Multiple Linear Regression - Estimated Regression Equation
intermediaire-goederen[t] = + 56.8144233107686 + 0.48733032890477`totale-consumptiegoederen`[t] + 0.0859384873333898`duurzame-consumptiegoederen`[t] -0.217827808454171`niet-duurzame-consumptiegoederen`[t] + 4.37494881242500`aanslagen-WTC9-11`[t] + 5.07926127284946M1[t] + 3.14424194856741M2[t] + 7.55592006481916M3[t] + 4.50318240294593M4[t] + 3.04115642517979M5[t] + 5.31492909401468M6[t] -0.439082047915904M7[t] -0.456107112610295M8[t] + 4.16331921146515M9[t] + 7.47935716091545M10[t] + 5.807952030928M11[t] + 0.0365005072086118t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)56.814423310768618.9218323.00260.0038350.001918
`totale-consumptiegoederen`0.487330328904770.9033220.53950.5914530.295726
`duurzame-consumptiegoederen`0.08593848733338980.1477050.58180.562760.28138
`niet-duurzame-consumptiegoederen`-0.2178278084541710.826739-0.26350.7930420.396521
`aanslagen-WTC9-11`4.374948812425002.5957251.68540.0968510.048425
M15.079261272849463.359791.51180.135590.067795
M23.144241948567413.3874850.92820.356850.178425
M37.555920064819163.3163112.27840.0261030.013052
M44.503182402945933.3825451.33130.1878880.093944
M53.041156425179793.3500490.90780.3674460.183723
M65.314929094014683.3003471.61040.1123070.056153
M7-0.4390820479159044.496348-0.09770.9225180.461259
M8-0.4561071126102954.153569-0.10980.9129080.456454
M94.163319211465153.695281.12670.264160.13208
M107.479357160915453.6756442.03480.046080.02304
M115.8079520309283.2988531.76060.083160.04158
t0.03650050720861180.0876510.41640.6785110.339256


Multiple Linear Regression - Regression Statistics
Multiple R0.752262475930964
R-squared0.565898832693784
Adjusted R-squared0.455650917187443
F-TEST (value)5.13296627963218
F-TEST (DF numerator)16
F-TEST (DF denominator)63
p-value1.18416107863784e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.55899397910031
Sum Squared Residuals1946.85208575943


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1103.196.93369921466126.16630078533878
2100.695.13129434731765.46870565268241
3103.1102.8104800240770.289519975922891
495.595.49382930543660.00617069456338041
590.595.5873505215899-5.0873505215899
690.9100.655133532570-9.75513353257048
788.887.23148103858631.56851896141371
890.789.35673201576081.3432679842392
994.3103.692277009603-9.39227700960314
10104.6110.307607074989-5.70760707498932
11111.1104.6452080638276.45479193617345
12110.895.890389486411714.9096105135882
13107.2100.9223765030136.27762349698723
149999.393646609288-0.393646609288089
1599106.278762102654-7.27876210265437
1691101.680983908455-10.6809839084555
1796.2100.217736405432-4.01773640543233
1896.9103.842409442453-6.94240944245283
1996.292.77130675608193.42869324391815
20100.192.06912840260758.03087159739247
2199103.566252225004-4.56625222500421
22115.4109.3439882293226.05601177067831
23106.9103.5715127844783.32848721552211
24107.196.2301036094710.86989639053
2599.3103.352834237749-4.05283423774927
2699.2100.052710954735-0.852710954734891
27108.3107.2471110546891.05288894531093
28105.6103.0283358808002.57166411920034
2999.598.21200241834141.28799758165859
30107.4103.0815482622154.31845173778486
3193.194.2385915824697-1.13859158246969
3288.193.0009940456307-4.90099404563074
33110.7107.8269897691432.87301023085725
34113.1111.4186541166741.68134588332648
3599.6105.206145116372-5.60614511637239
3693.6101.307776825272-7.70777682527167
3798.699.9344529046976-1.33445290469759
3899.699.6186677632067-0.018667763206698
39114.3109.3321836750504.96781632495012
40107.8102.1318622811325.66813771886757
41101.299.43803754099581.76196245900420
42112.5108.9240785007023.57592149929751
43100.594.60768114068435.8923188593157
4493.995.2788908834753-1.37889088347529
45116.2108.1999044048988.00009559510177
46112109.1387906461582.86120935384172
47106.4106.1977229131160.202277086884048
4895.7102.592141796973-6.89214179697288
4996101.871148494700-5.87114849469974
5095.899.4219854386811-3.62198543868115
51103108.672775912583-5.67277591258282
52102.2102.842138879379-0.64213887937857
5398.4100.628602044454-2.22860204445407
54111.4106.8851440829424.5148559170576
5586.692.2413272612448-5.64132726124481
5691.395.3470983242375-4.04709832423754
57107.9108.268708163685-0.368708163684588
58101.8107.659967992119-5.85996799211924
59104.4107.678669956679-3.27866995667867
6093.4100.101020760107-6.70102076010669
61100.1102.962789455149-2.86278945514869
6298.5102.179031664515-3.67903166451519
63112.9112.1255525799870.774447420013413
64101.4102.438017807624-1.03801780762376
65107.1104.2012620145222.89873798547751
66110.8108.3936314381552.40636856184481
6790.393.3642122688959-3.06421226889589
6895.597.110213983921-1.61021398392107
69111.4107.9458684276673.45413157233291
70113112.0309919407380.969008059262034
71107.5108.600741165529-1.10074116552854
7295.9100.378567521767-4.478567521767
73106.3104.6226991900311.67730080996927
74105.2102.1026632222563.09733677774361
75117.2111.3331346509605.86686534903983
76106.9102.7848319371734.11516806282651
77108.2102.8150090546645.384990945336
78110108.1180547409611.88194525903854
7996.197.1453999520372-1.04539995203718
80100.698.0369423443672.56305765563297