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CC-score

*The author of this computation has been verified*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Thu, 02 Dec 2010 13:49:13 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6.htm/, Retrieved Thu, 02 Dec 2010 14:49:19 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
162556 807 213118 6282154 56289 29790 444 81767 4321023 28328 87550 412 153198 4111912 17936 84738 428 -26007 223193 4145 54660 315 126942 1491348 3040 42634 168 157214 1629616 2964 40949 263 129352 1398893 2865 45187 267 234817 1926517 2854 37704 228 60448 983660 2167 16275 129 47818 1443586 1974 25830 104 245546 1073089 1910 12679 122 48020 984885 1871 18014 393 -1710 1405225 943 43556 190 32648 227132 929 24811 280 95350 929118 822 6575 63 151352 1071292 819 7123 102 288170 638830 769 21950 265 114337 856956 745 37597 234 37884 992426 652 17821 277 122844 444477 643 12988 73 82340 857217 601 22330 67 79801 711969 446 13326 103 165548 702380 436 16189 290 116384 358589 379 7146 83 134028 297978 305 15824 56 63838 585715 284 27664 236 74996 657954 247 11920 73 31080 209458 238 8568 34 32168 786690 223 14416 139 49857 439798 220 3369 26 87161 688779 217 11819 70 106113 574339 199 6984 40 80570 741409 195 4519 42 102129 597793 163 2220 12 301670 644190 154 18562 211 102313 etc...
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time26 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
CC-score[t] = + 2225.19734859957 + 0.0607507551073995kosten[t] + 0.221457567847118orders[t] -0.049251159404494dividenden[t] + 0.00579323022270509rijkdom[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)2225.19734859957970.082562.29380.0222870.011143
kosten0.06075075510739950.0582241.04340.2973550.148678
orders0.2214575678471180.0327716.757800
dividenden-0.0492511594044940.008248-5.971400
rijkdom0.005793230222705090.0015573.72060.0002250.000113


Multiple Linear Regression - Regression Statistics
Multiple R0.40483788847509
R-squared0.163893715944969
Adjusted R-squared0.156042952714406
F-TEST (value)20.8761506533425
F-TEST (DF numerator)4
F-TEST (DF denominator)426
p-value9.9920072216264e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7991.99994922802
Sum Squared Residuals27209498918.2843


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
15628938176.96917961118112.030820389
22832825138.85098894933189.14901105074
31793623911.2402292597-5975.24022925966
4414510041.7620096579-5896.7620096579
530408003.27438568724-4963.27438568724
629646550.21880123234-3586.21880123234
728656504.49659447667-3639.49659447667
828544625.21790132280-1771.21790132280
921677287.67090182128-5120.67090182128
1019749250.4180180946-7276.41801809461
111910-2059.352620603773969.35262060377
1218716363.09886916871-4492.09886916871
1394311631.6056975506-10688.6056975506
149294621.21029065388-3692.21029065388
158224480.98888140721-3658.98888140721
168191390.56510375827-571.565103758268
17769-7811.303693272418580.30369327241
187452950.67626458326-2205.67626458326
196528444.5859333671-7792.58593336711
20643-106.071534526227749.071534526227
216013941.10952483784-3340.10952483784
224463790.90792398461-3344.90792398461
23436-1026.809852622591462.80985262259
24379-381.744286094151760.744286094151
25305-2197.076014635982502.07601463598
262843448.00524704609-3164.00524704609
272474076.10927115281-3829.10927115281
282382648.22513362828-2410.22513362828
292235726.40436384266-5503.40436384266
302203224.09884721396-3004.09884721396
312172133.09955402989-1916.09955402989
321991059.80232795234-860.802327952338
331952985.32603794899-2790.32603794899
34163942.212044479443-779.212044479443
35154-8762.93476463728916.9347646372
36143550.005210554648-407.005210554648
371352215.68720442454-2080.68720442454
381321067.99209160858-935.992091608575
39120-3868.236079252003988.236079252
40119-227.561017019234346.561017019234
41108-1776.552700880741884.55270088074
42104324.002534815276-220.002534815276
43101806.251195115995-705.251195115995
4490-924.684702099171014.68470209917
45851353.55963566989-1268.55963566989
4674-372.715965537587446.715965537587
4773-5689.193304733885762.19330473388
4870-1537.948969433931607.94896943393
4967-751.27516619735818.27516619735
5066-263.161772730389329.161772730389
5166187.639573458709-121.639573458709
5266-1126.597994275221192.59799427522
5359-3766.734858461213825.73485846121
5458-1840.113006621531898.11300662153
5558-3825.404234542183883.40423454218
56541717.50851432496-1663.50851432496
57533756.96889129262-3703.96889129262
5849826.589507911453-777.589507911453
5949-607.930988902784656.930988902784
6044751.185994884342-707.185994884342
6139784.130968303094-745.130968303094
6238202.659170584225-164.659170584225
6338158.864168980380-120.864168980380
6437752.556169827759-715.556169827759
65361550.63657664052-1514.63657664052
6635685.065442491717-650.065442491717
6734-183.786116982569217.786116982569
6833-285.539775491196318.539775491196
6932-1957.611070762601989.61107076260
703212.378722317756119.6212776822439
71312245.55176484996-2214.55176484996
72312814.76230962993-2783.76230962993
7330-1143.976909066841173.97690906684
7430243.043437937883-213.043437937883
7530-1216.830178762801246.83017876280
7628-54.895704912461282.8957049124612
7726-1226.997086386321252.99708638632
78263.1279109171233622.8720890828766
7926702.919991140074-676.919991140074
8025-1567.900422002011592.90042200201
81251324.50273822489-1299.50273822489
8224554.426760763043-530.426760763043
8324-2514.618479707862538.61847970786
84232075.25926823205-2052.25926823205
8523-612.54314882945635.54314882945
86221492.19520963438-1470.19520963438
8722678.375725282294-656.375725282294
8822-807.144471623257829.144471623257
8920-1100.03717813461120.0371781346
9020-589.702605190745609.702605190745
9119-72.538894757363391.5388947573633
9218522.930538380274-504.930538380274
93171866.35602009729-1849.35602009729
9416-1508.364017619081524.36401761908
95142578.96616758538-2564.96616758538
96141632.16805615539-1618.16805615539
9714-835.475477485983849.475477485983
9813774.256688363327-761.256688363327
9913-1686.459917741971699.45991774197
10013546.611160223424-533.611160223424
10113341.546992407641-328.546992407641
10213-808.08783495278821.08783495278
10312-1167.268372984521179.26837298452
10412-1521.402505194801533.40250519480
105111083.45254993613-1072.45254993613
10610314.685718317125-304.685718317125
10710209.410741453744-199.410741453744
10810491.158937359152-481.158937359152
10910-551.232611848826561.232611848826
11010201.644521822094-191.644521822094
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1129994.347261177008-985.347261177008
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1146405.340499319375-399.340499319375
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1185567.540243786373-562.540243786373
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1214-371.001446219840375.001446219840
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1232-177.341066382604179.341066382604
1242-183.809163894828185.809163894828
12511336.23270225929-1335.23270225929
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12701061.73589719542-1061.73589719542
12801061.15884321882-1061.15884321882
12901245.13340292870-1245.13340292870
13001061.73589719542-1061.73589719542
13101063.83458275168-1063.83458275168
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13301061.73589719542-1061.73589719542
13401045.87234201709-1045.87234201709
13501082.65933862504-1082.65933862504
13601090.25867694008-1090.25867694008
13701080.26484891981-1080.26484891981
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13901114.40971628414-1114.40971628414
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14301223.17550339107-1223.17550339107
14401058.86917943540-1058.86917943540
14501047.11525159417-1047.11525159417
14601274.12243872482-1274.12243872482
14701061.73589719542-1061.73589719542
14801055.89223415727-1055.89223415727
14901135.81565603309-1135.81565603309
15001050.88730000387-1050.88730000387
15101059.35303960870-1059.35303960870
15201138.51958386001-1138.51958386001
15301062.84318503465-1062.84318503465
15401092.24337458900-1092.24337458900
15501051.87498860301-1051.87498860301
15601061.73589719542-1061.73589719542
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15801029.37476843810-1029.37476843810
15901203.20937596441-1203.20937596441
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17601060.75260450648-1060.75260450648
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18001061.73589719542-1061.73589719542
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18601223.46211286266-1223.46211286266
18701214.78985261556-1214.78985261556
1880995.191188044063-995.191188044063
1890692.406099037058-692.406099037058
19001061.73589719542-1061.73589719542
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19301026.95229635928-1026.95229635928
19401061.73589719542-1061.73589719542
19501057.51290885179-1057.51290885179
19601061.73589719542-1061.73589719542
1970-670.444619295417670.444619295417
19801061.73589719542-1061.73589719542
19901061.73589719542-1061.73589719542
2000-122.450099684981122.450099684981
20101061.73589719542-1061.73589719542
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20301061.73589719542-1061.73589719542
20401481.57349460038-1481.57349460038
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20601021.84136080571-1021.84136080571
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20801061.73589719542-1061.73589719542
20901900.83256305510-1900.83256305510
21001029.65070276457-1029.65070276457
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212267139.27021528727127.72978471273
213474-1265.108604274241739.10860427424
2145341718.23423810380-1184.23423810380
21501096.93472968430-1096.93472968430
21615139.391716797484-124.391716797484
217397149.047152752360247.952847247640
21807.27187899534681-7.27187899534681
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220288-1196.825340063161484.82534006316
22101015.12508699445-1015.12508699445
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22420299.024712512484-279.024712512484
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2270180.646183229562-180.646183229562
228192139.27021528727052.7297847127302
22901298.12517165803-1298.12517165803
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2360139.093865291472-139.093865291472
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2390139.27021528727-139.27021528727
2400139.27021528727-139.27021528727
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2660139.27021528727-139.27021528727
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273125139.270215287270-14.2702152872698
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3290139.27021528727-139.27021528727
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3320139.27021528727-139.27021528727
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3430139.27021528727-139.27021528727
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3480139.27021528727-139.27021528727
3491595139.2702152872701455.72978471273
350312-56.5851520261839368.585152026184
35160-493.50182904787553.50182904787
352587294.71762115064292.28237884936
353135-617.48475488464752.48475488464
3540494.815251300011-494.815251300011
3550139.27021528727-139.27021528727
356514139.27021528727374.72978471273
35703795.0455384202-3795.0455384202
3580139.27021528727-139.27021528727
3590139.27021528727-139.27021528727
3601139.27021528727-138.27021528727
3610139.069612574113-139.069612574113
3620139.27021528727-139.27021528727
3631763139.2702152872701623.72978471273
364180682.779062980999-502.779062980999
3650-150.011155391809150.011155391809
3660139.27021528727-139.27021528727
3670139.27021528727-139.27021528727
3680139.27021528727-139.27021528727
369218139.27021528727078.7297847127302
3700131.175019996045-131.175019996045
371448139.27021528727308.72978471273
372227-461.326688842690688.32668884269
3731742619.15538319041-2445.15538319041
37401439.27071994794-1439.27071994794
3750139.27021528727-139.27021528727
376121139.270215287270-18.2702152872698
377607119.593900115765487.406099884235
3782212-265.9865481508392477.98654815084
3790-80.964475931408580.9644759314085
3800139.27021528727-139.27021528727
381530139.27021528727390.72978471273
3825712861.63482329764-2290.63482329764
3830-2398.483193935322398.48319393532
38478139.27021528727-61.27021528727
3852489482.4454478470912006.55455215291
3861315514.73571760857-5383.73571760857
387923-670.8664013954481593.86640139545
38872-13.075319717734185.0753197177341
389572125.766987209078446.233012790922
39039732.7523704685520364.247629531448
391450238.424685240868211.575314759132
392622975.489568639023-353.489568639023
3936942756.86228508428-2062.86228508428
3943425-809.4826765146084234.48267651461
3955626761.11188255103-6199.11188255103
39649171204.201018978673712.79898102133
39714421922.11011521089-480.110115210894
3985297478.08862736367-6949.08862736367
39921261778.90234625530347.097653744696
4001061-4479.85050079825540.8505007982
4017762834.52731676273-2058.52731676273
40261124.3718473441693586.62815265583
4031526710.007121857298815.992878142702
4045923844.17276704166-3252.17276704166
40511822821.12977971315-1639.12977971315
4066211776.08405875345-1155.08405875345
4079891671.17545764071-682.175457640715
408438276.705012304751161.294987695249
409726459.546882246314266.453117753686
41013036303.52816196975-5000.52816196975
41174192460.928992735464958.07100726454
41211642101.48214628337-937.482146283368
41333101634.373636701191675.62636329881
4141920-3189.128002428245109.12800242824
41596511667.6521158017-10702.6521158017
41632568436.94602002951-5180.94602002951
4171135-10020.125275238211155.1252752382
41812701713.56505880768-443.565058807678
4196613993.40392803695-3332.40392803695
42010134681.2413623307-3668.2413623307
42128445192.67863088673-2348.67863088673
422115286461.34986279195066.6501372081
42365263108.981032670163417.01896732984
424226416665.8177769437-14401.8177769437
42551091013.501187283944095.49881271606
42639996469.24356204597-2470.24356204597
4273562411470.596305224724153.4036947753
428925221985.6195159779-12733.6195159779
429152367347.494063651487888.50593634852
430180731026.6718571884617046.3281428115
4311625568622.64792251147153933.352077489


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.2125167131630390.4250334263260780.787483286836961
90.1298748260125510.2597496520251010.87012517398745
100.08070539749555340.1614107949911070.919294602504447
110.1132189293466980.2264378586933960.886781070653302
120.08523411261263990.1704682252252800.91476588738736
130.0762788684661460.1525577369322920.923721131533854
140.05666891709069530.1133378341813910.943331082909305
150.03196655263008430.06393310526016860.968033447369916
160.02288814464036120.04577628928072240.977111855359639
170.02338291390958110.04676582781916230.976617086090419
180.01289508279253260.02579016558506520.987104917207467
190.00700553137039880.01401106274079760.992994468629601
200.004044242803434560.008088485606869110.995955757196565
210.002606657140514020.005213314281028040.997393342859486
220.001593433348390860.003186866696781720.99840656665161
230.00095000279920820.00190000559841640.999049997200792
240.0005234383248254980.001046876649651000.999476561675175
250.0004890163965120570.0009780327930241130.999510983603488
260.000306490016031910.000612980032063820.999693509983968
270.0001488651865739010.0002977303731478020.999851134813426
280.0001353729299363810.0002707458598727630.999864627070064
296.95370092179137e-050.0001390740184358270.999930462990782
303.86888697544557e-057.73777395089114e-050.999961311130246
312.10927149427105e-054.21854298854210e-050.999978907285057
321.09899921147204e-052.19799842294407e-050.999989010007885
335.32453365964768e-061.06490673192954e-050.99999467546634
342.81864572370921e-065.63729144741842e-060.999997181354276
351.82972329488405e-063.6594465897681e-060.999998170276705
369.08590084664337e-071.81718016932867e-060.999999091409915
374.20300499551869e-078.40600999103738e-070.9999995796995
381.98205601526389e-073.96411203052778e-070.999999801794399
391.08217356000135e-072.16434712000269e-070.999999891782644
407.58751067871814e-081.51750213574363e-070.999999924124893
413.8995824043246e-087.7991648086492e-080.999999961004176
422.10249233676175e-084.2049846735235e-080.999999978975077
431.00802923721687e-082.01605847443374e-080.999999989919708
445.13915763000892e-091.02783152600178e-080.999999994860842
452.73677636808491e-095.47355273616983e-090.999999997263224
461.46392864701814e-092.92785729403628e-090.999999998536071
477.78532980773889e-101.55706596154778e-090.999999999221467
483.4517122321572e-106.9034244643144e-100.999999999654829
491.58395097197166e-103.16790194394333e-100.999999999841605
501.04632762732490e-102.09265525464981e-100.999999999895367
514.58960671586639e-119.17921343173278e-110.999999999954104
522.00063349370651e-114.00126698741301e-110.999999999979994
539.01967405018123e-121.80393481003625e-110.99999999999098
544.25390836482142e-128.50781672964283e-120.999999999995746
552.16212703861893e-124.32425407723786e-120.999999999997838
569.7092626428169e-131.94185252856338e-120.99999999999903
574.05390782881894e-138.10781565763787e-130.999999999999595
581.71602462633704e-133.43204925267409e-130.999999999999828
598.13455145202905e-141.62691029040581e-130.999999999999919
603.32487887580687e-146.64975775161374e-140.999999999999967
611.34846131532277e-142.69692263064554e-140.999999999999986
629.06558584689765e-151.81311716937953e-140.99999999999999
633.82122900778245e-157.64245801556491e-150.999999999999996
641.54915616631547e-153.09831233263094e-150.999999999999998
656.56204744621773e-161.31240948924355e-151
662.45163788323765e-164.9032757664753e-161
671.19709765131027e-162.39419530262053e-161
684.87414758202253e-179.74829516404507e-171
692.05460750793188e-174.10921501586376e-171
707.89269691308249e-181.57853938261650e-171
714.28315034384225e-188.5663006876845e-181
721.63116384418287e-183.26232768836574e-181
736.86502996128151e-191.37300599225630e-181
742.80897816801889e-195.61795633603778e-191
751.11021745537741e-192.22043491075482e-191
764.91585104274915e-209.8317020854983e-201
771.94027470111384e-203.88054940222769e-201
788.61555027637528e-211.72311005527506e-201
793.32996491804111e-216.65992983608223e-211
801.30767655920586e-212.61535311841172e-211
814.75989789389794e-229.51979578779589e-221
821.64976555737664e-223.29953111475328e-221
836.54989732783297e-231.30997946556659e-221
842.47035855494859e-234.94071710989718e-231
859.03267426147026e-241.80653485229405e-231
863.07270854415339e-246.14541708830678e-241
871.06331329240423e-242.12662658480845e-241
883.83930869131153e-257.67861738262306e-251
891.39152898477634e-252.78305796955268e-251
904.9445545096924e-269.8891090193848e-261
911.68821927218420e-263.37643854436840e-261
925.56102805986073e-271.11220561197215e-261
931.81684410873052e-273.63368821746105e-271
946.04522676891876e-281.20904535378375e-271
951.94050633386946e-283.88101266773893e-281
966.09251271219822e-291.21850254243964e-281
972.24524169128481e-294.49048338256962e-291
987.29338531396318e-301.45867706279264e-291
992.64218988780329e-305.28437977560658e-301
1008.5583700563516e-311.71167401127032e-301
1012.80578945511282e-315.61157891022564e-311
1021.02823696588882e-312.05647393177765e-311
1033.39843255107255e-326.7968651021451e-321
1041.13965506748803e-322.27931013497606e-321
1053.45384287953964e-336.90768575907927e-331
1061.15628260412030e-332.31256520824061e-331
1076.60949540890408e-341.32189908178082e-331
1082.79489695985318e-345.58979391970636e-341
1098.49925812933704e-351.69985162586741e-341
1102.90529820594944e-355.81059641189888e-351
1118.90189867491932e-361.78037973498386e-351
1124.99961619870229e-369.99923239740457e-361
1131.57500998682651e-363.15001997365303e-361
1144.87607269716611e-379.75214539433222e-371
1151.39328429202117e-372.78656858404234e-371
1164.00232159752435e-388.00464319504871e-381
1171.19127543201098e-382.38255086402196e-381
1184.00894031674425e-398.0178806334885e-391
1191.50835728755709e-393.01671457511418e-391
1204.34688380689722e-408.69376761379444e-401
1211.21067365522288e-402.42134731044576e-401
1223.39438338874958e-416.78876677749915e-411
1231.02469283376955e-412.04938566753909e-411
1244.92479856034834e-429.84959712069668e-421
1251.33583800994708e-422.67167601989415e-421
1265.40141111871414e-431.08028222374283e-421
1271.49681068533955e-432.9936213706791e-431
1284.13840076187176e-448.27680152374352e-441
1291.13594220350556e-442.27188440701112e-441
1303.08715387434143e-456.17430774868285e-451
1318.36577220541451e-461.67315444108290e-451
1322.19026349476667e-464.38052698953334e-461
1335.84382851695938e-471.16876570339188e-461
1341.56710562494424e-473.13421124988848e-471
1354.14110281456365e-488.2822056291273e-481
1361.08464929578867e-482.16929859157734e-481
1372.82821473715952e-495.65642947431904e-491
1387.32440405362205e-501.46488081072441e-491
1391.88486449371722e-503.76972898743444e-501
1404.82009798149453e-519.64019596298905e-511
1411.21702949413456e-512.43405898826913e-511
1423.0761058856389e-526.1522117712778e-521
1438.0211394539467e-531.60422789078934e-521
1442.00730138409500e-534.01460276819000e-531
1454.97996365985182e-549.95992731970364e-541
1461.23470042670133e-542.46940085340265e-541
1473.0333365155595e-556.066673031119e-551
1487.4377123288965e-561.4875424657793e-551
1491.79591671427753e-563.59183342855505e-561
1504.33817225189178e-578.67634450378355e-571
1511.08542043393424e-572.17084086786849e-571
1522.75191072930722e-585.50382145861443e-581
1536.55836976320688e-591.31167395264138e-581
1541.59534085128208e-593.19068170256415e-591
1553.75585432744232e-607.51170865488464e-601
1568.78497999556471e-611.75699599911294e-601
1572.04375487131469e-614.08750974262939e-611
1584.73389300679684e-629.46778601359368e-621
1591.08215940959318e-622.16431881918636e-621
1602.47784033615476e-634.95568067230952e-631
1615.6435353809768e-641.12870707619536e-631
1621.27859916692747e-642.55719833385494e-641
1632.88158815832152e-655.76317631664304e-651
1646.54793785466473e-661.30958757093295e-651
1651.50607629064589e-663.01215258129179e-661
1663.34332653765498e-676.68665307530996e-671
1677.37981168737117e-681.47596233747423e-671
1681.63499277903732e-683.26998555807464e-681
1694.19997102269461e-698.39994204538923e-691
1709.14980650796639e-701.82996130159328e-691
1711.98318756561402e-703.96637513122805e-701
1724.29128523584202e-718.58257047168404e-711
1739.20711100580752e-721.84142220116150e-711
1741.98245898922632e-723.96491797845265e-721
1754.21049778719307e-738.42099557438613e-731
1768.92336769370406e-741.78467353874081e-731
1772.06449618236706e-744.12899236473413e-741
1784.3171005107129e-758.6342010214258e-751
1798.99273421319255e-761.79854684263851e-751
1801.86397779308739e-763.72795558617478e-761
1813.92374569638840e-777.84749139277681e-771
1828.14668858345143e-781.62933771669029e-771
1831.96260477261407e-783.92520954522814e-781
1844.13885743731321e-798.27771487462643e-791
1858.42427554211568e-801.68485510842314e-791
1861.68355633462796e-803.36711266925591e-801
1873.43652299377692e-816.87304598755385e-811
1886.884868920163e-821.3769737840326e-811
1891.41503505998548e-822.83007011997096e-821
1902.79771106340148e-835.59542212680296e-831
1915.49374207827479e-841.09874841565496e-831
1921.07571221855995e-842.15142443711990e-841
1932.10108228201764e-854.20216456403527e-851
1944.07511581758821e-868.15023163517643e-861
1957.8752119093052e-871.57504238186104e-861
1961.51343000647730e-873.02686001295460e-871
1973.504022077707e-887.008044155414e-881
1986.75659654818331e-891.35131930963666e-881
1991.30173662401066e-892.60347324802132e-891
2002.93358531879855e-905.8671706375971e-901
2015.61968736422764e-911.12393747284553e-901
2021.08035425553994e-912.16070851107989e-911
2032.09697739142373e-924.19395478284746e-921
2043.94175360442071e-937.88350720884142e-931
2057.45427395246989e-941.49085479049398e-931
2061.47338418176343e-942.94676836352687e-941
2073.15291086705818e-956.30582173411637e-951
2081.14338585158274e-952.28677170316548e-951
2091.57209023642937e-943.14418047285874e-941
2101.71952016957885e-313.4390403391577e-311
2113.17988203382764e-316.35976406765528e-311
2121.35968913879609e-312.71937827759217e-311
2136.02254172359099e-321.20450834471820e-311
2142.55313255112562e-325.10626510225123e-321
2151.10150618655033e-322.20301237310066e-321
2164.61396054608973e-339.22792109217946e-331
2171.93328365789691e-333.86656731579382e-331
2188.02065861700268e-341.60413172340054e-331
2194.72011689192578e-349.44023378385156e-341
2201.97378118992122e-343.94756237984244e-341
2218.33754149435312e-351.66750829887062e-341
2223.43769544530157e-356.87539089060314e-351
2231.39991158646545e-352.7998231729309e-351
2245.72727742002867e-361.14545548400573e-351
2252.30044819294166e-364.60089638588332e-361
2269.32888413657606e-371.86577682731521e-361
2273.74533959255003e-377.49067918510005e-371
2281.48317433050561e-372.96634866101122e-371
2296.14708504469033e-381.22941700893807e-371
2302.41467825219317e-384.82935650438635e-381
2319.59337404150469e-391.91867480830094e-381
2323.74311678026429e-397.48623356052857e-391
2331.45263879374721e-392.90527758749443e-391
2345.70946623445419e-401.14189324689084e-391
2352.19926122302145e-404.3985224460429e-401
2368.42925293132575e-411.68585058626515e-401
2373.20626969159433e-416.41253938318866e-411
2381.21440917728082e-412.42881835456164e-411
2394.58453595988691e-429.16907191977382e-421
2401.72201549142212e-423.44403098284424e-421
2416.41421276048034e-431.28284255209607e-421
2422.39409706032855e-434.7881941206571e-431
2438.84929692041223e-441.76985938408245e-431
2447.05736982739299e-441.41147396547860e-431
2453.09390321022494e-446.18780642044989e-441
2461.13566017024227e-442.27132034048454e-441
2474.13020732986474e-458.26041465972947e-451
2481.51732811379249e-453.03465622758498e-451
2495.48618209212973e-461.09723641842595e-451
2501.96676865288413e-463.93353730576826e-461
2517.16758793017609e-471.43351758603522e-461
2522.54398049685725e-475.08796099371449e-471
2539.12728946534778e-481.82545789306956e-471
2545.59537892226585e-481.11907578445317e-471
2554.99686807833782e-489.99373615667564e-481
2561.76858931613713e-483.53717863227425e-481
2576.4988843325977e-491.29977686651954e-481
2582.44332915767404e-494.88665831534808e-491
2598.48688653346722e-501.69737730669344e-491
2602.93298988462914e-505.86597976925828e-501
2611.00847889375414e-502.01695778750828e-501
2623.44103932938641e-516.88207865877282e-511
2631.33023622057496e-512.66047244114992e-511
2644.50238170603581e-529.00476341207161e-521
2651.51778580376358e-523.03557160752716e-521
2665.09048455763556e-531.01809691152711e-521
2671.69424968306953e-533.38849936613906e-531
2687.24432716697688e-541.44886543339538e-531
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4221.13187063881924e-842.26374127763848e-841
4235.88822186608892e-511.17764437321778e-501


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level4040.971153846153846NOK
5% type I error level4080.98076923076923NOK
10% type I error level4090.983173076923077NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/10bf291291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/10bf291291297725.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/1menx1291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/1menx1291297725.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/2menx1291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/2menx1291297725.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/3f5401291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/3f5401291297725.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/4f5401291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/4f5401291297725.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/5f5401291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/5f5401291297725.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/6pell1291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/6pell1291297725.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/70ok61291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/70ok61291297725.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/80ok61291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/80ok61291297725.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/90ok61291297725.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291297747exw35wms83qwnb6/90ok61291297725.ps (open in new window)


 
Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}
 




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Software written by Ed van Stee & Patrick Wessa


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