Home » date » 2010 » Dec » 21 »

*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: Tue, 21 Dec 2010 22:59:30 +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/21/t1292972265vjihaa7uzxfvir4.htm/, Retrieved Tue, 21 Dec 2010 23:57:57 +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/21/t1292972265vjihaa7uzxfvir4.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 «
12.42 10.4 12.37 10.5 12.53 10.6 12.02 10.6 11.70 10.7 11.67 10.7 11.51 10.8 11.50 10.9 11.77 10.9 11.75 11.0 11.87 11.0 12.18 10.9 12.29 10.9 12.41 10.8 12.55 10.8 12.39 10.8 12.39 10.8 12.40 10.8 12.33 10.8 12.16 10.9 12.12 10.9 12.13 10.8 11.90 10.7 11.84 10.6 11.75 10.6 11.73 10.6 11.73 10.4 11.64 10.2 11.45 10.1 10.78 10.0 10.67 9.9 10.80 9.9 10.76 9.9 10.38 9.9 9.99 9.9 9.94 10.0 10.05 10.0 9.99 10.1 9.48 10.1 8.29 10.1 8.48 10.1 8.58 10.1 8.23 10.0 7.92 10.0 8.04 10.0 8.09 10.0 8.09 10.1 8.27 10.1 8.26 10.1 8.20 10.0 8.11 10.0 8.02 10.0 8.05 9.9 8.10 9.9 8.07 9.8 7.96 9.7 8.18 9.7 8.64 9.6 8.35 9.6 8.27 9.5 8.16 9.4 7.78 9.4 7.67 9.3 7.79 9.2 7.93 9.1 7.95 8.9 8.09 8.8 8.20 8.7 8.24 8.5 8.09 8.3 8.05 8.2 8.14 8.1 8.17 7.9 8.30 7.8 8.51 7.7 8.42 7.6 8.36 7.5 8.35 7.4 8.39 7.3 8.36 7.3 8.43 7.2 8.68 7.1 9.10 7.0 9.40 6.9 9.80 6.8 10.40 6.7 10.26 6.7 10.00 6.6 9.82 6.6 9.75 6.6 9.56 6.5 10.01 6.5 10.30 6.4 10.22 6 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 time19 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 7.38742434365645 + 0.171303601673414Rente[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)7.387424343656450.21730233.99600
Rente0.1713036016734140.0287165.965500


Multiple Linear Regression - Regression Statistics
Multiple R0.330609450980076
R-squared0.109302609077348
Adjusted R-squared0.106231238763821
F-TEST (value)35.5875709926531
F-TEST (DF numerator)1
F-TEST (DF denominator)290
p-value7.08843606034293e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.25445518542109
Sum Squared Residuals456.360765546661


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
110.49.515015076440240.88498492355976
210.59.50644989635660.993550103643401
310.69.533858472624331.06614152737567
410.69.44649363577091.15350636422911
510.79.39167648323541.30832351676460
610.79.38653737518521.31346262481480
710.89.359128798917451.44087120108255
810.99.357415762900711.54258423709929
910.99.403667735352541.49633226464746
10119.400241663319071.59975833668093
11119.420798095519881.57920190448012
1210.99.473902212038641.42609778796136
1310.99.492745608222711.40725439177729
1410.89.513302040423521.28669795957648
1510.89.53728454465781.2627154553422
1610.89.509875968390051.29012403160995
1710.89.509875968390051.29012403160995
1810.89.511589004406791.28841099559321
1910.89.499597752289651.30040224771035
2010.99.470476140005171.42952385999483
2110.99.463623995938231.43637600406177
2210.89.465337031954971.33466296804504
2310.79.425937203570081.27406279642992
2410.69.415658987469681.18434101253032
2510.69.400241663319071.19975833668093
2610.69.39681559128561.2031844087144
2710.49.39681559128561.0031844087144
2810.29.3813982671350.818601732865007
2910.19.348850582817040.751149417182956
30109.234077169695860.765922830304144
319.99.215233773511780.68476622648822
329.99.237503241729320.662496758270676
339.99.230651097662390.669348902337613
349.99.16555572902650.73444427097351
359.99.098747324373860.801252675626142
36109.090182144290190.909817855709812
37109.109025540474260.890974459525736
3810.19.098747324373861.00125267562614
3910.19.011382487520421.08861751247958
4010.18.807531201529051.29246879847095
4110.18.8400788858471.25992111415300
4210.18.857209246014341.24279075398566
43108.797252985428651.20274701457135
44108.744148868909891.25585113109011
45108.76470530111071.2352946988893
46108.773270481194371.22672951880563
4710.18.773270481194371.32672951880563
4810.18.804105129495591.29589487050441
4910.18.802392093478851.29760790652115
50108.792113877378451.20788612262155
51108.776696553227841.22330344677216
52108.761279229077231.23872077092277
539.98.766418337127441.13358166287257
549.98.77498351721111.12501648278890
559.88.76984440916091.03015559083910
569.78.751001012976830.948998987023172
579.78.788687805344980.911312194655021
589.68.867487462114750.73251253788525
599.68.817809417629460.782190582370541
609.58.804105129495590.695894870504414
619.48.785261733311510.61473826668849
629.48.720166364675610.679833635324388
639.38.701322968491540.598677031508464
649.28.721879400692350.478120599307653
659.18.745861904926620.354138095073375
668.98.74928797696010.150712023039907
678.88.773270481194370.0267295188056296
688.78.79211387737845-0.0921138773784473
698.58.79896602144538-0.298966021445383
708.38.77327048119437-0.47327048119437
718.28.76641833712744-0.566418337127435
728.18.78183566127804-0.681835661278042
737.98.78697476932824-0.886974769328244
747.88.80924423754579-1.00924423754579
757.78.8452179938972-1.14521799389721
767.68.8298006697466-1.22980066974660
777.58.8195224536462-1.31952245364619
787.48.81780941762946-1.41780941762946
797.38.8246615616964-1.52466156169640
807.38.8195224536462-1.51952245364619
817.28.83151370576333-1.63151370576333
827.18.87433960618169-1.77433960618169
8378.94628711888452-1.94628711888452
846.98.99767819938655-2.09767819938654
856.89.06619964005591-2.26619964005591
866.79.16898180105996-2.46898180105996
876.79.14499929682568-2.44499929682568
886.69.10046036039059-2.50046036039059
896.69.06962571208938-2.46962571208938
906.69.05763445997224-2.45763445997224
916.59.02508677565429-2.52508677565429
926.59.10217339640733-2.60217339640733
936.49.15185144089262-2.75185144089262
946.49.13814715275874-2.73814715275874
956.49.10217339640733-2.70217339640733
966.49.09189518030692-2.69189518030692
976.49.07819089217305-2.67819089217305
986.48.97198265913553-2.57198265913553
996.48.96855658710206-2.56855658710206
1006.38.95827837100166-2.65827837100166
1016.48.95313926295146-2.55313926295146
1026.48.97369569515227-2.57369569515227
1036.49.00966945150368-2.60966945150368
1046.48.99939123540328-2.59939123540328
1056.58.96684355108533-2.46684355108533
1066.58.95142622693472-2.45142622693472
1076.68.9548522989682-2.35485229896819
1086.68.95142622693472-2.35142622693472
1096.78.88119175024862-2.18119175024862
1106.78.88119175024862-2.18119175024862
1116.88.89660907439923-2.09660907439923
1126.98.9051742544829-2.0051742544829
11378.9000351464327-1.90003514643270
11478.9154524705833-1.91545247058331
1157.18.91373943456657-1.81373943456657
1167.28.93429586676738-1.73429586676738
1177.28.8897569303323-1.68975693033229
1187.48.80581816551232-1.40581816551232
1197.58.7647053011107-1.2647053011107
1207.68.7492879769601-1.14928797696009
1217.88.6841926083242-0.884192608324196
1227.98.69789689645807-0.797896896458068
1238.18.64993188798951-0.549931887989513
1248.28.66192314010665-0.461923140106652
1258.48.66021010408992-0.260210104089917
1268.58.62594938375523-0.125949383755235
1278.78.595114735454020.104885264545979
1288.98.600253843504220.299746156495778
12998.62423634773850.375763652261499
1309.28.61909723968830.580902760311701
1319.38.572845267236480.727154732763524
1329.58.516315078684250.98368492131575
1339.68.504323826567111.09567617343289
1349.68.535158474868331.06484152513167
1359.78.610532059604631.08946794039537
1369.88.636227599855641.16377240014436
1379.98.6790535002741.22094649972601
1389.98.75442708501031.14557291498970
1399.88.74928797696011.05071202303991
1409.88.793826913395181.00617308660482
1419.88.857209246014340.942790753985657
1429.88.83493977779680.96506022220320
1439.78.817809417629460.88219058237054
1449.78.809244237545790.890755762454211
1459.78.83493977779680.8650602222032
1469.78.804105129495590.895894870504414
1479.68.785261733311510.81473826668849
1489.68.732157616792750.867842383207248
1499.68.687618680357660.912381319642336
1509.68.643079743922580.956920256077423
1519.68.643079743922580.956920256077423
1529.78.61909723968831.0809027603117
1539.78.593401699437291.10659830056271
1549.88.596827771470761.20317222852925
1559.88.552288835035671.24771116496433
1569.98.535158474868331.36484152513168
1579.98.490619538433241.40938046156676
1589.98.524880258767921.37511974123208
1599.88.55914097910261.24085902089740
1609.78.530019366818121.16998063318188
1619.78.533445438851591.16655456114841
1629.68.54886276300221.05113723699780
1639.58.54886276300220.951137236997802
1649.48.521454186734450.878545813265548
1659.48.492332574449970.907667425550029
1669.38.432376313864280.867623686135724
1679.28.415245953696940.784754046303064
1689.28.39811559352960.801884406470406
1699.28.396402557512860.80359744248714
1709.18.355289693111240.74471030688876
1719.18.389550413445920.710449586554077
1729.18.41867202573040.681327974269596
1739.18.39811559352960.701884406470406
1749.28.386124341412450.813875658587545
1759.38.35357665709450.946423342905495
1769.38.372420053278580.92757994672142
1779.38.362141837178180.937858162821824
1789.38.362141837178180.937858162821824
1799.38.360428801161440.939571198838558
1809.48.321028972776561.07897102722344
1819.48.281629144391671.11837085560833
1829.48.26278574820761.13721425179240
1839.58.249081460073721.25091853992628
1849.58.249081460073721.25091853992628
1859.48.259359676174131.14064032382587
1869.48.237090207956581.16290979204342
1879.38.223385919822711.07661408017729
1889.48.185699127454561.21430087254544
1899.48.130881974919071.26911802508093
1909.28.120603758818661.07939624118134
1919.18.129168938902330.970831061097667
1929.18.088056074500711.01194392549929
1939.18.055508390182761.04449160981723
19498.079490894417040.920509105582957
19598.11717768678520.882822313214806
1968.98.091482146534180.808517853465819
1978.88.12231679483540.677683205164604
1988.78.18227305542110.517726944578908
1998.58.233664135923120.266335864076884
2008.38.274777000324730.0252229996752657
2018.18.30218557659248-0.202185576592482
2027.98.3432984409941-0.443298440994100
2037.88.29533343252555-0.495333432525545
2047.68.31417682870962-0.71417682870962
2057.48.37927219734552-0.979272197345518
2067.28.37927219734552-1.17927219734552
20778.35015058506104-1.35015058506104
20878.3330202248937-1.33302022489370
2096.88.36214183717818-1.56214183717818
2106.88.33473326091043-1.53473326091043
2116.78.34672451302757-1.64672451302757
2126.88.33987236896063-1.53987236896063
2136.78.35186362107777-1.65186362107777
2146.78.3432984409941-1.6432984409941
2156.78.3330202248937-1.63302022489370
2166.58.29190736049208-1.79190736049208
2176.38.27135092829127-1.97135092829127
2186.38.27135092829127-1.97135092829127
2196.38.2576466401574-1.95764664015739
2206.58.27991610837494-1.77991610837494
2216.68.30903772065942-1.70903772065942
2226.58.30047254057575-1.80047254057575
2236.38.30389861260922-2.00389861260922
2246.38.26963789227453-1.96963789227453
2256.58.2679248562578-1.7679248562578
22678.22509895583945-1.22509895583944
2277.18.20111645160517-1.10111645160517
2287.38.23880324397332-0.938803243973318
2297.38.2576466401574-0.957646640157394
2307.48.26963789227453-0.869637892274532
2317.48.30732468464268-0.907324684642684
2327.38.30732468464268-1.00732468464268
2337.48.30903772065942-0.909037720659418
2347.58.28505521642514-0.78505521642514
2357.78.25935967617413-0.559359676174128
2367.78.20968163168884-0.509681631688837
2377.78.17542091135416-0.475420911354155
2387.78.18569912745456-0.485699127454559
2397.78.1822730554211-0.482273055421091
2407.88.15143840711988-0.351438407119877
24188.12060375881866-0.120603758818662
2428.18.091482146534180.008517853465818
2438.18.098334290601120.00166570939888135
2448.28.12231679483540.077683205164603
2458.28.064073570266440.135926429733564
2468.28.028099813915020.171900186084981
2478.18.082916966450510.0170830335494887
2488.18.11032554271826-0.0103255427182575
2498.28.115464650768460.0845353492315396
2508.38.125742866868860.174257133131136
2518.38.146299299069670.153700700930327
2528.48.1377341189860.262265881013997
2538.58.11717768678520.382822313214806
2548.58.11717768678520.382822313214806
2558.48.084630002467240.315369997532755
25688.1171776867852-0.117177686785194
2577.98.14116019101947-0.241160191019472
2588.18.15143840711988-0.0514384071198771
2598.58.130881974919070.369118025080933
2608.88.103473398651320.69652660134868
2618.88.091482146534180.708517853465819
2628.68.069212678316640.530787321683362
2638.38.04694321009910.253056789900907
2648.38.014395525781150.285604474218856
2658.38.0024042736640.297595726335995
2668.47.998978201630540.401021798369463
2678.48.031525885948490.368474114051514
2688.58.004117309680740.49588269031926
2698.67.974995697396260.62500430260374
2708.67.945874085111780.65412591488822
2718.67.952726229178720.647273770821284
2728.67.954439265195450.64556073480455
2738.67.925317652910970.67468234708903
2748.57.952726229178720.547273770821284
2758.47.985273913496660.414726086503336
2768.47.968143553329320.431856446670677
2778.47.964717481295850.435282518704145
2788.57.993839093580340.506160906419665
2798.58.021247669848080.478752330151918
2808.68.065786606283170.53421339371683
2818.68.07777785840030.522222141599691
2828.48.076064822383580.323935177616426
2838.28.079490894417040.120509105582957
28488.05893446221623-0.058934462216233
28588.03666499399869-0.036664993998689
28688.04351713806563-0.0435171380656256
28788.03152588594849-0.0315258859484866
2887.98.04180410204889-0.141804102048891
2897.98.08291696645051-0.182916966450511
2907.88.09148214653418-0.291482146534182
2917.88.07435178636684-0.274351786366840
29288.11032554271826-0.110325542718257


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.000575204651176170.001150409302352340.999424795348824
63.54891976134204e-057.09783952268407e-050.999964510802387
72.54813852889298e-065.09627705778596e-060.99999745186147
84.28073967731061e-078.56147935462123e-070.999999571926032
91.66980803258372e-073.33961606516743e-070.999999833019197
101.24710679954468e-072.49421359908936e-070.99999987528932
117.4940227355273e-081.49880454710546e-070.999999925059773
123.32233270213517e-086.64466540427034e-080.999999966776673
131.36201481893154e-082.72402963786309e-080.999999986379852
142.75343222601965e-095.50686445203931e-090.999999997246568
155.70854264848843e-101.14170852969769e-090.999999999429146
169.26492020714731e-111.85298404142946e-100.99999999990735
171.44106847964289e-112.88213695928579e-110.99999999998559
182.17733444486053e-124.35466888972106e-120.999999999997823
193.10092886647771e-136.20185773295542e-130.99999999999969
205.88980951203514e-141.17796190240703e-130.999999999999941
211.06009149677551e-142.12018299355102e-140.99999999999999
221.40152739359651e-152.80305478719301e-150.999999999999999
232.48566366318616e-164.97132732637233e-161
249.23855078334307e-171.84771015666861e-161
253.24458649982444e-176.48917299964887e-171
261.00711443341042e-172.01422886682085e-171
272.01096145311248e-174.02192290622496e-171
282.25367680857155e-164.5073536171431e-161
291.43713634718798e-152.87427269437596e-150.999999999999999
301.62786751344655e-153.2557350268931e-150.999999999999998
311.03302841212971e-152.06605682425942e-150.999999999999999
325.14596665212297e-161.02919333042459e-151
331.85682463493083e-163.71364926986165e-161
344.18438701550338e-178.36877403100676e-171
351.04408937391587e-172.08817874783174e-171
363.35621770346849e-186.71243540693698e-181
378.7096239051571e-191.74192478103142e-181
383.14871951251511e-196.29743902503023e-191
392.40441576374749e-194.80883152749497e-191
401.47167238065132e-182.94334476130264e-181
411.42588608133745e-182.8517721626749e-181
428.02203021888648e-191.60440604377730e-181
433.4144482936602e-196.8288965873204e-191
441.59858466051178e-193.19716932102357e-191
456.0710824397988e-201.21421648795976e-191
462.11795538489012e-204.23591076978023e-201
479.6712701543906e-211.93425403087812e-201
483.76336834929306e-217.52673669858613e-211
491.43491916771001e-212.86983833542001e-211
504.46548747787334e-228.93097495574668e-221
511.41860813862805e-222.83721627725609e-221
524.60801665988203e-239.21603331976405e-231
531.36277399357544e-232.72554798715088e-231
544.08147656580179e-248.16295313160358e-241
551.31122896606047e-242.62245793212095e-241
564.8650864722207e-259.7301729444414e-251
572.03943772881184e-254.07887545762369e-251
582.01290239790318e-254.02580479580636e-251
591.32428130100863e-252.64856260201725e-251
601.23676553897253e-252.47353107794505e-251
611.60274235066570e-253.20548470133139e-251
621.13854572472258e-252.27709144944517e-251
631.06465480573224e-252.12930961146448e-251
641.83977238873503e-253.67954477747006e-251
656.3162623840679e-251.26325247681358e-241
667.41640145317291e-241.48328029063458e-231
671.41626158204736e-222.83252316409473e-221
683.64976999242798e-217.29953998485596e-211
692.09877403322216e-194.19754806644433e-191
701.33826025062306e-172.67652050124613e-171
715.23899397188917e-161.04779879437783e-151
721.71005464015868e-143.42010928031736e-140.999999999999983
736.98762019306105e-131.39752403861221e-120.999999999999301
742.21046277924734e-114.42092555849467e-110.999999999977895
756.08390559979037e-101.21678111995807e-090.99999999939161
769.9244411480241e-091.98488822960482e-080.999999990075559
771.10468246583476e-072.20936493166953e-070.999999889531753
789.39880085516428e-071.87976017103286e-060.999999060119914
796.59538302858469e-061.31907660571694e-050.999993404616971
803.02771190267563e-056.05542380535126e-050.999969722880973
810.0001279760853689660.0002559521707379330.999872023914631
820.0005485865993936730.001097173198787350.999451413400606
830.002487148462644630.004974296925289260.997512851537355
840.01027943343465090.02055886686930190.98972056656535
850.03793398898959540.07586797797919080.962066011010405
860.1223226962697460.2446453925394920.877677303730254
870.2571543050402390.5143086100804770.742845694959761
880.4223681911681960.8447363823363920.577631808831804
890.5764039494276770.8471921011446460.423596050572323
900.7047508042880120.5904983914239760.295249195711988
910.8070778423633090.3858443152733820.192922157636691
920.8874886888047550.2250226223904900.112511311195245
930.9440976674475850.1118046651048290.0559023325524147
940.9732372143720880.05352557125582460.0267627856279123
950.9871685267717750.02566294645645020.0128314732282251
960.9939677487228880.01206450255422330.00603225127711163
970.9971969553455540.005606089308891090.00280304465444554
980.998557789719390.002884420561219730.00144221028060986
990.9992693015446040.00146139691079180.0007306984553959
1000.9996654817548640.0006690364902720870.000334518245136044
1010.9998355526072970.0003288947854064770.000164447392703239
1020.9999235605636740.0001528788726516717.64394363258353e-05
1030.9999674885454736.5022909053701e-053.25114545268505e-05
1040.9999866180600972.67638798054177e-051.33819399027088e-05
1050.9999938625866141.2274826771265e-056.1374133856325e-06
1060.9999972591256945.48174861274084e-062.74087430637042e-06
1070.9999987099340082.58013198426387e-061.29006599213193e-06
1080.9999994250033381.14999332307157e-065.74996661535783e-07
1090.9999996966745166.06650967106827e-073.03325483553413e-07
1100.9999998488352163.02329569006726e-071.51164784503363e-07
1110.9999999226713671.54657266032969e-077.73286330164843e-08
1120.9999999590824588.18350848686686e-084.09175424343343e-08
1130.9999999771131444.57737113050985e-082.28868556525493e-08
1140.9999999885201662.29596685070873e-081.14798342535437e-08
1150.999999994032561.19348803365742e-085.96744016828711e-09
1160.9999999969513526.0972954400877e-093.04864772004385e-09
1170.9999999984569483.08610470833248e-091.54305235416624e-09
1180.9999999989180422.16391656227114e-091.08195828113557e-09
1190.9999999991451641.70967128978192e-098.5483564489096e-10
1200.9999999992738221.45235500811457e-097.26177504057284e-10
1210.9999999992331461.53370808373175e-097.66854041865874e-10
1220.9999999991685241.6629520479693e-098.3147602398465e-10
1230.9999999989712472.05750578239478e-091.02875289119739e-09
1240.9999999986999222.60015632112087e-091.30007816056043e-09
1250.9999999982670143.46597275690901e-091.73298637845450e-09
1260.999999997648924.70216138770009e-092.35108069385004e-09
1270.9999999968190036.36199423786867e-093.18099711893433e-09
1280.999999995784688.43064117203391e-094.21532058601696e-09
1290.999999994399831.12003417255403e-085.60017086277016e-09
1300.999999992977891.40442217465211e-087.02211087326055e-09
1310.9999999918939651.62120707193441e-088.10603535967203e-09
1320.999999992240541.55189205378889e-087.75946026894443e-09
1330.9999999930564431.38871132739735e-086.94355663698675e-09
1340.9999999932513871.34972266367528e-086.74861331837638e-09
1350.9999999930137331.39725349563166e-086.98626747815829e-09
1360.9999999929217541.41564913266771e-087.07824566333854e-09
1370.9999999928404121.43191757180457e-087.15958785902284e-09
1380.9999999919219721.61560564554389e-088.07802822771943e-09
1390.9999999903345831.93308331140340e-089.66541655701701e-09
1400.9999999878712292.42575422956598e-081.21287711478299e-08
1410.9999999838918283.22163432420234e-081.61081716210117e-08
1420.9999999791023524.17952958735777e-082.08976479367889e-08
1430.99999997209085.58183995428849e-082.79091997714425e-08
1440.9999999631532697.36934625616553e-083.68467312808277e-08
1450.9999999506436179.87127669753741e-084.93563834876871e-08
1460.9999999359474961.28105008918457e-076.40525044592283e-08
1470.9999999147753561.70449287338524e-078.5224643669262e-08
1480.9999998920086352.15982729737191e-071.07991364868596e-07
1490.9999998692427452.61514509459075e-071.30757254729537e-07
1500.9999998490258273.01948346863234e-071.50974173431617e-07
1510.9999998274313343.45137332779072e-071.72568666389536e-07
1520.9999998221378973.55724205660209e-071.77862102830105e-07
1530.999999823944353.52111300194662e-071.76055650097331e-07
1540.9999998425939843.14812032646237e-071.57406016323119e-07
1550.9999998685254922.62949015367735e-071.31474507683868e-07
1560.9999999051584081.89683184009415e-079.48415920047077e-08
1570.999999936517411.26965179263019e-076.34825896315095e-08
1580.999999958593158.2813699521016e-084.1406849760508e-08
1590.9999999712196355.75607294802879e-082.87803647401440e-08
1600.999999979321174.13576600128428e-082.06788300064214e-08
1610.9999999861907852.76184298429012e-081.38092149214506e-08
1620.9999999905333891.89332225857429e-089.46661129287143e-09
1630.9999999933931461.32137076459783e-086.60685382298914e-09
1640.9999999952243679.55126614624268e-094.77563307312134e-09
1650.9999999967781126.44377600266014e-093.22188800133007e-09
1660.9999999975827234.83455365493294e-092.41727682746647e-09
1670.999999998038683.92264146009073e-091.96132073004536e-09
1680.9999999984527853.0944300413751e-091.54721502068755e-09
1690.9999999988420312.31593755838486e-091.15796877919243e-09
1700.9999999990024151.99517071215520e-099.97585356077602e-10
1710.999999999232571.53486077069216e-097.67430385346082e-10
1720.999999999488131.02373984747031e-095.11869923735155e-10
1730.9999999996706326.5873537919016e-103.2936768959508e-10
1740.9999999998282953.43409127605251e-101.71704563802625e-10
1750.9999999999235321.52936974136368e-107.64684870681842e-11
1760.9999999999730665.38673607344733e-112.69336803672366e-11
1770.9999999999917031.65936350820598e-118.2968175410299e-12
1780.9999999999979554.09003108409082e-122.04501554204541e-12
1790.9999999999996137.74224289394366e-133.87112144697183e-13
1800.9999999999999411.17611870549638e-135.88059352748192e-14
1810.999999999999991.97805869311003e-149.89029346555013e-15
1820.9999999999999983.14668726028554e-151.57334363014277e-15
18313.23615184148489e-161.61807592074245e-16
18412.29857255362048e-171.14928627681024e-17
18511.30371643405653e-186.51858217028265e-19
18616.82549403946177e-203.41274701973089e-20
18714.60807026042269e-212.30403513021134e-21
18812.83880723534548e-221.41940361767274e-22
18913.89762373694309e-231.94881186847155e-23
19011.22379125448193e-236.11895627240964e-24
19114.27274703158431e-242.13637351579216e-24
19212.63976551294672e-241.31988275647336e-24
19312.42007492371679e-241.21003746185839e-24
19412.06543526392154e-241.03271763196077e-24
19519.16495855302424e-254.58247927651212e-25
19617.96995241147007e-253.98497620573503e-25
19715.80662430549707e-252.90331215274854e-25
19812.03487479989098e-251.01743739994549e-25
19915.261633258893e-262.6308166294465e-26
20011.14811020059209e-265.74055100296043e-27
20112.74923463015045e-271.37461731507523e-27
20214.81367274159052e-282.40683637079526e-28
20313.35162301200402e-281.67581150600201e-28
20413.10447016023744e-281.55223508011872e-28
20511.37486014926819e-286.87430074634093e-29
20611.13885860601030e-285.69429303005149e-29
20712.3302995988979e-281.16514979944895e-28
20815.50614964764628e-282.75307482382314e-28
20911.27966481323086e-276.39832406615429e-28
21013.34201440371311e-271.67100720185656e-27
21118.54589426366514e-274.27294713183257e-27
21212.248357095736e-261.124178547868e-26
21315.82766353467986e-262.91383176733993e-26
21411.53554025925766e-257.6777012962883e-26
21514.04881607560384e-252.02440803780192e-25
21615.18142230644926e-252.59071115322463e-25
21711.69227803943477e-258.46139019717387e-26
21814.2440550694202e-262.1220275347101e-26
21914.85468833532438e-272.42734416766219e-27
22012.52442278584681e-271.26221139292341e-27
22113.20365569786052e-271.60182784893026e-27
22211.77603599821908e-278.8801799910954e-28
22311.73773869586427e-288.68869347932137e-29
22411.45245015958605e-307.26225079793023e-31
22512.08136135701047e-321.04068067850524e-32
22614.68516082037591e-332.34258041018796e-33
22717.79617781444636e-343.89808890722318e-34
22811.32025238620934e-336.60126193104668e-34
22912.7287581140266e-331.3643790570133e-33
23019.7017632920095e-334.85088164600476e-33
23114.52862273845954e-322.26431136922977e-32
23211.38144053230029e-316.90720266150146e-32
23315.51516032628866e-312.75758016314433e-31
23412.26678314886832e-301.13339157443416e-30
23511.20233358749131e-296.01166793745657e-30
23614.68076984063067e-292.34038492031534e-29
23711.21285233298826e-286.06426166494128e-29
23813.08198606232541e-281.54099303116271e-28
23916.40198452780079e-283.20099226390039e-28
24011.46514221207857e-277.32571106039285e-28
24116.03144754644322e-273.01572377322161e-27
24212.83763974807713e-261.41881987403856e-26
24311.34617293165656e-256.73086465828279e-26
24417.42624240900492e-253.71312120450246e-25
24513.76329235553395e-241.88164617776697e-24
24611.70360822046231e-238.51804110231153e-24
24717.27164991516452e-233.63582495758226e-23
24813.34281058665333e-221.67140529332666e-22
24911.73760846314904e-218.68804231574521e-22
25018.63270462284463e-214.31635231142232e-21
25114.05095222391302e-202.02547611195651e-20
25211.50799395979754e-197.53996979898768e-20
25314.16354323776442e-192.08177161888221e-19
25411.05179542242507e-185.25897711212535e-19
25514.35602756489321e-182.17801378244661e-18
25611.82757677574962e-179.1378838787481e-18
25716.24059342299002e-173.12029671149501e-17
25813.04952672274467e-161.52476336137233e-16
25917.0098455845222e-163.5049227922611e-16
26011.88572029753785e-169.42860148768923e-17
26111.91532520673116e-179.57662603365582e-18
26211.35811468076324e-176.79057340381618e-18
26317.87822884358789e-173.93911442179394e-17
26414.90334120122642e-162.45167060061321e-16
2650.9999999999999992.87872763032689e-151.43936381516344e-15
2660.9999999999999911.71986723297503e-148.59933616487515e-15
2670.9999999999999588.41783343094186e-144.20891671547093e-14
2680.99999999999983.9812745688698e-131.9906372844349e-13
2690.9999999999990851.83090762460215e-129.15453812301076e-13
2700.9999999999950489.9050595627464e-124.9525297813732e-12
2710.9999999999749275.01455217260418e-112.50727608630209e-11
2720.9999999998798832.40233290872297e-101.20116645436149e-10
2730.9999999993793091.24138297334767e-096.20691486673834e-10
2740.999999996855296.28942061379715e-093.14471030689857e-09
2750.9999999845044863.0991027839693e-081.54955139198465e-08
2760.999999924931621.50136760953847e-077.50683804769233e-08
2770.9999996485142987.0297140445979e-073.51485702229895e-07
2780.9999987905315072.41893698689502e-061.20946849344751e-06
2790.9999975612934334.87741313375221e-062.43870656687611e-06
2800.9999981279844633.74403107480390e-061.87201553740195e-06
2810.9999995985514668.02897066903525e-074.01448533451762e-07
2820.9999999175304951.64939010348046e-078.24695051740232e-08
2830.999999958333618.33327817232531e-084.16663908616266e-08
2840.9999995003582569.99283487528265e-074.99641743764133e-07
2850.999993400495941.31990081186195e-056.59950405930973e-06
2860.999927398042330.0001452039153385337.26019576692665e-05
2870.9994626984328680.001074603134265010.000537301567132506


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2700.954063604240283NOK
5% type I error level2730.964664310954064NOK
10% type I error level2750.97173144876325NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/101u731292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/101u731292972349.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/1utsr1292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/1utsr1292972349.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/2utsr1292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/2utsr1292972349.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/352au1292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/352au1292972349.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/452au1292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/452au1292972349.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/552au1292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/552au1292972349.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/6gurf1292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/6gurf1292972349.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/7gurf1292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/7gurf1292972349.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/8qkq01292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/8qkq01292972349.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/9qkq01292972349.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292972265vjihaa7uzxfvir4/9qkq01292972349.ps (open in new window)


 
Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
 




Copyright

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.


Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

  • personalize online software applications according to your needs
  • enforce strict security rules with respect to the data that you upload (e.g. statistical data)
  • manage user sessions of online applications
  • alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by