Multiple Linear Regression - Estimated Regression Equation
BouwV[t] = + 31.8082471358567 -7.02863629197244X[t] + 0.238965295567245Y1[t] + 0.0273747630402027Y2[t] + 0.402192343482086Y3[t] + 35.1646947722934D1[t] + 46.4488639594472D2[t] + 48.3693352090327D3[t] -2.53493234791146M1[t] + 2.46209784069195M2[t] -9.05815573602356M3[t] -8.97051564784286M4[t] -8.52014242938935M5[t] -0.581790688040753M6[t] -15.662456704794M7[t] -2.1316338495595M8[t] -9.61420426024542M9[t] + 1.19846234618739M10[t] + 11.0501843155142M11[t] + 0.183692662157701t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)31.80824713585679.3958793.38530.0010830.000541
X-7.028636291972443.593639-1.95590.0538060.026903
Y10.2389652955672450.0833352.86750.0052290.002614
Y20.02737476304020270.0840110.32580.745350.372675
Y30.4021923434820860.0812144.95234e-062e-06
D135.164694772293410.2397913.43410.0009260.000463
D246.448863959447210.6741054.35153.8e-051.9e-05
D348.369335209032710.1914964.7468e-064e-06
M1-2.534932347911464.924658-0.51470.6080850.304042
M22.462097840691954.7524380.51810.6057710.302885
M3-9.058155736023564.620506-1.96040.0532610.02663
M4-8.970515647842864.913921-1.82550.0714750.035737
M5-8.520142429389354.904506-1.73720.0860160.043008
M6-0.5817906880407534.812185-0.12090.9040590.45203
M7-15.6624567047944.608836-3.39840.0010390.000519
M8-2.13163384955955.154911-0.41350.6802830.340142
M9-9.614204260245425.289551-1.81760.0726930.036347
M101.198462346187394.8420680.24750.8051170.402559
M1111.05018431551424.8464272.28010.0251370.012569
t0.1836926621577010.0635832.8890.0049150.002458


Multiple Linear Regression - Regression Statistics
Multiple R0.888787196164306
R-squared0.78994268006561
Adjusted R-squared0.742429714842354
F-TEST (value)16.6258341560837
F-TEST (DF numerator)19
F-TEST (DF denominator)84
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.42444946867422
Sum Squared Residuals7460.90081415787


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1110.3672031100.4970610232869.87014207671448
296.8602511109.665005884637-12.8047547846368
394.194458390.56792757489863.62653072510141
499.5162196193.122079185336.39414042466996
594.0633348789.52249320592954.54084166407054
697.554147695.41500789719562.1391397028044
778.1506242283.3433178634725-5.19269364347253
881.243464390.3235163603003-9.08005206030026
992.3626246584.63653135349477.72609329650527
1096.0632437190.57070128588225.4925424241178
11114.0523777103.03873642439911.0136412756008
12110.6616666101.0443682205519.61729837944905
13104.917194999.86367518695315.05351971304688
1490.00187193110.813940703797-20.8120687737971
1595.700806794.3921636322741.30864306772604
1686.0274115793.3066580472825-7.2792464772825
1784.8528766885.7862964913284-0.933419811328375
18100.0432895.65492884871164.38835115128838
1980.9171382380.46521664220640.451921587793574
2074.0653970989.552692789929-15.4872956999291
2177.3028136986.2023770056123-8.89956331561229
2297.2304324990.0924139185297.1380185714711
2390.75515676102.222743552765-11.4675867927650
24100.561445591.6564637324978.90498176750306
2592.0129326799.486063300376-7.47313063037595
2699.24012138100.288126766076-1.04800538607567
27105.867275594.388613873858511.4786616261415
2890.992046393.003302637251-2.01125633725107
2993.3062442393.17084171318180.135402516818164
3091.17419413104.104083880653-12.9298897506527
3177.3329503982.7782718719272-5.44532148192725
3291.127772194.0575988104428-2.92982671044282
3385.0124994388.8188097154378-3.80631028543779
3483.9039024293.1646187619632-9.26071634196322
35104.8626302108.315884712483-3.4532545124831
36110.903910899.967938211152610.9359725888474
3795.4371437399.188225906755-3.75108217675502
38111.6238727109.2677466602902.35612603971050
39108.8925403103.8056099369225.08693036307776
4096.1751168297.6467416089771-1.47162478897713
41101.9740205101.6771935093560.296826990644036
4299.11953031109.738317211688-10.6187869016880
4386.7815814789.2031130268029-2.42153155680293
44118.4195003102.22342062108516.1960796789148
45118.7441447100.99910497204117.7450397279594
46106.5296192107.976894958966-1.44727575896614
47134.7772694127.8268776747866.95039172521446
48104.6778714123.506793851375-18.8289224513755
49105.2954304109.823526721456-4.52809632145642
50139.4139849125.68884948508013.7251354149198
51103.6060491110.416597143210-6.81054804321038
5299.78182974103.313440777609-3.53161103760905
53103.4610301115.775638581636-12.3146084816363
54120.0594945110.2705194731699.78897502683065
5596.7137716897.9026485615266-1.18887688152666
56107.1308929107.972471769716-0.841578869716332
57105.3608372109.199616137525-3.83877893752545
58111.6942359110.6686887762951.02554712370545
59132.0519998126.3587974405235.69320235947691
60126.8037879119.8185772931936.98521060680693
61154.4824253154.48242533.33066907387547e-16
62141.5570984139.1567553595062.40034304049400
63109.9506882123.378395378936-13.4277071789356
64127.904198126.8752012532041.02899674679640
65133.0888617125.7358474036827.35301429631823
66120.0796299122.876463389796-2.7968334897962
67117.5557142112.2334282334325.32228596656794
68143.0362309127.07392289539615.9623080046036
69159.982927159.9829271.88737914186277e-15
70128.5991124128.2625180882380.336594311761745
71149.7373327141.5102707025658.2270619974346
72126.8169313141.651787041729-14.8348557417290
73140.9639674121.77969069132719.1842767086729
74137.6691981138.215254007511-0.546055907511498
75117.9402337117.2602193816960.680014318304081
76122.3095247118.4166503964783.89287430352231
77127.7804207118.2296184808459.55080221915521
78136.1677176119.84378704327716.3239305567232
79116.2405856108.8581464396637.38243916033725
80123.1576893120.2407217172402.91696758276046
81116.3400234117.422597777971-1.0825743779713
82108.6119282118.964585656445-10.3526574564447
83125.8982264129.748627893001-3.85040149300129
84112.8003105120.059393798020-7.2590832980203
85107.5182447111.943234365835-4.42498966583474
86135.0955413122.45559124059812.6399500594017
87115.5096488112.2965703688153.21307843118489
88115.8640759106.5180700661299.34600583387147
89104.5883906117.792050103784-13.2036595037845
90163.7213386163.7213386-2.10942374678780e-15
91113.4482275114.419631085693-0.971403585693479
9298.0428844113.204373894184-15.1614894941843
93116.7868521124.630758207918-7.84390610791782
94126.5330444119.4650972736827.06794712631802
95113.0336597126.146714259477-13.1130545594774
96124.3392163119.8598181514824.47939814851835
97109.8298759123.760515604012-13.9306397040121
98124.4434777120.3541474025054.08933029749514
99111.5039454116.659548709390-5.15560330938978
100102.0350019108.403280567740-6.36827866774037
101116.8726598112.2978596902574.57480010974299
102112.2073122118.502198495510-6.29488629550977
103101.151390299.0882097652762.06318043472409
104124.4255108116.0006232317068.42488756829401


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.6996705976874690.6006588046250620.300329402312531
240.5632169467492660.8735661065014690.436783053250734
250.4460069031594570.8920138063189140.553993096840543
260.5110250523310270.9779498953379450.488974947668973
270.6952466845501430.6095066308997130.304753315449857
280.5961846627489850.807630674502030.403815337251015
290.5316724709875280.9366550580249450.468327529012472
300.4686676700677880.9373353401355760.531332329932212
310.3823928680722240.7647857361444470.617607131927776
320.4809493376978240.9618986753956470.519050662302176
330.3994795434232760.7989590868465520.600520456576724
340.3928921738986430.7857843477972860.607107826101357
350.3362327781512220.6724655563024450.663767221848778
360.3065984564527060.6131969129054110.693401543547294
370.2439661174572980.4879322349145960.756033882542702
380.3555753583362310.7111507166724630.644424641663769
390.3158327497922440.6316654995844890.684167250207756
400.2594775557813110.5189551115626220.740522444218689
410.2083196652646950.4166393305293910.791680334735305
420.2133321540684270.4266643081368540.786667845931573
430.1900698785153490.3801397570306980.809930121484651
440.4450870471441160.8901740942882310.554912952855884
450.5722933457694340.8554133084611330.427706654230566
460.521068787847010.957862424305980.47893121215299
470.4665798675182830.9331597350365670.533420132481717
480.7203728427246930.5592543145506140.279627157275307
490.676472462873340.647055074253320.32352753712666
500.755268375145520.4894632497089590.244731624854479
510.7238811138604270.5522377722791460.276118886139573
520.6798629191749410.6402741616501180.320137080825059
530.7644558051181330.4710883897637340.235544194881867
540.7504189665339430.4991620669321140.249581033466057
550.7422680575936220.5154638848127560.257731942406378
560.7565969822921050.486806035415790.243403017707895
570.7318587416865660.5362825166268680.268141258313434
580.7166197874846680.5667604250306640.283380212515332
590.6715979628304050.656804074339190.328402037169595
600.6114012646727530.7771974706544930.388598735327247
610.5383345195835330.9233309608329350.461665480416467
620.4808979510971230.9617959021942470.519102048902877
630.481565410080040.963130820160080.51843458991996
640.4202090628470890.8404181256941780.579790937152911
650.3709845798346570.7419691596693150.629015420165343
660.3385410618076010.6770821236152010.661458938192399
670.3501211861574550.7002423723149110.649878813842545
680.3259787372216420.6519574744432830.674021262778358
690.2552851957454460.5105703914908930.744714804254554
700.2054630399093080.4109260798186160.794536960090692
710.2492285025906280.4984570051812570.750771497409372
720.2413426286247910.4826852572495820.758657371375209
730.570919444977280.858161110045440.42908055502272
740.4763247380388620.9526494760777240.523675261961138
750.3893743968034960.7787487936069930.610625603196504
760.2931704369576880.5863408739153750.706829563042312
770.2452259472488740.4904518944977470.754774052751126
780.3596273350467050.7192546700934110.640372664953295
790.2936486680726040.5872973361452090.706351331927396
800.2072897661171960.4145795322343920.792710233882804
810.1242407778311600.2484815556623190.87575922216884


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