3. The data refer to 5 suppliers of the Levi-Strauss clothing manufacturing plant in Albuquerque. The firm's quality control department collects weekly data on percentage waste y (run-up). The sample means (;) and sample standard deviations S; are given in the JMP output below (last table) for each supplier (labeled as 1, 2, 3, 4, 5). Response Run-Up v Residual by Predicted Plot DOneway Analysis of Run-Up By Supplier Plant Number 20 25 15 10 20 -5 15 -10 -15 -20 -25 10 5- -15 -10 -5 10 15 20 25 Run-Up Predicted - Residual Normal Quantile Plot -5 20 -10- 15 -15 2 4 10 ... Supplier Plant Number Oneway Anova v Analysis of Variance Sum of Squares Mean Square 544.6349 F Ratio Prob > F 3.6566 Source DF Supplier Plant Number Error C. Total - Means and Std Deviations 4 136.159 0.0084" -5 88 3276.7896 37.236 92 3821.4245 -10 - Std Err Level Number Mean Std Dev Mean Lower 95% Upper 95% -15 - 21 2.7047619 5.4155772 1.1817758 0.2396207 2.6829071 2.7093228 5.1699031 21 5.9095238 7.0884346 1.5468232 9.1361405 -20 19 4.8315789 4.4031621 1.0101547 6.9538351 9.252136 16.15097 4 19 7.4894737 3.6570928 0.8389946 5.7268114 13 10.376923 9.5550296 2.6500884 4.6028765 Normal Quantile dn-uny Run-Up Residual Run-Up Residual 0.05 0.1 - -66'0
3. The data refer to 5 suppliers of the Levi-Strauss clothing manufacturing plant in Albuquerque. The firm's quality control department collects weekly data on percentage waste y (run-up). The sample means (;) and sample standard deviations S; are given in the JMP output below (last table) for each supplier (labeled as 1, 2, 3, 4, 5). Response Run-Up v Residual by Predicted Plot DOneway Analysis of Run-Up By Supplier Plant Number 20 25 15 10 20 -5 15 -10 -15 -20 -25 10 5- -15 -10 -5 10 15 20 25 Run-Up Predicted - Residual Normal Quantile Plot -5 20 -10- 15 -15 2 4 10 ... Supplier Plant Number Oneway Anova v Analysis of Variance Sum of Squares Mean Square 544.6349 F Ratio Prob > F 3.6566 Source DF Supplier Plant Number Error C. Total - Means and Std Deviations 4 136.159 0.0084" -5 88 3276.7896 37.236 92 3821.4245 -10 - Std Err Level Number Mean Std Dev Mean Lower 95% Upper 95% -15 - 21 2.7047619 5.4155772 1.1817758 0.2396207 2.6829071 2.7093228 5.1699031 21 5.9095238 7.0884346 1.5468232 9.1361405 -20 19 4.8315789 4.4031621 1.0101547 6.9538351 9.252136 16.15097 4 19 7.4894737 3.6570928 0.8389946 5.7268114 13 10.376923 9.5550296 2.6500884 4.6028765 Normal Quantile dn-uny Run-Up Residual Run-Up Residual 0.05 0.1 - -66'0
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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Suppose all assumptios hold for the ne-way ANOVA model. Give a single numerical value which provides an estimate of the standard deviation of percentage waste (run-up) for any supplier.
Transcribed Image Text:3. The data refer to 5 suppliers of the Levi-Strauss clothing manufacturing plant in Albuquerque. The
firm's quality control department collects weekly data on percentage waste y (run-up).
The sample means (;) and sample standard deviations S; are given in the JMP output below (last
table) for each supplier (labeled as 1, 2, 3, 4, 5).
Response Run-Up
v Residual by Predicted Plot
DOneway Analysis of Run-Up By Supplier Plant Number
20
25
15
10
20
-5
15
-10
-15
-20
-25
10
5-
-15
-10
-5
10
15
20
25
Run-Up Predicted
- Residual Normal Quantile Plot
-5
20
-10-
15
-15
2
4
10
...
Supplier Plant Number
Oneway Anova
v Analysis of Variance
Sum of
Squares Mean Square
544.6349
F Ratio Prob > F
3.6566
Source
DF
Supplier Plant Number
Error
C. Total
- Means and Std Deviations
4
136.159
0.0084"
-5
88 3276.7896
37.236
92 3821.4245
-10 -
Std Err
Level Number
Mean
Std Dev
Mean Lower 95% Upper 95%
-15 -
21 2.7047619 5.4155772 1.1817758
0.2396207
2.6829071
2.7093228
5.1699031
21 5.9095238 7.0884346 1.5468232
9.1361405
-20
19 4.8315789 4.4031621 1.0101547
6.9538351
9.252136
16.15097
4
19 7.4894737 3.6570928 0.8389946
5.7268114
13 10.376923 9.5550296 2.6500884
4.6028765
Normal Quantile
dn-uny
Run-Up Residual
Run-Up Residual
0.05
0.1 -
-66'0
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