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mini-tutorial

*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 13:54:25 +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/t12912127839qcwd6cwjo0liah.htm/, Retrieved Wed, 01 Dec 2010 15:13:17 +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/t12912127839qcwd6cwjo0liah.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:
workshop 4
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
162556 807 213118 6282154 29790 444 81767 4321023 87550 412 153198 4111912 84738 428 -26007 223193 54660 315 126942 1491348 42634 168 157214 1629616 40949 263 129352 1398893 45187 267 234817 1926517 37704 228 60448 983660 16275 129 47818 1443586 25830 104 245546 1073089 12679 122 48020 984885 18014 393 -1710 1405225 43556 190 32648 227132 24811 280 95350 929118 6575 63 151352 1071292 7123 102 288170 638830 21950 265 114337 856956 37597 234 37884 992426 17821 277 122844 444477 12988 73 82340 857217 22330 67 79801 711969 13326 103 165548 702380 16189 290 116384 358589 7146 83 134028 297978 15824 56 63838 585715 27664 236 74996 657954 11920 73 31080 209458 8568 34 32168 786690 14416 139 49857 439798 3369 26 87161 688779 11819 70 106113 574339 6984 40 80570 741409 4519 42 102129 597793 2220 12 301670 644190 18562 211 102313 377934 10327 74 88577 640273 5336 80 112477 697458 2365 83 191778 550608 4069 131 79804 207393 8636 203 128294 301607 13718 56 96448 345783 4525 89 93 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 time31 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
wealth[t] = + 152095.676365723 + 21.940040076093Costs[t] + 1042.23193118863orders[t] + 1.61363572897475dividends[t] + 40.6339512691246t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)152095.67636572347680.5530623.18990.0015280.000764
Costs21.9400400760932.16750210.122300
orders1042.23193118863362.2543882.87710.0042160.002108
dividends1.613635728974750.431293.74140.0002080.000104
t40.6339512691246115.0533240.35320.7241320.362066


Multiple Linear Regression - Regression Statistics
Multiple R0.809773238876724
R-squared0.6557326984009
Adjusted R-squared0.652500141578373
F-TEST (value)202.852644021997
F-TEST (DF numerator)4
F-TEST (DF denominator)426
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation253782.350973787
Sum Squared Residuals27436735189623.4


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
162821544903597.452683221378556.54731678
243210231400463.868233852920559.13176615
341119122749673.408938641362238.59106136
42231932415522.77028409-2192329.77028409
514913481884682.64171323-393334.641713233
616296161516512.2406122113103.759387799
713988931533636.82181748-134743.821817484
819265171801010.36549230125506.634507696
99836601314858.58581223-331198.585812226
101443586721184.920528277722401.079471723
1110730891223867.80454560-150778.804545603
12984885635400.135216118349484.864783882
131405225954689.631523555450535.368476445
142271321358990.9834432-1131858.98344320
159291181142744.62745326-213626.627453262
161071292606889.189603006464402.810396994
17638830880374.42399919-241544.423999190
188569561095100.69726758-238144.69726758
199924261282760.65603532-290334.656035323
204444771030825.52201658-586348.522016583
21857217646855.92675122210361.073248780
22711969841510.002390349-129541.002390349
23702380819887.287871663-117507.287871663
24358589998306.840713752-639717.840713752
25297978612672.671302893-314694.671302893
26585715661707.619075667-75992.6190756675
276579541127125.02260573-469171.022605732
28209458540993.434141594-331535.434141594
29786690428599.644114567358090.355885433
30439798694923.587615469-255125.587615469
31688779395015.457855495293763.542144505
32574339656889.259757577-82550.2597575769
33741409478365.744580078263043.255419922
34597793461197.016287121136595.983712879
35644190701517.027163131-57327.0271631313
36377934945816.372323243-567882.372323243
37640273600230.10130184740042.8986981534
38697458535587.280742964161870.719257036
39550608601533.678365149-50925.6783651492
40207393508302.026186924-300909.026186924
41301607761828.718709274-460221.718709274
42345783668772.699017587-322989.699017587
43501749497257.0408612554491.95913874541
44379983585940.586317957-205957.586317957
45387475407707.188341514-20232.1883415137
46377305425212.652368322-47907.6523683216
47370837652163.620894532-281326.620894532
48430866694709.26423023-263843.264230230
49469107518798.796476381-49691.7964763814
50194493370256.545242568-175763.545242568
51530670491723.74881567538946.2511843249
52518365604455.287990064-86090.2879900642
53491303719951.186101263-228648.186101263
54527021480769.88859319846251.111406802
55233773628049.954758744-394276.954758744
56405972385921.41658086920050.5834191305
57652925324143.032334271328781.967665729
58446211462825.427699558-16614.4276995576
59341340396378.86451897-55038.8645189695
60387699588820.603173866-201121.603173866
61493408473741.24266511919666.7573348806
62146494318834.973364847-172340.973364847
63414462401971.11315499412490.8868450056
64364304533840.675924554-169536.675924554
65355178364938.954502856-9760.95450285557
66357760853714.002633341-495954.002633341
67261216320126.109515145-58910.1095151447
68397144407806.033111373-10662.0331113726
69374943473798.42928867-98855.4292886705
70424898493877.409761276-68979.4097612755
71202055316958.138038660-114903.138038660
72378525369931.9246152358593.07538476472
73310768395409.724640325-84641.7246403251
74325738304574.56711425521163.4328857455
75394510425741.316617850-31231.3166178495
76247060340007.91221046-92947.9122104603
77368078392846.282517894-24768.2825178944
78236761303934.356749491-67173.3567494914
79312378349676.276148424-37298.2761484241
80339836373705.104001906-33869.1040019056
81347385314291.7417523433093.2582476597
82426280472267.137152351-45987.1371523506
83352850433329.994048289-80479.9940482894
84301881252941.91562272648939.0843772744
85377516348220.9520494729295.0479505302
86357312455003.021202426-97691.021202426
87458343485409.63737085-27066.6373708501
88354228348821.6712081625406.32879183801
89308636420695.997753609-112059.997753609
90386212349545.16615062436666.833849376
91393343391400.2431855321942.75681446824
92378509395999.69815362-17490.6981536196
93452469378793.25235870973675.7476412909
94364839534260.785970788-169421.785970788
95358649302771.51848237955877.4815176209
96376641351507.02781922525133.9721807752
97429112353717.71711572375394.282884277
98330546323281.5327462077264.46725379284
99403560442173.660757823-38613.6607578234
100317892314034.256009833857.74399016984
101307528338995.55572189-31467.5557218901
102235133343523.182248948-108390.182248948
103299243462499.494701397-163256.494701397
104314073360865.739994945-46792.739994945
105368186333385.96292820634800.0370717941
106269661318468.257502649-48807.2575026489
107125390373048.689071752-247658.689071752
108510834375986.037466754134847.962533246
109321896494365.15374589-172469.153745890
110249898337519.388027735-87621.3880277352
111408881309699.82274478599181.1772552148
112158492343938.265355449-185446.265355449
113292154454071.649719611-161917.649719611
114289513342308.554636961-52795.5546369613
115378049286329.15150241691719.8484975843
116343466317214.17676684626251.8232331541
117332743311150.50082987321592.4991701273
118442882366684.16440593176197.835594069
119214215341982.395616666-127767.395616666
120315688296076.98664512219611.0133548785
121375195662999.949731102-287804.949731102
122334280296870.9305027137409.0694972898
123355864458826.464917381-102962.464917381
124480382393178.70190496487203.2980950364
125353058334498.10925308018559.8907469195
126217193375822.605253235-158629.605253235
127315380255236.1496402560143.8503597503
128314533267879.26207877346653.7379212267
129318056284053.47336704434002.5266329563
130315380255358.05149405760021.9485059429
131314353263295.04311062651057.9568893737
132369448307475.58970448661972.4102955142
133315380255479.95334786459900.0466521355
134312846277578.12481291735267.8751870825
135312075273881.47482423738193.5251757628
136315009266356.90390874248652.0960912578
137318903268950.98111259849952.0188874024
138314887256835.05523577958051.9447642208
139314913279572.90072848935340.0992715109
140315380255764.39100674859615.6089932516
141325506269885.96618600155620.0338139994
142315380255845.65890928759534.3410907134
143298568282928.63041584615639.3695841537
144315834261166.46859616054667.5314038396
145329784285930.86559021443853.1344097860
146312878265414.68895281547463.3110471854
147315380256048.82866563259331.1713343678
148314987263768.11460473951218.8853952611
149325249264160.31129116961088.688708831
150315877256955.52725628158921.4727437186
151291650271575.5828696220074.4171303800
152305959342485.732755465-36526.7327554649
153315380261503.7920291953876.2079708097
154297765270296.86134548527468.1386545148
155315245259248.93752860155996.0624713991
156315380256414.53422705458965.4657729456
157315380256455.16817832358924.8318216765
158315236261972.88943073853263.1105692625
159336425270835.01643374465589.9835662555
160315380256577.07003213158802.9299678692
161315380256617.703983458762.2960166
162315380256658.33793466958721.6620653309
163315380256698.97188593858681.0281140618
164306268274809.76864246831458.231357532
165302187295854.8327188756332.16728112503
166314882258894.28755451155987.7124454893
167315380256861.50769101558518.4923089853
168382712268016.368099063114695.631900937
169341570416396.418363141-74826.4183631407
170315380256983.40954482258396.5904551779
171315380257024.04349609158355.9565039088
172312412262343.12323892050068.8767610797
173315380257105.31139862958274.6886013705
174309596272945.26918393436650.7308160655
175315380257186.57930116858193.4206988323
176315547268702.26360492746844.7363950731
177313267383816.071458461-70549.071458461
178316176258906.56489360257269.4351063979
179315380257349.11510624458030.8848937557
180315380257389.74905751357990.2509424867
181359335316182.63808719143152.3619128094
182330068298208.24527966531859.7547203354
183314289329609.890945914-15320.8909459141
184297413300518.210195626-3105.21019562613
185314806266269.72095422148536.279045779
186333210282531.07707160950678.9229283906
187352108338809.04249118213298.9575088185
188313332269368.89102411243963.1089758883
189291787276033.58287017415753.4171298264
190315380257796.08857020557583.9114297954
191318745269197.22147137149547.7785286293
192315380257877.35647274357502.6435272571
193315366269775.56810642945590.4318935713
194315380257958.62437528157421.3756247189
195315688262014.59331617353673.4066838275
196315380258039.89227781957340.1077221807
197409642545302.147809413-135660.147809413
198315380258121.16018035857258.8398196424
199315380258161.79413162757218.2058683733
200269587319544.044153832-49957.0441538319
201315380258243.06203416557136.937965835
202315380258283.69598543457096.3040145659
203315380258324.32993670357055.6700632968
204300962259324.7633091741637.23669083
205325479324830.305783002648.694216997705
206316155265387.15488953550767.8451104649
207318574269970.09443049748603.9055695029
208315380258527.49969304956852.5003069511
209343613290906.92075963152706.0792403689
210306948267549.57400931839398.4259906823
211315380258649.40154685656730.5984531438
212315380258690.03549812556689.9645018747
213330059271083.21334754458975.7866524556
214288985281621.5418199397363.4581800607
215304485278804.98629356225680.0137064385
216315380260937.03516557954442.9648344209
217315688265654.81100866050033.1889913395
218317736274820.24959032542915.7504096746
219315380261058.93701938754321.0629806135
220322331321884.423361809446.576638191487
221296656268162.30489194428493.6951080565
222315380259096.37501081756283.6249891834
223315354260209.56102746555144.4389725347
224312161290290.22029273921870.7797072606
225315576260836.46863429754739.5313657027
226314922289391.07128181625530.9287181840
227314551270017.97459250244533.0254074982
228315380259340.17871843156039.8212815687
229312339273029.48860789639309.5113921037
230315380259421.44662097055958.5533790304
231298700278868.26294875519831.7370512452
232321376285074.55148036236301.4485196378
233315380259543.34847477755836.651525223
234303230282765.70439369020464.2956063097
235315380259624.61637731555755.3836226848
236315487259746.24403050355740.755969497
237315380259705.88427985355674.1157201465
238315793266564.07308683849228.9269131621
239315380259787.15218239255592.8478176083
240315380259827.78613366155552.2138663392
241315380259868.4200849355511.57991507
242312887266130.84046346246756.1595365384
243315380259949.68798746855430.3120125318
244315637266357.00699134949279.993008651
245324385332578.676528369-8193.67652836941
246315380260071.58984127655308.4101587244
247315380260112.22379254555267.7762074553
248308989280416.11763473628572.8823652637
249315380260193.49169508355186.508304917
250315380260234.12564635255145.8743536479
251296702261894.72733649734807.2726635032
252315380260315.39354889055064.6064511097
253307322270750.31206869136571.6879313086
254304376333474.921965839-29098.9219658392
255253588406440.278443786-152852.278443786
256315380260477.92935396754902.0706460332
257309560266301.8506503843258.1493496202
258298466274641.93304250223824.0669574977
259315380260599.83120777454780.1687922258
260315380260640.46515904354739.5348409567
261315380260681.09911031254698.9008896875
262315380260721.73306158254658.2669384184
263343929298819.02803202945109.9719679714
264331955274953.09558055857001.9044194419
265315380260843.63491538954536.365084611
266315380260884.26886665854495.7311333419
267315380260924.90281792754455.0971820728
268381180301797.27275438679382.727245614
269315380261006.17072046554373.8292795345
270331420305851.80463962325568.1953603768
271315380261087.43862300454292.5613769963
272315380261128.07257427354251.9274257272
273315380261168.70652554254211.293474458
274310201283880.11576576726320.8842342329
275315380261249.9744280854130.0255719198
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277320398298227.60161705622170.3983829439
278315380261371.87628188854008.1237181124
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281315380261493.77813569553886.221864305
282315380261534.41208696453845.5879130359
283313491287691.28512656025799.7148734396
284315380261615.67998950253764.3200104977
285331323294625.63280789236697.3671921081
286315380261696.94789204153683.0521079594
287319210280134.70558498539075.2944150153
288318098267701.18013131950396.8198686809
289315380261818.84974584853561.150254152
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291315380261900.11764838653479.8823516138
292325176281490.16276763043685.8372323702
293365959324648.86744770741310.1325522925
294315380262022.01950219453357.9804978064
295302409271688.84111440730720.1588855931
296340968277677.99368173763290.0063182635
297315380262143.92135600153236.078643999
298315380262184.5553072753195.4446927299
299315380262225.18925853953154.8107414608
300315380271645.91059050643734.0894094938
301313164267259.44967497545904.5503250250
302301164266637.61770868134526.3822913190
303315380263429.95699480451950.0430051956
304315380262428.35901488552951.6409851152
305344425286845.93034377057579.0696562305
306315394275084.49470777440309.5052922262
307315380262550.26086869252829.7391313078
308316647292297.31325115024349.6867488502
309309836279032.92809483730803.071905163
310315380262672.16272250052707.8372775004
311315380262712.79667376952667.2033262313
312346611346829.132068267-218.132068266626
313315380262794.06457630752585.935423693
314322031283966.39174198338064.6082580169
315315656271345.16711347144310.8328865293
316339445278506.77977668660938.2202233141
317314964262477.50031020952486.4996897911
318297141292637.1028752794503.8971247205
319315372268269.35434421247102.6456557879
320315380263078.50223519152301.4977648092
321315380263119.1361864652260.86381354
322315380263159.77013772952220.2298622709
323315380263200.40408899852179.5959110018
324315380263241.03804026752138.9619597327
325315380263281.67199153652098.3280084635
326312502264850.44602712647651.5539728737
327315380263362.93989407552017.0601059253
328315380263403.57384534451976.4261546562
329315380263444.20779661351935.792203387
330315380263484.84174788251895.1582521179
331315380263525.47569915151854.5243008488
332315380263566.1096504251813.8903495797
333315380263606.74360168951773.2563983106
334313729269372.29934143844356.7006585622
335315388264763.47892559550624.5210744047
336315371274786.74576414540584.2542358554
337296139303462.107179201-7323.10717920116
338315380263809.91335803551570.0866419649
339313880265747.22017077148132.7798292291
340317698275464.96075320242233.0392467976
341295580286737.4373237038842.56267629741
342315380263972.44916311251407.5508368884
343315380264013.08311438151366.9168856193
344315380264053.7170656551326.2829343502
345308256291268.13100962516987.8689903749
346315380264134.98496818851245.0150318119
347303677267328.13504426136348.8649557392
348315380264216.25287072651163.7471292737
349315380264256.88682199551123.1131780045
350319369309714.2040065199654.79599348093
351318690270886.00800495647803.9919950437
352314049273642.72343319540406.2765668052
353325699285905.20526852839793.7947314716
354314210270634.53397527343575.4660247268
355315380264500.6905296150879.3094703898
356315380264541.32448087950838.6755191207
357322378320634.8792643731743.12073562713
358315380264622.59238341850757.4076165824
359315380264663.22633468750716.7736653133
360315380264703.86028595650676.1397140442
361315398268938.58927351446459.4107264864
362315380264785.12818849450594.8718115059
363315380264825.76213976350554.2378602368
364308336334149.180259964-25813.1802599639
365316386276485.52987075939900.4701292415
366315380264947.66399357150432.3360064294
367315380264988.29794484050391.7020551603
368315380265028.93189610950351.0681038912
369315380265069.56584737850310.4341526221
370315553277188.75205355638364.2479464441
371315380265150.83374991650229.1662500838
372323361280801.02522408842559.9747759117
373336639303203.64961624133435.350383759
374307424278832.90485932628591.0951406741
375315380265313.36955499350066.6304450073
376315380265354.00350626250025.9964937382
377295370272184.80006881023185.1999311897
378322340285590.2267062836749.7732937198
379319864324429.593320273-4565.59332027291
380315380265516.53931133849863.4606886617
381315380265557.17326260749822.8267373925
382317291316500.354238057790.645761943277
383280398261706.25875257618691.7412474241
384315380265679.07511641549700.9248835852
385317330283132.81411125334197.1858887471
386238125362024.824276876-123899.824276876
387327071270092.97777677256978.0222232279
388309038288957.0244680120080.97553199
389314210270069.03284707544140.9671529247
390307930283357.25673705824572.7432629420
391322327288307.16309805434019.8369019463
392292136279879.53450602212256.4654939782
393263276288400.47727234-25124.4772723401
394367655299409.28027972168245.7197202793
395283910387603.031415211-103693.031415211
396283587283181.569213519405.430786480996
397243650388422.278767506-144772.278767506
398438493644528.040501704-206035.040501703
399296261290273.4416502965987.55834970434
400230621291613.255198083-60992.2551980831
401304252308419.1289947-4167.12899469993
402333505297437.67419651736067.3258034825
403296919283641.01359882213277.9864011785
404278990335786.415829657-56796.4158296572
405276898292544.922174294-15646.9221742941
406327007320065.1034181466941.89658185405
407317046303458.30841151413587.6915884859
408304555297971.3648015296583.63519847128
409298096281812.0230826316283.9769173698
410231861300732.989588527-68871.989588527
411309422369524.755567599-60102.7555675989
412286963455567.420204395-168604.420204395
413269753302542.783841107-32789.7838411073
414448243396284.90253181551958.0974681847
415165404350703.143926758-185299.143926758
416204325310842.092351840-106517.092351840
417407159321328.79839640885830.2016035923
418290476315332.553485549-24856.5534855488
419275311333671.993452079-58360.9934520789
420246541291077.401574258-44536.4015742577
421253468305053.427188864-51585.427188864
422240897426049.726374734-185152.726374734
423-83265469610.059392657-552875.059392657
424-42143423439.094612848-465582.094612848
425272713328912.732721602-56199.7327216024
426215362475135.334711608-259773.334711608
42742754361773.522758312-319019.522758312
4283062751459172.98657135-1152897.98657135
429253537534168.490853007-280631.490853007
430372631718438.622944825-345807.622944825
431-7170682071.694139307-689241.694139307


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9999999999989752.04954532303717e-121.02477266151859e-12
913.33007206776349e-431.66503603388174e-43
1011.62610872065578e-778.1305436032789e-78
1111.0163285477126e-795.081642738563e-80
1212.68489029529006e-881.34244514764503e-88
1314.98615031955043e-1032.49307515977521e-103
1413.95475446787406e-1071.97737723393703e-107
1514.64246533473329e-1102.32123266736665e-110
1617.04413738287268e-1223.52206869143634e-122
1712.80450178648334e-1261.40225089324167e-126
1812.38286335966666e-1281.19143167983333e-128
1911.03220956514483e-1395.16104782572414e-140
2013.55765573900076e-1411.77882786950038e-141
2111.78446804359413e-1578.92234021797064e-158
2217.16068622422079e-1653.58034311211039e-165
2317.54828725159591e-1663.77414362579795e-166
2413.90777046071258e-1671.95388523035629e-167
2512.41553023741517e-1671.20776511870759e-167
2611.09951776640461e-1725.49758883202303e-173
2718.34024495155431e-1754.17012247577716e-175
2815.04633755889458e-1762.52316877944729e-176
2911.55277160122261e-1917.76385800611306e-192
3011.76017279339351e-1918.80086396696757e-192
3113.67325956179326e-1981.83662978089663e-198
3211.42219872799042e-1997.11099363995212e-200
3313.88184524046562e-2091.94092262023281e-209
3411.28646778101541e-2116.43233890507704e-212
3511.32723410146626e-2106.63617050733128e-211
3612.13541943081030e-2101.06770971540515e-210
3715.49467288678899e-2162.74733644339450e-216
3813.96128530335989e-2221.98064265167995e-222
3913.52674037363103e-2211.76337018681552e-221
4018.0439135062224e-2224.0219567531112e-222
4112.13778949469633e-2221.06889474734816e-222
4211.69958874438891e-2218.49794372194453e-222
4314.93241149059084e-2222.46620574529542e-222
4416.3450276434725e-2213.17251382173625e-221
4518.0414612939513e-2214.02073064697565e-221
4613.97538752839442e-2201.98769376419721e-220
4712.55545968394941e-2201.27772984197470e-220
4812.88934859606322e-2191.44467429803161e-219
4917.72580724434181e-2193.86290362217090e-219
5019.97830039997907e-2204.98915019998953e-220
5112.65538493172784e-2201.32769246586392e-220
5218.67115172226138e-2204.33557586113069e-220
5318.76167392689486e-2194.38083696344743e-219
5411.57545956893908e-2187.8772978446954e-219
5512.32874854724334e-2211.16437427362167e-221
5613.40286978877561e-2211.70143489438781e-221
5712.51323795851489e-2341.25661897925745e-234
5811.17126164714650e-2345.85630823573251e-235
5917.68929835582118e-2343.84464917791059e-234
6013.1112896022554e-2331.5556448011277e-233
6118.4056659294406e-2354.2028329647203e-235
6216.66698282958071e-2373.33349141479036e-237
6311.97057799575281e-2369.85288997876406e-237
6411.50141276035569e-2357.50706380177846e-236
6519.26016599280228e-2354.63008299640114e-235
6611.33260330998591e-2346.66301654992954e-235
6711.82953276146871e-2349.14766380734355e-235
6811.56428150688785e-2337.82140753443927e-234
6918.40745500584055e-2334.20372750292028e-233
7015.31201304047547e-2322.65600652023774e-232
7111.92245610388123e-2319.61228051940616e-232
7213.78817984435462e-2321.89408992217731e-232
7319.84316991321438e-2324.92158495660719e-232
7416.80096448649193e-2313.40048224324597e-231
7517.71233724967081e-2303.85616862483541e-230
7611.03933953382399e-2295.19669766911995e-230
7718.2762359538457e-2294.13811797692285e-229
7815.60977578771823e-2292.80488789385912e-229
7914.92862006306495e-2282.46431003153248e-228
8011.49134125434914e-2277.4567062717457e-228
8111.11669703943778e-2265.58348519718891e-227
8212.86799460791516e-2261.43399730395758e-226
8312.45852720365423e-2261.22926360182711e-226
8411.75735522559376e-2258.7867761279688e-226
8511.67946654512399e-2248.39733272561994e-225
8617.68745205028269e-2243.84372602514134e-224
8719.52975689337625e-2244.76487844668813e-224
8816.5010916032038e-2233.2505458016019e-223
8911.01011888399369e-2225.05059441996843e-223
9011.00394825247018e-2215.01974126235092e-222
9111.05417285569859e-2205.27086427849294e-221
9218.91635883106376e-2204.45817941553188e-220
9317.1956447285995e-2213.59782236429975e-221
9416.54184207815547e-2203.27092103907773e-220
9515.1702008402178e-2202.5851004201089e-220
9618.0638270266745e-2204.03191351333725e-220
9718.16530425862689e-2194.08265212931344e-219
9819.00126801840228e-2184.50063400920114e-218
9917.59737203605953e-2173.79868601802976e-217
10017.75358049922793e-2163.87679024961396e-216
10116.43049264406655e-2153.21524632203327e-215
10212.75222482607435e-2161.37611241303718e-216
10311.48649002855638e-2167.43245014278192e-217
10411.51738916486372e-2177.58694582431859e-218
10519.89277796179455e-2174.94638898089728e-217
10612.34252224784453e-2161.17126112392227e-216
10712.52260009440277e-2191.26130004720138e-219
10812.34196592932018e-2201.17098296466009e-220
10911.17856584657906e-2195.89282923289532e-220
11011.14442447791590e-2195.72212238957948e-220
11111.73682017250469e-2198.68410086252346e-220
11212.92530252305034e-2201.46265126152517e-220
11311.35034075036329e-2196.75170375181645e-220
11417.57331076943235e-2193.78665538471617e-219
11511.22876687968555e-2186.14383439842776e-219
11618.56157380055356e-2184.28078690027678e-218
11714.89936858762253e-2172.44968429381127e-217
11818.3079131749057e-2174.15395658745285e-217
11912.08118824045434e-2181.04059412022717e-218
12012.42484835662433e-2171.21242417831217e-217
12111.26917091986162e-2166.34585459930808e-217
12211.42991475203049e-2157.14957376015246e-216
12317.83493953399422e-2153.91746976699711e-215
12412.18905687808090e-2141.09452843904045e-214
12511.18541816336476e-2135.92709081682379e-214
12618.83418853593491e-2154.41709426796746e-215
12719.10118148916415e-2144.55059074458208e-214
12819.68287160497675e-2134.84143580248837e-213
12911.11480826648111e-2115.57404133240554e-212
13011.16153200675753e-2105.80766003378767e-211
13111.22945450115137e-2096.14727250575684e-210
13212.55845117788437e-2091.27922558894218e-209
13312.71840943636109e-2081.35920471818054e-208
13412.91588706963683e-2071.45794353481841e-207
13513.22285108302368e-2061.61142554151184e-206
13613.53402968097012e-2051.76701484048506e-205
13713.90428631850415e-2041.95214315925207e-204
13814.14510295881113e-2032.07255147940556e-203
13914.64259569940453e-2022.32129784970227e-202
14014.92579477110756e-2012.46289738555378e-201
14115.32132364731245e-2002.66066182365622e-200
14215.63916183370866e-1992.81958091685433e-199
14316.11803273001552e-1983.05901636500776e-198
14416.49467790220162e-1973.24733895110081e-197
14517.1652123452109e-1963.58260617260545e-196
14617.82595700518488e-1953.91297850259244e-195
14718.19540954774743e-1944.09770477387371e-194
14818.55875361389558e-1934.27937680694779e-193
14919.12837546461739e-1924.56418773230870e-192
15019.4806669485475e-1914.74033347427375e-191
15118.28932372357782e-1904.14466186178891e-190
15218.2390613481128e-1894.1195306740564e-189
15318.45378034876857e-1884.22689017438429e-188
15417.91668758390527e-1873.95834379195264e-187
15518.02485856855268e-1864.01242928427634e-186
15618.12097206105235e-1854.06048603052618e-185
15718.18625382400325e-1844.09312691200163e-184
15818.16793181898653e-1834.08396590949326e-183
15917.82031448514638e-1823.91015724257319e-182
16017.80429849042796e-1813.90214924521398e-181
16117.75482491043315e-1803.87741245521658e-180
16217.67186015296932e-1793.83593007648466e-179
16317.55588036354033e-1783.77794018177016e-178
16417.04421904799721e-1773.52210952399861e-177
16515.40100416936186e-1762.70050208468093e-176
16615.23380792216015e-1752.61690396108007e-175
16715.05236808896394e-1742.52618404448197e-174
16811.88506024903846e-1749.4253012451923e-175
16915.77660352622803e-1742.88830176311402e-174
17015.67339553574341e-1732.83669776787171e-173
17115.54593889323363e-1722.77296944661681e-172
17215.22781625209959e-1712.61390812604979e-171
17315.06067122864858e-1702.53033561432429e-170
17414.58932690608612e-1692.29466345304306e-169
17514.39848742850909e-1682.19924371425454e-168
17614.19033133655804e-1672.09516566827902e-167
17712.76418892117359e-1661.38209446058680e-166
17812.63038796457657e-1651.31519398228829e-165
17912.4763540155989e-1641.23817700779945e-164
18012.32017375874933e-1631.16008687937466e-163
18111.95735387086882e-1629.78676935434409e-163
18211.71025962680736e-1618.5512981340368e-162
18311.29652181451325e-1606.48260907256624e-161
18415.27536349618986e-1602.63768174809493e-160
18514.68746970101587e-1592.34373485050794e-159
18613.99748805322487e-1581.99874402661243e-158
18712.22852123396447e-1571.11426061698223e-157
18811.98202348169976e-1569.91011740849878e-157
18911.05460259315770e-1555.27301296578852e-156
19019.53876447077373e-1554.76938223538687e-155
19118.69175700391603e-1544.34587850195802e-154
19217.79083689549332e-1533.89541844774666e-153
19316.87620701317553e-1523.43810350658776e-152
19416.10420291501957e-1513.05210145750978e-151
19515.38571756222624e-1502.69285878111312e-150
19614.73513766913585e-1492.36756883456793e-149
19712.54128202297521e-1481.27064101148760e-148
19812.22057933514944e-1471.11028966757472e-147
19911.93112863369236e-1469.65564316846179e-147
20017.8543523407271e-1473.92717617036355e-147
20116.81099278245557e-1463.40549639122779e-146
20215.87813066400955e-1452.93906533200478e-145
20315.04886875578884e-1442.52443437789442e-144
20414.38065007254952e-1432.19032503627476e-143
20511.13905856091126e-1425.69529280455631e-143
20619.53662098910448e-1424.76831049455224e-142
20717.44806505138702e-1413.72403252569351e-141
20816.25175122697798e-1403.12587561348899e-140
20911.78795566739117e-1398.93977833695584e-140
21011.43239975611664e-1387.16199878058318e-139
21111.19862036864152e-1375.99310184320758e-138
21219.9821432245612e-1374.9910716122806e-137
21317.73871890767966e-1363.86935945383983e-136
21414.4709027478941e-1352.23545137394705e-135
21513.2937401066093e-1341.64687005330465e-134
21612.69274760983355e-1331.34637380491677e-133
21712.18724435334852e-1321.09362217667426e-132
21811.79844032251005e-1318.99220161255026e-132
21911.45024980857936e-1307.25124904289679e-131
22019.42326858011168e-1304.71163429005584e-130
22116.45325130704517e-1293.22662565352259e-129
22215.15572851807447e-1282.57786425903724e-128
22314.09871776715876e-1272.04935888357938e-127
22413.18158248002081e-1261.59079124001040e-126
22512.50707935674636e-1251.25353967837318e-125
22611.92412049279906e-1249.62060246399532e-125
22711.49057027801999e-1237.45285139009995e-124
22811.1582743120275e-1225.7913715601375e-123
22917.95079476253808e-1223.97539738126904e-122
23016.1162643194276e-1213.0581321597138e-121
23113.56781496928355e-1201.78390748464177e-120
23212.74656420807938e-1191.37328210403969e-119
23312.08411842806693e-1181.04205921403347e-118
23411.39958912538291e-1176.99794562691454e-118
23511.05152384984313e-1165.25761924921564e-117
23617.8652481115742e-1163.9326240557871e-116
23715.853286461237e-1152.9266432306185e-115
23814.35525675109779e-1142.17762837554890e-114
23913.21114053630130e-1131.60557026815065e-113
24012.35634014200579e-1121.17817007100290e-112
24111.72087557783398e-1118.6043778891699e-112
24211.22205521476039e-1106.11027607380196e-111
24318.83861436842726e-1104.41930718421363e-110
24416.41197189986108e-1093.20598594993054e-109
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35412.29593118374278e-301.14796559187139e-30
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35711.04995813493810e-285.24979067469051e-29
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36313.58246346054806e-251.79123173027403e-25
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37711.86436290814081e-179.32181454070403e-18
37816.42256928879768e-173.21128464439884e-17
37912.17724961560504e-161.08862480780252e-16
38017.28405299470982e-163.64202649735491e-16
3810.9999999999999992.41346607940094e-151.20673303970047e-15
3820.9999999999999967.0129405582772e-153.5064702791386e-15
3830.9999999999999892.28226845724639e-141.14113422862319e-14
3840.9999999999999637.3405408125383e-143.67027040626915e-14
3850.9999999999998842.31850165636937e-131.15925082818468e-13
3860.9999999999998113.77102744878564e-131.88551372439282e-13
3870.9999999999994041.19259360371299e-125.96296801856496e-13
3880.9999999999981473.70669381731603e-121.85334690865801e-12
3890.9999999999943151.13701532006569e-115.68507660032846e-12
3900.99999999998283.43989895522568e-111.71994947761284e-11
3910.9999999999485971.02806784195246e-105.14033920976232e-11
3920.9999999998521732.95654807596423e-101.47827403798212e-10
3930.9999999996374697.2506242800363e-103.62531214001815e-10
3940.9999999990291431.94171368120239e-099.70856840601197e-10
3950.999999998014963.97007852957307e-091.98503926478654e-09
3960.9999999946546431.06907134546905e-085.34535672734523e-09
3970.9999999866389842.67220314382104e-081.33610157191052e-08
3980.9999999770568794.58862415887541e-082.29431207943770e-08
3990.99999993995161.20096798254334e-076.0048399127167e-08
4000.9999998389196953.22160610583991e-071.61080305291995e-07
4010.9999995889519678.22096066272187e-074.11048033136093e-07
4020.9999989389886722.12202265693647e-061.06101132846823e-06
4030.9999972964335455.40713291101412e-062.70356645550706e-06
4040.9999939701052581.20597894831889e-056.02989474159446e-06
4050.9999861272709172.77454581655985e-051.38727290827993e-05
4060.9999666066722476.67866555062846e-053.33933277531423e-05
4070.9999215707536240.0001568584927526657.84292463763326e-05
4080.999820790917990.0003584181640198730.000179209082009937
4090.999600946808160.000798106383681880.00039905319184094
4100.9992772369878990.001445526024202580.000722763012101291
4110.9985135402863930.002972919427213580.00148645971360679
4120.996999436002810.006001127994378620.00300056399718931
4130.9939295196750750.01214096064984950.00607048032492475
4140.993149806177240.01370038764551930.00685019382275964
4150.9904063306964630.01918733860707370.00959366930353684
4160.9850378892428850.02992422151423020.0149621107571151
4170.994205570603230.01158885879354080.00579442939677041
4180.9892812537419050.02143749251619070.0107187462580954
4190.978329402527210.04334119494557990.0216705974727899
4200.9591699699386420.08166006012271650.0408300300613582
4210.9437181318114010.1125637363771970.0562818681885986
4220.8785828963537330.2428342072925350.121417103646267
4230.8364418704353970.3271162591292060.163558129564603


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level4050.973557692307692NOK
5% type I error level4120.990384615384615NOK
10% type I error level4130.992788461538462NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/10l3rv1291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/10l3rv1291211632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/1wkcj1291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/1wkcj1291211632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/2pbt41291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/2pbt41291211632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/3pbt41291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/3pbt41291211632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/4pbt41291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/4pbt41291211632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/5pbt41291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/5pbt41291211632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/6z2t71291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/6z2t71291211632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/7auas1291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/7auas1291211632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/8auas1291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/8auas1291211632.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/9l3rv1291211632.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/01/t12912127839qcwd6cwjo0liah/9l3rv1291211632.ps (open in new window)


 
Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 4 ; 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')
}
 




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Software written by Ed van Stee & Patrick Wessa


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