BUAL 2650 Auburn Exam 1 || with Accurate Answers 100%.
sample proportion correct answers sample 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 correct answers typical 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 correct answers assume symmetry, p-hat +/- 2*SD(p-hat) for 95% confidence
interval, so 95/100 will contain p.
conditions to check correct answers randomization condition, 10% condition (no larger than 10%
of the population), success/failure (nq >10, np>10)
confidence intervals for proportions correct answers 68%- (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 correct answers p-hat - p / SD(p-hat)
mu(0,1) standard normal distribution
positive z-score correct answers outlier > 3 is unusual
negative z-score correct answers outlier < -3 is unusual
null hypothesis correct answers 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
stat hypothesis testing correct answers the population perimeter is the initial hypothesis, p=x,
collect data to challenge the hypothesis and form p-hat, then decide if the data proves likely or
unlikely
Ho correct answers null hypothesis, population parameter, hypothesized value
Ha correct answers alternative hypothesis, the parameter we deem plausible when we reject the
null hypothesis
Two-sided test correct answers population parameter does not equal hypothesized value
One-sided test correct answers population paramater > or < hypothesized value
Reject the null correct answers less than 0.05, small
Fail to reject the null, accept the null correct answers more than 0.05, large
sample proportion correct answers sample 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 correct answers typical 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 correct answers assume symmetry, p-hat +/- 2*SD(p-hat) for 95% confidence
interval, so 95/100 will contain p.
conditions to check correct answers randomization condition, 10% condition (no larger than 10%
of the population), success/failure (nq >10, np>10)
confidence intervals for proportions correct answers 68%- (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 correct answers p-hat - p / SD(p-hat)
mu(0,1) standard normal distribution
positive z-score correct answers outlier > 3 is unusual
negative z-score correct answers outlier < -3 is unusual
null hypothesis correct answers 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
stat hypothesis testing correct answers the population perimeter is the initial hypothesis, p=x,
collect data to challenge the hypothesis and form p-hat, then decide if the data proves likely or
unlikely
Ho correct answers null hypothesis, population parameter, hypothesized value
Ha correct answers alternative hypothesis, the parameter we deem plausible when we reject the
null hypothesis
Two-sided test correct answers population parameter does not equal hypothesized value
One-sided test correct answers population paramater > or < hypothesized value
Reject the null correct answers less than 0.05, small
Fail to reject the null, accept the null correct answers more than 0.05, large
