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Paper - werkloosheid Brussel - MPR (1)

*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: Fri, 12 Dec 2008 02:25:01 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv.htm/, Retrieved Fri, 12 Dec 2008 09:26:01 +0000
 
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/2008/Dec/12/t12290739491vuikspz1devyjv.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
 
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Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
51.220 50.487 49.415 49.398 48.196 47.348 49.331 49.644 49.588 49.567 49.010 49.563 49.741 49.487 48.278 47.478 46.985 45.216 46.581 49.266 48.121 46.412 46.285 46.824 46.949 45.355 44.924 45.059 44.202 44.149 46.151 47.703 48.436 47.089 47.492 49.295 49.127 50.041 48.857 48.428 48.788 48.820 50.743 52.590 51.959 53.451 55.674 56.120 55.685 56.714 54.882 55.173 53.574 53.954 58.055 61.062 58.353 59.693 58.833 60.417 61.696 62.515 62.687 61.794 63.014 63.134 68.057 67.327 68.310 69.780 69.944 69.881 71.397 70.631 70.452 69.862 69.114 69.358 71.133 73.128 73.528 73.677 72.273 71.962 73.654 73.305 73.355 73.346 72.881 72.424 74.540 74.847 75.904 76.870 76.370 77.631 78.335 77.926 77.236 76.755 74.710 73.486 76.034 76.389 77.767 78.124 76.696 77.375 77.431 77.347 77.013 76.666 75.225 75.579 77.100 78.592 79.502 78.528 77.775 77.271 78.738 77.885 76.896 75.813 74.958 75.340 77.187 78.602 81.653 78.125 76.092 74.870 75.615 74.776 72.528 71.894 71.641 71.145 73.320 72.186 72.854 74.243 74.628 72.368 75.361 72.746 70.536 69.410 66.219 66.739 67.626 70.602 71.758 71.786 69.641 68.055 70.148 69.390 68.562 68.622 68.120 68.308 70.421 69.766 72.157 72.928 75.340 74.812 74.593 76.003 75.112 75.452 75.634 75.653 78.645 73.100 79.699 82.848 81.834 81.736 82.267 84.120 83.819 82.734 81.842 81.735 83.227 81.934 89.521 88.827 85.874 85.211 87.130 88.620 89.563 89.056 88.542 89.504 89.428 86.040 96.240 94.423 93.028 92.285 91.685 94.260 93.858 92.437 92.980 92.099 92.803 88.551 98.334 98.329 96.455 97.109 97.687 98.512 98.673 96.028 98.014 95.580 97.838 97.760 99.913 97.588 93.942 93.656 93.365 92.881 93.120 91.063 90.930 91.946 94.624 95.484 95.862 95.530 94.574 94.677 93.845 91.533 91.214 90.922 89.563 89.945 91.850 92.505 92.437 93.876
 
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 time8 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
Werkloosheid[t] = + 73.5559 -0.428804761904705M1[t] -0.482852380952313M2[t] -1.12828095238089M3[t] -1.77542380952377M4[t] -2.35913809523804M5[t] -2.53389999999994M6[t] -0.475233333333273M7[t] -0.361709523809474M8[t] + 1.77248095238100M9[t] + 1.76286190476196M10[t] + 0.0321000000000522M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)73.55593.55461420.693100
M1-0.4288047619047054.966777-0.08630.9312730.465637
M2-0.4828523809523134.966777-0.09720.9226360.461318
M3-1.128280952380894.966777-0.22720.820490.410245
M4-1.775423809523774.966777-0.35750.7210640.360532
M5-2.359138095238044.966777-0.4750.6352340.317617
M6-2.533899999999944.966777-0.51020.6104050.305203
M7-0.4752333333332734.966777-0.09570.9238530.461927
M8-0.3617095238094744.966777-0.07280.9420060.471003
M91.772480952381004.9667770.35690.7215070.360754
M101.762861904761964.9667770.35490.7229560.361478
M110.03210000000005225.0269830.00640.994910.497455


Multiple Linear Regression - Regression Statistics
Multiple R0.0841640134692795
R-squared0.00708358116325706
Adjusted R-squared-0.0388075138249959
F-TEST (value)0.154356333512424
F-TEST (DF numerator)11
F-TEST (DF denominator)238
p-value0.99925543865729
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.8967152076055
Sum Squared Residuals60143.9219452286


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
151.2273.127095238095-21.9070952380950
250.48773.0730476190477-22.5860476190477
349.41572.427619047619-23.0126190476191
449.39871.7804761904761-22.3824761904761
548.19671.196761904762-23.0007619047619
647.34871.022-23.674
749.33173.0806666666666-23.7496666666666
849.64473.1941904761905-23.5501904761905
949.58875.328380952381-25.7403809523809
1049.56775.3187619047619-25.7517619047619
1149.0173.588-24.578
1249.56373.5559-23.9929000000000
1349.74173.1270952380952-23.3860952380952
1449.48773.0730476190476-23.5860476190476
1548.27872.4276190476191-24.1496190476191
1647.47871.7804761904762-24.3024761904762
1746.98571.1967619047619-24.2117619047619
1845.21671.022-25.806
1946.58173.0806666666667-26.4996666666667
2049.26673.1941904761905-23.9281904761905
2148.12175.328380952381-27.2073809523809
2246.41275.3187619047619-28.9067619047619
2346.28573.588-27.303
2446.82473.5559-26.7318999999999
2546.94973.1270952380952-26.1780952380952
2645.35573.0730476190476-27.7180476190476
2744.92472.4276190476191-27.5036190476191
2845.05971.7804761904762-26.7214761904762
2944.20271.1967619047619-26.9947619047619
3044.14971.022-26.873
3146.15173.0806666666667-26.9296666666667
3247.70373.1941904761905-25.4911904761905
3348.43675.328380952381-26.8923809523810
3447.08975.3187619047619-28.2297619047619
3547.49273.588-26.096
3649.29573.5559-24.2608999999999
3749.12773.1270952380952-24.0000952380952
3850.04173.0730476190476-23.0320476190476
3948.85772.4276190476191-23.5706190476191
4048.42871.7804761904762-23.3524761904762
4148.78871.1967619047619-22.4087619047619
4248.8271.022-22.202
4350.74373.0806666666667-22.3376666666667
4452.5973.1941904761905-20.6041904761905
4551.95975.328380952381-23.3693809523809
4653.45175.3187619047619-21.8677619047619
4755.67473.588-17.914
4856.1273.5559-17.4359000000000
4955.68573.1270952380952-17.4420952380952
5056.71473.0730476190476-16.3590476190476
5154.88272.427619047619-17.5456190476191
5255.17371.7804761904762-16.6074761904762
5353.57471.1967619047619-17.6227619047619
5453.95471.022-17.068
5558.05573.0806666666667-15.0256666666667
5661.06273.1941904761905-12.1321904761905
5758.35375.328380952381-16.9753809523810
5859.69375.3187619047619-15.6257619047619
5958.83373.588-14.755
6060.41773.5559-13.1388999999999
6161.69673.1270952380952-11.4310952380952
6262.51573.0730476190476-10.5580476190476
6362.68772.427619047619-9.74061904761906
6461.79471.7804761904762-9.98647619047617
6563.01471.1967619047619-8.1827619047619
6663.13471.022-7.888
6768.05773.0806666666667-5.02366666666666
6867.32773.1941904761905-5.86719047619047
6968.3175.328380952381-7.01838095238095
7069.7875.3187619047619-5.5387619047619
7169.94473.588-3.64399999999999
7269.88173.5559-3.67489999999994
7371.39773.1270952380952-1.73009523809522
7470.63173.0730476190476-2.44204761904763
7570.45272.427619047619-1.97561904761906
7669.86271.7804761904762-1.91847619047617
7769.11471.1967619047619-2.08276190476190
7869.35871.022-1.66400000000000
7971.13373.0806666666667-1.94766666666667
8073.12873.1941904761905-0.0661904761904699
8173.52875.328380952381-1.80038095238095
8273.67775.3187619047619-1.64176190476189
8372.27373.588-1.31500000000000
8471.96273.5559-1.59389999999994
8573.65473.12709523809520.526904761904773
8673.30573.07304761904760.231952380952377
8773.35572.4276190476190.927380952380945
8873.34671.78047619047621.56552380952383
8972.88171.19676190476191.6842380952381
9072.42471.0221.402
9174.5473.08066666666671.45933333333334
9274.84773.19419047619051.65280952380953
9375.90475.3283809523810.575619047619044
9476.8775.31876190476191.55123809523810
9576.3773.5882.78200000000001
9677.63173.55594.07510000000006
9778.33573.12709523809525.20790476190477
9877.92673.07304761904764.85295238095237
9977.23672.4276190476194.80838095238094
10076.75571.78047619047624.97452380952383
10174.7171.19676190476193.51323809523809
10273.48671.0222.46400000000000
10376.03473.08066666666672.95333333333334
10476.38973.19419047619053.19480952380953
10577.76775.3283809523812.43861904761904
10678.12475.31876190476192.80523809523809
10776.69673.5883.10800000000001
10877.37573.55593.81910000000006
10977.43173.12709523809524.30390476190477
11077.34773.07304761904764.27395238095237
11177.01372.4276190476194.58538095238095
11276.66671.78047619047624.88552380952383
11375.22571.19676190476194.02823809523809
11475.57971.0224.55699999999999
11577.173.08066666666674.01933333333333
11678.59273.19419047619055.39780952380953
11779.50275.3283809523814.17361904761904
11878.52875.31876190476193.2092380952381
11977.77573.5884.18700000000001
12077.27173.55593.71510000000006
12178.73873.12709523809525.61090476190478
12277.88573.07304761904764.81195238095238
12376.89672.4276190476194.46838095238094
12475.81371.78047619047624.03252380952383
12574.95871.19676190476193.7612380952381
12675.3471.0224.318
12777.18773.08066666666674.10633333333333
12878.60273.19419047619055.40780952380953
12981.65375.3283809523816.32461904761905
13078.12575.31876190476192.8062380952381
13176.09273.5882.50400000000001
13274.8773.55591.31410000000006
13375.61573.12709523809522.48790476190477
13474.77673.07304761904761.70295238095237
13572.52872.4276190476190.100380952380946
13671.89471.78047619047620.113523809523836
13771.64171.19676190476190.444238095238106
13871.14571.0220.122999999999988
13973.3273.08066666666670.239333333333329
14072.18673.1941904761905-1.00819047619046
14172.85475.328380952381-2.47438095238095
14274.24375.3187619047619-1.07576190476191
14374.62873.5881.04000000000001
14472.36873.5559-1.18789999999995
14575.36173.12709523809522.23390476190478
14672.74673.0730476190476-0.327047619047635
14770.53672.427619047619-1.89161904761906
14869.4171.7804761904762-2.37047619047617
14966.21971.1967619047619-4.97776190476191
15066.73971.022-4.28300000000000
15167.62673.0806666666667-5.45466666666666
15270.60273.1941904761905-2.59219047619047
15371.75875.328380952381-3.57038095238096
15471.78675.3187619047619-3.5327619047619
15569.64173.588-3.94699999999999
15668.05573.5559-5.50089999999993
15770.14873.1270952380952-2.97909523809523
15869.3973.0730476190476-3.68304761904763
15968.56272.427619047619-3.86561904761906
16068.62271.7804761904762-3.15847619047617
16168.1271.1967619047619-3.07676190476190
16268.30871.022-2.714
16370.42173.0806666666667-2.65966666666666
16469.76673.1941904761905-3.42819047619046
16572.15775.328380952381-3.17138095238096
16672.92875.3187619047619-2.39076190476190
16775.3473.5881.75200000000001
16874.81273.55591.25610000000005
16974.59373.12709523809521.46590476190478
17076.00373.07304761904762.92995238095237
17175.11272.4276190476192.68438095238093
17275.45271.78047619047623.67152380952383
17375.63471.19676190476194.4372380952381
17475.65371.0224.631
17578.64573.08066666666675.56433333333333
17673.173.1941904761905-0.0941904761904757
17779.69975.3283809523814.37061904761904
17882.84875.31876190476197.5292380952381
17981.83473.5888.24600000000001
18081.73673.55598.18010000000006
18182.26773.12709523809529.13990476190477
18284.1273.073047619047611.0469523809524
18383.81972.42761904761911.3913809523809
18482.73471.780476190476210.9535238095238
18581.84271.196761904761910.6452380952381
18681.73571.02210.713
18783.22773.080666666666710.1463333333333
18881.93473.19419047619058.73980952380953
18989.52175.32838095238114.1926190476191
19088.82775.318761904761913.5082380952381
19185.87473.58812.2860000000000
19285.21173.555911.6551000000001
19387.1373.127095238095214.0029047619048
19488.6273.073047619047615.5469523809524
19589.56372.42761904761917.1353809523809
19689.05671.780476190476217.2755238095238
19788.54271.196761904761917.3452380952381
19889.50471.02218.482
19989.42873.080666666666716.3473333333333
20086.0473.194190476190512.8458095238095
20196.2475.32838095238120.9116190476190
20294.42375.318761904761919.1042380952381
20393.02873.58819.4400000000000
20492.28573.555918.7291000000001
20591.68573.127095238095218.5579047619048
20694.2673.073047619047621.1869523809524
20793.85872.427619047619121.4303809523809
20892.43771.780476190476220.6565238095238
20992.9871.196761904761921.7832380952381
21092.09971.02221.077
21192.80373.080666666666719.7223333333333
21288.55173.194190476190515.3568095238095
21398.33475.32838095238123.0056190476191
21498.32975.318761904761923.0102380952381
21596.45573.58822.867
21697.10973.555923.5531000000001
21797.68773.127095238095224.5599047619048
21898.51273.073047619047625.4389523809524
21998.67372.427619047619126.2453809523809
22096.02871.780476190476224.2475238095238
22198.01471.196761904761926.8172380952381
22295.5871.02224.558
22397.83873.080666666666724.7573333333333
22497.7673.194190476190524.5658095238095
22599.91375.32838095238124.5846190476191
22697.58875.318761904761922.2692380952381
22793.94273.58820.354
22893.65673.555920.1001000000001
22993.36573.127095238095220.2379047619048
23092.88173.073047619047619.8079523809524
23193.1272.427619047619120.6923809523809
23291.06371.780476190476219.2825238095238
23390.9371.196761904761919.7332380952381
23491.94671.02220.924
23594.62473.080666666666721.5433333333333
23695.48473.194190476190522.2898095238095
23795.86275.32838095238120.5336190476190
23895.5375.318761904761920.2112380952381
23994.57473.58820.986
24094.67773.555921.1211000000001
24193.84573.127095238095220.7179047619048
24291.53373.073047619047618.4599523809524
24391.21472.427619047619118.7863809523809
24490.92271.780476190476219.1415238095238
24589.56371.196761904761918.3662380952381
24689.94571.02218.923
24791.8573.080666666666718.7693333333333
24892.50573.194190476190519.3108095238095
24992.43775.32838095238117.1086190476190
25093.87675.318761904761918.5572380952381


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.0002569985025118390.0005139970050236780.999743001497488
164.04858878346574e-058.09717756693148e-050.999959514112165
173.59169743771555e-067.1833948754311e-060.999996408302562
186.81911757551511e-071.36382351510302e-060.999999318088242
192.07676467652219e-074.15352935304438e-070.999999792323532
201.70887478551314e-083.41774957102627e-080.999999982911252
212.04096034466086e-094.08192068932172e-090.99999999795904
229.20031906818506e-101.84006381363701e-090.999999999079968
232.49830266004884e-104.99660532009769e-100.99999999975017
246.66856425849253e-111.33371285169851e-100.999999999933314
254.65227520746421e-119.30455041492841e-110.999999999953477
266.97428956227612e-111.39485791245522e-100.999999999930257
274.33979166608765e-118.6795833321753e-110.999999999956602
281.82108784316294e-113.64217568632588e-110.999999999981789
297.71238311958019e-121.54247662391604e-110.999999999992288
301.95266257070194e-123.90532514140389e-120.999999999998047
314.58419112602596e-139.16838225205192e-130.999999999999542
321.05683560373585e-132.1136712074717e-130.999999999999894
332.00373812602538e-144.00747625205076e-140.99999999999998
344.17376938101026e-158.34753876202052e-150.999999999999996
357.89293971648583e-161.57858794329717e-151
361.62622630639265e-163.25245261278531e-161
373.08672642652795e-176.17345285305589e-171
387.79025535034956e-181.55805107006991e-171
391.86331812820255e-183.7266362564051e-181
404.28441242582847e-198.56882485165693e-191
411.63124772113488e-193.26249544226976e-191
421.15292126664639e-192.30584253329279e-191
439.02661799683471e-201.80532359936694e-191
448.39392861485626e-201.67878572297125e-191
456.16949161144433e-201.23389832228887e-191
463.21898164992954e-196.43796329985908e-191
479.49057189884143e-181.89811437976829e-171
487.70714799520895e-171.54142959904179e-161
492.39372809478734e-164.78745618957467e-161
501.56740722744306e-153.13481445488612e-150.999999999999998
514.90475640281482e-159.80951280562965e-150.999999999999995
521.77477700096559e-143.54955400193118e-140.999999999999982
533.60688624268522e-147.21377248537044e-140.999999999999964
541.09619594896989e-132.19239189793978e-130.99999999999989
558.33752203265016e-131.66750440653003e-120.999999999999166
568.41798057194066e-121.68359611438813e-110.999999999991582
572.77390690525558e-115.54781381051116e-110.99999999997226
581.50153752542756e-103.00307505085512e-100.999999999849846
594.2416683218278e-108.4833366436556e-100.999999999575833
601.30556051089887e-092.61112102179774e-090.99999999869444
615.54998204988655e-091.10999640997731e-080.999999994450018
622.63018235903987e-085.26036471807974e-080.999999973698176
631.49454687697986e-072.98909375395972e-070.999999850545312
645.67138982675914e-071.13427796535183e-060.999999432861017
652.82549655755207e-065.65099311510414e-060.999997174503442
661.27649685413553e-052.55299370827107e-050.999987235031459
677.4608332545781e-050.0001492166650915620.999925391667454
680.0002042600854734630.0004085201709469260.999795739914527
690.000674820609761170.001349641219522340.99932517939024
700.002227595770630390.004455191541260790.99777240422937
710.005754040531064150.01150808106212830.994245959468936
720.01144034587355680.02288069174711370.988559654126443
730.02397128612082640.04794257224165290.976028713879174
740.04216604206483520.08433208412967040.957833957935165
750.07117894553239090.1423578910647820.928821054467609
760.1082980483251380.2165960966502760.891701951674862
770.1522660265823850.3045320531647690.847733973417615
780.2078059375859070.4156118751718130.792194062414093
790.2614484620109100.5228969240218190.73855153798909
800.3182944733299580.6365889466599160.681705526670042
810.3915318460394670.7830636920789330.608468153960533
820.4636514675138060.9273029350276130.536348532486194
830.5168744790169050.966251041966190.483125520983095
840.5580148494658350.883970301068330.441985150534165
850.6118690559413760.7762618881172470.388130944058624
860.659905700162940.6801885996741190.340094299837059
870.708502455162860.5829950896742790.291497544837140
880.752111193206190.4957776135876190.247888806793810
890.789809436025310.420381127949380.21019056397469
900.8218156351591680.3563687296816640.178184364840832
910.8467441353570560.3065117292858880.153255864642944
920.8625446106171220.2749107787657560.137455389382878
930.8840964119318640.2318071761362730.115903588068136
940.9034848517194140.1930302965611710.0965151482805857
950.9171768779988690.1656462440022620.082823122001131
960.9291618690522860.1416762618954280.0708381309477139
970.9415204757361910.1169590485276180.0584795242638089
980.951200547143640.09759890571272080.0487994528563604
990.9591696787132470.08166064257350630.0408303212867531
1000.9653341899655240.06933162006895120.0346658100344756
1010.9692614904027910.06147701919441740.0307385095972087
1020.9722235940796960.05555281184060830.0277764059203042
1030.9745694470816590.05086110583668270.0254305529183413
1040.9756409695218480.04871806095630460.0243590304781523
1050.9782308343117930.04353833137641380.0217691656882069
1060.9803502761802920.03929944763941590.0196497238197079
1070.9813334826425390.03733303471492250.0186665173574613
1080.9819124756553970.03617504868920610.0180875243446030
1090.9828355358492380.03432892830152430.0171644641507622
1100.9836954505289570.03260909894208570.0163045494710429
1110.9845596763644460.03088064727110820.0154403236355541
1120.9852028953526450.02959420929471050.0147971046473553
1130.985610658130640.02877868373871910.0143893418693596
1140.9861683011039050.02766339779219010.0138316988960951
1150.9863289822107350.02734203557853070.0136710177892653
1160.9861999666610770.02760006667784580.0138000333389229
1170.9867642201315920.02647155973681570.0132357798684078
1180.9869937213816270.02601255723674550.0130062786183727
1190.9867830870376850.02643382592463000.0132169129623150
1200.9861471993098240.02770560138035210.0138528006901761
1210.9860019968070810.02799600638583710.0139980031929185
1220.9857386316505150.02852273669897090.0142613683494854
1230.9854076040483180.02918479190336490.0145923959516824
1240.9848278027691450.030344394461710.015172197230855
1250.9842538868287870.03149222634242530.0157461131712127
1260.983752416171350.03249516765730020.0162475838286501
1270.9829846625048330.03403067499033370.0170153374951668
1280.9817147132530180.03657057349396470.0182852867469824
1290.9816173168325240.03676536633495250.0183826831674763
1300.9810357793054760.03792844138904710.0189642206945236
1310.9798605246609330.04027895067813460.0201394753390673
1320.9783058970541970.04338820589160650.0216941029458033
1330.9770251958358820.04594960832823580.0229748041641179
1340.9759339691794360.04813206164112820.0240660308205641
1350.975302004631290.04939599073741980.0246979953687099
1360.9744127439137620.05117451217247550.0255872560862378
1370.9735479372406910.05290412551861740.0264520627593087
1380.972882195251980.05423560949603950.0271178047480197
1390.971840042158360.05631991568328060.0281599578416403
1400.970095651219520.05980869756095890.0299043487804794
1410.971305466843370.05738906631325930.0286945331566297
1420.9718505217114140.0562989565771720.028149478288586
1430.9708253773949290.05834924521014210.0291746226050710
1440.9704269760414370.05914604791712630.0295730239585631
1450.9691867280373920.06162654392521540.0308132719626077
1460.9696383878079770.06072322438404610.0303616121920231
1470.9716439176685870.05671216466282650.0283560823314133
1480.9736816206905540.05263675861889270.0263183793094463
1490.978223933448140.04355213310371790.0217760665518589
1500.9818655734631830.03626885307363320.0181344265368166
1510.9856672270434920.02866554591301650.0143327729565083
1520.9864001117144630.02719977657107380.0135998882855369
1530.9892263374467140.02154732510657180.0107736625532859
1540.9916495129483020.01670097410339670.00835048705169837
1550.9936764999708170.01264700005836570.00632350002918284
1560.9957096317438260.008580736512348360.00429036825617418
1570.99684686951770.006306260964598910.00315313048229946
1580.997989298490890.004021403018219730.00201070150910987
1590.9988612098144460.002277580371108250.00113879018555412
1600.999333302082740.001333395834518570.000666697917259286
1610.9996522361759160.00069552764816810.000347763824084050
1620.9998272540098910.0003454919802171250.000172745990108563
1630.9999174590613640.0001650818772716278.25409386358137e-05
1640.9999574128984628.51742030755114e-054.25871015377557e-05
1650.9999869401747412.61196505177057e-051.30598252588528e-05
1660.9999961901369877.61972602679348e-063.80986301339674e-06
1670.999998023947983.95210403884993e-061.97605201942496e-06
1680.999999080722041.83855591905622e-069.19277959528108e-07
1690.9999996342844797.31431042491615e-073.65715521245807e-07
1700.9999998539618862.92076227397383e-071.46038113698691e-07
1710.9999999567642218.6471558345473e-084.32357791727365e-08
1720.9999999842387343.15225312562767e-081.57612656281384e-08
1730.9999999946809151.06381691983396e-085.31908459916979e-09
1740.9999999983357533.32849331214559e-091.66424665607280e-09
1750.999999999344481.31103984916472e-096.55519924582358e-10
1760.9999999999292861.41428682277561e-107.07143411387806e-11
1770.9999999999917551.64893468694148e-118.24467343470738e-12
1780.999999999997495.01829085838669e-122.50914542919335e-12
1790.9999999999990031.99422961999573e-129.97114809997866e-13
1800.9999999999996237.53762591314331e-133.76881295657166e-13
1810.9999999999998562.88577364470445e-131.44288682235223e-13
1820.9999999999999241.51283204221085e-137.56416021105424e-14
1830.9999999999999676.50169605704745e-143.25084802852373e-14
1840.9999999999999852.97740958286027e-141.48870479143013e-14
1850.9999999999999968.71184805726896e-154.35592402863448e-15
1860.9999999999999992.29773419103405e-151.14886709551702e-15
18715.24533948632669e-162.62266974316335e-16
18819.89970844907205e-174.94985422453602e-17
18915.75598955509871e-172.87799477754936e-17
19013.21046946442661e-171.60523473221331e-17
19119.27192332211558e-184.63596166105779e-18
19211.53779846192314e-187.68899230961572e-19
19317.28307024533833e-193.64153512266917e-19
19416.0584981901041e-193.02924909505205e-19
19517.32167705567786e-193.66083852783893e-19
19611.31557612075314e-186.57788060376569e-19
19711.83992350585842e-189.1996175292921e-19
19813.97099524230866e-181.98549762115433e-18
19914.79504270422333e-182.39752135211167e-18
20011.26503625959382e-186.32518129796911e-19
20114.08985093583899e-182.04492546791949e-18
20211.19379200650072e-175.96896003250362e-18
20313.61849284690554e-171.80924642345277e-17
20419.3218346242384e-174.6609173121192e-17
20512.19573072587962e-161.09786536293981e-16
20617.96072248518051e-163.98036124259026e-16
2070.9999999999999992.85247454364149e-151.42623727182074e-15
2080.9999999999999951.05479776884002e-145.27398884420011e-15
2090.999999999999983.82044998795908e-141.91022499397954e-14
2100.999999999999931.40034290544258e-137.00171452721288e-14
2110.999999999999774.58855437858426e-132.29427718929213e-13
2120.999999999999833.38401786870158e-131.69200893435079e-13
2130.9999999999994711.05731882931337e-125.28659414656687e-13
2140.9999999999984483.10446635031015e-121.55223317515508e-12
2150.9999999999947521.04968421210237e-115.24842106051186e-12
2160.9999999999843833.12347798688630e-111.56173899344315e-11
2170.999999999964217.15809250039888e-113.57904625019944e-11
2180.9999999999634547.3092279236333e-113.65461396181665e-11
2190.9999999999697336.05333656752943e-113.02666828376472e-11
2200.9999999999518479.63057790653237e-114.81528895326619e-11
2210.9999999999898312.03374218304169e-111.01687109152085e-11
2220.9999999999852592.94826676516750e-111.47413338258375e-11
2230.9999999999807553.84895005320445e-111.92447502660223e-11
2240.999999999959918.01815095806911e-114.00907547903456e-11
2250.9999999999932731.34531051534233e-116.72655257671167e-12
2260.999999999982573.48608595739891e-111.74304297869945e-11
2270.9999999998191263.6174693161878e-101.8087346580939e-10
2280.9999999982986663.40266731163432e-091.70133365581716e-09
2290.999999982839563.43208791030154e-081.71604395515077e-08
2300.999999856485532.87028941030162e-071.43514470515081e-07
2310.9999989984508672.00309826575327e-061.00154913287663e-06
2320.999990380649741.92387005182066e-059.61935025910328e-06
2330.9999237657764320.0001524684471359047.62342235679519e-05
2340.999487056478220.001025887043558790.000512943521779395
2350.99721495820960.005570083580800620.00278504179040031


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1360.615384615384615NOK
5% type I error level1780.805429864253394NOK
10% type I error level1980.895927601809955NOK
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/10bhi81229073890.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/10bhi81229073890.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/1da8t1229073890.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/1da8t1229073890.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/25sb41229073890.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/25sb41229073890.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/3blpr1229073890.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/4dhr01229073890.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/51wk41229073890.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/51wk41229073890.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/6onvx1229073890.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/6onvx1229073890.ps (open in new window)


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http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/7eud61229073890.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/8l6k21229073890.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/8l6k21229073890.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/92i2h1229073890.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/12/t12290739491vuikspz1devyjv/92i2h1229073890.ps (open in new window)


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





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Error 001_3: History of computation (impact.txt) is not saved due to a technical problem. We are sorry for this inconveniance and will correct it A.S.A.P.