Multiple Linear Regression - Estimated Regression Equation
[t] = + 9152.70277777778 + 332.632870370369M1[t] -580.933754208754M2[t] + 285.699621212121M3[t] -10.4670033670033M4[t] + 200.766372053872M5[t] + 97.8997474747474M6[t] + 682.733122895623M7[t] + 540.766498316498M8[t] + 278.499873737374M9[t] + 379.633249158249M10[t] -392.733375420876M11[t] + 11.0666245791246t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)9152.70277777778106.59708285.862600
M1332.632870370369132.2154542.51580.013360.00668
M2-580.933754208754132.16696-4.39552.6e-051.3e-05
M3285.699621212121132.123072.16240.0328170.016408
M4-10.4670033670033132.083787-0.07920.9369860.468493
M5200.766372053872132.0491161.52040.1313620.065681
M697.8997474747474132.019060.74160.459980.22999
M7682.733122895623131.9936235.17251e-061e-06
M8540.766498316498131.9728074.09768.1e-054.1e-05
M9278.499873737374131.9566152.11050.0371420.018571
M10379.633249158249131.9450482.87720.0048440.002422
M11-392.733375420876131.938107-2.97660.0036040.001802
t11.06662457912460.78135614.163400


Multiple Linear Regression - Regression Statistics
Multiple R0.880797786458691
R-squared0.77580474063053
Adjusted R-squared0.750661347056384
F-TEST (value)30.8552120596901
F-TEST (DF numerator)12
F-TEST (DF denominator)107
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation295.017403252654
Sum Squared Residuals9312773.69974747


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
197699496.40227272729272.597727272714
293218593.90227272727727.097727272728
399399471.60227272727467.397727272727
493369186.50227272727149.497727272728
5101959408.80227272727786.197727272728
694649317.00227272727146.997727272727
7100109912.9022727272797.0977272727276
8102139782.00227272727430.997727272728
995639530.8022727272732.197727272728
1098909643.00227272727246.997727272728
1193058881.70227272727423.297727272727
1293919285.50227272727105.497727272727
1399289629.20176767676298.798232323235
1486868726.70176767677-40.7017676767675
1598439604.40176767677238.598232323233
1696279319.30176767677307.698232323232
17100749541.60176767677532.398232323233
1895039449.8017676767753.1982323232325
191011910045.701767676873.2982323232325
20100009914.8017676767785.1982323232324
2193139663.60176767677-350.601767676768
2298669775.8017676767790.1982323232326
2391729014.50176767677157.498232323233
2492419418.30176767677-177.301767676768
2596599762.00126262626-103.001262626261
2689048859.5012626262644.4987373737374
2797559737.2012626262617.7987373737377
2890809452.10126262626-372.101262626263
2994359674.40126262626-239.401262626263
3089719582.60126262626-611.601262626262
311006310178.5012626263-115.501262626263
32979310047.6012626263-254.601262626263
3394549796.40126262626-342.401262626263
3497599908.60126262626-149.601262626262
3588209147.30126262626-327.301262626262
3694039551.10126262626-148.101262626263
3796769894.80075757576-218.800757575756
3886428992.30075757576-350.300757575757
3994029870.00075757576-468.000757575757
4096109584.9007575757625.0992424242426
4192949807.20075757576-513.200757575757
4294489715.40075757576-267.400757575757
431031910311.30075757587.69924242424247
44954810180.4007575758-632.400757575757
4598019929.20075757576-128.200757575758
46959610041.4007575758-445.400757575757
4789239280.10075757576-357.100757575758
4897469683.9007575757662.0992424242425
49982910027.6002525253-198.600252525251
5091259125.10025252525-0.100252525252519
51978210002.8002525253-220.800252525252
5294419717.70025252525-276.700252525253
5391629940.00025252525-778.000252525252
5499159848.2002525252566.7997474747475
551044410444.1002525253-0.100252525252583
561020910313.2002525253-104.200252525253
57998510062.0002525253-77.0002525252524
58984210174.2002525253-332.200252525253
5994299412.9002525252516.0997474747475
60101329816.70025252525315.299747474748
61984910160.3997474747-311.399747474746
6291729257.89974747475-85.8997474747475
631031310135.5997474747177.400252525252
6498199850.49974747475-31.4997474747475
65995510072.7997474747-117.799747474747
66100489980.9997474747567.0002525252525
671008210576.8997474747-494.899747474747
681054110445.999747474795.0002525252524
691020810194.799747474713.2002525252525
701023310306.9997474747-73.9997474747474
7194399545.69974747475-106.699747474748
7299639949.4997474747513.5002525252525
731015810293.1992424242-135.199242424241
7492259390.69924242424-165.699242424243
751047410268.3992424242205.600757575758
7697579983.29924242424-226.299242424243
771049010205.5992424242284.400757575757
781028110113.7992424242167.200757575758
791044410709.6992424242-265.699242424243
801064010578.799242424261.2007575757573
811069510327.5992424242367.400757575757
821078610439.7992424242346.200757575757
8398329678.49924242424153.500757575757
84974710082.2992424242-335.299242424243
851041110425.9987373737-14.998737373736
8695119523.49873737374-12.4987373737376
871040210401.19873737370.801262626262459
88970110116.0987373737-415.098737373738
891054010338.3987373737201.601262626262
901011210246.5987373737-134.598737373737
911091510842.498737373772.5012626262623
921118310711.5987373737471.401262626263
931038410460.3987373737-76.3987373737374
941083410572.5987373737261.401262626262
9598869811.2987373737474.7012626262624
961021610215.09873737370.901262626262397
971094310558.7982323232384.201767676769
9898679656.29823232323210.701767676767
991020310533.9982323232-330.998232323233
1001083710248.8982323232588.101767676768
1011057310471.1982323232101.801767676767
1021064710379.3982323232267.601767676767
1031150210975.2982323232526.701767676768
1041065610844.3982323232-188.398232323233
1051086610593.1982323232272.801767676767
1061083510705.3982323232129.601767676767
10799459944.098232323230.901767676767608
1081033110347.8982323232-16.8982323232325
1091071810691.597727272726.4022727272738
11094629789.09772727273-327.097727272728
1111057910666.7977272727-87.7977272727276
1121063310381.6977272727251.302272727272
1131034610603.9977272727-257.997727272728
1141075710512.1977272727244.802272727272
1151120711108.097727272798.9022727272728
1161101310977.197727272735.8022727272727
1171101510725.9977272727289.002272727273
1181076510838.1977272727-73.1977272727276
1191004210076.8977272727-34.8977272727273
1201066110480.6977272727180.302272727272


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.7142793559420820.5714412881158360.285720644057918
170.6274357298684110.7451285402631780.372564270131589
180.5042280361469310.9915439277061380.495771963853069
190.4077290415955330.8154580831910660.592270958404467
200.3324565686099010.6649131372198020.667543431390099
210.2651954213119560.5303908426239130.734804578688044
220.1942234615211260.3884469230422520.805776538478874
230.1463328023343770.2926656046687530.853667197665623
240.09737181758102660.1947436351620530.902628182418973
250.06376494453963410.1275298890792680.936235055460366
260.04352502817858330.08705005635716650.956474971821417
270.02876026344741280.05752052689482560.971239736552587
280.03072563709193360.06145127418386720.969274362908066
290.1076108106388810.2152216212777630.892389189361119
300.1252052875554480.2504105751108970.874794712444552
310.1180101276215630.2360202552431260.881989872378437
320.08602371663592780.1720474332718560.913976283364072
330.08368378410190360.1673675682038070.916316215898096
340.06168732809537180.1233746561907440.938312671904628
350.05209250612419060.1041850122483810.947907493875809
360.05648990635191830.1129798127038370.943510093648082
370.0444890860707840.08897817214156810.955510913929216
380.03126389662098330.06252779324196660.968736103379017
390.02666552070920120.05333104141840250.973334479290799
400.07536645302166780.1507329060433360.924633546978332
410.09542522057904660.1908504411580930.904574779420953
420.122199358457590.244398716915180.87780064154241
430.1831980008754770.3663960017509550.816801999124523
440.216686302919710.4333726058394210.78331369708029
450.3251295737690820.6502591475381640.674870426230918
460.2999866698123940.5999733396247890.700013330187606
470.2610654808745670.5221309617491340.738934519125433
480.3755831238906320.7511662477812650.624416876109368
490.3532501324161340.7065002648322670.646749867583866
500.3869975838664980.7739951677329960.613002416133502
510.3488917363537180.6977834727074360.651108263646282
520.3150983473530280.6301966947060560.684901652646972
530.5200084944642810.9599830110714380.479991505535719
540.6695922439348480.6608155121303040.330407756065152
550.6821820880000080.6356358239999840.317817911999992
560.6866535803119820.6266928393760350.313346419688018
570.7237085402613650.552582919477270.276291459738635
580.7224003250421430.5551993499157140.277599674957857
590.7207658644460830.5584682711078340.279234135553917
600.8414755352213790.3170489295572430.158524464778621
610.8283695140881110.3432609718237770.171630485911889
620.7992372163431380.4015255673137240.200762783656862
630.839719385100120.3205612297997610.16028061489988
640.8223095430716010.3553809138567980.177690456928399
650.7970159633275480.4059680733449040.202984036672452
660.8003064790780560.3993870418438880.199693520921944
670.8496962104533580.3006075790932850.150303789546642
680.8485671087894260.3028657824211480.151432891210574
690.8481967604896680.3036064790206640.151803239510332
700.835280164343430.3294396713131410.16471983565657
710.8040236830177190.3919526339645610.195976316982281
720.76936635979020.4612672804195990.2306336402098
730.7484651004188010.5030697991623980.251534899581199
740.7023416146866160.5953167706267670.297658385313384
750.7300829286300150.5398341427399710.269917071369985
760.7266356655779480.5467286688441040.273364334422052
770.7583073746430650.4833852507138690.241692625356935
780.7424183775155080.5151632449689840.257581622484492
790.7843035154032860.4313929691934270.215696484596714
800.7498433082219660.5003133835560670.250156691778034
810.7764081021078470.4471837957843070.223591897892153
820.7935938781375780.4128122437248440.206406121862422
830.7652349056131080.4695301887737830.234765094386891
840.7734066452641820.4531867094716360.226593354735818
850.7428191030678080.5143617938643840.257180896932192
860.6838233930432430.6323532139135150.316176606956757
870.6354468515476330.7291062969047330.364553148452367
880.8916382325714530.2167235348570940.108361767428547
890.8762403963126560.2475192073746880.123759603687344
900.9100156987276020.1799686025447950.0899843012723977
910.9204847841968710.1590304316062580.079515215803129
920.9558846500235950.08823069995280970.0441153499764048
930.9792786917435890.04144261651282150.0207213082564108
940.9686570153476480.06268596930470460.0313429846523523
950.9483406564041310.1033186871917390.0516593435958694
960.9397892946398240.1204214107203520.0602107053601759
970.928307632256740.1433847354865190.0716923677432596
980.953016174277950.09396765144410070.0469838257220503
990.955466201870340.08906759625932070.0445337981296604
1000.9505926115709260.09881477685814760.0494073884290738
1010.9473262004956470.1053475990087060.052673799504353
1020.8946079493731230.2107841012537550.105392050626877
1030.9574488623444440.08510227531111190.0425511376555559
1040.9344304476628360.1311391046743280.0655695523371641


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0112359550561798OK
10% type I error level130.146067415730337NOK