Multiple Linear Regression - Estimated Regression Equation
Productie[t] = + 70.843171659092 -12.1409361201919Dummy[t] -0.170535875011005`Yt-1`[t] + 0.127341252226174`Yt-2`[t] + 0.406670131624456`Yt-3`[t] -0.115932126741984`Yt-4`[t] -0.0322030815182641`Yt-5`[t] + 0.125704389082812`Yt-6`[t] -19.0624672402896M1[t] -15.1805417856902M2[t] -0.526451480378921M3[t] + 14.7571552687538M4[t] -1.15645418805891M5[t] -16.4847096765184M6[t] -11.7529419581613M7[t] -7.21825953770693M8[t] + 2.70776198327648M9[t] -3.68093564071264M10[t] -7.4642631479732M11[t] + 0.128245177155806t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)70.84317165909215.4569064.58332.7e-051.3e-05
Dummy-12.14093612019192.59814-4.67292e-051e-05
`Yt-1`-0.1705358750110050.127664-1.33580.187110.093555
`Yt-2`0.1273412522261740.1278420.99610.3235730.161786
`Yt-3`0.4066701316244560.1315093.09230.0031170.001558
`Yt-4`-0.1159321267419840.121414-0.95480.3438330.171916
`Yt-5`-0.03220308151826410.120282-0.26770.7899090.394955
`Yt-6`0.1257043890828120.1281680.98080.3309950.165498
M1-19.06246724028962.382462-8.001200
M2-15.18054178569023.051581-4.97467e-063e-06
M3-0.5264514803789213.479885-0.15130.8803050.440153
M414.75715526875384.4554223.31220.001640.00082
M5-1.156454188058914.751963-0.24340.8086290.404315
M6-16.48470967651843.512189-4.69361.8e-059e-06
M7-11.75294195816133.371446-3.4860.0009710.000486
M8-7.218259537706933.443448-2.09620.0406760.020338
M92.707761983276484.2817350.63240.5297470.264873
M10-3.680935640712644.096295-0.89860.3727820.186391
M11-7.46426314797322.88189-2.59010.0122590.006129
t0.1282451771558060.0386193.32080.0015990.000799


Multiple Linear Regression - Regression Statistics
Multiple R0.956037433079494
R-squared0.914007573449228
Adjusted R-squared0.884301098822598
F-TEST (value)30.7679583301975
F-TEST (DF numerator)19
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.24878213411558
Sum Squared Residuals580.502194522171


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
187.485.24581244931662.15418755068342
289.991.1086770543519-1.20867705435185
3109.8107.5659792500282.23402074997159
4111.7112.015227856697-0.315227856696888
598.6100.654711118671-2.05471111867122
696.997.0887552358055-0.188755235805451
795.196.8060798854834-1.70607988548339
89795.68526181473231.31473818526770
9112.7108.4539992207654.24600077923484
10102.999.88385900117283.01614099882724
1197.499.288654725945-1.88865472594506
12111.4112.579884202804-1.17988420280436
1387.484.464827567272.93517243273008
1496.892.98333580801333.81666419198670
15114.1111.7266016592512.37339834074884
16110.3112.947271720740-2.64727172073952
17103.9105.475800427504-1.57580042750357
18101.699.3616896471372.23831035286297
1994.696.9283644438593-2.32836444385928
2095.9100.954519472762-5.05451947276176
21104.7111.999382717118-7.29938271711795
22102.8101.5521342114191.24786578858060
2398.199.9514281190936-1.85142811909361
24113.9111.4677935468132.43220645318740
2580.986.5259300780725-5.62593007807252
2695.796.3647263809863-0.664726380986282
27113.2112.5585231439940.641476856006124
28105.9111.531321988285-5.6313219882853
29108.8107.9642003240160.835799675983572
30102.399.78980769918752.51019230081246
319996.50523878868152.49476121131850
32100.7104.225735573141-3.52573557314125
33115.5113.0252154323372.47478456766280
34100.7102.857828576880-2.15782857688021
35109.9105.2591057301054.64089426989528
36114.6114.5088584447380.0911415552623357
3785.487.7405741431553-2.34057414315528
38100.5102.523148752392-2.02314875239176
39114.8114.1938325746930.60616742530706
40116.5114.5135322494241.98646775057574
41112.9110.7902998739042.10970012609582
42102102.016647218873-0.0166472188729180
43106103.1537477631992.84625223680116
44105.3105.523047331630-0.223047331629558
45118.8113.9335328980494.86646710195109
46106.1108.501676524901-2.40167652490070
47109.3107.8815869012351.41841309876503
48117.2117.383355621630-0.183355621630165
4992.591.30495748377981.19504251622018
50104.2102.7843078788611.41569212113938
51112.5117.373744315871-4.87374431587117
52122.4120.1999294760192.20007052398130
53113.3111.5526061988341.74739380116604
54100102.972587744027-2.97258774402668
55110.7108.5240455689422.17595443105776
56112.8106.0236301724666.77636982753436
57109.8113.453128457005-3.65312845700514
58117.3115.4024894526661.89751054733407
59109.1108.9842888671120.115711132888437
60115.9115.4503415509490.449658449051261
619698.9875101288926-2.98751012889258
6299.8103.413667620248-3.61366762024811
63116.8118.111239914068-1.31123991406762
64115.7111.2927167088354.40728329116467
6599.4100.462382057071-1.06238205707065
6694.395.8705124549704-1.57051245497038
679194.4825235498348-3.48252354983475
6893.292.48780563526950.712194364730514
69103.1103.734741274726-0.634741274725637
7094.195.702012232961-1.60201223296099
7191.894.23493565651-2.43493565651008
72102.7104.309766633066-1.60976663306648
7382.677.93038814951334.66961185048669
7489.186.82213650514812.27786349485193
75104.5104.1700791420950.329920857905177


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.7589497830207080.4821004339585840.241050216979292
240.6948077907708590.6103844184582810.305192209229141
250.7159134175749330.5681731648501340.284086582425067
260.7961560719737520.4076878560524960.203843928026248
270.7584851521130290.4830296957739420.241514847886971
280.8332945736280020.3334108527439970.166705426371998
290.7948731081699190.4102537836601620.205126891830081
300.8047284241779960.3905431516440090.195271575822004
310.7835918328431860.4328163343136280.216408167156814
320.8118522178659040.3762955642681930.188147782134096
330.8406491307424430.3187017385151150.159350869257557
340.8099502582326440.3800994835347110.190049741767356
350.8719528304449720.2560943391100560.128047169555028
360.815452710933650.3690945781326990.184547289066350
370.81436893136260.3712621372748010.185631068637401
380.7877504979608390.4244990040783230.212249502039161
390.7221803527843430.5556392944313140.277819647215657
400.6924187121056480.6151625757887040.307581287894352
410.6185666992441850.762866601511630.381433300755815
420.5248262860068060.9503474279863890.475173713993194
430.4581740907626540.9163481815253080.541825909237346
440.5578657001802630.8842685996394750.442134299819737
450.5601575474682410.8796849050635180.439842452531759
460.578081036698670.843837926602660.42191896330133
470.5774706664164660.8450586671670690.422529333583534
480.4938023450920070.9876046901840140.506197654907993
490.436883149728950.87376629945790.56311685027105
500.3271959850539990.6543919701079980.672804014946001
510.2603820910852080.5207641821704150.739617908914792
520.2224405069848590.4448810139697180.777559493015141


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