Home » date » 2010 » Nov » 16 »

Workshop 6 - Tutorial - Assignment 4.4

*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, 16 Nov 2010 10:12:17 +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/Nov/16/t1289902315rb3gednk56spk46.htm/, Retrieved Tue, 16 Nov 2010 11:11:55 +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/Nov/16/t1289902315rb3gednk56spk46.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 «
87.28 255 87.28 280.2 87.09 299.9 86.92 339.2 87.59 374.2 90.72 393.5 90.69 389.2 90.3 381.7 89.55 375.2 88.94 369 88.41 357.4 87.82 352.1 87.07 346.5 86.82 342.9 86.4 340.3 86.02 328.3 85.66 322.9 85.32 314.3 85 308.9 84.67 294 83.94 285.6 82.83 281.2 81.95 280.3 81.19 278.8 80.48 274.5 78.86 270.4 69.47 263.4 68.77 259.9 70.06 258 73.95 262.7 75.8 284.7 77.79 311.3 81.57 322.1 83.07 327 84.34 331.3 85.1 333.3 85.25 321.4 84.26 327 83.63 320 86.44 314.7 85.3 316.7 84.1 314.4 83.36 321.3 82.48 318.2 81.58 307.2 80.47 301.3 79.34 287.5 82.13 277.7 81.69 274.4 80.7 258.8 79.88 253.3 79.16 251 78.38 248.4 77.42 249.5 76.47 246.1 75.46 244.5 74.48 243.6 78.27 244 80.7 240.8 79.91 249.8 78.75 248 77.78 259.4 81.14 260.5 81.08 260.8 80.03 261.3 78.91 259.5 78.01 256.6 76.9 257.9 75.97 256.5 81.93 254.2 80.27 253.3 78.67 253.8 77.42 255.5 76.16 257.1 74.7 257.3 76.39 253.2 76.04 252.8 74.65 252 73.29 250.7 71.79 252.2 74.39 250 74.91 25 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 time12 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Multiple Linear Regression - Estimated Regression Equation
USA[t] = + 135.650154209725 + 1.88127975922978Colombia[t] -0.0365024424003371Q1[t] + 0.515883279331436Q2[t] -0.328110200415450Q3[t] + 0.148431456768387t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)135.6501542097258.19800116.546700
Colombia1.881279759229780.08742521.518900
Q1-0.03650244240033714.649678-0.00790.9937410.49687
Q20.5158832793314364.6497010.11090.9117190.45586
Q3-0.3281102004154504.649445-0.07060.943780.47189
t0.1484314567683870.0158379.372500


Multiple Linear Regression - Regression Statistics
Multiple R0.77509837506724
R-squared0.600777491031876
Adjusted R-squared0.595138755029502
F-TEST (value)106.544709803557
F-TEST (DF numerator)5
F-TEST (DF denominator)354
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation31.1890836696366
Sum Squared Residuals344356.664813663


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1255299.960180609669-44.9601806096686
2280.2300.660997788169-20.460997788169
3299.9299.6079926109370.292007389063275
4339.2299.76471670905139.4352832909485
5374.2301.13710316210373.0628968378965
6393.5307.72632598699385.7736740130071
7389.2306.97432557123882.2256744287625
8381.7306.71716812232274.9828318776783
9375.2305.41813731726769.7818626827326
10369304.97137384263764.0286261573626
11357.4303.27873354726754.1212664527329
12352.1302.64532014650549.4546798534947
13346.5301.34628934145145.1537106585489
14342.9301.57678658014441.3232134198562
15340.3300.09108705828940.2089129417112
16328.3299.85274240696528.4472575930347
17322.9299.28741070801123.6125892919894
18314.3299.34859276837314.9514072316274
19308.9298.05102122244110.8489787775593
20294297.906740559079-3.90674055907865
21285.6296.645335349209-11.0453353492089
22281.2295.257931994964-14.0579319949641
23280.3292.906843783863-12.6068437838633
24278.8291.953612824033-13.1536128240325
25274.5290.729833209347-16.2298332093475
26270.4288.382977177895-17.9829771778954
27263.4270.022198215749-6.62219821574922
28259.9269.181844041472-9.2818440414722
29258271.720623945247-13.7206239452467
30262.7279.739619387151-17.0396193871507
31284.7282.5244249187472.17557508125273
32311.3286.74471329679824.5552867032016
33322.1293.96787980105528.132120198945
34327297.490616618429.5093833816002
35331.3299.18427988964332.1157201103568
36333.3301.09059416384232.2094058361584
37321.4301.48471514209419.9152848579058
38327300.32306535895726.6769346410432
39320298.44229708766421.5577029123364
40314.7304.20523486828310.4947651317169
41316.7302.17250495712914.5274950428708
42314.4300.61578642455413.7842135754464
43321.3298.52807737974522.7719226202549
44318.2297.34909284880720.8509071511933
45307.2295.76787007986811.4321299201320
46301.3294.3804667256236.91953327437697
47287.5291.559058574715-4.0590585747149
48277.7297.28437076015-19.5843707601498
49274.4296.568536680457-22.1685366804568
50258.8295.406886897319-36.6068868973194
51253.3293.168675471773-39.8686754717725
52251292.290695702311-41.2906957023109
53248.4290.93522650448-42.5352265044797
54249.5289.830015114119-40.3300151141193
55246.1287.347237319872-41.2472373198725
56244.5285.923686420234-41.4236864202343
57243.6284.191961270557-40.5919612705571
58244292.022828736538-48.0228287365382
59240.8295.898776528488-55.098776528488
60249.8294.889107175880-45.0891071758803
61248292.818751669542-44.8187516695418
62259.4291.694727481589-32.2947274815891
63260.5297.320265449623-36.8202654496227
64260.8297.683930321253-36.8839303212527
65261.3295.820515588430-34.5205155884295
66259.5294.414299436592-34.9142994365923
67256.6292.025585630307-35.425585630307
68257.9290.413906754746-32.5139067547458
69256.5288.77624559303-32.2762455930301
70254.2300.68949013654-46.4894901365398
71253.3296.87100371324-43.5710037132398
72253.8294.337497755656-40.5374977556560
73255.5292.097827070987-36.5978270709869
74257.1290.428231752857-33.3282317528575
75257.3286.986001281404-29.6860012814035
76253.2290.641905731686-37.4419057316857
77252.8290.095386830323-37.2953868303233
78252288.181225143494-36.1812251434941
79250.7284.927122647963-34.2271226479631
80252.2282.581744666302-30.3817446663023
81250287.585001054668-37.5850010546677
82251289.264083707967-38.2640837079674
83253.4287.872448174074-34.4724481740739
84251.2285.602321382782-34.4023213827822
85255.6285.093428076604-29.4934280766044
86261.1283.104015199406-22.0040151994059
87258.9280.640050202751-21.7400502027515
88259.9281.060153467158-21.1601534671585
89261.2280.532447363388-19.3324473633884
90264.7280.010432698389-15.3104326983892
91267.1276.323635858235-9.22363585823527
92266.4279.603284356672-13.2032843566716
93267.7280.731104441024-13.0311044410237
94268.6278.760504361418-10.1605043614175
95267.5276.635169721424-9.1351697214244
96268.5278.560296793215-10.0602967932152
97268.5275.963182954292-7.46318295429233
98270.5273.390573351733-2.89057335173264
99270.9275.385241384453-4.48524138445277
100270.1269.8605006096940.239499390306444
101269.3264.7236590958114.57634090418949
102269.8268.5850262698171.21497373018328
103270.1264.0892791331946.01072086680596
104264.9261.4052707948723.49472920512811
105263.7264.941128971038-1.24112897103814
106264.8262.1239529997792.67604700022142
107263.7267.598988587074-3.89898858707378
108255.9268.037904649073-12.1379046490730
109276.2278.666187517536-2.46618751753556
110360.1297.46491597982662.6350840201738
111380.5324.38654082234156.1134591776591
112373.7327.51568694003946.1843130599612
113369.8329.50889571363740.2911042863634
114366.6328.25318194253838.3468180574622
115359.3324.54757230479234.7524276952084
116345.8322.10813033516923.6918696648307
117326.2318.8337557829247.36624421707624
118324.5317.3146628455337.18533715446723
119328.1318.5944445697469.50555543025448
120327.5315.70349545790811.7965045420919
121324.4312.82418965510111.5758103448992
122316.5310.8535895754955.64641042450537
123310.9307.2796695208953.62033047910541
124301.5305.028355527195-3.52835552719521
125291.7302.41242889068-10.7124288906801
126290.4300.686395179774-10.2863951797738
127287.4297.526356672204-10.1263566722043
128277.7295.820613808682-18.1206138086816
129281.6293.976011873451-12.3760118734506
130288297.479935893203-9.47993589320319
131276299.888485472954-23.8884854729538
132272.9298.5025601685-25.6025601685002
133283298.840242753976-15.8402427539758
134283.3303.153117070197-19.8531170701971
135276.8299.259379456528-22.459379456528
136284.5301.27857051628-16.7785705162803
137282.7300.148854889557-17.4488548895567
138281.2297.726747667735-16.5267476677354
139287.4294.246891601097-6.84689160109686
140283.1292.01439040499-8.91439040498974
141284289.342025375698-5.34202537569775
142285.5292.620195824343-7.12019582434269
143289.2300.070575158829-10.8705751588292
144292.5297.856886760314-5.35688676031441
145296.4298.100505357829-1.70050535782858
146305.2293.10104486586212.0989551341375
147303.9290.73114385716913.1688561428305
148311.5303.3607527589788.13924724102231
149316.3300.29331898024716.0066810197526
150316.7302.53678556131614.163214438684
151322.5300.84414526594621.6558547340543
152317.1298.49876728428518.6012327157152
153309.8295.69471267184714.1052873281533
154303.8295.6994563394328.10054366056811
155290.3295.963346993661-5.66334699366057
156293.7293.37340264330.326597356700113
157291.7290.1930920790161.50690792098418
158296.5287.5076056909028.99239430909764
159289.1290.536977591199-1.43697759119879
160288.5295.152334718688-6.65233471868818
161293.8292.0849009399581.71509906004211
162297.7289.7568577060987.94314229390188
163305.4286.25818884186719.1418111581327
164302.7283.91281086020618.7871891397936
165302.5295.857989560136.64201043987023
166303295.0161573360617.98384266393848
167294.5291.6679908525692.83200914743096
168294.1289.3414256685014.75857433149954
169294.5287.0453165910547.4546834089456
170297.1285.58266204644011.5173379535597
171289.4287.2198869249072.18011307509322
172292.4294.581912500872-2.18191250087161
173287.9292.511556994533-4.61155699453312
174286.6291.030089652327-4.43008965232669
175280.5288.246307096603-7.74630709660316
176272.4286.521751435488-14.1217514354882
177269.2283.811760811012-14.6117608110115
178270.6287.842443163348-17.2424431633484
179267.3285.99930048724-18.6993004872397
180262.5284.086616850202-21.5866168502017
181266.8284.19854586457-17.3985458645698
182268.8277.825751148366-9.02575114836594
183263.1273.63100877322-10.5310087732200
184261.2272.865905789312-11.6659057893123
185266271.755002960181-5.75500296018094
186262.5271.214175497589-8.71417549758944
187265.2263.3697503895381.83024961046223
188261.3257.5440048533023.75599514669818
189253.7256.602417202501-2.90241720250121
190249.2256.249717715833-7.04971771583269
191239.1254.53826462287-15.4382646228701
192236.4253.961289614885-17.5612896148853
193235.2253.132578749638-17.9325787496384
194245.2252.930381643708-7.73038164370828
195246.2251.350618133892-5.15061813389178
196247.7256.737299962665-9.03729996266536
197251.4257.470051297579-6.07005129757921
198253.3256.853972644619-3.55397264461852
199254.8255.217770742025-0.417770742025134
200250267.565187679949-17.5651876799489
201249.3266.642412826741-17.3424128267406
202241.5266.308526137664-24.8085261376644
203243.3265.086205782102-21.7862057821015
204248264.076536429494-16.0765364294938
205253263.304263957024-10.3042639570239
206252.9276.891847486248-23.9918474862480
207251.5313.163432732135-61.6634327321348
208251.6311.43887707102-59.8388770710198
209253.5309.970531087635-56.4705310876348
210259.8312.477376834996-52.6773768349956
211334.1309.37377672020324.7262232797971
212448346.723401658291101.276598341709
213445.8373.54950325372172.2504967462785
214445379.16046060381165.8395393961886
215448.2377.95695304584170.2430469541592
216438.2370.75787328536767.4421267146328
217439.8367.16368117405272.6363188259475
218423.4366.17134656924657.2286534307541
219410.8361.56272264706949.2372773529306
220408.4360.57186609205447.8281339079459
221406.7366.38407277688840.3159272231116
222405.9360.72616436919245.1738356308081
223402.7356.34329401812346.356705981877
224405.1349.93435175652655.1656482434742
225399.6347.28079952482652.3192004751739
226386.5341.86745748582944.6325425141706
227381.4341.39764903395840.0023509660415
228375.2341.70487551281233.4951244871883
229357.7329.9459292464427.7540707535602
230359335.53807379893723.4619262010626
231355333.99593588430521.0040641156945
232352.7328.94151504935423.7584849506462
233344.4330.46440388314413.9355961168558
234343.8328.98293654093814.8170634590623
235338333.366829867884.63317013212032
236339363.548778923302-24.5487789233018
237333.3367.291577872983-33.9915778729833
238334.4372.752032842335-38.3520328423349
239328.3372.131722009725-43.8317220097255
240330.7358.009532735286-27.3095327352863
241330349.072506107759-19.0725061077590
242331.6385.498825914033-53.8988259140327
243351.2467.372632523649-116.172632523649
244389.4449.130440576497-59.730440576497
245410.9495.352536489587-84.452536489587
246442.8496.166230453641-53.3662304536409
247462.8421.64925067848641.1507493215143
248466.9409.54003074642257.3599692535777
249461.7400.54656572611861.1534342738818
250439.2390.46764988423248.7323501157683
251430.3380.42212745788149.8778725421188
252416.1394.76370094058921.3362990594115
253402.5394.4617484079268.03825159207398
254397.3408.369149496219-11.0691494962193
255403.3379.3415142992423.9584857007598
256395.9382.9786059519312.9213940480699
257387.8361.47463053274826.3253694672520
258378.6359.22183848925719.3781615107427
259377.1360.38874342791616.7112565720837
260370.4358.55131098124811.8486890187524
261362342.82286442290119.1771355770992
262350.3340.4383827962649.86161720373588
263348.2349.958169865903-1.75816986590337
264344.6352.466493663055-7.86649366305534
265343.5350.283261371163-6.78326137116308
266342.8345.509554450305-2.70955445030457
267347.6347.0150897456250.584910254375115
268346.6344.7073373591491.89266264085134
269349.5341.7527803659727.74721963402784
270342.1342.566474330026-0.466474330026094
271342330.63967214444611.3603278555542
272342.8326.31895041559416.4810495844063
273339.3318.52950444119720.7704955588034
274348.2319.1738832269229.0261167730801
275333.7335.108834275533-1.40883427553273
276334.7358.593427388097-23.8934273880968
277354341.30351862958912.6964813704106
278367.7332.4286218336135.2713781663901
279363.3333.78365474819229.5163452518081
280358.4326.41525980938831.9847401906124
281353.1324.36371710064128.7362828993587
282343.1314.92443637689328.1755636231071
283344.6313.62686483096130.9731351690391
284344.4306.59710024881837.8028997511821
285333.9305.12875426543328.771245734567
286331.7307.97423036945523.7257696305449
287324.3309.74314483106814.5568551689324
288321.2307.36014125422213.8398587457778
289322.4301.35791105109321.0420889489066
290321.7290.13141455607731.5685854439233
291320.5289.83092128253730.6690787174635
292312.8296.32755816925616.4724418307444
293309.7303.7952910422125.90470895778784
294315.6284.44166598732331.1583340126772
295309.7283.01240485824526.6875951417553
296304.6284.65533996615119.944660033849
297302.5286.46042076382616.0395792361741
298301.5276.13693855323925.3630614467605
299298.8281.06640301035817.7335969896420
300291.3279.79335449145811.5066455085418
301293.6283.9124093929869.68759060701436
302294.6281.84774532541812.7522546745820
303285.9290.878399657657-4.97839965765755
304297.6297.1304701756770.469529824323219
305301.1286.0675974202215.0324025797801
306293.8281.89590002231511.9040999776851
307297.7268.20069486305929.4993051369413
308292.9265.21568176326027.6843182367403
309292.1275.54295987024516.5570401297546
310287.2276.24377704874610.9562229512544
311288.2281.8693150167796.33068498322079
312283.8268.93233199065514.8676680093453
313299.9276.98326158897222.9167384110276
314292.4275.08791269973517.3120873002645
315293.3268.33462985203724.9653701479629
316300.8273.70249888321827.0975011167817
317293.7276.65516033402317.0448396659766
318293.1270.90318793836522.1968120616347
319294.4277.69511935712116.7048806428786
320292.1272.65951131976219.4404886802380
321291.9273.65564182096818.2443581790320
322282.5272.080110490810.4198895091998
323277.9272.9460106679824.95398933201761
324287.5276.22565916641911.2743408335814
325289.2288.3401530446730.859846955327345
326285.6289.567728555757-3.96772855575712
327293.2293.38723795493-0.18723795493013
328290.8290.0447817008770.755218299122519
329283.1293.129132734829-10.0291327348286
330275304.026486208354-29.0264862083542
331287.8287.4717358150690.328264184931395
332287.8289.791931636298-1.99193163629764
333287.4302.696563013428-15.2965630134282
334284301.233908468814-17.2339084688141
335277.8315.983653269112-38.1836532691121
336277.6334.576919007679-56.9769190076788
337304.9336.137433436654-31.2374334366538
338294362.574157721417-68.5741577214174
339300.9388.122448339694-87.2224483396944
340324374.583455790616-50.5834557906163
341332.9369.277299098403-36.3772990984026
342341.6357.091349926179-15.4913499261787
343333.4337.940433465156-4.54043346515606
344348.2338.3040983367869.8959016632139
345344.7329.46113569722015.2388643027796
346344.7348.542056123396-3.84205612339553
347329.3354.920105995121-25.620105995121
348323.5355.528337235451-32.0283372354509
349323.2384.442659363627-61.242659363627
350317.4367.440634007775-50.0406340077749
351330.1355.363329441456-25.2633294414561
352329.2358.040968416939-28.8409684169388
353334.9351.624856666780-16.7248566667796
354315.8339.175528328264-23.3755283282635
355315.4340.81275320673-25.4127532067300
356319.6352.821539787992-33.2215397879923
357317.3345.953920895618-28.6539208956179
358313.8343.983320816012-30.1833208160118
359315.8366.201746260452-50.401746260452
360311.3381.314644444444-70.0146444444436


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.782388121352170.4352237572956610.217611878647830
100.7679187455426760.4641625089146470.232081254457324
110.7148930506689380.5702138986621230.285106949331062
120.6763715562663380.6472568874673240.323628443733662
130.5784641843326940.8430716313346110.421535815667306
140.4760855575473030.9521711150946060.523914442452697
150.3801070177133880.7602140354267750.619892982286612
160.3236141751684930.6472283503369850.676385824831507
170.2563717246254290.5127434492508570.743628275374571
180.1931884876171860.3863769752343710.806811512382814
190.1450980164633070.2901960329266140.854901983536693
200.1372922296798880.2745844593597760.862707770320112
210.1060895410971810.2121790821943610.89391045890282
220.07662471912106830.1532494382421370.923375280878932
230.0620042346947280.1240084693894560.937995765305272
240.05227089032272090.1045417806454420.94772910967728
250.04935120469520060.09870240939040120.9506487953048
260.07949836112987820.1589967222597560.920501638870122
270.6935947874451650.6128104251096710.306405212554835
280.7490304469973460.5019391060053080.250969553002654
290.742144332766030.5157113344679410.257855667233970
300.6941678538980180.6116642922039630.305832146101982
310.6458978387278740.7082043225442510.354102161272126
320.6188926655166180.7622146689667630.381107334483381
330.5830797434505280.8338405130989450.416920256549472
340.5387947636468430.9224104727063140.461205236353157
350.4930674538221310.9861349076442620.506932546177869
360.4517875274650710.9035750549301410.548212472534929
370.4089659412766070.8179318825532150.591034058723393
380.364726275858910.729452551717820.63527372414109
390.3245992460216500.6491984920433010.67540075397835
400.3227568687745620.6455137375491240.677243131225438
410.2845996109156230.5691992218312450.715400389084377
420.2467210802384040.4934421604768080.753278919761596
430.2133696566682180.4267393133364370.786630343331782
440.1827792685024040.3655585370048090.817220731497596
450.1535722114404530.3071444228809070.846427788559546
460.1271443784502660.2542887569005310.872855621549735
470.1073342041579540.2146684083159090.892665795842046
480.1184425450320090.2368850900640170.881557454967991
490.1144757549906490.2289515099812980.885524245009351
500.1280894276795960.2561788553591910.871910572320404
510.1508217160105010.3016434320210030.849178283989499
520.1635897420498080.3271794840996160.836410257950192
530.1574443326975720.3148886653951440.842555667302428
540.1431190744680470.2862381489360930.856880925531953
550.1317891330173200.2635782660346400.86821086698268
560.1167310833984680.2334621667969370.883268916601532
570.0990821517796230.1981643035592460.900917848220377
580.09426557045359760.1885311409071950.905734429546402
590.1155406997016940.2310813994033880.884459300298306
600.1123334665675640.2246669331351280.887666533432436
610.1003792927283250.2007585854566500.899620707271675
620.084752862627160.169505725254320.91524713737284
630.07458131348317810.1491626269663560.925418686516822
640.0650088191158550.130017638231710.934991180884145
650.05425945887699950.1085189177539990.945740541123
660.04515802884951450.09031605769902890.954841971150486
670.03744192523419480.07488385046838970.962558074765805
680.03127695616064160.06255391232128320.968723043839358
690.02754996661403180.05509993322806360.972450033385968
700.02513727982957090.05027455965914180.97486272017043
710.02173255770262020.04346511540524030.97826744229738
720.0181363606679090.0362727213358180.98186363933209
730.01549369364958000.03098738729916010.98450630635042
740.01407877367687270.02815754735374530.985921226323127
750.01342848548889080.02685697097778160.98657151451111
760.01143381240050090.02286762480100170.9885661875995
770.01013606717992090.02027213435984190.989863932820079
780.009532831867928180.01906566373585640.990467168132072
790.009186322312861590.01837264462572320.990813677687138
800.009811922720107060.01962384544021410.990188077279893
810.009152198028732770.01830439605746550.990847801971267
820.008415831301380790.01683166260276160.99158416869862
830.007733636420983870.01546727284196770.992266363579016
840.007401310724354960.01480262144870990.992598689275645
850.008069196503630970.01613839300726190.991930803496369
860.01067329866205740.02134659732411470.989326701337943
870.01328475708635380.02656951417270760.986715242913646
880.01587162054384370.03174324108768740.984128379456156
890.02057365347206250.0411473069441250.979426346527937
900.02792156526006720.05584313052013430.972078434739933
910.03996148171473720.07992296342947440.960038518285263
920.04721051464852330.09442102929704670.952789485351477
930.05617624290239430.1123524858047890.943823757097606
940.06922331478229830.1384466295645970.930776685217702
950.08119278470337110.1623855694067420.918807215296629
960.08997259510001880.1799451902000380.910027404899981
970.1067572904449750.2135145808899490.893242709555025
980.1308434118544600.2616868237089190.86915658814554
990.1448132719118470.2896265438236940.855186728088153
1000.1682782988186150.336556597637230.831721701181385
1010.2047286224369000.4094572448738010.7952713775631
1020.2255515113010070.4511030226020150.774448488698993
1030.2478401606638590.4956803213277180.752159839336141
1040.2584079695870330.5168159391740670.741592030412967
1050.2624231820311010.5248463640622020.7375768179689
1060.2660713263381240.5321426526762470.733928673661876
1070.259577368495260.519154736990520.74042263150474
1080.2458922544757910.4917845089515810.75410774552421
1090.2490875157374890.4981750314749790.75091248426251
1100.4999418048744360.9998836097488720.500058195125564
1110.6364465382118850.727106923576230.363553461788115
1120.6725122081062320.6549755837875370.327487791893768
1130.677465378727110.6450692425457790.322534621272890
1140.6724143075284820.6551713849430360.327585692471518
1150.6608002796770260.6783994406459470.339199720322974
1160.636835836308880.726328327382240.36316416369112
1170.6061065759125990.7877868481748020.393893424087401
1180.5748627777869960.8502744444260090.425137222213004
1190.5435596427692680.9128807144614640.456440357230732
1200.5124824988321840.9750350023356310.487517501167816
1210.482416136298480.964832272596960.51758386370152
1220.4505092510420640.9010185020841280.549490748957936
1230.4188765942500280.8377531885000560.581123405749972
1240.3883669369145630.7767338738291250.611633063085437
1250.3610548425822580.7221096851645160.638945157417742
1260.3339088690028250.6678177380056510.666091130997175
1270.3074283898505740.6148567797011480.692571610149426
1280.2863120007880330.5726240015760660.713687999211967
1290.2634465807360050.526893161472010.736553419263995
1300.2401156966314410.4802313932628810.75988430336856
1310.2284657621478270.4569315242956550.771534237852173
1320.2182242791065100.4364485582130210.78177572089349
1330.2004969245672710.4009938491345430.799503075432729
1340.1871382971427800.3742765942855590.81286170285722
1350.1756573876832860.3513147753665710.824342612316714
1360.1613021180760140.3226042361520280.838697881923986
1370.1481383953534750.2962767907069500.851861604646525
1380.1349560425051670.2699120850103340.865043957494833
1390.1212215007934650.2424430015869300.878778499206535
1400.1093653370134450.2187306740268890.890634662986555
1410.1000838468742390.2001676937484780.899916153125761
1420.08973738737062350.1794747747412470.910262612629377
1430.07965254131116820.1593050826223360.920347458688832
1440.07032604131367920.1406520826273580.92967395868632
1450.06229243319444680.1245848663888940.937707566805553
1460.05920372211281690.1184074442256340.940796277887183
1470.05692196362951730.1138439272590350.943078036370483
1480.04998688459250550.0999737691850110.950013115407494
1490.04648219859076290.09296439718152570.953517801409237
1500.04168867472147970.08337734944295930.95831132527852
1510.03944578348813560.07889156697627120.960554216511864
1520.0370123769539460.0740247539078920.962987623046054
1530.03420323585171420.06840647170342850.965796764148286
1540.03013641332537730.06027282665075450.969863586674623
1550.02570971645288950.05141943290577890.97429028354711
1560.02212136219258530.04424272438517060.977878637807415
1570.01936788967740440.03873577935480890.980632110322596
1580.01767074430717670.03534148861435330.982329255692823
1590.01505390849796150.03010781699592290.984946091502038
1600.01271801443051070.02543602886102140.98728198556949
1610.01087330346490320.02174660692980640.989126696535097
1620.009534395442963370.01906879088592670.990465604557037
1630.009414588178109540.01882917635621910.99058541182189
1640.00948462745015860.01896925490031720.990515372549841
1650.007977878613068860.01595575722613770.992022121386931
1660.006680833927675040.01336166785535010.993319166072325
1670.005527762137397460.01105552427479490.994472237862603
1680.004654030806611090.009308061613222190.995345969193389
1690.004034435676833960.008068871353667910.995965564323166
1700.003571133123922550.00714226624784510.996428866876077
1710.002944863104044840.005889726208089680.997055136895955
1720.002375845589214180.004751691178428360.997624154410786
1730.001931749256005880.003863498512011750.998068250743994
1740.001553853653119790.003107707306239590.99844614634688
1750.001262578085273650.002525156170547300.998737421914726
1760.001065486721533420.002130973443066830.998934513278467
1770.0009096785776200080.001819357155240020.99909032142238
1780.0007845658403065020.001569131680613000.999215434159693
1790.0006889634816352250.001377926963270450.999311036518365
1800.0006305897671281070.001261179534256210.999369410232872
1810.0005602696404878990.001120539280975800.999439730359512
1820.0004756865416112690.0009513730832225370.999524313458389
1830.0004141512963633170.0008283025927266340.999585848703637
1840.0003703239532117270.0007406479064234550.999629676046788
1850.000335567010871430.000671134021742860.999664432989129
1860.0002966087657989250.0005932175315978490.999703391234201
1870.0002874772795173480.0005749545590346970.999712522720483
1880.0003043443305386910.0006086886610773820.999695655669461
1890.0003085902175168140.0006171804350336290.999691409782483
1900.0002973725430970930.0005947450861941860.999702627456903
1910.0002966398101282180.0005932796202564350.999703360189872
1920.0003136418693382080.0006272837386764170.999686358130662
1930.000346197763889370.000692395527778740.99965380223611
1940.0003544808365458640.0007089616730917280.999645519163454
1950.0003693147952956320.0007386295905912650.999630685204704
1960.0003927915933008640.0007855831866017280.9996072084067
1970.0004225322803208030.0008450645606416050.99957746771968
1980.0004393252917548270.0008786505835096550.999560674708245
1990.000465910562152570.000931821124305140.999534089437847
2000.0005829775262517330.001165955052503470.999417022473748
2010.0007722037416890290.001544407483378060.99922779625831
2020.001200570380156980.002401140760313950.998799429619843
2030.001898000147330770.003796000294661550.99810199985267
2040.00302902179681110.00605804359362220.996970978203189
2050.004749284446710560.009498568893421120.99525071555329
2060.009176868117028850.01835373623405770.990823131882971
2070.05613180010210750.1122636002042150.943868199897893
2080.2253935602847520.4507871205695050.774606439715248
2090.5503525234304420.8992949531391160.449647476569558
2100.8467639950117620.3064720099764760.153236004988238
2110.8581807916045850.2836384167908310.141819208395415
2120.9411400346495020.1177199307009970.0588599653504985
2130.9534264631474130.09314707370517440.0465735368525872
2140.9607125632792320.07857487344153550.0392874367207677
2150.9703873573748950.05922528525021030.0296126426251052
2160.975978143448170.04804371310365920.0240218565518296
2170.982893736974870.03421252605026060.0171062630251303
2180.9840315847855810.03193683042883740.0159684152144187
2190.9831534340951990.03369313180960210.0168465659048011
2200.9819260436658530.03614791266829430.0180739563341472
2210.9798643985155660.0402712029688680.020135601484434
2220.9785605179772440.04287896404551210.0214394820227560
2230.9773202442166940.04535951156661220.0226797557833061
2240.9784132525049270.04317349499014660.0215867474950733
2250.9784421454834890.0431157090330220.021557854516511
2260.9764202445639250.04715951087215060.0235797554360753
2270.9732016152196030.05359676956079340.0267983847803967
2280.96843547267790.0631290546441990.0315645273220995
2290.9623884718922620.07522305621547610.0376115281077381
2300.9554245006348320.08915099873033660.0445754993651683
2310.9477306860059620.1045386279880760.0522693139940382
2320.9387767455960120.1224465088079760.0612232544039878
2330.9317401865839380.1365196268321230.0682598134160617
2340.9231362522373820.1537274955252360.0768637477626179
2350.9203607790115890.1592784419768220.0796392209884111
2360.9509782846382340.09804343072353180.0490217153617659
2370.978352300526550.04329539894689930.0216476994734496
2380.9921804486535140.01563910269297220.00781955134648609
2390.99810330842410.00379338315179860.0018966915758993
2400.9992035852461470.001592829507705240.000796414753852618
2410.999605878317210.0007882433655793220.000394121682789661
2420.9999672216377456.55567245104986e-053.27783622552493e-05
2430.9999999973040885.39182437728164e-092.69591218864082e-09
2440.999999999845483.09040410496429e-101.54520205248214e-10
2450.9999999999992251.55010490865539e-127.75052454327693e-13
2460.9999999999998353.30257669276612e-131.65128834638306e-13
2470.9999999999999321.36739899992773e-136.83699499963864e-14
2480.9999999999999976.92132724862332e-153.46066362431166e-15
24911.54154867457458e-167.70774337287289e-17
25011.50075958727794e-177.50379793638972e-18
25111.03558688904358e-185.17793444521789e-19
25219.12938049530682e-194.56469024765341e-19
25311.89782886148401e-189.48914430742003e-19
25414.04851391779795e-182.02425695889897e-18
25513.53714945073478e-181.76857472536739e-18
25615.49947871782033e-182.74973935891017e-18
25716.73291618367248e-183.36645809183624e-18
25811.16212516510232e-175.8106258255116e-18
25911.96977126931958e-179.84885634659792e-18
26014.08436722322626e-172.04218361161313e-17
26118.54045542136656e-174.27022771068328e-17
26211.94028726185634e-169.70143630928169e-17
26313.92311662857402e-161.96155831428701e-16
26416.23214296377151e-163.11607148188576e-16
26518.53228449087217e-164.26614224543608e-16
26611.36129578696089e-156.80647893480447e-16
2670.9999999999999992.71564059830886e-151.35782029915443e-15
2680.9999999999999975.37794897447838e-152.68897448723919e-15
2690.9999999999999941.12142965676828e-145.60714828384139e-15
2700.9999999999999911.79615557266354e-148.9807778633177e-15
2710.9999999999999813.79852401812856e-141.89926200906428e-14
2720.999999999999968.17322289676964e-144.08661144838482e-14
2730.9999999999999161.68743365618718e-138.43716828093592e-14
2740.9999999999998483.03703570695282e-131.51851785347641e-13
2750.999999999999774.60033371771845e-132.30016685885923e-13
2760.9999999999999051.90865786806835e-139.54328934034177e-14
2770.9999999999997934.13249029357538e-132.06624514678769e-13
2780.9999999999997934.12924878526553e-132.06462439263277e-13
2790.9999999999997564.88573319597291e-132.44286659798646e-13
2800.9999999999997335.34477957113601e-132.67238978556801e-13
2810.9999999999996347.32134242675482e-133.66067121337741e-13
2820.9999999999994771.04559626443999e-125.22798132219995e-13
2830.9999999999994651.07009501641007e-125.35047508205036e-13
2840.9999999999996586.84838419764964e-133.42419209882482e-13
2850.9999999999995229.5628299921993e-134.78141499609965e-13
2860.9999999999993291.3426981816236e-126.713490908118e-13
2870.9999999999987172.56547747132208e-121.28273873566104e-12
2880.9999999999974265.14818600511174e-122.57409300255587e-12
2890.9999999999954659.06913066742284e-124.53456533371142e-12
2900.9999999999952889.4247205959544e-124.7123602979772e-12
2910.9999999999955698.86286626267864e-124.43143313133932e-12
2920.9999999999919521.60958106104931e-118.04790530524653e-12
2930.9999999999821273.57461927481152e-111.78730963740576e-11
2940.9999999999839943.20114183577935e-111.60057091788967e-11
2950.9999999999813333.73337595519562e-111.86668797759781e-11
2960.9999999999681376.37266058906712e-113.18633029453356e-11
2970.9999999999349791.30042593766470e-106.50212968832348e-11
2980.999999999906551.86901876702386e-109.3450938351193e-11
2990.9999999998398933.20214631659549e-101.60107315829774e-10
3000.9999999996493127.01376854530607e-103.50688427265304e-10
3010.9999999992287471.54250679142292e-097.71253395711462e-10
3020.999999998499053.00190144247845e-091.50095072123922e-09
3030.9999999968551566.28968747894114e-093.14484373947057e-09
3040.9999999934758911.30482171558398e-086.52410857791988e-09
3050.9999999877695132.44609740977777e-081.22304870488888e-08
3060.999999977082454.58351012516864e-082.29175506258432e-08
3070.9999999702314355.95371302618423e-082.97685651309211e-08
3080.99999995018599.96281993303576e-084.98140996651788e-08
3090.9999999002328741.99534252369217e-079.97671261846083e-08
3100.9999998026352453.94729510058251e-071.97364755029125e-07
3110.9999996121746927.7565061533789e-073.87825307668945e-07
3120.99999922522731.54954539859554e-067.7477269929777e-07
3130.9999987697893882.46042122406653e-061.23021061203327e-06
3140.9999979639839274.07203214604083e-062.03601607302041e-06
3150.9999970134686975.97306260612006e-062.98653130306003e-06
3160.9999965068730086.9862539841719e-063.49312699208595e-06
3170.9999937658253481.24683493032783e-056.23417465163914e-06
3180.9999911543253311.76913493377162e-058.84567466885811e-06
3190.9999878193392512.4361321497482e-051.2180660748741e-05
3200.999981222103983.75557920383168e-051.87778960191584e-05
3210.9999672114095866.55771808277073e-053.27885904138536e-05
3220.9999400510577790.000119897884442025.994894222101e-05
3230.9998882144272730.0002235711454532940.000111785572726647
3240.9998062220484860.0003875559030279850.000193777951513993
3250.9996502751590570.000699449681885270.000349724840942635
3260.9993854803382030.001229039323593210.000614519661796603
3270.9990235008510550.001952998297890640.000976499148945322
3280.9983839270293180.003232145941364620.00161607297068231
3290.9975020134446940.004995973110611080.00249798655530554
3300.9968671919317190.006265616136562890.00313280806828144
3310.994770187566610.01045962486678090.00522981243339047
3320.9915267481518140.01694650369637290.00847325184818645
3330.9895072906150880.02098541876982320.0104927093849116
3340.9882885945391580.02342281092168350.0117114054608417
3350.994017749455320.01196450108936150.00598225054468073
3360.9996472103113680.0007055793772642110.000352789688632105
3370.9999552827461018.94345077975025e-054.47172538987512e-05
3380.999998747170822.50565835947207e-061.25282917973604e-06
3390.9999998473101723.05379656891068e-071.52689828445534e-07
3400.99999987531192.49376198290157e-071.24688099145079e-07
3410.9999997418063455.16387309619497e-072.58193654809749e-07
3420.9999989329536882.13409262461609e-061.06704631230805e-06
3430.9999972756997595.44860048208426e-062.72430024104213e-06
3440.9999909556574851.80886850294011e-059.04434251470055e-06
3450.9999608652580437.82694839147434e-053.91347419573717e-05
3460.9999880250985552.39498028891526e-051.19749014445763e-05
3470.9999326570593720.0001346858812564546.73429406282271e-05
3480.99979859971680.0004028005664013840.000201400283200692
3490.9994562778299180.001087444340164040.000543722170082021
3500.9994103678525580.001179264294883730.000589632147441866
3510.9954154827429430.00916903451411330.00458451725705665


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1460.425655976676385NOK
5% type I error level1960.571428571428571NOK
10% type I error level2210.644314868804665NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/107nia1289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/107nia1289902320.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/1i4lz1289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/1i4lz1289902320.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/2te2k1289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/2te2k1289902320.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/3te2k1289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/3te2k1289902320.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/4te2k1289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/4te2k1289902320.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/5l5251289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/5l5251289902320.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/6l5251289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/6l5251289902320.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/7ee1q1289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/7ee1q1289902320.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/8ee1q1289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/8ee1q1289902320.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/97nia1289902320.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/16/t1289902315rb3gednk56spk46/97nia1289902320.ps (open in new window)


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




Copyright

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.


Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

  • personalize online software applications according to your needs
  • enforce strict security rules with respect to the data that you upload (e.g. statistical data)
  • manage user sessions of online applications
  • alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by