Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1781.33973794529 + 0.0538006313056124X[t] + 0.119501855681305`yt-1`[t] + 0.063586039545287`yt-2`[t] + 0.311071408572704`yt-3`[t] + 0.140670870471306`yt-4`[t] + 445.120233686327M1[t] -139.148884123066M2[t] + 317.064608308976M3[t] -33.4253531581604M4[t] + 117.406862236303M5[t] + 623.118184091766M6[t] + 183.848976303235M7[t] + 330.214447746130M8[t] + 600.959567741939M9[t] -370.074056974433M10[t] + 22.8431456711585M11[t] -11.0948669738725t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)1781.339737945292001.2022330.89010.3788520.189426
X0.05380063130561243.0678790.01750.9860980.493049
`yt-1`0.1195018556813050.1550380.77080.4454750.222737
`yt-2`0.0635860395452870.1459080.43580.6653870.332693
`yt-3`0.3110714085727040.1458282.13310.0392610.019631
`yt-4`0.1406708704713060.1533520.91730.364620.18231
M1445.120233686327379.2799411.17360.2476760.123838
M2-139.148884123066371.690113-0.37440.7101580.355079
M3317.064608308976366.7940290.86440.3926420.196321
M4-33.4253531581604331.088768-0.1010.9201030.460051
M5117.406862236303374.3776190.31360.7554920.377746
M6623.118184091766348.4764061.78810.0815280.040764
M7183.848976303235365.7177990.50270.6179970.308999
M8330.214447746130389.6726650.84740.4019350.200967
M9600.959567741939351.3128951.71060.0950980.047549
M10-370.074056974433377.365426-0.98070.3327960.166398
M1122.8431456711585382.7998070.05970.952720.47636
t-11.09486697387257.208704-1.53910.1318570.065928


Multiple Linear Regression - Regression Statistics
Multiple R0.775654730445256
R-squared0.601640260862102
Adjusted R-squared0.427996272007121
F-TEST (value)3.46479175483905
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value0.000662408025575933
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation471.696718242219
Sum Squared Residuals8677413.9660187


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
146344983.90799608805-349.907996088049
239964189.87790454883-193.877904548833
343084685.31353429033-377.313534290328
441434527.83586762746-384.835867627459
544294538.89584602054-109.895846020537
652195064.23539605947154.76460394053
749294718.43411682344210.565883176561
857554934.71507982185820.284920178148
955925560.7197971582631.2802028417440
1041634635.35148367051-472.351483670506
1149625052.62197922258-90.6219792225772
1252085088.73719753734119.262802462664
1347555134.65406150597-379.65406150597
1444914547.41067264111-56.4106726411114
1557325121.20352556347610.796474436529
1657314784.60826911029946.39173088971
1750404857.12823267029182.87176732971
1861025617.4698211578484.530178842202
1949045424.01743886163-520.017438861632
2053695268.50837939603100.491620603971
2155785740.70518434306-162.705184343058
2246194592.3776355205626.6223644794431
2347314849.33447980839-118.334479808385
2450114897.90473844875113.095261551251
2552995101.87337208654197.126627913458
2641464457.48303190161-311.483031901609
2746254885.49972335967-260.499723359668
2847364637.0331921204298.966807879584
2942194501.69522849957-282.695228499566
3051164927.48235470416188.517645295838
3142054653.02493156247-448.024931562468
3241214590.44956189859-469.449561898586
3351034987.79338289792115.206617102081
3443003963.48303912106336.516960878944
3545784158.04372050775419.956279492253
3638094398.68598956603-589.68598956603
3755264646.03279250251879.967207497492
3842474179.5603613762867.4396386237243
3938304381.12113464115-551.121134641149
4043944314.4725988234379.527401176571
4148264338.28096271089487.719037289114
4244094609.94291537314-200.942915373140
4345694253.83885542975315.161144570251
4441064594.3057845092-488.305784509202
4547944740.3376890284853.6623109715226
4639143804.78784168788109.212158312118
4737934003.99982046129-210.999820461290
4844054047.67207444788357.327925552115
4940224369.53177781693-347.531777816930
5041003605.66802953217494.331970467829
5147884209.86208214538578.137917854617
5231633903.05007231841-740.050072318406
5335853862.99973009872-277.999730098722
5439034529.86951270543-626.869512705431
5541783735.68465732271442.315342677286
5638633826.0211943743336.9788056256688
5741874224.44394657229-37.4439465722893


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.6629649616032540.6740700767934930.337035038396746
220.7331302402690570.5337395194618850.266869759730943
230.7366756276937790.5266487446124420.263324372306221
240.6820708069569290.6358583860861430.317929193043071
250.6345364803886280.7309270392227440.365463519611372
260.6024663438413650.795067312317270.397533656158635
270.4747143301308050.949428660261610.525285669869195
280.4080805104102560.8161610208205130.591919489589743
290.2978484010888340.5956968021776670.702151598911166
300.2483676503961240.4967353007922470.751632349603876
310.1914488187564840.3828976375129670.808551181243516
320.147799659970610.295599319941220.85220034002939
330.3458899844066080.6917799688132150.654110015593393
340.2531524229190820.5063048458381630.746847577080918
350.1667521034151770.3335042068303540.833247896584823
360.1468942566399690.2937885132799390.85310574336003


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