BUAL 2650 AUBURN EXAM 1
QUESTIONS & ANSWERS(RATED
A+)
sample proportion - ANSWERsample of the population, p-hat, that we use because
we do not know the parameter of the whole population, p. p=p-hat most of the time
but not always
standard deviation - ANSWERtypical difference between p and p-hat. the proportion
from sample, p-hat, is not equal to p, typically the estimate p-hat will be off by the
sq.rt of pq/n,
confidence interval - ANSWERassume symmetry, p-hat +/- 2*SD(p-hat) for 95%
confidence interval, so 95/100 will contain p.
conditions to check - ANSWERrandomization condition, 10% condition (no larger
than 10% of the population), success/failure (nq >10, np>10)
bell-shaped model - ANSWERnormal distribution sample size gets larger and each
sample average tends to become closer to the population mean and approach the
normal model
CLT sampling distribution - ANSWERof any mean becomes normal as the sample
size grows regardless of the shape of the population distribution
Mean Standard Deviation - ANSWERSD(y-bar)= st.dev population/sqrt(n)
Standard Error - ANSWERwhen 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)
confidence intervals for proportions - ANSWER68%- (p-sq.rt.pq/n,p+sq.rt.pq/n)
95%- (p-2sq.rt.pq/n,p+2sq.rt.pq/n)
99.7%- (p-3sq.rt.pq/n,p+3sq.rt.pq/n)
z-score - ANSWERp-hat - p / SD(p-hat)
mu(0,1) standard normal distribution
positive z-score - ANSWERoutlier > 3 is unusual
negative z-score - ANSWERoutlier < -3 is unusual
QUESTIONS & ANSWERS(RATED
A+)
sample proportion - ANSWERsample of the population, p-hat, that we use because
we do not know the parameter of the whole population, p. p=p-hat most of the time
but not always
standard deviation - ANSWERtypical difference between p and p-hat. the proportion
from sample, p-hat, is not equal to p, typically the estimate p-hat will be off by the
sq.rt of pq/n,
confidence interval - ANSWERassume symmetry, p-hat +/- 2*SD(p-hat) for 95%
confidence interval, so 95/100 will contain p.
conditions to check - ANSWERrandomization condition, 10% condition (no larger
than 10% of the population), success/failure (nq >10, np>10)
bell-shaped model - ANSWERnormal distribution sample size gets larger and each
sample average tends to become closer to the population mean and approach the
normal model
CLT sampling distribution - ANSWERof any mean becomes normal as the sample
size grows regardless of the shape of the population distribution
Mean Standard Deviation - ANSWERSD(y-bar)= st.dev population/sqrt(n)
Standard Error - ANSWERwhen 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)
confidence intervals for proportions - ANSWER68%- (p-sq.rt.pq/n,p+sq.rt.pq/n)
95%- (p-2sq.rt.pq/n,p+2sq.rt.pq/n)
99.7%- (p-3sq.rt.pq/n,p+3sq.rt.pq/n)
z-score - ANSWERp-hat - p / SD(p-hat)
mu(0,1) standard normal distribution
positive z-score - ANSWERoutlier > 3 is unusual
negative z-score - ANSWERoutlier < -3 is unusual
