented by:

mu

The probability distribution of the sample mean is commonly referred to as: A. sampling distribution of mu B. sampling distribution of x-bar C. sampling distribution of p-bar D. sampling distribution of sigma

B. sampling distribution of x-bar

Nonresponse bias occurs when A. the population has been divided into strata B. those responding to a survey or poll differ systematically from the non-respondents C. portions of the population are excluded from the consideration of the sample D. cluster sampling is used instead of stratified random sampling

B. those responding to a survey or poll differ systematically from the non-respondents

Stratified sampling is preferred to cluster sampling when the objective is A. to access every possible individual in the population B. to increase precision C. to reduce costs

B. to increase precision

The expected value of X-bar is equal to A. x-bar B. sigma C. x D. mu

D. mu

How is the standard deviation of the mean represented by:

sigma/*srt*n

Which of the following is considered an estimate? A. mu=5.2 B. sigma^2=10 C. sigma=3.7 D. x-bar=20

D. x-bar=20

The central limit theorem states that, for any distribution, a n gets larger, the sampling distribution of the sample mean becomes A. larger B. smaller C. closer to a normal distribution D. more spread out than a normal distribution

C. closer to a normal distribution

The central limit theorem states that, for any distribution, as n gets larger, the sampling distribution of the sample mean becomes A. more spread out than a normal distribution B. smaller C. closer to a normal distribution D. larger

C. closer to a normal distribution

The standard deviation of the sampling distribution of x-bar is defined as: A. sigma^2/n B. mu C. sigma/*sqrt*n D. sigma

C. sigma/*sqrt*n

The standard deviation of x-bar is calculated as A. the positive square root of the variance of x-bar divided by "n" B. the expected value of x C. the positive square root of the variance of x-bar D. the square of the variance of x

C. the positive square root of the variance of x-bar

A population has a mean of 50 and a standard deviation of 10. A random sample of 144 is selected. The expected value of x-bar is equal to ______. A. 10 B. 0.625 C. 50 D. 144

C. 50

If the population from which the sample is drawn is normally distributed, then the sampling distribution of the sample mean is A. unknown B. never normally distributed C. always normally distributed D. only sometimes normally distributed

C. always normally distributed

A sample statistic is considered biased if A. it systematically over-or under-estimates the unknown parameter being estimated B. it approaches the unknown population parameter being estimated as the sample size grows larger C. its expected value equals the unknown parameter being estimated D. its standard error is lower than that of any other

A. it systematically over-or under-estimates the unknown parameter being estimated

For any population portion p, the sampling distribution of the sample proportion is approximately normally distributed if A. np>(or equaled to) 5 and n(1-p) >(or equaled to) 5 B. np>(or equaled to) 30 and n(1-p)>(or equaled to) 30 C. n>(or equaled to)30 D. np >(or equaled to) 10 and n(1-p) >(or equaled to) 10

A. np>(or equaled to) 5 and n(1-p) >(or equaled to) 5

A sample of n observations that have the same probability of being selected from the population as any other sample of n observations is called a(n) ________. A. simple random sample B. biased sample C. cluster sample D. stratified sample

A. simple random sample

The standard error of p-bar equals: A. *sqrt* p(1-p)/n B. *sqrt* p(1+p)/n-1 C. *sqrt* p(1+p)/n D. *sqrt* p(1-p)/n-1

A. *sqrt* p(1-p)/n

Random samples of size 100 are taken from a population whose proportion is 0.40. The expected value of the sample proportion is A. 0.40 B. 0.04 C. 0.004 D. 40

A. 0.40

If X is normally distributed with expected value mu and a standard deviation sigma, the x-bar is normally distributed with A. expected value mu and standard deviation sigma/*sqrt*n B. expected value of x-bar and a standard deviation sigma/*sqrt*n C. expected value mu and standard deviation sigma D. expected value x-bar and standard deviation sigma

A. expected value mu and standard deviation sigma/*sqrt*n

What is a primary requirement for a "good" sample? A. it is representative of the population we are trying to describe B. it is easy to analyze C. it proves our hypothesis about the population

A. it is representative of the population we are trying to describe

The variance of x-bar, which is equal to sigma^2/n, is A. smaller than the variance of the individual observation of sigma^2 B. larger than the variance of the individual observation of sigma^2 C. equal to the variance of the individual observation sigma^2

A. smaller than the variance of the individual observation of sigma^2

A population has a mean of 50 and a standard deviation of 10. A random sample of 256 is selected. The standard error of x bar is equal to A. 50 B. 0.625 C. 144 D. 10

B. 0.625

A population has a mean of 50 and a standard deviation of 10. A random sample of 144 is selected. The expected value of x-bar is equal to _______. A. 0.625 B. 50 C. 10 D. 144

B. 50

The sample size required to approximate the normal distribution depends on A. the magnitude of the mean B. how much the population varies from normality C. the magnitude of the standard deviation

B. how much the population varies from normality

A population consists of all items of interest in a statistical problem, whereas a __________ is a subset of the population. A. probability B. sample C. parameter D. census

B. sample

True or false: If we had access to data that included the entire population, then the values of the parameters would be known and no statistical inference would be required.

True If you have data that describes the entire population, then there is no need to make inferences using sample data.

Random samples of size 400 are taken from a population whose population proportion is 0.25. The expected value of the sample proportion is A. 0.75 B. 400 C. 0.25 D. 0.02

C. 0.25

A population has a mean 100 and a standard deviation of 10. A random sample of 25 is selected. The expected value of x-bar is equal to ______. A. 2 B. 100 C. 100 D. 10

C. 100

A population has a mean of 100 and a standard deviation of 10. A random sample of 25 is selected. The expected value of x-bar is equal to ________. A. 10 B. 2 C. 100 D. 25

C. 100

In general, the variability between sample mean is ________ the variability between observations. A. equal to B. more than C. biased when compared to D. less than

D. less than

As the sample size increases, the shape of the sampling distribution p-bar becomes: A. less normal B. less symmetric C. more skewed D. more normal

D. more normal

If p-bar is the value that a normal random variable assumes, then we can transform it into its standard normal value as A. z= p-p-bar/p-bar B. z= p-bar-p/p C. p-p-bar/*sqrt*p-bar(1-p-bar)/n D. p-bar-p/*sqrt*p(1-p)/n

D. p-bar-p/*sqrt*p(1-p)/n

We use a calculated sample _____ to make inferences about an unknown population _____. A. parameter, statistic B. statistic, statistic C. parameter, parameter D. statistic, parameter

D. statistic, parameter