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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

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
Section10.4: Distributions Of Data
Problem 19PFA
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Using your previous answer to part (b), give two-sided 95% confidence limits for the difference in mean percentage waste for suppliers 3 an 4: u3 - u4.

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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
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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