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hypothese 1

*The author of this computation has been verified*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Wed, 24 Nov 2010 19:16:52 +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/24/t1290626266lnzwpttycw11vxf.htm/, Retrieved Wed, 24 Nov 2010 20:17:50 +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/24/t1290626266lnzwpttycw11vxf.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 «
1 1 1 0 0 1 0 0 0 1 1 0 1 1 0 0 1 1 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 0 0 1 0 1 1 1 0 1 1 1 1 0 0 1 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0 0 1 0 1 1 1 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1 0 1 0 1 1 0 0 1 1 1 0 1 1 1 0 1 0 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 0 1 1 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 0 1 0 1 1 0 0 1 1 1 0 1 1 0 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 0 0 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 0 0 1 1 0 0 1 1 1 1 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 1 1 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 time82 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework
error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.


Multiple Linear Regression - Estimated Regression Equation
survived[t] = + 0.396578159185763 + 0.505349894156293`sex `[t] -0.000297551549688478t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)0.3965781591857630.02445716.215300
`sex `0.5053498941562930.02314321.835700
t-0.0002975515496884782.9e-05-10.141700


Multiple Linear Regression - Regression Statistics
Multiple R0.575014121687333
R-squared0.330641240139855
Adjusted R-squared0.329616188440222
F-TEST (value)322.560550124775
F-TEST (DF numerator)2
F-TEST (DF denominator)1306
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.39796724344235
Sum Squared Residuals206.841572470152


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
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114100.0570718409912098-0.0570718409912098
114200.0567742894415213-0.0567742894415213
114300.0564767378918328-0.0564767378918328
114400.0561791863421443-0.0561791863421443
114500.0558816347924558-0.0558816347924558
114600.0555840832427673-0.0555840832427673
114700.560636425849372-0.560636425849372
114800.560338874299683-0.560338874299683
114900.0546914285937019-0.0546914285937019
115010.5597437712003070.440256228799693
115100.054096325494325-0.054096325494325
115200.55914866810093-0.55914866810093
115300.053501222394948-0.053501222394948
115400.558553565001553-0.558553565001553
115500.0529061192955711-0.0529061192955711
115600.0526085677458826-0.0526085677458826
115700.557660910352487-0.557660910352487
115800.0520134646465056-0.0520134646465056
115900.55706580725311-0.55706580725311
116010.5567682557034220.443231744296578
116100.0511208099974402-0.0511208099974402
116200.0508232584477517-0.0508232584477517
116310.05052570689806330.949474293101937
116400.0502281553483748-0.0502281553483748
116500.0499306037986863-0.0499306037986863
116600.0496330522489978-0.0496330522489978
116700.0493355006993093-0.0493355006993093
116800.0490379491496208-0.0490379491496208
116900.0487403975999324-0.0487403975999324
117000.0484428460502439-0.0484428460502439
117100.0481452945005554-0.0481452945005554
117200.0478477429508669-0.0478477429508669
117300.552900085557472-0.552900085557472
117400.552602534007783-0.552602534007783
117500.552304982458095-0.552304982458095
117600.552007430908406-0.552007430908406
117700.0463599852024245-0.0463599852024245
117800.0460624336527361-0.0460624336527361
117900.0457648821030476-0.0457648821030476
118000.0454673305533591-0.0454673305533591
118100.550519673159964-0.550519673159964
118200.0448722274539822-0.0448722274539822
118310.5499245700605870.450075429939413
118400.0442771243546052-0.0442771243546052
118500.0439795728049167-0.0439795728049167
118600.0436820212552282-0.0436820212552282
118700.0433844697055398-0.0433844697055398
118810.5484368123121440.451563187687856
118910.5481392607624560.451860739237544
119010.5478417092127680.452158290787232
119110.04219426350678590.957805736493214
119200.0418967119570974-0.0418967119570974
119300.0415991604074089-0.0415991604074089
119400.0413016088577204-0.0413016088577204
119500.041004057308032-0.041004057308032
119600.0407065057583435-0.0407065057583435
119710.0404089542086550.959591045791345
119800.0401114026589665-0.0401114026589665
119910.5451637452655710.454836254734429
120000.0395162995595896-0.0395162995595896
120100.0392187480099011-0.0392187480099011
120200.0389211964602126-0.0389211964602126
120300.0386236449105241-0.0386236449105241
120400.0383260933608357-0.0383260933608357
120500.0380285418111472-0.0380285418111472
120610.5430808844177520.456919115582248
120700.0374334387117702-0.0374334387117702
120800.0371358871620817-0.0371358871620817
120900.542188229768686-0.542188229768686
121000.541890678218998-0.541890678218998
121100.0362432325130163-0.0362432325130163
121200.541295575119621-0.541295575119621
121300.0356481294136394-0.0356481294136394
121400.0353505778639509-0.0353505778639509
121500.0350530263142624-0.0350530263142624
121600.0347554747645739-0.0347554747645739
121710.5398078173711790.460192182628821
121800.034160371665197-0.034160371665197
121900.0338628201155085-0.0338628201155085
122000.03356526856582-0.03356526856582
122100.0332677170161315-0.0332677170161315
122200.0329701654664431-0.0329701654664431
122300.0326726139167546-0.0326726139167546
122410.5377249565233590.462275043476641
122500.0320775108173776-0.0320775108173776
122600.0317799592676891-0.0317799592676891
122700.0314824077180007-0.0314824077180007
122800.536534750324605-0.536534750324605
122910.03088730461862380.969112695381376
123000.0305897530689352-0.0305897530689352
123100.53564209567554-0.53564209567554
123200.535344544125851-0.535344544125851
123310.02969709841986990.97030290158013
123410.02939954687018130.970600453129819
123500.0291019953204929-0.0291019953204929
123600.0288044437708044-0.0288044437708044
123710.02850689222111590.971493107778884
123800.0282093406714274-0.0282093406714274
123910.0279117891217390.972088210878261
124000.0276142375720505-0.0276142375720505
124110.0273166860223620.972683313977638
124200.0270191344726735-0.0270191344726735
124300.026721582922985-0.026721582922985
124400.0264240313732966-0.0264240313732966
124510.5314763739799010.468523626020099
124600.0258289282739196-0.0258289282739196
124700.0255313767242311-0.0255313767242311
124810.5305837193308360.469416280669164
124900.0249362736248542-0.0249362736248542
125000.0246387220751657-0.0246387220751657
125100.0243411705254772-0.0243411705254772
125200.0240436189757887-0.0240436189757887
125300.0237460674261003-0.0237460674261003
125400.0234485158764118-0.0234485158764118
125510.02315096432672340.976849035673277
125600.0228534127770348-0.0228534127770348
125710.02255586122734630.977444138772654
125810.5276082038339510.472391796166049
125910.5273106522842630.472689347715737
126000.0216632065782809-0.0216632065782809
126110.5267155491848860.473284450815114
126210.5264179976351970.473582002364803
126300.0207705519292155-0.0207705519292155
126400.020473000379527-0.020473000379527
126500.0201754488298385-0.0201754488298385
126600.525227791436443-0.525227791436443
126700.0195803457304616-0.0195803457304616
126800.524632688337066-0.524632688337066
126900.0189852426310846-0.0189852426310846
127000.0186876910813961-0.0186876910813961
127100.0183901395317076-0.0183901395317076
127200.0180925879820192-0.0180925879820192
127300.0177950364323307-0.0177950364323307
127400.522847379038935-0.522847379038935
127500.0171999333329538-0.0171999333329538
127600.0169023817832653-0.0169023817832653
127700.52195472438987-0.52195472438987
127810.01630727868388840.983692721316112
127900.0160097271341998-0.0160097271341998
128000.521062069740804-0.521062069740804
128100.0154146240348229-0.0154146240348229
128200.0151170724851344-0.0151170724851344
128300.0148195209354459-0.0148195209354459
128400.0145219693857574-0.0145219693857574
128500.014224417836069-0.014224417836069
128600.0139268662863805-0.0139268662863805
128710.5189792088929850.481020791107015
128800.0133317631870035-0.0133317631870035
128900.0130342116373151-0.0130342116373151
129000.0127366600876266-0.0127366600876266
129110.5177890026942310.482210997305769
129200.0121415569882496-0.0121415569882496
129300.0118440054385611-0.0118440054385611
129400.0115464538888727-0.0115464538888727
129500.0112489023391842-0.0112489023391842
129600.0109513507894957-0.0109513507894957
129700.0106537992398072-0.0106537992398072
129800.0103562476901188-0.0103562476901188
129900.0100586961404303-0.0100586961404303
130000.00976114459074182-0.00976114459074182
130110.5148134871973470.485186512802653
130200.00916604149136485-0.00916604149136485
130300.00886848994167638-0.00886848994167638
130400.0085709383919879-0.0085709383919879
130500.513623280998593-0.513623280998593
130600.513325729448904-0.513325729448904
130700.00767828374292246-0.00767828374292246
130800.00738073219323398-0.00738073219323398
130900.0070831806435455-0.0070831806435455


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.9240911344476530.1518177311046940.0759088655523468
70.9680470731007790.06390585379844290.0319529268992215
80.9657352689548740.06852946209025110.0342647310451256
90.9695072645278510.06098547094429810.030492735472149
100.9623505381104370.07529892377912690.0376494618895634
110.9445263221996150.1109473556007690.0554736778003846
120.9478440339462380.1043119321075240.052155966053762
130.934922431613910.1301551367721790.0650775683860897
140.912367708831370.1752645823372590.0876322911686296
150.9166221966655620.1667556066688770.0833778033344384
160.9277396066790950.144520786641810.072260393320905
170.923333206625530.153333586748940.0766667933744699
180.8998316654811590.2003366690376820.100168334518841
190.869225869612250.2615482607755010.130774130387751
200.8605973321960360.2788053356079290.139402667803964
210.8890258730717490.2219482538565020.110974126928251
220.8564572540557040.2870854918885920.143542745944296
230.8597498725270390.2805002549459220.140250127472961
240.8233168388396780.3533663223206430.176683161160322
250.7822918947249610.4354162105500780.217708105275039
260.817365005897370.365269988205260.18263499410263
270.8185665779055180.3628668441889640.181433422094482
280.7799233775152150.4401532449695710.220076622484785
290.737663439802050.5246731203958980.262336560197949
300.7226302922112890.5547394155774220.277369707788711
310.7849917365606850.4300165268786290.215008263439315
320.7751091275317460.4497817449365080.224890872468254
330.7372185652827350.525562869434530.262781434717265
340.6966409497945140.6067181004109720.303359050205486
350.7560128394736260.4879743210527480.243987160526374
360.7163938504231510.5672122991536980.283606149576849
370.6744202427506990.6511595144986020.325579757249301
380.6654001302000930.6691997395998140.334599869799907
390.7232031519851520.5535936960296950.276796848014848
400.7570019382402020.4859961235195960.242998061759798
410.7754921075794160.4490157848411690.224507892420584
420.7400429367457740.5199141265084510.259957063254226
430.7022219012293720.5955561975412570.297778098770628
440.66240409895160.6751918020968010.3375959010484
450.6210229699974130.7579540600051730.378977030002587
460.6372473333233650.725505333353270.362752666676635
470.6426635972547620.7146728054904760.357336402745238
480.6779729139479720.6440541721040560.322027086052028
490.6391722666556990.7216554666886020.360827733344301
500.6603609855114750.6792780289770510.339639014488525
510.6217658289340350.756468342131930.378234171065965
520.6427837189572840.7144325620854330.357216281042716
530.6540802720284960.6918394559430070.345919727971504
540.6579007614189530.6841984771620940.342099238581047
550.6917995766715710.6164008466568580.308200423328429
560.656306135667070.6873877286658590.343693864332929
570.6792010615931850.641597876813630.320798938406815
580.6438812847533590.7122374304932820.356118715246641
590.6595119948269460.6809760103461070.340488005173054
600.6238955090662210.7522089818675570.376104490933778
610.6335176167883580.7329647664232840.366482383211642
620.5976688160405170.8046623679189660.402331183959483
630.6021290338839220.7957419322321560.397870966116078
640.5662016149134030.8675967701731930.433798385086597
650.6013932738679870.7972134522640260.398606726132013
660.5658056008208320.8683887983583360.434194399179168
670.5298639892394980.9402720215210040.470136010760502
680.4938633707932090.9877267415864170.506136629206791
690.5195527894718160.9608944210563670.480447210528184
700.4842191070315540.9684382140631070.515780892968446
710.50109573168860.99780853662280.4989042683114
720.5115781131352290.9768437737295420.488421886864771
730.4768150939444710.9536301878889410.523184906055529
740.442384237009780.8847684740195590.55761576299022
750.4484729054053730.8969458108107460.551527094594627
760.449923721083370.899847442166740.55007627891663
770.4166564384023890.8333128768047790.58334356159761
780.4146836475765060.8293672951530130.585316352423493
790.3825721101118740.7651442202237470.617427889888126
800.3513330119600530.7026660239201060.648666988039947
810.347116064314650.69423212862930.65288393568535
820.3401064101199110.6802128202398220.659893589880089
830.3111557536228970.6223115072457950.688844246377103
840.283366807370370.5667336147407390.71663319262963
850.275455943418440.550911886836880.72454405658156
860.2497291255907520.4994582511815050.750270874409248
870.3003872704077610.6007745408155220.699612729592239
880.3452348495609370.6904696991218730.654765150439063
890.3164576065753620.6329152131507240.683542393424638
900.3150917666546610.6301835333093220.684908233345339
910.287789320634370.575578641268740.71221067936563
920.3276008854844620.6552017709689240.672399114515538
930.2999709911822680.5999419823645350.700029008817732
940.333083076533510.6661661530670210.66691692346649
950.3610365070907490.7220730141814990.63896349290925
960.3329006078547860.6658012157095710.667099392145214
970.3421531463212070.6843062926424140.657846853678793
980.3147716537477460.6295433074954910.685228346252254
990.2884642262326460.5769284524652920.711535773767354
1000.2633281289226660.5266562578453320.736671871077334
1010.2871190265891150.574238053178230.712880973410885
1020.2970932550597910.5941865101195820.702906744940209
1030.2720002390625010.5440004781250020.727999760937499
1040.2480873883919940.4961747767839870.751912611608006
1050.2254185516262650.450837103252530.774581448373735
1060.3606866179329030.7213732358658050.639313382067097
1070.3638802943464740.7277605886929490.636119705653526
1080.337326670720930.674653341441860.66267332927907
1090.3116204208844780.6232408417689570.688379579115522
1100.3420997498157820.6841994996315630.657900250184218
1110.3466973621120910.6933947242241820.653302637887909
1120.3209979007884520.6419958015769040.679002099211548
1130.2961914885238280.5923829770476550.703808511476172
1140.2723641112041970.5447282224083940.727635888795803
1150.2748854598690330.5497709197380660.725114540130967
1160.2752724065641080.5505448131282170.724727593435892
1170.2527316493840550.5054632987681090.747268350615945
1180.2312455526360760.4624911052721530.768754447363924
1190.2302568949118040.4605137898236070.769743105088196
1200.2628612428452030.5257224856904060.737138757154797
1210.2927211696850130.5854423393700250.707278830314988
1220.269775314712320.539550629424640.73022468528768
1230.2478226140004010.4956452280008030.752177385999599
1240.2725960319586380.5451920639172760.727403968041362
1250.2506740555444010.5013481110888020.749325944455599
1260.2561413903075320.5122827806150650.743858609692468
1270.2596310641034180.5192621282068370.740368935896582
1280.2385421428435180.4770842856870370.761457857156482
1290.2403092561404520.4806185122809040.759690743859548
1300.2202806880142670.4405613760285340.779719311985733
1310.2012866089788150.4025732179576310.798713391021185
1320.1833495611368030.3666991222736060.816650438863197
1330.1838998769195530.3677997538391060.816100123080447
1340.2096910115791450.419382023158290.790308988420855
1350.1914798492189730.3829596984379450.808520150781027
1360.1927383078822320.3854766157644640.807261692117768
1370.2177514555627050.435502911125410.782248544437295
1380.1993108142424170.3986216284848330.800689185757583
1390.2012342978796350.402468595759270.798765702120365
1400.1837916384581210.3675832769162420.816208361541879
1410.2068388809088080.4136777618176170.793161119091192
1420.189193408479130.3783868169582590.81080659152087
1430.1917318228938510.3834636457877030.808268177106149
1440.2142116034473240.4284232068946490.785788396552676
1450.1963753265762640.3927506531525270.803624673423736
1460.2164847239567160.4329694479134320.783515276043284
1470.198675363059340.3973507261186810.80132463694066
1480.2035982628477460.4071965256954920.796401737152254
1490.2071729797246420.4143459594492840.792827020275358
1500.1899806032416150.379961206483230.810019396758385
1510.1922672955010370.3845345910020730.807732704498963
1520.2144662541729610.4289325083459220.785533745827039
1530.2355294978310990.4710589956621980.764470502168901
1540.2172583966061920.4345167932123830.782741603393808
1550.2218039825631730.4436079651263470.778196017436827
1560.2042489687549360.4084979375098720.795751031245064
1570.2074678526952790.4149357053905580.792532147304721
1580.2095428856132590.4190857712265170.790457114386741
1590.2105456723592770.4210913447185540.789454327640723
1600.1937593490901130.3875186981802270.806240650909887
1610.177847444618280.355694889236560.82215255538172
1620.1628161368549060.3256322737098130.837183863145094
1630.1630555196586030.3261110393172070.836944480341397
1640.1489541327205970.2979082654411930.851045867279403
1650.1700423666348560.3400847332697120.829957633365144
1660.1907745077478350.3815490154956710.809225492252165
1670.1927773981033910.3855547962067810.80722260189661
1680.1772277959053910.3544555918107830.822772204094609
1690.1625255464533470.3250510929066950.837474453546653
1700.2469899303880650.493979860776130.753010069611935
1710.2730267504287940.5460535008575880.726973249571206
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13000.7864238338560340.4271523322879320.213576166143966
1301100
1302100
1303100


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1790.137904468412943NOK
5% type I error level3760.289676425269646NOK
10% type I error level6220.479198767334361NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/10sr7t1290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/10sr7t1290626124.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/137s01290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/137s01290626124.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/2eh9l1290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/2eh9l1290626124.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/3eh9l1290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/3eh9l1290626124.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/4eh9l1290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/4eh9l1290626124.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/5eh9l1290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/5eh9l1290626124.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/678851290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/678851290626124.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/7hz7q1290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/7hz7q1290626124.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/8hz7q1290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/8hz7q1290626124.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/9sr7t1290626124.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/24/t1290626266lnzwpttycw11vxf/9sr7t1290626124.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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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