1. QUIZ CHAPTER Seven Psy302 Quantitative Methods

2. 1. A distribution of all sample means or sample variances that could be obtained in samples of a given size from the same population is called A. a conditional procedure B. a sampling distribution C. sampling without replacement D. random sampling E. all of the above

3. 1. A distribution of all sample means or sample variances that could be obtained in samples of a given size from the same population is called A. a conditional procedure B. a sampling distribution C. sampling without replacement D. random sampling E. all of the above

4. 2. What is the central limit theorem? A. It explains that sample means will vary minimally from the population mean. B. It explains that a sampling distribution of possible sample means is approximately normally distributed, regardless of the shape of the distribution in the population. C. It explains that if we select a sample at random, then on average we can expect the sample mean to exceed the population mean. D. all of the above

5. 2. What is the central limit theorem? A. It explains that sample means will vary minimally from the population mean. B. It explains that a sampling distribution of possible sample means is approximately normally distributed, regardless of the shape of the distribution in the population. C. It explains that if we select a sample at random, then on average we can expect the sample mean to exceed the population mean. D. all of the above

6. 3. A sample statistic is an unbiased estimator if its value equals the value of the _____ on average. A. proportion B. p-value C. parameter D. mean E. all of the above

7. 3. A sample statistic is an unbiased estimator if its value equals the value of the _____ on average. A. proportion B. p-value C. parameter D. mean E. all of the above

8. 4..It happens to be the case that the standard error of the sampling distribution of sample means A. is minimal B. is approximately equal to that in the population C. gets larger as the sample size increases D. both A and C

9. 4..It happens to be the case that the standard error of the sampling distribution of sample means A. is minimal B. is approximately equal to that in the population C. gets larger as the sample size increases D. both A and C

10. 5. The mean of the sampling distribution of sample means is A. equal to the population mean B. equal to the population variance C. both A and B D. none of the above

11. 5. The mean of the sampling distribution of sample means is A. equal to the population mean B. equal to the population variance C. both A and B D. none of the above

12. 6. The Law of Large numbers states that _____ the number of observations in a sample will decrease the standard error.

13. A. increasing B. decreasing C. multiplying D. dividing E. all of the above

14. 6. The Law of Large numbers states that _____ the number of observations in a sample will decrease the standard error. A. increasing B. decreasing C. multiplying D. dividing E. all of the above

15. 7. If a random sample is selected from a population with a mean equal to 15 then we expect the value of the sample mean on average to be: A. greater than 15 B. less than 15 C. equal to 15

16. 7. If a random sample is selected from a population with a mean equal to 15 then we expect the value of the sample mean on average to be: A. greater than 15 B. less than 15 C. equal to 15

17. 8. In the bar graph below the vertical lines (error bars) above the bars represent: A. the mean B. the standard deviation C. the variance D. the correlation E. SEM

18. 8. In the bar graph below the vertical lines (error bars) above the bars represent: A. the mean B. the standard deviation C. the variance D. the correlation E. SEM

19. 9. The standard error of the mean tells us: A. the value of the population mean. B. the standard deviation of the sampling distribution C. how far possible sample means deviate from the population mean. D. how nasty the distribution is E. b & c

20. 9. The standard error of the mean tells us: A. the value of the population mean. B. the standard deviation of the sampling distribution C. how far possible sample means deviate from the population mean. D. how nasty the distribution is E. b & c

21. 10. _____ is the extent to which sample means elected from the same population vary from each other. A. mean square B. SEM C. sampling error D. the law of large numbers E. the central limit theorem

22. 10. _____ is the extent to which sample means elected from the same population vary from each other. A. mean square B. SEM C. sampling error D. the law of large numbers E. the central limit theorem

23. The End