Explore all topics
Start Free Trial
Need Help? Contact us
back arrowfull exam logo
search
ai logoChat with AI
Servicesarrow
Discoverarrow

Topics

Explore all topics
Discover more topics

Exam 9: Hypothesis Testing

arrow
Exam 1: Introduction to Business Analytics29 Questionsadd course
Exam 2: Describing the Distribution of a Single Variable100 Questionsadd course
Exam 3: Finding Relationships Among Variables85 Questionsadd course
Exam 4: Probability and Probability Distributions114 Questionsadd course
Exam 5: Normal, Binomial, Poisson, and Exponential Distributions125 Questionsadd course
Exam 6: Decision Making Under Uncertainty107 Questionsadd course
Exam 7: Sampling and Sampling Distributions90 Questionsadd course
Exam 8: Confidence Interval Estimation84 Questionsadd course
Exam 9: Hypothesis Testing87 Questionsadd course
Exam 10: Regression Analysis: Estimating Relationships92 Questionsadd course
Exam 11: Regression Analysis: Statistical Inference82 Questionsadd course
Exam 12: Time Series Analysis and Forecasting106 Questionsadd course
Exam 13: Introduction to Optimization Modeling97 Questionsadd course
Exam 14: Optimization Models114 Questionsadd course
Exam 15: Introduction to Simulation Modeling82 Questionsadd course
Exam 16: Simulation Models102 Questionsadd course
Exam 17: Data Mining20 Questionsadd course
Exam 18: Importing Data Into Excel19 Questionsadd course
Exam 19: Analysis of Variance and Experimental Design20 Questionsadd course
Exam 20: Statistical Process Control20 Questionsadd course
Select questions type
  • Essay
  • Multiple Choice
  • Short Answer
  • True False
  • Matching
  • Select Tags
search iconSearch Question
flashcardsStudy Flashcards
Select questions type
  • Essay
  • Multiple Choice
  • Short Answer
  • True False
  • Matching
  • Select Tags

(A) Construct a 90% confidence interval for the difference between the proportions of unacceptable assemblies generated by the two teams. ​ (B) Based on the confidence interval constructed in (A), is there sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies? ​ (C) Is there sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies? Conduct the appropriate hypothesis test. ​ (D) Calculate the p-value and explain how to use it for testing the null hypothesis of equal proportion.

Free
(Essay)
4.9/5
(36)
Question 1
Correct Answer:
Verified

(A) (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ ​ (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ ​ (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ →(-0.0382,0.117)
​
(B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies
​
(C)
​ (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ ​ (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ = (15 + 8) / (145 + 125) = 0.0852 (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ Z - value = (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ = 0.0394 / 0.0341 = 1.155
Critical values = (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ Since z-value does not fall in the rejection region, we cannot reject (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies
​
​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248
​
Since p-value=0.248 > (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ =0.10, we cannot reject (A) ​ ​ →(-0.0382,0.117) ​ (B) Because the 90% confidence interval includes the value 0, we cannot reject the null hypothesis of equal proportions. Therefore, there is sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ (C) ​ ​ = (15 + 8) / (145 + 125) = 0.0852 Z - value = = 0.0394 / 0.0341 = 1.155 Critical values = Since z-value does not fall in the rejection region, we cannot reject at the 10% significance level. Therefore, there is no sufficient evidence to conclude, at the 10% significance level, that the two teams differ with respect to their proportions of unacceptable assemblies ​ ​(D) p-value = 2p(Z >1.155) =2(0.124) = 0.248 ​ Since p-value=0.248 > =0.10, we cannot reject ​ ​

Do graduates of undergraduate business programs with different majors tend to earn disparate starting salaries? Below you will find output from an analysis for randomly selected graduates with majors in accounting (Acct) and marketing (Mktg). Do graduates of undergraduate business programs with different majors tend to earn disparate starting salaries? Below you will find output from an analysis for randomly selected graduates with majors in accounting (Acct) and marketing (Mktg). -Assuming that the variances of the underlying populations are unequal, can you reject at a 5% significance level that the mean starting salaries for accounting and marketing majors are the same? Explain why or why not. ​ -Assuming that the variances of the underlying populations are unequal, can you reject at a 5% significance level that the mean starting salaries for accounting and marketing majors are the same? Explain why or why not. ​

Free
(Essay)
4.9/5
(50)
Question 2
Correct Answer:
Verified

(A) (A) The salaries are equal. The salaries are not equal. Because the p - value = 0.667 < = 0.05, we cannot reject and conclude that in this test the difference between the salaries is not statistically significant. The salaries are equal. (A) The salaries are equal. The salaries are not equal. Because the p - value = 0.667 < = 0.05, we cannot reject and conclude that in this test the difference between the salaries is not statistically significant. The salaries are not equal.
Because the p - value = 0.667 < (A) The salaries are equal. The salaries are not equal. Because the p - value = 0.667 < = 0.05, we cannot reject and conclude that in this test the difference between the salaries is not statistically significant. = 0.05, we cannot reject (A) The salaries are equal. The salaries are not equal. Because the p - value = 0.667 < = 0.05, we cannot reject and conclude that in this test the difference between the salaries is not statistically significant. and conclude that in this test the difference between the salaries is not statistically significant.

If a null hypothesis about a population mean If a null hypothesis about a population mean is rejected at the 0.025 level of significance, then it must also be rejected at the 0.01 level. is rejected at the 0.025 level of significance, then it must also be rejected at the 0.01 level.

Free
(True/False)
4.8/5
(35)
Question 3
Correct Answer:
Verified

False

A test for independence is applied to a contingency table with 4 rows and 4 columns. The degrees of freedom for this chi-square test must equal 9.

(True/False)
4.9/5
(46)
Question 4

The significance level The significance level also determines the rejection region. also determines the rejection region.

(True/False)
4.7/5
(26)
Question 5

Do undergraduate business students who major in information systems (IS) earn, on average, higher annual starting salaries than their peers who major in marketing (Mktg)? To address this question with a statistical hypothesis test, a comparison should be done to determine whether the variances of annual starting salaries of the two types of majors are equal. Below you will find output from a test of 20 randomly selected IS majors and 20 randomly selected Mktg majors. Do undergraduate business students who major in information systems (IS) earn, on average, higher annual starting salaries than their peers who major in marketing (Mktg)? To address this question with a statistical hypothesis test, a comparison should be done to determine whether the variances of annual starting salaries of the two types of majors are equal. Below you will find output from a test of 20 randomly selected IS majors and 20 randomly selected Mktg majors. -(A) Use the information above to perform the test of equal variance. Explain how the ratio of sample variances is calculated. What type of distribution is used to test for equal variances? Also, would you conclude that the variances are equal or not? Explain. ​ (B) Based on your conclusion in (A), which test statistic should be used in performing a test for the existence of a difference between population means? ​ (C) Using a 5% level of significance, is there sufficient evidence to conclude that IS majors earn, on average, a higher annual starting salaries than their peers who major in Mktg? ​ (D) Using a 1% level of significance, is there sufficient evidence to conclude that IS majors earn, on average, a higher annual starting salaries than their peers who major in Mktg? Explain your answer. -(A) Use the information above to perform the test of equal variance. Explain how the ratio of sample variances is calculated. What type of distribution is used to test for equal variances? Also, would you conclude that the variances are equal or not? Explain. ​ (B) Based on your conclusion in (A), which test statistic should be used in performing a test for the existence of a difference between population means? ​ (C) Using a 5% level of significance, is there sufficient evidence to conclude that IS majors earn, on average, a higher annual starting salaries than their peers who major in Mktg? ​ (D) Using a 1% level of significance, is there sufficient evidence to conclude that IS majors earn, on average, a higher annual starting salaries than their peers who major in Mktg? Explain your answer.

(Essay)
4.8/5
(34)
Question 6

What is the idea behind the chi-square test for independence?

(Multiple Choice)
4.9/5
(34)
Question 7

A recent study of educational levels of 1000 voters and their political party affiliations in a Midwestern state showed the results given in the table below. Use A recent study of educational levels of 1000 voters and their political party affiliations in a Midwestern state showed the results given in the table below. Use = .10 and test to determine if party affiliation is independent of the educational level of the voters. = .10 and test to determine if party affiliation is independent of the educational level of the voters. A recent study of educational levels of 1000 voters and their political party affiliations in a Midwestern state showed the results given in the table below. Use = .10 and test to determine if party affiliation is independent of the educational level of the voters.

(Essay)
4.7/5
(31)
Question 8

Which of the following values is typically used for Which of the following values is typically used for ? ?

(Multiple Choice)
4.8/5
(27)
Question 9

An alternative hypothesis can have the signs >, <, or ≠.

(True/False)
4.8/5
(36)
Question 10

(A) State the appropriate null and alternative hypotheses in this situation. ​ (B) Test this CEO's belief at the (A) State the appropriate null and alternative hypotheses in this situation. ​ (B) Test this CEO's belief at the =.05 significance level and report the p-value. Do you find statistical support for his hypothesis that the proportion of women in similar sales positions across the country is less than 40%? ​ (C) Suppose the sample size above is 100, instead of 50, and the sample proportion is again 0.36. Would this change your results? Explain your answer. =.05 significance level and report the p-value. Do you find statistical support for his hypothesis that the proportion of women in similar sales positions across the country is less than 40%? ​ (C) Suppose the sample size above is 100, instead of 50, and the sample proportion is again 0.36. Would this change your results? Explain your answer.

(Essay)
4.8/5
(29)
Question 11

The power of a test is the probability that we:

(Multiple Choice)
4.8/5
(28)
Question 12

A p-value is considered "convincing" if it is:

(Multiple Choice)
4.9/5
(37)
Question 13

Lilliefors test for normality compare two cumulative distribution functions (cdf's): the cdf from a normal distribution and the cdf corresponding to the given data (called the empirical cdf).

(True/False)
4.9/5
(34)
Question 14

Which of the following statements are true of the null and alternative hypotheses?

(Multiple Choice)
4.9/5
(28)
Question 15

The Lilliefors test is used to test for normality.

(True/False)
4.9/5
(40)
Question 16

Larger p-values indicate more evidence in support of the:

(Multiple Choice)
4.9/5
(27)
Question 17

Which sign is possible in an alternative hypothesis?

(Multiple Choice)
4.8/5
(37)
Question 18

The number of cars sold by three salespersons over a 6-month period are shown in the table below. Use the 5% level of significance to test for independence of salespersons and type of car sold. The number of cars sold by three salespersons over a 6-month period are shown in the table below. Use the 5% level of significance to test for independence of salespersons and type of car sold.

(Essay)
4.9/5
(38)
Question 19

A Type I error probability is represented by A Type I error probability is represented by ; it is the probability of incorrectly rejecting a null hypothesis that is true. ; it is the probability of incorrectly rejecting a null hypothesis that is true.

(True/False)
4.8/5
(38)
Question 20
Showing 1 - 20 of 87
close modal

Filters

  • Essay(0)
  • Multiple Choice(0)
  • Short Answer(0)
  • True False(0)
  • Matching(0)