Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 815.4400473079 -146.985860400957X[t] + 0.322066277846830Y1[t] -0.0980332042753272Y2[t] + 0.385737324810814Y3[t] + 0.120494337086232Y4[t] + 756.041550490447M1[t] + 1003.38137563738M2[t] -76.4336368920148M3[t] + 546.369797069734M4[t] + 196.75507538071M5[t] + 882.128458892924M6[t] + 58.5773967719282M7[t] + 847.254865489325M8[t] + 597.636910820808M9[t] + 482.014123979181M10[t] + 1430.83045970275M11[t] -1.58386023416724t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)815.44004730791073.5564610.75960.4520790.22604
X-146.985860400957193.289434-0.76040.4515620.225781
Y10.3220662778468300.1560032.06450.0456680.022834
Y2-0.09803320427532720.156715-0.62560.5352540.267627
Y30.3857373248108140.1588172.42880.0198580.009929
Y40.1204943370862320.1706110.70630.484230.242115
M1756.041550490447385.7004591.96020.0571470.028574
M21003.38137563738327.0095593.06840.0039040.001952
M3-76.4336368920148327.677084-0.23330.816780.40839
M4546.369797069734392.6387661.39150.1719510.085975
M5196.75507538071335.5848010.58630.5610490.280524
M6882.128458892924378.262282.33210.0249540.012477
M758.5773967719282314.2799160.18640.8531080.426554
M8847.254865489325376.5745292.24990.0301720.015086
M9597.636910820808315.1133411.89660.0653080.032654
M10482.014123979181365.3548461.31930.1947610.09738
M111430.83045970275367.6030953.89230.0003770.000189
t-1.583860234167244.618476-0.34290.7334850.366743


Multiple Linear Regression - Regression Statistics
Multiple R0.837277838225756
R-squared0.701034178383995
Adjusted R-squared0.570715743320609
F-TEST (value)5.37939377527833
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value7.01224103738518e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation438.580853085848
Sum Squared Residuals7501773.4230469


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
134994014.44692413249-515.44692413249
241454276.40948502811-131.409485028112
337963655.59463305087140.405366949125
437113876.13782116545-165.137821165453
539493700.83477158281248.165228417188
637404412.82590675054-672.82590675054
732433422.20703345585-179.207033455847
844074151.28610619539255.713893804612
948144271.75049257232542.249507427683
1039083954.6194039217-46.6194039217033
1152504959.27287808975290.727121910246
1239374345.1400856631-408.140085663099
1340044244.7273718845-240.727371884502
1455605049.27099512249510.729004877507
1539224117.66931889528-195.669318895280
1637593926.43999982545-167.439999825446
1741384291.60340120661-153.603401206612
1846344669.08690655155-35.086906551549
1939963706.29736549665289.702634503346
2043084365.84208855129-57.8420885512853
2141434661.64906392728-518.649063927276
2244294274.37989923832154.620100761676
2352195373.37346717731-154.373467177309
2449294141.30158489376787.698415106238
2557554815.35313247362939.646867526383
2655925694.75933913491-102.759339134912
2741634463.21493845389-300.214938453886
2849624923.8568859739538.1431140260491
2952085006.73184744888201.268152551116
3047555120.56193076156-365.561930761556
3144914261.43253111766229.567468882337
3257325199.07604102141532.923958978588
3357315228.34184163925502.658158360755
3450404832.7353333298207.264666670195
3561026004.4075591307897.5924408692193
3649045130.91630542065-226.916305420648
3753695128.76234609467240.237653905330
3855785968.11436095055-390.114360950549
3946194574.2935711311744.7064288688274
4047314901.17828491782-170.178284917823
4150114816.71393664407194.286063355926
4252995234.9635207978564.0364792021519
4341464402.78290037860-256.782900378595
4446254911.80234437382-286.802344373821
4547365072.73332501888-336.733325018880
4642194534.26536351017-315.265363510168
4751165349.94609560216-233.946095602156
4842054357.64202402249-152.642024022491
4941214544.71022541472-423.710225414722
5051034989.44581976393113.554180236066
5143003989.22753846879310.772461531213
5245784113.38700811733464.612991882673
5338094299.11604311762-490.116043117618
5455264516.561735138511009.43826486149
5542474330.28016955124-83.2801695512407
5638304273.99341985809-443.993419858094
5743944583.52527684228-189.525276842281


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.4611570421694150.922314084338830.538842957830585
220.3222645881151250.644529176230250.677735411884875
230.2823878612628160.5647757225256310.717612138737184
240.4572472291821250.914494458364250.542752770817875
250.8986205170923090.2027589658153830.101379482907691
260.8298376873805630.3403246252388740.170162312619437
270.7611461768886720.4777076462226560.238853823111328
280.7285397684760590.5429204630478830.271460231523941
290.6465258290164070.7069483419671870.353474170983593
300.9146311583277350.1707376833445310.0853688416722653
310.9338303143052150.1323393713895700.0661696856947852
320.8917502769102480.2164994461795040.108249723089752
330.8203168980652990.3593662038694010.179683101934701
340.8098167742900420.3803664514199160.190183225709958
350.7428165513321030.5143668973357940.257183448667897
360.7117627255736350.576474548852730.288237274426365


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