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The waiting time of patients in different states can be found via https://projects.propublica.org/emergency/ Problem 3. Georgia claims that it has a lower average (mean) emergency room waiting time compared with South Carolina. Use this dataset to test this hypothesis with the confidence of 95%. (Hint: You need to test the variance equality first). Problem 4. Use the Georgia waiting time data to define and test a two-population hypothesis test.  Establish your hypothesis, test it and check whether reject or not for both questions. important inormation will be found on the link above. Also I have attached helpful information to answer the question.

The waiting time of patients in different states can be found via https://projects.propublica.org/emergency/ Problem 3. Georgia claims that it has a lower average (mean) emergency room waiting time compared with South Carolina. Use this dataset to test this hypothesis with the confidence of 95%. (Hint: You need to test the variance equality first). Problem 4. Use the Georgia waiting time data to define and test a two-population hypothesis test.  Establish your hypothesis, test it and check whether reject or not for both questions. important inormation will be found on the link above. Also I have attached helpful information to answer the question.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.3: Measures Of Spread
Problem 1GP
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The waiting time of patients in different states can be found via https://projects.propublica.org/emergency/

Problem 3. Georgia claims that it has a lower average (mean) emergency room waiting time compared with South Carolina. Use this dataset to test this hypothesis with the confidence of 95%. (Hint: You need to test the variance equality first).

Problem 4. Use the Georgia waiting time data to define and test a two-population hypothesis test. 

Establish your hypothesis, test it and check whether reject or not for both questions. important inormation will be found on the link above. Also I have attached helpful information to answer the question.

R # Perform two-sample t-test assuming equal variances t_test<- t.test(waiting times_georgia, waiting times_south_carolina, var.equal = TRUE) t_test Problem 4: To define and test a two-population hypothesis test for Georgia's waiting time data, we can set up the following hypotheses: Null hypothesis: The average waiting time in Georgia is equal to a specific value (e.g., 40 minutes) Alternative hypothesis: The average waiting time in Georgia is not equal to the specific value We can conduct a one-sample t-test in R to test this hypothesis: R #Load the Georgia waiting time data waiting times_georgia <- c(45, 30, 50, 40, 35) #Perform one-sample t-test t_test_georgia <- t.test(waiting times_georgia, mu= =40) t_test_georgia
Transcribed Image Text:R # Perform two-sample t-test assuming equal variances t_test<- t.test(waiting times_georgia, waiting times_south_carolina, var.equal = TRUE) t_test Problem 4: To define and test a two-population hypothesis test for Georgia's waiting time data, we can set up the following hypotheses: Null hypothesis: The average waiting time in Georgia is equal to a specific value (e.g., 40 minutes) Alternative hypothesis: The average waiting time in Georgia is not equal to the specific value We can conduct a one-sample t-test in R to test this hypothesis: R #Load the Georgia waiting time data waiting times_georgia <- c(45, 30, 50, 40, 35) #Perform one-sample t-test t_test_georgia <- t.test(waiting times_georgia, mu= =40) t_test_georgia
THE ANSWER IS IN THE EXPLANATION SECTION BELOW Explanation: Problem 3: We must use an F-test to check for equality of variances before comparing the average ER wait times in Georgia and South Carolina. Null hypothesis: There is no difference in waiting times between South Carolina and Georgia. Hypothesis alternative: There is a difference in waiting times between Georgia and South Carolina. We can conduct an F-test in R using the var.test function: R # Load the data waiting times_georgia <- c(45, 30, 50, 40, 35) waiting times_south_carolina <- c(50, 40, 55, 45, 42) # Perform the F-test var_test <-var.test(waiting times_georgia, waiting times_south_carolina) var_test If the result of the F-test indicates that the variances are not significantly different, we can proceed with a two-sample t-test to compare the means of waiting times in Georgia and South Carolina:
Transcribed Image Text:THE ANSWER IS IN THE EXPLANATION SECTION BELOW Explanation: Problem 3: We must use an F-test to check for equality of variances before comparing the average ER wait times in Georgia and South Carolina. Null hypothesis: There is no difference in waiting times between South Carolina and Georgia. Hypothesis alternative: There is a difference in waiting times between Georgia and South Carolina. We can conduct an F-test in R using the var.test function: R # Load the data waiting times_georgia <- c(45, 30, 50, 40, 35) waiting times_south_carolina <- c(50, 40, 55, 45, 42) # Perform the F-test var_test <-var.test(waiting times_georgia, waiting times_south_carolina) var_test If the result of the F-test indicates that the variances are not significantly different, we can proceed with a two-sample t-test to compare the means of waiting times in Georgia and South Carolina:
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