Example of preprocessing responses for analyze variabilitymain topic interpreting results session command see also
Example of preprocessing responses for analyze variabilityYou are investigating how processing conditions affect the yield of a chemical reaction. You believe that three processing conditions (factors)-reaction time, reaction temperature, and type of catalyst-affect the variability in yield. You decide to conduct a 2-level full factorial experiment with 8 replicates so you can analyze the variability in the responses at different factor settings.
In order to analyze the variability in your responses, you must first preprocess the replicate responses to calculate and store the standard deviations and number of replicates.
1 Open the worksheet YIELDSTDEV.MTW. (The design and response data have been saved for you.)
2 Choose Stat > DOE > Factorial > Preprocess Responses for Analyze Variability.
3 Under Standard deviations to use for analysis, choose Compute for repeat responses across rows.
3 In Repeat responses across rows of, enter 'Yield_1'-'Yield_8'.
4 In Store standard deviations in, enter StdYield to name the column in which the standard deviations are stored.
5 In Store number of repeats in, enter NYield to name the column in which the number of repeats are stored. Click OK.
Data window output
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Note |
Preprocessing responses does not produce output in the Session window. Instead, columns are stored in the worksheet. |
|
StdOrder |
RunOrder |
CenterPt |
Blocks |
Time |
Temp |
Catalyst |
Yield_1 |
Yield_2 |
Yield_3 |
Yield_4 |
Yield_5 |
Yield_6 |
Yield_7 |
Yield_8 |
StdYield |
NYield |
|
1 |
3 |
1 |
1 |
20 |
150 |
A |
45.1931 |
48.4485 |
47.3932 |
42.1684 |
45.4934 |
42.9325 |
45.9203 |
43.2976 |
0.2800 |
8 |
|
2 |
2 |
1 |
1 |
50 |
150 |
A |
59.6118 |
49.7662 |
43.5734 |
41.8365 |
45.7207 |
49.7236 |
47.3297 |
55.2384 |
3.0456 |
8 |
|
3 |
4 |
1 |
1 |
20 |
200 |
A |
44.8025 |
48.3112 |
43.3937 |
51.2708 |
43.1891 |
46.3449 |
43.3953 |
49.1860 |
1.0240 |
8 |
|
4 |
7 |
1 |
1 |
50 |
200 |
A |
43.2365 |
46.2602 |
59.7650 |
38.0484 |
42.7636 |
39.8702 |
59.4553 |
48.5013 |
8.0317 |
8 |
|
5 |
8 |
1 |
1 |
20 |
150 |
B |
38.8697 |
56.4470 |
48.4665 |
41.6720 |
42.2025 |
44.7592 |
49.2040 |
49.1959 |
0.4915 |
8 |
|
6 |
5 |
1 |
1 |
50 |
150 |
B |
47.2578 |
39.9795 |
44.7077 |
43.2094 |
60.0008 |
43.0617 |
43.6365 |
53.0645 |
3.9723 |
8 |
|
7 |
1 |
1 |
1 |
20 |
200 |
B |
42.3529 |
42.1531 |
42.9112 |
43.1371 |
43.8015 |
45.5991 |
43.2711 |
43.0210 |
2.0003 |
8 |
|
8 |
6 |
1 |
1 |
50 |
200 |
B |
40.7675 |
45.8798 |
40.4579 |
42.3855 |
43.3958 |
44.0803 |
40.3418 |
44.8547 |
10.0303 |
8 |
In the example, Minitab calculates and stores the standard deviations of the repeats of yield in the column StdYield. Minitab calculates and stores the number of repeats in the column NYield. Minitab stores one standard deviation and the number of repeats for each combination of factor settings in the row where that combination first appears. In this example, Minitab stored 8 standard deviations and 8 numbers of repeats, filling the remaining rows with the missing data symbol (*).
To analyze this data using Analyze Variability, see Example of analyzing variability. Keep this worksheet active in order to use the stored standard deviations and number of repeats in the analyzing variability example.
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Note |
If this data contained replicates instead of repeats, the worksheet will look different than the worksheet above, but the results produced by analyzing the variability in the data will be the same. |