BUS 308 Week 2 Quiz
1.
Question :
The one-sample t-test differs from the z-test in which way?
There are no parameter values involved in a t-test.
The t-test is more sensitive to minor differences between sample and population.
With the t-test one can be confident of the normality of the data.
The t-test requires no <known> parameter standard error of the mean.
Question 2.
If a certifying agency raises the requirements for real estate agents, what sort of decision error is the agency protecting against?
Student Answer:
Type I
Type II
Type III
Type IV
Question 3.
What is the alternate hypothesis in a problem where sales group two is predicted to be “. . . significantly less productive than sales group one?”
HA: μ1≠ μ 2
HA: μ 1= μ 2
HA: μ 1> μ2
HA: μ 1< μ 2
Question 4.
Which of the following defines statistical significance?
The outcome is unlikely to have occurred by chance.
The outcome is important.
The outcome is unusual.
The outcome is one that can be explained by normal circumstances.
Question 5.
The standard error of the mean can be calculated by dividing μ by the square root of the number of values in the distribution.
Question 6.
How does variability in the distribution of sample means compare to variability in a population based on individual scores?
Samples tend to vary less than individual scores.
Samples exaggerate differences among scores.
Individual scores tend to be more stable over time than samples.
Sample means vary less than individual scores.
Question 7.
The z-test can be used to test mean differences even when the initial data set is not normally distributed.
True
False
Question 8.
What is the advantage of a one-tailed test over a two-tailed test?
Less data variability in the groups involved.
Smaller critical values indicate significance.
Rejecting at HO= .05 involves less chance of error.
There are fewer calculations to make.
Question 9.
The z- test requires an estimate of the population standard deviation
False.
Question 10.
The standard error of the mean is actually the standard deviation of all of the means that make up the distribution of sample means.
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