Levi-Strauss Co manufactures clothing. The quality control department measures weekly values of different suppliers for the percentage difference of waste between the layout on the computer and the actual waste when the clothing is made (called run-up). The data is in the following table, and there are some negative values because sometimes the supplier is able to layout the pattern better than the computer ("Waste run up," 2013). Table #11.3.3: Run-ups for Different Plants Making Levi Strauss Clothing Plant 1 Plant 2 Plant 3 Plant 4 Plant 5 1.2 16.4 12.1 11.5 24 10.1 -6 9.7 10.2 -3.7 -2 -11.6 7.4 3.8 8.2 1.5 -1.3 -2.1 8.3 9.2 -3 4 10.1 6.6 -9.3 -0.7 17 4.7 10.2 8 3.2 3.8 4.6 8.8 15.8 2.7 4.3 3.9 2.7 22.3 -3.2 10.4 3.6 5.1 3.1 -1.7 4.2 9.6 11.2 16.8 2.4 8.5 9.8 5.9 11.3 0.3 6.3 6.5 13 12.3 3.5 9 5.7 6.8 16.9 -0.8 7.1 5.1 14.5 19.4 4.3 3.4 5.2 2.8 19.7 -0.8 7.3 13 3 -3.9 7.1 42.7 7.6 0.9 3.4 1.4 70.2 1.5 0.7 3 8.5 2.4 6 1.3 2.9 Do the data show that there is a difference between some of the suppliers? Test at the 1% level ********************************************************************** Let x1 = percentage difference of waste between the layout on the computer and the actual waste when the clothing is made (called run-up) from plant 1 Let x2 = percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 2 Let x3 = percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 3 Let x4 = percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 4 Let x5 = percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 5 Let μ1 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 1 Let μ2 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 2 Let μ3 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 3 Let μ4 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 4 Let μ5 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 5 (vii) Calculate sample mean and sample standard deviation for Plant 4 sample Enter sample mean in decimal form to nearest hundredth, then comma, then sample standard deviation in decimal form to nearest hundredth. Examples of correctly entered answers: 13.27,2.31 0.27,0.06 -10.30,0.79 (viii) Calculate sample mean and sample standard deviation for Plant 5 sample Enter sample mean in decimal form to nearest hundredth, then comma, then sample standard deviation in decimal form to nearest hundredth. Examples of correctly entered answers: 13.27,2.31 0.27,0.06 -10.30,0.79 (ix) Using technology, determine F ratio test statistic and corresponding p-value. Enter test statistic to nearest hundredth, then enter comma, then enter p-value to nearest thousandth. Examples of correctly entered responses: 12.33,0.004 7.50,0.000 6.77,0.504

Levi-Strauss Co manufactures clothing. The quality control department measures weekly values of different suppliers for the percentage difference of waste between the layout on the computer and the actual waste when the clothing is made (called run-up). The data is in the following table, and there are some negative values because sometimes the supplier is able to layout the pattern better than the computer ("Waste run up," 2013). Table #11.3.3: Run-ups for Different Plants Making Levi Strauss Clothing Plant 1 Plant 2 Plant 3 Plant 4 Plant 5 1.2 16.4 12.1 11.5 24 10.1 -6 9.7 10.2 -3.7 -2 -11.6 7.4 3.8 8.2 1.5 -1.3 -2.1 8.3 9.2 -3 4 10.1 6.6 -9.3 -0.7 17 4.7 10.2 8 3.2 3.8 4.6 8.8 15.8 2.7 4.3 3.9 2.7 22.3 -3.2 10.4 3.6 5.1 3.1 -1.7 4.2 9.6 11.2 16.8 2.4 8.5 9.8 5.9 11.3 0.3 6.3 6.5 13 12.3 3.5 9 5.7 6.8 16.9 -0.8 7.1 5.1 14.5 19.4 4.3 3.4 5.2 2.8 19.7 -0.8 7.3 13 3 -3.9 7.1 42.7 7.6 0.9 3.4 1.4 70.2 1.5 0.7 3 8.5 2.4 6 1.3 2.9 Do the data show that there is a difference between some of the suppliers? Test at the 1% level ********************************************************************** Let x1 = percentage difference of waste between the layout on the computer and the actual waste when the clothing is made (called run-up) from plant 1 Let x2 = percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 2 Let x3 = percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 3 Let x4 = percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 4 Let x5 = percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 5 Let μ1 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 1 Let μ2 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 2 Let μ3 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 3 Let μ4 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 4 Let μ5 = mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 5 (vii) Calculate sample mean and sample standard deviation for Plant 4 sample Enter sample mean in decimal form to nearest hundredth, then comma, then sample standard deviation in decimal form to nearest hundredth. Examples of correctly entered answers: 13.27,2.31 0.27,0.06 -10.30,0.79 (viii) Calculate sample mean and sample standard deviation for Plant 5 sample Enter sample mean in decimal form to nearest hundredth, then comma, then sample standard deviation in decimal form to nearest hundredth. Examples of correctly entered answers: 13.27,2.31 0.27,0.06 -10.30,0.79 (ix) Using technology, determine F ratio test statistic and corresponding p-value. Enter test statistic to nearest hundredth, then enter comma, then enter p-value to nearest thousandth. Examples of correctly entered responses: 12.33,0.004 7.50,0.000 6.77,0.504

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Q9C

Table #11.3.3: Run-ups for Different Plants Making Levi Strauss Clothing

Plant 1	Plant 2	Plant 3	Plant 4	Plant 5
1.2	16.4	12.1	11.5	24
10.1	-6	9.7	10.2	-3.7
-2	-11.6	7.4	3.8	8.2
1.5	-1.3	-2.1	8.3	9.2
-3	4	10.1	6.6	-9.3
-0.7	17	4.7	10.2	8
3.2	3.8	4.6	8.8	15.8
2.7	4.3	3.9	2.7	22.3
-3.2	10.4	3.6	5.1	3.1
-1.7	4.2	9.6	11.2	16.8
2.4	8.5	9.8	5.9	11.3
0.3	6.3	6.5	13	12.3
3.5	9	5.7	6.8	16.9
-0.8	7.1	5.1	14.5
19.4	4.3	3.4	5.2
2.8	19.7	-0.8	7.3
13	3	-3.9	7.1
42.7	7.6	0.9	3.4
1.4	70.2	1.5	0.7
3	8.5
2.4	6
1.3	2.9

Do the data show that there is a difference between some of the suppliers? Test at the 1% level

**********************************************************************

Let x₁= percentage difference of waste between the layout on the computer and the actual waste when the clothing is made (called run-up) from plant 1

Let x₂= percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 2

Let x₃= percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 3

Let x₄= percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 4

Let x₅= percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 5

Let μ₁= mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 1

Let μ₂= mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 2

Let μ₃= mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 3

Let μ₄= mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 4

Let μ₅= mean percentage difference of waste between the layout on the computer and the actual waste of run-up from plant 5

(vii) Calculate sample mean and sample standard deviation for Plant 4 sample

Enter sample mean in decimal form to nearest hundredth, then comma, then sample standard deviation in decimal form to nearest hundredth. Examples of correctly entered answers:

13.27,2.31

0.27,0.06

-10.30,0.79

(viii) Calculate sample mean and sample standard deviation for Plant 5 sample

Enter sample mean in decimal form to nearest hundredth, then comma, then sample standard deviation in decimal form to nearest hundredth. Examples of correctly entered answers:

13.27,2.31

0.27,0.06

-10.30,0.79

(ix) Using technology, determine F ratio test statistic and corresponding p-value.

Enter test statistic to nearest hundredth, then enter comma, then enter p-value to nearest thousandth. Examples of correctly entered responses:

12.33,0.004

7.50,0.000

6.77,0.504

Definition Definition Measure of central tendency that is the average of a given data set. The mean value is evaluated as the quotient of the sum of all observations by the sample size. The mean, in contrast to a median, is affected by extreme values. Very large or very small values can distract the mean from the center of the data. Arithmetic mean: The most common type of mean is the arithmetic mean. It is evaluated using the formula: μ = 1 N ∑ i = 1 N x i Other types of means are the geometric mean, logarithmic mean, and harmonic mean. Geometric mean: The nth root of the product of n observations from a data set is defined as the geometric mean of the set: G = x 1 x 2 ... x n n Logarithmic mean: The difference of the natural logarithms of the two numbers, divided by the difference between the numbers is the logarithmic mean of the two numbers. The logarithmic mean is used particularly in heat transfer and mass transfer. ln x 2 − ln x 1 x 2 − x 1 Harmonic mean: The inverse of the arithmetic mean of the inverses of all the numbers in a data set is the harmonic mean of the data. 1 1 x 1 + 1 x 2 + ...

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