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Aantal reizigers inclusief lags

*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, 26 Nov 2010 01:59:36 +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/Nov/26/t1290736728p034eej00ivsc65.htm/, Retrieved Fri, 26 Nov 2010 02:58:59 +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/Nov/26/t1290736728p034eej00ivsc65.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 1742117 5 0 1522522 1276674 1086979 1149822 0 1737275 6 0 1742117 1522522 1276674 1086979 0 1979900 7 0 1737275 1742117 1522522 1276674 0 2061036 8 0 1979900 1737275 1742117 1522522 0 1867943 9 0 2061036 1979900 1737275 1742117 0 1707752 10 0 1867943 2061036 1979900 1737275 0 1298756 11 0 1707752 1867943 2061036 1979900 0 1281814 12 0 1298756 1707752 1867943 2061036 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 148812 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 time16 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
Yt[t] = + 526103.942017139 + 381943.276250048`9/11`[t] + 5463.46107822309t -3715.08271553495`9/11_t`[t] + 0.658302508465071`Yt-1`[t] + 0.266841562077032`Yt-2`[t] -0.00740687286284289`Yt-3`[t] -0.216866072731440`Yt-4`[t] + 134988.954627636M1[t] -158013.501846681M2[t] + 291621.008463846M3[t] + 8276.71590354365M4[t] -309010.132693305M5[t] -192510.530115477M6[t] -664535.837845057M7[t] -295913.329782779M8[t] -171119.104806719M9[t] -208806.873901883M10[t] + 186715.925498493M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)526103.94201713965838.0759017.990900
`9/11`381943.27625004893026.984244.10575.8e-052.9e-05
t5463.46107822309982.7440795.559400
`9/11_t`-3715.08271553495793.174656-4.68385e-063e-06
`Yt-1`0.6583025084650710.0685869.598200
`Yt-2`0.2668415620770320.0823453.24050.0013940.000697
`Yt-3`-0.007406872862842890.081794-0.09060.9279350.463968
`Yt-4`-0.2168660727314400.067537-3.21110.0015380.000769
M1134988.95462763652947.621522.54950.0115270.005763
M2-158013.50184668155679.697739-2.83790.0050020.002501
M3291621.00846384660951.5861714.78453e-062e-06
M48276.7159035436559493.5464090.13910.8894940.444747
M5-309010.13269330572943.682597-4.23633.4e-051.7e-05
M6-192510.53011547781953.119188-2.3490.0197830.009892
M7-664535.83784505772223.610054-9.201100
M8-295913.32978277989615.08133-3.3020.0011340.000567
M9-171119.10480671972876.937176-2.34810.0198340.009917
M10-208806.87390188364991.089984-3.21290.0015290.000764
M11186715.92549849352506.3977313.55610.0004680.000234


Multiple Linear Regression - Regression Statistics
Multiple R0.992669608595858
R-squared0.985392951829854
Adjusted R-squared0.984097745588166
F-TEST (value)760.800033318057
F-TEST (DF numerator)18
F-TEST (DF denominator)203
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation112494.287273394
Sum Squared Residuals2568957827837.21


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
117421171773951.44151448-31834.4415144845
217372751708798.3176792428476.6823207589
319799002176346.48659992-196446.486599925
420610362001951.6518954259084.3481045806
518679431760695.88953950107247.110460504
617077521776448.77690500-68696.776905003
712987561099689.46044091199066.539559088
812818141145621.28978312136192.710216880
912811511198650.7181684382500.2818315674
1011649761199238.50027400-34262.5002740048
1114543291612392.39439893-158063.394398926
1216452881594300.4729971850987.5270028242
1318177431938677.35958975-120934.359589752
1418957851838672.9362597657112.0637402373
1522363112326999.05582406-90688.0558240638
1622959512251422.3128848944528.6871151082
1720873152031748.8909941055566.109005896
1819808912012833.88841971-31942.8884197112
1914654461346249.81728148119196.182718522
2014450261341230.15129262103795.848707376
2114881201366537.49014721121582.509852794
2213383331384130.75623961-45797.7562396054
2317157891809944.91036181-94155.910361811
2418060901841312.49394392-35222.4939439248
2520833162133695.05984925-50379.0598492541
2620922782082438.645394929839.35460507755
2724308002534885.4943776-104085.494377599
2824248942460609.37777547-35715.3777754697
2922990162175042.50064565123973.499354352
3021306882208112.85171641-77424.8517164112
3116522211523780.58658695128440.413413051
3216081621540186.7963161767975.2036838283
3316470741542310.90206623104763.097933775
3414796911563994.26540393-84303.2654039293
3518849781969264.81460472-84286.8146047189
3620078982019415.74380923-11517.7438092275
3722089542341735.21200340-132781.212003402
3822171642252649.83512981-35485.8351298136
3925342912677999.11438661-143708.114386615
4025603122582928.17784073-22616.1778407297
4124290692329194.1084063899874.8915936197
4223150772367573.67040326-52496.670403257
4317996081721982.6957894177625.3042105935
4417725901721645.3539862350944.6460137651
4517447991725874.9479394018924.052060604
4616590931696685.24029604-37592.2402960444
4720998212145823.08866826-46002.0886682647
4821357362237898.18123620-102162.181236203
4924278942526259.81797753-98365.8179775335
5024688822455955.08891812926.9110820002
5127032172920150.49227537-216933.492275368
5227668412797517.55889685-30676.5588968512
5326552362526445.57664415128790.423355846
5425503732581291.72024712-30918.7202471202
5520520971964626.8790824487470.1209175642
5619980551969743.4578253028311.5421746953
5719207481956444.45848783-35696.4584878311
5818766941885340.00071866-8646.00071865728
5923809302345156.1233294335773.8766705678
6024674022496380.52381477-28978.5238147740
6127707712845400.36378985-74629.3637898478
6227813402786463.27166346-5123.27166346102
6331439263219478.13194992-75552.1319499206
6431722353162108.9636169910126.0363830121
6529525402899805.7495800152734.2504199886
6629208772879719.3754931241157.6245068777
6723845522254847.65652445129704.343475554
6822489872262908.51971302-13921.5197130218
6922086162208688.54109046-72.5410904624812
7021787562124552.6476903754203.3523096343
7126328702612424.1437409320445.856259073
7227069052751846.64762033-44941.6476203272
7330297453071189.24810737-41444.2481073722
7430154023019044.30586104-3642.30586103816
7533914143451817.08668706-60403.086687058
7635078053399191.67496336108613.325036637
7731778523194417.59800406-16565.5980040582
7831429613130555.1673385312405.8326614743
7925458152470572.9748530475242.0251469624
8024140072419448.52618166-5441.52618165517
8123725782375407.34826914-2829.34826913662
8223326642292728.0316609739935.968339025
8328253282786860.8197070038467.1802929957
8429014782948171.13085885-46693.1308588472
8532639553279496.69637408-15541.6963740783
8632267383255904.09710595-29166.0971059466
8736107863675819.81270948-65033.8127094819
8837092743621628.5280893787645.4719106293
8934671853398786.7043988868398.2956011246
9034496463392889.1737685956756.8262314141
9128029512766165.4829047136785.5170952865
9224625302690284.79384945-227754.793849446
9324906452476508.1775067614136.8224932425
9425615202380547.17480716180972.825192840
9530675542978460.5360629289093.4639370823
9632269513222861.440038244089.55996175882
9735464933596654.051961-50161.0519609973
9834927873542885.58878812-50098.5887881247
9939522633936973.6125241715289.3874758304
10039320723910301.3831251721770.6168748347
10137202843638893.4781362581390.5218637499
10236515553624291.9011361927263.0988638091
10329149722956476.53716699-41504.5371669898
10427135142833275.74565917-119761.745659169
10527039972680800.8654203423196.1345796551
10625913732608914.88996412-17541.8899641182
10731637483094452.9938270069295.0061729949
10833551373303703.5600914351433.4399085677
10936137023725779.37974724-112077.379747244
11036867733679709.727897227063.27210278262
11140987164126360.09801046-27644.0980104629
11240635174095739.81767976-32222.8176797618
11335514893761331.74860206-209842.748602064
11432266633514220.02636638-287557.026366381
11526568422604403.2289563352438.7710436728
11625974842524408.4396542773075.5603457253
11725723992573271.04332434-872.043324335353
11825966312579643.2833678916987.7166321143
11931652253110187.2265206755037.7734793322
12033031453319051.17836626-15906.1783662588
12136982473703566.70656297-5319.70656296867
12236686313699745.45224957-31114.4522495667
12341304334112731.3850279417701.6149720629
12441314004094401.4671090636998.5328909398
12538643583817262.6853292547095.3146707484
12637211103762976.48050297-41866.4805029749
12728925323026984.78041772-134452.780417721
12828434512815444.4075500128006.5924499862
12927475022747551.18716633-49.1871663298084
13026687752672554.28143042-3779.28143041837
13130186023172450.09010848-153848.090108476
13230133923208121.58470389-194729.584703892
13333936573456168.74845422-62511.7484542163
13435442333428335.92038057115897.079619426
13540758324004486.8563538471345.1436461567
13640329234111337.13013480-78414.1301348044
13737345093825822.39068809-91313.390688088
13837612853699581.4703057261703.5296942782
13929700903052334.02510955-82244.0251095464
14028478492920520.02888899-72671.0288889918
14127416802819984.91138323-78304.9113832278
14228306392681588.69705093149050.302949074
14332576733281580.28182076-23907.2818207562
14434800853428764.5524873751320.4475126262
14538432713848232.22868325-4961.22868325275
14637969613832957.99540547-35996.9954054680
14743377674256511.4505662181255.5494337926
14842436304267648.36052586-24018.3605258569
14939272023956029.07566419-28827.0756641888
15039152963846889.4328696068406.5671303956
15130873963167753.39949311-80357.399493107
15229637923012697.48697672-48905.4869767157
15329557922905663.8217061450128.178293859
15428299252840189.48397206-10264.4839720637
15532811953332926.30813324-51731.3081332366
15635480113438309.15613531109701.843864686
15740596483873776.93239124185871.067608765
15839411754014486.15548997-73311.1554899675
15945285944424562.56254292104031.437457081
16044331514436399.56090856-3248.56090855673
16141457374104699.3355072841037.6644927236
16240771324029615.6364549947516.3635450116
16331985193310797.54536086-112278.545360861
16430786603107295.81208622-28635.8120862197
16530282022983322.9636224544879.0363775456
16628586422903569.45383524-44927.4538352389
16733989543367183.6978589431770.3021410609
16838088833519026.32701356289856.672986437
16941759614081997.1847459493963.8152540636
17042275424174547.358539452994.6414606017
17147446164637626.1823504106989.817649598
17246080124618566.74162288-10554.7416228776
17342950494271089.5312365223959.4687634762
17442011444131845.4891980769298.5108019264
17533532763405115.12775047-51839.1277504745
17632868513224217.4761925162633.5238074857
17731698893149352.5129677820536.4870322246
17830517203045336.652519566383.34748043977
17936954263517972.36326074177453.636739263
18039055013740497.34163484165003.658365163
18142964584213535.4410074682922.5589925372
18242462474256565.0762967-10318.0762966969
18349218494638063.523272283785.476728002
18448214464739365.8097020682080.1902979439
18544250644453597.08302929-28533.0830292945
18643790994289999.0699602389099.930039775
18734728893537920.59351906-65031.5935190602
18833591603324175.746726634984.2532734017
18932009443220338.03666287-19394.036662869
19031531703066577.4635277686592.5364722375
19137414983587548.87267961153949.127320386
19239187193802966.88233992115752.117660080
19344034494248025.14522401155423.854775985
19444004074329162.8595745371244.140425466
19548474734778988.2661490368484.7338509692
19647161364748861.73341068-32725.7334106787
19742974404361060.61668591-63620.6166859093
19842722534165982.14587357106270.854126427
19932718343471418.40534-199584.405340000
20031683883208073.78355932-39685.7835593244
20129117483090551.77101101-178803.771011011
20227209992870934.1143919-149935.114391899
20331999183291875.67946239-91957.679462394
20436726233395616.77786484277006.222135163
20538920134028401.85468502-136388.854685023
20638508454045529.79887002-194684.798870019
20745324674420991.51165573111475.488344274
20844847394472985.0657076111753.9342923921
20940149724260638.89102096-245666.891020964
21039837584060781.51838025-77023.5183802484
21131584593297135.80342253-138676.803422535
21231005693129711.1837586-29142.1837586002
21329354043100028.30306006-164624.303060063
21428557192952795.06284939-97076.0628493859
21534656113432944.6554541732666.3445458274
21630069853641985.00504385-635000.005043853
21740951103675961.12733192419148.872668076
21841047933991405.56849625113387.431503751
21947307884610651.87673727120136.123262726
22046427264835134.68411049-192408.684110491
22142469194392617.14588746-145698.145887462
22243081174220069.2046602888047.7953397174


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.0790145448081980.1580290896163960.920985455191802
230.0628446915817780.1256893831635560.937155308418222
240.04890776188589330.09781552377178660.951092238114107
250.02606674082738820.05213348165477650.973933259172612
260.01233721275798120.02467442551596240.987662787242019
270.005588552351158020.01117710470231600.994411447648842
280.003019321861275680.006038643722551360.996980678138724
290.001322037439280120.002644074878560240.99867796256072
300.0004869680049231930.0009739360098463850.999513031995077
310.0002083021247013810.0004166042494027610.999791697875299
329.63711434056156e-050.0001927422868112310.999903628856594
333.73668965064064e-057.47337930128129e-050.999962633103494
342.02718349839846e-054.05436699679692e-050.999979728165016
357.77839482982633e-061.55567896596527e-050.99999222160517
362.50893894722377e-065.01787789444755e-060.999997491061053
371.49411687464965e-062.98823374929931e-060.999998505883125
387.61050292803874e-071.52210058560775e-060.999999238949707
393.69727317396865e-077.3945463479373e-070.999999630272683
401.82210291379957e-073.64420582759914e-070.999999817789709
416.26952568592451e-081.25390513718490e-070.999999937304743
422.46447709964328e-084.92895419928656e-080.999999975355229
431.10152186153346e-082.20304372306692e-080.999999988984781
443.88031097510637e-097.76062195021274e-090.99999999611969
453.91232437677316e-097.82464875354631e-090.999999996087676
461.21503682554146e-092.43007365108292e-090.999999998784963
471.64590487122202e-093.29180974244404e-090.999999998354095
481.06065473055357e-092.12130946110714e-090.999999998939345
495.02554549673799e-101.00510909934760e-090.999999999497445
502.55724391866675e-105.1144878373335e-100.999999999744276
512.16747308806028e-104.33494617612057e-100.999999999783253
527.27086321335364e-111.45417264267073e-100.999999999927291
534.41002396520728e-118.82004793041455e-110.9999999999559
544.35731924081998e-118.71463848163996e-110.999999999956427
552.29722254893477e-114.59444509786954e-110.999999999977028
567.85190233341756e-121.57038046668351e-110.999999999992148
571.05356050585698e-112.10712101171395e-110.999999999989464
584.57567497474122e-129.15134994948244e-120.999999999995424
595.91725637240019e-111.18345127448004e-100.999999999940827
605.17072575245557e-111.03414515049111e-100.999999999948293
613.69600113275323e-107.39200226550647e-100.9999999996304
622.05131306455766e-104.10262612911531e-100.999999999794869
633.30701406440963e-106.61402812881926e-100.999999999669299
641.34360872875210e-102.68721745750419e-100.99999999986564
658.82423212762023e-111.76484642552405e-100.999999999911758
668.47318886572995e-111.69463777314599e-100.999999999915268
675.73768844296282e-111.14753768859256e-100.999999999942623
681.02517904141547e-102.05035808283094e-100.999999999897482
698.27127192557873e-111.65425438511575e-100.999999999917287
704.7890006458447e-119.5780012916894e-110.99999999995211
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1897.73248125110784e-111.54649625022157e-100.999999999922675
1907.33820073233732e-111.46764014646746e-100.999999999926618
1911.06474955716416e-102.12949911432832e-100.999999999893525
1925.08112691673284e-091.01622538334657e-080.999999994918873
1931.05191026896108e-082.10382053792217e-080.999999989480897
1944.27296769383148e-088.54593538766296e-080.999999957270323
1951.83673163843157e-083.67346327686315e-080.999999981632684
1967.65243252906752e-091.53048650581350e-080.999999992347568
1974.48302146669536e-098.96604293339073e-090.999999995516978
1982.44618365063111e-094.89236730126222e-090.999999997553816
1994.74682598781442e-099.49365197562884e-090.999999995253174
2002.62468233987025e-095.2493646797405e-090.999999997375318


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1730.966480446927374NOK
5% type I error level1750.977653631284916NOK
10% type I error level1770.988826815642458NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/10yfb21290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/10yfb21290736759.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/1see81290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/1see81290736759.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/2k5vb1290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/2k5vb1290736759.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/3k5vb1290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/3k5vb1290736759.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/4k5vb1290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/4k5vb1290736759.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/5dwve1290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/5dwve1290736759.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/6dwve1290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/6dwve1290736759.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/765uh1290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/765uh1290736759.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/865uh1290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/865uh1290736759.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/9yfb21290736759.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/26/t1290736728p034eej00ivsc65/9yfb21290736759.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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Software written by Ed van Stee & Patrick Wessa


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