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Задачи для решения. 1. Провести обработку и анализ результатов ЦКОП (табл
1. Провести обработку и анализ результатов ЦКОП (табл. 8.8)для двух факторов (N=9) по рассмотренной методике.
В каждом варианте приведены результаты проведения пяти параллельных опытов (Y1x Y2x Y3x Y4x Y5x).
Таблица8.8
Номер
опыта
| X0б
| X1б
| X2б
| X1бX2б
| X21б
| X22б
| Y1x
| Y2x
| Y3x
| Y4x
| Y5x
|
| +1
| –1
| –1
| +1
| +1
| +1
|
|
|
|
|
|
| +1
| +1
| –1
| –1
| +1
| +1
|
|
|
|
|
|
| +1
| –1
| +1
| –1
| +1
| +1
|
|
|
|
|
|
| +1
| +1
| +1
| +1
| +1
| +1
|
|
|
|
|
|
| +1
| –1,00
|
|
| 1,00
|
|
|
|
|
|
|
| +1
| +1,00
|
|
| 1,00
|
|
|
|
|
|
|
| +1
|
| –1,00
|
|
| 1,00
|
|
|
|
|
|
| +1
|
| +1,00
|
|
| 1,00
|
|
|
|
|
|
| +1
|
|
|
|
|
|
|
|
|
|
|
Вариант 1
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 3,004
| 3,031
| 3,035
| 3,039
| 3,001
| 5,193
| 5,152
| 5,177
| 5,209
| 5,151
| 3,927
| 3,950
| 3,936
| 3,898
| 3,897
| 7,141
| 7,099
| 7,111
| 7,138
| 7,097
| 4,684
| 4,697
| 4,688
| 4,730
| 4,729
| 9,135
| 9,123
| 9,166
| 9,134
| 9,117
| 6,371
| 6,403
| 6,343
| 6,339
| 6,337
| 14,672
| 14,680
| 14,695
| 14,668
| 14,672
| 5,828
| 5,847
| 5,842
| 5,905
| 5,886
|
| Вариант 2
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 3,651
| 3,605
| 3,653
| 3,592
| 3,627
| 6,547
| 6,514
| 6,535
| 6,562
| 6,581
| 4,761
| 4,793
| 4,816
| 4,792
| 4,801
| 9,515
| 9,566
| 9,534
| 9,552
| 9,528
| 5,828
| 5,847
| 5,842
| 5,905
| 5,886
| 13,041
| 13,081
| 13,051
| 13,089
| 13,063
| 8,364
| 8,371
| 8,338
| 8,365
| 8,366
| 25,575
| 25,563
| 25,611
| 25,578
| 25,534
| 5,081
| 5,148
| 5,123
| 5,092
| 5,073
|
|
Вариант 3
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 2,124
| 2,150
| 2,139
| 2,140
| 2,157
| 3,382
| 3,394
| 3,368
| 3,374
| 3,372
| 2,705
| 2,652
| 2,655
| 2,674
| 2,713
| 4,307
| 4,242
| 4,276
| 4,317
| 4,255
| 3,107
| 3,089
| 3,096
| 3,119
| 3,137
| 5,081
| 5,148
| 5,123
| 5,092
| 5,073
| 3,948
| 3,901
| 3,914
| 3,951
| 3,919
| 6,873
| 6,920
| 6,932
| 6,858
| 6,869
| 6,718
| 6,752
| 6,760
| 6,709
| 6,743
|
|
Вариант 4
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 2,588
| 2,597
| 2,542
| 2,537
| 2,539
| 4,191
| 4,165
| 4,152
| 4,129
| 4,138
| 3,201
| 3,231
| 3,202
| 3,199
| 3,248
| 5,509
| 5,453
| 5,448
| 5,511
| 5,445
| 3,793
| 3,830
| 3,850
| 3,789
| 3,852
| 6,718
| 6,752
| 6,760
| 6,709
| 6,743
| 4,963
| 4,966
| 5,001
| 4,952
| 5,007
| 9,738
| 9,753
| 9,702
| 9,746
| 9,737
| 7,094
| 7,126
| 7,149
| 7,102
| 7,158
|
|
Вариант 5
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 3,072
| 3,028
| 3,080
| 3,049
| 3,069
| 5,193
| 5,159
| 5,163
| 5,220
| 5,168
| 3,932
| 3,955
| 3,893
| 3,915
| 3,939
| 7,094
| 7,126
| 7,149
| 7,102
| 7,158
| 4,740
| 4,704
| 4,668
| 4,698
| 4,724
| 9,163
| 9,167
| 9,160
| 9,133
| 9,191
| 6,336
| 6,396
| 6,369
| 6,405
| 6,357
| 14,676
| 14,668
| 14,725
| 14,722
| 14,741
| 8,385
| 8,390
| 8,404
| 8,421
| 8,390
|
|
Вариант 6
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 4,292
| 4,285
| 4,333
| 4,304
| 4,277
| 8,385
| 8,390
| 8,404
| 8,421
| 8,390
| 5,881
| 5,886
| 5,847
| 5,900
| 5,909
| 13,349
| 13,332
| 13,357
| 13,342
| 13,356
| 7,389
| 7,368
| 7,439
| 7,419
| 7,442
| 20,252
| 20,271
| 20,271
| 20,258
| 20,310
| 11,282
| 11,269
| 11,293
| 11,249
| 11,254
| 66,571
| 66,613
| 66,562
| 66,585
| 66,620
| 7,379
| 7,415
| 7,415
| 7,368
| 7,368
|
|
Вариант 7
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 4,307
| 4,284
| 4,284
| 4,316
| 4,286
| 8,387
| 8,396
| 8,430
| 8,389
| 8,404
| 5,832
| 5,873
| 5,856
| 5,843
| 5,862
| 13,329
| 13,304
| 13,328
| 13,340
| 13,312
| 7,379
| 7,415
| 7,415
| 7,368
| 7,368
| 20,255
| 20,278
| 20,304
| 20,279
| 20,261
| 11,226
| 11,238
| 11,271
| 11,234
| 11,273
| 66,599
| 66,605
| 66,588
| 66,595
| 66,562
| 13,040
| 13,011
| 13,045
| 13,061
| 13,036
|
|
Вариант 8
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 3,583
| 3,605
| 3,623
| 3,623
| 3,587
| 6,555
| 6,564
| 6,523
| 6,559
| 6,511
| 4,795
| 4,790
| 4,776
| 4,798
| 4,744
| 9,504
| 9,530
| 9,524
| 9,557
| 9,530
| 5,855
| 5,839
| 5,827
| 5,881
| 5,863
| 13,040
| 13,011
| 13,045
| 13,061
| 13,036
| 8,328
| 8,301
| 8,303
| 8,319
| 8,310
| 25,586
| 25,544
| 25,578
| 25,562
| 25,556
| 4,701
| 4,682
| 4,690
| 4,718
| 4,719
|
|
Вариант 9
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 3,054
| 3,032
| 3,024
| 3,046
| 3,019
| 5,147
| 5,170
| 5,178
| 5,190
| 5,177
| 3,926
| 3,895
| 3,937
| 3,931
| 3,915
| 7,117
| 7,121
| 7,101
| 7,130
| 7,091
| 4,701
| 4,682
| 4,690
| 4,718
| 4,719
| 9,150
| 9,159
| 9,115
| 9,162
| 9,156
| 6,390
| 6,383
| 6,384
| 6,378
| 6,378
| 14,677
| 14,670
| 14,718
| 14,690
| 14,693
| 6,721
| 6,714
| 6,741
| 6,704
| 6,722
|
|
Вариант 10
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 2,549
| 2,537
| 2,563
| 2,564
| 2,569
| 4,118
| 4,164
| 4,155
| 4,126
| 4,151
| 3,236
| 3,220
| 3,202
| 3,212
| 3,207
| 5,445
| 5,485
| 5,449
| 5,472
| 5,455
| 3,825
| 3,812
| 3,790
| 3,782
| 3,781
| 6,721
| 6,714
| 6,741
| 6,704
| 6,722
| 4,951
| 4,989
| 4,955
| 4,941
| 4,981
| 9,735
| 9,693
| 9,705
| 9,711
| 9,726
| 3,950
| 3,932
| 3,908
| 3,935
| 3,901
|
|
Вариант 11
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 2,164
| 2,165
| 2,145
| 2,150
| 2,163
| 3,347
| 3,338
| 3,322
| 3,318
| 3,358
| 2,639
| 2,658
| 2,651
| 2,648
| 2,670
| 4,281
| 4,251
| 4,296
| 4,276
| 4,269
| 3,086
| 3,084
| 3,081
| 3,122
| 3,068
| 5,082
| 5,128
| 5,117
| 5,106
| 5,078
| 3,950
| 3,932
| 3,908
| 3,935
| 3,901
| 6,855
| 6,870
| 6,875
| 6,872
| 6,907
| 2,788
| 2,823
| 2,815
| 2,777
| 2,773
|
|
Вариант 12
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 1,983
| 1,951
| 1,969
| 1,981
| 1,935
| 3,004
| 3,024
| 2,984
| 2,983
| 3,007
| 2,435
| 2,415
| 2,428
| 2,394
| 2,438
| 3,767
| 3,794
| 3,784
| 3,783
| 3,803
| 2,788
| 2,823
| 2,815
| 2,777
| 2,773
| 4,491
| 4,467
| 4,492
| 4,473
| 4,460
| 3,485
| 3,510
| 3,515
| 3,524
| 3,475
| 5,883
| 5,879
| 5,863
| 5,870
| 5,877
| 5,083
| 5,076
| 5,136
| 5,098
| 5,140
|
|
Вариант 13
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 2,132
| 2,114
| 2,160
| 2,146
| 2,120
| 3,373
| 3,324
| 3,377
| 3,327
| 3,385
| 2,708
| 2,645
| 2,657
| 2,645
| 2,657
| 4,277
| 4,254
| 4,311
| 4,288
| 4,265
| 3,075
| 3,074
| 3,090
| 3,099
| 3,096
| 5,083
| 5,076
| 5,136
| 5,098
| 5,140
| 3,978
| 3,928
| 3,905
| 3,948
| 3,904
| 6,898
| 6,908
| 6,887
| 6,940
| 6,904
| 3,781
| 3,808
| 3,820
| 3,814
| 3,842
|
|
Вариант 14
Y1x
| Y2x
| Y3x
| Y4x
| Y5x
| 2,567
| 2,587
| 2,585
| 2,527
| 2,583
| 4,148
| 4,183
| 4,155
| 4,144
| 4,169
| 3,234
| 3,259
| 3,216
| 3,240
| 3,200
| 5,458
| 5,485
| 5,490
| 5,513
| 5,469
| 3,781
| 3,808
| 3,820
| 3,814
| 3,842
| 6,713
| 6,722
| 6,750
| 6,751
| 6,700
| 4,998
| 4,949
| 4,950
| 4,947
| 4,968
| 9,758
| 9,689
| 9,701
| 9,711
| 9,686
| 9,178
| 9,194
| 9,157
| 9,159
| 9,121
|
|
2. Провести обработку и анализ результатов ЦКРП (табл. 8.5)для трех факторов (N=20) по рассмотренной методике.
В каждом варианте приведены результаты проведения пяти параллельных опытов (Y1x Y2x Y3x Y4x).
Вариант 1
-2.794 -2.836 -2.837 -2.751 -2.769
5.059 5.118 5.135 4.983 5.025
0.943 0.911 0.929 0.941 0.895
3.387 3.225 3.428 3.35 3.288
8.127 8.153 8.238 8.212 8.1
10.69 10.6 10.59 10.77 10.64
5.547 5.503 5.599 5.53 5.484
2.874 2.9 2.889 2.89 2.907
0.148 0.130 0.176 0.162 0.136
13.01 13.01 12.99 12.99 13.01
3.835 3.815 3.828 3.794 3.838
0.985 0.964 0.972 1.04 0.968
9.451 9.483 9.368 9.353 9.319
2.659 2.647 2.673 2.674 2.679
2.677 2.697 2.695 2.637 2.693
2.698 2.707 2.652 2.647 2.649
2.749 2.768 2.761 2.758 2.78
2.815 2.762 2.765 2.784 2.823
2.818 2.755 2.767 2.755 2.767
2.898 2.933 2.925 2.887 2.883
| Вариант 2
-1.694 -1.736 -1.737 -1.651 -1.669
12.16 12.22 12.24 12.08 12.13
4.043 4.011 4.029 4.041 3.995
8.487 8.325 8.528 8.45 8.388
13.23 13.25 13.34 13.31 13.2
17.79 17.7 17.69 17.87 17.74
8.647 8.603 8.699 8.63 8.584
3.974 4 3.989 3.99 4.007
-0.433 -0.451 -0.405 -0.419 -0.445
23.13 23.13 23.11 23.11 23.13
6.082 6.062 6.075 6.041 6.085
7.743 7.722 7.73 7.801 7.726
16.21 16.24 16.12 16.11 16.08
3.759 3.747 3.773 3.774 3.779
3.777 3.797 3.795 3.737 3.793
3.798 3.807 3.752 3.747 3.749
3.849 3.868 3.861 3.858 3.88
3.915 3.862 3.865 3.884 3.923
3.918 3.855 3.867 3.855 3.867
3.998 4.033 4.025 3.987 3.983
| Вариант 3
-8.114 -8.156 -8.157 -8.071 -8.089
7.739 7.798 7.815 7.663 7.705
-2.377 -2.409 -2.391 -2.379 -2.425
4.067 3.905 4.108 4.03 3.968
8.807 8.833 8.918 8.892 8.78
15.37 15.28 15.27 15.45 15.32
4.227 4.183 4.279 4.21 4.164
1.554 1.58 1.569 1.57 1.587
-4.535 -4.553 -4.507 -4.521 -4.547
19.56 19.56 19.54 19.55 19.56
0.833 0.813 0.826 0.792 0.836
0.812 0.791 0.799 0.870 0.795
12.64 12.67 12.56 12.54 12.51
1.339 1.327 1.353 1.354 1.359
1.357 1.377 1.375 1.317 1.373
1.378 1.387 1.332 1.327 1.329
1.429 1.448 1.441 1.438 1.46
1.495 1.442 1.445 1.464 1.503
1.498 1.435 1.447 1.435 1.447
1.578 1.613 1.605 1.567 1.563
| Вариант 4
-0.248 -0.29 -0.291 -0.205 -0.223
7.205 7.264 7.281 7.129 7.171
7.981 7.949 7.967 7.979 7.933
14.03 13.86 14.07 13.99 13.93
6.849 6.875 6.96 6.934 6.822
5.007 4.917 4.91 5.093 4.958
4.761 4.717 4.813 4.744 4.698
1.688 1.714 1.703 1.704 1.721
0.164 0.146 0.192 0.178 0.152
8.574 8.575 8.555 8.56 8.573
10.12 10.1 10.11 10.08 10.12
10.07 10.05 10.06 10.13 10.06
5.38 5.412 5.297 5.282 5.248
2.339 2.327 2.353 2.354 2.359
2.357 2.377 2.375 2.317 2.373
2.378 2.387 2.332 2.327 2.329
2.429 2.448 2.441 2.438 2.46
2.495 2.442 2.445 2.464 2.503
2.498 2.435 2.447 2.435 2.447
2.578 2.613 2.605 2.567 2.563
| Вариант 5
4.262 4.22 4.219 4.305 4.287
3.535 3.594 3.611 3.459 3.501
12.49 12.46 12.48 12.49 12.44
10.36 10.19 10.4 10.32 10.26
11.36 11.39 11.47 11.44 11.33
1.337 1.247 1.24 1.423 1.288
9.271 9.227 9.323 9.254 9.208
-1.982 -1.956 -1.967 -1.966 -1.949
7.463 7.445 7.491 7.477 7.451
2.116 2.117 2.097 2.102 2.115
10.54 10.52 10.53 10.5 10.54
10.49 10.47 10.48 10.55 10.48
5.8 5.832 5.717 5.702 5.668
2.759 2.747 2.773 2.774 2.779
2.777 2.797 2.795 2.737 2.793
2.798 2.807 2.752 2.747 2.749
2.849 2.868 2.861 2.858 2.88
2.915 2.862 2.865 2.884 2.923
2.918 2.855 2.867 2.855 2.867
2.998 3.033 3.025 2.987 2.983
| Вариант 6
-3.901 -3.943 -3.944 -3.858 -3.876
-4.628 -4.569 -4.552 -4.704 -4.662
4.328 4.296 4.314 4.326 4.28
2.192 2.03 2.233 2.155 2.093
3.196 3.222 3.307 3.281 3.169
-6.826 -6.916 -6.923 -6.74 -6.875
1.108 1.064 1.16 1.091 1.045
-10.14 -10.12 -10.13 -10.13 -10.11
7.504 7.486 7.532 7.518 7.492
-9.858 -9.857 -9.877 -9.872 -9.859
-1.433 -1.453 -1.44 -1.474 -1.43
-0.654 -0.675. -0.667 -0.596 -0.671
-5.348 -5.316 -5.431 -5.446 -5.48
2.8 2.788 2.814 2.815 2.82
2.818 2.838 2.836 2.778 2.834
2.839 2.848 2.793 2.788 2.79
2.89 2.909 2.902 2.899 2.921
2.956 2.903 2.906 2.925 2.964
2.959 2.896 2.908 2.896 2.908
3.039 3.074 3.066 3.028 3.024
| Вариант 7
-3.558 -3.6 -3.601 -3.515 -3.533
-4.285 -4.226 -4.209 -4.361 -4.319
4.671 4.639 4.657 4.669 4.623
2.535 2.373 2.576 2.498 2.436
3.539 3.565 3.65 3.624 3.512
-6.483 -6.573 -6.58 -6.397 -6.532
1.451 1.407 1.503 1.434 1.388
-9.802 -9.776 -9.787 -9.786 -9.769
7.847 7.829 7.875 7.861 7.835
-9.515 -9.514 -9.534 -9.529 -9.516
-1.09 -1.11 -1.097 -1.131 -1.087
-0.311 -0.332 -0.324 -0.253 -0.328
-5.005 -4.973 -5.088 -5.103 -5.137
3.143 3.131 3.157 3.158 3.163
3.161 3.181 3.179 3.121 3.177
3.182 3.191 3.136 3.131 3.133
3.233 3.252 3.245 3.242 3.264
3.299 3.246 3.249 3.268 3.307
3.302 3.239 3.251 3.239 3.251
3.382 3.417 3.409 3.371 3.367
| Вариант 8
-0.558 -0.6 -0.601 -0.515 -0.533
-1.285 -1.226 -1.209 -1.361 -1.319
7.671 7.639 7.657 7.669 7.623
5.535 5.373 5.576 5.498 5.436
6.539 6.565 6.65 6.624 6.512
-3.483 -3.573 -3.58 -3.397 -3.532
4.451 4.407 4.503 4.434 4.388
-6.802 -6.776 -6.787 -6.786 -6.769
10.85 10.83 10.88 10.86 10.84
-6.515 -6.514 -6.534 -6.529 -6.516
1.91 1.89 1.903 1.869 1.913
2.688 2.667 2.675 2.746 2.671
-2.005 -1.973 -2.088 -2.103 -2.137
6.143 6.131 6.157 6.158 6.163
6.161 6.181 6.179 6.121 6.177
6.182 6.191 6.136 6.131 6.133
6.233 6.252 6.245 6.242 6.264
6.299 6.246 6.249 6.268 6.307
6.302 6.239 6.251 6.239 6.251
6.382 6.417 6.409 6.371 6.367
| Вариант 9
-3.558 -3.6 -3.601 -3.515 -3.533
-4.285 -4.226 -4.209 -4.361 -4.319
4.671 4.639 4.657 4.669 4.623
2.535 2.373 2.576 2.498 2.436
3.539 3.565 3.65 3.624 3.512
-6.483 -6.573 -6.58 -6.397 -6.532
1.451 1.407 1.503 1.434 1.388
-9.802 -9.776 -9.787 -9.786 -9.769
7.847 7.829 7.875 7.861 7.835
-9.515 -9.514 -9.534 -9.529 -9.516
-1.09 -1.11 -1.097 -1.131 -1.087
-0.319 -0.339 -0.329 -0.259 -0.328
-5.005 -4.973 -5.088 -5.103 -5.137
3.143 3.131 3.157 3.158 3.163
3.161 3.181 3.179 3.121 3.177
3.182 3.191 3.136 3.131 3.133
3.233 3.252 3.245 3.242 3.264
3.299 3.246 3.249 3.268 3.307
3.302 3.239 3.251 3.239 3.251
3.382 3.417 3.409 3.371 3.367
| Вариант 10
0.312 0.27 0.269 0.355 0.337
-0.415 -0.356 -0.339 -0.491 -0.449
8.541 8.509 8.527 8.539 8.493
6.405 6.243 6.446 6.368 6.306
7.409 7.435 7.52 7.494 7.382
-2.613 -2.703 -2.71 -2.527 -2.662
5.321 5.277 5.373 5.304 5.258
-5.932 -5.906 -5.917 -5.916 -5.899
11.72 11.7 11.75 11.73 11.71
-5.645 -5.644 -5.664 -5.659 -5.646
2.78 2.76 2.773 2.739 2.783
3.558 3.537 3.545 3.616 3.541
-1.135 -1.103 -1.218 -1.233 -1.267
7.013 7.001 7.027 7.028 7.033
7.031 7.051 7.049 6.991 7.047
7.052 7.061 7.006 7.001 7.003
7.103 7.122 7.115 7.112 7.134
7.169 7.116 7.119 7.138 7.177
7.172 7.109 7.121 7.109 7.121
7.252 7.287 7.279 7.241 7.237
|
Контрольные вопросы
1. Когда и для чего используется ЦКП и в чем его отличие от планирования ПФЭ и ДФЭ?
2. Что является критерием оптимальности плана при ЦКОП и ЦКРП?
3. Как достигается ортогональность матрицы планирования при ЦКОП?
4. Почему при рототабельном планировании можно не проводить параллельных опытов?
5. В чем преимущество рототабельного планирования перед ортогональным и как оно достигается?
6. Каков порядок обработки результатов ЦКОП?
7. Каков порядок обработки результатов ЦКРП?
МОДУЛЬ 3 «ПАССИВНЫЙ ЭКСПЕРИМЕНТ»
ПРАКТИЧЕСКОЕ ЗАНЯТИЕ №9
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