Multiple Linear Regression - Estimated Regression Equation
intermediaire-goederen[t] = + 68.0873734438617 + 0.189856395390054`totale-consumptiegoederen`[t] -0.0184033439239251`duurzame-consumptiegoederen`[t] + 0.096680713353948`niet-duurzame-consumptiegoederen`[t] -2.87863091100782`inval-USA-in-Irak`[t] + 4.25369055093826M1[t] + 2.88273763365504M2[t] + 7.90784012799898M3[t] + 4.24573766642082M4[t] + 3.0642567361827M5[t] + 5.90145014519717M6[t] -3.17162982247658M7[t] -3.1057799907332M8[t] + 5.7594812951929M9[t] + 8.92848470101635M10[t] + 6.6561488976943M11[t] + 0.0374113990247136t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)68.087373443861718.5325093.67390.0004950.000247
`totale-consumptiegoederen`0.1898563953900540.9113740.20830.8356520.417826
`duurzame-consumptiegoederen`-0.01840334392392510.170058-0.10820.9141660.457083
`niet-duurzame-consumptiegoederen`0.0966807133539480.8433880.11460.90910.45455
`inval-USA-in-Irak`-2.878630911007823.013434-0.95530.3430950.171547
M14.253690550938263.3822751.25760.2131630.106582
M22.882737633655043.4376780.83860.4048810.20244
M37.907840127998983.3884682.33380.0228130.011406
M44.245737666420823.4318831.23710.2206250.110312
M53.06425673618273.4454710.88940.3771940.188597
M65.901450145197173.4309781.720.0903330.045166
M7-3.171629822476584.566401-0.69460.4898860.244943
M8-3.10577999073324.254788-0.72990.4681270.234063
M95.75948129519293.8180931.50850.1364330.068217
M108.928484701016353.7615032.37360.0206750.010337
M116.65614889769433.3889171.96410.0539350.026967
t0.03741139902471360.0892780.4190.6766080.338304


Multiple Linear Regression - Regression Statistics
Multiple R0.743506799184985
R-squared0.552802360434302
Adjusted R-squared0.439228356735077
F-TEST (value)4.86733180506935
F-TEST (DF numerator)16
F-TEST (DF denominator)63
p-value2.58953877430024e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.64222600287052
Sum Squared Residuals2005.58699885050


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1103.198.82566627704664.27433372295339
2100.697.26499246176963.33500753823039
3103.1104.664999472846-1.56499947284622
495.598.4548774942852-2.95487749428521
590.598.1903854393855-7.69038543938552
690.9103.085777451152-12.1857774511523
788.890.148513809067-1.34851380906700
890.791.9746101766381-1.27461017663809
994.3103.016090025409-8.71609002540881
10104.6109.010723032559-4.4107230325595
11111.1103.5366917333287.5633082666724
12110.894.733980038272516.0660199617275
13107.299.78095686928687.41904313071322
149997.84184512188811.15815487811189
1599104.551274042598-5.55127404259823
169199.9915384163747-8.99153841637466
1796.299.03767859809-2.83767859808993
1896.9102.574696633689-5.67469663368915
1996.292.41081825133933.78918174866075
20100.192.19667537627037.90332462372973
2199104.145206977163-5.14520697716264
22115.4109.6477272464195.75227275358128
23106.9103.8998450690733.00015493092736
24107.196.59956754396110.5004324560390
2599.3103.804514492526-4.5045144925258
2699.2100.444033921589-1.24403392158924
27108.3107.8990191719540.400980828045647
28105.6102.6672948560202.93270514397982
2999.596.49903544717043.0009645528296
30107.4102.6529578169044.74704218309552
3193.192.67481600093230.425183999067729
3288.192.132809309287-4.03280930928695
33110.7106.1977412697874.50225873021319
34113.1109.7022015891073.39779841089323
3599.6104.257709087700-4.65770908770031
3693.6100.485012162614-6.88501216261364
3798.698.7781633667652-0.178163366765164
3899.698.35939747075911.24060252924088
39114.3107.8668061126546.4331938873457
40107.8100.6722777579047.12772224209647
41101.298.1912425200693.00875747993099
42112.5107.3986979880355.10130201196487
43100.594.11361479855686.38638520144321
4493.994.5148280962495-0.614828096249517
45116.2108.3098092445947.89019075540626
46112109.5100660733062.48993392669372
47106.4107.049081354036-0.649081354036092
4895.7103.203678588899-7.50367858889925
4996101.784647389267-5.78464738926660
5095.899.4273972020658-3.62739720206584
51103108.399236506178-5.39923650617759
52102.2102.441105946527-0.241105946527237
5398.4101.259834326945-2.85983432694520
54111.4106.5197099480424.8802900519581
5586.691.6805669636791-5.08056696367913
5691.394.6692841426174-3.36928414261744
57107.9108.454130820253-0.554130820252828
58101.8109.061932391418-7.26193239141758
59104.4107.312907930597-2.91290793059671
6093.499.8816871593461-6.4816871593461
61100.1102.895392660814-2.79539266081363
6298.5101.760049386057-3.26004938605743
63112.9111.9755094779030.924490522096986
64101.4102.313030792966-0.913030792966373
65107.1103.8453377536973.25466224630285
66110.8108.0625676164712.73743238352924
6790.393.4493017047557-3.14930170475568
6895.596.6251526721475-1.12515267214746
69111.4109.3770216627952.02297833720484
70113112.9673496671910.0326503328088348
71107.5109.843764825267-2.34376482526664
7295.9101.596074506907-5.69607450690742
73106.3104.7306589442951.56934105570456
74105.2102.8022844358712.39771556412935
75117.2112.4431552158664.7568447841337
76106.9103.8598747359233.04012526407719
77108.2104.0764859146434.1235140853572
78110109.6055925457060.394407454293737
7996.197.122368471670-1.02236847166987
80100.698.08664022679032.51335977320973