ch 7 test 1 prep qmb3200 ucf
The sample mean is the point estimator of:
μ.
The sample statistic characteristic s is the point estimator of:
σ.
Which of the following is(are) true?
II. The standard deviations of two different samples from the same population may be the same. III. Statistical inferences can be used to draw conclusions about the populations based on sample data.
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.
Which of the following is a nonprobability sampling technique?
Judgment sampling
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 random sample of 121 bottles of cologne showed an average content of 4 ounces. It is known that the standard deviation of the contents (i.e., of the population) is .22 ounces. In this problem, the value .22 ounces is:
a parameter.
The medical director of a company looks at the medical records of all 50 employees and finds that the mean systolic blood pressure for these employees is 126.07. The value of 126.07 is:
a parameter.
Cluster sampling is:
a probability sampling method.
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.
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 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 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.
For a(n) _____ , it is impossible to construct a sampling frame.
infinite population
When drawing a sample from a population, the goal is for the sample to:
match the targeted population.
A simple random sample of size n from a finite population of size N is a sample selected such that each possible sample of size:
n has the same probability of being selected.
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.
In computing the standard error of the mean, the finite population correction factor is used when:
n/N>.05
Convenience sampling is a:
nonprobability sampling technique.
A sample of 92 observations is taken from an infinite population. The sampling distribution of xbar is approximately:
normal because of the central limit theorem.
Parameters are:
numerical characteristics of a population.
A doctor would like to determine if there is a difference between the blood pressure of people who walk every day for 60 minutes and those who walk one day per week for 60 minutes. Fifty of her patients who report that they have routinely walked 60 minutes every day for the past two years and 50 who report that they have walked 60 minutes only one day per week will be identified. The doctor will examine their medical records and collect their blood pressure readings over this two-year period. This is an example of a(n):
observational study.
Sample statistics, such as x̅ , s, or p̅, that provide the point estimate of the population parameter are known as:
point estimators.
The sampling distribution of pbar is the:
probability distribution of all possible values of the sample proportion.
In stratified random sampling:
randomly selected elements within each of the strata form the sample.
In a recent Gallup Poll, the decision was made to increase the size of its random sample of voters from 1500 people to about 4000 people. The purpose of this increase is to:
reduce the standard error of the estimate.
Doubling the size of the sample will:
reduce the standard error of the mean.
Which of the following is a point estimator?
s
Which of the following is not a symbol for a parameter?
s
The value of the _____ is used to estimate the value of the population parameter.
sample statistic
The probability distribution of all possible values of the sample proportion is the:
sampling distribution of pbar .
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.
As the sample size increases, the:
standard error of the mean decreases.
The standard deviation of a point estimator is called the:
standard error.
A probability sampling method in which we randomly select one of the first k elements and then select every kth element thereafter is:
systematic sampling.
In a survey of public opinion concerning state aid to a particular city, every 40th person registered as a voter was interviewed, beginning with a person selected at random from among the first 40 listed. This is an example of:
systematic sampling.
The population we want to make inferences about is called the:
target population.
A simple random sample of size n from an infinite population of size N is to be selected. Each possible sample should have:
the same probability of being selected.
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.