BUAL 2650 Auburn Exam 1 Questions and Answers
1. sample propor- sample of the population, p-hat, that we use because we do not know the para-
tion meter of the whole population, p. p=p-hat most of the time but not always
2. standard devia- typical ditterence between p and p-hat. the proportion from sample, p-hat, is not
tion equal to p, typically the estimate p-hat will be ott by the sq.rt of pq/n,
3. confidence inter- assume symmetry, p-hat +/- 2*SD(p-hat) for 95% confidence interval, so 95/100
val will contain p.
4. conditions to randomization condition, 10% condition (no larger than 10% of the population),
check success/failure (nq >10, np>10)
5. confidence inter- 68%- (p-sq.rt.pq/n,p+sq.rt.pq/n)
vals for propor- 95%- (p-2sq.rt.pq/n,p+2sq.rt.pq/n)
tions 99.7%- (p-3sq.rt.pq/n,p+3sq.rt.pq/n)
6. z-score p-hat - p / SD(p-hat)
mu(0,1) standard normal distribution
7. positive z-score outlier > 3 is unusual
8. negative z-score outlier < -3 is unusual
9. null hypothesis we assume someone is innocent until proven guilty, retain the hypothesis until the
facts make it unlikely beyond a reasonable doubt, consider if the data is consistent
with the hypothesis
10. stat hypothesis the population perimeter is the initial hypothesis, p=x, collect data to challenge the
testing hypothesis and form p-hat, then decide if the data proves likely or unlikely
11. Ho null hypothesis, population parameter, hypothesized value
12. Ha alternative hypothesis, the parameter we deem plausible when we reject the null
hypothesis
1/5
, BUAL 2650 Auburn Exam 1 Questions and Answers
13. Two-sided test population parameter does not equal hypothesized value
14. One-sided test population paramater > or < hypothesized value
15. Reject the null less than 0.05, small
16. Fail to reject the more than 0.05, large
null, accept the
null
17. conclusion statement about if we reject or fail to reject the null hypothesis
18. p-value probability of deviating in either direction from the hypothesized value
19. central limit theo- when we sample at random, the proportion (p-hat) we will get varying from sample
rem to sample because of point estimates
20. bell-shaped mod- normal distribution sample size gets larger and each sample average tends to
el become closer to the population mean and approach the normal model
21. CLT sampling dis- of any mean becomes normal as the sample size grows regardless of the shape of
tribution the population distribution
22. Mean Standard SD(y-bar)= st.dev population/sqrt(n)
Deviation
23. Standard Error when we estimate the standard deviation of the sampling distribution, mu is the
average at the center, standard deviation is the spread, SE(p-hat) sq.rt(p-hat q-hat
/n)
24. normal (y-bar - mu) / (S/sp.rt.n)
distribution/t-dis-
tribution
25.
2/5
1. sample propor- sample of the population, p-hat, that we use because we do not know the para-
tion meter of the whole population, p. p=p-hat most of the time but not always
2. standard devia- typical ditterence between p and p-hat. the proportion from sample, p-hat, is not
tion equal to p, typically the estimate p-hat will be ott by the sq.rt of pq/n,
3. confidence inter- assume symmetry, p-hat +/- 2*SD(p-hat) for 95% confidence interval, so 95/100
val will contain p.
4. conditions to randomization condition, 10% condition (no larger than 10% of the population),
check success/failure (nq >10, np>10)
5. confidence inter- 68%- (p-sq.rt.pq/n,p+sq.rt.pq/n)
vals for propor- 95%- (p-2sq.rt.pq/n,p+2sq.rt.pq/n)
tions 99.7%- (p-3sq.rt.pq/n,p+3sq.rt.pq/n)
6. z-score p-hat - p / SD(p-hat)
mu(0,1) standard normal distribution
7. positive z-score outlier > 3 is unusual
8. negative z-score outlier < -3 is unusual
9. null hypothesis we assume someone is innocent until proven guilty, retain the hypothesis until the
facts make it unlikely beyond a reasonable doubt, consider if the data is consistent
with the hypothesis
10. stat hypothesis the population perimeter is the initial hypothesis, p=x, collect data to challenge the
testing hypothesis and form p-hat, then decide if the data proves likely or unlikely
11. Ho null hypothesis, population parameter, hypothesized value
12. Ha alternative hypothesis, the parameter we deem plausible when we reject the null
hypothesis
1/5
, BUAL 2650 Auburn Exam 1 Questions and Answers
13. Two-sided test population parameter does not equal hypothesized value
14. One-sided test population paramater > or < hypothesized value
15. Reject the null less than 0.05, small
16. Fail to reject the more than 0.05, large
null, accept the
null
17. conclusion statement about if we reject or fail to reject the null hypothesis
18. p-value probability of deviating in either direction from the hypothesized value
19. central limit theo- when we sample at random, the proportion (p-hat) we will get varying from sample
rem to sample because of point estimates
20. bell-shaped mod- normal distribution sample size gets larger and each sample average tends to
el become closer to the population mean and approach the normal model
21. CLT sampling dis- of any mean becomes normal as the sample size grows regardless of the shape of
tribution the population distribution
22. Mean Standard SD(y-bar)= st.dev population/sqrt(n)
Deviation
23. Standard Error when we estimate the standard deviation of the sampling distribution, mu is the
average at the center, standard deviation is the spread, SE(p-hat) sq.rt(p-hat q-hat
/n)
24. normal (y-bar - mu) / (S/sp.rt.n)
distribution/t-dis-
tribution
25.
2/5
