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ws7inleiding

*The author of this computation has been verified*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Wed, 01 Dec 2010 19:37:04 +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/t1291232176x5zuf87a7189qeh.htm/, Retrieved Wed, 01 Dec 2010 20:36:27 +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/t1291232176x5zuf87a7189qeh.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 807 213118 6282154 1 5626 37 29790 444 81767 4321023 2 13337 138 87550 412 153198 4111912 3 8541 45 84738 428 -26007 223193 415 39 0 54660 315 126942 1491348 6 4276 24 42634 168 157214 1629616 5 9164 34 40949 263 129352 1398893 9 2493 29 45187 267 234817 1926517 4 4891 38 37704 228 60448 983660 14 1734 21 16275 129 47818 1443586 7 11409 76 25830 104 245546 1073089 10 7592 34 12679 122 48020 984885 13 7135 62 18014 393 -1710 1405225 8 5043 67 43556 190 32648 227132 414 110 1 24811 280 95350 929118 15 1444 29 6575 63 151352 1071292 11 5480 133 7123 102 288170 638830 28 4026 62 21950 265 114337 856956 17 1266 30 37597 234 37884 992426 12 3195 21 17821 277 122844 444477 46 655 14 12988 73 82340 857217 16 5523 51 22330 67 79801 711969 20 6095 23 13326 103 165548 702380 21 4925 38 16189 290 116384 358589 89 538 10 7146 83 134028 297978 367 933 14 15824 56 63838 585715 30 6027 24 27664 236 74996 657954 24 1624 17 11920 73 31080 209458 419 52 1 8568 34 32168 786690 18 15856 68 1441 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] = + 243934.341691069 + 21.1772030433850Costs[t] + 1198.56923273516Orders[t] + 1.17254151275796Dividends[t] -514.841012265932Wrank[t] + 2.87190204268727`Profit/Trades`[t] -0.175298344199932`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)243934.34169106957861.1148574.21593.3e-051.6e-05
Costs21.17720304338502.4497738.644600
Orders1198.56923273516415.4683582.88490.0041930.002096
Dividends1.172541512757960.4900812.39250.0173330.008666
Wrank-514.841012265932123.966191-4.15314.3e-052.1e-05
`Profit/Trades`2.871902042687270.8970563.20150.0015110.000755
`Profit/Cost`-0.1752983441999321.20116-0.14590.8840640.442032


Multiple Linear Regression - Regression Statistics
Multiple R0.830158281661101
R-squared0.689162772610513
Adjusted R-squared0.683087777938405
F-TEST (value)113.442531196733
F-TEST (DF numerator)6
F-TEST (DF denominator)307
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation282409.286178234
Sum Squared Residuals24484786510347.8


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
162821544919186.826385921362967.17361408
243210231540091.845908872780931.15409113
341119122794416.510582101317495.48941790
42231932307394.50175907-2084201.50175907
514913481937061.33296726-445713.332967258
616296161556240.7338431373375.2661568668
713988931580534.92611230-181641.926112298
819265171808199.72865872118317.271341283
99836601384319.60237073-400659.602370731
101443586828425.662947052615160.337052948
1110730891220154.68483954-147065.684839544
12984885728758.208332248256126.791667752
1314052251104805.66870679300419.331293212
142271321219509.4418252-992377.4418252
159291181213182.47252148-284064.472521477
161071292646202.273781805425089.726218195
17638830852060.769222621-213230.769222621
188569561155337.94593959-298381.945939587
199924261368013.36125653-375587.361256527
204444771075572.59928502-631095.599285018
21857217710641.595538662146575.404461338
22711969897898.800345651-185929.800345651
23702380847032.077738563-144652.077738563
243585891026544.7108983-667955.710898301
25297978465631.653755451-167653.653755451
26585715722874.50085724-137159.500857240
276579541192883.55345065-534929.553450653
28209458404735.525642851-195277.525642851
29786690540108.106942841246581.893057159
30439798751477.020611994-311679.020611994
31688779486908.190103966201870.809896034
32574339699231.623964318-124892.623964318
33741409559006.567710054182402.432289946
34597793518092.80734799379700.1926520069
35644190703613.937648002-59423.9376480016
36377934976116.57016272-598182.570162719
37640273655171.667240608-14898.6672406085
38697458590998.932908142106459.067091858
39550608614611.984157383-64003.9841573833
40207393364603.344502841-157210.344502841
41301607636071.991616228-334464.991616228
42345783670030.190269515-324247.190269515
43501749541204.265813238-39455.2658132381
44379983602580.413966223-222597.413966223
45387475445506.055693307-58031.0556933073
46377305453637.500261690-76332.5002616904
47370837614723.266935245-243886.266935245
48430866718062.396944947-287196.396944947
49469107548280.541773724-79173.5417737238
50194493212773.095950618-18280.0959506181
51530670537255.84085818-6585.84085817992
52518365640896.914423619-122531.914423619
53491303723121.63103856-231818.63103856
54527021503146.77131801523874.2286819854
55233773451857.73113639-218084.731136390
56405972434864.895059380-28892.8950593804
57652925521171.613153389131753.386846611
58446211504401.480018193-58190.480018193
59341340398130.413514427-56790.4135144272
60387699618732.410018672-231033.410018672
61493408522315.143245543-28907.1432455434
62146494162022.288828179-15528.2888281788
63414462441320.753260853-26858.7532608525
64364304554136.578351978-189832.578351978
65355178392256.015025401-37078.0150254008
66357760886079.277060931-528319.277060931
67261216172220.84842860588995.1515713954
68397144437674.508336975-40530.508336975
69374943481005.475861916-106062.475861916
70424898531809.782834411-106911.782834411
71202055182031.81100280020023.1889972002
72378525418593.096883763-40068.0968837632
73310768280961.56681783229806.4331821682
74325738327852.382103318-2114.38210331833
75394510450440.257858188-55930.2578581881
76247060195056.76305787152003.2369421294
77368078402718.282430205-34640.2824302055
78236761152935.4115873283825.58841268
79312378249720.78758055862657.2124194425
80339836358535.628404704-18699.6284047038
81347385345228.9527263692156.04727363146
82426280509652.410276325-83372.4102763255
83352850422973.481910003-70123.4819100032
84301881157533.051779826144347.948220174
85377516389589.331095112-12073.3310951122
86357312480720.248971499-123408.248971499
87458343533746.005511777-75403.0055117765
88354228376040.529449175-21812.5294491752
89308636297660.12866324610975.8713367543
90386212408991.306537014-22779.3065370141
91393343420236.51653972-26893.5165397195
92378509427990.589243839-49481.5892438391
93452469432880.27366148919588.7263385105
94364839528397.332495552-163558.332495552
95358649344284.11305910314364.8869408973
96376641388404.412857244-11763.4128572437
97429112448816.551420025-19704.5514200248
98330546323151.4589848127394.5410151884
99403560469784.2994947-66224.2994947002
100317892322886.479680404-4994.47968040357
101307528229792.55493423077735.4450657703
102235133186806.94394816948326.056051831
103299243325520.746040733-26277.7460407326
104314073256129.75687191957943.243128081
105368186364104.652560764081.34743923976
106269661183204.93378920686456.0662107942
107125390215814.773611966-90424.7736119657
108510834447273.09807998463560.9019200164
109321896474696.511962555-152800.511962555
110249898190856.64667838059041.3533216196
111408881360912.89040069647968.1095993041
112158492191239.745620522-32747.7456205218
113292154314900.168784549-22746.1687845486
114289513217211.68608578772301.3139142133
115378049368235.893395259813.1066047502
116343466340808.3398327872657.66016721328
117332743317089.80131148315653.1986885174
118442882424601.18810498118280.8118950187
119214215182294.33373167331920.6662683267
120315688278505.07421060637182.9257893937
121375195673915.428314704-298720.428314705
122334280325262.9049726029017.09502739791
123355864477704.213787423-121840.213787423
124480382442312.3337142838069.6662857203
125353058357103.382367507-4045.38236750696
126217193218148.75399793-955.753997930224
127314533217399.57514096497133.4248590355
128318056283202.10990562234853.8900943783
129314353205515.141680576108837.858319424
130369448338648.37499039230799.6250096079
131312846281483.77496432231362.2250356780
132312075228545.12457910883529.8754208917
133315009209064.003951837105944.996048163
134318903281248.63978436537654.3602156346
135314887320544.665789948-5657.66578994795
136314913248906.95014903566006.0498509649
137325506291234.39856250434271.6014374959
138298568176331.852169608122236.147830392
139315834347810.922415968-31976.9224159683
140329784307917.86709304121866.1329069593
141312878191183.523866656121694.476133344
142314987222250.31627251592736.6837274854
143325249436624.434543579-111375.434543579
144315877564263.03151606-248386.031516061
145291650161212.053640827130437.946359173
146305959231810.62960204674148.3703979539
147297765166790.365793747130974.634206253
148315245274629.0524437140615.94755629
149315236332350.377166773-17114.3771667726
150336425328611.3144374917813.68556250852
151306268165979.184146985140288.815853015
152302187178408.210284153123778.789715847
153314882269848.74262996945033.2573700314
154382712328706.26079417554005.7392058251
155341570426602.569810241-85032.5698102409
156312412233313.01991862679098.9800813738
157309596194564.729893464115031.270106536
158315547272644.36611129642902.6338887045
159313267296895.9415397216371.0584602802
160316176568161.78698603-251985.78698603
161359335339932.4335046619402.5664953399
162330068327896.7048856782171.29511432194
163314289237583.49134832676705.5086516743
164297413170748.020338704126664.979661296
165314806231291.05694003383514.943059967
166333210310013.62510194823196.3748980519
167352108361387.264934292-9279.2649342917
168313332228268.49609300785063.5039069927
169291787187781.045483100104005.954516900
170318745337927.219828282-19182.2198282818
171315366230773.30989504684592.690104954
172315688399450.619975637-83762.6199756367
173409642567470.80373162-157828.80373162
174269587176132.40352021893454.5964797824
175300962146979.081450048153982.918549952
176325479326542.97691928-1063.97691928035
177316155404809.774938239-88654.7749382388
178318574333291.384105455-14717.3841054554
179343613315886.8881943327726.1118056702
180306948156040.980122636150907.019877364
181330059279648.48638278650410.5136172143
182288985152644.138961741136340.861038259
183304485196782.81991716107702.18008284
184315688278519.41895153237168.5810484675
185317736265610.07912800852125.9208719915
186322331325535.664110862-3204.66411086236
187296656201881.78924174894774.2107582516
188315354323217.855704655-7863.85570465539
189312161195442.458507242116718.541492758
190315576394767.718446748-79191.7184467476
191314922205328.289414904109593.710585096
192314551192323.19747659122227.80252341
193312339239137.95595586873201.0440441319
194298700178534.834937464120165.165062536
195321376304814.45864709116561.5413529091
196303230174258.719206650128971.280793350
197315487549154.51672784-233667.516727840
198315793294130.47866247621662.5213375238
199312887264060.33271205548826.6672879446
200315637277155.19767125738481.8023287427
201324385402788.237301284-78403.2373012844
202308989191451.884075321117537.115924679
203296702160613.164317516136088.835682484
204307322300375.8780631026946.12193689769
205304376211109.26453152093266.7354684804
206253588254256.568471507-668.568471506616
207309560191139.101022696118420.898977304
208298466158855.921633829139610.078366171
209343929331524.17078811112404.8292118886
210331955296899.72518628735055.2748137125
211381180362396.25325053718783.7467494635
212331420362382.563943891-30962.5639438906
213310201195933.158333897114267.841666103
214320016276340.10184389543675.8981561046
215320398292179.67347367828218.3265263220
216291841191895.58773466699945.4122653338
217310670177969.490956037132700.509043963
218313491188125.611137057125365.388862943
219331323312993.95789926818329.0421007320
220319210316738.5424841472471.45751585324
221318098328311.533499850-10213.5334998505
222292754138105.914408930154648.085591070
223325176283412.5502061141763.4497938902
224365959350808.69793322115150.3020667786
225302409164201.567712486138207.432287514
226340968296545.48826422944422.5117357708
227313164236484.43427582376679.5657241772
228301164280070.79622511621093.2037748843
229344425296695.53636363247729.4636363676
230315394265761.23983418149632.7601658191
231316647291036.33165703525610.6683429646
232309836179407.366646510130428.633353489
233346611346286.524688511324.475311488743
234322031309756.6418750812274.3581249200
235315656315243.635420554412.364579446104
236339445301223.26120375738221.7387962426
237314964489517.034265082-174553.034265082
238297141173292.378747255123848.621252745
239315372210756.901495745104615.098504255
240312502310633.2612914801868.73870852032
241313729223984.78366700789744.2163329927
242315388371313.202935228-55925.2029352283
243315371241413.65666675673957.3433332442
244296139167672.068986837128466.931013163
245313880481205.2006876-167325.200687600
246317698275673.82370280542024.1762971946
247295580163253.304249869132326.695750131
248308256174748.357472917133507.642527083
249303677186588.334502015117088.665497985
250319369307425.47754118911943.5224588109
251318690271445.49234907147244.5076509286
252314049196023.479246494118025.520753506
253325699303404.35616368022294.6438363195
254314210324501.555959397-10291.5559593973
255322378303412.79279062218965.2072093776
256315398283540.35815694531857.6418430546
257308336224965.37481030883370.6251896919
258316386277387.17199377438998.8280062256
259315553256325.69867326559227.3013267348
260323361272757.14269372450603.8573062759
261336639333862.8236761572776.17632384297
262307424458516.586013234-151092.586013234
263295370142731.823921275152638.176078725
264322340298959.97134053823380.0286594620
265319864325433.06324219-5569.06324219004
266317291288611.21705781728679.7829421834
267280398123513.547914450156884.452085550
268317330274996.61316318542333.3868368154
269238125203585.07271343234539.9272865683
270327071317587.9669384449483.03306155585
271309038205028.617477988104009.382522012
272314210268297.98556846445912.0144315358
273307930177588.831109093130341.168890907
274322327302917.07002170819409.9299782916
275292136167616.698705383124519.301294617
276263276159196.040299434104079.959700566
277367655363038.3115474684616.68845253225
278283910245621.82227282438288.1777271755
279283587152133.627242857131453.372757143
280243650235105.4164940538544.58350594718
281438493683558.2680292-245065.2680292
282296261176552.714512496119708.285487504
283230621150728.72822370479892.271776296
284304252230686.67346158173565.326538419
285333505329663.3556790303841.64432096964
286296919177226.965546899119692.034453101
287278990190500.89301900488489.1069809965
288276898169927.851892781106970.148107219
289327007329241.147464597-2234.14746459709
290317046290668.14135626526377.8586437353
291304555195443.790638806109111.209361194
292298096169520.245840985128575.754159015
293231861163336.54337420368524.4566257965
294309422253914.83831062755507.1616893725
295286963309754.337245857-22791.3372458574
296269753162183.920371333107569.079628667
297448243439534.5643685998708.43563140073
298165404165990.040096695-586.040096695229
299204325140330.07225420963994.9277457913
300407159361444.23919676545714.7608032352
301290476182544.890015671107931.109984329
302275311186718.99886858588592.0011314155
303246541172456.78609494074084.2139050604
304253468218195.69041792935272.3095820705
305240897270289.37921763-29392.3792176299
306-83265318998.548503369-402263.548503369
307-42143223449.973105017-265592.973105017
308272713186393.61427045486319.3857295464
309215362314780.719569092-99418.7195690916
31042754178487.401038712-135733.401038712
3113062751297936.43986259-991661.43986259
312253537368198.688444269-114661.688444269
313372631725817.556902627-353186.556902627
314-7170517192.406755547-524362.406755547


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9999987374329992.52513400224188e-061.26256700112094e-06
110.9999979762427264.04751454894857e-062.02375727447428e-06
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27312.71600636382427e-161.35800318191214e-16
27417.0754947838814e-163.5377473919407e-16
2750.9999999999999983.17092402699063e-151.58546201349531e-15
2760.9999999999999931.40175224258583e-147.00876121292915e-15
2770.9999999999999872.66650213218903e-141.33325106609451e-14
2780.9999999999999411.17002415669482e-135.8501207834741e-14
2790.9999999999997644.7292207189558e-132.3646103594779e-13
2800.9999999999993741.25128728856853e-126.25643644284265e-13
2810.999999999999735.41868299356559e-132.70934149678279e-13
2820.9999999999987572.48564449965343e-121.24282224982672e-12
2830.9999999999981753.65071856251133e-121.82535928125566e-12
2840.9999999999916651.66690550337841e-118.33452751689204e-12
2850.999999999988872.22595570282524e-111.11297785141262e-11
2860.9999999999478741.04251070144507e-105.21255350722534e-11
2870.9999999997999094.00182321763381e-102.00091160881690e-10
2880.9999999990889651.82207105871992e-099.1103552935996e-10
2890.9999999985975052.80498913309138e-091.40249456654569e-09
2900.9999999993381431.32371385691773e-096.61856928458863e-10
2910.9999999966413896.71722288405603e-093.35861144202801e-09
2920.9999999962393827.52123511453412e-093.76061755726706e-09
2930.9999999813767313.72465372940552e-081.86232686470276e-08
2940.9999999319697841.36060432253036e-076.8030216126518e-08
2950.9999999671001286.57997448257271e-083.28998724128635e-08
2960.999999808163283.83673441393933e-071.91836720696966e-07
2970.9999995112481659.77503670031514e-074.88751835015757e-07
2980.999997076788065.84642388033105e-062.92321194016553e-06
2990.999985073434252.98531315009233e-051.49265657504616e-05
3000.999920292856190.0001594142876209887.9707143810494e-05
3010.9996356878867840.000728624226431410.000364312113215705
3020.9985047228081080.002990554383784810.00149527719189240
3030.9977155615501230.004568876899753170.00228443844987658
3040.9993467216184180.001306556763164240.000653278381582121


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level2951NOK
5% type I error level2951NOK
10% type I error level2951NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/10icpc1291232200.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/10icpc1291232200.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/1bbai1291232200.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/1bbai1291232200.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/2bbai1291232200.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/2bbai1291232200.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/3mkal1291232200.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/3mkal1291232200.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/4mkal1291232200.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/4mkal1291232200.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/5mkal1291232200.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/5mkal1291232200.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/6xcr61291232200.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/01/t1291232176x5zuf87a7189qeh/9plq91291232200.ps (open in new window)


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




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