Dec 22nd, 2013
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Question description

1. How do statistical tests like the one sample t adjust for the absence of parameter values?
@Answer found in section 4.3 The One-sample t-Test, in Statistics for Managers
(Points : 1)  The values are estimated from sample data.
 The values are assumed to have a constant value.
 The test is reconstructed so that the values aren’t needed.
 The test is reformulated so that data are always normal.

Question 2. 2. If a certifying agency raises the requirements for real estate agents, what sort of decision error is the agency protecting against? (Points : 1)
 Type I
 Type II
 Type III
 Type IV

Question 3. 3. What question does the z test answer? (Points : 1)
 Is the individual characteristic of the group?
 Has there been a type I error?
 Does the sample represent the population?
 Are the data normal?

Question 4. 4. Statistical significance for a tested mean difference means practical significance as well. (Points : 1)

Question 5. 5. The desired sample depends on all of these factors except?
@Answer found in section 3.4, in Statistics for Managers
(Points : 1)
 The desired certainty of results, expressed as a z score.
 The variance in the data set.
 The allowable error or variation from the actual population mean.
 The size of the population to be sampled.

Question 6. 6. Type I errors may occur with ______ results and type II errors with ______ results.
@Answer found in section 4.5 The Independent Samples t-Test, in Statistics for Managers (Points : 1)
 statistically significant, statistically significant
 statistically significant, statistically non-significant
 statistically non-significant, statistically significant
 statistically non-significant, statistically non-significant

Question 7. 7. What is the relationship between the power of a statistical test and decision errors?
@Answer found in section 4.3 The One-sample t-Test, in Statistics for Managers (Points : 1)
 Powerful tests minimize the risk of decision errors.
 Powerful tests are more inclined to type II than type I errors.
 Powerful tests compensate for decision errors with stronger effect sizes.
 Powerful tests minimize type II errors.

Question 8. 8. The one-sample t-test differs from the z-test in which way?
@Answer found in section 4.3 The One-sample t-Test, in Statistics for Managers (Points : 1)
 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 9. 9. What is the advantage of a one-tailed test over a two-tailed test?
@Answer found in section 4.3 The One-sample t-Test, in Statistics for Managers (Points : 1)
 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 10. 10. The z- test requires an estimate of the population standard deviation. (Points : 1)

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