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ws7beursmulti

*The author of this computation has been verified*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Tue, 30 Nov 2010 23:31:47 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd.htm/, Retrieved Wed, 01 Dec 2010 00:30:06 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd.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 time22 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
Wealth[t] = + 186647.561462056 + 30.2212730981770Costs[t] -156.311213130649trades[t] -6.84758095793277orders[t] + 0.898279264054195dividends[t] -1.36321463969614Wrank[t] + 1.55137604546330`Profit/trades`[t] + 1.59904591655984`Profit/Cost`[t] + 5.46535468491439CCscore[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)186647.56146205639107.888014.77263e-061e-06
Costs30.22127309817701.83370516.48100
trades-156.311213130649191.457447-0.81640.4148930.207446
orders-6.847580957932771.173976-5.832800
dividends0.8982792640541950.3223192.78690.0056550.002827
Wrank-1.363214639696141.171966-1.16320.2456630.122832
`Profit/trades`1.551376045463300.9725831.59510.1117230.055862
`Profit/Cost`1.599045916559849.6098950.16640.8679560.433978
CCscore5.465354684914391.7090523.19790.001530.000765


Multiple Linear Regression - Regression Statistics
Multiple R0.858189962167756
R-squared0.736490011165495
Adjusted R-squared0.729578273753443
F-TEST (value)106.556422395506
F-TEST (DF numerator)8
F-TEST (DF denominator)305
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation286006.941457617
Sum Squared Residuals24948991021391.9


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
162821545432663.08515391849490.914846095
243210231284779.608577763036243.39142224
341119123007067.640788771104844.35921123
42231932651483.15922357-2428290.15922357
514913481926487.30100547-435139.301005467
616296161621252.124954118363.87504589237
713988931482946.01066890-84053.0106689033
819265171729422.52194128197094.478058721
99836601322743.71514014-339083.715140138
101443586732131.683246506711454.316753494
1110730891191401.59762988-118312.597629876
12984885616304.98076885368580.019231150
131405225702541.687480826702683.312519174
142271321497067.63796618-1269935.63796618
15929118948022.643501924-18904.6435019244
161071292509199.258729525562092.741270475
17638830653544.118322566-14714.1183225660
18856956875831.461654983-18875.4616549833
199924261325076.94208147-332650.942081475
20444477779858.277976644-335381.277976644
21857217645937.53126316211279.468736840
22711969931485.942417836-219516.942417836
23702380731491.095699936-29111.0956999361
24358589735148.153259057-376559.153259057
25297978508658.779525436-210680.779525436
26585715722725.741271622-137010.741271622
276579541048224.26486235-390270.264862349
28209458546666.980200679-337208.980200679
29786690494364.53402534292325.46597466
30439798611917.023222751-172119.023222751
31688779390670.508328898298108.491671102
32574339633314.887474848-58975.887474848
33741409482608.559125237258800.440874763
34597793420597.116074491177195.883925509
35644190553649.98824892890540.0117510725
36377934816327.76586146-438393.765861459
37640273571853.82210283668419.1778971644
38697458446102.164418499251355.835581501
39550608425208.023363335125399.976636665
40207393357902.186406669-150509.186406669
41301607428150.836230043-126543.836230043
42345783682099.229424732-336316.229424732
43501749374411.314377322127337.685622678
44379983485887.818043963-105904.818043963
45387475383811.5895670193663.41043298057
46377305396875.182397386-19570.1823973864
47370837490921.788032634-120084.788032634
48430866486159.162705839-55293.1627058391
49469107436121.7705299132985.2294700902
50194493318386.954702425-123893.954702425
51530670366981.430515186163688.569484814
52518365412467.254585155105897.745414845
53491303562093.11847432-70790.1184743202
54527021330370.866634556196650.133365444
55233773442718.07178447-208945.071784470
56405972301760.413409098104211.586590902
57652925422219.43505138230705.564948620
58446211423191.34642606623019.6535739340
59341340300124.84109549841215.1589045022
60387699533747.604420122-146048.604420122
61493408441876.3499767251531.6500232802
62146494278242.962912209-131748.962912209
63414462370583.18738269243878.8126173081
64364304478115.646445283-113811.646445283
65355178310994.48014093144183.5198590693
66357760519361.305898797-161601.305898797
67261216275182.238946422-13966.2389464219
68397144336337.13693198760806.8630680132
69374943335422.02818638339520.9718136167
70424898393991.13243273730906.8675672634
71202055293469.775128877-91414.7751288771
72378525374281.7188470274243.28115297307
73310768325244.145325151-14476.1453251513
74325738290919.48844525934818.5115547412
75394510356277.71054082738232.2894591729
76247060309747.917203707-62687.9172037071
77368078318623.4755315449454.5244684602
78236761271799.154864683-35038.1548646829
79312378270413.00225271741964.9977472834
80339836287430.20385615352405.7961438471
81347385287838.00057615159546.9994238487
82426280401597.51984700624682.4801529937
83352850332805.98178491720044.0182150827
84301881252544.01952503249336.980474968
85377516310948.09382135466567.906178646
86357312408082.281945766-50770.2819457662
87458343368503.24728149489839.7527185056
88354228311614.16696575942613.833034241
89308636337899.418399395-29263.4183993952
90386212321608.88232082364603.1176791773
91393343330519.56091616162823.4390838389
92378509350412.66314219728096.3368578031
93452469300089.312244895152379.687755105
94364839489583.312307411-124744.312307411
95358649275677.84558519382971.154414807
96376641332605.44333063844035.556669362
97429112340843.13959146088268.8604085395
98330546262403.30261737868142.6973826223
99403560391939.84550596111620.1544940395
100317892309062.6567595358829.34324046507
101307528292551.43874858514976.5612514151
102235133295653.055543547-60520.055543547
103299243374479.019553364-75236.0195533644
104314073313162.358806233910.641193767173
105368186299497.77881554868688.221184452
106269661274043.30571997-4382.30571997021
107125390324658.87758035-199268.87758035
108510834357506.95584588153327.044154120
109321896443270.477587006-121374.477587006
110249898306501.031665575-56603.0316655749
111408881282254.551643828126626.448356172
112158492324163.415101298-165671.415101298
113292154409240.248518575-117086.248518575
114289513332787.826292826-43274.826292826
115378049309686.58352594868362.4164740516
116343466282115.38436105661350.6156389435
117332743279341.96404088853401.0359591122
118442882348571.77556558294310.2244344184
119214215321777.461205641-107562.461205641
120315688258663.13538150357024.8646184971
121375195593335.34872734-218140.348727340
122334280285992.97586094848287.0241390524
123355864302987.5280151652876.4719848403
124480382343939.971707571136442.028292429
125353058323649.82621225629408.1737877436
126217193351168.888511331-133975.888511331
127314533269836.58652915144696.4134708490
128318056253692.39161113164363.6083888695
129314353267934.28298674446418.7170132557
130369448277929.51280804491518.4871919556
131312846304127.7190986458718.28090135521
132312075294478.68356634617596.3164336539
133315009269113.16541200545895.8345879954
134318903258417.54584037560485.4541596254
135314887369402.717036294-54515.7170362938
136314913302937.5134493811975.4865506198
137325506263399.84362578062106.1563742204
138298568275341.2221197223226.7778802803
139315834306759.2931771749074.70682282559
140329784268711.48774444061072.5122555598
141312878261054.01909374451823.9809062564
142314987289824.64315108425162.356848916
143325249346774.15421484-21525.1542148399
144315877434131.008326047-118254.008326047
145291650267413.33944285624236.6605571443
146305959284454.05211944821504.9478805524
147297765265010.73406995232754.2659300484
148315245309772.9224846895472.07751531134
149315236335384.342744691-20148.3427446906
150336425283036.89189039953388.1081096014
151306268257602.03570163148665.9642983693
152302187252039.92545926950147.0745407308
153314882305017.1904796289864.80952037205
154382712262688.405984785120023.594015215
155341570320527.51015110321042.4898488966
156312412290577.27901167721834.7209883234
157309596266252.54806798343343.4519320165
158315547262754.33030791552792.6696920853
159313267229919.94624878583347.053751215
160316176425850.876442557-109674.876442557
161359335282345.86866991476989.1313300859
162330068285817.17542758544250.8245724155
163314289304819.4511293959469.54887060532
164297413257210.7388379940202.2611620103
165314806285876.37222488528929.6277751152
166333210272834.30969549660375.6903045037
167352108317850.31331092634257.6866890741
168313332282004.89455701931327.1054429813
169291787280326.22014337211460.7798566277
170318745294277.15859431524467.8414056848
171315366276021.24055618939344.7594438109
172315688338001.161133409-22313.1611334085
173409642427855.860257587-18213.8602575869
174269587274651.137600657-5064.13760065717
175300962240220.88532147560741.1146785245
176325479289079.24994430436399.7500556961
177316155350479.667839372-34324.6678393719
178318574295728.21012720222845.7898727982
179343613266408.88259657277204.1174034282
180306948251319.36473919555628.6352608053
18112185491.8020150435-85370.8020150435
18238618030.7990518980-17644.7990518980
18335110833.3047509609-10482.3047509609
18416447363.9779935308-47199.9779935308
18515261463.6370532534-61311.6370532534
18613583611.6521343447-83476.6521343447
1873736027.71099347305-5654.71099347305
18829634757.820605488-34461.820605488
18932841977.7780931948-41649.7780931948
190169-14407.817621843814576.8176218438
19130241362.2053624042-41060.2053624042
19230745236.1880157954-44929.1880157954
193327-15603.482784516015930.4827845160
1943636043.78842879811-5680.78842879811
19513944162.2311727374-44023.2311727374
19635527284.0476880488-26929.0476880488
197172-21943.044502402522115.0445024025
19816333930.0994058114-33767.0994058114
199321-2141.955754803822462.95575480382
20016860159.8334789413-59991.8334789413
201131-61430.378866004561561.3788660045
20233827871.4349790081-27533.4349790081
20337275351.5498235296-74979.5498235296
204345-46066.192605136346411.1926051363
205352-32558.225473427132910.2254734271
206400-156889.100049469157289.100049469
20733572267.6714499776-71932.6714499776
208365122904.053991757-122539.053991757
209102-18506.956430934918608.9564309349
21011670407.2158611469-70291.2158611469
21167-23973.377744221724040.3777442217
212117-25300.356658181325417.3566581813
21333236760.8976311902-36428.8976311902
21414142533.0829435269-42392.0829435269
21514035233.1438389274-35093.1438389274
216381-13996.891988871814377.8919888718
217331-4305.825189170114636.82518917011
21831778303.4018702478-77986.4018702478
219118-11505.111476457711623.1114764577
22014430388.7257841194-30244.7257841194
22114914363.1338865965-14214.1338865965
22237876924.0196015672-76546.0196015672
22313043829.9329795956-43699.9329795956
22484-52380.346737230352464.3467372303
22535644796.2375548362-44440.2375548362
22610777238.4063423363-77131.4063423363
22732020058.5895379029-19738.5895379029
228360-11902.139668738912262.1396687389
22910169122.8966044975-69021.8966044975
230174133956.119407176-133782.119407176
23115720885.4555551761-20728.4555551761
23233384160.1406948456-83827.1406948456
23399-103062.818085828103161.818085828
23413718097.2319392404-17960.2319392404
23516773138.4133636286-72971.4133636286
23610927719.2729441713-27610.2729441713
237301-90289.023067925790590.0230679257
23837024730.3333680289-24360.3333680289
239293158114.207326523-157821.207326523
240324-16694.772066486117018.7720664861
24131625380.4074506266-25064.4074506266
242175159755.630163681-159580.630163681
24329427406.8367597856-27112.8367597856
24437533343.3620570369-32968.3620570369
245315-96362.281399136796677.2813991367
24615352608.8720346813-52455.8720346813
24737614747.9191834942-14371.9191834942
24834145027.2614815823-44686.2614815823
24935440845.108120502-40491.108120502
25014348944.6961546211-48801.6961546211
25114760981.4571735973-60834.4571735973
25231440040.8985628139-39726.8985628139
25312656724.3194484864-56598.3194484864
254311-29918.255981801930229.2559818019
255133-66825.50700617966958.507006179
256173291169.168457778-290996.168457778
25734033081.8530999787-32741.8530999787
25815849191.7008787906-49033.7008787906
25917049628.119966353-49458.119966353
26013265338.5877288726-65206.5877288726
261110-55414.660529111855524.6605291118
262344-126320.655266590126664.655266590
26337765417.3314803307-65040.3314803307
26413458836.2701012935-58702.2701012935
26514248358.198450258-48216.198450258
266155-31376.763490735431531.7634907354
267390154010.51099111-153620.51099111
26815446913.9441365467-46759.9441365467
269408-67627.153760799168035.1537607991
27012349303.8080116005-49180.8080116005
27133744144.6481904383-43807.6481904383
2723128634.26533807444-8322.26533807444
27334254621.5642682845-54279.5642682845
27413631773.950231458-31637.950231458
27538030154.1757744714-29774.1757744714
276398-3630.165906251144028.16590625114
2778340682.6013238411-40599.6013238411
278388-138388.28140274138776.28140274
27938958582.1142312078-58193.1142312078
28040647564.7347998608-47158.7347998608
28149-282610.17058201282659.17058201
28237411911.4452692813-11537.4452692813
283413253278.381666712-252865.381666712
284353-46575.687651046346928.6876510463
28511328807.41048865-28694.41048865
28637139424.3867685468-39053.3867685468
287391-41153.09023862241544.090238622
288392-13916.945503681714308.9455036817
289124-14784.886034852014908.8860348520
290156254.767155265021-98.7671552650208
29135044744.6771545319-44394.6771545319
29236646145.9409278584-45779.9409278584
293412-91944.212354019292356.2123540192
29433617360.3176527184-17024.3176527184
29538711154.6352946602-10767.6352946602
29639547207.3833430915-46812.3833430915
2974470491.5575347887-70447.5575347887
298424-201494.317887545201918.317887545
299421-117439.358635028117860.358635028
30057311422.808203390-311365.808203390
30138418257.7771904418-17873.7771904418
302393-43166.772498152743559.7724981527
303405-58871.054491767359276.0544917673
304402-84359.857304721684761.8573047216
305407-42033.808451231942440.8084512319
306431244041.803969254-243610.803969254
307430-210882.530859637211312.530859637
30839475202.6792558176-74808.6792558176
309417-67235.758740168167652.7587401681
31042873414.9946864041-72986.9946864041
311347-596798.45786881597145.45786881
312401-52054.717455007652455.7174550076
3137876299.868196325-76221.868196325
314429877424.79600348-876995.79600348


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.999948941593350.0001021168133017885.10584066508938e-05
130.999980143241553.97135169009004e-051.98567584504502e-05
1411.14858458973364e-165.7429229486682e-17
1518.7916540870661e-184.39582704353305e-18
1613.72090879371186e-231.86045439685593e-23
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180100
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30012.81062394019919e-171.40531197009960e-17
3010.9999999999999578.64433578541149e-144.32216789270575e-14
3020.9999999998854752.2905075573731e-101.14525377868655e-10


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2911NOK
5% type I error level2911NOK
10% type I error level2911NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/1065h01291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/1065h01291159883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/1i4kp1291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/1i4kp1291159883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/2aeja1291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/2aeja1291159883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/3aeja1291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/3aeja1291159883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/4aeja1291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/4aeja1291159883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/5aeja1291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/5aeja1291159883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/63niv1291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/63niv1291159883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/7ewig1291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/7ewig1291159883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/8ewig1291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/8ewig1291159883.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/9ewig1291159883.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12911597946tu0ahye86qt5hd/9ewig1291159883.ps (open in new window)


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




Copyright

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Software written by Ed van Stee & Patrick Wessa


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