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Schiphol: MR - Model 4

*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, 10 Dec 2010 20:07:01 +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/10/t1292011685h4s15d3u29asj0g.htm/, Retrieved Fri, 10 Dec 2010 21:08:08 +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/10/t1292011685h4s15d3u29asj0g.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 «
0 1281151 13 0 1281814 1298756 1707752 1867943 0 1164976 14 0 1281151 1281814 1298756 1707752 0 1454329 15 0 1164976 1281151 1281814 1298756 0 1645288 16 0 1454329 1164976 1281151 1281814 0 1817743 17 0 1645288 1454329 1164976 1281151 0 1895785 18 0 1817743 1645288 1454329 1164976 0 2236311 19 0 1895785 1817743 1645288 1454329 0 2295951 20 0 2236311 1895785 1817743 1645288 0 2087315 21 0 2295951 2236311 1895785 1817743 0 1980891 22 0 2087315 2295951 2236311 1895785 0 1465446 23 0 1980891 2087315 2295951 2236311 0 1445026 24 0 1465446 1980891 2087315 2295951 0 1488120 25 0 1445026 1465446 1980891 2087315 0 1338333 26 0 1488120 1445026 1465446 1980891 0 1715789 27 0 1338333 1488120 1445026 1465446 0 1806090 28 0 1715789 1338333 1488120 1445026 0 2083316 29 0 1806090 1715789 1338333 1488120 0 2092278 30 0 2083316 1806090 1715789 1338333 0 2430800 31 0 2092278 2083316 1806090 1715789 0 2424894 32 0 2430800 2092278 2083316 1806090 0 2299016 33 0 2424894 2430800 2092278 2083316 0 2130688 34 0 2 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 time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Multiple Linear Regression - Estimated Regression Equation
Passagiers[t] = + 194011.243574092 + 380088.931765371`9/11`[t] + 5404.82792194756t -3721.53389639468`T_9/11`[t] + 0.658733587626391`Passagiers-1`[t] + 0.264676600256945`Passagiers-2`[t] -0.00276110284221941`Passagiers-3`[t] -0.209733720217022`Passagiers-4`[t] + 134292.960372113M1[t] + 100284.659944656M2[t] + 501151.929790886M3[t] + 315108.782038711M4[t] + 453693.579681728M5[t] + 155835.777651903M6[t] + 614751.568544971M7[t] + 314371.933454732M8[t] -7045.16201447281M9[t] + 117213.089006908M10[t] -373974.447140203M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)194011.24357409297451.2223441.99090.0478930.023947
`9/11`380088.93176537194581.1455054.01878.4e-054.2e-05
t5404.827921947561009.8356035.352200
`T_9/11`-3721.53389639468818.258646-4.54819e-065e-06
`Passagiers-1`0.6587335876263910.0699149.422100
`Passagiers-2`0.2646766002569450.0845453.13060.0020130.001006
`Passagiers-3`-0.002761102842219410.083693-0.0330.9737160.486858
`Passagiers-4`-0.2097337202170220.069196-3.0310.0027680.001384
M1134292.96037211364097.7603192.09510.0374510.018725
M2100284.65994465666288.2122751.51290.1319350.065967
M3501151.92979088667795.2176987.392100
M4315108.78203871192335.6321233.41260.0007820.000391
M5453693.57968172887895.1158955.16181e-060
M6155835.77765190399351.6355581.56850.1183790.05919
M7614751.56854497177840.158087.897600
M8314371.93345473296035.3815883.27350.0012570.000628
M9-7045.1620144728178844.90347-0.08940.9288920.464446
M10117213.08900690868424.4870511.7130.0882970.044148
M11-373974.44714020360169.123361-6.215400


Multiple Linear Regression - Regression Statistics
Multiple R0.992347755876047
R-squared0.984754068592227
Adjusted R-squared0.983346751846894
F-TEST (value)699.738755939612
F-TEST (DF numerator)18
F-TEST (DF denominator)195
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation111893.973577065
Sum Squared Residuals2441450957958.64


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
112811511190205.3110202290945.6889797843
211649761191407.6815779-26431.6815778986
314543291606802.95545584-152473.955455843
416452881589577.5106703355710.489329674
518177431936403.03628818-118660.036288184
618957851831661.2234983964123.7765016104
722363112331821.19347265-95510.1934726522
822959512241291.483732154659.5162678999
920873152018310.2406219469004.7593780614
1019808912009014.82491694-28123.8249169449
1114654461326321.52920689139124.47079311
1214450261326059.47406806118966.525931942
1314881201359931.54434342128188.455656575
1413383331378054.73898072-39721.7389807244
1517157891805225.66140788-89436.661407882
1618060901837748.94830702-31658.9483070241
1720833162132501.95876722-49185.9587672188
1820922782076940.8138181915337.1861818079
1924308002541125.65678236-110325.656782356
2024248942451814.07869017-26920.0786901735
2122990162163341.79732202135674.20267798
2221306882205707.30206406-75019.3020640577
2316522211504741.15405185147479.845948145
2416081621525971.9123302982190.0876697073
2516470741536873.17074469110200.829255314
2614796911558866.10752047-79175.1075204727
2718849781965648.81240138-80670.8124013841
2820078982016817.50666393-8919.50666393091
2922089542341349.65326422-132395.653264221
3022171642247859.88725721-30695.8872572107
3125342912685461.78234094-151170.782340938
3225603122575226.57132183-14914.5713218282
3324290692318100.41516159110968.584838406
3423150772365598.93757533-50521.9375753312
3517996081703404.8470336296203.1529663834
3617725901707961.2576856464628.7423143558
3717447991721269.62572923529.374270999
3816590931692539.48485998-33446.4848599785
3920998212143184.36487245-43363.3648724477
4021357362235927.32840991-100191.328409914
4124278942526291.11235127-98397.1123512692
4224688822452556.8287268516325.1712731544
4327032172928670.31795697-225453.317956974
4427668412790569.14569067-23728.145690673
4526552362517102.57872931138133.421270693
4625503732580843.89088155-30470.8908815533
4720520971947121.54675116104975.453248842
4819980551957478.9570400640576.0429599449
4919207481953392.33649415-32644.3364941457
5018766941882929.59208184-6235.59208184376
5123809302344374.9811393236555.0188606788
5224674022495781.66993715-28379.6699371469
5327707712846528.29883193-75757.2988319293
5427813402784649.15130379-3309.1513037925
5531439263230232.55071746-86306.5507174581
5631722353157928.9598776414306.0401223633
5729525402892876.920178859663.0798211972
5829208772882094.440541138782.5594588975
5923845522241181.4502985143370.549701495
6022489872253554.22738629-4567.22738629405
6122086162208162.99370335453.00629665095
6221787562125206.5514830353549.4485169739
6326328702613983.3516979118886.648302093
6427069052753125.95325944-46220.9532594421
6530297453074627.87618316-44882.8761831579
6630154023019444.5790342-4042.5790341963
6733914143464317.73875728-72903.7387572823
6835078053396819.37844423110985.62155577
6931778523189328.51797341-11476.5179734135
7031429613134416.450606328544.54939367833
7125458152459135.2673635586679.732636446
7224140072412419.500985711587.49901428959
7323725782376538.96725091-3960.96725091332
7423326642294724.9283668737939.0716331259
7528253282789344.6343826335983.3656173712
7629014782950435.30886813-48957.3088681296
7732639553283783.39467355-19828.3946735457
7832267383257272.33405537-30534.3340553692
7936107863689477.5334526-78691.5334525964
8037092743620665.5150438288608.4849561835
8134671853395258.2303115671926.7696884399
8234496463398261.8866070851384.1133929226
8328029512756030.605230346920.3947697007
8424625302684780.17793973-222250.177939731
8524906452479888.8391736310756.1608263686
8625615202385168.2996696176351.700330397
8730675542982143.2086634285410.7913365757
8832269513224925.569520932025.43047906915
8935464933601758.35477936-55265.3547793574
9034927873545724.99544391-52937.9954439074
9139522633952670.45351512-407.453515123203
9239320723911839.9846358120232.0153641857
9337202843637269.3281641183014.6718358906
9436515553632071.7515038819483.2484961244
9529149722948647.75331657-33675.7533165705
9627135142829443.6101387-115929.610138702
9727039972686086.8160642317910.1839357704
9825913732614341.52774234-22968.5277423417
9931637483098948.0258280464799.9741719574
10033551373307822.2190113647314.7809886372
10136137023731687.47851243-117985.47851243
10236867733682256.808602154516.19139784634
10340987164142573.07172297-43857.0717229672
10440635174097444.48716461-33927.4871646148
10535514893757515.03877964-206026.038779638
10632266633520387.32111085-293724.321110851
10726568422595086.3025301861755.6974698178
10825974842518206.1498900379277.8501099665
10925723992572549.42624547-150.426245467784
11025966312577689.7139457718941.286054226
11131652253109237.8693257755987.130674233
11233031453318362.26092885-15217.2609288504
11336982473705170.67917692-6923.67917691988
11436686313699115.11180068-30484.1118006803
11541304334125145.25068585287.74931419839
11641314004092797.3437134538602.6562865505
11738643583813144.2814974851213.7185025205
11837211103768368.62515314-47258.6251531398
11928925323016964.22393535-124432.223935351
12028434512809429.9308169434021.069183057
12127475022750172.90848676-2670.90848675579
12226687752673984.20589292-5209.2058929239
12330186023173195.45961217-154593.45961217
12430133923209000.07171428-195608.071714276
12533936573458768.29649396-65111.2964939606
12635442333427253.94936864116979.05063136
12740758324014333.6175778561498.3824221526
12840329234105716.09863209-72793.0986320865
12937345093818248.36475707-83739.3647570697
13037612853703208.9125804258076.0874195829
13129700903040983.95824027-70893.9582402748
13228478492912365.3751368-64516.3751367991
13327416802820920.12140427-79240.1214042733
13428306392682872.43711637147766.562883628
13532576733282200.62297677-24527.6229767745
13634800853428618.9770115651466.0229884372
13738432713850444.8250794-7173.82507939705
13837969613832543.59903924-35582.5990392406
13943377674268586.035371369180.9646286967
14042436304266229.50746083-22599.5074608329
14139272023951578.71151924-24376.7115192365
14239152963852382.191381962913.8086181004
14330873963158118.64754253-70722.647542528
14429637923005877.00537996-42085.0053799636
14529557922907703.891371248088.108628796
14628299252842176.93648611-12251.9364861062
14732811953333677.09740545-52482.0974054454
14835480113439215.91569889108795.084301114
14940596483876710.67917471182937.320825292
15039411754013341.15079994-72166.1507999442
15145285944435933.1907790892660.8092209187
15244331514435459.44949375-2308.44949375145
15341457374101349.7868189644387.213181042
15440771324035926.4065183141205.5934816858
15531985193302219.9419738-103700.941973805
15630786603101758.84642471-23098.8464247138
15730282022986701.4828844641500.517115541
15828586422906228.9472149-47586.9472149044
15933989543368335.308219630618.6917804039
16038088833520296.3460497288586.653950298
16141759614084657.2984510491303.7015489627
16242275424172858.6049933354683.3950066722
16347446164650139.2821995294476.717800483
16446080124618719.76163705-10707.7616370524
16542950494268726.6505950226322.3494049837
16642011444140107.1040425861036.8959574198
16733532763397839.82557391-44563.8255739131
16832868513219638.5692642667212.4307357361
16931698893153345.8010398716543.1989601298
17030517203048428.949331553291.05066845367
17136954263520190.43551711175235.564482886
17239055013742839.28184741162661.718152589
17342964584216721.8997273879736.1002726237
17442462474256692.5212579-10445.5212579044
17549218494652106.21082927269742.789170726
17648214464740022.6384981781423.3615018261
17744250644451107.82082279-26043.820822787
17843790994298030.4374670181068.5625329939
17934728893531915.16798976-59026.1679897591
18033591603320608.4309595538551.5690404525
18132009443225076.57683041-24132.5768304121
18231531703070570.5215144182599.4784855887
18337414983590151.48604769151346.513952311
18439187193804988.0434753113730.956524703
18544034494251029.35435761152419.645642388
18644004074329464.4146943170942.585305693
18748474734792474.7749272854998.2250727237
18847161364748963.0687191-32827.0687190981
18942974404359385.25612188-61945.2561218796
19042722534174159.4670896998093.5329103117
19132718343463842.48787685-192008.487876847
19231683883202526.08284009-34138.0828400946
19329117483093455.69639698-181707.696396982
19427209992872738.38943188-151739.389431877
19531999183291816.80119798-91898.8011979806
19636726233394854.90555639277768.094443612
19738920134027621.05004489-135608.050044892
19838508454039764.20894002-188919.208940017
19945324674429561.29818196102905.701818041
20044847394469228.1197352315510.8802647713
20140149724252563.86344508-237591.86344508
20239837584063173.91180884-79415.9118088383
20331584593285944.29108489-127485.291084892
20431005693120995.49171315-20426.4917131541
20529354043099012.49081698-163608.490816979
20628557192951390.98678332-95671.986783322
20734656113430987.9238485834623.0761514177
20830069853639891.18306942-632906.183069419
20940951103674331.75384278420778.246157218
21041047933988582.81736588116210.182634118
21147307884616913.04072959113874.959270407
21246427264826328.40750944-183602.407509439
21342469194386028.19718011-139109.19718011
21443081174224171.13815183945.8618489987


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.05503967632002510.110079352640050.944960323679975
230.02268070845452660.04536141690905320.977319291545473
240.006910214718080270.01382042943616050.99308978528192
250.002244674615698080.004489349231396150.997755325384302
260.0008240685928134330.001648137185626870.999175931407187
270.0002592132064982730.0005184264129965470.999740786793502
288.32018651248867e-050.0001664037302497730.999916798134875
293.27640293291676e-056.55280586583351e-050.99996723597067
302.10057963748113e-054.20115927496226e-050.999978994203625
311.99746104649324e-053.99492209298648e-050.999980025389535
327.47563901927888e-061.49512780385578e-050.99999252436098
332.29746487205881e-064.59492974411761e-060.999997702535128
347.53813199803475e-071.50762639960695e-060.9999992461868
352.22778622243286e-074.45557244486571e-070.999999777221378
365.94268819458335e-081.18853763891667e-070.999999940573118
374.05641700464774e-088.11283400929548e-080.99999995943583
381.06183418458592e-082.12366836917184e-080.999999989381658
397.00068084457541e-081.40013616891508e-070.999999929993192
403.92774722906654e-087.85549445813309e-080.999999960722528
411.7211599413829e-083.44231988276579e-080.9999999827884
421.61846837099459e-083.23693674198918e-080.999999983815316
431.63508086259031e-083.27016172518062e-080.999999983649191
445.74618948849512e-091.14923789769902e-080.99999999425381
453.44441887508848e-096.88883775017695e-090.999999996555581
462.78344919556229e-095.56689839112457e-090.99999999721655
471.85448672780743e-093.70897345561486e-090.999999998145513
486.4212959591108e-101.28425919182216e-090.99999999935787
497.13157651105415e-101.42631530221083e-090.999999999286842
503.19476056514971e-106.38952113029942e-100.999999999680524
519.30591385548094e-091.86118277109619e-080.999999990694086
527.97959850454016e-091.59591970090803e-080.999999992020401
533.21915941266127e-086.43831882532255e-080.999999967808406
541.53545806504427e-083.07091613008854e-080.99999998464542
551.26412640309136e-082.52825280618272e-080.999999987358736
565.13580665351919e-091.02716133070384e-080.999999994864193
574.3936490820731e-098.7872981641462e-090.99999999560635
583.20831056335028e-096.41662112670056e-090.99999999679169
591.80911549130557e-093.61823098261115e-090.999999998190884
602.70284103363745e-095.4056820672749e-090.99999999729716
612.45250706422233e-094.90501412844466e-090.999999997547493
621.52888276061527e-093.05776552123054e-090.999999998471117
631.08922948323598e-092.17845896647196e-090.99999999891077
644.70015318423262e-109.40030636846524e-100.999999999529985
655.59926235286428e-101.11985247057286e-090.999999999440074
662.42422703044591e-104.84845406089181e-100.999999999757577
672.20612681035524e-104.41225362071048e-100.999999999779387
683.78519727948566e-107.57039455897132e-100.99999999962148
691.06250073937878e-092.12500147875756e-090.9999999989375
704.80379314902116e-109.60758629804232e-100.99999999951962
715.20069285214807e-101.04013857042961e-090.99999999947993
726.0211068534391e-101.20422137068782e-090.99999999939789
735.52430242832583e-101.10486048566517e-090.99999999944757
742.81217044367302e-105.62434088734604e-100.999999999718783
752.66720841392045e-105.33441682784089e-100.99999999973328
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1912.34782556928822e-094.69565113857645e-090.999999997652174
1921.28948051838197e-092.57896103676395e-090.99999999871052


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1680.982456140350877NOK
5% type I error level1700.994152046783626NOK
10% type I error level1700.994152046783626NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/10g2p41292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/10g2p41292011607.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/191aa1292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/191aa1292011607.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/2karv1292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/2karv1292011607.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/3karv1292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/3karv1292011607.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/4karv1292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/4karv1292011607.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/5vj8x1292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/5vj8x1292011607.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/6vj8x1292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/6vj8x1292011607.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/7ns801292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/7ns801292011607.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/8ns801292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/8ns801292011607.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/9g2p41292011607.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/10/t1292011685h4s15d3u29asj0g/9g2p41292011607.ps (open in new window)


 
Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 2 ; par2 = Include Monthly 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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