QMB 3200 Final Practice Questions
and Answers (A+ Score)
• Which of the following statements regarding the sampling distribution of sample means
is incorrect? -✓✓The standard deviation of the sampling distribution is the standard
deviation of the population.
• A simple random sample of size n from an infinite population is a sample selected
such that -✓✓each element is selected independently and is selected from the same
population
• The fact that the sampling distribution of sample means can be approximated by a
normal probability distribution whenever the sample size becomes large is based on the
-✓✓central limit theorem.
• Cluster sampling is -✓✓a probability sampling method.
• The central limit theorem states that -✓✓if the sample size n is large, then the
sampling distribution of the sample mean can be approximated by a normal distribution.
• The value of the _____ is used to estimate the value of the population parameter -
✓✓sample statistic
• The sampling distribution of is the -✓✓probability distribution of all possible values of
the sample proportion.
• Which of the following is not a symbol for a parameter? -✓✓S.
• The sample statistic characteristic s is the point estimator of -✓✓σ..
• The distribution of values taken by a statistic in all possible samples of the same size
from the same population is called a -✓✓sampling distribution.
• Which of the following is a point estimator? -✓✓S.
• As a rule of thumb, the sampling distribution of the sample proportion can be
approximated by a normal probability distribution when -✓✓n(1 - p) ≥ 5 and np ≥ 5.
• A sample of 92 observations is taken from an infinite population. The sampling
distribution of is approximately -✓✓normal because of the central limit theorem.
, • The central limit theorem is important in Statistics because it enables reasonably
accurate probabilities to be determined for events involving the sample average -
✓✓when the sample size is large regardless of the distribution of the variable.
• The distribution of values taken by a statistic in all possible samples of the same size
from the same population is the sampling distribution of -✓✓The sample
• Which of these best describes a sampling distribution of a statistic? -✓✓It is the
distribution of all of the statistics calculated from all possible samples of the same
sample size.
• The probability distribution of all possible values of the sample proportion is the -
✓✓sampling distribution of p.
• For a fixed confidence level and population standard deviation, if we would like to cut
our margin of error in half, we should take a sample size that is -✓✓four times as large
as the original sample size.
• We can reduce the margin of error in an interval estimate of p by doing any of the
following except -✓✓increasing the planning value p* to .5.
• When computing the sample size needed to estimate a proportion within a given
margin of error for a specific confidence level, what planning value of p should be used
when no estimate of p is available? -✓✓0.50
• A statistics teacher started class one day by drawing the names of 10 students out of a
hat and asked them to do as many pushups as they could. The 10 randomly selected
students averaged 15 pushups per person with a standard deviation of 9 pushups.
Suppose the distribution of the population of number of pushups that can be done is
approximately normal. Which of the following statements is true? -✓✓A t distribution
should be used because σ is unknown.
• In interval estimation, as the sample size becomes larger, the interval estimate -
✓✓becomes narrower.In general, higher confidence levels provide larger confidence
intervals.
• One way to have high confidence and a small margin of error is to -✓✓increase the
sample size.
• From a population that is normally distributed, a sample of 30 elements is selected and
the standard deviation of the sample is computed. For the interval estimation of μ, the
proper distribution to use is the -✓✓t distribution with 29 degrees of freedom.
• As the number of degrees of freedom for a t distribution increases, the difference
between the t distribution and the standard normal distribution -✓✓becomes smaller.
and Answers (A+ Score)
• Which of the following statements regarding the sampling distribution of sample means
is incorrect? -✓✓The standard deviation of the sampling distribution is the standard
deviation of the population.
• A simple random sample of size n from an infinite population is a sample selected
such that -✓✓each element is selected independently and is selected from the same
population
• The fact that the sampling distribution of sample means can be approximated by a
normal probability distribution whenever the sample size becomes large is based on the
-✓✓central limit theorem.
• Cluster sampling is -✓✓a probability sampling method.
• The central limit theorem states that -✓✓if the sample size n is large, then the
sampling distribution of the sample mean can be approximated by a normal distribution.
• The value of the _____ is used to estimate the value of the population parameter -
✓✓sample statistic
• The sampling distribution of is the -✓✓probability distribution of all possible values of
the sample proportion.
• Which of the following is not a symbol for a parameter? -✓✓S.
• The sample statistic characteristic s is the point estimator of -✓✓σ..
• The distribution of values taken by a statistic in all possible samples of the same size
from the same population is called a -✓✓sampling distribution.
• Which of the following is a point estimator? -✓✓S.
• As a rule of thumb, the sampling distribution of the sample proportion can be
approximated by a normal probability distribution when -✓✓n(1 - p) ≥ 5 and np ≥ 5.
• A sample of 92 observations is taken from an infinite population. The sampling
distribution of is approximately -✓✓normal because of the central limit theorem.
, • The central limit theorem is important in Statistics because it enables reasonably
accurate probabilities to be determined for events involving the sample average -
✓✓when the sample size is large regardless of the distribution of the variable.
• The distribution of values taken by a statistic in all possible samples of the same size
from the same population is the sampling distribution of -✓✓The sample
• Which of these best describes a sampling distribution of a statistic? -✓✓It is the
distribution of all of the statistics calculated from all possible samples of the same
sample size.
• The probability distribution of all possible values of the sample proportion is the -
✓✓sampling distribution of p.
• For a fixed confidence level and population standard deviation, if we would like to cut
our margin of error in half, we should take a sample size that is -✓✓four times as large
as the original sample size.
• We can reduce the margin of error in an interval estimate of p by doing any of the
following except -✓✓increasing the planning value p* to .5.
• When computing the sample size needed to estimate a proportion within a given
margin of error for a specific confidence level, what planning value of p should be used
when no estimate of p is available? -✓✓0.50
• A statistics teacher started class one day by drawing the names of 10 students out of a
hat and asked them to do as many pushups as they could. The 10 randomly selected
students averaged 15 pushups per person with a standard deviation of 9 pushups.
Suppose the distribution of the population of number of pushups that can be done is
approximately normal. Which of the following statements is true? -✓✓A t distribution
should be used because σ is unknown.
• In interval estimation, as the sample size becomes larger, the interval estimate -
✓✓becomes narrower.In general, higher confidence levels provide larger confidence
intervals.
• One way to have high confidence and a small margin of error is to -✓✓increase the
sample size.
• From a population that is normally distributed, a sample of 30 elements is selected and
the standard deviation of the sample is computed. For the interval estimation of μ, the
proper distribution to use is the -✓✓t distribution with 29 degrees of freedom.
• As the number of degrees of freedom for a t distribution increases, the difference
between the t distribution and the standard normal distribution -✓✓becomes smaller.
