Home » date » 2010 » Dec » 21 »

Workshop 7

*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, 21 Dec 2010 13:34:27 +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/21/t1292938369o3esxowf8usrjs3.htm/, Retrieved Tue, 21 Dec 2010 14:33:02 +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/21/t1292938369o3esxowf8usrjs3.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
162556 807 213118 6282154 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 time24 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
Costs[t] = -2184.84324440858 + 103.395389966996Orders[t] -0.0173740227096243Dividends[t] + 0.0089231436628916Wealth[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)-2184.84324440858628.474615-3.47640.000560.00028
Orders103.3953899669965.04927420.477300
Dividends-0.01737402270962430.008381-2.07290.0387770.019389
Wealth0.00892314366289160.00087310.21700


Multiple Linear Regression - Regression Statistics
Multiple R0.90267403569178
R-squared0.814820414712084
Adjusted R-squared0.813519387180787
F-TEST (value)626.289909407135
F-TEST (DF numerator)3
F-TEST (DF denominator)427
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5104.05464384181
Sum Squared Residuals11123936615.7270


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1162556133609.08214153728946.9178584632
22979080859.1971857006-51069.1971857006
38755074443.573396093213106.4266039068
48473844511.813073626940226.1869263731
55466041486.723859756313173.2761402437
64263426995.440347122715638.5596528773
74094935243.30293938965705.69706061039
84518738532.59794617666654.40205382337
93770429116.42023875528587.57976124481
101627523203.6963111442-6928.69631114415
112583013877.482841970411952.5171580296
121267918383.3641074758-5704.36410747579
131801451018.2791451413-33004.2791451413
144355618919.785222336824636.2147776632
152481133399.9062747663-8588.90627476629
16657511258.7656592716-4683.7656592716
1771239055.18627415772-1932.18627415772
182195030875.1829630721-8925.18296307213
193759730207.04030432607389.95969567397
201782128287.5174565595-10466.5174565595
211298811581.51363454461406.48636545538
22223309709.185167654712620.8148323453
231332611856.08485660101469.91514339902
241618928977.5027499162-12788.5027499162
2571466727.26910950771418.730890492293
26158247722.594822516748101.40517748326
272766426784.5046462458879.495353754216
28119206692.059422708945227.94057729106
2985687791.46034010624776.539659893762
301441615245.2800474226-829.280047422564
3133695135.17367032254-1766.17367032254
32118198334.133789696293484.86621030371
3369847166.84636451761-182.846364517608
3445195717.56438856498-1198.56438856498
352220-437.1200794188042657.12007941880
361856221226.3550302292-2664.35503022917
37103279640.72476606933686.275233930667
38533610356.1279334738-5020.12793347376
3923657978.17308158323-5613.17308158323
40406911824.0338666272-7755.03386662716
41863619266.7206401169-10630.7206401169
42137185015.0802366318702.919763369
4345259864.65042795371-5339.65042795372
4468698262.79882230859-1393.79882230859
4546284103.50201850826524.497981491744
4636891998.251704835541690.74829516446
4748912532.149476569832358.85052343017
48748914685.5774630017-7196.57746300166
4949015879.75767918789-978.757679187892
5022842462.42926758159-178.429267581587
5131609977.95888943364-6817.95888943364
52415014161.0692295915-10011.0692295915
5372858946.99304216486-1661.99304216485
5411346491.39031536256-5357.39031536256
5546588967.31032682534-4309.31032682534
5623848333.28671697151-5949.28671697151
5737483386.89062805726361.109371942745
5853715132.2956689145238.704331085499
5912854405.62335644182-3120.62335644182
6093278544.43908649286782.560913507141
6155655024.58307982565540.416920174346
6215281382.89094830611145.109051693894
6331222453.54674365554668.453256344458
6475617595.2848901754-34.2848901753985
6526755542.4494354538-2867.44943545380
661325325381.6784054512-12128.6784054512
67880293.290831408959586.709168591041
6820534163.57539842606-2110.57539842606
6914246430.67811598262-5006.67811598262
7040368653.1149157901-4617.1149157901
7130454209.66461612572-1164.66461612572
7251194443.55005677225675.449943227749
7314312350.00049694757-919.00049694757
74554274.822845224765279.177154775235
7519752785.42822717379-810.428227173794
761765738.9926634961381026.00733650386
7710122104.05409944594-1092.05409944594
78810-220.7798288546291030.77982885463
7912804772.2243174779-3492.2243174779
80666143.843592224069522.156407775931
8113802853.88667308956-1473.88667308956
8246776877.07213318944-2200.07213318944
838761961.25055597439-1085.25055597439
848141371.36948732482-557.369487324824
85514949.970625290361-435.970625290361
8656926918.29973697871-1226.29973697871
87364210676.3421503557-7034.34215035566
88540513.92075768856426.0792423114365
8920993369.69073655069-1270.69073655069
90567913.398998535902-346.398998535902
9120013387.14164826872-1386.14164826872
9229493888.10393427270-939.103934272696
9322537778.6229371789-5525.6229371789
9465331864.196653381584668.80334661842
9518894809.44012550055-2920.44012550055
9630553231.10746074969-176.107460749686
97272611.565592691099-339.565592691099
9814141819.98637020719-405.986370207192
9925641119.637706130361444.36229386964
1001383310.1679337568771072.83206624312
10112612442.12400440965-1181.12400440965
102975304.854931857879670.145068142121
10333664287.37487337354-921.374873373539
104576-531.6478225068571107.64782250686
10516862802.07838327233-1116.07838327233
1067461976.54446878986-1230.54446878986
10731922857.50330515765334.496694842347
10820452326.98508190777-281.985081907771
10957023783.9311379551918.06886204500
1101932845.4358572426591086.56414275734
1119362557.98551002291-1621.98551002291
11234371807.116191359691629.88380864031
11351313475.391558279201655.60844172080
1142397456.6377554706741940.36224452933
1151389809.387107521165579.612892478835
11615033519.52244974971-2016.52244974971
117402134.815277057918267.184722942082
11822391985.61342489346253.386575106539
1192234-477.2972882083362711.29728820834
1208371739.02194231222-902.021942312216
121105798758.940448077751820.05955192225
122875874.6703772819040.329622718096061
123158510953.3618881030-9368.36188810295
12416592817.51386280024-1158.51386280024
12526471630.092131473931016.90786852607
1263294813.7881762904822480.21182370952
1270-425.612854934268425.612854934268
12894599.514838395426-505.514838395426
1294222024.74609999485-1602.74609999485
1300-425.612854934268425.612854934268
13134290.554468336784-256.554468336784
13215582730.04058323889-1172.04058323889
1330-322.217464967272322.217464967272
134431616.78223657139-1573.78223657139
135645-41.5222848721389686.522284872139
136316-11.6063664826463327.606366482646
137115635.868124750399-520.868124750399
1385-326.616574793077331.616574793077
139897-16.1984031568525913.198403156852
1400-425.612854934268425.612854934268
141389521.963076819794-132.963076819794
1420-425.612854934268425.612854934268
1431002630.695413599369371.304586400631
14436-11.003267794804147.0032677948041
1454601208.2227722555-748.2227722555
146309555.330234363923-246.330234363923
1470-425.612854934268425.612854934268
1489292.650066763584-283.650066763584
149271-130.759570191198401.759570191198
15014-426.32076325586440.32076325586
151520122.372902723211397.627097276789
15217664251.40962150359-2485.40962150359
153091.3640949007147-91.3640949007147
154458177.372276789534280.627723210466
15520-223.814236345463243.814236345463
1560-425.612854934268425.612854934268
1570-425.612854934268425.612854934268
15898-233.485005174142331.485005174142
159405276.979900447565128.020099552435
1600-425.612854934268425.612854934268
1610-425.612854934268425.612854934268
1620-425.612854934268425.612854934268
1630-425.612854934268425.612854934268
164483214.936047280380268.063952719620
1654541894.00607822397-1440.00607822397
16647-326.661190511392373.661190511392
1670-425.612854934268425.612854934268
1687572297.46620892985-1540.46620892985
16946555494.83072578168-839.83072578168
1700-425.612854934268425.612854934268
1710-425.612854934268425.612854934268
17236-156.574250591664192.574250591664
1730-425.612854934268425.612854934268
174203432.172632162214-229.172632162214
1750-425.612854934268425.612854934268
176126399.027030547484-273.027030547484
17740011239.2116087767-10839.2116087767
17871-418.944383146347489.944383146347
1790-425.612854934268425.612854934268
1800-425.612854934268425.612854934268
1819721741.35725810656-769.357258106556
182531653.057866811444-122.057866811444
18324612235.15592841746225.844071582540
184378874.618787066069-496.618787066069
1852354.3608787660548-31.3608787660548
186638876.916994160421-238.916994160421
18723002208.0560184442791.9439815557312
188149153.533802646696-4.53380264669636
189226-206.464097653747432.464097653747
1900-425.612854934268425.612854934268
191275349.377560966079-74.3775609660786
1920-425.612854934268425.612854934268
193141282.219590157669-141.219590157669
1940-425.612854934268425.612854934268
19528-115.631940645745143.631940645745
1960-425.612854934268425.612854934268
19749808700.32444698907-3720.32444698907
1980-425.612854934268425.612854934268
1990-425.612854934268425.612854934268
200472794.529047820392-322.529047820392
2010-425.612854934268425.612854934268
2020-425.612854934268425.612854934268
2030-425.612854934268425.612854934268
204203858.758748526751-655.758748526751
205496441.88146685556454.1185331444365
2061082.017445983248-72.017445983248
20763-276.635698962411339.635698962411
2080-425.612854934268425.612854934268
20911362726.52272825406-1590.52272825406
210265-190.666632398782455.666632398782
2110-425.612854934268425.612854934268
2120-425.612854934268425.612854934268
213267896.231250475923-629.231250475923
214474350.975521207676123.024478792324
215534-38.8641984547746572.864198454775
2160-218.822075000275218.822075000275
21715195.579296595114-180.579296595114
218397320.46347898499276.5365210150078
2190-218.822075000275218.822075000275
22018662609.57906782216-743.579067822158
221288-278.855082208241566.855082208241
2220-425.612854934268425.612854934268
2233-322.067238202895325.067238202895
2244681613.57134495482-1145.57134495482
22520-321.945320739663341.945320739663
2262781833.11509300867-1555.11509300867
22761497.548368672164-436.548368672164
2280-425.612854934268425.612854934268
229192-325.113402344175517.113402344175
2300-425.612854934268425.612854934268
23131793.8068359804272223.193164019573
232738558.4488241714179.551175828600
2330-425.612854934268425.612854934268
234368793.327713195059-425.327713195059
2350-425.612854934268425.612854934268
2362-425.05768108466427.05768108466
2370-425.612854934268425.612854934268
23853204.890505625756-151.890505625756
2390-425.612854934268425.612854934268
2400-425.612854934268425.612854934268
2410-425.612854934268425.612854934268
24294-148.791456719028242.791456719028
2430-425.612854934268425.612854934268
24424316.119491240152-292.119491240152
2452332-346.2624779230382678.26247792304
2460-425.612854934268425.612854934268
2470-425.612854934268425.612854934268
2481311049.38724671307-918.38724671307
2490-425.612854934268425.612854934268
2500-425.612854934268425.612854934268
251206470.49549025345-264.495490253450
2520-425.612854934268425.612854934268
253167-455.363586654276622.363586654276
2546223191.747073912-2569.74707391200
25523284534.1084920829-2206.10849208290
2560-425.612854934268425.612854934268
257365348.72891270345916.2710872965406
2583642337.86413785925-1973.86413785925
2590-425.612854934268425.612854934268
2600-425.612854934268425.612854934268
2610-425.612854934268425.612854934268
2620-425.612854934268425.612854934268
263226962.787757941487-736.787757941487
264307788.627329278865-481.627329278865
2650-425.612854934268425.612854934268
2660-425.612854934268425.612854934268
2670-425.612854934268425.612854934268
268188797.859433199488-609.859433199488
2690-425.612854934268425.612854934268
2701382247.74302397098-2109.74302397098
2710-425.612854934268425.612854934268
2720-425.612854934268425.612854934268
2730-425.612854934268425.612854934268
2741251491.33141963720-1366.33141963720
2750-425.612854934268425.612854934268
276282826.407711357788-544.407711357788
2773351937.23020074529-1602.23020074529
2780-425.612854934268425.612854934268
27913242246.05814281916-922.058142819157
280176230.676505734914-54.6765057349137
2810-425.612854934268425.612854934268
2820-425.612854934268425.612854934268
2832492315.20331253068-2066.20331253068
2840-425.612854934268425.612854934268
285333471.886071023367-138.886071023367
2860-425.612854934268425.612854934268
287601125.539735129589475.460264870411
28830-114.194160647792144.194160647792
2890-425.612854934268425.612854934268
290249827.773789393567-578.773789393567
2910-425.612854934268425.612854934268
292165865.692637824264-700.692637824264
2934531635.70630437419-1182.70630437419
2940-425.612854934268425.612854934268
29553513.691011853814-460.691011853814
296382756.812855586312-374.812855586312
2970-425.612854934268425.612854934268
2980-425.612854934268425.612854934268
2990-425.612854934268425.612854934268
3000504.945654768702-504.945654768702
30130-33.160155194600763.1601551946007
302290-415.537011449365705.537011449365
3030-322.217464967272322.217464967272
3040-425.612854934268425.612854934268
3053661262.1923755843-896.1923755843
3062814.996138340327-812.996138340327
3070-425.612854934268425.612854934268
3082091492.94039860863-1283.94039860863
3093841387.52584113560-1003.52584113560
3100-425.612854934268425.612854934268
3110-425.612854934268425.612854934268
3123652701.79319457623-2336.79319457623
3130-425.612854934268425.612854934268
314491022.42461818808-973.42461818808
3153403.300717521568-400.300717521568
316133111.14305750148621.8569424985142
31732-416.607098074586448.607098074586
3183681471.77953898698-1103.77953898698
319191.3100837741215-90.3100837741215
3200-425.612854934268425.612854934268
3210-425.612854934268425.612854934268
3220-425.612854934268425.612854934268
3230-425.612854934268425.612854934268
3240-425.612854934268425.612854934268
3250-425.612854934268425.612854934268
32622-347.933020474493369.933020474493
3270-425.612854934268425.612854934268
3280-425.612854934268425.612854934268
3290-425.612854934268425.612854934268
3300-425.612854934268425.612854934268
3310-425.612854934268425.612854934268
3320-425.612854934268425.612854934268
3330-425.612854934268425.612854934268
33496-20.8388635097347116.838863509735
3351-322.267697976936323.267697976936
336314-12.1116033592482326.111603359248
3378441467.04306253250-623.043062532495
3380-425.612854934268425.612854934268
33926-338.660008458503364.660008458503
340125646.155678137969-521.155678137969
341304599.379403070493-295.379403070493
3420-425.612854934268425.612854934268
3430-425.612854934268425.612854934268
3440-425.612854934268425.612854934268
345621854.958739182249-233.958739182249
3460-425.612854934268425.612854934268
347119-192.021050939283311.021050939283
3480-425.612854934268425.612854934268
3490-425.612854934268425.612854934268
3501595643.935464806972951.064535193028
351312-49.0234033192867361.023403319287
35260279.259065444711-219.259065444711
353587719.964739741403-132.964739741403
354135-356.025603597299491.025603597299
3550-425.612854934268425.612854934268
3560-425.612854934268425.612854934268
357514873.987303787789-359.987303787789
3580-425.612854934268425.612854934268
3590-425.612854934268425.612854934268
3600-425.612854934268425.612854934268
3611-11.905426525769412.9054265257694
3620-425.612854934268425.612854934268
3630-425.612854934268425.612854934268
36417632391.31430019576-628.314300195761
365180532.773151933513-352.773151933513
3660-425.612854934268425.612854934268
3670-425.612854934268425.612854934268
3680-425.612854934268425.612854934268
3690-425.612854934268425.612854934268
370218299.698578688387-81.6985786883872
3710-425.612854934268425.612854934268
372448385.68469173258262.3153082674178
373227211.18804833606915.8119516639315
374174-257.652709124703431.652709124703
3750-425.612854934268425.612854934268
3760-425.612854934268425.612854934268
377121612.097141155346-491.097141155346
378607365.194177177966241.805822822034
3792212648.3524209201031563.64757907990
3800-425.612854934268425.612854934268
3810-425.612854934268425.612854934268
3825301231.68034130401-701.68034130401
3835711044.10151198970-473.101511989704
3840-425.612854934268425.612854934268
38578798.131389847274-720.131389847274
38624891760.41841355697728.581586443031
3871317.29287757907764123.707122420922
388923174.654355447863748.345644552137
38972-120.272327806364192.272327806364
390572372.253253111757199.746746888243
391397635.755818569654-238.755818569654
39245011.5866952473846438.413304752615
393622-67.3772378328133689.377237832813
394694533.69149315673160.30850684327
3953425-173.7307963528853598.73079635288
396562157.191545741980404.80845425802
39749171760.021334878853156.97866512115
398144224334.1285380826-22892.1285380826
39952972.8251196085678456.174880391432
40021263747.36020799312-1621.36020799312
4011061-383.1645778837411444.16457788374
402776502.299423980835273.700576019165
40361156.6115844527942554.388415547206
40415261271.02194647327254.978053526728
405592-106.903320219852698.903320219852
4061182642.197323565347539.802676434653
407621600.03263660546220.9673633945379
408989749.638295169116239.361704830884
409438392.4156942023845.5843057976197
410726-1021.562170905441747.56217090544
41113035255.90959360694-3952.90959360694
41274191448.078213335375970.92178666463
4131164777.253395876648386.746604123352
41433103816.49819097575-506.498190975748
4151920-951.4151839531022871.4151839531
416965-1431.64806448462396.6480644846
41732563213.9546749573042.0453250426963
41811351392.88262908445-257.882629084451
41912701550.89910413138-280.899104131380
420661-1026.822669667111687.82266966711
4211013-1031.902040946312044.90204094631
42228446353.1780595051-3509.1780595051
423115284355.762876567937172.23712343207
4246526-1462.975176332277988.97517633227
42522641357.92750368864906.072496311357
42651097050.56506400497-1941.56506400497
4273999-522.4933828521034521.49338285210
4283562423044.016189166412579.9838108336
42992522004.747763751597247.25223624841
430152368686.755920366746549.24407963326
431180734411.426887641813661.5731123582


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
714.08564299509308e-252.04282149754654e-25
818.01317396630329e-344.00658698315164e-34
911.74397693882242e-358.7198846941121e-36
1011.43756901599076e-377.18784507995381e-38
1114.36834507400726e-392.18417253700363e-39
1211.41875753505095e-387.09378767525475e-39
1314.75588929597354e-642.37794464798677e-64
1412.82537690649251e-811.41268845324625e-81
1513.52189761944528e-871.76094880972264e-87
1611.80790083731418e-869.03950418657088e-87
1711.50830879499184e-927.5415439749592e-93
1815.62195537914268e-962.81097768957134e-96
1919.30998410829649e-1014.65499205414824e-101
2012.9779779599268e-1061.4889889799634e-106
2118.85431201133807e-1074.42715600566903e-107
2213.16839544007808e-1161.58419772003904e-116
2312.23698617539458e-1161.11849308769729e-116
2412.8882981982553e-1211.44414909912765e-121
2511.99062518537329e-1209.95312592686645e-121
2615.99224589338272e-1252.99612294669136e-125
2713.30569983620402e-1281.65284991810201e-128
2812.26340232042175e-1291.13170116021087e-129
2911.52325257943499e-1307.61626289717493e-131
3017.90152879720159e-1313.95076439860080e-131
3113.50558826777061e-1301.75279413388531e-130
3216.3126172928039e-1323.15630864640195e-132
3319.08433838577144e-1334.54216919288572e-133
3412.7146464781987e-1321.35732323909935e-132
3511.37401609666689e-1316.87008048333447e-132
3612.60100618576338e-1321.30050309288169e-132
3713.69372543277252e-1341.84686271638626e-134
3815.97238990025701e-1342.98619495012851e-134
3916.51888828581008e-1343.25944414290504e-134
4014.08970810662917e-1352.04485405331458e-135
4112.16351496017715e-1371.08175748008857e-137
4213.64197661930279e-1421.82098830965140e-142
4311.72525584676711e-1418.62627923383555e-142
4411.06663819699153e-1405.33319098495764e-141
4513.17530994762532e-1401.58765497381266e-140
4611.175777922535e-1395.878889612675e-140
4718.34019417330209e-1394.17009708665104e-139
4818.26948502866503e-1394.13474251433251e-139
4913.69046638769839e-1381.84523319384919e-138
5011.88297605450768e-1379.41488027253841e-138
5115.29706626612171e-1372.64853313306086e-137
5218.54743881802654e-1384.27371940901327e-138
5314.20444114071471e-1372.10222057035735e-137
5411.29929921561411e-1366.49649607807054e-137
5512.96456271916003e-1371.48228135958002e-137
5619.85953263699617e-1374.92976631849808e-137
5715.21712127589493e-1392.60856063794746e-139
5817.22713703049789e-1393.61356851524895e-139
5912.18609299262790e-1381.09304649631395e-138
6011.42705876243737e-1387.13529381218685e-139
6115.38179331453581e-1392.69089665726790e-139
6211.11050000529416e-1385.55250002647079e-139
6313.36635249401481e-1381.68317624700740e-138
6417.2587593619518e-1383.6293796809759e-138
6514.85980052531961e-1372.42990026265980e-137
6612.58526368293429e-1381.29263184146714e-138
6711.32492987509511e-1376.62464937547553e-138
6818.73932814324767e-1374.36966407162384e-137
6914.58011872417406e-1372.29005936208703e-137
7012.16201022847737e-1361.08100511423868e-136
7111.17090247735914e-1355.85451238679571e-136
7216.70442379100747e-1363.35221189550373e-136
7312.49222087438354e-1351.24611043719177e-135
7411.51068572690391e-1347.55342863451954e-135
7519.5984177445231e-1344.79920887226155e-134
7614.85100078802768e-1332.42550039401384e-133
7712.35172942681079e-1321.17586471340539e-132
7811.00007292035739e-1315.00036460178697e-132
7912.60631030804078e-1311.30315515402039e-131
8011.23561065830020e-1306.17805329150102e-131
8117.73424308483656e-1303.86712154241828e-130
8213.71437271931682e-1291.85718635965841e-129
8316.58775501338783e-1293.29387750669391e-129
8413.82687808930155e-1281.91343904465078e-128
8512.13845782639027e-1271.06922891319513e-127
8617.87341995714258e-1273.93670997857129e-127
8711.83444491347150e-1269.1722245673575e-127
8819.14584459574598e-1264.57292229787299e-126
8912.26957239617851e-1251.13478619808925e-125
9011.24998223543057e-1246.24991117715283e-125
9117.28916199336352e-1243.64458099668176e-124
9214.00499633900516e-1232.00249816950258e-123
9311.76732832507307e-1228.83664162536537e-123
9411.49855513261458e-1227.4927756630729e-123
9518.04931611995113e-1224.02465805997557e-122
9612.24116877788469e-1211.12058438894234e-121
9711.25823887175801e-1206.29119435879003e-121
9816.96549211469773e-1203.48274605734887e-120
9913.44968965421688e-1191.72484482710844e-119
10011.66204014031692e-1188.3102007015846e-119
10117.26968156099135e-1183.63484078049568e-118
10211.52841950479333e-1177.64209752396663e-118
10314.30752671818624e-1172.15376335909312e-117
10411.24069836589253e-1166.20349182946266e-117
10516.42650586405653e-1163.21325293202827e-116
10611.66563008264699e-1158.32815041323495e-116
10711.91477827085698e-1159.5738913542849e-116
10812.28045080453877e-1151.14022540226938e-115
10919.15763263139627e-1154.57881631569813e-115
11013.86182669195844e-1141.93091334597922e-114
11111.69548845332761e-1138.47744226663805e-114
11216.45418616774734e-1133.22709308387367e-113
11312.81009469033336e-1121.40504734516668e-112
11411.21313184115233e-1116.06565920576165e-112
11512.47318133881195e-1111.23659066940597e-111
11611.27188171759201e-1106.35940858796007e-111
11715.99949330935774e-1102.99974665467887e-110
11811.09065641022666e-1095.45328205113331e-110
11913.78111915417085e-1091.89055957708543e-109
12011.88547405831494e-1089.4273702915747e-109
12119.57115001945042e-1094.78557500972521e-109
12214.67368716814263e-1082.33684358407132e-108
12314.412795060044e-1102.206397530022e-110
12411.45007191459051e-1097.25035957295257e-110
12513.08330879906596e-1091.54165439953298e-109
12611.23449805387842e-1086.17249026939208e-109
12716.01913728728833e-1083.00956864364416e-108
12812.94444370604112e-1071.47222185302056e-107
12911.34861384155214e-1066.74306920776071e-107
13016.48202018664168e-1063.24101009332084e-106
13113.15028979537049e-1051.57514489768525e-105
13211.18231286441721e-1045.91156432208603e-105
13315.64566805319287e-1042.82283402659644e-104
13412.25376305826506e-1031.12688152913253e-103
13511.02091189736530e-1025.10455948682651e-103
13614.79059770535088e-1022.39529885267544e-102
13712.26832886610263e-1011.13416443305131e-101
13811.05865260811858e-1005.29326304059292e-101
13914.42161439626283e-1002.21080719813141e-100
14012.03425059899853e-991.01712529949927e-99
14119.40345691733974e-994.70172845866987e-99
14214.28709407167711e-982.14354703583855e-98
14311.9462873165961e-979.7314365829805e-98
14418.93009872152703e-974.46504936076352e-97
14514.11497798015746e-962.05748899007873e-96
14611.88197385976603e-959.40986929883015e-96
14718.38125233044575e-954.19062616522288e-95
14813.74964484093331e-941.87482242046666e-94
14911.60583424161758e-938.02917120808791e-94
15017.0479475188091e-933.52397375940455e-93
15113.07925065045076e-921.53962532522538e-92
15211.03868179679396e-915.19340898396978e-92
15314.56248013830607e-912.28124006915304e-91
15411.97902290330973e-909.89511451654866e-91
15518.57538082984116e-904.28769041492058e-90
15613.6667198903221e-891.83335994516105e-89
15711.56084822663336e-887.80424113316682e-89
15816.65004734938375e-883.32502367469188e-88
15912.67677767392423e-871.33838883696212e-87
16011.12414012086070e-865.62070060430352e-87
16114.69975373456515e-862.34987686728258e-86
16211.95602014966862e-859.7801007483431e-86
16318.10424290741402e-854.05212145370701e-85
16413.36995574819849e-841.68497787409925e-84
16511.10293571451741e-835.51467857258704e-84
16614.52243257844644e-832.26121628922322e-83
16711.84113749855451e-829.20568749277255e-83
16815.10636638898071e-822.55318319449036e-82
16911.43532125691820e-817.17660628459102e-82
17015.7763098006243e-812.88815490031215e-81
17112.31419696319969e-801.15709848159984e-80
17219.30706859715374e-804.65353429857687e-80
17313.69511812596226e-791.84755906298113e-79
17411.45512740345428e-787.27563701727139e-79
17515.72498313701297e-782.86249156850649e-78
17612.26216130907412e-771.13108065453706e-77
17713.52036909829050e-801.76018454914525e-80
17811.40384945615854e-797.01924728079271e-80
17915.6115906810394e-792.8057953405197e-79
18012.23310198703315e-781.11655099351658e-78
18118.86646236657674e-784.43323118328837e-78
18213.54495938206145e-771.77247969103072e-77
18311.25153611430167e-766.25768057150836e-77
18413.98813023488641e-761.99406511744321e-76
18511.56443907659881e-757.82219538299406e-76
18616.02956981530082e-753.01478490765041e-75
18711.80978573258536e-749.04892866292679e-75
18817.02303502370105e-743.51151751185052e-74
18912.58257981318432e-731.29128990659216e-73
19019.84523560829723e-734.92261780414861e-73
19113.78314989068332e-721.89157494534166e-72
19211.42917538499046e-717.14587692495232e-72
19315.42277257380116e-712.71138628690058e-71
19412.03019854805502e-701.01509927402751e-70
19517.6413520965127e-703.82067604825635e-70
19612.83514057714691e-691.41757028857345e-69
19718.0969736779908e-694.0484868389954e-69
19812.98059330979072e-681.49029665489536e-68
19911.09229359565242e-675.46146797826208e-68
20012.07460270938853e-671.03730135469427e-67
20117.56197619898615e-673.78098809949307e-67
20212.74403968509303e-661.37201984254651e-66
20319.91285496749983e-664.95642748374992e-66
20413.54490670906696e-651.77245335453348e-65
20511.15128863056005e-645.75644315280027e-65
20614.13624273118416e-642.06812136559208e-64
20711.47725606012494e-637.3862803006247e-64
20815.22130088453078e-632.61065044226539e-63
20911.78084421326907e-628.90422106634533e-63
21016.25709640290788e-623.12854820145394e-62
21112.18162862341148e-611.09081431170574e-61
21217.5724424709076e-613.7862212354538e-61
21312.64512827706169e-601.32256413853085e-60
21418.72667237019943e-604.36333618509972e-60
21512.99259899178511e-591.49629949589255e-59
21611.02737302428994e-585.13686512144968e-59
21713.50619884855435e-581.75309942427718e-58
21811.19257457487662e-575.96287287438311e-58
21914.03792381062128e-572.01896190531064e-57
22011.34585618123748e-566.72928090618742e-57
22114.48249849464838e-562.24124924732419e-56
22211.48723762480403e-557.43618812402017e-56
22314.93061214432229e-552.46530607216115e-55
22411.51624035082852e-547.58120175414259e-55
22514.97940565225105e-542.48970282612553e-54
22611.41789980613506e-537.08949903067531e-54
22714.5699382399396e-532.2849691199698e-53
22811.47735681017452e-527.38678405087261e-53
22914.76521849365172e-522.38260924682586e-52
23011.52656509228363e-517.63282546141815e-52
23114.78189116562144e-512.39094558281072e-51
23211.50384189328225e-507.51920946641124e-51
23314.75301453654807e-502.37650726827403e-50
23411.44193801987582e-497.20969009937909e-50
23514.51712985579533e-492.25856492789766e-49
23611.40838966571860e-487.04194832859301e-49
23714.37166170165594e-482.18583085082797e-48
23811.35851535110401e-476.79257675552007e-48
23914.17808789384584e-472.08904394692292e-47
24011.27903969438679e-466.39519847193396e-47
24113.89745050596095e-461.94872525298048e-46
24211.18854917809312e-455.94274589046562e-46
24313.58823354228016e-451.79411677114008e-45
24411.08116925051872e-445.40584625259362e-45
24512.02733652300919e-441.01366826150459e-44
24616.05259147986786e-443.02629573993393e-44
24711.79856025594208e-438.9928012797104e-44
24814.94836787132634e-432.47418393566317e-43
24911.45760407394997e-427.28802036974986e-43
25014.27341279939395e-422.13670639969698e-42
25111.24226732811425e-416.21133664057126e-42
25213.60825659101215e-411.80412829550607e-41
25311.04430947940149e-405.22154739700744e-41
25411.58633362040943e-407.93166810204713e-41
25511.18484856818498e-405.92424284092488e-41
25613.42557024702307e-401.71278512351153e-40
25719.89795813975591e-404.94897906987796e-40
25812.47962490956515e-391.23981245478257e-39
25917.07402528920679e-393.53701264460339e-39
26012.00847327010062e-381.00423663505031e-38
26115.67517602161304e-382.83758801080652e-38
26211.59588412632066e-377.97942063160328e-38
26314.43148081245033e-372.21574040622516e-37
26411.23860739510232e-366.19303697551162e-37
26513.43293813907372e-361.71646906953686e-36
26619.4686289645864e-364.7343144822932e-36
26712.59891106657685e-351.29945553328842e-35
26817.00133886509528e-353.50066943254764e-35
26911.90292501833185e-349.51462509165926e-35
27014.13897899450516e-342.06948949725258e-34
27111.11607213199347e-335.58036065996735e-34
27212.99465526298974e-331.49732763149487e-33
27317.99559206858744e-333.99779603429372e-33
27411.89639615985287e-329.48198079926434e-33
27515.01862712064064e-322.50931356032032e-32
27611.31403050198654e-316.57015250993268e-32
27713.06572698869492e-311.53286349434746e-31
27818.00192829871052e-314.00096414935526e-31
27911.78220808931137e-308.91104044655685e-31
28014.49000518256243e-302.24500259128121e-30
28111.15640313347519e-295.78201566737595e-30
28212.96320693437763e-291.48160346718882e-29
28316.47477753177087e-293.23738876588544e-29
28411.64466045841301e-288.22330229206504e-29
28514.16149600423348e-282.08074800211674e-28
28611.04626829432675e-275.23134147163376e-28
28712.57996642389376e-271.28998321194688e-27
28816.44416649821438e-273.22208324910719e-27
28911.59521464478299e-267.97607322391497e-27
29013.77519028417694e-261.88759514208847e-26
29119.25224884410161e-264.62612442205081e-26
29212.23517890911416e-251.11758945455708e-25
29315.33000022149924e-252.66500011074962e-25
29411.28616399092552e-246.43081995462761e-25
29512.98002205663746e-241.49001102831873e-24
29617.04426658854812e-243.52213329427406e-24
29711.67351599192665e-238.36757995963324e-24
29813.95437261735015e-231.97718630867508e-23
29919.29327299676325e-234.64663649838162e-23
30012.14497472600754e-221.07248736300377e-22
30114.99530544057666e-222.49765272028833e-22
30211.15272192819418e-215.76360964097089e-22
30312.65546309957494e-211.32773154978747e-21
30416.07300722592092e-213.03650361296046e-21
30511.38377107930030e-206.91885539650148e-21
30613.03838791639356e-201.51919395819678e-20
30716.83553356516448e-203.41776678258224e-20
30811.41332917543008e-197.0666458771504e-20
30913.08012215985726e-191.54006107992863e-19
31016.82127553843877e-193.41063776921938e-19
31111.50199938304365e-187.50999691521827e-19
31212.43209518348098e-181.21604759174049e-18
31315.31349902770235e-182.65674951385118e-18
31411.10876866260017e-175.54384331300085e-18
31512.37314003387214e-171.18657001693607e-17
31615.09345359477145e-172.54672679738572e-17
31711.08668429239611e-165.43342146198056e-17
31812.07391254799812e-161.03695627399906e-16
31914.37629214851671e-162.18814607425836e-16
32019.19240842267648e-164.59620421133824e-16
32111.91918653537857e-159.59593267689286e-16
3220.9999999999999983.98248368337041e-151.99124184168520e-15
3230.9999999999999968.21341033844824e-154.10670516922412e-15
3240.9999999999999921.68348468897782e-148.4174234448891e-15
3250.9999999999999833.42920986576596e-141.71460493288298e-14
3260.9999999999999656.94694030236285e-143.47347015118142e-14
3270.999999999999931.39741563227422e-136.9870781613711e-14
3280.999999999999862.79321360443755e-131.39660680221877e-13
3290.9999999999997235.54767401340895e-132.77383700670447e-13
3300.9999999999994531.09478110800175e-125.47390554000874e-13
3310.9999999999989272.14651631699558e-121.07325815849779e-12
3320.999999999997914.1813084608768e-122.0906542304384e-12
3330.9999999999959548.09173236409899e-124.04586618204950e-12
3340.9999999999922141.55724704986871e-117.78623524934356e-12
3350.9999999999851172.97649762959177e-111.48824881479589e-11
3360.999999999971765.64781423259244e-112.82390711629622e-11
3370.9999999999486841.02631852713815e-105.13159263569077e-11
3380.9999999999039021.92196962868063e-109.60984814340316e-11
3390.9999999998211513.57697400325139e-101.78848700162569e-10
3400.999999999671446.57118036662153e-103.28559018331077e-10
3410.9999999994193231.16135444496863e-095.80677222484313e-10
3420.9999999989414042.11719258360864e-091.05859629180432e-09
3430.9999999980837383.83252314685158e-091.91626157342579e-09
3440.9999999965558426.88831654314522e-093.44415827157261e-09
3450.9999999938827311.2234537660535e-086.11726883026749e-09
3460.99999998916152.16770002102393e-081.08385001051197e-08
3470.999999980953553.80928994752572e-081.90464497376286e-08
3480.9999999667441456.65117103122227e-083.32558551561114e-08
3490.9999999423641061.15271787973208e-075.76358939866039e-08
3500.9999999042237061.91552588474298e-079.5776294237149e-08
3510.9999998369202043.26159591563107e-071.63079795781553e-07
3520.9999997245199745.50960052891627e-072.75480026445813e-07
3530.9999995365139769.26972047868178e-074.63486023934089e-07
3540.9999992262773161.54744536776506e-067.73722683882532e-07
3550.9999987183839032.56323219385914e-061.28161609692957e-06
3560.9999978938478124.21230437673525e-062.10615218836763e-06
3570.999996673949386.6521012386954e-063.3260506193477e-06
3580.9999946166708481.07666583031335e-055.38332915156673e-06
3590.9999913576722121.7284655576041e-058.6423277880205e-06
3600.9999862395316152.75209367707283e-051.37604683853641e-05
3610.999978273692524.34526149615844e-052.17263074807922e-05
3620.999965979784456.80404310993976e-053.40202155496988e-05
3630.9999471796298930.0001056407402145415.28203701072704e-05
3640.999918984648310.0001620307033801458.10153516900724e-05
3650.9998765539056030.0002468921887935590.000123446094396779
3660.9998132151424120.0003735697151768630.000186784857588431
3670.9997198676827820.0005602646344354520.000280132317217726
3680.9995836090268870.000832781946226180.00041639097311309
3690.9993866411818950.001226717636210980.000613358818105488
3700.9991048454975330.001790309004934330.000895154502467165
3710.99870536790850.00258926418299840.0012946320914992
3720.9981456368944730.003708726211054620.00185436310552731
3730.997373799247920.005252401504160450.00262620075208022
3740.9963129647607830.007374070478434840.00368703523921742
3750.994864983037870.01027003392426190.00513501696213096
3760.9929176371351090.01416472572978230.00708236286489114
3770.99042973862620.01914052274760110.00957026137380057
3780.9870664080850050.02586718382999060.0129335919149953
3790.9833306002516350.03333879949673020.0166693997483651
3800.9778617886847540.04427642263049220.0221382113152461
3810.9708985589071750.05820288218565050.0291014410928252
3820.9627698019412230.0744603961175540.037230198058777
3830.9520630519799680.09587389604006380.0479369480200319
3840.93886575828270.1222684834346010.0611342417173005
3850.923320040874670.1533599182506590.0766799591253296
3860.9066587150690450.1866825698619100.0933412849309552
3870.8845274031400120.2309451937199750.115472596859988
3880.8589359579807340.2821280840385320.141064042019266
3890.8291497078312920.3417005843374150.170850292168707
3900.795307421664180.4093851566716410.204692578335820
3910.7575009666330830.4849980667338350.242499033366917
3920.716085099783250.56782980043350.28391490021675
3930.673233183221370.653533633557260.32676681677863
3940.6271912754481790.7456174491036430.372808724551821
3950.5808355049722730.8383289900554540.419164495027727
3960.5289825979100670.9420348041798650.471017402089933
3970.4832403095251480.9664806190502970.516759690474852
3980.9993776786445060.001244642710987660.000622321355493828
3990.9989266106445590.002146778710882680.00107338935544134
4000.9986825385168010.00263492296639810.00131746148319905
4010.9978587591627230.004282481674554630.00214124083727731
4020.9964591565207550.007081686958491030.00354084347924552
4030.9942167319024980.01156653619500320.00578326809750158
4040.9910051471858370.01798970562832640.0089948528141632
4050.9858041916279670.02839161674406620.0141958083720331
4060.978124983030960.04375003393808060.0218750169690403
4070.966882528988430.06623494202314210.0331174710115711
4080.9508919833969670.09821603320606680.0491080166030334
4090.9288965814987880.1422068370024230.0711034185012117
4100.8993718903899270.2012562192201460.100628109610073
4110.9293866314581660.1412267370836690.0706133685418343
4120.9344830279650980.1310339440698050.0655169720349025
4130.9039306915252320.1921386169495360.0960693084747681
4140.8627064396210340.2745871207579320.137293560378966
4150.8089671377406270.3820657245187460.191032862259373
4160.740585453518050.5188290929638990.259414546481950
4170.6806728675314740.6386542649370520.319327132468526
4180.5903045896932580.8193908206134840.409695410306742
4190.5013504060216420.9972991879567160.498649593978358
4200.3948548635161790.7897097270323590.60514513648382
4210.2925943497846490.5851886995692990.70740565021535
4220.4531569942654770.9063139885309530.546843005734523
4230.3410712395219950.682142479043990.658928760478005
4240.2134217199741530.4268434399483070.786578280025847


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level3730.892344497607656NOK
5% type I error level3830.916267942583732NOK
10% type I error level3880.92822966507177NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/10tfpo1292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/10tfpo1292938441.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/1e5sx1292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/1e5sx1292938441.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/2e5sx1292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/2e5sx1292938441.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/3e5sx1292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/3e5sx1292938441.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/47fr01292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/47fr01292938441.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/57fr01292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/57fr01292938441.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/67fr01292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/67fr01292938441.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/706831292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/706831292938441.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/8tfpo1292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/8tfpo1292938441.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/9tfpo1292938441.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292938369o3esxowf8usrjs3/9tfpo1292938441.ps (open in new window)


 
Parameters (Session):
par1 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;
 
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = yes ;
 
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