Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 25 Nov 2020 15:26:41 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Nov/25/t1606314422mmcghyfgu7ffwj5.htm/, Retrieved Sun, 09 May 2021 01:10:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319300, Retrieved Sun, 09 May 2021 01:10:04 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact25
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [women] [2020-11-25 14:26:41] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
11.455804
2.9332886
6.2191311
6.9153776
4.9465626
5.1345641
3.0622244
5.5872164
7.655561
16.656451
3.2168822
6.8287604
1.03288
3.4909358
6.130999
4.6384438
7.9617834
8.9458241
2.4497795
2.6329406
6.1893723
2.0414829
1.0927938
0.73493386
3.5031847
9.033562
8.7579618
4.899559
6.3482659
2.9117379
3.2663727
4.3329434
3.2527249
10.564674
7.944759
6.1244488
3.2393779
2.5294579
2.9625241
5.7379961
9.7991181
6.1244488
15.716767
7.464172
2.3590469
6.1197413
12.20489
5.5732484
4.423213
10.968499
2.0029644
7.8392945
4.8253233
3.3406084
6.2782903
2.0747318
3.6746693
5.4109208
6.6348195
3.3684468
6.3553039
4.899559
4.749134
2.4655845
7.0431161
2.5922086
4.899559
4.9865907
3.075532
4.3940856
7.7248869
14.382577
9.634427
5.7382223
4.3388466
4.4113344
10.634978
7.6963907
11.660951
1.384658
1.4861996
5.0862936
9.0346479
2.4497795
7.1962273
5.5120039
3.2143846
1.031706
6.1244488
6.4555001
2.3437285
4.899559
3.9808917
0.86855819
6.7368937
0.83195229
4.5495905
6.0088932
8.6855819
3.0622244
1.5250024
5.345367
7.0431161
6.1244488
3.5956441
3.676112
2.0489884
2.4497795
3.0013006
5.0752404
4.4855118
8.9530095
1.0734989
14.290381
4.899559
8.2680059
3.4344948
7.3493386
4.8510486
4.7283954
5.5120039
6.8086976
2.6175726
4.0256208
5.4373155
12.283894
3.9808917
4.0483645
11.092494
5.2057815
3.122268
3.9808917
7.2640521
5.8301389
3.1919678
3.9156312
3.3328396
4.6044049
2.8467856
1.0881709
1.4329641
3.8277805
8.1148947
7.464172
5.8168281
6.4337644
2.5320159
4.4960659
1.7978221
5.2057815
0.31847134
5.9144677
10.1985
4.7464478
5.4284887
4.2540191
6.1244488
4.899559
7.9617834
5.8519878
4.1904123
2.3382624
7.9617834
2.756002
5.9429339
2.1149176
5.0526703
3.1847134
1.6472655
5.2057815
10.615711
4.8131688
9.933416
5.678538
3.5783296
4.6161699
8.0394371
7.1451903
7.4332435
2.008016
2.0029644
3.2812198
1.9935608
9.3755581
5.9713376
9.7531847
4.5933366
7.0431161
5.5975028
6.255687
2.7613122
4.899559
10.105341
2.5697342
7.7351672
2.756002
9.353908
5.7758026
5.0065363
5.709151
0.52581949
2.1435571
7.655561
8.5742283
3.1120978
3.9769108
3.7659204
7.3493386
1.0411563
5.8182264
0.22116065
4.899559
3.1070374
3.8370041
4.3866575
3.7284449
4.5933366
6.3220117
6.0509554
4.3927081
6.4306712
5.7324841
4.5933366
3.6860109
7.1560455
7.0431161
4.7770701
9.4928956
3.7913254
3.3684468
3.9808917
5.7324841
2.2058621
1.9053841
4.7770701
2.3810941
4.5569882
7.9617834
5.538632
9.6630192
5.2057815
6.407874
5.6962353
39.808917
4.5933366
9.5694512
8.5742283
0.76253701
5.7253274
18.473419
3.3641338
4.732115
2.1435571
4.1340029
7.6846912
3.7052915
5.4075133
3.9676662
6.0983873
3.3174098
11.722871
3.7454626
7.9617834
2.5409947
4.2200266
4.4585987
7.655561
5.3841308
5.6653097
5.2057815
5.4491294
4.1141335
11.889597
3.0622244
4.5627144
4.899559
0.82612539
1.8373346
6.27756
4.7464478
5.2057815
3.2094011
5.2057815
5.8182264
6.2394385
11.811437
5.8182264
2.9047966
4.1033807
7.3493386
0.50470893
0.46972174
4.6315084
3.5985462
6.4306712
6.3694268
4.3517823
4.6848373
3.9704432
10.707113
4.7464478
9.8436595
4.5062611
5.463969
4.199622
3.1847134
5.6267021
2.1435571
3.6746693
5.7406851
7.9617834
1.378001
5.1328714
3.0622244
6.8243858
1.3132839
5.5120039
5.9395187
6.5867908
4.0946315
6.6097825
5.3524595
2.0603506
3.6396724
6.5787506
2.5142474
5.3287527
7.3493386
9.0991811
8.6660951
2.1713955
9.2993631
2.8152162
7.0431161
7.4630507
5.3928785
3.2519961
4.2871142
5.9151437
7.1349829
11.636453
5.1778246
4.6540697
0.77675936
5.3841308
11.198992
7.4535845
7.8115611
8.2680059
1.8515775
4.512271
3.7898089
3.5713791
10.615711
5.5120039
12.086732
4.899559
2.5875796
3.1531816
2.4419527
3.8781215
6.0776973
5.47977
8.7172811
5.6543214
7.6007479
8.1065431
2.3645204
4.1904123
5.6617127
7.9617834
5.8460648
4.1218217
5.0616149
5.2488066
4.099136
12.327923
25.076734
5.8494735
5.5120039
3.0622244
8.3204223
0.89124441
7.8283457
4.3987754
8.4503174
0.23144719
2.6724867
3.5812038
10.411563
7.0063694
9.5823234
4.5933366
6.7572332
7.3493386
10.394821
6.5109696
18.373346
3.5649777
9.0239812
6.2101911
6.4306712
10.105341
11.785314
3.4702176
4.2462845
6.2554875
13.609886
6.4306712
6.7368937
5.8877054
1.7416401
10.436795
5.1297397
7.0117979
10.564674
9.0991811
3.3075658
3.558339
7.3493386
5.4927119
1.561134
12.15153
4.7961201
5.4140127
6.6348195
12.489072
3.0622244
5.984347
7.5081651
12.853238
5.8182264
7.556947
2.3161552
4.2871142
11.33023
3.2563531
4.8253233
9.9102912
5.6068897
4.153974
4.8745613
1.6634305
3.2587765
6.9665605
10.227235
2.8141797
5.2057815
10.49425
9.7991181
9.7640676
7.9617834
4.2871142
7.0431161
3.6419848
6.5639131
13.771734
4.1513955
2.5477707
6.7368937
4.2871142
9.1866732
5.8182264
7.0431161
4.1596256
4.9761146
5.1309919
5.3588927
2.6423386
3.8216561
4.5933366
4.5924179
5.5120039
5.7555061
4.0943318
3.1800023
6.9563705
10.555395
3.613127
9.7644514
2.7936082
5.2057815
4.6044049
9.1866732
8.1570315
10.110201
12.16923
5.9883499
16.229789
7.5687886
10.184616
7.0035302
4.1226063
3.3843925
4.8149014
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3.6746693
9.1866732
6.130999
2.756002
5.8182264
11.33023
1.6003585
1.3099078
2.4430678
4.0904575
4.3498524
11.12634
0.68326578
7.3162334
4.899559
6.7368937
6.2650099
3.3649802
4.1690793
5.2423265
2.8204104
8.5742283
9.1866732
4.1728932
10.133179
5.846666
7.655561
3.2497075
5.6729184
4.0946315
7.3493386
8.2009787
5.2057815
9.9522293
2.9694297
5.5120039
5.5168263
14.392455
5.0526703
10.42566
6.2605477
2.756002
4.069356
3.7671769
2.2500692
4.5987089
1.5833092
8.9214234
20.845397
4.899559
4.0180588
6.2520415
3.4351965
5.7449731
5.9551551
4.7842464
4.0355397
8.9419598
9.1866732
5.4595086
5.8159123
2.6407242
2.4497795
2.78953
7.9123314
5.9250481
7.9617834
4.0300385
4.6115952
5.1537484
5.3078556
7.6128607
8.8804508
3.3684468
5.1644001
4.7996567
3.8026428
2.5578013
5.5120039
13.469397
7.0431161
5.9713376
11.071436
3.1154805
4.899559
5.3588927
3.7688916
5.5501229
2.1055956
3.8680729
7.3493386
10.702018
5.790388
6.8898338
6.5837825
5.3323723
3.4920103
5.5547326
9.6282032
4.0880379
2.3451498
0.81381091
3.7988629
1.6116971
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3.3188502
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3.694112
8.4628747
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15.923567
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11.024008
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5.3343949
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13.52581
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6.902827
11.082336
8.2325924
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4.6584903
4.6809435
3.9808917
13.859401
6.7254817
6.6450269
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9.7991181
3.5840826
5.0526703
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16.854051
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5.1122725
6.255687
10.947452
7.0041806
7.847343
6.9695855
6.6400397
12.248898
4.4657439
4.8770496
7.655561
7.8392945
12.950775
6.414327
4.7264966
6.3247812
9.9700329
1.2550595
11.758942
7.655561
9.1866732
9.5066071
4.209145
7.6402499
4.170458
8.4211171
21.873031
6.3796342
6.2752973
3.7273754
2.4454049
6.3694268
8.5742283
9.5925102
10.411563
4.4134611
9.7991181
4.0059288
3.7467216
3.3544069
5.5363395
4.5036351
6.0509554
1.8699984
15.923567
4.3036667
6.4773831
6.9611221
5.5479094
12.926881
9.5137069
5.5120039
5.4830072
5.8692634
4.1618413
4.6545811
7.9617834
18.702176
4.2314374
12.744686
6.8025478
10.432682
6.4207931
0.42462845
3.1100717
6.7368937
4.4096031
2.1826696
6.239919
3.9667248
3.0761436
5.4354483
4.8045245
3.6746693
4.683402
3.9808917
5.5120039
5.8182264
3.3352773
4.3169251
6.9205039
4.14337
4.9128972
5.5732484
15.923567
8.3442272
4.00413
4.899559
1.0484653
3.8663337
2.2781854
0.15348016
4.6954107
4.804252
4.2747831
6.4306712
5.0921654
10.068145
3.0622244
4.3036667
3.6396724
3.3523299
3.2587765
8.1188444
4.4541446
6.5089032
6.4306712
5.1248261
5.4867721
13.78001
11.072174
6.1845449
6.1244488
4.899559
3.3289687
5.1935965
8.8532573
7.0431161
7.0431161
6.4306712
4.1315201
3.9196472
5.1295556
8.1065431
10.717785
6.8648929
3.9371457
6.7368937
6.1639614
1.935996
5.2057815
11.222323
1.2962342
6.1244488
20.614742
8.4335343
8.9082892
3.280168
6.526052
7.655561
7.6215363
8.1413725
3.1980385
6.8593827
5.4861557
7.2983015
1.3673188
4.9712599
7.5659711
6.8593827
1.6331863
4.4541446
2.048646
6.4380998
1.4361729
12.384996
7.3187163
3.9374638
7.2255911
5.9184939
7.0431161
6.1069504
8.9082892
8.1659317
8.6056662
5.3201708
9.380599
5.9916898
7.5161163
4.4096031
10.044974
4.0734706
8.5742283
4.5905778
9.1646428
6.4050101
6.3262929
9.4580815
6.230961
1.0704919
11.942675
10.176007
2.3945213
11.795235
1.7515924
3.6746693
5.1182894
6.6776248
5.8115208
4.7120759
5.6012547
6.6144047
6.3838192
11.636453
5.2057815
7.5073889
3.81911
4.3074221
2.5376202
3.3684468
4.0829659
3.5454215
3.5385704
5.2470206
7.9617834
8.8158156
5.1886714
8.2921748
4.4386249
10.050839
5.8182264
5.6036012
4.3695276
18.93964
9.3120274
9.7991181
6.7177548
5.8593211
3.3011714
11.548961
6.8900049
28.308563
9.1866732
8.5304823
6.9107704
12.356394
8.2680059
4.3126327
11.022862
8.0239466
4.2690528
7.3984226
4.9914982
9.7494604
92.547226
4.4637203
2.3189661
11.163177
9.2310533
5.9883499
8.5742283
4.2871142
7.3737875
4.5933366
3.7913254
5.0045496
7.8392945
8.3598726
5.3978193
9.7292669
6.1392065
5.7665592
3.5451355
4.0087301
5.1160054
5.4987857
3.8512813
5.5120039
7.0836998
7.655561
6.3847502
7.655561
6.0048468
4.6001415
4.7784755
7.6740081
9.5466818
13.473787
6.9715119
6.2257555
6.9213621
3.2364973
13.167565
3.8277805
4.0996895
9.8896829
4.899559
2.8886289
11.636453
2.2747953
8.8119274
6.8243858
3.3736404
3.9129584
27.454426
8.3580713
6.3137175
7.1266313
2.7874953
8.1659317
4.5116773
4.7464478
3.3684468
4.5933366
5.6651151
0.52509701
5.3674945
1.7860774
4.0167556
8.6395405
2.9936306
7.6619673
4.2389125
5.8593211
7.6215363
17.23347
8.9046262
7.5661684
4.0846023
7.9617834
3.966242
3.4821467
4.2652411
1.9337918
10.499055
2.9488087
2.5325752
8.1178968
6.4306712
3.2996835
4.5333998
2.3094368
2.511024
2.2419665
3.6491507
5.144537
4.3203476
4.8348046
112.79193
2.8266687
2.5905504
8.7961214
3.3790062
6.1244488
1.961746
3.8435029
4.0829659
2.6539278
3.6087404
5.6344929
5.5120039
4.899559
1.9324717
5.2057815
6.4313584
7.893734
4.3342253
7.0147872
5.268432
7.3493386
5.0685094
8.6596857
9.4054035
2.6152928
11.263354
6.1244488
7.9216737
5.9316169
8.5742283
3.9398516
4.9261871
2.0679957
3.8663337
7.9617834
4.7180939
1.6703042
4.4437861
8.3243993
11.200782
4.9761146
3.2447411
2.9652825
4.1467622
3.9664308
5.4626301
7.0890753
14.623684
6.3893046
3.7031551
7.3891262
9.4928956
11.65139
6.7368937
11.268986
4.5512039
4.2871142
4.6044049
3.9808917
10.846945
4.5964935
3.3700671
10.795639
3.122268
6.727021
4.7668133
4.1340029
7.0186521
8.5742283
4.3649249
5.7382223
7.655561
4.4402254
2.1362886
7.2106718
5.0729948
9.4783136
5.5120039
3.7913254
10.593641
8.0629187
5.1819065
8.2232974
4.423213
10.166585
5.287712
7.3588927
8.5304823
24.880573
3.8299033
15.311122
9.9714051
13.381921
7.655561
4.7775478
5.3189367
2.6305452
4.1596256
6.1244488
4.1010394
5.4207887
6.1244488
7.4235743
10.07024
3.899649
6.1433514
5.2018912
5.5120039
8.1659317
4.5933366
7.979595
7.4703153
7.0431161
6.1132524
6.8243858
3.2663727
7.742415
10.564674
4.516606
1.5923567
8.6752049
5.4765165
4.899559
5.8976174
7.8392945
5.9319458
6.1426222
5.8197808
2.8155619
4.2564919
5.5120039
5.5608358
10.441683
7.655561
3.0819807
4.5933366
3.7349518
7.3493386
3.1453959
8.3292504
5.538632
6.410638
10.411563
1.6147842
5.0270249
6.5357924
5.0674754
3.0603852
7.025103
8.8804508
2.0061187
12.248898
4.7315742
11.231423
5.0955414
1.7296475
4.899559
6.1244488
3.6746693
3.2449772
2.5275503
7.4504762
3.6746693
3.3036446
2.8478687
4.7770701
4.5951776
4.7629784
6.7368937
0.35385704
1.9730211
4.8621578
5.9806824
1.3269639
4.8862931
3.2663727
2.3371184
4.2356953
7.0458261
0.39908689
5.466202
1.4262039
2.8697417
22.116065
2.9973773
4.8580374
3.3406084
7.655561
4.5933366
4.0829659
4.2871142
2.987536
4.6545811
5.5120039
6.4306712
7.7090964
3.3273125
4.4096031
6.4500524
3.5168614
6.6438465
3.814028
4.028096
2.1254094
1.0669057
10.698497
5.5520427
3.6746693
4.696022
6.1396837
2.756002
3.4286769
3.629303
6.8900049
2.9869835
7.2793449
10.411563
8.5687355
6.3666586
5.5120039
3.4020677
4.2255275
3.9808917
6.5723545
5.8657977
5.0021676
6.1146497
8.9082892
5.6533374
9.1866732
1.4421344
4.7662377
3.5605491
4.2274956
7.0458261
2.1231423
7.5467142
4.2871142
4.5919123
6.9358549
3.6746693
5.6200824
5.8144145
7.7205173
10.474168
8.1774151
3.655921
4.5933366
5.3078556
4.5933366
8.8875722
5.0955414
7.6331346
2.5332947
3.899649
6.5841265
1.5165302
4.2819676
3.5934707
4.342791
5.790388
2.076987
6.1244488
2.8562452
5.5120039
4.2871142
7.1451903
4.5387839
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4.153974












































\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline R Engine error message &
Error in array(list(11.455804, 2.9332886, 6.2191311, 6.9153776, 4.9465626,  : 
  length of 'dimnames' [1] not equal to array extent
Execution halted
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319300&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [ROW]
R Engine error message[/C][C]
Error in array(list(11.455804, 2.9332886, 6.2191311, 6.9153776, 4.9465626,  : 
  length of 'dimnames' [1] not equal to array extent
Execution halted
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319300&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319300&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
R Engine error message
Error in array(list(11.455804, 2.9332886, 6.2191311, 6.9153776, 4.9465626,  : 
  length of 'dimnames' [1] not equal to array extent
Execution halted



Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')