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Zonder lineair, mét dummies zonder orders

*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: Fri, 03 Dec 2010 19:20:38 +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/03/t1291403908z23trldmov9ltoo.htm/, Retrieved Fri, 03 Dec 2010 20:18:39 +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/03/t1291403908z23trldmov9ltoo.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 «
350 1 1595 17 60720 319369 143 7022 75 0 61 0 5565 47 94355 493408 38 6243 53 39 287 0 601 6 60720 319210 144 19868 198 0 268 1 188 11 77655 381180 67 16471 964 0 25 1 7146 105 134028 297978 367 933 14 305 418 0 1135 17 62285 290476 384 5322 80 -9 392 1 450 8 59325 292136 380 11517 205 -1 131 1 34 8 60630 314353 309 14294 3340 0 316 1 133 14 65990 339445 109 9960 1052 0 347 1 119 6 59118 303677 354 17280 872 0 68 0 2053 53 100423 397144 60 3720 96 33 70 0 4036 63 100269 424898 53 3570 56 32 147 1 0 0 60720 315380 203 0 169 1 4655 393 60720 341570 105 360 30 0 248 1 131 11 61808 308989 338 9908 834 0 152 1 1766 73 60438 305959 349 1451 60 0 351 1 312 14 58598 318690 147 8478 381 0 372 1 448 40 59781 323361 132 3084 275 0 137 1 115 13 60945 318903 145 9146 1037 0 352 1 60 10 61124 314049 314 11405 1916 0 338 1 0 0 60720 315380 180 0 258 1 364 35 47705 298466 365 2813 271 0 343 1 0 0 60720 315380 183 0 398 0 1442 118 121173 438493 49 2021 165 -3 115 0 1389 9 57530 378049 71 19 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 time32 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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] = + 60019.6603271018 + 0.00809446600599193Rank[t] -0.0439900757638617group[t] -0.0129786525268384Costs[t] -0.108011699109833Trades[t] -0.101256190436436Dividends[t] -0.0340716239793206Wealth[t] -0.128781548334873WRank[t] -0.221094507310106`Profit/Trades`[t] -0.0189223049288562`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)60019.66032710189177.5085476.539900
Rank0.008094466005991930.0409470.19770.8433910.421696
group-0.04399007576386170.047663-0.92290.3565680.178284
Costs-0.01297865252683840.015728-0.82520.4097340.204867
Trades-0.1080116991098330.042637-2.53330.0116630.005831
Dividends-0.1012561904364360.036089-2.80570.0052530.002627
Wealth-0.03407162397932060.020266-1.68120.0934560.046728
WRank-0.1287815483348730.042713-3.0150.0027250.001363
`Profit/Trades`-0.2210945073101060.084799-2.60730.0094510.004725
`Profit/Cost`-0.01892230492885620.021713-0.87150.383990.191995


Multiple Linear Regression - Regression Statistics
Multiple R0.247537176524676
R-squared0.0612746537618086
Adjusted R-squared0.0412068910156238
F-TEST (value)3.05338739234688
F-TEST (DF numerator)9
F-TEST (DF denominator)421
p-value0.00148630772671066
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation103319.79996662
Sum Squared Residuals4494167028424.93


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1041397.8553238988-41397.8553238988
23932191.4220552047-32152.4220552047
3038574.2593941697-38574.2593941697
4035499.1693461769-35499.1693461769
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6-942575.1151091794-42584.1151091794
7-141456.3519384379-41457.3519384379
8039906.3724209728-39906.3724209728
9039535.552708787-39535.552708787
10039804.7985611683-39804.7985611683
113335456.0345098915-35423.0345098915
123234534.0260544088-34502.0260544088
13143097.6810457855-43096.6810457855
14122326.9782575084-22325.9782575084
15125361.5391152730-25360.5391152730
16126100.0436703887-26099.0436703887
17124478.9497139146-24477.9497139146
18124162.4291952230-24161.4291952230
19124317.2721227499-24316.2721227499
20124631.9103425533-24630.9103425533
213525108.3063131626-25073.3063131626
22046895.8571640183-46895.8571640183
2343849344090.2265516671394402.773448333
2437804958648.2234845876319400.776515412
2531333257934.3471488782255397.652851122
2631986457539.9459243566262324.054075643
2743086656010.6880353399374855.31196466
2831538058794.5048163096256585.495183690
295106-24536.203429912429642.2034299124
302999-12334.786281439215333.7862814392
310-17534.713564207217534.7135642072
32040778.3247403238-40778.3247403238
335826769.9272522635-26711.9272522635
343738725.0403362886-38688.0403362886
35940936.7061894102-40927.7061894102
36038616.3657004504-38616.3657004504
37-3144391.3348926156-44422.3348926156
382865-1876.886887444884741.88688744488
39033977.6405312230-33977.6405312230
401427599.55738659-27585.55738659
41040204.0621415683-40204.0621415683
423233516.6160784916-33484.6160784916
43043098.7807004715-43098.7807004715
44126821.5412211669-26820.5412211669
45125486.468125884-25485.4681258840
46925111.0322733683-25102.0322733683
472545451.7626032560-45426.7626032560
481143449.0353062586-43438.0353062586
4928241476.483656143-41194.483656143
504031451.7608939836-31411.7608939836
511743838.2345137379-43821.2345137379
526447860.0147322001-47796.0147322001
5315933980.5695078829-33821.5695078829
5424812771.2703433266-12523.2703433266
55516250.3886474313-16245.3886474313
563644053.9995648282-44017.9995648282
577249340.463084928-49268.463084928
58044542.6045388114-44542.6045388114
5931538045452.6297746549269927.370225345
602198-37653.362657281839851.3626572818
612226092.60226267177-5870.60226267177
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6731096-21299.973541601652395.9735416016
680-17537.944442052917537.9444420529
6919743244.74573866969-1270.74573866969
70-12942910.2534673984-43039.2534673984
7128328-98855.6252283493127183.625228349
72043097.923975757-43097.923975757
73120962.1968597959-20961.1968597959
74023403.0542437688-23403.0542437688
75122131.6152142277-22130.6152142277
76124717.8934543073-24716.8934543073
77036983.8072213701-36983.8072213701
781-103105.875855746103106.875855746
79120033.4412860309-20032.4412860309
80025116.3687773052-25116.3687773052
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89126819.9748974076-26818.9748974076
901-59556.8674697759557.8674697699
91143422.4650554665-43421.4650554665
92121891.4726625530-21890.4726625530
93122432.9344549544-22431.9344549544
942625110.9156990297-25084.9156990297
953046098.9993164105-46068.9993164105
968045910.280089746-45830.280089746
979947837.9449431476-47738.9449431476
98240297.7576967631-40295.7576967631
991641692.6716422907-41676.6716422907
100245591.1906087391-45589.1906087391
101039750.1810304589-39750.1810304589
102-717043706.6626795084-50876.6626795084
10332233158780.5053392248263550.494660775
104162961651333.92429029641578282.07570970
10529275458192.898592295234561.101407705
10631805658782.1735286714259273.826471329
10735517858346.467813013296831.532186987
10820432558371.6811068935145953.318893107
10931538058781.1745591771256598.825440823
1107315-18905.212314756926220.2123147569
1110-17534.811266884617534.8112668846
112040841.3112464778-40841.3112464778
1133834865.7375664048-34827.7375664048
11460121070.0696602595-20469.0696602595
11513223427.1112923978-23295.1112923978
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129143092.4103939195-43091.4103939195
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1326417553151.027599369311023.9724006307
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13426943131.2468652401-42862.2468652401
13567-8339.6290487688406.629048768
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14638817272.5859916074-16884.5859916074
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14811425879.6528973893-25765.6528973893
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15034618010.2475064896-17664.2475064896
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19123-36928.887405976936951.8874059769
19226917269.4732421306-17000.4732421306
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19931041622.8358859849-41312.8358859849
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20141639014.2848908196-38598.2848908196
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204219703.80271265945-9682.80271265945
20542339392.0845408488-38969.0845408488
20616040380.5069935161-40220.5069935161
20720740567.5879737378-40360.5879737378
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209984824.8708711059-4726.8708711059
210261316862.5599752834-14249.5599752834
21133717285.7674565463-16948.7674565463
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21315422372.5766763974-22218.5766763974
2143622312.400906789-22276.400906789
2151929726.74368757606-9534.74368757606
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21762238484.7657685143-37862.7657685143
21817452587.0286490409-52413.0286490409
219222042234.9679165261-40014.9679165261
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2247069462025.86821651658668.1317834835
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3259026252426.202883682537835.7971163175
3266072063673.0492798623-2953.04927986234
32739739725.0698877251-39328.0698877251
32829840460.4537851759-40162.4537851759
32942848107.30493407-47679.30493407
33021640561.8545166845-40345.8545166845
33117418047.9582138763-17873.9582138763
33217317260.3862092167-17087.3862092167
3333021454.0317281431-21424.0317281431
334050170.4082662992-50170.4082662992
3357156153054.831999208118506.1680007919
3366072062127.5451685281-1407.54516852812
33725543139.3277111258-42884.3277111258
33824017267.2685044594-17027.2685044594
339415021508.9139023434-17358.9139023434
340465847123.2073193525-42465.2073193525
34181450005.3226051696-49191.3226051696
342100253708.0560802816-52706.0560802816
34349652895.0005378774-52399.0005378774
34438951150.2155584375-50761.2155584375
3451267952265.0474585511-39586.0474585511
34640044119.5846477142-43719.5846477142
3475353111.856465906-53058.856465906
348051146.5785059219-51146.5785059219
3496703852079.426427212814958.5735727872
35011376161613.957865056252147.0421349438
3515832062332.1859282042-4012.18592820423
3526284162230.043510237610.956489763032
3537980461592.521233045718211.4787669543
3546255561655.5096266938899.490373306231
3559948961430.862661475438058.1373385246
3569013161705.702708609528425.2972913905
3579535057925.467848175437424.5321518246
3586424567388.8989107133-3143.89891071325
3596072061714.5189718364-994.518971836367
3606439771.0420241122-39707.0420241122
36138239955.2306802706-39573.2306802706
36240438242.5059357356-37838.5059357356
36312239758.0617797568-39636.0617797568
36416240620.3843986078-40458.3843986078
36535041319.5023045116-40969.5023045116
3667539524.8079981494-39449.8079981494
36728840628.9811553284-40340.9811553284
36824617281.7391888249-17035.7391888249
369021466.5533935130-21466.5533935130
3706072053225.59187452227494.40812547785
3719035130.9049368382-35040.9049368382
37216840793.7150879365-40625.7150879365
37320840517.5431331868-40309.5431331868
37422217269.2469137403-17047.2469137403
375021439.9983017691-21439.9983017691
3765966153181.76869744776479.23130255235
37710272562165.225139607540559.7748603925
37810847962129.593111196246349.4068888038
3798741960339.390369443327079.6096305567
3805468363028.4019907561-8345.40199075611
38112284458469.281394753364374.7186052467
3826271063438.9053099431-728.90530994314
3836072062227.5272957519-1507.52729575189
3846072062181.9387855622-1461.9387855622
3858716161691.185868719225469.8141312808
38610148164064.079378451437416.9206215486
38712829460662.877316801267631.1226831988
3886262062305.7881262227314.211873777259
3896072062242.5330709926-1522.53307099264
39022743134.3371061802-42907.3371061802
3914427387.09451536-27343.09451536
39210712920.8831240846-12813.8831240846
39364716971.5473204199-16324.5473204199
39440-10543.704598413310583.7045984133
3957023418.2873762101-23348.2873762101
39611774316189.2578858568101553.742114143
39733417259.0607863014-16925.0607863014
3989518524.0467247597-18429.0467247597
399819091.2817595321-19083.2817595321
400221-163099.868760634163320.868760634
40141920234.2503122333-19815.2503122333
40242424212.1372399092-23788.1372399092
40332759162.600297068-58835.600297068
40422621460.2597903393-21234.2597903393
40569451017.7216550903-50323.7216550903
406050051.8974279063-50051.8974279063
4075672653163.15868391093562.84131608913
4086072062108.6367680074-1388.63676800739
4099435561265.895634083533089.1043659165
4106072063778.3937592974-3058.39375929743
4117765562266.880175634315388.1198243657
41213402861201.35811707572826.641882925
4136228562105.4784923477179.521507652290
4145932562164.0854617252-2839.08546172518
4156063062183.8519801818-1553.85198018181
4166599061963.745235364026.25476464008
4175911862481.2203203481-3363.22032034807
41810042361686.506900196138736.4930998039
41910026962273.783814848337995.2161851517
4206072063395.8913170621-2675.89131706215
42110542447.8430608132-42342.8430608132
42233840476.2931097631-40138.2931097631
42334940825.122671135-40476.122671135
42414740992.2740446597-40845.2740446597
42513240674.8418476939-40542.8418476939
42614540503.0505768334-40358.0505768334
42731440549.9095120011-40235.9095120011
42818040598.5085039148-40418.5085039148
42927119769.2093175782-19498.2093175782
43039817280.1447357479-16882.1447357479
4311152168.10198202098-2053.10198202098


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
133.31598092171345e-126.6319618434269e-120.999999999996684
141.83821670930240e-153.67643341860481e-150.999999999999998
151.88623849795415e-183.7724769959083e-181
163.93192386793126e-227.86384773586253e-221
173.99694256999967e-257.99388513999933e-251
189.28818258727166e-291.85763651745433e-281
192.56813774660627e-325.13627549321255e-321
206.26358157937151e-361.25271631587430e-351
211.40257090364495e-392.8051418072899e-391
221.07441155037750e-422.14882310075501e-421
233.32946859412867e-196.65893718825734e-191
242.81992825242069e-095.63985650484138e-090.999999997180072
258.8082391864263e-091.76164783728526e-080.99999999119176
262.60085468532312e-095.20170937064625e-090.999999997399145
274.58250497970409e-089.16500995940818e-080.99999995417495
283.55960444433925e-087.11920888867851e-080.999999964403956
294.67702383793149e-079.35404767586299e-070.999999532297616
302.34053800517881e-074.68107601035761e-070.9999997659462
317.41404167550149e-081.48280833510030e-070.999999925859583
322.4177300219882e-084.8354600439764e-080.9999999758227
331.12035168305511e-072.24070336611021e-070.999999887964832
343.91622375453908e-087.83244750907817e-080.999999960837762
351.79775614448728e-083.59551228897455e-080.999999982022439
361.34689203435637e-082.69378406871275e-080.99999998653108
376.26669923464584e-091.25333984692917e-080.9999999937333
388.4177513076154e-091.68355026152308e-080.999999991582249
392.98687386212791e-095.97374772425583e-090.999999997013126
401.53650807699648e-093.07301615399296e-090.999999998463492
415.26506503504327e-101.05301300700865e-090.999999999473493
422.07027824053739e-104.14055648107478e-100.999999999792972
436.97615059133823e-111.39523011826765e-100.999999999930238
448.13776595131927e-111.62755319026385e-100.999999999918622
453.50864063154520e-117.01728126309041e-110.999999999964914
461.16276055437107e-112.32552110874214e-110.999999999988372
473.8238486424881e-127.6476972849762e-120.999999999996176
481.41359543228055e-122.8271908645611e-120.999999999998586
491.77492040196164e-123.54984080392329e-120.999999999998225
505.80365036049867e-131.16073007209973e-120.99999999999942
511.88358471133343e-133.76716942266686e-130.999999999999812
522.19156094491563e-134.38312188983125e-130.99999999999978
538.02274790222267e-141.60454958044453e-130.99999999999992
542.97326579776298e-145.94653159552595e-140.99999999999997
559.56289897198673e-151.91257979439735e-140.99999999999999
563.22752567035235e-156.4550513407047e-150.999999999999997
571.03548072329959e-152.07096144659918e-150.999999999999999
583.37304994573161e-166.74609989146321e-161
591.21461364290275e-142.42922728580551e-140.999999999999988
604.52292299552199e-159.04584599104398e-150.999999999999996
611.58480163235656e-153.16960326471312e-150.999999999999998
622.67373318138855e-145.34746636277709e-140.999999999999973
631.27382568135883e-142.54765136271766e-140.999999999999987
644.86513320523506e-159.73026641047012e-150.999999999999995
652.0616102494514e-154.1232204989028e-150.999999999999998
667.61146098208114e-161.52229219641623e-151
672.90633513147049e-165.81267026294099e-161
681.2776093348412e-162.5552186696824e-161
695.62489802270545e-171.12497960454109e-161
702.26113518351725e-174.5222703670345e-171
712.45913413820340e-174.91826827640681e-171
729.02284736806526e-181.80456947361305e-171
733.20030872499825e-186.4006174499965e-181
741.09960866424865e-182.19921732849731e-181
753.77480592687731e-197.54961185375462e-191
761.25936308631381e-192.51872617262762e-191
774.19401002239854e-208.38802004479709e-201
789.42098282304899e-201.88419656460980e-191
793.14464946287855e-206.2892989257571e-201
801.04949450440070e-202.09898900880140e-201
811.64470973726590e-183.28941947453179e-181
821.74850621327049e-183.49701242654098e-181
837.8816036860188e-191.57632073720376e-181
843.1398310225516e-196.2796620451032e-191
856.99118271167245e-171.39823654233449e-161
862.83899500855054e-175.67799001710109e-171
871.11676356697449e-172.23352713394898e-171
884.50814200506957e-189.01628401013913e-181
891.78835410134935e-183.57670820269870e-181
902.43450474832411e-184.86900949664821e-181
919.79803971075946e-191.95960794215189e-181
923.91861643496629e-197.83723286993259e-191
931.52913684314540e-193.05827368629079e-191
946.00062130619144e-201.20012426123829e-191
952.38634376267816e-204.77268752535632e-201
969.34727054910993e-211.86945410982199e-201
973.51831340630265e-217.03662681260531e-211
981.40419805861883e-212.80839611723767e-211
995.33393843667114e-221.06678768733423e-211
1002.05635879635022e-224.11271759270044e-221
1017.8449920648641e-231.56899841297282e-221
1024.77238489058781e-229.54476978117562e-221
1031.87021030361549e-213.74042060723098e-211
1040.999999999999882.42313177689581e-131.21156588844791e-13
1050.9999999999999968.0007543301294e-154.0003771650647e-15
10614.67878776265781e-172.33939388132891e-17
10717.75925121856933e-213.87962560928467e-21
10819.80279924235932e-244.90139962117966e-24
10913.94393722248700e-281.97196861124350e-28
11014.47086027995811e-282.23543013997906e-28
11113.73446569594803e-281.86723284797402e-28
11218.3618437767634e-284.1809218883817e-28
11311.86308295974662e-279.3154147987331e-28
11413.97059408481765e-271.98529704240882e-27
11518.23125955288729e-274.11562977644364e-27
11611.70361260007857e-268.51806300039284e-27
11713.72801357284768e-261.86400678642384e-26
11818.12138299183846e-264.06069149591923e-26
11911.47966760327823e-257.39833801639117e-26
12013.13413871303629e-251.56706935651815e-25
12116.57232683783046e-253.28616341891523e-25
12211.38275771899628e-246.9137885949814e-25
12319.26383174010796e-254.63191587005398e-25
12411.42473566012606e-387.12367830063031e-39
12511.48167440970683e-467.40837204853416e-47
12611.57851923502927e-597.89259617514636e-60
12719.06181247984976e-784.53090623992488e-78
12815.86871381230191e-802.93435690615096e-80
12912.26926301755806e-791.13463150877903e-79
13017.9085275224251e-793.95426376121255e-79
13112.40635298876550e-781.20317649438275e-78
13211.69521235989933e-788.47606179949664e-79
13316.64356050853263e-793.32178025426631e-79
13416.49459445700001e-1053.24729722850001e-105
13514.03823774382881e-1072.01911887191441e-107
13611.09765042421898e-1065.48825212109491e-107
13714.19075513534433e-1062.09537756767217e-106
13812.09170121532293e-1061.04585060766146e-106
13916.67469417375671e-1063.33734708687835e-106
14011.1951481622652e-1055.975740811326e-106
14118.20656534228528e-1094.10328267114264e-109
14212.91110364261690e-1101.45555182130845e-110
14313.56840781298813e-1101.78420390649407e-110
14415.52817967443732e-1172.76408983721866e-117
14511.98178670565429e-1169.90893352827147e-117
14619.5095123149004e-1164.7547561574502e-116
14711.40789865321338e-1157.0394932660669e-116
14815.38537778903772e-1152.69268889451886e-115
14911.88517276246818e-1149.42586381234088e-115
15015.90707582081081e-1142.95353791040541e-114
15112.41949760059859e-1131.20974880029929e-113
15219.2356630024167e-1134.61783150120835e-113
15313.29131284103762e-1121.64565642051881e-112
15411.09149244814741e-1115.45746224073703e-112
15512.89645584442903e-1111.44822792221451e-111
15612.74816068788574e-1111.37408034394287e-111
15714.14643285199169e-1112.07321642599585e-111
15811.11247360086287e-1185.56236800431435e-119
15913.7696953330679e-1201.88484766653395e-120
16011.90026023347435e-1199.50130116737175e-120
16111.04900076451479e-1185.24500382257395e-119
16215.55506304471725e-1182.77753152235862e-118
16313.2551748409474e-1171.6275874204737e-117
16411.91468964976898e-1169.57344824884489e-117
16518.71640097648188e-1164.35820048824094e-116
16614.18418163014043e-1152.09209081507022e-115
16712.27182961060741e-1141.13591480530370e-114
16811.27876463496692e-1136.3938231748346e-114
16916.79127300824574e-1133.39563650412287e-113
17012.96374393334103e-1121.48187196667051e-112
17111.38089794078469e-1116.90448970392347e-112
17216.64775441161e-1113.323877205805e-111
17312.48865597036875e-1101.24432798518437e-110
17419.0089176056159e-1104.50445880280795e-110
17512.16204408157825e-1091.08102204078912e-109
17612.27920658140388e-1091.13960329070194e-109
17711.47646233836391e-1097.38231169181955e-110
17817.63770129744466e-1093.81885064872233e-109
17914.03456742590281e-1082.01728371295140e-108
18012.10174384170639e-1071.05087192085320e-107
18111.10155683489228e-1065.50778417446139e-107
18214.80501582951476e-1062.40250791475738e-106
18312.36331594163998e-1051.18165797081999e-105
18411.13683274294458e-1045.68416371472291e-105
18513.98754057810158e-1041.99377028905079e-104
18611.32596324680967e-1036.62981623404836e-104
18715.19188534429182e-1052.59594267214591e-105
18811.96999329594667e-1049.84996647973337e-105
18918.83330512958602e-1044.41665256479301e-104
19011.04374224379369e-1035.21871121896846e-104
19113.73999630019042e-1031.86999815009521e-103
19211.92378495636844e-1029.61892478184219e-103
19319.30833154197516e-1024.65416577098758e-102
19413.03498482522711e-1011.51749241261355e-101
19517.42139017685384e-1023.71069508842692e-102
19611.31212550685370e-1016.56062753426851e-102
19714.83098289070164e-1012.41549144535082e-101
19819.00188502012122e-1014.50094251006061e-101
19916.53008124703922e-1013.26504062351961e-101
20017.46142477947334e-1023.73071238973667e-102
20112.23017192959441e-1011.11508596479721e-101
20216.11708175654312e-1013.05854087827156e-101
20311.90452709711467e-1009.52263548557336e-101
20419.89422694141583e-1014.94711347070792e-101
20513.58289049230793e-1001.79144524615397e-100
20611.29287611752066e-996.4643805876033e-100
20715.31149781474225e-992.65574890737112e-99
20812.80726020736682e-981.40363010368341e-98
20911.55645505003964e-977.7822752501982e-98
21018.1501160908834e-974.0750580454417e-97
21114.16835679501934e-962.08417839750967e-96
21212.05448785138225e-951.02724392569113e-95
21311.01331041615294e-945.06655208076468e-95
21414.91192937566547e-942.45596468783273e-94
21512.33508263544015e-931.16754131772007e-93
21611.20344846751689e-926.01724233758447e-93
21714.32446265428855e-922.16223132714428e-92
21811.37188186471419e-916.85940932357093e-92
21915.08545294706628e-912.54272647353314e-91
22011.05637299389769e-905.28186496948844e-91
22114.46069157231264e-902.23034578615632e-90
22211.07621705616342e-895.38108528081711e-90
22312.87280443696760e-891.43640221848380e-89
22411.39319404343841e-886.96597021719207e-89
22517.09604409134361e-883.54802204567180e-88
22613.62528181011369e-871.81264090505684e-87
22717.81402193755517e-873.90701096877759e-87
22813.95666494930697e-861.97833247465348e-86
22912.04988386173404e-851.02494193086702e-85
23011.01341970667862e-845.06709853339312e-85
23114.88453591068514e-842.44226795534257e-84
23212.32247379369723e-831.16123689684861e-83
23311.0642038121785e-825.3210190608925e-83
23414.52649449293098e-822.26324724646549e-82
23512.10930350750899e-811.05465175375450e-81
23615.69643205175038e-812.84821602587519e-81
23711.49913549256273e-807.49567746281367e-81
23817.41244069474127e-803.70622034737064e-80
23912.21197678739249e-791.10598839369624e-79
24018.3044169922714e-794.1522084961357e-79
24113.87792736752232e-781.93896368376116e-78
24211.77028114025128e-778.8514057012564e-78
24314.47690716399349e-772.23845358199674e-77
24418.93169139354907e-774.46584569677454e-77
24513.9517332727095e-761.97586663635475e-76
24611.91108561213326e-759.5554280606663e-76
24718.57296547107396e-754.28648273553698e-75
24814.16798112161532e-742.08399056080766e-74
24911.84408101463823e-739.22040507319115e-74
25018.61858231118023e-734.30929115559012e-73
25113.60975748307392e-721.80487874153696e-72
25211.62178080105379e-718.10890400526896e-72
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3900.9999999999997185.64051751227154e-132.82025875613577e-13
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3980.9999999999999975.28048759863586e-152.64024379931793e-15
3990.999999999999991.86110537567686e-149.3055268783843e-15
4000.9999999999999967.07318435394407e-153.53659217697203e-15
4010.9999999999999754.91889680990304e-142.45944840495152e-14
4020.9999999999998273.45609912131984e-131.72804956065992e-13
4030.9999999999996127.76434217104047e-133.88217108552023e-13
4040.9999999999998682.63342024549121e-131.31671012274560e-13
4050.999999999998922.16000262560666e-121.08000131280333e-12
4060.999999999999549.19952840710994e-134.59976420355497e-13
4070.9999999999962557.49075547391329e-123.74537773695665e-12
4080.9999999999640277.19467453367086e-113.59733726683543e-11
4090.9999999996665876.66826138911177e-103.33413069455589e-10
4100.999999999231871.53626194980552e-097.68130974902761e-10
4110.9999999952432339.51353341081928e-094.75676670540964e-09
4120.9999999682960996.34078026461624e-083.17039013230812e-08
4130.9999997308164845.38367031987586e-072.69183515993793e-07
4140.999997525675624.94864876139207e-062.47432438069604e-06
4150.9999874885649552.50228700894899e-051.25114350447449e-05
4160.9999218706555950.0001562586888089627.81293444044809e-05
4170.9996912308803860.0006175382392273030.000308769119613652
4180.9997463574025830.0005072851948347470.000253642597417373


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level4061NOK
5% type I error level4061NOK
10% type I error level4061NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/106y8o1291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/106y8o1291404004.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/16our1291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/16our1291404004.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/26our1291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/26our1291404004.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/3zxbu1291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/3zxbu1291404004.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/4zxbu1291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/4zxbu1291404004.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/5zxbu1291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/5zxbu1291404004.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/6aosf1291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/6aosf1291404004.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/72ga01291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/72ga01291404004.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/82ga01291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/82ga01291404004.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/9dprl1291404004.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291403908z23trldmov9ltoo/9dprl1291404004.ps (open in new window)


 
Parameters (Session):
par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 10 ; 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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