Multiple Linear Regression - Estimated Regression Equation
y[t] = + 9.56057142857143 + 0.960314153439154M1[t] -0.474578042328042M2[t] -0.0797202380952383M3[t] -0.813362433862434M4[t] -1.01412962962963M5[t] -1.27639682539683M6[t] -1.10003902116402M7[t] -1.33568121693122M8[t] -1.58519841269841M9[t] -1.01121560846561M10[t] -0.970857804232805M11[t] -0.00423280423280423t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)9.560571428571430.1649657.956800
M10.9603141534391540.2036184.71631e-055e-06
M2-0.4745780423280420.203501-2.33210.022120.01106
M3-0.07972023809523830.203395-0.39190.6961010.348051
M4-0.8133624338624340.2033-4.00080.0001366.8e-05
M5-1.014129629629630.203216-4.99043e-062e-06
M6-1.276396825396830.203144-6.283200
M7-1.100039021164020.203082-5.41671e-060
M8-1.335681216931220.203032-6.578700
M9-1.585198412698410.202993-7.809100
M10-1.011215608465610.202965-4.98223e-062e-06
M11-0.9708578042328050.202948-4.78387e-064e-06
t-0.004232804232804230.001507-2.80950.0061870.003094


Multiple Linear Regression - Regression Statistics
Multiple R0.880761246189551
R-squared0.77574037278937
Adjusted R-squared0.743317294156508
F-TEST (value)23.9255618373983
F-TEST (DF numerator)12
F-TEST (DF denominator)83
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.405884762262812
Sum Squared Residuals13.6736225396825


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
112.00810.51665277777781.49134722222222
29.1699.077527777777780.0914722222222216
38.7889.46815277777778-0.680152777777778
48.4178.73027777777778-0.313277777777778
58.2478.52527777777778-0.278277777777778
68.1978.25877777777778-0.0617777777777789
78.2368.43090277777778-0.194902777777777
88.2538.191027777777780.0619722222222226
97.7337.93727777777778-0.204277777777777
108.3668.50702777777778-0.141027777777778
118.6268.543152777777780.0828472222222215
128.8639.50977777777778-0.646777777777779
1310.10210.4658591269841-0.363859126984126
148.4639.02673412698413-0.563734126984127
159.1149.41735912698413-0.303359126984126
168.5638.67948412698413-0.116484126984126
178.8728.474484126984130.397515873015873
188.3018.207984126984130.0930158730158734
198.3018.38010912698413-0.0791091269841268
208.2788.140234126984130.137765873015873
217.7367.88648412698413-0.150484126984127
227.9738.45623412698413-0.483234126984127
238.2688.49235912698413-0.224359126984126
249.4769.458984126984130.0170158730158738
2511.110.41506547619050.684934523809523
268.9628.97594047619048-0.0139404761904761
279.1739.36656547619048-0.193565476190476
288.7388.628690476190480.109309523809523
298.4598.423690476190480.0353095238095237
308.0788.15719047619048-0.0791904761904766
318.4118.329315476190480.0816845238095234
328.2918.089440476190480.201559523809524
337.817.83569047619048-0.0256904761904765
348.6168.405440476190480.210559523809524
358.3128.44156547619048-0.129565476190477
369.6929.408190476190480.283809523809524
379.91110.3642718253968-0.453271825396826
388.9158.92514682539682-0.0101468253968259
399.4529.315771825396830.136228174603175
409.1128.577896825396830.534103174603174
418.4728.372896825396830.0991031746031743
428.238.106396825396820.123603174603175
438.3848.278521825396830.105478174603175
448.6258.038646825396820.586353174603174
458.2217.784896825396830.436103174603175
468.6498.354646825396830.294353174603174
478.6258.390771825396830.234228174603175
4810.4439.357396825396831.08560317460317
4910.35710.31347817460320.0435218253968246
508.5868.87435317460317-0.288353174603174
518.8929.26497817460317-0.372978174603175
528.3298.52710317460317-0.198103174603174
538.1018.32210317460317-0.221103174603174
547.9228.05560317460317-0.133603174603175
558.128.22772817460318-0.107728174603176
567.8387.98785317460317-0.149853174603175
577.7357.734103174603170.000896825396825762
588.4068.303853174603170.102146825396826
598.2098.33997817460317-0.130978174603175
609.4519.306603174603180.144396825396826
6110.04110.2626845238095-0.221684523809523
629.4118.823559523809520.587440476190476
6310.4059.214184523809521.19081547619048
648.4678.47630952380952-0.00930952380952354
658.4648.271309523809520.192690476190477
668.1028.004809523809520.0971904761904766
677.6278.17693452380952-0.549934523809524
687.5137.93705952380952-0.424059523809524
697.517.68330952380952-0.173309523809524
708.2918.253059523809520.0379404761904766
718.0648.28918452380952-0.225184523809523
729.3839.255809523809520.127190476190475
739.70610.2118908730159-0.505890873015874
748.5798.77276587301587-0.193765873015872
759.4749.163390873015870.310609126984127
768.3188.42551587301587-0.107515873015874
778.2138.22051587301587-0.00751587301587378
788.0597.954015873015870.104984126984126
799.1118.126140873015870.984859126984128
807.7087.88626587301587-0.178265873015873
817.687.632515873015870.0474841269841267
828.0148.20226587301587-0.188265873015874
838.0078.23839087301587-0.231390873015873
848.7189.20501587301587-0.487015873015873
859.48610.1610972222222-0.675097222222222
869.1138.721972222222220.391027777777778
879.0259.11259722222222-0.087597222222222
888.4768.374722222222220.101277777777778
897.9528.16972222222222-0.217722222222222
907.7597.90322222222222-0.144222222222222
917.8358.07534722222222-0.240347222222222
927.67.83547222222222-0.235472222222223
937.6517.581722222222220.0692777777777776
948.3198.151472222222220.167527777777779
958.8128.187597222222220.624402777777777
968.639.15422222222222-0.524222222222222


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.9870200141847270.02595997163054660.0129799858152733
170.9932154756383890.0135690487232220.00678452436161099
180.9871333518408680.0257332963182630.0128666481591315
190.9767902721992480.04641945560150420.0232097278007521
200.959403009663850.08119398067230050.0405969903361503
210.9354202044054390.1291595911891230.0645797955945613
220.9201473653960610.1597052692078790.0798526346039394
230.8884825892708360.2230348214583280.111517410729164
240.9009655652563980.1980688694872040.0990344347436022
250.90582650657650.1883469868470.0941734934234999
260.8759877516938590.2480244966122830.124012248306141
270.8605860320196940.2788279359606130.139413967980306
280.8228777827474550.3542444345050890.177122217252544
290.767449765979030.4651004680419390.232550234020969
300.712405518453040.575188963093920.28759448154696
310.6500217084276130.6999565831447740.349978291572387
320.5786926518213660.8426146963572690.421307348178634
330.5126373851158930.9747252297682140.487362614884107
340.4848099847516790.9696199695033580.515190015248321
350.4323147136910920.8646294273821850.567685286308907
360.4170191033670980.8340382067341970.582980896632902
370.6568587337257660.6862825325484680.343141266274234
380.603283016180020.7934339676399590.396716983819979
390.5892885097674150.821422980465170.410711490232585
400.5977951473916290.8044097052167420.402204852608371
410.5301899595042850.939620080991430.469810040495715
420.4608798269553220.9217596539106440.539120173044678
430.3941804757562040.7883609515124070.605819524243796
440.4108588572669930.8217177145339860.589141142733007
450.3897490729268090.7794981458536180.610250927073191
460.3400589923204760.6801179846409530.659941007679524
470.2847275397655370.5694550795310740.715272460234463
480.6277138923070870.7445722153858260.372286107692913
490.6649382710412840.6701234579174320.335061728958716
500.6756024225583840.6487951548832330.324397577441616
510.7619573373239350.4760853253521290.238042662676065
520.7414211116603380.5171577766793250.258578888339662
530.722945628407210.5541087431855790.27705437159279
540.6844361158012440.6311277683975130.315563884198756
550.6420586901257070.7158826197485850.357941309874293
560.6094835762807440.7810328474385120.390516423719256
570.5436813553372560.9126372893254880.456318644662744
580.4730491065436290.9460982130872580.526950893456371
590.4315820804518550.863164160903710.568417919548145
600.3842567633777450.768513526755490.615743236622255
610.3750225192685650.7500450385371290.624977480731435
620.3971446601651430.7942893203302850.602855339834857
630.7663055738847830.4673888522304340.233694426115217
640.7052513275415790.5894973449168430.294748672458421
650.6657681928451140.6684636143097710.334231807154885
660.6004336950298210.7991326099403580.399566304970179
670.7542783813596050.491443237280790.245721618640395
680.7311383347314160.5377233305371670.268861665268584
690.681162108107320.637675783785360.31883789189268
700.5979369681615690.8041260636768610.402063031838431
710.6017243044882250.796551391023550.398275695511775
720.6045650795246490.7908698409507030.395434920475351
730.5534950762812130.8930098474375740.446504923718787
740.5612801550858850.8774396898282310.438719844914115
750.4929241626094850.9858483252189710.507075837390515
760.4019148994646510.8038297989293010.598085100535349
770.2982534340515690.5965068681031380.701746565948431
780.2083212614708940.4166425229417880.791678738529106
790.7786635679125190.4426728641749620.221336432087481
800.6886517893577960.6226964212844080.311348210642204


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0615384615384615NOK
10% type I error level50.0769230769230769OK