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17 4,45 3,59 3,20 2,96 2,81 2,70 2,55 2,38 2,19
18 4,41 3,55 3,16 2,93 2,77 2,66 2,51 2,34 2,15
19 4,38 3,52 3,13 2,90 2,74 2,63 2,48 2,31 2,11
20 4,35 3,49 3,10 2,87 2,71 2,60 2,45 2,28 2,08
21 4,32 3,47 3,07 2,84 2,68 2,57 2,42 2,25 2,05
22 4,30 3,44 3,05 2,82 2,66 2,55 2,40 2,23 2,03
23 4,28 3,42 3,03 2,80 2,64 2,53 2,38 2,20 2,00
24 4,26 3,40 3,01 2,78 2,62 2,51 2,36 2,18 1,98
25 4,24 3,38 2,99 2,76 2,60 2,49 2,34 2,16 1,96
26 4,22 3,37 2,98 2,74 2,59 2,47 2,32 2,15 1,95
27 4,21 3,35 2,96 2,73 2,57 2,46 2,30 2,13 1,93
28 4,20 3,34 2,95 2,71 2,56 2,44 2,29 2,12 1,91
29 4,18 3,33 2,93 2,70 2,54 2,43 2,28 2,10 1,90
30 4,17 3,32 2,92 2,69 2,53 2,42 2,27 2,09 1,89
40 4,08 3,23 2,84 2,61 2,45 2,34 2,18 2,00 1,79
60 4,00 3,15 2,76 2,52 2,37 2,25 2,10 1,92 1,70
120 3,92 3,07 2,68 2,45 2,29 2,17 2,02 1,83 1,61

4.6 Methods of excluding minor factors and
anomalous values

When processing the results of experiments or industrial
data, it is necessary to establish the factors, which significantly
affect the process and its characteristics, and eliminate
insignificant ones.
There are many approaches to exclude insignificant
factors or their abnormal values. The most common is selective
experiment, associative analysis and method of D. Himelblau.
When using selective experiment, it is necessary to build a
planning matrix and conduct a preliminary experiment, results
of that experiment let to assess the impact of factors on the
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