See the attached image for the introduction. Using your previous answer to part (b) above, give two-sided 95% confidence limits for the difference in mean percentage waste for supplier 3 and 4: µ3 − µ4. (Plug in completely, but you need not simplify.) 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 (ỹ;) 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-UpResidual by Predicted PlotOneway Analysis of Run-Up By Supplier Plant Number202515102015-10-1510-20-25-15-10-510152025Run-Up PredictedResidual Normal Quantile Plot20-1015-1510Supplier Plant Number• Oneway Anova* Analysis of VarianceSum ofSquares Mean Square544.6349F Ratio Prob >F0.0084SourceDFSupplier Plant NumberError136.15937.2363.6566-588 3276.789692 3821.4245C. TotalMeans and Std Deviations-10Std ErrMean Lower 96% Upper 95%Level NumberMeanStd Dev-1521 2.7047619 5.4155772 1.18177580.23962075.169903121 5.9095238 7.0884346 1.546823219 4.8315789 4.4031621 1.01015472.68290719.1361405-202.70932286.953835119 7.4894737 3.6570928 0.83899465.72681149.25213613 10.376923 9.5550296 2.65008844.602876516.15097Normal Quantle--- ...S0'0L00empe dn-unuempsy dn-unydn-ure

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

Answered: 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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See the attached image for the introduction.

Using your previous answer to part (b) above, give two-sided 95% confidence limits for the difference in mean percentage waste for supplier 3 and 4: µ3 − µ4. (Plug in completely, but you need not simplify.)

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 (ỹ;) 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
Residual by Predicted Plot
Oneway Analysis of Run-Up By Supplier Plant Number
20
25
15
10
20
15
-10
-15
10
-20
-25
-15
-10
-5
10
15
20
25
Run-Up Predicted
Residual Normal Quantile Plot
20
-10
15
-15
10
Supplier Plant Number
• Oneway Anova
* Analysis of Variance
Sum of
Squares Mean Square
544.6349
F Ratio Prob >F
0.0084
Source
DF
Supplier Plant Number
Error
136.159
37.236
3.6566
-5
88 3276.7896
92 3821.4245
C. Total
Means and Std Deviations
-10
Std Err
Mean Lower 96% Upper 95%
Level Number
Mean
Std Dev
-15
21 2.7047619 5.4155772 1.1817758
0.2396207
5.1699031
21 5.9095238 7.0884346 1.5468232
19 4.8315789 4.4031621 1.0101547
2.6829071
9.1361405
-20
2.7093228
6.9538351
19 7.4894737 3.6570928 0.8389946
5.7268114
9.252136
13 10.376923 9.5550296 2.6500884
4.6028765
16.15097
Normal Quantle
--- ...
S0'0
L00
empe dn-unu
empsy dn-uny
dn-ure

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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