Home » date » 2010 » Dec » 01 »

ws7beursexperimental

*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, 01 Dec 2010 19:24:35 +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/01/t12912313670trexr3xnfkabg3.htm/, Retrieved Wed, 01 Dec 2010 20:22:57 +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/01/t12912313670trexr3xnfkabg3.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 «
84738 428 -26007 223193 40949 263 129352 1398893 25830 104 245546 1073089 12679 122 48020 984885 43556 190 32648 227132 6575 63 151352 1071292 7123 102 288170 638830 17821 277 122844 444477 13326 103 165548 702380 16189 290 116384 358589 7146 83 134028 297978 15824 56 63838 585715 11920 73 31080 209458 8568 34 32168 786690 14416 139 49857 439798 2220 12 301670 644190 18562 211 102313 377934 10327 74 88577 640273 4069 131 79804 207393 8636 203 128294 301607 13718 56 96448 345783 4525 89 93811 501749 6869 88 117520 379983 4628 39 69159 387475 4901 58 121920 469107 2284 41 76403 194493 2384 77 61348 405972 3748 6 50350 652925 5371 47 87720 446211 1285 51 99489 341340 1528 32 60326 146494 2675 54 59017 355178 13253 251 90829 357760 880 15 80791 261216 1424 73 131116 374943 5119 38 39039 378525 1431 35 106885 310768 554 9 79285 325738 1975 34 118881 394510 1012 29 114768 368078 810 11 74015 236761 1280 52 69465 312378 1380 29 60982 347385 876 33 138971 352850 814 15 3962 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 time13 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
Wealth[t] = + 192265.220743186 + 4.98330302199025Costsex[t] -161.389387250037Ordersex[t] + 1.90916484532142Dividendsex[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)192265.22074318618925.3894810.159100
Costsex4.983303021990251.8182112.74080.0066260.003313
Ordersex-161.389387250037269.232715-0.59940.5494870.274743
Dividendsex1.909164845321420.2482137.691600


Multiple Linear Regression - Regression Statistics
Multiple R0.528356900965356
R-squared0.279161014797715
Adjusted R-squared0.269463629346563
F-TEST (value)28.7872454079421
F-TEST (DF numerator)3
F-TEST (DF denominator)223
p-value8.88178419700125e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation118018.124280139
Sum Squared Residuals3106005917868.3


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1223193495814.044345305-272621.044345305
21398893600835.378415922798057.621584078
31073089772987.232636484300101.767363516
4984885327437.110386831657447.889613169
5227132440984.39746154-213852.397461540
61071292503818.824385107567473.175614893
7638830761463.604145592-122633.604145592
8444477470897.249888479-26420.2498884785
9702380558108.031740745144271.968259255
10358589448333.232421564-89744.2324215636
11297978470362.130885315-172384.130885315
12585715383960.467472787201754.532527213
13209458299221.610888647-89763.6108886469
14786690290888.936613397495801.063386603
15439798336856.623973632102941.376026368
16644190777329.239693117-133139.239693117
17377934446044.513546982-68110.5135469817
18640273400893.070898812239379.929101188
19207393343759.26232594-136366.26232594
20301607447473.374695003-145866.374695003
21345783435723.496914407-89940.4969144066
22501749379551.674756886122197.325243114
23379983436658.315745407-56675.315745407
24387475341069.69256379046405.3074362105
25469107440093.18233504629013.8176649544
26194493342896.041645253-148403.041645253
27405972308841.87726013697130.1227398636
28652925306100.754108039346824.245891961
29446211378917.18030513967293.8196948611
30341340380378.807672874-39038.8076728743
31146494309887.525827646-163393.525827646
32355178309553.71109184245624.2889081578
33357760391207.733229563-33447.7332295628
34261216348473.02361215-87257.0236121497
35374943437902.076836411-62959.0768364107
36378525286173.83859375692351.1614062444
37310768397808.783306083-87040.7833060828
38325738344941.600893427-19203.6008934270
39394510423583.431021771-29073.4310217712
40368078411739.062139038-43661.0621390378
41236761335833.248957713-99072.2489577126
42312378322871.736454584-10493.7364545840
43347385310886.57728067236498.4227193277
44352850456623.292130361-103773.292130361
45301881269551.44559019732329.5544098032
46377516388524.756423381-11008.7564233813
47458343366600.50889267391742.4911073272
48354228391334.919660425-37106.9196604251
49308636399000.624684654-90364.6246846538
50386212390133.943074896-3921.94307489609
51393343378849.40228449414493.5977155058
52452469313927.644127253138541.355872747
53358649266384.16565035192264.834349649
54429112407894.25637701121217.7436229891
55403560450434.724303948-46874.7243039479
56235133366901.872938205-131768.872938205
57299243407266.895672076-108023.895672075
58314073411032.990154462-96959.9901544622
59368186327300.15500946140885.8449905392
60269661327924.218063386-58263.2180633858
61321896406838.671041886-84942.6710418857
62408881323085.7762785685795.22372144
63292154375133.509505489-82979.5095054892
64343466301714.66397318341751.3360268175
65332743355237.313663893-22494.3136638933
66442882369812.13027319673069.8697268038
67315688310006.3249944225681.67500557812
68375195440733.824708378-65538.824708378
69334280322602.98287696211677.0171230379
70355864359829.482595615-3965.48259561525
71314533307183.6157963787349.38420362221
72318056301263.68402541116792.3159745885
73314353307057.5919070217295.4080929789
74369448302214.67666934467233.3233306563
75312846305495.0349652167350.96503478388
76312075310758.3830512861316.61694871382
77315009308708.4059153076300.59408469273
78318903307578.45821632811324.5417836716
79314887308053.2372789636833.76272103748
80314913312014.1754128282898.82458717228
81325506305534.24474625019971.7552537495
82298568303828.312083460-5260.3120834604
83315834308055.746193987778.25380601997
84329784313074.65561628216709.3443837184
85312878300287.19142197712590.8085780229
86314987307324.3881247627662.61187523778
87325249309217.40649556216031.5935044381
88315877308824.5891876267052.41081237438
89291650305699.330781972-14049.3307819719
90305959309027.926988055-3068.92698805504
91297765305342.636873476-7577.6368734755
92315245308382.7953733226862.20462667758
93315236309825.3520036555410.64799634493
94336425309639.64654442426785.3534555763
95306268309676.927932959-3408.927932959
96302187311429.972301047-9242.97230104702
97314882308262.5360058866619.4639941139
98382712280360.404778222102351.595221778
99341570322510.56941971519059.4305802848
100312412309496.2760275952915.72397240458
101309596310074.178960918-478.178960917794
102315547307967.5083131427579.49168685767
103313267291946.03060064421320.9693993555
104359335330818.98077132728516.019228673
105314289307010.5198492267278.48015077426
106313332310485.8927385492846.10726145146
107291787318102.543045562-26315.5430455624
108318745306101.21166010812643.7883398925
109315366309499.9701756955866.02982430452
110315688308169.6324976737518.36750232714
111330059301864.74107463428194.2589253664
112288985311337.722121595-22352.7221215946
113304485313671.260234706-9186.26023470591
114315688307508.0406707638179.95932923708
115317736308897.0775415298838.9224584713
116322331304387.95476741617943.0452325844
117296656308739.808642168-12083.8086421682
118315354308001.2690463217352.73095367853
119312161307294.1082203934866.89177960669
120315576308290.2658361457285.73416385531
121314922307330.3706259157591.62937408508
122314551307041.1871501947509.81284980634
123312339317521.880592109-5182.88059210905
124298700314739.473179125-16039.4731791252
125321376310414.88329608110961.1167039189
126303230309769.756869525-6539.75686952504
127315487308243.5875485897243.41245141102
128315793306777.1887301549015.81126984637
129312887309395.8379744253491.16202557470
130315637305457.517022410179.4829775998
131324385342321.605573928-17936.605573928
132308989308498.853389942490.14661005752
133296702293235.0216154863466.97838451374
134307322315590.338448283-8268.33844828296
135304376328614.436369743-24238.4363697434
136253588352616.181088259-99028.181088259
137309560297614.81745598211945.1825440176
138298466280959.72792074817506.2720792522
139343929330347.40670089113581.5932991091
140331955304547.00703467427407.9929653259
141381180340005.77328950541174.2267104953
142331420322048.9544975259371.04550247472
143310201305895.1395290364305.86047096426
144320016310965.00246849050.99753159988
145320398312743.3252973047654.67470269602
146310670321934.022877876-11264.0228778762
147313491297609.22414286315881.7758571365
148331323327662.0280263133660.97197368653
149318098310384.6845000637713.31549993694
150292754295119.563053807-2365.56305380657
151325176311124.62113965714051.3788603427
152365959346337.25673109719621.7432689029
153302409304522.205216548-2113.20521654752
154301164305788.790488759-4624.79048875940
155344425309831.3189873634593.6810126398
156315394306291.6415828269102.35841717402
157309836295834.05075709114001.9492429092
158346611355383.567147476-8772.5671474765
159315656306991.8207208268664.17927917356
160339445318268.23063887121176.7693611290
161314964306951.6671810318012.33281896898
162297141307649.174604053-10508.1746040530
163315372307385.8373530297986.16264697092
164312502308141.7717600274360.22823997300
165313729307371.5244799596357.47552004109
166315388308046.6682207927341.33177920819
167315371309108.9097510076262.0902489926
168296139309559.208949953-13420.2089499525
169313880308493.8996552015386.1003447991
170317698305316.29261886412381.7073811359
171295580312061.673359915-16481.6733599151
172308256309186.279293508-930.279293508072
173303677305240.072966764-1563.07296676443
174319369314524.1845986774844.81540132331
175318690305209.08473044113480.9152695586
176314049308130.2852191825918.71478081839
177325699307353.20470152418345.7952984761
178314210311268.8933887792941.10661122142
179322378342809.80420216-20431.8042021599
180315398307552.9542348157845.04576518492
181316386305562.75635263710823.2436473632
182315553308146.3444991467406.65550085378
183323361307499.79840444715861.2015955528
184336639338592.735223242-1953.73522324242
185307424316400.212580997-8976.21258099662
186295370298345.979829563-2975.97982956312
187322340309542.64655862912797.3534413708
188319864317598.8825632452265.11743675531
189317291332197.064460828-14906.064460828
190280398272037.6656366388360.33436336157
191317330310421.8815335546908.1184664462
192238125329437.382906842-91312.3829068422
193327071306336.54911403820734.4508859622
194309038307811.7163417241226.2836582756
195314210307449.5887267426760.4112732577
196307930305663.4316126932266.56838730710
197322327312353.4256205229973.574379478
198292136306800.575228275-14664.5752282746
199263276310437.317447204-47161.3174472042
200367655324693.05608952342961.9439104771
201283587305376.862141653-21789.8621416531
202243650313396.902280081-69746.9022800806
203296261315710.021001748-19449.0210017477
204304252331502.179671075-27250.1796710753
205333505317466.80680524516038.1931947551
206296919307345.149760697-10426.1497606967
207276898316787.635030262-39889.6350302616
208327007331346.701230581-4339.70123058053
209317046323417.323966175-6371.32396617469
210304555307764.119119725-3209.11911972518
211298096304337.896760713-6241.8967607126
212231861329002.286450994-97141.2864509938
213309422322939.253096278-13517.2530962775
214165404351683.751301084-186279.751301084
215204325337055.853867578-132730.853867578
216407159272076.960273615135082.039726385
217275311326846.332572427-51535.3325724269
218246541321249.941257060-74708.9412570595
219253468330354.348575288-76886.3485752876
220240897333258.74833372-92361.7483337203
221-42143328147.141833989-370290.141833989
222272713305655.892215735-32942.8922157352
22342754306652.789048288-263898.789048288
224306275630681.02565202-324406.02565202
225253537385003.976762145-131466.976762145
226372631402157.063904417-29526.0639044171
227-7170323282.242179113-330452.242179113


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
713.23628973892804e-211.61814486946402e-21
817.45233128743543e-253.72616564371772e-25
917.93029794633198e-263.96514897316599e-26
1017.78912754433532e-263.89456377216766e-26
1111.22229371702441e-296.11146858512203e-30
1211.99811271258615e-309.99056356293076e-31
1312.35311260223077e-311.17655630111539e-31
1411.26803495648770e-386.34017478243852e-39
1519.3412149810602e-394.6706074905301e-39
1612.86747587620996e-401.43373793810498e-40
1715.02984391737373e-402.51492195868687e-40
1813.11440634443009e-431.55720317221505e-43
1919.80512802766635e-454.90256401383318e-45
2014.45562728584114e-452.22781364292057e-45
2116.80327107143507e-463.40163553571754e-46
2215.06653076748258e-462.53326538374129e-46
2318.19087377406804e-464.09543688703402e-46
2411.2812553490627e-456.4062767453135e-46
2511.36433227308585e-456.82166136542923e-46
2616.65764517034865e-473.32882258517433e-47
2712.01200298764217e-461.00600149382109e-46
2812.02440914537983e-541.01220457268992e-54
2916.92550678603769e-553.46275339301884e-55
3011.59182408406604e-547.95912042033021e-55
3115.9274799226417e-572.96373996132085e-57
3212.51198989005891e-561.25599494502946e-56
3311.10101216011481e-555.50506080057403e-56
3417.93433471993147e-563.96716735996573e-56
3511.93491384220232e-559.6745692110116e-56
3619.933713034685e-564.9668565173425e-56
3711.17856186861197e-555.89280934305984e-56
3814.30993895768459e-552.15496947884229e-55
3911.52834316346005e-547.64171581730025e-55
4015.25079499899099e-542.62539749949550e-54
4113.29453128598836e-541.64726564299418e-54
4211.31263425173409e-536.56317125867044e-54
4315.42697941770429e-532.71348970885214e-53
4416.50535360270667e-533.25267680135333e-53
4512.9525535531779e-521.47627677658895e-52
4611.22185410267973e-516.10927051339863e-52
4711.69095651854958e-518.45478259274791e-52
4816.23175105144669e-513.11587552572335e-51
4918.45982671272631e-514.22991335636316e-51
5013.39393424802813e-501.69696712401406e-50
5111.19123874375880e-495.95619371879398e-50
5212.72709393413369e-501.36354696706684e-50
5313.69888720948051e-501.84944360474026e-50
5411.04216476264838e-495.21082381324191e-50
5513.26113995479622e-491.63056997739811e-49
5616.69174270258012e-503.34587135129006e-50
5716.5457542375186e-503.2728771187593e-50
5815.82781576265661e-502.91390788132831e-50
5911.59881084345793e-497.99405421728964e-50
6012.79867073795034e-491.39933536897517e-49
6117.61548341855167e-493.80774170927584e-49
6216.78750185563898e-493.39375092781949e-49
6311.75489208553806e-488.77446042769028e-49
6415.22393103673607e-482.61196551836803e-48
6511.98674312464642e-479.93371562323212e-48
6611.13115870639832e-475.65579353199158e-48
6714.80173589957858e-472.40086794978929e-47
6811.09434330277642e-465.47171651388212e-47
6914.22216078007373e-462.11108039003686e-46
7011.16749617051151e-455.83748085255754e-46
7114.8249775936302e-452.4124887968151e-45
7211.96815157780394e-449.84075788901971e-45
7317.93722043480012e-443.96861021740006e-44
7411.02236586339860e-435.11182931699298e-44
7514.11894546309033e-432.05947273154516e-43
7611.57189309199954e-427.85946545999772e-43
7715.98912812616091e-422.99456406308046e-42
7812.29875861187132e-411.14937930593566e-41
7918.67130844234076e-414.33565422117038e-41
8013.08838537971617e-401.54419268985809e-40
8111.05300519009244e-395.26502595046222e-40
8213.87199893208726e-391.93599946604363e-39
8311.41325458609007e-387.06627293045036e-39
8414.93223842972059e-382.46611921486029e-38
8511.73514005959611e-378.67570029798055e-38
8616.21742190571644e-373.10871095285822e-37
8712.01559475988593e-361.00779737994296e-36
8816.94642585312351e-363.47321292656175e-36
8912.37621000397012e-351.18810500198506e-35
9018.25681281314935e-354.12840640657468e-35
9112.81946120176713e-341.40973060088356e-34
9219.44812020770985e-344.72406010385492e-34
9313.13517618037575e-331.56758809018787e-33
9418.58206859444319e-334.29103429722160e-33
9512.83390343725976e-321.41695171862988e-32
9619.02195118579316e-324.51097559289658e-32
9712.88008289851282e-311.44004144925641e-31
9811.30577067141724e-316.5288533570862e-32
9912.40166663818311e-311.20083331909156e-31
10017.75327987150134e-313.87663993575067e-31
10112.50186714725157e-301.25093357362578e-30
10217.89185832428516e-303.94592916214258e-30
10311.45491871828857e-297.27459359144285e-30
10413.71091690521540e-291.85545845260770e-29
10519.83932822650987e-294.91966411325494e-29
10613.03478879115187e-281.51739439557594e-28
10718.67560518120266e-284.33780259060133e-28
10812.51724629880414e-271.25862314940207e-27
10917.54238307571368e-273.77119153785684e-27
11012.20758959875439e-261.10379479937719e-26
11115.6902014677107e-262.84510073385535e-26
11211.60511251015262e-258.02556255076312e-26
11314.67186616072843e-252.33593308036422e-25
11411.33771925670762e-246.6885962835381e-25
11513.68774530833963e-241.84387265416981e-24
11619.14661947103061e-244.57330973551530e-24
11712.58201391062177e-231.29100695531089e-23
11817.02156261363401e-233.51078130681700e-23
11911.97051380641876e-229.85256903209382e-23
12015.25838790189252e-222.62919395094626e-22
12111.45432499298823e-217.27162496494113e-22
12213.92049183330143e-211.96024591665072e-21
12311.04904127691886e-205.24520638459432e-21
12412.76920857504497e-201.38460428752249e-20
12516.76226531852862e-203.38113265926431e-20
12611.77987467921838e-198.89937339609191e-20
12714.45690681256731e-192.22845340628366e-19
12811.12316420120870e-185.61582100604351e-19
12912.81939278359435e-181.40969639179717e-18
13016.98887246599455e-183.49443623299727e-18
13111.50750785977788e-177.53753929888939e-18
13213.78921579743484e-171.89460789871742e-17
13319.3336330033812e-174.6668165016906e-17
13412.27549892726494e-161.13774946363247e-16
13514.56356001416695e-162.28178000708348e-16
13612.14649873352832e-161.07324936676416e-16
13714.97664328527243e-162.48832164263621e-16
13811.21874951376937e-156.09374756884685e-16
1390.9999999999999992.87703767724540e-151.43851883862270e-15
1400.9999999999999976.03677228627355e-153.01838614313678e-15
1410.9999999999999951.07127699584172e-145.35638497920861e-15
1420.9999999999999872.52850878091245e-141.26425439045622e-14
1430.999999999999975.9361200160455e-142.96806000802275e-14
1440.9999999999999321.35720547592527e-136.78602737962633e-14
1450.9999999999998433.13369092328756e-131.56684546164378e-13
1460.9999999999996447.12011092378186e-133.56005546189093e-13
1470.9999999999991941.61191362212541e-128.05956811062706e-13
1480.9999999999982363.52760546757231e-121.76380273378616e-12
1490.999999999996257.49979636475145e-123.74989818237572e-12
1500.9999999999917581.64831729160171e-118.24158645800856e-12
1510.9999999999825163.49675566260086e-111.74837783130043e-11
1520.9999999999640047.19913274665002e-113.59956637332501e-11
1530.9999999999231231.53754821574732e-107.6877410787366e-11
1540.9999999998418263.1634897345305e-101.58174486726525e-10
1550.9999999997125235.74953556875028e-102.87476778437514e-10
1560.9999999994064851.18703010713058e-095.9351505356529e-10
1570.9999999987962162.40756797282512e-091.20378398641256e-09
1580.9999999976766264.64674871892735e-092.32337435946367e-09
1590.99999999539589.20839866725099e-094.60419933362549e-09
1600.9999999918102971.63794052045420e-088.18970260227098e-09
1610.999999984755973.04880587732367e-081.52440293866184e-08
1620.9999999705974765.88050471317919e-082.94025235658959e-08
1630.9999999445975491.10804902487254e-075.5402451243627e-08
1640.9999998978084852.04383029001861e-071.02191514500930e-07
1650.9999998126132453.74773509596509e-071.87386754798255e-07
1660.999999664972036.70055941042626e-073.35027970521313e-07
1670.9999994083356461.18332870735129e-065.91664353675646e-07
1680.9999989134743962.17305120733760e-061.08652560366880e-06
1690.999998108318853.78336230017160e-061.89168115008580e-06
1700.9999966794660766.64106784755888e-063.32053392377944e-06
1710.9999941000814841.17998370320532e-055.89991851602662e-06
1720.999989693349482.06133010387390e-051.03066505193695e-05
1730.9999823931752143.52136495723382e-051.76068247861691e-05
1740.999972889560535.42208789395275e-052.71104394697638e-05
1750.9999575108972338.49782055334539e-054.24891027667270e-05
1760.9999297741146540.0001404517706925587.02258853462789e-05
1770.9998920137049430.0002159725901144770.000107986295057238
1780.9998299999977070.0003400000045856740.000170000002292837
1790.9997222445730750.0005555108538493880.000277755426924694
1800.9995685580505420.000862883898916030.000431441949458015
1810.9993343747999890.001331250400022660.000665625200011329
1820.9989884024299920.002023195140015620.00101159757000781
1830.9985453032181340.002909393563731780.00145469678186589
1840.9978174011299040.004365197740192670.00218259887009633
1850.9967410531256280.006517893748743520.00325894687437176
1860.995115341826730.00976931634653930.00488465817326965
1870.9933067934601710.01338641307965720.00669320653982858
1880.9915227712963880.01695445740722370.00847722870361186
1890.9876427308854320.02471453822913610.0123572691145681
1900.9826443156598140.03471136868037240.0173556843401862
1910.9759091252199230.04818174956015340.0240908747800767
1920.9699199977273070.0601600045453860.030080002272693
1930.9618902229522940.07621955409541260.0381097770477063
1940.9509334340886160.0981331318227680.049066565911384
1950.9367882453964460.1264235092071090.0632117546035543
1960.9189898736775040.1620202526449930.0810101263224963
1970.8980670548468990.2038658903062030.101932945153101
1980.8697071283649280.2605857432701430.130292871635072
1990.833588528936810.3328229421263810.166411471063190
2000.825554871303040.3488902573939210.174445128696960
2010.7827716110547720.4344567778904560.217228388945228
2020.736558124718180.526883750563640.26344187528182
2030.6838898812624790.6322202374750430.316110118737521
2040.6326079833767840.7347840332464330.367392016623216
2050.5976632368315030.8046735263369950.402336763168497
2060.5429784988766120.9140430022467770.457021501123388
2070.4745805493324530.9491610986649060.525419450667547
2080.4301756577035960.8603513154071920.569824342296404
2090.3786179577690770.7572359155381540.621382042230923
2100.3320639329452250.664127865890450.667936067054775
2110.2850419381208870.5700838762417730.714958061879113
2120.2214241542973350.4428483085946710.778575845702665
2130.1622666953436040.3245333906872080.837733304656396
2140.1356845500699810.2713691001399620.864315449930019
2150.09652561419784970.1930512283956990.90347438580215
2160.3173317193183130.6346634386366270.682668280681687
2170.2316059392756460.4632118785512920.768394060724354
2180.1543938548761630.3087877097523250.845606145123837
2190.0936359737896420.1872719475792840.906364026210358
2200.04789063630873710.09578127261747420.952109363691263


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1800.841121495327103NOK
5% type I error level1850.864485981308411NOK
10% type I error level1890.883177570093458NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/10ttj11291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/10ttj11291231460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/1mam71291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/1mam71291231460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/2f2la1291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/2f2la1291231460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/3f2la1291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/3f2la1291231460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/4f2la1291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/4f2la1291231460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/5pb2v1291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/5pb2v1291231460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/6pb2v1291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/6pb2v1291231460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/7ikky1291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/7ikky1291231460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/8ikky1291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/8ikky1291231460.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/9ttj11291231460.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912313670trexr3xnfkabg3/9ttj11291231460.ps (open in new window)


 
Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 4 ; 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