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CC-score()

*The author of this computation has been verified*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Fri, 03 Dec 2010 19:01:27 +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/03/t1291402810nfqxvauxiyjh980.htm/, Retrieved Fri, 03 Dec 2010 20:00:22 +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/03/t1291402810nfqxvauxiyjh980.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 «
162556 1081 807 213118 6282154 1 5626 37 56289 29790 309 444 81767 4321023 2 13337 138 28328 87550 458 412 153198 4111912 3 8541 45 17936 84738 588 428 -26007 223193 415 39 0 4145 54660 302 315 126942 1491348 6 4276 24 3040 42634 156 168 157214 1629616 5 9164 34 2964 40949 481 263 129352 1398893 9 2493 29 2865 45187 353 267 234817 1926517 4 4891 38 2854 37704 452 228 60448 983660 14 1734 21 2167 16275 109 129 47818 1443586 7 11409 76 1974 25830 115 104 245546 1073089 10 7592 34 1910 12679 110 122 48020 984885 13 7135 62 1871 18014 239 393 -1710 1405225 8 5043 67 943 43556 247 190 32648 227132 414 110 1 929 24811 505 280 95350 929118 15 1444 29 822 6575 159 63 151352 1071292 11 5480 133 819 7123 109 102 288170 638830 28 4026 62 769 21950 519 265 114337 856956 17 1266 30 745 37597 248 234 37884 992426 12 3195 21 652 17821 373 277 122844 444477 46 655 14 643 12988 119 73 82340 857217 16 5523 51 601 22330 84 67 79801 711969 20 6095 23 446 13326 102 103 165548 702380 21 4925 38 436 16189 295 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 time32 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework
error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.


Multiple Linear Regression - Estimated Regression Equation
CCScore [t] = + 12643.2666340819 + 0.0816834896133914Costs[t] + 0.409653715041103Trades[t] -0.0146582706350887Orders[t] + 0.00695828453847412Dividends[t] -0.00620844609687738Wealth[t] -0.0220034826398095WRank[t] -0.0251392946483144`Profit/Trades`[t] + 0.00519706906003111`Profit/Cost`[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)12643.26663408194776.8775462.64680.0084310.004215
Costs0.08168348961339140.0340532.39870.0168850.008443
Trades0.4096537150411030.03750410.922900
Orders-0.01465827063508870.0384-0.38170.7028570.351428
Dividends0.006958284538474120.0061581.130.2591120.129556
Wealth-0.006208446096877380.006098-1.01810.3092190.15461
WRank-0.02200348263980950.032918-0.66840.5042180.252109
`Profit/Trades`-0.02513929464831440.03867-0.65010.5159830.257991
`Profit/Cost`0.005197069060031110.0376230.13810.8901990.445099


Multiple Linear Regression - Regression Statistics
Multiple R0.506378733356555
R-squared0.256419421595789
Adjusted R-squared0.242323107313244
F-TEST (value)18.1905295566031
F-TEST (DF numerator)8
F-TEST (DF denominator)422
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation57399.7665423969
Sum Squared Residuals1390377410029.34


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
156289-11308.327767397267597.3277673972
228328-11395.797395718839723.7973957188
317936-4700.8972465889722636.8972465890
4414518222.8071280746-14077.8071280746
530408744.02621408481-5704.02621408481
629646933.45075793953-3969.45075793953
728658333.7088199408-5468.7088199408
828546025.39221821394-3171.39221821394
9216710174.7061688595-8007.70616885945
1019745099.15888864883-3125.15888864883
1119109654.19975006326-7744.19975006326
1218717762.40389363329-5891.40389363329
139435344.09193332195-4401.09193332195
1492915104.6398314583-14175.6398314583
158229731.29943659368-8909.29943659368
168197509.32453058586-6690.32453058586
1776911205.7777978304-10436.7777978304
1874510088.1248192059-9343.12481920593
196529834.19425465133-9182.19425465133
2064312325.5549680399-11682.5549680399
216018863.8788170424-8262.8788170424
2244610482.2001324410-10036.2001324410
2343610439.2218023252-10003.2218023252
2437912650.3588253935-12271.3588253935
2530512319.9409824743-12014.9409824743
2628410616.9962073987-10332.9962073987
2724711410.7273026228-11163.7273026228
2823812595.7540280720-12357.7540280720
292238298.84989849045-8075.84989849045
3022011565.4600189412-11345.4600189412
312178821.20721160293-8604.20721160293
3219910703.8607536702-10504.8607536702
331958886.75983622106-8691.75983622106
341639826.8671794801-9663.8671794801
3515410426.0322333949-10272.0322333949
3614312555.0269075050-12412.0269075050
3713510042.2548415694-9907.25484156938
381329409.27443849377-9277.27443849377
3912010672.1167628804-10552.1167628804
4011912290.3138138876-12171.3138138876
4110812705.3304409404-12597.3304409404
4210412250.0801737968-12146.0801737968
4310110607.8167046858-10506.8167046858
449011660.2579981803-11570.2579981803
458511044.5186607253-10959.5186607253
467411140.6251824876-11066.6251824876
477312272.4867135539-12199.4867135539
487011644.0035751169-11574.0035751169
496710931.9240727636-10864.9240727636
506612161.7642096438-12095.7642096438
516610324.6655939105-10258.6655939105
526610707.4424227212-10641.4424227212
535911483.6949773899-11424.6949773899
545810496.7596053299-10438.7596053299
555812710.7716674850-12652.7716674850
565410728.8293812728-10674.8293812728
57537984.65900078683-7931.65900078683
584910853.9199810693-10804.9199810693
594911329.4273738886-11280.4273738886
604411598.5444591951-11554.5444591951
613910552.2053344402-10513.2053344402
623812319.2828991176-12281.2828991176
633810838.0118083148-10800.0118083148
643711557.895450047-11520.895450047
653611047.7762079128-11011.7762079128
663512514.8449040538-12479.8449040538
673411656.4581248879-11622.4581248879
683310970.8147463360-10937.8147463360
693211333.7110933846-11301.7110933846
703210966.6249013154-10934.6249013154
713111840.5967125803-11809.5967125803
723110909.5940531539-10878.5940531539
733011499.1559639005-11469.1559639005
743010933.3163509075-10903.3163509075
753011055.8128193986-11025.8128193986
762811733.7002837438-11705.7002837438
772611167.3152480195-11141.3152480195
782611741.7239999946-11715.7239999946
792611308.1144998629-11282.1144998629
802511455.9916837604-11430.9916837604
812510889.7099005820-10864.7099005820
822410958.9376655939-10934.9376655939
832411448.5128158021-11424.5128158021
842310968.9049752420-10945.9049752420
852310764.8724407092-10741.8724407092
862211308.5621529476-11286.5621529476
872210680.9195698235-10658.9195698235
882210939.5816941178-10917.5816941178
892011616.1078816979-11596.1078816979
902010589.9313089732-10569.9313089732
911910955.9838273620-10936.9838273620
921811016.5909443824-10998.5909443824
931710435.7580038420-10418.7580038420
941611768.1998968101-11752.1998968101
951410759.7005115763-10745.7005115763
961410886.2244250862-10872.2244250862
971410074.0623776820-10060.0623776820
981311257.0321812754-11244.0321812754
991311028.0060886198-11015.0060886198
1001311024.1877764556-11011.1877764556
1011311290.6762731324-11277.6762731324
1021311854.9551654492-11841.9551654492
1031211808.1606551488-11796.1606551488
1041211374.0831606229-11362.0831606229
1051110882.1307854401-10871.1307854401
1061011452.5885046662-11442.5885046662
1071012587.4531485461-12577.4531485461
108109937.21194402893-9927.21194402893
1091011787.8298853422-11777.8298853422
1101011728.1145897553-11718.1145897553
111910490.7945973157-10481.7945973157
112912305.7286431033-12296.7286431033
113911835.3109709227-11826.3109709227
114611393.2132206277-11387.2132206277
115610315.3095175312-10309.3095175312
116610947.0248713567-10941.0248713567
117511083.0281444561-11078.0281444561
118510410.1009455996-10405.1009455996
119512007.8880870927-12002.8880870927
120411160.8685172773-11156.8685172773
121411932.4701959070-11928.4701959070
122310868.3644296087-10865.3644296087
123211215.2300968081-11213.2300968081
124210313.3701319118-10311.3701319118
125111014.4743662284-11013.4743662284
126112072.7148882625-12071.7148882625
127711104.0398005323-11097.0398005323
1282832813.7374285901-32785.7374285901
129031684.9903689328-31684.9903689328
1306063032894.97274725427735.027252746
1315563738225.279717579417411.7202824206
1326072042815.168506979717904.8314930203
13332338579.9642616480-38256.9642616480
13432916886.0026465219-16557.0026465219
13529914663.6072329870-14364.6072329870
13614513961.9599603323-13816.9599603323
13730413925.1866176918-13621.1866176918
13830327616.8477758998-27313.8477758998
13918814685.6276204987-14497.6276204987
1403225317.36955801454-4995.36955801454
14110025321.30078731378-4319.30078731378
1423613745.3539071525-13709.3539071525
14346013018.8073375146-12558.8073375146
14430913818.6039454233-13509.6039454233
145013759.2661543374-13759.2661543374
146713946.2648330682-13939.2648330682
1472146692.887330167-146690.887330167
1480151273.199675522-151273.199675522
1497147768.455560672-147761.455560672
15046136922.558556966-136876.558556966
1515142921.902607245-142916.902607245
152297765147098.044544738150666.955455262
15331524516627.6647023206298617.335297679
15431538028636.7690995426286743.230900457
15503397.64789737754-3397.64789737754
15609673.89523697691-9673.89523697691
157010523.2123889282-10523.2123889282
158011103.0671994226-11103.0671994226
1596072032895.989974805627824.0100251944
16021838601.9975764737-38383.9975764737
1612205735.86710521184-5515.86710521184
1622255788.1901289333-5563.1901289333
16324325548.35495228769-3116.35495228769
1647575321.97954826928-4564.97954826928
165465514427.4106966856-9772.41069668556
166014175.2629372413-14175.2629372413
167013946.5281972127-13946.5281972127
168312412147116.149301704165295.850698296
16931538024451.7632359135290928.236764087
170121773511.594980607888665.40501939212
17103424.04058904543-3424.04058904543
172010833.1352405433-10833.1352405433
173011189.8796488463-11189.8796488463
17408193.66828359333-8193.66828359333
175011103.1552133532-11103.1552133532
1767164232873.693515714038768.3064842860
1777164141951.217675518229689.7823244818
1785579239409.12219931616382.877800684
1797187338403.870394658633469.1296053414
1806255537061.895056602425493.1049433976
1816037038166.782253070222203.2177469298
1826487339718.486051022425154.5139489776
1836204141364.616789559220676.3832104408
1846574538035.566077122427709.4339228776
1856072036367.677926524524352.3220734755
18614638642.4682879710-38496.4682879710
18717915357.2771270347-15178.2771270347
1888195471.72487909899-4652.72487909899
189285322.15552333182-5294.15552333182
190012570.6859465471-12570.6859465471
1918913821.3666434757-13732.3666434757
1920189742.390123307-189742.390123307
193315380147111.446314301168268.553685699
19434794121.25596850432-642.255968504321
19503392.13646625165-3392.13646625165
196011102.2530705649-11102.2530705649
1975080432891.150411661817912.8495883382
1988739037312.157247463250077.8427525368
1996165639175.286247844422480.7137521556
2006568837761.657594832727926.3424051673
2016072038304.774403630822415.2255963692
20210339071.1121217936-38968.1121217936
20334613131.8359594805-12785.8359594805
20403827.18394655269-3827.18394655269
2053211104.6088412401-11072.6088412401
2062031385.8696820704-31365.8696820704
207733770.874527888-33763.874527888
208033738.4295619125-33738.4295619125
2096083132895.576816472527935.4231835275
2106064638329.059490639422316.9405093606
2116072038599.758231194222120.2417688058
21213538760.0343197319-38625.0343197319
21337313037.3208800818-12664.3208800818
21419814866.8941407456-14668.8941407456
215365055621.0568323470830883.9431676529
2162395608.7217806056-5369.7217806056
21759245626.24143371037297.758566289629
2184135461.69712832148-5048.69712832148
21918745404.72326022641-3530.72326022641
2201925321.42648378702-5129.42648378702
221013226.1934374802-13226.1934374802
222713938.4692286166-13931.4692286166
2239140278.190454296-140269.190454296
2240149337.364421406-149337.364421406
225303230147114.111410451156115.888589549
22631538015471.1286899009299908.871310099
2271154873393.12340688192112093.876593118
22807648.3683466844-7648.3683466844
229010631.9756726758-10631.9756726758
230011103.4632621101-11103.4632621101
2316072032895.864725683927824.1352743161
23232138570.6540016873-38249.6540016873
23323716321.1127919005-16084.1127919005
23447725407.43991822687-635.43991822687
235536168.01961450046-6115.01961450046
23605320.89855859941-5320.89855859941
2371513943.0375710835-13928.0375710835
2380144329.261860630-144329.261860630
239315380147111.43165603168268.56834397
240107454033.817061392466711.18293860754
24103418.34127521037-3418.34127521037
24209843.50097149913-9843.50097149913
243011298.9016520183-11298.9016520183
244011849.3718348946-11849.3718348946
245811104.4140425635-11096.4140425635
2463530476.037863634-30441.037863634
247027792.0379486660-27792.0379486660
2486072032896.177848488127823.8221515119
24926238613.4678804949-38351.4678804949
2502265319.71701175094-5093.71701175094
25130713694.5614118989-13387.5614118989
252013890.6038778185-13890.6038778185
253013945.9321863874-13945.9321863874
254315380147111.123832347168268.876167653
255164711372.6377135766815098.3622864233
25603458.67585825686-3458.67585825686
257010536.5767348302-10536.5767348302
258011101.4609451899-11101.4609451899
2596072032896.497929576927823.5020704231
26033238597.2582243456-38265.2582243456
26128413867.6840186899-13583.6840186899
2624255346.48518967923-4921.48518967923
2633595347.31749381756-4988.31749381756
26413245320.999115778-3996.999115778
26517613685.4897432994-13509.4897432994
266013706.9799557024-13706.9799557024
267013946.1308566625-13946.1308566625
268313491147086.964078068166404.035921932
26931538013623.6710753803301756.328924620
270101022771.485540309067330.51445969094
27103412.22021055843-3412.22021055843
272010633.8450129696-10633.8450129696
273010378.9589972417-10378.9589972417
2746211103.4591268303-11041.4591268303
275030591.81501729-30591.81501729
2766284132892.138503016029948.8614969840
2778112539176.459075382241948.5409246178
2786072042457.188140884418262.8118591156
27935638585.6500191600-38229.6500191600
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28631538033704.2167204012281675.783279599
28703398.36373012316-3398.36373012316
288010917.7235417721-10917.7235417721
289011112.9926901729-11112.9926901729
2901311103.9553555124-11090.9553555124
2912234162.8152157182-34140.8152157182
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29625005255.34345209927-2755.34345209927
297379205467.7997115722132452.2002884278
29810524867.92829764982-3815.92829764982
29936035943.53264379644-2340.53264379644
3002645896.41483241283-5632.41483241283
301842135419.9387915809278793.061208419
30205319.94326540277-5319.94326540277
303013946.1929411235-13946.1929411235
304315380147112.648292493168267.351707507
30503395.16914950547-3395.16914950547
306211101.8613259835-11099.8613259835
307032477.2195455604-32477.2195455604
3086072032896.268306187127823.7316938129
30925338619.2030325055-38366.2030325055
31005319.86784751883-5319.86784751883
311013945.9942708484-13945.9942708484
312315380147111.211781971168268.788218029
313227463450.5822656654319295.4177343346
314576943476.3718058701854217.6281941298
3152307412993.604992171210080.3950078288
31612333877.95261483602-2644.95261483602
31703389.03181225402-3389.03181225402
31808273.50897793355-8273.50897793355
319010875.8195730112-10875.8195730112
320011110.3020279038-11110.3020279038
321011103.6392899712-11103.6392899712
3226072032895.78818455427824.211815446
32334138558.4693576255-38217.4693576255
32418113011.3363186842-12830.3363186842
3258725723.87188048882-4851.87188048882
32605322.35663768901-5322.35663768901
3271013906.6187075602-13896.6187075602
3283148472.314049277-148469.314049277
3297148035.313970479-148028.313970479
33010146335.594562485-146325.594562485
3311151001.328603101-151000.328603101
3320146817.78178256-146817.78178256
333315380147111.255756782168268.744243218
33423532820.87516347252-467.87516347252
33503399.67507480302-3399.67507480302
336011102.2310670823-11102.2310670823
3376072232896.291782923827825.7082170762
3386072039252.529671800821467.4703281992
33928938626.9864530912-38337.9864530912
340616213.33527393183-6152.33527393183
3416475436.08932407688-4789.08932407688
34205319.61645457234-5319.61645457234
343013946.1805242313-13946.1805242313
344315380147110.977249640168269.022750360
34555033396.077566594622106.92243340538
34603423.6655050194-3423.6655050194
347011025.5433973857-11025.5433973857
348010727.7474145273-10727.7474145273
34908495.33491342573-8495.33491342573
350011101.9010148427-11101.9010148427
3515617832893.254541442923284.7454585571
3526043636858.193305752223577.8066942478
3536072038817.41392003321902.586079967
3546072038703.91681750322016.083182497
35526438628.2154142364-38364.2154142364
3562215428.48070277937-5207.48070277937
3571416444.28214519812-6303.28214519812
358785320.11924046531-5242.11924046531
359248913738.1951651599-11249.1951651599
36013113297.5866006023-13166.5866006023
36192313555.1763198753-12632.1763198753
3627213632.5183853774-13560.5183853774
36357213066.1667636465-12494.1667636465
36439713776.5185575540-13379.5185575540
36545013694.0417834121-13244.0417834121
36662213548.6094592426-12926.6094592426
36769413381.7083298522-12687.7083298522
368342513739.2729760007-10314.2729760007
36956213240.8123874456-12678.8123874456
370491713616.3620933193-8699.3620933193
371144213552.9923420892-12110.9923420892
37252913976.8481311167-13447.8481311167
373212613529.8517996947-11403.8517996947
374106113960.4065521889-12899.4065521889
37577613266.3652396412-12490.3652396412
37661113694.9550144772-13083.9550144772
377152613571.9084104688-12045.9084104688
37859213505.3509867494-12913.3509867494
379118213383.9231486830-12201.9231486830
38062113669.5309155286-13048.5309155286
38198913691.6641801865-12702.6641801865
38243813690.4426469340-13252.4426469340
38372613663.0619434458-12937.0619434458
384130312994.9426982910-11691.9426982910
385741913756.8771739501-6337.87717395009
386116413655.0807103308-12491.0807103308
387331013593.3339498620-10283.3339498620
388192014507.6097435815-12587.6097435815
38996512684.2952939662-11719.2952939662
390325612954.3612598274-9698.36125982744
391113514903.4306095122-13768.4306095122
392127013639.5281056938-12369.5281056938
39366113488.7548933743-12827.7548933743
394101313047.4520006034-12034.4520006034
395284412792.6925361572-9948.6925361572
3961152813273.4977205610-1745.49772056102
397652612397.5347248842-5871.53472488423
398226411745.0026822220-9481.00268222197
399510913672.1970927236-8563.19709272357
400399913194.3684658433-9195.36846584328
4013562412313.912720908823310.0872790912
402925212574.0734620785-3322.07346207847
4031523613231.15512159502004.84487840498
4041807314099.79980786423973.20019213578
40516255612257.4456025096150298.554397490
4062979053819.0406243371-24029.0406243371
4078755041569.018282934245980.9817170658
4088473839119.823943170345618.1760568297
4095466014818.993571715339841.0064282847
4104263421234.521111534521399.4788884655
4114094921572.555958947919376.4440410521
4124518720587.364234407724599.6357655923
4133770422650.954040844315053.0459591556
4141627518704.6048105048-2429.60481050484
4152583021806.23565759014023.76434240992
4161267916404.8022387191-3725.80223871912
4171801418702.5390044614-688.539004461355
4184355622519.007204334821036.9927956652
4192481113842.974784060210968.0252159398
420657517838.2979409097-11263.2979409097
421712317798.1239712058-10675.1239712058
4222195012828.71992842439121.28007157572
4233759717056.335280030320540.6647199697
4241782119042.1366109178-1221.13661091785
4251298814067.6173893156-1079.61738931562
4262233017320.90624838875009.09375161132
4271332616329.4173691517-3003.41736915165
4281618915047.31828925201141.68171074805
429714613564.6668365170-6418.66683651696
4301582412773.06707928333050.93292071675
4312766415679.323314397511984.6766856025


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.001163617087822660.002327234175645330.998836382912177
139.0823687975027e-050.0001816473759500540.999909176312025
141.93932016471739e-053.87864032943478e-050.999980606798353
151.58927022762658e-063.17854045525317e-060.999998410729772
161.50057872790421e-073.00115745580842e-070.999999849942127
171.71145244004348e-083.42290488008696e-080.999999982885476
181.27768652474457e-092.55537304948915e-090.999999998722313
191.21922912193294e-102.43845824386587e-100.999999999878077
201.19701584524726e-112.39403169049452e-110.99999999998803
211.2774007407896e-122.5548014815792e-120.999999999998723
221.73598290661375e-133.47196581322749e-130.999999999999826
231.64677533927847e-143.29355067855693e-140.999999999999984
241.43023701819014e-152.86047403638029e-150.999999999999999
259.62261043988556e-171.92452208797711e-161
261.19264499066228e-172.38528998132456e-171
278.24569964643992e-191.64913992928798e-181
285.36912488776641e-201.07382497755328e-191
294.37718748541734e-218.75437497083468e-211
303.1019316716117e-226.2038633432234e-221
312.03126583384819e-234.06253166769638e-231
321.85424182016025e-243.70848364032049e-241
331.19755690609742e-252.39511381219483e-251
348.853132742265e-271.770626548453e-261
355.16373755846271e-281.03274751169254e-271
365.12083241590366e-291.02416648318073e-281
373.50859237567511e-307.01718475135022e-301
382.10178091756919e-314.20356183513838e-311
391.20700678712523e-322.41401357425045e-321
406.560432331844e-341.3120864663688e-331
415.37973767071587e-351.07594753414317e-341
424.44908772286388e-368.89817544572775e-361
432.57092079968087e-375.14184159936173e-371
441.95083087855621e-383.90166175711243e-381
451.39882509617418e-392.79765019234835e-391
461.13350250790524e-402.26700501581047e-401
476.75237803647649e-421.35047560729530e-411
483.91516255252477e-437.83032510504955e-431
492.23920366805907e-444.47840733611814e-441
501.12609636144100e-452.25219272288201e-451
515.58714727194176e-471.11742945438835e-461
522.79994509149110e-485.59989018298221e-481
531.51347646723466e-493.02695293446931e-491
541.08620695152985e-502.1724139030597e-501
555.3432715916889e-521.06865431833778e-511
562.90429062027696e-535.80858124055393e-531
572.63919168713601e-545.27838337427201e-541
581.56580691103818e-553.13161382207636e-551
598.09221958124026e-571.61844391624805e-561
604.90382214247897e-589.80764428495794e-581
612.63399505482026e-595.26799010964052e-591
621.21863251630239e-602.43726503260478e-601
636.80182975869786e-621.36036595173957e-611
643.82964464089386e-637.65928928178771e-631
652.02288886779035e-644.04577773558071e-641
668.91184504089584e-661.78236900817917e-651
674.29760577231226e-678.59521154462451e-671
682.15906259370947e-684.31812518741893e-681
691.0087030411679e-692.0174060823358e-691
705.22810340277217e-711.04562068055443e-701
712.24486576311578e-724.48973152623155e-721
721.18382246003698e-732.36764492007395e-731
735.06832067354201e-751.01366413470840e-741
742.11637254432915e-764.2327450886583e-761
751.00638720379709e-772.01277440759418e-771
764.19207122359194e-798.38414244718389e-791
771.73173148266159e-803.46346296532317e-801
787.27834676065999e-821.45566935213200e-811
793.0835188558635e-836.167037711727e-831
801.22681235116379e-842.45362470232759e-841
815.45003692761972e-861.09000738552394e-851
822.51981248201769e-875.03962496403539e-871
839.8601285348248e-891.97202570696496e-881
844.19769046308174e-908.39538092616348e-901
851.63747240935247e-913.27494481870494e-911
867.71722111454533e-931.54344422290907e-921
873.27898718122652e-946.55797436245304e-941
881.22397108598132e-952.44794217196264e-951
894.61686418458988e-979.23372836917976e-971
901.69917830831678e-983.39835661663356e-981
917.00370970851299e-1001.40074194170260e-991
923.11796514521465e-1016.23593029042931e-1011
931.17010626775865e-1022.34021253551729e-1021
945.02984605243775e-1041.00596921048755e-1031
952.02061644229874e-1054.04123288459747e-1051
968.3541114248359e-1071.67082228496718e-1061
975.03355915973809e-1081.00671183194762e-1071
981.80319700636229e-1093.60639401272458e-1091
997.79623493490903e-1111.55924698698181e-1101
1003.17265438075954e-1126.34530876151908e-1121
1011.14721688723570e-1132.29443377447140e-1131
1024.06907179503017e-1158.13814359006034e-1151
1031.43038267372130e-1162.86076534744261e-1161
1045.16226935078704e-1181.03245387015741e-1171
1051.92884891467352e-1193.85769782934703e-1191
1066.85968996107255e-1211.37193799221451e-1201
1072.36503896467568e-1224.73007792935136e-1221
1088.49637748098515e-1241.69927549619703e-1231
1093.18474496315126e-1256.36948992630252e-1251
1101.07987763302327e-1262.15975526604653e-1261
1113.59598900761971e-1287.19197801523942e-1281
1121.18461883722135e-1292.36923767444270e-1291
1133.98495825427327e-1317.96991650854653e-1311
1141.29610098320852e-1322.59220196641703e-1321
1155.66225749605722e-1341.13245149921144e-1331
1161.98424612132936e-1353.96849224265871e-1351
1176.27588259935837e-1371.25517651987167e-1361
1182.23239572958458e-1384.46479145916915e-1381
1197.0760810172426e-1401.41521620344852e-1391
1202.20441899333715e-1414.40883798667431e-1411
1218.00790185141156e-1431.60158037028231e-1421
1222.72069169528496e-1445.44138339056993e-1441
1239.70616345003787e-1461.94123269000757e-1451
1243.06455676471641e-1476.12911352943283e-1471
1251.03170150692834e-1482.06340301385668e-1481
1263.15350167596286e-1506.30700335192573e-1501
1279.4462200344423e-1521.88924400688846e-1511
1286.16404697535334e-1531.23280939507067e-1521
1291.04140738111053e-1532.08281476222107e-1531
1303.81766001867616e-1137.63532003735232e-1131
1314.72926576916283e-1079.45853153832565e-1071
1324.38352974015962e-1088.76705948031925e-1081
1333.63894372849482e-1097.27788745698963e-1091
1345.86019199704391e-1051.17203839940878e-1041
1351.05237086055257e-1022.10474172110515e-1021
1361.77551455640705e-1023.55102911281409e-1021
1376.42988757185014e-1031.28597751437003e-1021
1381.04652215966246e-1032.09304431932492e-1031
1391.76778553970104e-1043.53557107940208e-1041
1401.58347133746342e-1053.16694267492683e-1051
1411.41009960095629e-1062.82019920191258e-1061
1421.37345703699485e-1072.74691407398970e-1071
1431.30290115491832e-1082.60580230983664e-1081
1441.22332168539088e-1092.44664337078175e-1091
1451.14092486085455e-1102.28184972170909e-1101
1461.05692952891844e-1112.11385905783688e-1111
1473.09961958707206e-1126.19923917414412e-1121
1481.11034469672451e-1122.22068939344902e-1121
1494.35245303935348e-1138.70490607870696e-1131
1501.7241572930039e-1133.4483145860078e-1131
1519.32838298595676e-1141.86567659719135e-1131
1522.66824388584054e-345.33648777168109e-341
1531.04532606917590e-152.09065213835180e-150.999999999999999
1542.98144092176763e-085.96288184353527e-080.99999997018559
1554.91419971064376e-079.82839942128751e-070.99999950858003
1563.32595911952664e-076.65191823905327e-070.999999667404088
1572.24734271213279e-074.49468542426558e-070.999999775265729
1581.51783135431106e-073.03566270862211e-070.999999848216865
1591.16505143860696e-072.33010287721392e-070.999999883494856
1609.03055016482228e-081.80611003296446e-070.999999909694498
1616.0092203950867e-081.20184407901734e-070.999999939907796
1623.9736704000809e-087.9473408001618e-080.999999960263296
1632.60796478703016e-085.21592957406032e-080.999999973920352
1641.70428590062994e-083.40857180125987e-080.99999998295714
1651.26542766831342e-082.53085533662685e-080.999999987345723
1668.75859167508256e-091.75171833501651e-080.999999991241408
1675.96104351283611e-091.19220870256722e-080.999999994038957
1683.72786305552011e-067.45572611104023e-060.999996272136944
1690.006111401007207930.01222280201441590.993888598992792
1700.005545513838861830.01109102767772370.994454486161138
1710.004582230213531670.009164460427063330.995417769786468
1720.00371705935077410.00743411870154820.996282940649226
1730.003005096047256110.006010192094512220.996994903952744
1740.002406270456128550.00481254091225710.997593729543871
1750.001927960018502240.003855920037004480.998072039981498
1760.001678963686708220.003357927373416440.998321036313292
1770.001501230357318890.003002460714637790.99849876964268
1780.001218753532762650.002437507065525310.998781246467237
1790.001041220783484350.002082441566968700.998958779216516
1800.0008468726574281830.001693745314856370.999153127342572
1810.0006776524435010180.001355304887002040.9993223475565
1820.0005437648946849250.001087529789369850.999456235105315
1830.0004289855231052620.0008579710462105250.999571014476895
1840.0003437309152866660.0006874618305733320.999656269084713
1850.0002711307442669350.000542261488533870.999728869255733
1860.0002436245071778330.0004872490143556660.999756375492822
1870.0001920168747191410.0003840337494382830.99980798312528
1880.0001460871463026110.0002921742926052220.999853912853697
1890.0001106882456146970.0002213764912293950.999889311754385
1908.72689719311977e-050.0001745379438623950.999912731028069
1916.82226303828072e-050.0001364452607656140.999931777369617
1920.0004739073218695080.0009478146437390160.99952609267813
1930.006791974578325550.01358394915665110.993208025421674
1940.005625643965163850.01125128793032770.994374356034836
1950.004627577946956290.009255155893912570.995372422053044
1960.003779369777264170.007558739554528330.996220630222736
1970.003109618519990930.006219237039981870.99689038148001
1980.002874818238822720.005749636477645430.997125181761177
1990.002365217377214960.004730434754429920.997634782622785
2000.001985508606132180.003971017212264360.998014491393868
2010.001635163479989140.003270326959978270.99836483652001
2020.001461758444473800.002923516888947610.998538241555526
2030.001187675375566600.002375350751133190.998812324624433
2040.0009448219540012370.001889643908002470.999055178045999
2050.0007482221472335340.001496444294467070.999251777852767
2060.0006594012142996610.001318802428599320.9993405987857
2070.0005722415733248660.001144483146649730.999427758426675
2080.0004877083758020690.0009754167516041370.999512291624198
2090.0004064154578585530.0008128309157171060.99959358454214
2100.0003279971309434960.0006559942618869920.999672002869056
2110.0002647202655270150.0005294405310540310.999735279734473
2120.0002268120183670790.0004536240367341580.999773187981633
2130.0001798196380499970.0003596392760999940.99982018036195
2140.0001431078511452210.0002862157022904430.999856892148855
2150.0001189685491992130.0002379370983984250.9998810314508
2169.05091621216066e-050.0001810183242432130.999909490837878
2176.83037774754386e-050.0001366075549508770.999931696222525
2185.14559880484152e-050.0001029119760968300.999948544011952
2193.85285547007305e-057.7057109401461e-050.9999614714453
2202.87545287877011e-055.75090575754022e-050.999971245471212
2212.22136987186844e-054.44273974373687e-050.999977786301281
2221.71054579074467e-053.42109158148934e-050.999982894542093
2236.31262825824582e-050.0001262525651649160.999936873717417
2240.0002779532175139940.0005559064350279890.999722046782486
2250.002357386261451080.004714772522902160.997642613738549
2260.1480488096346590.2960976192693170.851951190365341
2270.2027940527364520.4055881054729040.797205947263548
2280.1839976880052680.3679953760105360.816002311994732
2290.1665363782246500.3330727564493010.83346362177535
2300.1502401133259840.3004802266519690.849759886674015
2310.1382767875981230.2765535751962450.861723212401877
2320.1283751616468350.2567503232936710.871624838353165
2330.1162597830025270.2325195660050540.883740216997473
2340.1031607040756550.2063214081513110.896839295924345
2350.09126295595942180.1825259119188440.908737044040578
2360.08042251342817790.1608450268563560.919577486571822
2370.07179800496235440.1435960099247090.928201995037646
2380.1534022190690440.3068044381380880.846597780930956
2390.3222565886932870.6445131773865750.677743411306712
2400.2977009537947660.5954019075895330.702299046205234
2410.2740340245329920.5480680490659850.725965975467008
2420.2515851308437310.5031702616874620.748414869156269
2430.2302472783155650.460494556631130.769752721684435
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3110.9993293395228160.001341320954368530.000670660477184267
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3130.9999930087413451.39825173092319e-056.99125865461597e-06
3140.9999933905813791.32188372424294e-056.6094186212147e-06
3150.9999904972336611.90055326774781e-059.50276633873907e-06
3160.9999861469821752.77060356495064e-051.38530178247532e-05
3170.9999799198907224.01602185570453e-052.00801092785226e-05
3180.999971148947215.77021055814556e-052.88510527907278e-05
3190.999958979533868.20409322789365e-054.10204661394682e-05
3200.9999420010649950.0001159978700098145.79989350049072e-05
3210.9999185001193740.0001629997612516498.14998806258243e-05
3220.9998958583473080.0002082833053846600.000104141652692330
3230.999873714780790.0002525704384203640.000126285219210182
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3280.9998666320353320.0002667359293365050.000133367964668252
3290.9999778483243374.43033513261310e-052.21516756630655e-05
3300.9999989746401082.05071978491301e-061.02535989245651e-06
3310.9999999996016827.96635590674451e-103.98317795337225e-10
33213.82728889951519e-261.91364444975760e-26
33314.41091063669418e-262.20545531834709e-26
33411.37115660372767e-256.85578301863837e-26
33514.20589084664665e-252.10294542332332e-25
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33713.58476089772239e-241.79238044886119e-24
33811.02144035902658e-235.10720179513292e-24
33912.96812776532533e-231.48406388266266e-23
34018.84340406577869e-234.42170203288934e-23
34112.56576728101098e-221.28288364050549e-22
34217.47784766291311e-223.73892383145656e-22
34311.89045298917281e-219.45226494586405e-22
34412.45636433288414e-211.22818216644207e-21
34517.08576504135241e-213.54288252067621e-21
34612.03593992076391e-201.01796996038195e-20
34715.78968570102462e-202.89484285051231e-20
34811.63912899736258e-198.19564498681291e-20
34913.15265196284053e-191.57632598142027e-19
35013.47670374054942e-191.73835187027471e-19
35111.83703575006897e-199.18517875034485e-20
35214.98169271313084e-192.49084635656542e-19
35311.42302743585187e-187.11513717925933e-19
35411.70525881119477e-188.52629405597383e-19
35513.27836336686057e-181.63918168343028e-18
35616.62874099238506e-183.31437049619253e-18
35711.81010109947701e-179.05050549738504e-18
35812.82267135470339e-171.41133567735169e-17
35917.90761267291934e-173.95380633645967e-17
36011.95710584061603e-169.78552920308016e-17
36115.30774707957483e-162.65387353978741e-16
36211.42129569511573e-157.10647847557864e-16
3630.9999999999999992.3395855096282e-151.1697927548141e-15
3640.9999999999999976.34392005254476e-153.17196002627238e-15
3650.9999999999999921.55532390695818e-147.77661953479088e-15
3660.9999999999999794.17948549508861e-142.08974274754431e-14
3670.9999999999999461.08101189873329e-135.40505949366645e-14
3680.999999999999882.38742871257802e-131.19371435628901e-13
3690.9999999999997125.75054644550893e-132.87527322275447e-13
3700.9999999999992541.49212715444348e-127.46063577221738e-13
3710.9999999999983963.2079136538712e-121.6039568269356e-12
3720.9999999999959688.06371721784439e-124.03185860892220e-12
3730.9999999999898322.0335699414047e-111.01678497070235e-11
3740.9999999999815573.68861138882967e-111.84430569441483e-11
3750.999999999954289.14411904197026e-114.57205952098513e-11
3760.9999999998924442.15112807218725e-101.07556403609362e-10
3770.9999999997376935.24614833658696e-102.62307416829348e-10
3780.9999999994317931.13641421244578e-095.6820710622289e-10
3790.9999999986541882.6916242595003e-091.34581212975015e-09
3800.999999996834866.33028067567071e-093.16514033783536e-09
3810.99999999291231.41753983543565e-087.08769917717824e-09
3820.9999999840554573.18890850153361e-081.59445425076680e-08
3830.9999999643016877.13966265709806e-083.56983132854903e-08
3840.9999999241294831.51741033644186e-077.58705168220932e-08
3850.9999998330226643.33954672018704e-071.66977336009352e-07
3860.9999996928756446.14248712533528e-073.07124356266764e-07
3870.9999993787486841.24250263136691e-066.21251315683455e-07
3880.9999986770590222.64588195573111e-061.32294097786555e-06
3890.9999977040072124.59198557625525e-062.29599278812763e-06
3900.999995871259078.25748186037655e-064.12874093018827e-06
3910.999991727031731.65459365392680e-058.27296826963402e-06
3920.9999848095132963.03809734073010e-051.51904867036505e-05
3930.9999750098048684.9980390264174e-052.4990195132087e-05
3940.9999563577986238.7284402754675e-054.36422013773375e-05
3950.999945517019130.0001089659617389225.44829808694612e-05
3960.9999089012234440.0001821975531126489.1098776556324e-05
3970.9998541977130570.0002916045738855090.000145802286942754
3980.9997585864533440.0004828270933114010.000241413546655701
3990.999733757918680.0005324841626403640.000266242081320182
4000.999766126390460.0004677472190794280.000233873609539714
4010.9995902132519470.0008195734961055280.000409786748052764
4020.9994171134165380.001165773166924000.000582886583462001
4030.9996705142733760.0006589714532477130.000329485726623856
4040.9993387030057530.001322593988493560.000661296994246778
4050.999999989887332.02253389498652e-081.01126694749326e-08
4060.999999999466961.06608058454480e-095.33040292272398e-10
4070.9999999973767385.24652371281779e-092.62326185640889e-09
4080.9999999901400641.97198729934662e-089.8599364967331e-09
4090.9999999687323676.2535265260401e-083.12676326302005e-08
4100.9999998418958353.1620832902518e-071.5810416451259e-07
4110.999999250979881.49804023890334e-067.49020119451672e-07
4120.9999988792920332.24141593396462e-061.12070796698231e-06
4130.9999945284486051.09431027899943e-055.47155139499716e-06
4140.9999737654906475.24690187067153e-052.62345093533577e-05
4150.9998994003722770.0002011992554465320.000100599627723266
4160.9997358580907820.0005282838184365370.000264141909218268
4170.998724712807910.002550574384179900.00127528719208995
4180.9957716628366260.008456674326747670.00422833716337384
4190.99388448677330.01223102645339810.00611551322669907


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3430.840686274509804NOK
5% type I error level3480.852941176470588NOK
10% type I error level3500.857843137254902NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/10tuuq1291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/10tuuq1291402852.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/14bxe1291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/14bxe1291402852.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/24bxe1291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/24bxe1291402852.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/3x2ez1291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/3x2ez1291402852.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/4x2ez1291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/4x2ez1291402852.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/5x2ez1291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/5x2ez1291402852.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/6pcv21291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/6pcv21291402852.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/7i3vn1291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/7i3vn1291402852.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/8i3vn1291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/8i3vn1291402852.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/9i3vn1291402852.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/03/t1291402810nfqxvauxiyjh980/9i3vn1291402852.ps (open in new window)


 
Parameters (Session):
par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 9 ; 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')
}
 




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Software written by Ed van Stee & Patrick Wessa


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