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*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, 18 Dec 2009 02:53:18 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154.htm/, Retrieved Fri, 18 Dec 2009 10:54:53 +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/2009/Dec/18/t1261130079v9zh9yezy4ir154.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
0.122359934 0.358589807 0.931905548 4.214511881 -0.055775589 0.420547929 1.468985837 3.033704781 1.055123159 -0.56052437 0.944996502 -2.838342909 1.068458953 -3.59195797 0.316141818 -6.503810163 0.760538937 5.033758581 -2.20217258 1.822352229 -0.131541265 -1.643968707 -0.532224765 4.637069866 -1.741321669 2.605785323 0.204726282 2.374789523 -0.147865278 -0.774121623 1.396025769 -0.18761552 0.162345476 -5.476045205 0.015665853 1.447158493 0.217672981 -5.204314911 -0.917486426 -2.825441851 -0.903483082 -2.981766221 0.37057944 3.477155845 -1.528186024 -0.074804163 1.425442977 -0.747083419 0.823776349 0.087556846 1.16416148 1.717535987 0.548322968 0.265092469 -0.145197568 0.981838958 0.144016721 0.684387041 -0.008729414 0.745580981 -0.044488825 -7.057264511 2.265085141 -1.339602775 -2.30024615 6.03224455 -0.802974595 3.333000547 1.905081291 0.387430727 0.141275448 -0.492571244 -0.274557318 -1.114554463 -1.089390275 0.986362193 -0.106056668 -4.178129754 0.153839662 0.90977669 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 time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
JapanRES[t] = -0.216606458736063 -0.0300696870655007VSARES[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)-0.2166064587360630.10119-2.14060.0329210.016461
VSARES-0.03006968706550070.018947-1.5870.1133150.056658


Multiple Linear Regression - Regression Statistics
Multiple R0.0797986780766265
R-squared0.00636782902277707
Adjusted R-squared0.00383950288797508
F-TEST (value)2.51859478693223
F-TEST (DF numerator)1
F-TEST (DF denominator)393
p-value0.113315017829027
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.00330172425999
Sum Squared Residuals1577.19459478025


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
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2550.642873603-0.3384143783815160.981287981381516
256-1.942390074-0.345378648709025-1.59701142529098
2570.969546937-0.2659225597479831.23546949674798
2582.916260999-0.1534275470896843.06968854608968
2592.804886635-0.1800818045929172.98496843959292
260-1.177267984-0.197431552820850-0.97983643117915
2611.442031421-0.3362371582128131.77826857921281
262-1.0082797120.105911177052674-1.11419088905267
2631.685048733-0.1534222384668511.83847097146685
264-3.575122053-0.268127569118684-3.30699448388132
265-3.36169991-0.0997224100542042-3.26197749994580
2660.544584549-0.2642132034024930.808797752402493
267-3.812968258-0.382722716107807-3.43024554189219
268-0.255478375-0.2729513616987430.0174729866987429
2692.36107515-0.1248260678233812.48590121782338
2700.231398527-0.09939622109515470.330794748095155
2710.961587017-0.2131970518915561.17478406889156
272-2.7664061770.0198432885618289-2.78624946556183
273-1.889330686-0.257240587594175-1.63209009840582
274-1.722863291-0.108404245932190-1.61445904506781
275-0.492311851-0.280393642173762-0.211918208826238
276-0.3782960180.110756873144533-0.489052891144533
277-0.661148328-0.128832452916209-0.532315875083791
278-0.391163065-0.174573053107709-0.216590011892291
279-0.147328808-0.2527284505717350.105399642571735
280-0.155178326-0.00799634884162423-0.147181977158376
281-1.410346167-0.251894438693516-1.15845172830648
282-1.656752775-0.214310165102364-1.44244260989764
283-0.498316342-0.177742888861706-0.320573453138294
2840.635940123-0.3147813648981410.95072148789814
285-2.533052084-0.0456667686883443-2.48738531531166
2860.066688919-0.2171814662668330.283870385266833
2870.010332456-0.05787194073418680.0682043967341868
2880.100325395-0.1470604626717560.247385857671756
289-0.159212102-0.0564843684392878-0.102727733560712
290-1.118768301-0.430122409869871-0.688645891130129
291-0.360506506-0.312317240764100-0.0481892652358996
292-0.794671339-0.282443369184673-0.512227969815327
293-1.82637031-0.227521505291809-1.59884880470819
2940.020284413-0.3170392204207930.337323633420793
2952.059692348-0.3250961876639622.38478853566396
2961.01468765-0.07438354728301061.08907119728301
2973.219689867-0.2202208432100823.43991071021008
298-2.433588329-0.0266781413572552-2.40691018764274
299-2.372306799-0.421795916655193-1.95051088234481
300-0.37056341-0.4133408833464280.0427774733464281
3011.266050442-0.1634325305671251.42948297256713
3020.824592148-0.1670072112669000.9915993592669
303-1.852999286-0.0828740515764494-1.77012523442355
304-3.161642025-0.436243824252871-2.72539820074713
3050.442927211-0.3394796165008990.7824068275009
306-0.428942112-0.156591757376555-0.272350354623445
307-1.310961025-0.533886234986776-0.777074790013224
3081.014820082-0.3471783526166901.36199843461669
309-0.305111502-0.241286089173921-0.0638254128260788
3100.018050147-0.2159389342354740.233989081235474
311-2.696506438-0.240049705220849-2.45645673277915
312-1.10764417-0.35425947044047-0.75338469955953
3131.141463332-0.1276218720623041.26908520406230
3140.159323081-0.1291788900808560.288501971080856
315-2.055277082-0.228291471653339-1.82698561034666
3160.202725995-0.1778518286617420.380577823661742
317-0.3671714-0.330514775643549-0.0366566243564509
318-0.056145558-0.09615489008956740.0400093320895674
319-0.897022272-0.305465082047359-0.591557189952641
320-0.261083776-0.259664242409809-0.00141953359019098
321-0.651847454-0.359323548205966-0.292523905794034
322-0.339317343-0.129591061415679-0.209726281584321
3233.954885606-0.2750939754163454.22997958141634
324-2.972657905-0.183819286667942-2.78883861833206
3250.291575514-0.2330998508425740.524675364842574
3260.387896939-0.001509848976149970.38940678797615
3270.061920658-0.2740535254239120.335974183423912
328-4.019270705-0.388038209443618-3.63123249555638
3292.799936975-0.02129058009266422.82122755509266
330-0.961852566-0.290316746975832-0.671535819024168
331-0.689420887-0.398658712860098-0.290762174139902
332-0.528085855-0.177483319865674-0.350602535134326
3330.0888213190.208152191727730-0.119330872727730
334-3.596177355-0.264721436833030-3.33145591816697
335-1.4647901910.218117068617400-1.6829072596174
3360.759085451-0.3726381159472051.13172356694720
337-3.392063692-0.559746817773922-2.83231687422608
3382.3837555570.07630470885561592.30745084814438
3393.7731971590.02496274554971483.74823441345029
3400.17271416-0.1238996856552770.296613845655277
341-1.373664483-0.355909711946207-1.01775477105379
342-0.554515625-0.282171648757471-0.272343976242529
343-0.6167081590.364256423722298-0.980964582722298
3440.338243237-0.6542688183886690.992512055388669
345-4.969501265-0.269328666955519-4.70017259804448
346-1.459226067-0.0619383550558825-1.39728771194412
347-3.458393406-0.0179489255453813-3.44044448045462
348-0.708571911-0.0099092115068391-0.698662699493161
349-0.9671006470.327538223302014-1.29463887030201
350-2.764883729-0.0195568418616956-2.74532688713830
351-6.3583993490.379945690994377-6.73834503999438
352-1.216802997-0.0478186426367044-1.16898435436330
353-7.378604269-0.810998811997552-6.56760545700245
3546.326253049-0.3831796369404336.70943268594043
3552.2445534690.3167478775727781.92780559142722
3561.064937768-0.3703460255445741.43528379354457
357-5.865037234-0.181412615452120-5.68362461854788
358-2.548316402-0.681602550025991-1.86671385197401
3591.936100071-0.1232549924065442.05935506340654
360-0.6780526890.0632473218555839-0.741300010855584
361-0.197814711-0.182230281027475-0.0155844299725250
362-2.763460164-0.0615452479627985-2.7019149160372
363-0.426954939-0.091974604839854-0.334980334160146
364-6.333456722-0.705224395898218-5.62823232610178
3652.598460922-0.2039507568388132.80241167883881
3660.687911291-0.5041246965715701.19203598757157
3675.047098676-0.2456691399164805.29276781591648
368-1.221070779-0.383133352385105-0.837937426614895
369-1.439614033-0.425870889140594-1.01374314385941
3708.7875908180.1467034878002938.6408873301997
371-2.3867244610.0849156543337668-2.47164011533377
3720.539774275-0.2869223367262170.826696611726217
3730.369474032-0.1516029265372260.521076958537226
374-2.149167875-0.208255274008339-1.94091260099166
3750.019334090.163534601823699-0.144200511823699
3761.016870414-0.5527393763966941.56960979039669
377-5.4859518050.0727729832751374-5.55872478827514
3780.506223899-0.04910195908672180.555325858086722
379-1.934614759-0.177535182317883-1.75707957668212
380-0.5211105360.17098926033505-0.69209979633505
3810.0557162820.259471156594316-0.203754874594316
382-3.494650335-0.441514346567494-3.05313598843251
383-0.275822553-0.5171602079344010.241337654934401
3842.610696262-0.3028611946693712.91355745666937
385-0.486156759-0.49743039718680.0112736381868001
386-0.247611741-0.2838090223315340.0361972813315337
3871.077841547-0.4347420002875761.51258354728758
3881.0915400850.3217885605504140.769751524449586
389-3.133113963-0.264870322738918-2.86824364026108
390-0.451612277-0.333329006376219-0.118283270623781
3910.819605096-0.07852685271129210.898131948711292
392-0.476518511-0.416782749756588-0.0597357612434117
393-0.344000554-0.3893600214678520.0453594674678522
3942.175325119-0.1552574767160212.33058259571602
3950.390570472-0.1557300426865480.546300514686548


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.04308082112602480.08616164225204970.956919178873975
60.01786648742465790.03573297484931570.982133512575342
70.006061457529147950.01212291505829590.993938542470852
80.001777198461742800.003554396923485600.998222801538257
90.0004566567055536010.0009133134111072010.999543343294446
100.0338385276924230.0676770553848460.966161472307577
110.0184710910235890.0369421820471780.981528908976411
120.01112012464264100.02224024928528190.98887987535736
130.01736898197980730.03473796395961460.982631018020193
140.008983474488811470.01796694897762290.991016525511188
150.004669975977757060.009339951955514120.995330024022243
160.003722515302472590.007445030604945170.996277484697527
170.001902658005648950.003805316011297910.998097341994351
180.000908064276300550.00181612855260110.9990919357237
190.0004275623951615390.0008551247903230770.999572437604838
200.0003532178141765340.0007064356283530680.999646782185823
210.0002638876843789870.0005277753687579750.999736112315621
220.0001265360818341450.000253072163668290.999873463918166
230.0001665963348785550.0003331926697571110.999833403665122
240.0001597956178328740.0003195912356657490.999840204382167
259.31756713361482e-050.0001863513426722960.999906824328664
266.73045643786576e-050.0001346091287573150.999932695435621
273.37607349002401e-056.75214698004802e-050.9999662392651
281.63248260386758e-053.26496520773517e-050.999983675173961
297.46268936412754e-061.49253787282551e-050.999992537310636
303.38861360896887e-066.77722721793773e-060.99999661138639
311.52901562223316e-063.05803124446632e-060.999998470984378
324.59040938912062e-069.18081877824124e-060.99999540959061
331.52839894296956e-053.05679788593913e-050.99998471601057
349.12466178426482e-061.82493235685296e-050.999990875338216
351.34555634282212e-052.69111268564424e-050.999986544436572
366.77709669151952e-061.35541933830390e-050.999993222903308
373.6537453782747e-067.3074907565494e-060.999996346254622
382.88102960717452e-065.76205921434905e-060.999997118970393
391.52314461846888e-063.04628923693776e-060.999998476855382
407.42479995593218e-071.48495999118644e-060.999999257520004
414.54214494293897e-079.08428988587795e-070.999999545785506
423.27096003337356e-076.54192006674712e-070.999999672903997
434.10575018992179e-078.21150037984359e-070.99999958942498
441.70006854946878e-063.40013709893756e-060.99999829993145
458.72507738016027e-071.74501547603205e-060.999999127492262
465.60486436560903e-071.12097287312181e-060.999999439513563
473.05347445957154e-076.10694891914307e-070.999999694652554
482.88245663792034e-065.76491327584068e-060.999997117543362
492.18852409985808e-064.37704819971617e-060.9999978114759
502.8971654540567e-065.7943309081134e-060.999997102834546
513.55925268023907e-067.11850536047814e-060.99999644074732
522.19579569960614e-064.39159139921227e-060.9999978042043
531.26453855792846e-062.52907711585692e-060.999998735461442
542.12628082620708e-064.25256165241416e-060.999997873719174
559.83813003855478e-061.96762600771096e-050.999990161869961
566.59272463088855e-061.31854492617771e-050.99999340727537
574.59081918425323e-069.18163836850646e-060.999995409180816
586.71391737368569e-061.34278347473714e-050.999993286082626
594.00738949643688e-068.01477899287376e-060.999995992610504
600.002305628510302340.004611257020604680.997694371489698
610.001846427638649050.003692855277298110.998153572361351
620.003431492000552870.006862984001105740.996568507999447
630.002875044638441760.005750089276883530.997124955361558
640.004782548381079820.009565096762159640.99521745161892
650.003520935065092010.007041870130184020.996479064934908
660.002672819285535810.005345638571071620.997327180714464
670.002726765927479800.005453531854959590.99727323407252
680.005844692338533940.01168938467706790.994155307661466
690.004422905272275990.008845810544551990.995577094727724
700.00847238011599860.01694476023199720.991527619884001
710.006511072512491930.01302214502498390.993488927487508
720.004943162180607690.009886324361215370.995056837819392
730.003950141767997970.007900283535995940.996049858232002
740.003062486231574020.006124972463148050.996937513768426
750.003072582713248050.006145165426496090.996927417286752
760.004538322455315250.00907664491063050.995461677544685
770.003569119731717730.007138239463435470.996430880268282
780.002665821644735770.005331643289471530.997334178355264
790.002314583841389030.004629167682778070.99768541615861
800.002079605377871710.004159210755743410.997920394622128
810.001566758548933190.003133517097866380.998433241451067
820.00328182264346790.00656364528693580.996718177356532
830.007076530413658350.01415306082731670.992923469586342
840.007828023073404470.01565604614680890.992171976926596
850.006820861204683610.01364172240936720.993179138795316
860.005495971178615220.01099194235723040.994504028821385
870.005332651230675630.01066530246135130.994667348769324
880.004094790918750390.008189581837500770.99590520908125
890.006729642420001360.01345928484000270.993270357579999
900.007230963541035280.01446192708207060.992769036458965
910.005719241228496810.01143848245699360.994280758771503
920.006043678508920190.01208735701784040.99395632149108
930.00532986357394420.01065972714788840.994670136426056
940.004126651033533490.008253302067066980.995873348966467
950.003179500485268470.006359000970536930.996820499514732
960.002446825990745170.004893651981490330.997553174009255
970.001857647149258670.003715294298517340.998142352850741
980.001400763303253330.002801526606506660.998599236696747
990.001050517921287990.002101035842575970.998949482078712
1000.0007825373227920640.001565074645584130.999217462677208
1010.0005808559077009170.001161711815401830.999419144092299
1020.0005804830022286110.001160966004457220.999419516997771
1030.0005870509807138470.001174101961427690.999412949019286
1040.0005309645257263830.001061929051452770.999469035474274
1050.0003961219389324530.0007922438778649060.999603878061068
1060.0006243500601040340.001248700120208070.999375649939896
1070.0004673276664179620.0009346553328359250.999532672333582
1080.0003474578509966760.0006949157019933520.999652542149003
1090.0003047525597481570.0006095051194963140.999695247440252
1100.0003479265118771480.0006958530237542950.999652073488123
1110.0004242123879810810.0008484247759621630.999575787612019
1120.0003222133078933950.000644426615786790.999677786692107
1130.0002426078713731510.0004852157427463030.999757392128627
1140.0001774907721617580.0003549815443235160.999822509227838
1150.0001358102470628180.0002716204941256370.999864189752937
1160.0001248738697320640.0002497477394641290.999875126130268
1179.66837060025604e-050.0001933674120051210.999903316293997
1187.49251111512786e-050.0001498502223025570.999925074888849
1190.0001381851993556440.0002763703987112880.999861814800644
1200.0001009636780788690.0002019273561577380.99989903632192
1210.00010311798858040.00020623597716080.99989688201142
1220.0001379740711166190.0002759481422332370.999862025928883
1230.0001826482558274540.0003652965116549080.999817351744173
1240.0001529005162047560.0003058010324095120.999847099483795
1250.0001122814549112490.0002245629098224990.999887718545089
1260.0001264647497496560.0002529294994993120.99987353525025
1279.2286194301805e-050.000184572388603610.999907713805698
1287.33825860129045e-050.0001467651720258090.999926617413987
1295.35306439926934e-050.0001070612879853870.999946469356007
1303.87157213985493e-057.74314427970986e-050.999961284278601
1318.44782371915997e-050.0001689564743831990.999915521762808
1326.30457530686834e-050.0001260915061373670.999936954246931
1335.43935225547097e-050.0001087870451094190.999945606477445
1340.0001055089071918860.0002110178143837720.999894491092808
1358.19008701742592e-050.0001638017403485180.999918099129826
1366.39782880184623e-050.0001279565760369250.999936021711981
1374.66847602227828e-059.33695204455657e-050.999953315239777
1383.42942442055764e-056.85884884111527e-050.999965705755794
1393.33894259959319e-056.67788519918638e-050.999966610574004
1402.80927957426579e-055.61855914853159e-050.999971907204257
1412.01665135469771e-054.03330270939542e-050.999979833486453
1422.20758964789943e-054.41517929579886e-050.999977924103521
1432.24593683696128e-054.49187367392256e-050.99997754063163
1445.7107489146946e-050.0001142149782938920.999942892510853
1458.55519262934122e-050.0001711038525868240.999914448073707
1460.0001140072239553190.0002280144479106390.999885992776045
1479.98440337311056e-050.0001996880674622110.999900155966269
1488.52754968752579e-050.0001705509937505160.999914724503125
1496.30266136299561e-050.0001260532272599120.99993697338637
1508.26669358495983e-050.0001653338716991970.99991733306415
1516.1106978189801e-050.0001222139563796020.99993889302181
1524.58397773893983e-059.16795547787966e-050.99995416022261
1533.43436543438639e-056.86873086877278e-050.999965656345656
1543.96661113597072e-057.93322227194143e-050.99996033388864
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1670.0001015323797459490.0002030647594918980.999898467620254
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1800.01598751287726860.03197502575453730.984012487122731
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1850.01660706991562120.03321413983124230.983392930084379
1860.01442986073453010.02885972146906030.98557013926547
1870.01200480981626460.02400961963252920.987995190183735
1880.01157094188029560.02314188376059120.988429058119704
1890.01654410609032160.03308821218064320.983455893909678
1900.01948966981723370.03897933963446740.980510330182766
1910.01634036459420620.03268072918841240.983659635405794
1920.01659386271398280.03318772542796550.983406137286017
1930.01499589397557710.02999178795115410.985004106024423
1940.01591956282852810.03183912565705610.984080437171472
1950.01390073435067740.02780146870135480.986099265649323
1960.01160753890385970.02321507780771930.98839246109614
1970.02513192110504940.05026384221009880.97486807889495
1980.02288868354480890.04577736708961790.97711131645519
1990.01995577209995320.03991154419990650.980044227900047
2000.02412510295400980.04825020590801950.97587489704599
2010.02154986283889660.04309972567779310.978450137161103
2020.01816545386781470.03633090773562930.981834546132185
2030.01525048419745420.03050096839490840.984749515802546
2040.01392915284250010.02785830568500020.9860708471575
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2080.01917985178985500.03835970357971000.980820148210145
2090.03329831089311830.06659662178623650.966701689106882
2100.03009732247547630.06019464495095250.969902677524524
2110.0365731068350350.073146213670070.963426893164965
2120.03502384814877750.0700476962975550.964976151851223
2130.03386988123016170.06773976246032330.966130118769838
2140.03571984290429870.07143968580859740.964280157095701
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2220.02988919889423090.05977839778846190.97011080110577
2230.02690013740569350.0538002748113870.973099862594307
2240.03238860005980600.06477720011961190.967611399940194
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2260.02594084497011740.05188168994023480.974059155029883
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2310.02131768756738240.04263537513476480.978682312432618
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2400.01357626256453490.02715252512906970.986423737435465
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2490.005400412598853540.01080082519770710.994599587401146
2500.004519846071820640.009039692143641280.99548015392818
2510.003990996053795750.00798199210759150.996009003946204
2520.003267155033974940.006534310067949870.996732844966025
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2800.0008697545186278570.001739509037255710.999130245481372
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2900.0001015066154658070.0002030132309316150.999898493384534
2917.47026964464708e-050.0001494053928929420.999925297303554
2925.51148472360121e-050.0001102296944720240.999944885152764
2934.68445353886544e-059.36890707773088e-050.999953155464611
2943.44811727389332e-056.89623454778664e-050.999965518827261
2954.03193843921282e-058.06387687842564e-050.999959680615608
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2976.16341974512062e-050.0001232683949024120.99993836580255
2986.4744641098384e-050.0001294892821967680.999935255358902
2995.92441725428841e-050.0001184883450857680.999940755827457
3004.32059200014340e-058.64118400028679e-050.999956794079998
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3022.96806333439712e-055.93612666879424e-050.999970319366656
3032.56941249058151e-055.13882498116301e-050.999974305875094
3042.98537710681181e-055.97075421362363e-050.999970146228932
3052.28597836444572e-054.57195672889143e-050.999977140216356
3061.61064684629359e-053.22129369258718e-050.999983893531537
3071.16663901916600e-052.33327803833201e-050.999988333609808
3089.94712652717927e-061.98942530543585e-050.999990052873473
3096.90606113333691e-061.38121222666738e-050.999993093938867
3104.82374558344985e-069.6474911668997e-060.999995176254417
3115.05032427563909e-061.01006485512782e-050.999994949675724
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3141.96000630206962e-063.92001260413924e-060.999998039993698
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3161.13203878661417e-062.26407757322835e-060.999998867961213
3177.53613223025137e-071.50722644605027e-060.999999246386777
3184.95459189786813e-079.90918379573626e-070.99999950454081
3193.27477421127092e-076.54954842254183e-070.999999672522579
3202.12692392854092e-074.25384785708183e-070.999999787307607
3211.36937812836988e-072.73875625673976e-070.999999863062187
3228.68648962106798e-081.73729792421360e-070.999999913135104
3233.67758453576811e-077.35516907153622e-070.999999632241546
3244.32333735417595e-078.64667470835191e-070.999999567666265
3252.94023002120853e-075.88046004241706e-070.999999705976998
3261.92652437690896e-073.85304875381792e-070.999999807347562
3271.26621181776661e-072.53242363553321e-070.999999873378818
3282.3102725947794e-074.6205451895588e-070.99999976897274
3293.42843899749566e-076.85687799499131e-070.9999996571561
3302.20823730355024e-074.41647460710049e-070.99999977917627
3311.39345424204713e-072.78690848409427e-070.999999860654576
3328.67330708102428e-081.73466141620486e-070.99999991326693
3335.3191182128907e-081.06382364257814e-070.999999946808818
3348.21841332267654e-081.64368266453531e-070.999999917815867
3356.38234570104709e-081.27646914020942e-070.999999936176543
3364.75948437453452e-089.51896874906905e-080.999999952405156
3375.39207944783158e-081.07841588956632e-070.999999946079206
3385.94725749278075e-081.18945149855615e-070.999999940527425
3391.69696067302023e-073.39392134604046e-070.999999830303933
3401.07051169092111e-072.14102338184223e-070.999999892948831
3416.795538075475e-081.359107615095e-070.99999993204462
3424.06526439859466e-088.13052879718932e-080.999999959347356
3432.54308270095322e-085.08616540190645e-080.999999974569173
3441.82987951065709e-083.65975902131418e-080.999999981701205
3457.62465043784381e-081.52493008756876e-070.999999923753496
3465.1545754883396e-081.03091509766792e-070.999999948454245
3478.7729169271511e-081.75458338543022e-070.99999991227083
3485.28458050700227e-081.05691610140045e-070.999999947154195
3493.62858849432874e-087.25717698865748e-080.999999963714115
3504.31322538744686e-088.62645077489372e-080.999999956867746
3514.12814314032766e-068.25628628065533e-060.99999587185686
3523.04789440162822e-066.09578880325643e-060.999996952105598
3534.19460971542541e-058.38921943085082e-050.999958053902846
3540.001134153820982350.002268307641964700.998865846179018
3550.0009072420009024760.001814484001804950.999092757999098
3560.0007691573210720330.001538314642144070.999230842678928
3570.005287334179584420.01057466835916880.994712665820416
3580.004297994261600020.008595988523200050.9957020057384
3590.003944370842138480.007888741684276960.996055629157861
3600.002990781491808620.005981562983617230.997009218508191
3610.002065932148755190.004131864297510370.997934067851245
3620.002680019306189630.005360038612379270.99731998069381
3630.001879560627026140.003759121254052290.998120439372974
3640.01052659888326230.02105319776652460.989473401116738
3650.01163802488352530.02327604976705050.988361975116475
3660.008931410653562920.01786282130712580.991068589346437
3670.03755211714084910.07510423428169810.96244788285915
3680.02873397175855370.05746794351710750.971266028241446
3690.02214337165494270.04428674330988540.977856628345057
3700.6726963087051480.6546073825897040.327303691294852
3710.6730621250456120.6538757499087760.326937874954388
3720.6254694136760970.7490611726478060.374530586323903
3730.5711509911075370.8576980177849250.428849008892463
3740.5581383887010310.8837232225979380.441861611298969
3750.4925259403438510.9850518806877010.507474059656149
3760.4565750120228860.9131500240457720.543424987977114
3770.8870449248036010.2259101503927980.112955075196399
3780.8465987089598670.3068025820802650.153401291040133
3790.8492715768928880.3014568462142240.150728423107112
3800.8174401227362420.3651197545275160.182559877263758
3810.7780665056864810.4438669886270380.221933494313519
3820.8994769935381950.2010460129236100.100523006461805
3830.8481730881434320.3036538237131360.151826911856568
3840.9148150963442220.1703698073115550.0851849036557776
3850.8613679269490450.277264146101910.138632073050955
3860.7858252129733880.4283495740532240.214174787026612
3870.770588285537070.4588234289258610.229411714462931
3880.6919240901672780.6161518196654450.308075909832722
3890.967102114585080.0657957708298420.032897885414921
3900.9145714869395560.1708570261208880.0854285130604439


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2560.663212435233161NOK
5% type I error level3390.878238341968912NOK
10% type I error level3660.94818652849741NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/1035hs1261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/1035hs1261129985.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/1a8ix1261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/1a8ix1261129985.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/2kvqf1261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/2kvqf1261129985.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/3zmhr1261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/3zmhr1261129985.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/45nk21261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/45nk21261129985.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/5zcon1261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/5zcon1261129985.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/6kh6u1261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/6kh6u1261129985.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/7v9bu1261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/7v9bu1261129985.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/8yw2p1261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/8yw2p1261129985.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/90iwt1261129985.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/18/t1261130079v9zh9yezy4ir154/90iwt1261129985.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 1 ; 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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