Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 30.1351868004738 -7.37172943300236X[t] + 0.212933144893385Y1[t] + 0.0216016225641451Y2[t] + 0.377218616670764Y3[t] + 0.0813878620545899Y4[t] + 35.9249251015229O1[t] + 45.6077804994825O2[t] + 47.6216268602616O3[t] -3.09242413991911M1[t] + 2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] + 11.3017701244168M11[t] + 0.179561185199603t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)30.13518680047389.5687433.14930.0022750.001137
X-7.371729433002363.614497-2.03950.0445820.022291
Y10.2129331448933850.0878562.42360.0175390.008769
Y20.02160162256414510.0842910.25630.7983740.399187
Y30.3772186166707640.0854894.41253e-051.5e-05
Y40.08138786205458990.0864550.94140.3492390.174619
O135.924925101522910.2785083.49510.0007630.000382
O245.607780499482510.7186444.2555.5e-052.7e-05
O347.621626860261610.2292884.65541.2e-056e-06
M1-3.092424139919114.963452-0.6230.5349670.267484
M22.309922982934434.7584070.48540.6286430.314321
M3-10.22268354893604.786261-2.13580.0356390.017819
M4-9.881266791696275.011521-1.97170.0519740.025987
M5-9.421180128019325.000292-1.88410.0630490.031524
M6-2.164966473804665.100668-0.42440.6723380.336169
M7-16.12343316008294.637884-3.47650.0008110.000405
M8-2.776300371482075.203663-0.53350.5950940.297547
M9-9.902671091624245.302-1.86770.065330.032665
M10-0.3897469234169475.130664-0.0760.939630.469815
M1111.30177012441684.8570712.32690.0224090.011205
t0.1795611851996030.0637782.81540.0060830.003042


Multiple Linear Regression - Regression Statistics
Multiple R0.890034725391375
R-squared0.7921618124025
Adjusted R-squared0.742080321415151
F-TEST (value)15.8174566448631
F-TEST (DF numerator)20
F-TEST (DF denominator)83
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.4308394231125
Sum Squared Residuals7382.08077463624


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1110.3672031100.3817055499459.98549755005454
296.8602511110.323673922685-13.4634228226846
394.194458391.28672234871562.90773595128442
499.5162196193.0710546143776.44516499562306
594.0633348790.35690797531993.70642689468011
697.554147694.6416529789992.91249462100100
778.1506242283.2787703156515-5.12814609565152
881.243464391.1254153900696-9.88195109006963
992.3626246585.2910273495937.07159730040694
1096.0632437190.38270038594395.68054332405608
11114.0523777102.86942055092311.1829571490766
12110.6616666100.10370778984910.5579588101514
13104.917194999.15835163125635.75884326874369
1490.00187193110.530848365456-20.5289764354558
1595.700806795.0628042871760.638002412824003
1686.0274115794.0321947600613-8.00478319006127
1784.8528766886.6412946317926-1.78841795179265
18100.0432894.55382907483445.4894509251656
1980.9171382380.79893145297060.118206777029389
2074.0653970989.3508198942467-15.2854228042467
2177.3028136986.1664019181804-8.86358822818042
2297.2304324990.42180955277536.8086229372247
2390.75515676102.464831675975-11.7096749159750
24100.561445591.05785606013989.50358943986022
2592.0129326797.8737557571752-5.86082308717519
2699.24012138101.02650582883-1.78638444882995
27105.867275593.199812580577212.6674629194228
2890.992046392.8615049851083-1.86945868510829
2993.3062442392.50736572601840.798878503981583
3091.17419413103.202672269222-12.0284781392221
3177.3329503983.9479295893513-6.6149791993513
3291.127772194.1436037025066-3.01583160250657
3385.0124994389.2192742469271-4.20677481692712
3483.9039024292.5129080747115-8.60900565471151
35104.8626302108.092983774383-3.23035357438289
36110.9039108100.22557150939910.6783392906011
3795.4371437398.1359475598993-2.69880382989934
38111.6238727108.3707659375563.25310676244409
39108.8925403103.1149739877215.77756631227933
4096.1751168298.0613547758284-1.88623795582838
41101.9740205100.7811688423301.19285165767012
4299.11953031109.464099332186-10.3445690221864
4386.7815814790.1830977891265-3.40151631912654
44118.4195003102.17338241169116.2461178883087
45118.7441447101.09200824904817.6521364509516
46106.5296192106.650626689001-0.121007489000737
47134.7772694126.8580931369037.91917626309703
48104.6778714124.184296105877-19.5064247058772
49105.2954304110.891344454809-5.59591405480864
50139.4139849125.61598112407213.7980037759276
51103.6060491111.486209744393-7.88016064439317
5299.78182974102.902736540884-3.12090680088364
53103.4610301114.874987558719-11.4139574587191
54120.0594945112.2809929571007.77850154289966
5596.7137716897.7590293024544-1.04525762245440
56107.1308929107.749816692012-0.618923792011776
57105.3608372109.077544077532-3.71670687753243
58111.6942359111.1626248884670.531611011532999
59132.0519998126.3735311372675.67846866273272
60126.8037879119.9031058388156.90068206118511
61154.4824253154.4824253-8.88178419700125e-16
62141.5570984138.1145272384763.44257116152416
63109.9506882123.284306511538-13.3336183115377
64127.904198126.8097806632981.09441733670216
65133.0888617127.9666072613495.12225443865054
66120.0796299123.919702733700-3.84007283370018
67117.5557142111.6827177189135.87299648108748
68143.0362309127.80791531788815.2283155821116
69159.982927159.982927-4.66293670342566e-15
70128.5991124126.2157041024202.38340829757957
71149.7373327141.1765132855888.56081941441244
72126.8169313142.343804792927-15.5268734929274
73140.9639674124.54774477769016.4162226223104
74137.6691981138.066376760411-0.39717866041074
75117.9402337118.391757207436-0.451523507435588
76122.3095247118.1116952720934.19782942790694
77127.7804207119.1640810569978.61633964300348
78136.1677176120.14888787352516.0188297264745
79116.2405856108.3165757827837.92400981721704
80123.1576893120.2006331673392.95705613266098
81116.3400234117.905354955729-1.56533155572921
82108.6119282119.461292927434-10.8493647274336
83125.8982264130.526964518678-4.62873811867777
84112.8003105120.909859805133-8.10954930513275
85107.5182447112.111371882877-4.59312718287737
86135.0955413122.17735742123412.9181838787660
87115.5096488112.0484485117563.46120028824364
88115.8640759105.9361501944089.92792570559219
89104.5883906116.200953895234-11.6125632952336
90163.7213386163.7213386-1.55431223447522e-15
91113.4482275113.2082403084810.239987191519269
9298.0428844113.082937909027-15.0400535090272
93116.7868521123.158184372989-6.37133227298933
94126.5330444122.3578520992484.17519230075247
95113.0336597126.806314580283-13.7726548802831
96124.3392163118.8369383978615.5022779021395
97109.8298759123.241771186348-13.4118952863481
98124.4434777121.6793809112812.76409678871919
99111.5039454115.290610820688-3.78666542068773
100102.0350019108.818952733943-6.78395083394276
101116.8726598111.4944722322405.37818756775954
102112.2073122118.193469020432-5.98615682043211
103101.151390299.11669123026942.03469896973057
104124.4255108115.0148176052209.4106931947805


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
240.7089185801805870.5821628396388270.291081419819413
250.5918129001813640.8163741996372730.408187099818636
260.6332498937860740.7335002124278520.366750106213926
270.7776644602749280.4446710794501440.222335539725072
280.6903004708762110.6193990582475770.309699529123789
290.6195048063108470.7609903873783050.380495193689152
300.5329617366829350.934076526634130.467038263317065
310.4666499584032140.9332999168064270.533350041596786
320.5794637498995480.8410725002009040.420536250100452
330.5023088804200150.995382239159970.497691119579985
340.4930000400869880.9860000801739760.506999959913012
350.430828074594670.861656149189340.56917192540533
360.3997921525030940.7995843050061890.600207847496906
370.3231622272127950.646324454425590.676837772787205
380.4302044024355490.8604088048710980.569795597564451
390.3922825063251190.7845650126502390.607717493674881
400.3343105128831950.668621025766390.665689487116805
410.2727368936740970.5454737873481940.727263106325903
420.2595194622423490.5190389244846980.740480537757651
430.2456527330721830.4913054661443650.754347266927817
440.5214141338726980.9571717322546040.478585866127302
450.6450097225972450.709980554805510.354990277402755
460.5964185232577930.8071629534844150.403581476742207
470.5553467130283990.8893065739432010.444653286971601
480.7689233293540220.4621533412919560.231076670645978
490.7197745027869930.5604509944260150.280225497213007
500.7984161678612620.4031676642774770.201583832138738
510.7643609305981740.4712781388036530.235639069401826
520.718017980884340.563964038231320.28198201911566
530.7764197415368960.4471605169262080.223580258463104
540.7860544502646310.4278910994707370.213945549735368
550.7750617821662470.4498764356675060.224938217833753
560.7756620344860190.4486759310279630.224337965513981
570.7477765367414870.5044469265170250.252223463258513
580.7225280915237330.5549438169525350.277471908476267
590.670269415259650.65946116948070.32973058474035
600.6143248175559160.7713503648881680.385675182444084
610.5399237484457910.9201525031084170.460076251554209
620.4859805614521980.9719611229043960.514019438547802
630.4818223209537510.9636446419075020.518177679046249
640.4170489394836590.8340978789673180.582951060516341
650.3617537919358670.7235075838717350.638246208064132
660.3334255145449250.6668510290898510.666574485455075
670.3327155003023610.6654310006047220.667284499697639
680.3061523132957800.6123046265915610.69384768670422
690.2360133442057890.4720266884115780.763986655794211
700.2239462373949340.4478924747898690.776053762605066
710.3301372223467670.6602744446935330.669862777653233
720.3500232351488930.7000464702977850.649976764851107
730.5267165255405760.9465669489188470.473283474459424
740.4272748364782150.854549672956430.572725163521785
750.3290284619170120.6580569238340240.670971538082988
760.2369733626613850.473946725322770.763026637338615
770.1784632645569330.3569265291138660.821536735443067
780.2666008599217090.5332017198434170.733399140078291
790.2071790895659370.4143581791318740.792820910434063
800.1425809883659120.2851619767318240.857419011634088


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