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lineaire trend zonder dummies

*Unverified author*
R Software Module: rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Sat, 13 Dec 2008 08:00:35 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd.htm/, Retrieved Sat, 13 Dec 2008 16:05:46 +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/2008/Dec/13/t12291807468geayizr4fxcpyd.htm/},
    year = {2008},
}
@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 = {2008},
    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 «
122.36 358.59 123.33 362.96 123.04 362.42 124.53 364.97 125.13 364.04 125.85 361.06 126.50 358.48 126.53 352.96 127.07 359.59 124.55 360.39 124.90 357.40 124.32 362.93 122.84 364.55 123.31 365.73 123.31 364.70 124.87 364.65 124.64 359.43 124.73 362.14 124.90 356.97 124.04 354.82 123.28 353.17 123.86 357.06 122.29 356.18 124.09 355.01 124.54 355.65 125.65 357.31 125.70 357.07 125.53 357.91 125.61 358.48 125.55 358.97 125.41 351.77 127.60 352.16 124.68 359.08 124.41 360.35 126.43 359.53 126.38 359.30 125.78 358.41 124.70 359.68 125.07 355.31 125.25 357.08 126.58 349.71 127.13 354.13 125.82 345.49 123.70 341.69 124.39 344.25 123.70 340.17 124.42 342.47 121.05 344.43 121.02 333.23 123.23 339.72 121.32 342.61 120.91 346.36 120.72 339.09 123.31 339.73 119.58 341.12 119.53 335.94 120.59 333.46 118.63 335.66 118.47 341.12 111.81 342.21 114.71 342.62 117.34 346.06 115.77 344.43 118.38 346.65 117.84 343.74 118.83 335.67 120.02 342.75 116.21 341.77 117.08 345.84 120.20 346.52 119.83 350.79 118.92 345.44 118.03 345.87 117.71 338.48 119.55 337.21 116.13 340.81 115.97 339.86 115.99 342.86 114.96 343.33 116.46 341.73 116.55 351.38 113.05 351.13 117.44 345.99 118.84 347.55 117.06 346.02 117.54 345.29 119.31 347.03 118.72 348.01 121.55 345.48 122.61 349.40 121.53 351.05 123.31 349.70 124.07 350.86 123.59 354.45 122.97 355.30 123.22 357.48 123.04 355.24 122.96 351.79 122.81 355.22 122.81 351.02 122.62 350.28 120.82 350.17 119.41 348.16 121.56 340.30 121.59 343.75 118.50 344.71 118.77 344.13 118.86 342.14 117.60 345.04 119.90 346.02 121.83 346.43 121.84 347.07 122.12 339.33 122.12 339.10 121.36 337.19 119.66 339.58 119.32 327.85 120.36 326.81 117.06 321.73 117.48 320.45 115.60 327.69 113.86 323.95 116.92 320.47 117.75 322.13 117.75 316.34 115.31 314.78 116.28 308.90 115.22 308.62 115.65 314.41 115.11 306.88 118.67 310.60 118.04 321.60 116.50 321.50 119.78 325.68 119.95 324.35 120.37 320.01 119.79 326.88 119.43 332.39 121.06 331.48 121.74 332.62 121.09 324.79 122.97 327.12 120.50 328.91 117.18 328.37 115.03 324.83 113.36 325.90 112.59 326.18 111.65 328.94 111.98 333.78 114.87 328.06 114.67 325.87 114.09 325.41 114.77 318.86 117.05 319.13 117.22 310.16 113.18 311.73 110.95 306.54 112.14 311.16 112.72 311.98 110.01 306.72 110.29 308.05 110.74 300.76 110.32 301.90 105.89 293.09 108.97 292.76 109.34 294.58 106.57 289.90 99.49 296.69 101.81 297.21 104.29 293.31 109.73 296.25 105.06 298.60 107.97 296.87 108.13 301.02 109.86 304.73 108.95 301.92 111.20 295.72 110.69 293.18 106.10 298.35 105.68 297.99 104.12 299.85 104.71 299.85 104.30 304.45 103.52 299.45 107.76 298.14 107.80 298.78 107.30 297.02 108.64 301.33 105.03 294.96 108.30 296.69 107.21 300.73 109.27 301.96 109.50 297.38 111.68 293.87 111.80 285.96 111.75 285.41 106.68 283.70 106.37 284.76 105.76 277.11 109.01 274.73 109.01 274.73 109.01 274.73 109.01 274.73 107.69 274.69 105.19 275.42 105.48 264.15 102.22 276.24 100.54 268.88 105.00 277.97 105.44 280.49 107.89 281.09 108.64 276.16 106.70 272.58 109.10 270.94 105.23 284.31 108.41 283.94 108.80 284.18 110.39 282.83 110.22 283.84 110.86 282.71 108.58 279.29 107.70 280.70 106.62 274.47 109.84 273.44 107.16 275.49 107.26 279.46 108.70 280.19 109.85 288.21 109.41 284.80 112.36 281.41 111.03 283.39 110.67 287.97 109.21 290.77 113.58 290.60 113.88 289.67 114.08 289.84 112.33 298.55 113.92 296.07 114.41 297.14 114.57 295.34 115.35 296.25 113.13 294.30 113.29 296.15 112.56 296.49 113.06 298.05 113.46 301.03 115.39 300.52 116.62 301.50 117.04 296.93 117.42 289.84 115.62 291.44 115.16 286.88 115.69 286.74 112.85 288.93 114.05 292.19 112.00 295.39 113.74 295.86 116.26 293.36 118.63 292.86 116.49 292.73 118.23 296.73 116.83 285.02 118.82 285.24 114.36 288.62 112.02 283.36 113.24 285.84 109.75 291.48 110.33 291.41 112.86 287.77 113.04 284.97 113.80 286.05 110.90 278.19 109.96 281.21 108.69 277.92 108.84 280.08 108.47 269.24 108.07 268.48 107.94 268.83 108.11 269.54 108.11 262.37 106.81 265.12 105.58 265.34 105.61 263.32 106.52 267.18 103.86 260.75 104.60 261.78 104.73 257.27 105.12 255.63 104.76 251.39 103.85 259.49 103.83 261.18 103.22 261.65 101.64 262.01 102.13 265.23 104.33 268.10 104.92 262.27 107.78 263.59 104.49 257.85 102.80 265.69 102.86 271.15 104.51 266.69 104.73 265.77 102.58 262.32 99.93 270.48 101.41 273.03 101.05 269.13 99.86 280.65 101.11 282.75 100.89 281.44 101.09 281.99 98.31 282.86 98.08 287.21 99.55 283.11 99.62 280.66 97.37 282.39 98.16 280.83 97.98 284.71 98.15 279.99 97.10 283.50 97.24 284.88 96.70 288.60 96.64 284.80 100.65 287.20 96.75 286.22 97.74 286.54 97.92 279.58 98.34 283.08 93.84 288.88 97.80 280.18 96.20 284.16 95.99 290.57 95.18 286.82 95.95 273.00 92.23 278.69 91.78 264.54 92.97 271.92 89.76 283.60 92.88 269.25 96.23 263.58 95.79 264.16 93.97 268.85 93.90 269.67 93.60 249.41 93.96 268.99 88.69 268.65 88.57 260.16 85.62 256.55 86.25 251.47 85.33 234.93 83.33 232.96 77.78 215.49 78.70 213.68 72.05 236.07 80.75 235.41 81.41 214.77 82.65 225.85 75.85 224.64 75.70 238.26 78.25 232.44 77.41 222.50 76.84 225.28 74.25 220.49 74.95 216.86 68.78 234.70 73.21 230.06 73.26 238.27 78.67 238.56 75.63 242.70 74.99 249.14 83.87 234.89 79.62 227.78 80.13 234.04 79.76 230.70 78.20 230.17 78.05 218.23 79.05 232.20 73.32 220.76 75.17 215.60 73.26 217.69 73.72 204.35 73.57 191.44 70.60 203.84 71.25 211.86 74.22 210.57 73.32 219.57 73.01 219.98 74.21 226.01 75.32 207.04 71.73 212.52 71.94 217.92 72.94 210.45 72.47 218.53 71.94 223.32 74.30 218.76 74.30 217.63
 
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 time15 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Multiple Linear Regression - Estimated Regression Equation
Japan[t] = + 32.3756864590004 + 0.266839763422737Amerika[t] -0.0207513918279697t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)32.37568645900048.1734613.96118.9e-054.4e-05
Amerika0.2668397634227370.02215512.044300
t-0.02075139182796970.008113-2.55790.0109050.005452


Multiple Linear Regression - Regression Statistics
Multiple R0.910611391292349
R-squared0.829213105951387
Adjusted R-squared0.828341744247058
F-TEST (value)951.62904432357
F-TEST (DF numerator)2
F-TEST (DF denominator)392
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.10301775529827
Sum Squared Residuals14600.7556828225


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1122.36128.041005832932-5.68100583293155
2123.33129.186344207261-5.85634420726104
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5125.13129.412276976274-4.28227697627368
6125.85128.596343089446-2.74634308944595
7126.5127.887145107987-1.38714510798732
8126.53126.3934382220660.136561777934171
9127.07128.141834461731-1.07183446173061
10124.55128.334554880641-3.78455488064083
11124.9127.515952596179-2.61595259617887
12124.32128.970825096079-4.65082509607865
13122.84129.382354120996-6.5423541209955
14123.31129.676473650006-6.36647365000637
15123.31129.380877301853-6.07087730185297
16124.87129.346783921854-4.47678392185386
17124.64127.933128964959-3.29312896495921
18124.73128.635513332007-3.90551333200685
19124.9127.235200363283-2.33520036328335
20124.04126.640743480096-2.60074348009648
21123.28126.179706478621-2.89970647862101
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23122.29126.941391382868-4.6513913828675
24124.09126.608437467835-2.51843746783492
25124.54126.758463524598-2.2184635245975
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27125.7127.095873205002-1.39587320500186
28125.53127.299267214449-1.76926721444899
29125.61127.430614487772-1.82061448777198
30125.55127.540614580021-1.99061458002116
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223106.62100.9876359480025.63236405199827
224109.84100.6920395998489.14796040015166
225107.16101.2183097230375.94169027696301
226107.26102.2569121919975.00308780800273
227108.7102.4309538274686.2690461725321
228109.85104.5502573382905.29974266170972
229109.41103.6195823531915.79041764680921
230112.36102.6942441633609.66575583664026
231111.03103.2018355031097.82816449689122
232110.67104.4032102277576.26678977224305
233109.21105.1296101735134.08038982648736
234113.58105.0634960219038.51650397809719
235113.88104.7945836500929.0854163499083
236114.08104.8191950180469.26080498195442
237112.33107.1226179656305.20738203437034
238113.92106.4401039605137.4798960394867
239114.41106.7048711155487.70512888445235
240114.57106.2038081495598.36619185044124
241115.35106.4258809424458.92411905755451
242113.13105.8847920119437.24520798805682
243113.29106.3576941824476.93230581755274
244112.56106.4276683101836.13233168981697
245113.06106.8231869492956.23681305070547
246113.46107.5976180524665.86238194753369
247115.39107.4407783812937.94922161870726
248116.62107.6815299576198.93847004238094
249117.04106.44132084694910.5986791530508
250117.42104.52867553245412.891324467546
251115.62104.93486776210210.6851322378976
252115.16103.69732704906711.4626729509332
253115.69103.63921809036012.0507819096404
254112.85104.2028457804278.64715421957255
255114.05105.0519920173588.9980079826424
256112105.8851278684826.11487213151762
257113.74105.9897911654637.7502088345369
258116.26105.30194036507810.9580596349217
259118.63105.14776909153913.4822309084610
260116.49105.09232853046611.3976714695340
261118.23106.13893619232912.091063807671
262116.83102.99349117082113.8365088291792
263118.82103.03144452694615.7885554730542
264114.36103.91261153548710.4473884645133
265112.02102.4882829880559.53171701194486
266113.24103.12929420951610.1107057904845
267109.75104.6135190833925.13648091660818
268110.33104.5740889081245.75591109187574
269112.86103.5820407774389.27795922256248
270113.04102.81413804802610.2258619519741
271113.8103.08157360069410.7184263993055
272110.9100.9634616683649.93653833163621
273109.96101.7485663620728.21143363792751
274108.69100.8499121485847.84008785141628
275108.84101.4055346457497.43446535425115
276108.4798.49224021841849.97775978158158
277108.0798.26869060638929.80130939361082
278107.9498.34133313175929.59866686824084
279108.1198.51003797196139.59996202803866
280108.1196.576045476392311.5339545236077
281106.8197.28910343397699.5208965660231
282105.5897.3270567901028.25294320989808
283105.6196.767289076168.84271092383997
284106.5297.77653917114388.74346082885617
285103.8696.04000810050767.81999189949234
286104.696.29410166500518.3058983349949
287104.7395.06990294014069.66009705985942
288105.1294.611534336299310.5084656637007
289104.7693.45938234755911.3006176524410
290103.8595.60003303945528.24996696054483
291103.8396.03024084781167.79975915218838
292103.2296.13490414479237.08509585520768
293101.6496.21021506779655.42978493220346
294102.1397.04868771418985.0813122858102
295104.3397.7937664433856.53623355661492
296104.9296.21733923080258.70266076919746
297107.7896.548816326692611.2311836733074
298104.4994.99640469281819.49359530718188
299102.897.06767704622445.7323229537756
300102.8698.50387076268464.35612923731543
301104.5197.29301402599127.21698597400881
302104.7397.02677005181437.7032299481857
303102.5896.08542147617796.4945785238221
30499.9398.24208255387951.68791744612054
305101.4198.90177255877952.50822744122054
306101.0597.84034608960283.20965391039717
30799.86100.893588772405-1.03358877240478
308101.11101.433200883765-0.323200883764561
309100.89101.062889401853-0.172889401852805
310101.09101.188899879907-0.0988998799073402
31198.31101.400299082257-3.09029908225715
31298.08102.540300661318-4.46030066131808
31399.55101.425506239457-1.87550623945690
31499.62100.750997427243-1.13099742724322
31597.37101.191878826137-3.82187882613658
31698.16100.754857403369-2.59485740336915
31797.98101.769444293621-3.78944429362139
31898.15100.489209218438-2.33920921843811
31997.1101.405065396224-4.30506539622395
32097.24101.752552877919-4.51255287791936
32196.7102.724445406024-6.02444540602397
32296.64101.689702913190-5.0497029131896
323100.65102.309366953576-1.65936695357618
32496.75102.027112593594-5.27711259359395
32597.74102.091749926061-4.35174992606126
32697.92100.213793780811-2.29379378081102
32798.34101.126981560963-2.78698156096263
32893.84102.653900796987-8.81390079698654
32997.8100.311643463381-2.51164346338077
33096.2101.352914329975-5.15291432997529
33195.99103.042605821687-7.05260582168706
33295.18102.021205317024-6.84120531702382
33395.9598.3127283946936-2.36272839469363
33492.2399.810295256741-7.58029525674103
33591.7896.0137612124813-4.23376121248135
33692.9797.9622872747132-4.99228727471317
33789.76101.058224319663-11.2982243196628
33892.8897.2083223227185-4.32832232271853
33996.2395.67458947228360.555410527716374
34095.7995.8086051432409-0.0186051432408520
34193.9797.0393322418655-3.06933224186553
34293.997.2373894560442-3.33738945604419
34393.691.81046445727161.78953554272842
34493.9697.0144356332608-3.0544356332608
34588.6996.902958721869-8.21295872186909
34688.5794.616737738582-6.0467377385821
34785.6293.632694800798-8.01269480079804
34886.2592.2563974107826-6.00639741078257
34985.3387.8221163319425-2.49211633194253
35083.3387.2756906061718-3.94569060617177
35177.7882.5932485473486-4.81324854734859
35278.782.0895171837255-3.38951718372546
35372.0588.0433080949326-15.9933080949326
35480.7587.8464424592456-7.0964424592456
35581.4182.3181183503723-0.908118350372343
35682.6585.2539515372683-2.60395153726828
35775.8584.9103240316988-9.06032403169881
35875.788.5239302176885-12.8239302176885
35978.2586.9501714027402-8.70017140274022
36077.4184.2770327624903-6.86703276249025
36176.8484.9980959129775-8.15809591297748
36274.2583.6991820543546-9.4491820543546
36374.9582.7098023213021-7.7598023213021
36468.7887.4494723089358-18.6694723089358
36573.2186.1905844148263-12.9805844148263
36673.2688.360587480699-15.1005874806990
36778.6788.4172196202636-9.74721962026361
36875.6389.5011848490058-13.8711848490058
36974.9991.1988815336202-16.2088815336202
37083.8787.3756635130183-3.50566351301825
37179.6285.4576814032546-5.83768140325462
37280.1387.107346930453-6.97734693045299
37379.7686.195350728793-6.43535072879307
37478.286.033174262351-7.83317426235105
37578.0582.8263560952556-4.77635609525561
37679.0586.5333561984433-7.48335619844327
37773.3283.4599579130592-10.1399579130592
37875.1782.0623133419699-6.8923133419699
37973.2682.5992570556955-9.33925705569545
38073.7279.0188632198082-5.29886321980818
38173.5775.5532104821927-1.98321048219268
38270.678.8412721568066-8.24127215680665
38371.2580.960575667629-9.71057566762902
38474.2280.5956009809857-6.37560098098572
38573.3282.9764074599624-9.65640745996239
38673.0183.0650603711377-10.0550603711377
38774.2184.6533527527489-10.4433527527489
38875.3279.5706510487916-4.25065104879158
38971.7381.0121815605202-9.2821815605202
39071.9482.432364891175-10.4923648911750
39172.9480.4183204665792-7.4783204665792
39272.4782.553634363207-10.0836343632069
39371.9483.8110454381739-11.8710454381739
39474.382.5735047251382-8.27350472513824
39574.382.2512244006426-7.95122440064257


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.0004902855947002350.000980571189400470.9995097144053
72.85509244254486e-055.71018488508973e-050.999971449075575
82.25725873580229e-064.51451747160458e-060.999997742741264
92.64830059254858e-075.29660118509716e-070.99999973516994
103.86320061219876e-057.72640122439752e-050.999961367993878
112.59415941366378e-055.18831882732756e-050.999974058405863
121.23089712104860e-052.46179424209720e-050.99998769102879
138.62780665702993e-061.72556133140599e-050.999991372193343
142.22678258722806e-064.45356517445611e-060.999997773217413
155.65023677559019e-071.13004735511804e-060.999999434976322
161.33793872411872e-072.67587744823744e-070.999999866206128
173.19323582503929e-086.38647165007857e-080.999999968067642
186.2569172615261e-091.25138345230522e-080.999999993743083
191.51134670854880e-093.02269341709761e-090.999999998488653
207.29436726126674e-101.45887345225335e-090.999999999270563
215.00346722795039e-101.00069344559008e-090.999999999499653
221.13924835466023e-102.27849670932047e-100.999999999886075
237.45839812862613e-111.49167962572523e-100.999999999925416
241.53169033392324e-113.06338066784648e-110.999999999984683
253.28375283311892e-126.56750566623785e-120.999999999996716
261.42714392824743e-122.85428785649486e-120.999999999998573
275.28570775131305e-131.05714155026261e-120.999999999999471
281.58579861923732e-133.17159723847464e-130.999999999999841
294.62912058658197e-149.25824117316393e-140.999999999999954
301.20616078986149e-142.41232157972298e-140.999999999999988
312.41394701200215e-154.8278940240043e-150.999999999999998
322.22591047602676e-154.45182095205353e-150.999999999999998
334.59658513233016e-169.19317026466032e-161
349.6112352400823e-171.92224704801646e-161
353.7628622637384e-177.5257245274768e-171
361.16258498954914e-172.32516997909827e-171
372.40698024498864e-184.81396048997728e-181
385.29551041527486e-191.05910208305497e-181
391.10313518761606e-192.20627037523212e-191
402.11665506238180e-204.23331012476360e-201
414.34322267297162e-218.68644534594325e-211
421.44624519573566e-212.89249039147131e-211
433.16466865857032e-226.32933731714064e-221
445.5651564623246e-221.11303129246492e-211
451.93068294699750e-223.86136589399499e-221
469.83337811362108e-231.96667562272422e-221
472.38060932436223e-234.76121864872446e-231
484.79788643148157e-229.59577286296314e-221
497.91335216340224e-221.58267043268045e-211
502.03569053267867e-224.07138106535735e-221
513.09465106834657e-226.18930213669313e-221
529.14221844355456e-221.82844368871091e-211
538.87285912662354e-221.77457182532471e-211
542.10471181007620e-224.20942362015241e-221
557.87257997795891e-221.57451599559178e-211
568.9829764911205e-221.7965952982241e-211
573.0443855332264e-226.0887710664528e-221
585.40210432275592e-221.08042086455118e-211
592.08649329065753e-214.17298658131505e-211
601.98931701894906e-163.97863403789813e-161
614.19530723331423e-158.39061446662846e-150.999999999999996
626.5891420336462e-151.31782840672924e-140.999999999999993
631.90413111001187e-143.80826222002374e-140.99999999999998
641.19452675825288e-142.38905351650576e-140.999999999999988
657.58951777511636e-151.51790355502327e-140.999999999999992
663.12763638551656e-156.25527277103313e-150.999999999999997
671.25156118215243e-152.50312236430486e-150.999999999999999
681.25726172184227e-152.51452344368454e-150.999999999999999
698.72068814359366e-161.74413762871873e-151
703.7108534496882e-167.4217068993764e-161
711.58363956069440e-163.16727912138881e-161
726.69650914373798e-171.33930182874760e-161
733.16744504110271e-176.33489008220541e-171
741.37975631501751e-172.75951263003502e-171
755.84066723020075e-181.16813344604015e-171
763.92439714648989e-187.84879429297979e-181
772.54852545968786e-185.09705091937573e-181
781.65847631829566e-183.31695263659133e-181
791.54398591564893e-183.08797183129786e-181
807.74877738521113e-191.54975547704223e-181
814.40056229685611e-198.80112459371223e-191
821.05235221399469e-182.10470442798939e-181
835.17873449546024e-191.03574689909205e-181
843.1993258514557e-196.3986517029114e-191
851.59676847774609e-193.19353695549217e-191
868.11155249330206e-201.62231049866041e-191
876.09078271774581e-201.21815654354916e-191
883.84156227417423e-207.68312454834846e-201
898.19838728762554e-201.63967745752511e-191
902.62134239320787e-195.24268478641574e-191
913.33744814034194e-196.67489628068387e-191
921.00443958582857e-182.00887917165713e-181
933.35937189604461e-186.71874379208922e-181
945.12803036540946e-181.02560607308189e-171
954.90281952667776e-189.80563905335552e-181
964.19414872686601e-188.38829745373201e-181
973.43684914446139e-186.87369828892277e-181
983.03046332077683e-186.06092664155367e-181
992.11289271042041e-184.22578542084081e-181
1001.69536383352238e-183.39072766704475e-181
1011.28909970238411e-182.57819940476822e-181
1027.23986054009928e-191.44797210801986e-181
1034.07555295412175e-198.1511059082435e-191
1043.89324341021587e-197.78648682043174e-191
1052.9705126831716e-195.9410253663432e-191
1061.72525727187575e-193.45051454375149e-191
1079.74818409031548e-201.94963681806310e-191
1085.40906058195652e-201.08181211639130e-191
1093.79465795221481e-207.58931590442962e-201
1102.23801976015529e-204.47603952031058e-201
1111.75846733985112e-203.51693467970224e-201
1121.32759914082306e-202.65519828164613e-201
1131.72708515066229e-203.45417030132459e-201
1142.13381053200143e-204.26762106400286e-201
1152.10819628784633e-204.21639257569266e-201
1161.26280656364045e-202.52561312728091e-201
1171.01992052130063e-202.03984104260126e-201
1181.18453890840716e-202.36907781681433e-201
1196.36151416203126e-211.27230283240625e-201
1203.65205709358145e-217.3041141871629e-211
1212.17856143785378e-214.35712287570755e-211
1221.63694412022059e-213.27388824044117e-211
1238.87348511969623e-221.77469702393925e-211
1245.23005206373561e-221.04601041274712e-211
1253.57101959344997e-227.14203918689994e-221
1261.68532415568221e-223.37064831136442e-221
1279.4866751818471e-231.89733503636942e-221
1284.38950985356415e-238.7790197071283e-231
1292.03419598346284e-234.06839196692568e-231
1309.28128430368386e-241.85625686073677e-231
1319.64687788372814e-241.92937557674563e-231
1325.24945057961809e-241.04989011592362e-231
1332.51673277395167e-245.03346554790335e-241
1341.90177696624385e-243.8035539324877e-241
1351.55226331164589e-243.10452662329179e-241
1361.75092266704678e-243.50184533409357e-241
1371.17843810618951e-242.35687621237903e-241
1386.74350878220275e-251.34870175644055e-241
1395.70045261428128e-251.14009052285626e-241
1405.7053611724462e-251.14107223448924e-241
1415.9031220280423e-251.18062440560846e-241
1421.28280431241197e-242.56560862482395e-241
1438.5543973713985e-251.7108794742797e-241
1444.65170491656885e-259.3034098331377e-251
1453.56211962266084e-257.12423924532169e-251
1465.89127292588805e-251.17825458517761e-241
1471.47654477037039e-242.95308954074077e-241
1488.54400800676868e-241.70880160135374e-231
1496.11275539049011e-231.22255107809802e-221
1505.84230615394795e-231.16846123078959e-221
1515.28781618641686e-231.05756323728337e-221
1525.66329319444968e-231.13265863888994e-221
1533.65915108664859e-237.31830217329718e-231
1542.13450344007886e-234.26900688015773e-231
1551.34287801615806e-232.68575603231612e-231
1569.08999585061046e-241.81799917012209e-231
1579.34609816804238e-241.86921963360848e-231
1588.04969438338248e-241.60993887667650e-231
1596.0429152520953e-241.20858305041906e-231
1608.64419663396166e-241.72883932679233e-231
1611.15553169674749e-232.31106339349498e-231
1627.95644343623996e-241.59128868724799e-231
1636.47048305447999e-241.29409661089600e-231
1641.87253010256366e-233.74506020512732e-231
1651.24289323311553e-232.48578646623106e-231
1668.09325976085643e-241.61865195217129e-231
1679.45304385205416e-241.89060877041083e-231
1687.75294121927292e-211.55058824385458e-201
1692.98859661957695e-195.97719323915391e-191
1709.25140368117697e-191.85028073623539e-181
1716.40146082722971e-191.28029216544594e-181
1722.36733761045475e-184.7346752209095e-181
1732.36591682932712e-184.73183365865424e-181
1743.05438680403579e-186.10877360807158e-181
1753.25926729091242e-186.51853458182483e-181
1763.70740515620322e-187.41481031240643e-181
1772.80722636967188e-185.61445273934375e-181
1782.06682829482186e-184.13365658964373e-181
1794.93277699476563e-189.86555398953127e-181
1801.38562454483969e-172.77124908967939e-171
1811.14240818423953e-162.28481636847906e-161
1826.64235159686188e-161.32847031937238e-151
1839.49030165516612e-151.89806033103322e-140.99999999999999
1841.03127314539209e-132.06254629078417e-130.999999999999897
1851.69772804557048e-133.39545609114095e-130.99999999999983
1863.0147419872533e-136.0294839745066e-130.999999999999699
1875.63173377001959e-131.12634675400392e-120.999999999999437
1881.05802970915136e-122.11605941830272e-120.999999999998942
1893.91243621422314e-127.82487242844627e-120.999999999996088
1906.59227370945346e-121.31845474189069e-110.999999999993408
1912.11661295237596e-114.23322590475192e-110.999999999978834
1924.45746240190058e-118.91492480380117e-110.999999999955425
1937.3891016269962e-111.47782032539924e-100.999999999926109
1941.09128399408836e-102.18256798817673e-100.999999999890872
1951.82833965224371e-103.65667930448741e-100.999999999817166
1962.90410438355126e-105.80820876710253e-100.99999999970959
1974.00625898435830e-108.01251796871661e-100.999999999599374
1986.32324384496484e-101.26464876899297e-090.999999999367676
1998.07924917941791e-101.61584983588358e-090.999999999192075
2001.04250049349825e-092.08500098699650e-090.9999999989575
2011.28948971026196e-092.57897942052393e-090.99999999871051
2021.54012005030052e-093.08024010060104e-090.99999999845988
2031.78654223220031e-093.57308446440061e-090.999999998213458
2041.90712584881100e-093.81425169762199e-090.999999998092874
2052.59820873337806e-095.19641746675612e-090.999999997401791
2062.55620090048635e-095.1124018009727e-090.9999999974438
2079.0849509745795e-091.8169901949159e-080.999999990915049
2083.53119727624355e-087.06239455248709e-080.999999964688027
2097.10877609631572e-081.42175521926314e-070.999999928912239
2101.57513419148931e-073.15026838297863e-070.99999984248658
2112.49389789722264e-074.98779579444527e-070.99999975061021
2123.53363099111678e-077.06726198223356e-070.9999996466369
2135.21473845489754e-071.04294769097951e-060.999999478526155
2147.49858203305851e-071.49971640661170e-060.999999250141797
2152.62098682508133e-065.24197365016265e-060.999997379013175
2164.79594976866715e-069.5918995373343e-060.999995204050231
2178.6569663866915e-061.7313932773383e-050.999991343033613
2181.39555637023010e-052.79111274046021e-050.999986044436298
2192.28107221320706e-054.56214442641412e-050.999977189277868
2203.62586319682383e-057.25172639364766e-050.999963741368032
2216.15524341294646e-050.0001231048682589290.99993844756587
2220.0001237531602219680.0002475063204439350.999876246839778
2230.00025300953108710.00050601906217420.999746990468913
2240.0004016287825211480.0008032575650422960.999598371217479
2250.0008144647668169310.001628929533633860.999185535233183
2260.001874326863032510.003748653726065010.998125673136968
2270.003627733176015840.007255466352031680.996372266823984
2280.007134669281411360.01426933856282270.992865330718589
2290.01379169551449340.02758339102898680.986208304485507
2300.02129289965308740.04258579930617470.978707100346913
2310.03344821686286620.06689643372573240.966551783137134
2320.05514852976753920.1102970595350780.94485147023246
2330.1083112431411640.2166224862823280.891688756858836
2340.1458919638195150.2917839276390300.854108036180485
2350.1886656187716230.3773312375432470.811334381228377
2360.2353928946837770.4707857893675530.764607105316223
2370.3138031772070680.6276063544141350.686196822792932
2380.3734878310473890.7469756620947780.626512168952611
2390.4297711426835590.8595422853671180.570228857316441
2400.4823763005043140.9647526010086280.517623699495686
2410.5270099475746080.9459801048507840.472990052425392
2420.5882475338939720.8235049322120550.411752466106028
2430.6463949035789560.7072101928420870.353605096421044
2440.7139470715902980.5721058568194050.286052928409702
2450.7690535494706330.4618929010587350.230946450529367
2460.815917886283080.3681642274338410.184082113716921
2470.836225377133260.3275492457334810.163774622866741
2480.8481486717066180.3037026565867640.151851328293382
2490.8602833131955340.2794333736089330.139716686804466
2500.8806237891446530.2387524217106940.119376210855347
2510.8875646783935030.2248706432129940.112435321606497
2520.894958411113610.2100831777727800.105041588886390
2530.9018535555152930.1962928889694150.0981464444847074
2540.9081759243140770.1836481513718460.0918240756859232
2550.9090692171613750.181861565677250.090930782838625
2560.9220707211639150.1558585576721710.0779292788360853
2570.9227958983535720.1544082032928560.077204101646428
2580.9214817808940650.1570364382118710.0785182191059353
2590.933844061565830.1323118768683420.0661559384341709
2600.9324813394225350.1350373211549290.0675186605774646
2610.9360936678202270.1278126643595460.0639063321797728
2620.9446454954052660.1107090091894680.0553545045947341
2630.9660014656209740.06799706875805130.0339985343790257
2640.9619621174816190.07607576503676220.0380378825183811
2650.9569562155144740.08608756897105210.0430437844855261
2660.951185815864470.0976283682710590.0488141841355295
2670.9534464983340440.09310700333191130.0465535016659556
2680.9521630555668570.09567388886628570.0478369444331428
2690.9447763129340270.1104473741319460.0552236870659729
2700.9379552975300490.1240894049399030.0620447024699513
2710.9330166799302420.1339666401395170.0669833200697584
2720.9236347761975360.1527304476049280.0763652238024641
2730.9125818050981260.1748363898037470.0874181949018736
2740.9019844088720740.1960311822558520.0980155911279262
2750.8901173068737190.2197653862525630.109882693126281
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2790.8239286332311910.3521427335376180.176071366768809
2800.8101566826027430.3796866347945150.189843317397258
2810.7882183880304030.4235632239391930.211781611969597
2820.7663832884998810.4672334230002370.233616711500119
2830.7416508681685840.5166982636628330.258349131831416
2840.7149142770836040.5701714458327910.285085722916396
2850.692333488393530.6153330232129390.307666511606469
2860.6644587509239740.6710824981520520.335541249076026
2870.6348727504112740.7302544991774520.365127249588726
2880.6085620011127720.7828759977744560.391437998887228
2890.5869864252253330.8260271495493330.413013574774667
2900.5554824712780650.8890350574438690.444517528721935
2910.5243739717812140.9512520564375710.475626028218786
2920.4948928371155420.9897856742310850.505107162884458
2930.4785288134310750.957057626862150.521471186568925
2940.46183274057170.92366548114340.5381672594283
2950.4353329545892120.8706659091784230.564667045410788
2960.4176693517204020.8353387034408040.582330648279598
2970.4689327849203370.9378655698406740.531067215079663
2980.4748911768908370.9497823537816740.525108823109163
2990.4585720118244670.9171440236489340.541427988175533
3000.4473474474536990.8946948949073980.552652552546301
3010.4607104463715630.9214208927431260.539289553628437
3020.4979566598038410.9959133196076810.502043340196159
3030.5185337470094350.962932505981130.481466252990565
3040.5265261810259990.9469476379480030.473473818974001
3050.5350541427112460.9298917145775080.464945857288754
3060.5472088889105790.9055822221788420.452791111089421
3070.5763713189343420.8472573621313160.423628681065658
3080.5950670166050880.8098659667898230.404932983394912
3090.6109699932030160.7780600135939670.389030006796984
3100.6259557939797390.7480884120405220.374044206020261
3110.6624014262222530.6751971475554950.337598573777748
3120.709272624477790.581454751044420.29072737552221
3130.7196873652451540.5606252695096920.280312634754846
3140.7258674369739290.5482651260521420.274132563026071
3150.7482236297866720.5035527404266570.251776370213328
3160.7556648143068030.4886703713863950.244335185693197
3170.7668431419937340.4663137160125310.233156858006266
3180.7688552249241470.4622895501517070.231144775075853
3190.7790880132756560.4418239734486890.220911986724344
3200.78647763933760.4270447213247990.213522360662400
3210.8021655858564770.3956688282870470.197834414143523
3220.8074670609910550.3850658780178890.192532939008944
3230.8061701870749180.3876596258501640.193829812925082
3240.8069807356871010.3860385286257980.193019264312899
3250.800913210178060.3981735796438790.199086789821939
3260.7966387348896420.4067225302207160.203361265110358
3270.7924043055516690.4151913888966620.207595694448331
3280.8171531504254210.3656936991491580.182846849574579
3290.813715407876840.3725691842463210.186284592123161
3300.8055676801887750.3888646396224500.194432319811225
3310.7990070869244410.4019858261511180.200992913075559
3320.792074456565020.4158510868699590.207925543434979
3330.7912816690056840.4174366619886320.208718330994316
3340.793874953713480.4122500925730420.206125046286521
3350.7883144315241460.4233711369517070.211685568475854
3360.7794974301100830.4410051397798340.220502569889917
3370.8183962494602810.3632075010794370.181603750539719
3380.8081495749660250.3837008500679490.191850425033975
3390.8466754795715920.3066490408568150.153324520428408
3400.8850626374015450.2298747251969090.114937362598455
3410.8979907664437260.2040184671125470.102009233556274
3420.9166940515900470.1666118968199060.083305948409953
3430.9669440912300240.06611181753995150.0330559087699757
3440.9860192384209680.02796152315806470.0139807615790323
3450.9885102422474650.022979515505070.011489757752535
3460.9929892640097580.01402147198048420.0070107359902421
3470.9945242658008420.01095146839831690.00547573419915846
3480.9969542026739250.006091594652149340.00304579732607467
3490.998611632168330.002776735663338220.00138836783166911
3500.999149057283380.001701885433239350.000850942716619675
3510.9991372831565740.001725433686851540.000862716843425769
3520.9990626015468790.001874796906242090.000937398453121044
3530.99983602806160.0003279438768008950.000163971938400448
3540.9998256643976820.0003486712046355440.000174335602317772
3550.9998732807396640.0002534385206718250.000126719260335912
3560.9999497281393350.000100543721330425.027186066521e-05
3570.9999388013330690.0001223973338622336.11986669311167e-05
3580.9999382826116530.0001234347766943656.17173883471825e-05
3590.9999181795846770.0001636408306460738.18204153230365e-05
3600.999887774079590.0002244518408219320.000112225920410966
3610.9998405104619250.0003189790761497770.000159489538074889
3620.9997953674569980.0004092650860047780.000204632543002389
3630.9996997655067960.0006004689864080990.000300234493204050
3640.9999885643317152.28713365702383e-051.14356682851191e-05
3650.9999948565509811.02868980378908e-055.14344901894542e-06
3660.9999990638278881.87234422374992e-069.36172111874958e-07
3670.999998066621973.86675606166694e-061.93337803083347e-06
3680.9999989566068322.08678633606915e-061.04339316803458e-06
3690.9999999703419555.93160893360145e-082.96580446680072e-08
3700.9999999882905262.34189488476318e-081.17094744238159e-08
3710.9999999705606125.88787770077108e-082.94393885038554e-08
3720.9999999332995221.33400955339729e-076.67004776698644e-08
3730.9999998940756952.11848609622328e-071.05924304811164e-07
3740.9999997412902655.17419470021382e-072.58709735010691e-07
3750.9999997606018894.78796222428808e-072.39398111214404e-07
3760.9999999753920734.92158536054286e-082.46079268027143e-08
3770.999999902380961.95238080495976e-079.76190402479879e-08
3780.999999860911342.78177317960716e-071.39088658980358e-07
3790.999999472262771.05547445876241e-065.27737229381205e-07
3800.9999982862159543.42756809261865e-061.71378404630933e-06
3810.9999944781689781.10436620441677e-055.52183102208387e-06
3820.9999916779099741.66441800528663e-058.32209002643313e-06
3830.999989443891222.1112217558647e-051.05561087793235e-05
3840.9999523285554229.5342889156625e-054.76714445783125e-05
3850.9997808664372580.0004382671254835150.000219133562741758
3860.999044685493860.001910629012280320.000955314506140162
3870.9994277614017160.001144477196568500.000572238598284248
3880.9999236836268320.0001526327463355317.63163731677655e-05
3890.9989829166359290.002034166728142090.00101708336407104


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2640.6875NOK
5% type I error level2710.705729166666667NOK
10% type I error level2790.7265625NOK
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/10e7q81229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/10e7q81229180410.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/1qpn51229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/1qpn51229180410.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/2wz8h1229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/2wz8h1229180410.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/3vceh1229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/3vceh1229180410.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/4vuwt1229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/4vuwt1229180410.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/5ynpj1229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/5ynpj1229180410.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/68jmg1229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/68jmg1229180410.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/7b1lh1229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/7b1lh1229180410.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/8qy2m1229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/8qy2m1229180410.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/9rw731229180410.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t12291807468geayizr4fxcpyd/9rw731229180410.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
 
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
 




Copyright

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Software written by Ed van Stee & Patrick Wessa


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