Question

College students Suppose a recent study of 1,000 college students in the U.S. found that 8% of them do not use Facebook. Which of the following describes the population for this example?

-All College students in the US

-The 1000 college students who participated in the study

-all college students in the US who do not use facebook

-The 8% of college students who do not use facebook

Which of the following defines what is meant by a control group in an experiment

-A group that is handled identically to the treatment groups in all respects except that they are controlled to greater extend than the other groups, providing baseline data.

-a group that is used by researchers to monitor how the experiment is going

-a group that is handled identically to the treatment group in all respects except that they dont recieve the active treatment

-none of the above

Which of the following studies can result in researchers extending the results inappropriately because the sample doesn’t represent the intended population?

-Studies involving randomly selected participants

-studies involving convenience samples

-experimental studies

-all of the above

Without random assignment, which of the following can happen?

-naturally occuring confounding variables can result in an apparent relationship between the explanatory and response variables

-the results may not be able to extend to a larger population

-Many people in the study will drop out because they aren’t happy with the treatment they were assigned to. This will cause bias in the results

-none of the above

Making a Type I error is only possible if the ____ hypothesis is true.

-alternative

-null

-power

-none of above

The National Collegiate Athletic Association (NCAA) requires colleges to report the graduation rates of their athletes. Here are data from a Big Ten university's report: 45 of the 74 athletes admitted in a speci fic year graduated within 6 years. Does the proportion of athletes who graduate di ffer significantly from the all-university proportion, which is .70 ?

What is the sample size

-45

-74

-119

-0.029

What is the sample proportion?

-6

-0.61

-6

-65

What are the null and alternative hypotheses?

-Null: p>0.70; alternative: p <0.61

-Null: p=.61; alternative: p <.0..61

-Null: p=0.70; alternative: p is not 0.70

-Null: p=.70; alternative: p > .70

What is the value of the test statistic for your observed results?

-1.70

- (-)2.70

- (-) 1.70

-0.53

What is the p-value for your observed results?

-0.9554

-0.0892

-0.002

-0.998

What is your conclusion? Please use words that a non-statistics student would understand, and justify your answer. Assume a significance level of .05.

-Since the p-value (.9554) > .05, do not reject the null hypothesis. This means that the sample evidence is not strong enough to conclude that the proportion of athletes who graduate di ffer signi ficantly from the all-university proportion of 0.70

-Since the p-value (.0892) > .05, we failed to reject the null hypothesis. This means that the sample evidence is not strong enough to conclude that the proportion of athletes who graduate di ffer signi ficantly from the all-university proportion of 0.70

-Since the p-value (.998) > .05, reject the null hypothesis. This means that the sample evidence is not strong enough to conclude that the proportion of athletes who graduate di ffer signi ficantly from the all-university proportion of 0.70

-since the p-value (.002) < .05, reject the null hypothesis. This means that the sample evidence is strong enough to conclude that a larger proportion of peopleown bread machine than 3 years ago.

Based on your conclusion, what type of error are you risk at?

-type I error

-type II error

-type I and type II error

-none of the above

If the power of the test is 87% for this test, what is the probability of the test making type II error?

-larger than 87%

-13%

-87%

-not enough information

What is the 95% of confidence interval of proportion of athletes graduate within 6 years ?

-(0.302,0.398)

-(1.10.1.32)

-(0.496, 0.724)

-(0.38,0.42)

Base on your result of 95% confidence interval, can you conclude that majority of the athletes graduate within 6 years?

-No, because 0.50 is in the interval.

-No, because 0.70 is in the interval

-Yes, because 0.40 is not in the interval.

-Yes, because 0.90 is not in the interval.

Answer 1

dear student please post the question one at a time

Question : The National Collegiate Athletic Association (NCAA) requires colleges to report the graduation rates of their athletes. Here are data from a Big Ten university's report: 45 of the 74 athletes admitted in a speci fic year graduated within 6 years. Does the proportion of athletes who graduate differ significantly from the all-university proportion, which is .70

The sample size is 74

The sample proportion :

The null hypothesis :

The alternate hypothesis :

Test statistic :

option c is right

The p value for the two tailed test =

option B is right

Conclusion :

option B is right

Since the p-value (.0892) > .05, we failed to reject the null hypothesis. This means that the sample evidence is not strong enough to conclude that the proportion of athletes who graduate differ significantly from the all-university proportion of 0.70

a type I error is the rejection of a true null hypothesis , while a type II error is failing to reject a false null hypothesis

Hence we are at risk of committing type II error since we fail to reject the null hypothesis in our test for the population proportion.

If the power of the test is 87%( 0.87) for this test, what is the probability of the test making type II error

The probability of making a type II error is and

hence

the probability of the test making type II error =0.13

95% confidence interval:

for 95% confidence

option C is right

Base on your result of 95% confidence interval, can you conclude that majority of the athletes graduate within 6 years

option A is right

( answer may vary slightly because of the rounding used within the calculation, but the procedure is right )