Home » date » 2010 » Dec » 02 »

CC-score 1

*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 14:17:29 +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/t1291299947ipxqao5n3v836lc.htm/, Retrieved Thu, 02 Dec 2010 15:25:47 +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/t1291299947ipxqao5n3v836lc.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 1081 213118 6282154 1 5626 37 56289 29790 309 81767 4321023 2 13337 138 28328 87550 458 153198 4111912 3 8541 45 17936 84738 588 -26007 223193 415 39 0 4145 54660 302 126942 1491348 6 4276 24 3040 42634 156 157214 1629616 5 9164 34 2964 40949 481 129352 1398893 9 2493 29 2865 45187 353 234817 1926517 4 4891 38 2854 37704 452 60448 983660 14 1734 21 2167 16275 109 47818 1443586 7 11409 76 1974 25830 115 245546 1073089 10 7592 34 1910 12679 110 48020 984885 13 7135 62 1871 18014 239 -1710 1405225 8 5043 67 943 43556 247 32648 227132 414 110 1 929 24811 505 95350 929118 15 1444 29 822 6575 159 151352 1071292 11 5480 133 819 7123 109 288170 638830 28 4026 62 769 21950 519 114337 856956 17 1266 30 745 37597 248 37884 992426 12 3195 21 652 17821 373 122844 444477 46 655 14 643 12988 119 82340 857217 16 5523 51 601 22330 84 79801 711969 20 6095 23 446 13326 102 165548 702380 21 4925 38 436 16189 295 116384 358589 89 538 10 379 7146 105 134028 297978 367 933 14 305 15824 64 63838 58571 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 time18 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
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.


Multiple Linear Regression - Estimated Regression Equation
CCScore [t] = + 61495.3852296926 + 0.0149360638041560Costs[t] + 0.196174139503746Trades[t] -0.194408421050888Dividends[t] -0.0225234792367258Wealth[t] -0.176946965216327WRank[t] -0.134058324708214`Profit/Trades`[t] -0.103407843549211`Profit/Cost`[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)61495.38522969266383.1203869.634100
Costs0.01493606380415600.0075051.99010.0472220.023611
Trades0.1961741395037460.0478594.0995e-052.5e-05
Dividends-0.1944084210508880.03778-5.145700
Wealth-0.02252347923672580.007422-3.03490.0025550.001277
WRank-0.1769469652163270.041363-4.27792.3e-051.2e-05
`Profit/Trades`-0.1340583247082140.058823-2.2790.0231640.011582
`Profit/Cost`-0.1034078435492110.038057-2.71720.0068550.003427


Multiple Linear Regression - Regression Statistics
Multiple R0.417418469336707
R-squared0.174238178543399
Adjusted R-squared0.160573089299436
F-TEST (value)12.7506067053590
F-TEST (DF numerator)7
F-TEST (DF denominator)423
p-value6.99440505513849e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation71369.2501530559
Sum Squared Residuals2154580053914.20


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
156289-119550.717968177175839.717968177
228328-53022.276859431181350.276859431
317936-60654.836816946578590.8368169465
4414562826.623438117-58681.623438117
530403525.31870309473-485.318703094735
62964-6338.486196381679302.48619638167
728655207.5075941873-2342.50759418730
82854-27463.033790613430317.0337906134
9216728003.0535114669-25836.0535114669
10197418410.4824226095-16436.4824226095
111910-11025.220319740912935.2203197409
12187129222.5917674437-27351.5917674437
1394329808.8113592281-28865.8113592281
1492950643.4405535775-49714.4405535775
1582222301.9856676967-21479.9856676967
168197321.11579681114-6502.11579681114
17769-9339.2757582578610108.2757582579
1874520219.5117253176-19474.5117253176
1965231955.1213033159-31303.1213033159
2064327844.2617476104-27201.2617476104
2160125649.1117799555-25048.1117799555
2244629472.3779563809-29026.3779563809
2343613042.5837102399-12606.5837102399
2437931003.4492291341-30624.4492291341
2530528663.5798106787-28358.5798106787
2628435325.5445038543-35041.5445038543
2724732340.9150234261-32093.9150234261
2823850867.9749442461-50629.9749442461
2922335522.0443231148-35299.0443231148
3022042083.8545048896-41863.8545048896
3121726733.4797785245-26516.4797785245
3219927524.0287764068-27325.0287764068
3319527621.6463253864-27426.6463253864
3416327150.8611457882-26987.8611457882
35154-14355.649111185214509.6491111852
3614333232.4648361201-33089.4648361201
3713529368.3931942605-29233.3931942605
3813223178.5554807044-23046.5554807044
3912011245.4232409506-11125.4232409506
4011941317.4622450143-41198.4622450143
4110829976.7111982517-29868.7111982517
4210434834.4735821856-34730.4735821856
4310131876.0814764652-31775.0814764652
449029968.6655177853-29878.6655177853
458538983.3383196418-38898.3383196418
467432299.1461027819-32225.1461027819
477312199.1734461563-12126.1734461563
487025224.4592191169-25154.4592191169
496726874.4039079539-26807.4039079539
506642249.6732949123-42183.6732949123
516628169.6677430708-28103.6677430708
526623414.5009756967-23348.5009756967
535913491.792870092-13432.792870092
545820941.6732801056-20883.6732801056
555825782.5200742931-25724.5200742931
565440175.7901481283-40121.7901481283
575330295.1032693940-30242.1032693940
584933972.0665079098-33923.0665079098
594934298.6826119692-34249.6826119692
604435664.8911654494-35620.8911654494
613931281.9224762888-31242.9224762888
623846615.219165397-46577.219165397
633832946.5838797469-32908.5838797469
643737125.1476863365-37088.1476863365
653641791.8465914696-41755.8465914696
663536123.6335248187-36088.6335248187
673439744.3953320372-39710.3953320372
683332549.0637540844-32516.0637540844
693227324.1925203390-27292.1925203390
703232010.9496553136-31978.9496553136
713151608.6128310405-51577.6128310405
723144962.0420536184-44931.0420536184
733033241.3910539025-33211.3910539025
743037177.3803210651-37147.3803210651
753028768.6549100076-28738.6549100076
762840480.7373938768-40452.7373938768
772630406.3899308217-30380.3899308217
782641602.453648366-41576.453648366
792640801.5548077613-40775.5548077613
802530819.3775263022-30794.3775263022
812541052.9822735724-41027.9822735724
822434037.8013494721-34013.8013494721
832426173.1761725168-26149.1761725168
842346173.1870886972-46150.1870886972
852331397.2836816704-31374.2836816704
862240746.0988370477-40724.0988370477
872233277.9964685163-33255.9964685163
882231794.0565236116-31772.0565236116
892033568.7992520807-33548.7992520807
902030401.1736029984-30381.1736029984
911933580.6712914413-33561.6712914413
921836302.7097769739-36284.7097769739
931738633.2274486153-38616.2274486153
941628405.5733523312-28389.5733523312
951445661.8919242289-45647.8919242289
961440783.6802311421-40769.6802311421
971425933.1455967883-25919.1455967883
981340347.0260662652-40334.0260662652
991325889.0591629067-25876.0591629067
1001338651.1751378327-38638.1751378327
1011339290.6457725039-39277.6457725039
1021338336.7626040595-38323.7626040595
1031233498.7093373688-33486.7093373688
1041231392.0196986572-31380.0196986572
1051139292.1150361462-39281.1150361462
1061041039.5239920049-41029.5239920049
1071047192.4188162035-47182.4188162035
1081028887.8310682948-28877.8310682948
1091034319.099660282-34309.099660282
1101041460.0565413786-41450.0565413786
111938131.4970839236-38122.4970839236
112948694.6801332743-48685.6801332743
113938021.9904924650-38012.9904924650
114639693.8624421314-39687.8624421314
115639140.7264483423-39134.7264483423
116642310.3725552762-42304.3725552762
117536750.2130028569-36745.2130028569
118532765.0580432183-32760.0580432183
119541505.0659017768-41500.0659017768
120442232.8995848133-42228.8995848133
121431505.2936066442-31501.2936066442
122339635.2950213684-39632.2950213684
123235203.7884486667-35201.7884486667
124228471.4748080536-28469.4748080536
125140780.5001429228-40779.5001429228
126141740.3726144495-41739.3726144495
127742543.9338141542-42536.9338141542
12828123420.129184295-123392.129184295
1290124581.402798310-124581.402798310
130314353117956.698463822196396.301536178
13136944857617.3582593536311830.641740646
13231538055724.1737718594259655.826228141
133376158743.104309396728871.8956906033
134224158794.4145236600913620.5854763399
135127798469.674448784644309.32555121536
13691467927.543326419341218.45667358066
137574438520.9774340242648922.0225659757
138229838699.2502324817114283.7497675183
13908454.32219686057-8454.32219686057
140041446.6589350295-41446.6589350295
1411542444.9080302202-42429.9080302202
1423120674.497401647-120671.497401647
14315122845.764752786-122830.764752786
14411126836.174511962-126825.174511962
1450123360.046918704-123360.046918704
146314987117939.025196148197047.974803852
14732524956510.4583184401268738.54168156
14831587767380.5620715625248496.437928437
14929165076508.3995562497215141.600443750
15030595956692.7880018735249266.211998126
15131538055494.657826809259885.342173191
152888811079.7581368765-2191.75813687652
153384158419.8880038967829995.1119961032
15408534.15877925547-8534.15877925547
155242538.5656677539-42536.5656677539
1567122648.762648851-122641.762648851
1570127881.884365545-127881.884365545
158315380117951.369886580197428.630113420
15908453.61207719269-8453.61207719269
1602742498.9305949527-42471.9305949527
16151122243.625310312-122192.625310312
1623121562.316161700-121559.316161700
1630122821.864717382-122821.864717382
164382712119373.085390551263338.914609449
16534157056180.437392477285389.562607523
16631538055282.8196936738260097.180306326
16708452.8997245424-8452.8997245424
168038341.2724568907-38341.2724568907
169942533.0162531378-42524.0162531378
1700122720.294915918-122720.294915918
171315547117905.402041225197641.597958775
17231326757215.6014110307256051.398588969
17331617655297.0752355671260878.924764433
17431538077689.5663734669237690.433626533
17508455.3252023416-8455.3252023416
176038834.7399100982-38834.7399100982
177038353.1193967781-38353.1193967781
178043289.6111545036-43289.6111545036
179040496.676244663-40496.676244663
180038598.3548676274-38598.3548676274
181040948.8268281061-40948.8268281061
182040265.7478141737-40265.7478141737
183039206.4434355609-39206.4434355609
184039575.8155809105-39575.8155809105
185442526.4556232283-42522.4556232283
1860124140.327346058-124140.327346058
187315366117838.124763223197527.875236777
18831538058833.6829657441256546.317034256
189578448380.6040302744849463.3959697255
19008506.6095850871-8506.6095850871
191029968.6716338798-29968.6716338798
192042541.6217578131-42541.6217578131
193269587115857.481571705153729.518428295
19431538055876.1079717329259503.892028267
19508449.628557898-8449.628557898
1962742522.7533291552-42495.7533291552
19717121001.351780582-120984.351780582
1982126414.278858302-126412.278858302
1994121032.516381113-121028.516381113
2000123941.443054573-123941.443054573
201343613119004.521017002224608.478982998
20230694856022.8867071485250925.113292852
20321021346.0257121192-21136.0257121192
2042674294.60966562367-4027.60966562367
2054747849.96732933543-7375.96732933543
20653416651.7169613234-16117.7169613234
207011910.1747385103-11910.1747385103
2086083112087.185792385348743.8142076147
2096064663202.0991625395-2556.09916253952
2106072065446.7519839222-4726.75198392224
21113525328.8164859814-25193.8164859814
21237322775.7120261159-22402.7120261159
21319821169.2314373570-20971.2314373570
21436505-1676.4139327726938181.4139327727
2152394383.27245908997-4144.27245908997
2165924-1709.224074353517633.22407435351
2174133939.79484007005-3526.79484007005
21818743590.92073768784-1716.92073768784
2191924293.4031407013-4101.4031407013
22008524.09740749099-8524.09740749099
2216391512046.303965249851868.6960347502
2226072063788.947622856-3068.94762285604
2236072063959.3226108976-3239.32261089760
22435526611.1156902341-26256.1156902341
22520420888.4147674790-20684.4147674790
22652257-7662.3474047466559919.3474047467
227534295.1458989225-4242.1458989225
22808228.47253385441-8228.47253385441
2296072012090.159070879748629.8409291203
23020025491.1916404780-25291.1916404780
2311200832.630114791936367.369885208064
232244290.45385755771-4266.45385755771
23323328666.4799475775-6334.47994757751
23407177.80727313122-7177.80727313122
2356072012089.235608231048630.764391769
23633826008.9695103618-25670.9695103618
23726021054.1938073773-20794.1938073773
2382064290.85603253184-4084.85603253184
239012260.4136308565-12260.4136308565
2406424512066.550958400752178.4490415993
2417300755615.70290201317391.2970979870
2428273265507.021779422917224.9782205771
2436072065250.130593441-4530.13059344101
24433526882.9644222080-26547.9644222080
24536524485.4173621526-24120.4173621526
24623920837.7763941462-20598.7763941462
24704290.72197420713-4290.72197420713
2486072012088.649997770948631.3500022291
24910220925.1303396773-20823.1303396773
25011619572.8387546750-19456.8387546750
25128820958.3454881314-20670.3454881314
25204284.55529127055-4284.55529127055
2537765512061.859076862065593.140923138
2546072063978.0439607942-3258.0439607942
25511722614.1280299187-22497.1280299187
25628621256.8927894486-20970.8927894486
25704284.01905797172-4284.01905797172
2586079812070.584242622148727.4157573779
2596072064388.5562094446-3668.55620944455
26014124785.5995018434-24644.5995018434
26114019890.2745692359-19750.2745692359
26223220867.511331699-20635.511331699
263698440.75991080992-8371.75991080992
2646294564.65387424123-3935.65387424123
26504287.90674938826-4287.90674938826
2665672612047.981774537644678.0182254624
2676072065923.2581553151-5203.2581553151
26811822489.3935657049-22371.3935657049
26924120970.7747823736-20729.7747823736
2701981687.44157665677-1489.44157665677
2713912743.4565383958463168.54346160415
2722494288.17486603768-4039.17486603768
273014916.0058593474-14916.0058593474
2746284112064.138679865850776.8613201342
2758112565283.707720658915841.2922793411
2766072065433.1974861503-4713.19748615028
27735627003.9295542268-26647.9295542268
27810718754.1930730127-18647.1930730127
27922920915.2521086538-20686.2521086538
28004291.39226583067-4291.39226583067
2816072012089.505889981948630.4941100181
28232025714.1924302635-25394.1924302635
28336023634.0781268466-23274.0781268466
28422521566.3626347487-21341.3626347487
2853664291.25820750596-3925.25820750596
28626124.91330351711-6122.91330351711
28701368.85761358624-1368.85761358624
2886401612060.188445110251955.8115548898
2895468364444.8961322396-9761.89613223957
2906072065211.3185216031-4491.31852160309
29124425497.6653870817-25253.6653870817
292402-1940.668896394522342.66889639452
293494291.92849912951-4242.92849912951
29438428.72150409525-8425.72150409525
2951331845.49966286287-1712.49966286287
296326544.28312643482-6512.28312643482
297368-8800.482101868589168.48210186858
298114673.5100500343-14672.5100500343
2990-2602.566413271252602.56641327125
3006072012088.379716020048631.62028398
30117725501.3926957322-25324.3926957322
30204297.42489044254-4297.42489044254
3036072012089.032896917948630.9671030821
30432425803.1796441104-25479.1796441104
30525222593.8480949095-22341.8480949095
30604288.84515766121-4288.84515766121
3076072012088.852709084048631.147290916
30827825507.4740940568-25229.4740940568
30904285.3596412188-4285.3596412188
3106037912074.948030671848304.0519693282
3116072761794.1399064213-1067.13990642135
3126072052329.57818211518390.42181788493
3136092561648.2047426286-723.204742628583
3146072065749.8282711168-5029.82827111677
31531525619.2001452748-25304.2001452748
31615323179.9250580119-23026.9250580119
31737622799.4943520734-22423.4943520734
31818720899.3693332239-20712.3693332239
31904297.69300709196-4297.69300709196
3206072012002.238079975748717.7619200243
3216072065724.1018204204-5004.1018204204
32235426909.842830083-26555.842830083
32317821171.7570870427-20993.7570870427
32415954298.63141536492-2703.63141536492
32531210063.5130492106-9751.51304921057
326609480.44331225313-9420.44331225313
32758710150.7042098337-9563.7042098337
3281357982.88807781833-7847.88807781833
32902360.78443997179-2360.78443997179
3306072012088.424762978548631.5752370215
33113322340.3849570342-22207.3849570342
33227920824.4320581927-20545.4320581927
33304288.5770410118-4288.5770410118
3346072212087.922120238148634.0778797619
3356072056752.30802229553967.69197770455
33628925505.7085268013-25216.7085268013
337615377.645811427-5316.645811427
3386473086.48825305881-2439.48825305881
33904283.7509413223-4283.7509413223
3406072012088.965326480248631.0346735198
34129225510.2205320099-25218.2205320099
3425313738.45396284919-3207.45396284919
3434484283.34876634818-3835.34876634818
3442279773.82388720076-9546.82388720076
3451748315.91036981259-8141.91036981259
3460-4653.353457684854653.35345768485
3476072012088.559903853948631.4400961461
34837728179.1744085670-27802.1744085670
34913420259.2749989468-20125.2749989468
35014220432.1679944703-20290.1679944703
35127120881.0740366840-20610.0740366840
3525304286.83428279059-3756.83428279059
35357113838.8453100019-13267.8453100019
354014916.0443724382-14916.0443724382
3556270012076.426305550750623.5736944493
3566780464467.81976151553336.18023848452
3575966164928.2833069267-5267.28330692671
3585862062140.9036760695-3520.90367606949
3596039863514.900228231-3116.90022823099
3605858058734.3528370188-154.352837018837
3616271064791.9244802522-2081.92448025222
3625932563890.249542245-4565.24954224505
3636095063605.7430538002-2655.74305380024
3646806063652.70471239154407.29528760847
3658362062463.304020411121156.6959795889
3665845664375.7832555117-5919.78325551174
3675281163890.9912791016-11079.9912791016
36812117364807.027426556456365.9725734436
3696387067586.4247117193-3716.42471171927
3702100163611.4883719249-42610.4883719249
3717041564835.27904086585579.72095913417
3726423061949.08696289872280.91303710133
3735919063529.0471988798-4339.04719887978
3746935163437.6772981525913.32270184802
3756427065044.2213322477-774.221332247732
3767069463410.45367616617283.54632383385
3776800564418.10794313673586.89205686326
3785893064702.2392316345-5772.23923163455
3795832064203.6892618308-5883.68926183075
3806998064184.03444979115795.96555020886
3816986362793.59005949157069.40994050846
3826325564835.4929917072-1580.49299170720
3835732065117.1840741556-7797.18407415555
3847523064356.829042003110873.1709579969
3857942066476.897707786812943.1022922132
3867349064369.14417879179120.85582120832
3873525063907.4554949331-28657.4554949331
3886228566823.7318655684-4538.73186556841
3896920664701.49052004164504.50947995842
3906592065032.8609409336887.139059066393
3916977062105.67443976817664.32556023189
3927268359775.84003783612907.1599621640
393-1454563504.3678893436-78049.3678893436
3945583060250.4893412919-4420.48934129189
3955517462460.6132464009-7286.61324640087
3966703864366.70915592332671.29084407667
3975125264229.3501979997-12977.3501979997
39815727858449.400807587898828.5991924122
3997951064812.315301313714697.6846986863
4007744063115.002155339914324.9978446601
4012728464289.712200347-37005.712200347
40221311839857.4578880892173260.542111911
40381767140245.940988026-58478.9409880259
404153198106642.19870230946555.8012976908
405-26007106655.938961173-132662.938961173
40612694256810.538089682270131.4619103178
40715721476670.107171353680543.8928286464
40812935277990.325767734351361.6742322657
40923481775304.64298285159512.35701715
4106044883712.7612196609-23264.7612196609
4114781873276.0482308788-25458.0482308788
41224554677014.6218527056168531.378147294
4134802073999.2891463842-25979.2891463842
414-171072049.0312455299-73759.0312455299
4153264875472.1270249887-42824.1270249887
4169535061404.926131604133945.0738683959
41715135274050.98889299177301.011107009
41828817075318.9865736561212851.013426344
41911433767126.077872345547210.9221276545
4203788468853.81144965-30969.8114496499
42112284473156.075526358549687.9244736415
4228234066148.261273765316191.7387262347
4237980170118.55163933929682.44836066076
42416554869071.958575146196476.041424854
42511638468754.055606116147629.9443938839
42613402865728.034434504768299.9655654953
4276383863654.3753170146183.624682985369
4287499665289.30350302339706.69649697671
4293108069350.7327998504-38270.7327998504
4303216863501.377291029-31333.3772910290
4314985768155.5226637661-18298.5226637661


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.0004872156464774470.0009744312929548950.999512784353523
124.73973527598959e-059.47947055197919e-050.99995260264724
132.89075526478385e-065.78151052956771e-060.999997109244735
146.79485070952728e-071.35897014190546e-060.99999932051493
154.47449142064719e-088.94898284129439e-080.999999955255086
163.02152396198083e-096.04304792396166e-090.999999996978476
173.02522842484945e-106.0504568496989e-100.999999999697477
181.86166164918597e-113.72332329837194e-110.999999999981383
191.49957952109718e-122.99915904219437e-120.9999999999985
201.35486419110366e-132.70972838220731e-130.999999999999865
211.14031200023311e-142.28062400046622e-140.999999999999989
221.35241483413318e-152.70482966826636e-150.999999999999999
231.10106964389467e-162.20213928778935e-161
248.08262364252348e-181.61652472850470e-171
254.54494460782962e-199.08988921565925e-191
264.81467320294039e-209.62934640588078e-201
272.79644426794376e-215.59288853588752e-211
281.54052522654139e-223.08105045308278e-221
291.07709409867061e-232.15418819734122e-231
306.5252400062218e-251.30504800124436e-241
313.65063908765008e-267.30127817530016e-261
322.92722581305765e-275.8544516261153e-271
331.64289369136625e-283.28578738273251e-281
341.06703176951123e-292.13406353902246e-291
355.73027186945307e-311.14605437389061e-301
364.5931542716493e-329.1863085432986e-321
372.75715264579947e-335.51430529159895e-331
381.46320200284978e-342.92640400569955e-341
397.39992584145573e-361.47998516829115e-351
403.45950616070002e-376.91901232140005e-371
412.37973587314009e-384.75947174628018e-381
421.70265726566880e-393.40531453133759e-391
438.54606624516092e-411.70921324903218e-401
445.61599404240575e-421.12319880848115e-411
453.57719396653457e-437.15438793306914e-431
462.55031105136224e-445.10062210272448e-441
471.35795232687807e-452.71590465375614e-451
486.80786773819759e-471.36157354763952e-461
493.44423224354379e-486.88846448708758e-481
501.50673107815463e-493.01346215630926e-491
516.53945682004148e-511.30789136400830e-501
522.86148915436394e-525.72297830872787e-521
531.36326433015569e-532.72652866031137e-531
548.17628438914068e-551.63525687782814e-541
553.48447673480134e-566.96895346960267e-561
561.65128471244638e-573.30256942489276e-571
571.31282211795910e-582.62564423591820e-581
586.88521409581706e-601.37704281916341e-591
593.10064831614712e-616.20129663229424e-611
601.64008080195210e-623.28016160390419e-621
617.86324302804714e-641.57264860560943e-631
623.27091430411627e-656.54182860823255e-651
631.62631237714839e-663.25262475429678e-661
647.99480137608985e-681.59896027521797e-671
653.731800350834e-697.463600701668e-691
661.43350761041975e-702.8670152208395e-701
675.86611953394767e-721.17322390678953e-711
682.59228132990728e-735.18456265981456e-731
691.04885315499781e-742.09770630999561e-741
704.69388769759248e-769.38777539518497e-761
711.80490380633734e-773.60980761267469e-771
728.57328959142833e-791.71465791828567e-781
733.17220600049939e-806.34441200099877e-801
741.15091325339210e-812.30182650678420e-811
754.83501308318154e-839.67002616636308e-831
761.75194384482264e-843.50388768964528e-841
776.33309151056987e-861.26661830211397e-851
782.30736366990700e-874.61472733981401e-871
798.43360729300478e-891.68672145860096e-881
802.8874855532166e-905.7749711064332e-901
811.1264155732652e-912.2528311465304e-911
824.59614271912876e-939.19228543825751e-931
831.56960271098495e-943.13920542196990e-941
845.80130493072597e-961.16026098614519e-951
851.98615212799383e-973.97230425598766e-971
868.22356858109022e-991.64471371621804e-981
873.03502707348560e-1006.07005414697119e-1001
889.86030034938982e-1021.97206006987796e-1011
893.22761980354423e-1036.45523960708845e-1031
901.04126549687426e-1042.08253099374852e-1041
913.81283096828332e-1067.62566193656663e-1061
921.50728644265897e-1073.01457288531794e-1071
934.99461262703792e-1099.98922525407585e-1091
941.97123123692332e-1103.94246247384664e-1101
957.01078352819963e-1121.40215670563993e-1111
962.61375367866098e-1135.22750735732196e-1131
971.58055672654252e-1143.16111345308504e-1141
985.16605239889997e-1161.03321047977999e-1151
992.04832551979518e-1174.09665103959036e-1171
1007.4604840345818e-1191.49209680691636e-1181
1012.34985160454275e-1204.6997032090855e-1201
1027.30682958722883e-1221.46136591744577e-1211
1032.25054948943731e-1234.50109897887461e-1231
1047.04830627314206e-1251.40966125462841e-1241
1052.36679864112316e-1264.73359728224633e-1261
1067.40711247517197e-1281.48142249503439e-1271
1072.41617119239246e-1294.83234238478493e-1291
1088.33925142876367e-1311.66785028575273e-1301
1092.78141388794857e-1325.56282777589715e-1321
1108.25826751835416e-1341.65165350367083e-1331
1112.46340288190075e-1354.92680576380151e-1351
1127.61710719257836e-1371.52342143851567e-1361
1132.23355504703006e-1384.46711009406011e-1381
1146.3459822178029e-1401.26919644356058e-1391
1152.50218413862267e-1415.00436827724534e-1411
1167.82901363163113e-1431.56580272632623e-1421
1172.16500195027335e-1444.33000390054671e-1441
1187.21826860240171e-1461.44365372048034e-1451
1192.03685469395233e-1474.07370938790466e-1471
1205.64708639958744e-1491.12941727991749e-1481
1211.7954803843567e-1503.5909607687134e-1501
1225.46250612479391e-1521.09250122495878e-1511
1231.61103715648738e-1533.22207431297476e-1531
1245.00001020418331e-1551.00000204083666e-1541
1251.55952122715819e-1563.11904245431639e-1561
1264.23727470573405e-1588.4745494114681e-1581
1271.13014572459619e-1592.26029144919239e-1591
1285.37056769499347e-1611.07411353899869e-1601
1291.34688321410721e-1602.69376642821442e-1601
1302.41831065217842e-1614.83662130435685e-1611
1311.07802694100884e-1272.15605388201769e-1271
1322.26210593773096e-1204.52421187546191e-1201
1333.04601873131969e-1046.09203746263939e-1041
1341.58527871946297e-1043.17055743892593e-1041
1351.46184643101986e-1042.92369286203973e-1041
1367.19891253401617e-1051.43978250680323e-1041
1371.03607084940632e-1002.07214169881265e-1001
1381.26694238376627e-1012.53388476753255e-1011
1394.57025731632454e-1009.14051463264908e-1001
1404.4769938191007e-1018.9539876382014e-1011
1416.21659278219045e-1021.24331855643809e-1011
1422.42935742567800e-1024.85871485135599e-1021
1434.43474516681106e-1038.86949033362212e-1031
1441.36075906255773e-1032.72151812511546e-1031
1454.67953093928645e-1049.3590618785729e-1041
1467.79146409842522e-1031.55829281968504e-1021
1473.07113666188247e-1026.14227332376494e-1021
1485.11594995649651e-1011.02318999129930e-1001
1496.04806162178147e-991.20961232435629e-981
1502.87854183980975e-975.75708367961949e-971
1511.24542082727244e-962.49084165454487e-961
1527.26049731133327e-971.45209946226665e-961
1536.16531294793365e-971.23306258958673e-961
1542.60617858758811e-965.21235717517622e-961
1553.20534950965968e-976.41069901931936e-971
1569.32209390465192e-981.86441878093038e-971
1574.49247658935480e-988.98495317870961e-981
1586.25326775464376e-981.25065355092875e-971
1591.26165805446636e-972.52331610893272e-971
1601.66753164411691e-983.33506328823381e-981
1617.45532993484081e-991.49106598696816e-981
1624.48395899224057e-998.96791798448114e-991
1632.86911078605070e-995.73822157210141e-991
1642.58869636551549e-655.17739273103097e-651
1651.04877940023318e-632.09755880046635e-631
1663.53876388170938e-627.07752776341877e-621
1671.84682254797462e-623.69364509594924e-621
1685.57319128003127e-631.11463825600625e-621
1691.27965987864729e-632.55931975729459e-631
1701.99373126604967e-633.98746253209935e-631
1712.04536401541122e-624.09072803082245e-621
1721.54851065709567e-603.09702131419134e-601
1732.02364416453697e-584.04728832907394e-581
1741.98511920841242e-563.97023841682484e-561
1751.08927041041973e-562.17854082083945e-561
1762.87177378072716e-575.74354756145432e-571
1777.60862191162395e-581.52172438232479e-571
1781.97260282250606e-583.94520564501212e-581
1795.088584942562e-591.0177169885124e-581
1803.54125621804463e-597.08251243608927e-591
1819.21733060031996e-601.84346612006399e-591
1822.37933977656079e-604.75867955312158e-601
1836.18823581006563e-611.23764716201313e-601
1841.62083235588125e-613.24166471176250e-611
1854.08797925592341e-628.17595851184683e-621
1868.58746378452145e-621.71749275690429e-611
1879.25368384362987e-601.85073676872597e-591
1881.50436858398852e-563.00873716797704e-561
1892.66056563832705e-555.32113127665411e-551
1902.08348743237144e-554.16697486474289e-551
1916.43181307742845e-561.28636261548569e-551
1921.80024568579214e-563.60049137158428e-561
1936.75467851688437e-371.35093570337687e-361
1944.31122619893291e-338.62245239786583e-331
1951.26996654851171e-322.53993309702343e-321
1965.23339132967112e-331.04667826593422e-321
1976.72096857113843e-331.34419371422769e-321
1982.06509893996134e-324.13019787992269e-321
1994.24603663753676e-318.49207327507352e-311
2004.257662038834e-268.515324077668e-261
2018.32513400971259e-231.66502680194252e-221
2021.99846343721176e-173.99692687442351e-171
20311.14321684934520e-235.71608424672599e-24
20412.77503363841111e-281.38751681920556e-28
20513.29197752659744e-281.64598876329872e-28
20614.79644969621431e-282.39822484810716e-28
20717.26686100540414e-283.63343050270207e-28
20816.07108402539357e-283.03554201269679e-28
20919.87308718845256e-284.93654359422628e-28
21012.07338639300639e-271.03669319650319e-27
21113.88364543696093e-321.94182271848047e-32
21211.11406251115924e-345.57031255579621e-35
21315.91562619541912e-362.95781309770956e-36
21416.62973505760982e-363.31486752880491e-36
21511.63660406013544e-358.1830203006772e-36
21613.37393331822171e-351.68696665911085e-35
21718.44415182551488e-354.22207591275744e-35
21812.1042722481524e-341.0521361240762e-34
21915.2103129318892e-342.6051564659446e-34
22019.12071899140565e-344.56035949570282e-34
22118.7799798729057e-344.38998993645285e-34
22211.49990549561869e-337.49952747809344e-34
22312.84550899791646e-331.42275449895823e-33
22412.37935540907307e-331.18967770453653e-33
22511.03722624583113e-335.18613122915565e-34
22612.63132485941513e-341.31566242970756e-34
22716.59026336381127e-343.29513168190563e-34
22811.20806008976163e-336.04030044880816e-34
22911.45634936755172e-337.2817468377586e-34
23012.28716511908799e-331.14358255954400e-33
23115.35153528544388e-332.67576764272194e-33
23211.3236223623023e-326.6181118115115e-33
23312.49650460109128e-321.24825230054564e-32
23414.94694924937807e-322.47347462468903e-32
23516.64478062766874e-323.32239031383437e-32
23611.21917308267889e-316.09586541339446e-32
23711.18255843173331e-315.91279215866655e-32
23812.88709225657048e-311.44354612828524e-31
23915.3785678581463e-312.68928392907315e-31
24016.93684877416315e-313.46842438708157e-31
24111.13448125660380e-305.67240628301902e-31
24212.08791968865692e-301.04395984432846e-30
24314.52478177889843e-302.26239088944921e-30
24418.6615236722574e-304.3307618361287e-30
24511.25074119069987e-296.25370595349937e-30
24611.93843526908707e-299.69217634543534e-30
24714.62206101463649e-292.31103050731824e-29
24816.62156479048407e-293.31078239524203e-29
24911.38180133443753e-286.90900667218766e-29
25012.31582032871650e-281.15791016435825e-28
25114.20510612405719e-282.10255306202859e-28
25219.87736106559608e-284.93868053279804e-28
25318.8096759413397e-284.40483797066985e-28
25411.85133193798308e-279.2566596899154e-28
25514.02759081102119e-272.01379540551060e-27
25617.76930248759858e-273.88465124379929e-27
25711.79999560677412e-268.99997803387058e-27
25812.5786798825273e-261.28933994126365e-26
25915.53556834510234e-262.76778417255117e-26
26011.20420264308266e-256.02101321541328e-26
26112.36122884456310e-251.18061442228155e-25
26214.70185037562337e-252.35092518781169e-25
26311.05276293544381e-245.26381467721903e-25
26412.37429445315522e-241.18714722657761e-24
26515.32874112957648e-242.66437056478824e-24
26618.24419184338577e-244.12209592169288e-24
26711.72708722552234e-238.6354361276117e-24
26813.70440466325447e-231.85220233162723e-23
26917.38300174113262e-233.69150087056631e-23
27011.60638204860644e-228.03191024303219e-23
27113.35602435285875e-221.67801217642937e-22
27217.31434705976269e-223.65717352988134e-22
27311.32070432377824e-216.60352161889121e-22
27411.79896429970116e-218.99482149850582e-22
27513.54636306892347e-211.77318153446174e-21
27617.2414861686362e-213.6207430843181e-21
27711.49878791370395e-207.49393956851973e-21
27812.88151969060891e-201.44075984530446e-20
27915.70392723690652e-202.85196361845326e-20
28011.20400484313822e-196.02002421569109e-20
28111.68392435935643e-198.41962179678217e-20
28213.45535604378626e-191.72767802189313e-19
28316.79902887144323e-193.39951443572161e-19
28411.34698021683842e-186.73490108419209e-19
28512.78462912244132e-181.39231456122066e-18
28615.34431335497556e-182.67215667748778e-18
28715.49561742848501e-182.74780871424250e-18
28817.37313095634765e-183.68656547817382e-18
28911.41868644199744e-177.09343220998722e-18
29012.79323252925218e-171.39661626462609e-17
29115.57252049386734e-172.78626024693367e-17
29211.10809763356478e-165.54048816782389e-17
29312.22475692567991e-161.11237846283996e-16
29414.05066321489584e-162.02533160744792e-16
29515.65967150938697e-162.82983575469349e-16
29611.02105976205633e-155.10529881028165e-16
29712.0039596012936e-151.0019798006468e-15
2980.9999999999999983.30886148154991e-151.65443074077495e-15
29911.52107800269713e-157.60539001348566e-16
3000.9999999999999992.37683045844736e-151.18841522922368e-15
3010.9999999999999984.62865345115939e-152.31432672557970e-15
3020.9999999999999959.09527641002939e-154.54763820501469e-15
3030.9999999999999931.39148448418627e-146.95742242093135e-15
3040.9999999999999872.67814325525012e-141.33907162762506e-14
3050.9999999999999755.02112808676985e-142.51056404338493e-14
3060.9999999999999529.6776855677341e-144.83884278386705e-14
3070.9999999999999281.4425903973497e-137.2129519867485e-14
3080.9999999999998642.72859734167388e-131.36429867083694e-13
3090.999999999999745.18555879327079e-132.59277939663539e-13
3100.9999999999996227.57020311263437e-133.78510155631718e-13
3110.9999999999993021.39671607176062e-126.98358035880311e-13
3120.999999999998722.55857291606673e-121.27928645803336e-12
3130.9999999999976634.67358408742141e-122.33679204371070e-12
3140.9999999999957628.4757850452668e-124.2378925226334e-12
3150.9999999999922741.54523339810575e-117.72616699052874e-12
3160.9999999999861642.76727509421335e-111.38363754710667e-11
3170.9999999999756344.87326067529907e-112.43663033764954e-11
3180.9999999999568338.63349514111044e-114.31674757055522e-11
3190.9999999999223161.55367387462162e-107.7683693731081e-11
3200.999999999891462.17081801393150e-101.08540900696575e-10
3210.9999999998100573.79885045525500e-101.89942522762750e-10
3220.9999999996658656.68270197858256e-103.34135098929128e-10
3230.999999999421451.15709819998524e-095.78549099992619e-10
3240.9999999989866662.02666868409507e-091.01333434204754e-09
3250.999999998361293.27741908765403e-091.63870954382702e-09
3260.999999997405655.18870113190879e-092.59435056595440e-09
3270.9999999959710558.05789070877779e-094.02894535438889e-09
3280.9999999938412931.23174147718786e-086.15870738593929e-09
3290.9999999901042221.97915566131622e-089.89577830658108e-09
3300.9999999855497992.89004028914002e-081.44502014457001e-08
3310.9999999760731174.78537655050618e-082.39268827525309e-08
3320.9999999603829267.92341479294859e-083.96170739647429e-08
3330.9999999338917881.32216423926380e-076.61082119631901e-08
3340.9999999072935141.85412972663861e-079.27064863319303e-08
3350.9999998517458562.96508287326552e-071.48254143663276e-07
3360.999999757868024.84263959214645e-072.42131979607322e-07
3370.999999605789367.88421281533587e-073.94210640766793e-07
3380.9999993634733231.27305335381122e-066.36526676905612e-07
3390.999998977130822.04573835952564e-061.02286917976282e-06
3400.9999986237528762.75249424826509e-061.37624712413254e-06
3410.9999978205367934.35892641408695e-062.17946320704347e-06
3420.999996560349696.87930062171926e-063.43965031085963e-06
3430.999994608608781.07827824413597e-055.39139122067983e-06
3440.9999921312789731.57374420549358e-057.86872102746791e-06
3450.9999883883983062.32232033872870e-051.16116016936435e-05
3460.9999829299042463.41401915070675e-051.70700957535338e-05
3470.9999775119451934.49761096129847e-052.24880548064924e-05
3480.9999659596656956.8080668609159e-053.40403343045795e-05
3490.9999491164026750.000101767194650025.088359732501e-05
3500.9999246130697970.0001507738604068537.53869302034265e-05
3510.999890724658610.0002185506827782940.000109275341389147
3520.9998431226087330.0003137547825340870.000156877391267043
3530.9997965519938750.0004068960122507430.000203448006125372
3540.9997356279924160.0005287440151670970.000264372007583548
3550.9998204906262620.0003590187474763390.000179509373738169
3560.9997372119646280.0005255760707436550.000262788035371827
3570.9996167337698070.0007665324603861550.000383266230193078
3580.999460850799580.001078298400839260.000539149200419628
3590.9992274733741190.001545053251762090.000772526625881043
3600.9989025910334250.002194817933149920.00109740896657496
3610.9984520127502470.003095974499506850.00154798724975342
3620.9979264185095490.004147162980901990.00207358149045100
3630.9971201938383680.005759612323263550.00287980616163177
3640.9960530975068220.007893804986355620.00394690249317781
3650.9946815711291140.01063685774177100.00531842887088552
3660.9927928961679870.01441420766402530.00720710383201266
3670.990438142598620.01912371480275840.00956185740137919
3680.990459511284090.01908097743182100.00954048871591052
3690.9881386177442420.02372276451151590.0118613822557580
3700.98660021495820.02679957008360170.0133997850418008
3710.9824743799824870.03505124003502530.0175256200175126
3720.9770408939207920.04591821215841520.0229591060792076
3730.9729758554036660.0540482891926680.027024144596334
3740.96524023540610.0695195291878010.0347597645939005
3750.955526597354090.08894680529181940.0444734026459097
3760.9436890453179030.1126219093641940.056310954682097
3770.9320979311662430.1358041376675140.0679020688337569
3780.9280124004968560.1439751990062870.0719875995031437
3790.9123835826269220.1752328347461560.0876164173730778
3800.9108697461286610.1782605077426780.089130253871339
3810.8903546590302870.2192906819394250.109645340969713
3820.877057548897380.2458849022052390.122942451102619
3830.8504501185760420.2990997628479170.149549881423958
3840.8209589466273240.3580821067453510.179041053372676
3850.7980734141400220.4038531717199570.201926585859978
3860.7684359392182270.4631281215635450.231564060781773
3870.7305300061418220.5389399877163570.269469993858178
3880.721070979319350.55785804136130.27892902068065
3890.689406583803410.621186832393180.31059341619659
3900.653452505083050.6930949898338990.346547494916949
3910.6067458429232120.7865083141535770.393254157076788
3920.5766948219293380.8466103561413230.423305178070662
3930.6141979743666690.7716040512666630.385802025633331
3940.562537544498810.874924911002380.43746245550119
3950.5127025039823460.9745949920353070.487297496017654
3960.4625907565847020.9251815131694030.537409243415298
3970.4132957153980630.8265914307961260.586704284601937
3980.4306196066243940.8612392132487870.569380393375606
3990.3748533165779770.7497066331559540.625146683422023
4000.3201808649269810.6403617298539620.679819135073019
4010.3149654080555740.6299308161111480.685034591944426
4020.3228041338385540.6456082676771070.677195866161446
4030.2867307632755070.5734615265510130.713269236724493
4040.2522553219380220.5045106438760450.747744678061978
4050.3699063146024360.7398126292048730.630093685397564
4060.3098995133425780.6197990266851560.690100486657422
4070.2755245624497680.5510491248995370.724475437550232
4080.2336091533449950.4672183066899910.766390846655005
4090.5611365688793150.8777268622413710.438863431120686
4100.4843312141615660.9686624283231320.515668785838434
4110.4173255260939730.8346510521879460.582674473906027
4120.847503239687020.3049935206259610.152496760312981
4130.8011516515102530.3976966969794940.198848348489747
4140.9256543902745720.1486912194508560.0743456097254282
4150.8945129963986490.2109740072027020.105487003601351
4160.8495060347747950.3009879304504110.150493965225205
4170.7795462880256090.4409074239487820.220453711974391
4180.8115482464631080.3769035070737830.188451753536892
4190.733899501950690.5322009960986210.266100498049311
4200.6080603389021020.7838793221957970.391939661097898


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3540.863414634146341NOK
5% type I error level3620.882926829268293NOK
10% type I error level3650.890243902439024NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/10jqxo1291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/10jqxo1291299426.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/1nyif1291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/1nyif1291299426.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/2nyif1291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/2nyif1291299426.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/3nyif1291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/3nyif1291299426.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/4f7z01291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/4f7z01291299426.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/5f7z01291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/5f7z01291299426.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/6qgg31291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/6qgg31291299426.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/7qgg31291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/7qgg31291299426.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/8jqxo1291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/8jqxo1291299426.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/9jqxo1291299426.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299947ipxqao5n3v836lc/9jqxo1291299426.ps (open in new window)


 
Parameters (Session):
par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 8 ; 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')
}
 




Copyright

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.


Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

  • personalize online software applications according to your needs
  • enforce strict security rules with respect to the data that you upload (e.g. statistical data)
  • manage user sessions of online applications
  • alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by