Multiple Linear Regression - Estimated Regression Equation
y[t] = + 9.81564665770551 -0.00130798098140336V2[t] + 0.467047542017969M1[t] -0.496870365252102M2[t] + 0.0191390733849997M3[t] -0.914554765271227M4[t] -1.17461898261787M5[t] -1.39948880362134M6[t] -1.18904313812691M7[t] -1.46415884470312M8[t] -1.58241850777879M9[t] -1.06574537070756M10[t] -1.04666703073587M11[t] -0.0091376845130207t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)9.815646657705510.22366343.885800
V2-0.001307980981403360.000196-6.689800
M10.4670475420179690.2414311.93450.0592160.029608
M2-0.4968703652521020.24102-2.06150.0449290.022465
M30.01913907338499970.2411510.07940.9370860.468543
M4-0.9145547652712270.243112-3.76190.0004760.000238
M5-1.174618982617870.240717-4.87971.3e-057e-06
M6-1.399488803621340.240897-5.80951e-060
M7-1.189043138126910.240689-4.94021.1e-055e-06
M8-1.464158844703120.240945-6.076700
M9-1.582418507778790.239542-6.60600
M10-1.065745370707560.242209-4.40016.4e-053.2e-05
M11-1.046667030735870.241932-4.32638.1e-054e-05
t-0.00913768451302070.002878-3.17470.0026760.001338


Multiple Linear Regression - Regression Statistics
Multiple R0.914398840553984
R-squared0.83612523960647
Adjusted R-squared0.789812807321342
F-TEST (value)18.0540126776060
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value7.37188088351104e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.378355541378205
Sum Squared Residuals6.58503412181335


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
199.081985841152-0.0819858411519943
288.1036983254433-0.103698325443292
399.21616527395713-0.216165273957128
498.718047284465020.281952715534975
587.977972229300150.0220277706998493
688.06049612128328-0.0604961212832768
788.06037503112856-0.0603750311285617
887.460898223521120.539101776478877
987.86192519241940.138074807580605
1087.809644784936960.190355215063036
1187.850976983949310.149023016050689
12109.126558868787570.873441131212428
13109.696955090693210.30304490930679
1488.42437185416875-0.424371854168749
1588.5310014279834-0.531001427983402
1688.32456319734425-0.324563197344247
1788.35358095924455-0.353580959244549
1877.0457210679959-0.0457210679959024
1988.2960297960628-0.296029796062801
2077.07264606032596-0.07264606032596
2177.07997075382182-0.0799707538218178
2288.01783194926173-0.0178319492617327
2388.28544485805686-0.28544485805686
2499.0064428067801-0.00644280678009763
251010.0006248666604-0.000624866660424087
2698.543616311758090.456383688241911
27109.058335951770590.94166404822941
2888.03440960775433-0.0344096077543344
2987.769131648838880.230868351161119
3088.00861325859041-0.00861325859040978
3177.52323122433505-0.523231224335049
3277.3880876651258-0.388087665125803
3377.26461426048133-0.264614260481326
3488.05859754796687-0.0585975479668706
3588.3654498862041-0.365449886204099
3698.985733299359280.0142667006407225
3799.02116529987094-0.0211652998709404
3888.21422329272608-0.214223292726076
3998.858433049897510.141566950102490
4088.11964655982719-0.119646559827187
4187.987782661014880.0122173389851243
4287.95520422663450.044795773365494
4398.088497196582940.911502803417065
4477.0233791595959-0.0233791595958998
4576.932605279486510.0673947205134941
4688.31125606565935-0.311256065659353
4788.33035258798784-0.330352587987842
4888.4379074564329-0.437907456432903
4999.19926890162343-0.199268901623431
5098.71409021590380.285909784096206
5199.33606429639137-0.33606429639137
5287.80333335060920.196666649390793
5376.911532501601540.0884674983984564
5476.92996532549590.0700346745040952
5577.03186675189065-0.0318667518906529
5677.05498889143121-0.054988891431214
5776.860884513790960.139115486209045
5887.802669652175080.197330347824921
5987.167775683801890.832224316198112
6088.44335756864015-0.44335756864015