One of the bartleby experts helped me with this homework problem. I am having difficulty figuring out where 21.76 and 4.21 came from on part (a). Look at images below. Also, there is a spreadsheet on Excel with data that is used for this problem.

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One of the bartleby experts helped me with this homework problem. I am having difficulty figuring out where 21.76 and 4.21 came from on part (a). Look at images below. Also, there is a spreadsheet on Excel with data that is used for this problem.

 

 

If there is one thing engineers like, it is a good pizza that is delivered in a timely
manner. A particular pizza chain is thinking about offering a guarantee of “less
than 30 minutes, or it’s free” on all of its deliveries. If the pizza business can pull
this off, they will impress customers across the nation, but having to walk this
promise back would be devastating to the reputation of the business. However,
the CEO will not green light this policy if it means taking a substantial hit
to profits. Accounting has run some numbers, and they have determined that
the business can only miss 5% of all deliveries before the costs of the missed
deliveries begin to impact profitability. The CEO agrees to a pilot test. 120
drivers time their deliveries with a stopwatch and log them in a spreadsheet.
The CEO then sends this spreadsheet to you for analysis.
Using all of the technical terms and symbols that you want:


ˆ(a) Should the pizza chain implement this policy?


ˆ(b) Create a graphic that visually represents the shape of the distribution
well.


ˆ(c) Describe the level of information contained by the data (e.g., nominal,
discrete, etc.).


ˆ(d) Describe the distribution of the data in detail (shape, center, spread, skew,
kurtosis, etc.). We have not covered how to formally calculate all of these
numbers, and that is fine – a verbal description will be sufficient in these
cases.


ˆ (e) Now that you have the details of the distribution, how did the distribution
impact your analysis?

 

(f)ˆ Would the analysis have been better/easier/different if the data followed a different shape?

ˆ
(g) In your professional opinion, should any of the data points be removed?
Give a bit of color regarding the ethics of your decision. Are we throwing
out the data just because we want to see better symmetry and make the
math cleaner, or can we actually provide some justification for why the
data should be removed? Would such a removal change your decision of
the business case?
 
 
Here is the data from the Excel spreadsheet
 
Delivery Time
20.58
19.2
26.79
27.86
20.95
18.12
20.41
20.68
18.47
18.73
30.01
19.8
18.34
18.15
21.49
21.28
19.57
19.59
20.94
21.09
25.94
21.07
20.16
20.38
18.42
19.77
20.35
20.72
23.88
20.98
18.94
20.41
20.17
20.68
18.91
21.34
19.99
20.03
18.77
20.04
21.33
31.04
20.77
32.01
20.41
21
20.55
22.69
29.55
20.97
19.38
18.88
19.26
21.51
20.26
20.84
20.1
21.86
21.66
20.24
20.22
20.27
18.24
20.85
18.55
21.31
19.14
29.85
19.1
18.72
20.36
18.66
20.72
21.95
18.22
21.28
20.27
18.94
18.31
20.62
19.15
18.77
32.67
19.51
19.23
20.2
20.53
20.4
20.03
20.77
33.44
22.02
22.1
19.47
18.9
18.86
19.08
20.46
21.21
21.12
21.69
50.03
23.86
20.49
27.46
21.9
20.11
18.01
29.08
24.13
23.49
24.15
23.94
26.32
28.62
25.56
23.92
24.65
23.54
19.37
a)
x =
Ex
n
= 21.75941667
≈ 21.76
S =
Σ(-x)2
n-1
= 4.209253571
≈ 4.21
Null hypothesis: Ho: μ = 30
Alternative hypothesis: HA: μ< 30
This hypotheses constitute a one-tailed test and significance level for the test is 0.05.
Use the sample data to calculate the standard error (SE), degrees of freedom (DF), and the t statistic test
statistic (t).
S.E=
=
S.D
√n
4.21
√120
= 0.384319
≈ 0.3843
D.F=n-1
Transcribed Image Text:a) x = Ex n = 21.75941667 ≈ 21.76 S = Σ(-x)2 n-1 = 4.209253571 ≈ 4.21 Null hypothesis: Ho: μ = 30 Alternative hypothesis: HA: μ< 30 This hypotheses constitute a one-tailed test and significance level for the test is 0.05. Use the sample data to calculate the standard error (SE), degrees of freedom (DF), and the t statistic test statistic (t). S.E= = S.D √n 4.21 √120 = 0.384319 ≈ 0.3843 D.F=n-1
D.F=n-1
= 120 - 1
= 119
t =
x-μ
S.E
21.76 - 30
0.3843
= -21.441582
≈
21.44
The P-value for this test is the probability that a t statistic having 119 degrees of freedom is less than -21.44.
P-value = P(t < -21.44)
Use the excel formula, " =T.DIST(-21.44,119)" to determine the P-value.
P-value = 0.000
Decision:
Since the P-value (0.000) is less than the significance level(0.05), hence we have to reject the null
hypothesis.
Conclusion:
From the above test we have sufficient evidence in the favor of the claim that the pizza chain should
implement this policy.
The percentage of deliveries within 30 minutes is high enough to meet the CEO's requirement (less
than 5% missed deliveries), then implementing the policy may be feasible.
Transcribed Image Text:D.F=n-1 = 120 - 1 = 119 t = x-μ S.E 21.76 - 30 0.3843 = -21.441582 ≈ 21.44 The P-value for this test is the probability that a t statistic having 119 degrees of freedom is less than -21.44. P-value = P(t < -21.44) Use the excel formula, " =T.DIST(-21.44,119)" to determine the P-value. P-value = 0.000 Decision: Since the P-value (0.000) is less than the significance level(0.05), hence we have to reject the null hypothesis. Conclusion: From the above test we have sufficient evidence in the favor of the claim that the pizza chain should implement this policy. The percentage of deliveries within 30 minutes is high enough to meet the CEO's requirement (less than 5% missed deliveries), then implementing the policy may be feasible.
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