The advantages of stratified over simple random sampling include stratified sampling
a. ensures adequate representation from each stratum of interest.
b. can produce more precise sample statistics.
c. allows for the comparison of variables between strata.
d. Both b and c.
e. All of the above.
Question 2A researcher divides the population of the purchasers of Brand X into heavy and light users. He then chooses a random sample independently from each group. This is an example of __________ sampling.
a. nonprobability
b. random
c. judgmental
d. stratified
e. quota
Question 3Which of the following is FALSE with regard to stratified sampling?
a. A stratified random sample is drawn by dividing the parent population into mutually exclusive and exhaustive subsets and by then taking a random sample of these subsets.
b. The basic rule by which we stratify suggests the resulting strata should be as homogeneous as possible.
c. When sampling from a population that is highly skewed, a stratified sample generally provides the most representative sample.
d. A market researcher needs information about parts of the population, as well as the total population. He should therefore plan on using a stratified random sample.
e. Stratified samples are more statistically efficient than simple random samples.
Question 4The feature that distinguishes stratified sampling from other forms of probability sampling is that only stratifid samples
a. involve partitioning the parent population into mutually exclusive and exhaustive subsets.
b. allow the a priori determination of the probability that any population element will be included in the sample.
c. make the probability of selection of any population element equal.
d. involve partitioning the parent population into mutually exclusive and exhaustive subsets and selecting a simple random sample from each subset.
e. None of the above.
Question 5In order to use a table of random numbers to draw a simple random sample, the researcher must
a. serially number the sample elements.
b. specify the parent population distribution.
c. calculate the population variance.
d. serially number the population elements.
e. assume that the central limit theorem holds for the population in question.
Question 6Suppose a researcher has constructed a 95 percent confidence interval using the
formula, x-zs_ < < x + zs_. The interval means that
x x
a. there is a 95 probability that the population mean is within the established interval.
b. 95 of the sample values lie within the established interval.
c. 95 of the population values lie within the established interval.
d. 95 of the intervals constructed employing this sampling procedure would contain the true population mean and this interval is hopefully one of those 95 out of 100.
e. 95 of the possible sample means lie within this interval.
Question 7Which of the following is needed before a confidence interval can be established for a population mean?
a. A confidence level
b. A point estimate of the population mean
c. An estimate of the sampling error associated with the sample mean
d. All of the above.
e. Both a and b.
Question 8For simple random sampling, the mean of all possible sample means is equal to the population mean when which of the following conditions is true. Choose the best possible answer.
a. Sampling is with replacement.
b. Sampling is without replacement.
c. Sampling is from an infinite population.
d. Sampling is from a finite population.
e. Sampling is with or without replacement and from an infinite or finite population.
Question 9With a normal distribution, we know that approximately
a. 95 of all observations will fall within one standard deviation of the mean.
b. 99 of all observations will fall within one standard deviation of the mean.
c. 95 of all observations will fall within two standard deviations of the mean.
d. 99 of all observations will fall within two standard deviations of the mean.
e. 95 of all observations will fall within three standard deviations of the mean.
Question 10In essence, the Central Limit Theorem holds that
a. if the sample is large enough, the distribution of sample means will be normal.
b. if the sample is large enough, all sample means will fall within one standard deviation of the population mean.
c. when the variable of interest is normally distributed within the population, the sample mean will be normally distributed.
d. the sample variance will be equal to the population variance if the sample size is large enough.
e. sample sizes larger than n=2 will insure that the distribution of sample means will be normal.
Question 11The sampling distribution of a statistic refers to the
a. distribution of all possible sample values of the statistic that could be drawn from the parent population under the specified sampling plan.
b. distribution of the variable in the parent population.
c. distribution of the variable in a particular sample.
d. spread of the variable in the parent population.
e. unbiased nature of most sample statistics.
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