Multiple Linear Regression - Estimated Regression Equation
Orders[t] = + 45.7945811606209 + 10.0989931803165Group[t] + 0.00487769208390905Costs[t] -4.40797450353478e-05Dividends[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)45.794581160620913.8521643.3060.0013320.000666
Group10.098993180316512.4214040.8130.4182120.209106
Costs0.004877692083909050.00028617.080700
Dividends-4.40797450353478e-050.000115-0.38370.7020690.351035


Multiple Linear Regression - Regression Statistics
Multiple R0.874706146469247
R-squared0.76511084267108
Adjusted R-squared0.75777055650455
F-TEST (value)104.234470606873
F-TEST (DF numerator)3
F-TEST (DF denominator)96
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation60.4832287994374
Sum Squared Residuals351189.212736488


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1807839.397501630414-32.3975016304135
2444197.595753008283246.404246991717
3412476.182587507249-64.1825875072493
4428460.26683489604-32.2668348960402
5315316.912652653129-1.91265265312888
6168256.919145610329-88.9191456103286
7263239.82939112480023.1706088751998
8267265.9511730465701.04882695342970
9228237.137544244747-9.13754424474746
10129133.170207758457-4.17020775845691
11104160.961762613542-56.9617626135422
12122105.52212973590616.4778702640936
13393143.835695904485249.164304095515
14190256.808222051449-66.8082220514494
15280172.710988945684107.289011054316
166371.193849041733-8.19384904173297
1710267.835921747468934.1640782525311
18265157.918969774634107.081030225366
19234237.610246558747-3.61024655874678
20277127.304999588842149.695000411158
2173115.615512920538-42.6155129205376
2267161.294830841061-94.2948308410607
23103103.497392239681-0.497392239681171
24290119.629361260831170.370638739169
258374.74264872463748.25735127536259
2656120.165217932831-64.1652179328312
27236187.52424359152648.4757564084736
2873102.566672325118-29.5666723251182
293486.1686896972566-52.1686896972566
30139113.91370639402625.0862936059736
312668.484484314601-42.484484314601
3270108.865583095723-38.8655830957226
334086.4078707974602-46.4078707974602
344273.4340445874074-31.4340445874074
351243.3255209020857-31.3255209020857
36211131.82437066833979.1756293316608
377492.2620557351537-18.2620557351537
388076.96298181833533.03701818166473
398358.975790775993424.0242092240066
4013162.12417027724668.875829722754
4120382.2631631876945120.736836812305
4256108.455357918516-52.455357918516
438963.730972878798425.2690271212016
448874.119196448438113.8808035515619
453965.3200290380524-26.3200290380524
462569.4004150318398-44.4004150318398
474970.4685825707334-21.4685825707334
4814986.383861606469762.6161383935303
495864.3259475491496-6.32594754914958
504153.5674051203335-12.5674051203335
519066.542325366239123.4576746337609
5213670.195854127941565.8041458720585
539783.102087488153613.8979125118464
546354.97971316400198.02028683599815
5511471.7106233572942.28937664271
567754.718794890231622.2812051097684
57661.8567559285823-55.8567559285823
584768.1259901087957-21.1259901087957
595147.67696573462233.32303426537766
608597.534401176312-12.5344011763120
614378.878786445081-35.878786445081
623250.5885399658316-18.5885399658316
632566.9486995644051-41.9486995644051
647789.1405312028352-12.1405312028352
655456.2409531723265-2.24095317232652
66251106.434915186852144.565084813148
671546.5257035133101-31.5257035133101
684461.480855953518-17.4808559535180
697346.960854838052826.0391451619472
708571.16010763664513.8398923633550
714969.5414473046244-20.5414473046244
723869.0426577717164-31.0426577717164
733548.0630949845917-13.0630949845917
74945.001959989979-36.001959989979
753450.1877788567941-16.1877788567941
762061.0810988201581-41.0810988201581
772945.6718613713201-16.6718613713201
781146.482949419796-35.482949419796
795248.97602753914413.02397246085592
801353.9464818012494-40.9464818012494
812949.8377252246699-20.8377252246699
826674.7335887175991-8.73358871759909
833343.9416331788179-10.9416331788179
841548.0183626198973-33.0183626198973
851543.7736230829941-28.7736230829941
866880.825758941222-12.825758941222
8710059.580409783837140.4195902161629
881343.8460045920571-30.8460045920571
894551.3334711467826-6.33347114678256
901444.0048113215194-30.0048113215194
913651.3429793026504-15.3429793026504
924066.6235451937197-26.6235451937198
936853.980858199635114.0191418003649
942982.1653762826792-53.1653762826792
954353.3543609151836-10.3543609151836
963068.139251258135-38.139251258135
97942.1405226171751-33.1405226171751
982259.696938122024-37.6969381220240
991952.5644456448636-33.5644456448636
100959.4118594817504-50.4118594817504


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.999380301386360.001239397227278540.00061969861363927
80.9983580079792990.003283984041402800.00164199202070140
90.9981050727201630.003789854559674320.00189492727983716
100.9972015129027820.005596974194435930.00279848709721797
110.996251498260270.007497003479461750.00374850173973088
120.9925739979620.01485200407599880.00742600203799938
130.9999549348104849.01303790316722e-054.50651895158361e-05
140.999987737407642.45251847220188e-051.22625923610094e-05
150.999984142931633.17141367395205e-051.58570683697603e-05
160.9999667929654996.64140690025053e-053.32070345012527e-05
170.9999720454369865.59091260279875e-052.79545630139938e-05
180.9999693345018726.13309962563112e-053.06654981281556e-05
190.9999753398486164.93203027675686e-052.46601513837843e-05
200.9999983758417863.24831642883184e-061.62415821441592e-06
210.9999994475318771.10493624703781e-065.52468123518904e-07
220.9999999888137962.23724077721399e-081.11862038860699e-08
230.9999999808841613.82316774225091e-081.91158387112546e-08
240.9999999997963254.07350790931446e-102.03675395465723e-10
250.9999999994921181.01576474914219e-095.07882374571095e-10
260.9999999999254631.49074695388788e-107.45373476943942e-11
270.9999999998025973.94805219135855e-101.97402609567927e-10
280.9999999997777744.4445124927184e-102.2222562463592e-10
290.9999999998615982.76803983186828e-101.38401991593414e-10
300.9999999996426157.1477065875434e-103.5738532937717e-10
310.9999999996547086.90584992181556e-103.45292496090778e-10
320.9999999997985514.02897361442436e-102.01448680721218e-10
330.99999999983073.38598788635981e-101.69299394317991e-10
340.999999999714655.70700414088547e-102.85350207044273e-10
350.9999999997908054.183891170377e-102.0919455851885e-10
360.9999999996337767.32447181492801e-103.66223590746400e-10
370.9999999995839878.3202681113691e-104.16013405568455e-10
380.999999998971332.05734125917419e-091.02867062958710e-09
390.999999997730514.53898133844747e-092.26949066922374e-09
400.9999999988920312.21593774935317e-091.10796887467658e-09
410.9999999999161851.67630566929695e-108.38152834648473e-11
420.9999999999989542.09214154651619e-121.04607077325810e-12
430.9999999999973075.38511449329614e-122.69255724664807e-12
440.9999999999930131.39747943055888e-116.98739715279438e-12
450.9999999999889672.20649366923947e-111.10324683461973e-11
460.9999999999845553.08900884735146e-111.54450442367573e-11
470.9999999999839913.20175321391646e-111.60087660695823e-11
480.9999999999802363.95288782497152e-111.97644391248576e-11
490.9999999999612137.75744100376064e-113.87872050188032e-11
500.9999999998964972.07005778482932e-101.03502889241466e-10
510.9999999998771972.45605720504706e-101.22802860252353e-10
520.9999999999834663.30671960278908e-111.65335980139454e-11
530.9999999999570288.59448466081113e-114.29724233040556e-11
540.9999999999511589.76845988736136e-114.88422994368068e-11
550.999999999973195.36182649433174e-112.68091324716587e-11
560.9999999999627727.44567407789866e-113.72283703894933e-11
570.999999999991511.69806471456701e-118.49032357283505e-12
580.9999999999934351.31307367250917e-116.56536836254585e-12
590.9999999999837233.25538325723286e-111.62769162861643e-11
600.9999999999853582.92839998359911e-111.46419999179955e-11
610.9999999999811933.76137130680994e-111.88068565340497e-11
620.9999999999405651.18870866808293e-105.94354334041463e-11
630.9999999998578772.84245243842806e-101.42122621921403e-10
640.999999999796824.06359281193314e-102.03179640596657e-10
650.9999999993472861.30542819704512e-096.52714098522558e-10
660.9999999999667666.64688880811658e-113.32344440405829e-11
670.999999999906251.87497987565740e-109.37489937828701e-11
680.9999999997492575.0148527920526e-102.5074263960263e-10
690.9999999998921392.15723027667232e-101.07861513833616e-10
700.9999999999613557.72892082457807e-113.86446041228903e-11
710.9999999998746322.50736198417672e-101.25368099208836e-10
720.999999999952159.56984960346385e-114.78492480173192e-11
730.9999999998080433.83913225876775e-101.91956612938388e-10
740.9999999995067619.86477609720943e-104.93238804860472e-10
750.999999997985084.02983902960578e-092.01491951480289e-09
760.9999999925047741.49904521366898e-087.4952260683449e-09
770.999999972076745.58465195542801e-082.79232597771400e-08
780.9999999389735121.22052976665153e-076.10264883325763e-08
790.9999998340996483.31800703639066e-071.65900351819533e-07
800.9999996157102247.6857955262764e-073.8428977631382e-07
810.9999988030756512.39384869735073e-061.19692434867536e-06
820.999997403366065.19326787856315e-062.59663393928158e-06
830.9999949787257681.00425484634813e-055.02127423174065e-06
840.9999967205973176.55880536538087e-063.27940268269044e-06
850.9999867722978182.64554043632050e-051.32277021816025e-05
860.9999474482311010.000105103537797935.2551768898965e-05
870.9999957218370738.55632585383927e-064.27816292691964e-06
880.9999787343012124.25313975753259e-052.12656987876630e-05
890.9999441929613120.0001116140773761855.58070386880927e-05
900.9997076707352330.0005846585295339230.000292329264766961
910.9985138704858370.002972259028326710.00148612951416336
920.9964059655670340.00718806886593120.0035940344329656
930.9993082145996620.001383570800676750.000691785400338373


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level860.988505747126437NOK
5% type I error level871NOK
10% type I error level871NOK