Can someone check my work. We use the six-step hyptheisis and minitiab. Do not handwrite because I have trouble reading some honest feedback is what I am asking for

Assigned Problem

**Assigned: You and some of your friends have decided to determine which ride service arrives faster to pick you up at your residence.Both Uber and Lyft are tested. The variable of interest is arrrival time in minutes from the time you submit your ride request. You collect the data ordering 18 rides from Uber and 18 from Lyft at varying times. The following table is the record of the arrival times:

At the .01 level of significance, is there sufficient evidence that the mean arrival time for Uber differs from the mean arrival time of Lyft?

Uber arrival time in minutes

Lyft arrival times in minutes

Uber

16.8

18.1

15.6

16.7

17.5

18.2

19.2

13.7

14.2

11.7

14.1

21.8

13.9

20.8

21.5

12.2

11.4

10.75

Lyft

22.0

15.2

18.7

15.6

20.8

19.7

21.2

23.9

14.5

19.5

17.0

19.5

16.5

24.0

23.0

16.7

17.5

16.25

Complete testing in Minitab and prepare report in Word. Enter the data presented above into a Minitab worksheet. Conduct the test with alpha of .01 and complete the following.

Using the six-step process for each hypothesis test complete the following for the ride arrival time analysis.

Vertical Box Plot in T-Test Should be included with 2 boxes and a line going to each oth

F-Test Problem Definition: We are trying to determine if there is enough data that would suggest does the mean arrival time for Uber significantly differ from the mean arrival time for Lyft. Hypothesis A two-tailed test is being done. Ho: HU=UL H1: HU#L Decision Rule: If F critical ratio exceeds critical value of 3.31, we reject null hypothesis. Test: Test Method 01: standard deviation of Uber Arrival 52: Standard deviation of Lyft Arrival Ratio: 01/02 F method was used. This method is accurate for normal data only. Descriptive Statistics 99% Lower Variable N StDev Variance Bound for o Uber 18 3.499 12.245 2.496 Arrival Lyft Arrival 18 3.017 9.104 2.152Ratio of Standard Deviations 99% Lower Bound for Bma'bed Ratio Ratio using F 1.159736 0.644 Null hypothesis Hg: 01 I o; = 1 Alternative H1: 01 log > 1 hypothesis Signicance level a = 0.01 Test Method Statistic DF'I DF2 P-Value F 1.35 'l? 1? 0.2?388385069963?2 Conclusion: Since 1.35 1' critical ratio does not exceed critical value of 3.31, we fail to reject the null hypothesis. It a type 2 error, and pvalue of 0.274 is > Alpha of .01, therefore we have a confidence interval of 99% Interpretation: Variance for both Uher and Ly'l't arrival times do not significantly differ, and we can pool the variance t test. Assumptions T-Test Problem Definition: We are trying to determine if there is enough evidence to suggest the that mean arrival time for Uber significantly differ from the mean arrival time for Lyft. Hypothesis It is a two-tailed test, and we are using a case 2 t-test. HO: HU=UL H1: HULL Decision Rule: If critical ratio t is 2.72, then we fail to reject the null. Test: Method Hi: population mean of Uber Arrival H2: population mean of Lyft Arrival Difference: H1 - H2 Equal variances are assumed for this analysis.Descriptive Statistics Sample N Mean m SEMean Uber 18 16.01 3.50 0.82 Arrival Ly'ft Arrival 18 18.98 3.02 0.?'1 Estimation for Difference 99% CI for Difference Pooled Difference 2.9? 3.2? (5.94, 0.00) Test Null hypothesis Ha: p1 u; = 0 Alternative H1: p1 - u; e D hypothesis T-Vdue DF PVdue -2.72 34 0.010 Critical ratio of 2.72 is not