Site image for background.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.Plug in but do not evaluate. 3. The data refer to 5 suppliers of the Levi-Strauss clothing manufacturing plant in Albuquerque. Thefirm'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 (lasttable) for each supplier (labeled as 1, 2, 3, 4, 5).Response Run-Upv Residual by Predicted PlotDOneway Analysis of Run-Up By Supplier Plant Number2025151020-515-10-15-20-25105--15-10-510152025Run-Up Predicted- Residual Normal Quantile Plot-520-10-15-152410...Supplier Plant NumberOneway Anovav Analysis of VarianceSum ofSquares Mean Square544.6349F Ratio Prob > F3.6566SourceDFSupplier Plant NumberErrorC. Total- Means and Std Deviations4136.1590.0084"-588 3276.789637.23692 3821.4245-10 -Std ErrLevel NumberMeanStd DevMean Lower 95% Upper 95%-15 -21 2.7047619 5.4155772 1.18177580.23962072.68290712.70932285.169903121 5.9095238 7.0884346 1.54682329.1361405-2019 4.8315789 4.4031621 1.01015476.95383519.25213616.15097419 7.4894737 3.6570928 0.83899465.726811413 10.376923 9.5550296 2.65008844.6028765Normal Quantiledn-unyRun-Up ResidualRun-Up Residual0.050.1 --66'0

Based on assumption of one-way ANOVA, the variance are assumed to be equal in this situation. Here,…

Answered: 3. The data refer to 5 suppliers of the…

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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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.

Plug in but do not evaluate.

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

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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