Multiple Linear Regression - Estimated Regression Equation
Vruchtesappen[t] = + 9131.73309840353 + 0.454083662567809Mineraalwater[t] + 0.00771101131009535Appelen[t] -0.0655831488070655Sinaasappelen[t] + 0.0340529156698439Citroenen[t] + 0.0192571859658415Pompelmoezen[t] -0.0490769779229899Bananen[t] + 57.4062005061049M1[t] + 77.7493115271082M2[t] + 250.489345731327M3[t] + 344.236338901192M4[t] + 356.036377383132M5[t] + 359.805646551251M6[t] + 302.852781869608M7[t] + 192.587979854707M8[t] + 49.6309153787849M9[t] + 39.9297724453515M10[t] + 44.9352240195256M11[t] + 4.55639570066448t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)9131.73309840353991.9047599.206300
Mineraalwater0.4540836625678090.112384.04060.0001738.7e-05
Appelen0.007711011310095350.0132220.58320.5622410.28112
Sinaasappelen-0.06558314880706550.022302-2.94070.0048450.002423
Citroenen0.03405291566984390.0411150.82820.4112510.205626
Pompelmoezen0.01925718596584150.0179531.07260.2882910.144145
Bananen-0.04907697792298990.028234-1.73820.0879740.043987
M157.406200506104979.1575280.72520.471510.235755
M277.749311527108281.5002090.9540.3444260.172213
M3250.48934573132797.2710292.57520.0128460.006423
M4344.236338901192110.5006073.11520.0029640.001482
M5356.036377383132115.7374363.07620.0033130.001656
M6359.805646551251123.0435582.92420.0050710.002535
M7302.852781869608114.1852842.65230.0105240.005262
M8192.587979854707101.5545791.89640.0633640.031682
M949.630915378784992.7296410.53520.5947350.297368
M1039.929772445351577.7532220.51350.6097050.304852
M1144.935224019525679.9549670.5620.5764810.28824
t4.556395700664482.8762371.58420.1191090.059554


Multiple Linear Regression - Regression Statistics
Multiple R0.799200768910404
R-squared0.638721869026981
Adjusted R-squared0.516023635866333
F-TEST (value)5.20563216416252
F-TEST (DF numerator)18
F-TEST (DF denominator)53
p-value1.3085519341427e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation129.986803912926
Sum Squared Residuals895518.167149366


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
11080010941.9391714998-141.939171499841
21090010874.977993708025.0220062919670
31100011007.4666254807-7.46662548067558
41100011087.0630233624-87.0630233623686
51110011117.7802357804-17.7802357803734
61100011141.0062181885-141.006218188493
71100011104.9191983194-104.919198319354
81110011106.9471516536-6.94715165356724
91110011090.13063098469.869369015438
101110011126.4208771537-26.4208771536528
111110011063.038753628036.9612463720369
121110011012.846042761487.1539572385911
131120011071.5643335593128.435666440744
141110011052.867743250247.1322567497655
151110011122.0070289226-22.0070289225945
161120011035.1079584052164.892041594792
171120011059.4037687044140.596231295606
181110011167.4783734250-67.478373424953
191120011089.9998283136110.000171686368
201110011037.042383775162.9576162248964
211110011097.65686118942.34313881063982
221100011150.6323044162-150.632304416210
231100011143.1765555573-143.176555557299
241100011188.8364334207-188.836433420730
251110011179.4948537127-79.4948537126964
261100011190.7545403590-190.754540358985
271100011140.2045892504-140.204589250432
281090011150.7382354840-250.738235484013
291100011114.1019851576-114.101985157647
301100011064.4661514704-64.4661514704467
311110011129.5968179685-29.596817968493
321130011281.677145308018.3228546920307
331130011207.137375113092.8626248870277
341130011215.053425973384.9465740267101
351130011210.023273084589.9767269155478
361140011218.9637798106181.036220189367
371140011216.0874346639183.9125653361
381140011220.3207937966179.679206203378
391150011248.6859495975251.314050402483
401150011279.5755638071220.424436192851
411150011362.4399732866137.560026713448
421150011329.8527219905170.14727800953
431150011379.1734328689120.826567131071
441150011455.193028797344.8069712027277
451140011397.55806991582.44193008420276
461140011285.0284987938114.971501206220
471140011225.8310319541174.168968045868
481130011205.472528440994.5274715591098
491120011184.289449450515.7105505495091
501130011225.669441362774.3305586373422
511130011279.955823305320.0441766947427
521130011347.1410881156-47.1410881155754
531120011318.5546458387-118.554645838654
541130011377.2201233352-77.2201233352117
551120011295.0689557265-95.0689557265167
561120011297.5326409155-97.5326409154885
571110011208.0162445651-108.016244565063
581110011155.5045876359-55.5045876359424
591110011157.8341436636-57.8341436635955
601110011200.3643778261-100.364377826066
611140011506.6247571138-106.624757113815
621150011635.4094875235-135.409487523468
631150011601.6799834435-101.679983443523
641160011600.3741308257-0.374130825685482
651150011527.7193912324-27.7193912323806
661160011419.9764115904180.023588409574
671130011301.2417668031-1.24176680307487
681130011321.6076495506-21.6076495505992
691120011199.50081823220.499181767755088
701120011167.360306027132.6396939728755
711110011200.0962421126-100.096242112558
721110011173.5168377403-73.5168377402719


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.008290462989271370.01658092597854270.991709537010729
230.00735697997271750.0147139599454350.992643020027282
240.001887408013320640.003774816026641280.99811259198668
250.002749855322397290.005499710644794570.997250144677603
260.001260149113857500.002520298227714990.998739850886142
270.02319167484480850.0463833496896170.976808325155192
280.1513911346682110.3027822693364220.848608865331789
290.1508209882413160.3016419764826330.849179011758684
300.4298954357627970.8597908715255930.570104564237203
310.6523094342887320.6953811314225350.347690565711268
320.7916654047282750.4166691905434510.208334595271725
330.795101445845610.4097971083087810.204898554154390
340.8930250527191790.2139498945616430.106974947280821
350.960245262947420.07950947410515820.0397547370525791
360.9851351619749640.02972967605007230.0148648380250361
370.9799179584589660.04016408308206780.0200820415410339
380.9829459348639360.03410813027212850.0170540651360643
390.988506008184740.02298798363052130.0114939918152606
400.9908552129390780.01828957412184430.00914478706092214
410.9985580703922990.002883859215402230.00144192960770111
420.9983553511827630.003289297634474140.00164464881723707
430.9985948319511740.002810336097652320.00140516804882616
440.9965764751367150.00684704972657080.0034235248632854
450.9933001331265410.01339973374691720.00669986687345861
460.988161276864690.02367744627062220.0118387231353111
470.9710688388150220.05786232236995660.0289311611849783
480.9688017585192780.06239648296144350.0311982414807217
490.9740474167346490.05190516653070250.0259525832653512
500.944797667618590.1104046647628180.0552023323814091


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.241379310344828NOK
5% type I error level170.586206896551724NOK
10% type I error level210.724137931034483NOK