An IT manufacturing company uses different production lines to produce IT equipment. The production lines use different equipment. A production analyst also suspects that layout of the lines may affect the production output. He considered a study to record the output for each line for three different layouts over four random weeks. The output (in thousands of units) is given in the following table.

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An IT manufacturing company uses different production lines to produce IT equipment. The
production lines use different equipment. A production analyst also suspects that layout of
the lines may affect the production output. He considered a study to record the output for
each line for three different layouts over four random weeks. The output (in thousands of
units) is given in the following table.
Layout
1
Layout
2
Layout
3
Line 1 Line 2 Line 3
12
12
11
10
14
10
12
10
14
12
11
12
17
16
18
18
18
15
17
17
18
12
11
11
12
11
10
11
12
15
16
17
10
I
11
11
(i) For layout 2, line 2 versus line 3.
(ii) For line 3, layout 2 versus layout 3.
11
(b)
Do the data provide sufficient evidence to indicate an interaction between layout
and production lines? Conduct the appropriate test. Please provide hypotheses, test
statistic, critical value, p-value, decision with justification (using both critical value
and p-value approach).
(c)
Test at .05 level of significance the difference in mean output of three production
lines.
(d) Test at .05 level of significance the difference in mean output of three production
layouts.
(e) Plot the residuals against the fitted values. What key model assumptions can be
examined and do these appear to be warranted?
(f)
Calculate 95% Bonferroni margin of error for the confidence intervals based on all
the pairwise differences between the average output means and test whether the mean
expected output is different for the following comparisons
Now, draw two interaction plots (Plot 1:layout:x axis vs line:y axis; Plot 2: line: x
axis vs layout: y axis) using STATCRUNCH and verify whether you notice the same
as you concluded in the above two comparisons
Transcribed Image Text:An IT manufacturing company uses different production lines to produce IT equipment. The production lines use different equipment. A production analyst also suspects that layout of the lines may affect the production output. He considered a study to record the output for each line for three different layouts over four random weeks. The output (in thousands of units) is given in the following table. Layout 1 Layout 2 Layout 3 Line 1 Line 2 Line 3 12 12 11 10 14 10 12 10 14 12 11 12 17 16 18 18 18 15 17 17 18 12 11 11 12 11 10 11 12 15 16 17 10 I 11 11 (i) For layout 2, line 2 versus line 3. (ii) For line 3, layout 2 versus layout 3. 11 (b) Do the data provide sufficient evidence to indicate an interaction between layout and production lines? Conduct the appropriate test. Please provide hypotheses, test statistic, critical value, p-value, decision with justification (using both critical value and p-value approach). (c) Test at .05 level of significance the difference in mean output of three production lines. (d) Test at .05 level of significance the difference in mean output of three production layouts. (e) Plot the residuals against the fitted values. What key model assumptions can be examined and do these appear to be warranted? (f) Calculate 95% Bonferroni margin of error for the confidence intervals based on all the pairwise differences between the average output means and test whether the mean expected output is different for the following comparisons Now, draw two interaction plots (Plot 1:layout:x axis vs line:y axis; Plot 2: line: x axis vs layout: y axis) using STATCRUNCH and verify whether you notice the same as you concluded in the above two comparisons
Options
(1 of 3)
Two Way Analysis of Variance results:
Responses: output
Row factor: layout
Column factor: line
ANOVA table
Source DF SS
layout
line
Interaction
Error
Total
MS
F-Stat P-value
2 240.38889 120.19444 102.2126 <0.0001
2 2.7222222 1.3611111 1.1574803 0.3294
4 4.7777778 1.1944444 1.015748 0.4168
27 31.75 1.1759259
35 279.63889
Fitted values stored in new column: Fit
Residuals stored in new column: Residuals
Transcribed Image Text:Options (1 of 3) Two Way Analysis of Variance results: Responses: output Row factor: layout Column factor: line ANOVA table Source DF SS layout line Interaction Error Total MS F-Stat P-value 2 240.38889 120.19444 102.2126 <0.0001 2 2.7222222 1.3611111 1.1574803 0.3294 4 4.7777778 1.1944444 1.015748 0.4168 27 31.75 1.1759259 35 279.63889 Fitted values stored in new column: Fit Residuals stored in new column: Residuals
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