QUIZ CHAPTER Seven

1. Psy302 Quantitative Methods QUIZ CHAPTER Seven

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 conditional procedure • a sampling distribution • sampling without replacement • random sampling • 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 conditional procedure • a sampling distribution • sampling without replacement • random sampling • all of the above

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

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

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

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

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

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

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

11. 5. The mean of the sampling distribution of sample means is • equal to the population mean • equal to the population variance • both A and B • 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. 6. The Law of Large numbers states that _____ the number of observations in a sample will decrease the standard error. • increasing • decreasing • multiplying • dividing • 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. • increasing • decreasing • multiplying • dividing • 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: • greater than 15 • less than 15 • 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: • greater than 15 • less than 15 • equal to 15

17. 8. In the bar graph below the vertical lines (error bars) above the bars represent: • the mean • the standard deviation • the variance • the correlation • SEM

18. 8. In the bar graph below the vertical lines (error bars) above the bars represent: • the mean • the standard deviation • the variance • the correlation • SEM

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

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

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

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

23. The End