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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 12:08:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322154564bemig9lbiq549dt.htm/, Retrieved Thu, 28 Mar 2024 21:14:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147101, Retrieved Thu, 28 Mar 2024 21:14:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact59
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multi linear regr...] [2011-11-24 17:08:33] [050dc696fa22882d0c3b1ebe5a70a85e] [Current]
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Dataseries X:
63031	68	13	5	20	10345
66751	17	26	7	21	17607
7176	1	0	0	0	1423
78306	114	37	12	28	20050
137944	95	47	15	59	21212
261308	148	80	16	58	93979
69266	56	21	12	36	15524
80226	26	36	13	50	16182
73226	63	35	15	29	19238
178519	96	40	13	48	28909
66476	74	35	6	24	22357
98606	65	46	16	44	25560
50001	40	20	7	16	9954
91093	173	24	12	46	18490
73884	28	19	9	35	17777
72961	55	15	10	35	25268
69388	58	48	16	63	37525
15629	25	0	5	15	6023
71693	103	38	20	62	25042
19920	29	12	7	12	35713
39403	31	10	13	33	7039
99933	43	51	13	44	40841
56088	74	4	11	29	9214
62006	99	24	9	26	17446
81665	25	39	10	31	10295
65223	69	19	7	22	13206
88794	62	23	13	46	26093
90642	25	39	15	39	20744
203699	38	37	13	45	68013
99340	57	20	7	23	12840
56695	52	20	14	41	12672
108143	91	41	11	32	10872
58313	48	26	3	12	21325
29101	52	0	8	18	24542
113060	35	31	12	41	16401
0	0	0	0	0	0
65773	31	8	12	32	12821
67047	107	35	8	24	14662
41953	242	3	20	54	22190
109835	41	47	18	71	37929
86584	57	42	9	32	18009
59588	32	11	14	53	11076
40064	17	10	7	24	24981
70227	36	26	13	35	30691
60437	29	27	11	42	29164
47000	22	0	11	33	13985
40295	21	15	14	30	7588
103397	41	32	9	36	20023
78982	64	13	12	48	25524
60206	71	24	11	31	14717
39887	28	10	17	34	6832
49791	36	14	10	30	9624
129283	45	24	11	43	24300
104816	22	29	12	41	21790
101395	27	40	17	66	16493
72824	38	22	6	20	9269
76018	26	27	8	23	20105
33891	41	8	12	30	11216
62164	21	27	13	49	15569
28266	28	0	14	37	21799
35093	36	0	17	61	3772
35252	58	17	8	25	6057
36977	65	7	9	28	20828
42406	29	18	9	25	9976
56353	21	7	9	29	14055
58817	19	24	15	53	17455
76053	55	18	16	55	39553
70872	119	39	13	33	14818
42372	34	17	12	37	17065
19144	25	0	10	27	1536
114177	113	39	9	26	11938
53544	46	20	3	2	24589
51379	28	29	12	46	21332
40756	63	27	8	15	13229
46956	52	23	17	63	11331
17799	35	0	9	28	853
71154	32	31	8	24	19821
58305	45	19	9	31	34666
27454	42	12	12	25	15051
34323	28	23	5	7	27969
44761	32	33	14	35	17897
113862	32	21	14	42	6031
35027	27	17	10	10	7153
62396	69	27	12	33	13365
29613	30	14	10	28	11197
65559	48	12	12	25	25291
110811	57	21	17	62	28994
27883	36	14	11	29	10461
40181	20	14	10	30	16415
53398	54	22	11	36	8495
56435	26	25	7	17	18318
77283	58	36	10	34	25143
71738	35	10	11	37	20471
48503	28	16	5	20	14561
25214	8	12	6	7	16902
119424	96	20	14	46	12994
79201	50	38	13	43	29697
19349	15	13	1	0	3895
78760	65	12	13	45	9807
54133	33	11	9	26	10711
21623	7	8	1	1	2325
25497	17	22	6	16	19000
69535	55	14	12	29	22418
30709	32	7	9	21	7872
37043	22	14	9	19	5650
24716	41	2	12	10	3979
54865	50	35	10	39	14956
27246	7	5	2	7	3738
0	0	0	0	0	0
38814	26	34	8	11	10586
27646	22	12	7	28	18122
65373	26	34	11	27	17899
43021	37	30	14	46	10913
43116	29	21	4	9	18060
3058	0	0	0	0	0
0	0	0	0	0	0
96347	42	28	13	49	15452
48626	51	16	17	27	33996
73073	77	12	13	31	8877
45266	32	14	12	46	18708
43410	63	7	1	3	2781
83842	50	41	12	41	20854
39296	18	21	6	15	8179
38490	37	28	11	21	7139
39841	23	1	8	23	13798
19764	19	10	2	4	5619
59975	39	31	12	41	13050
64589	38	7	12	46	11297
63339	55	26	14	54	16170
11796	22	1	2	1	0
7627	7	0	0	0	0
68998	21	12	9	21	20539
6836	5	0	1	0	0
33365	21	17	3	3	10056
5118	1	5	0	0	0
20898	22	4	2	3	2418
0	0	0	0	0	0
42690	31	6	12	44	11806
14507	25	0	14	19	15924
7131	0	0	0	0	0
4194	4	0	0	0	0
21416	20	15	4	12	7084
30591	29	0	7	24	14831
42419	33	12	10	26	6585




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147101&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147101&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147101&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
time[t] = + 11489.3212413663 + 158.305304711761comp[t] + 998.640799783652blog[t] -1782.2058980465review[t] + 886.630562666766fbm[t] + 0.965385019668044charac[t] -52.6499459504649t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
time[t] =  +  11489.3212413663 +  158.305304711761comp[t] +  998.640799783652blog[t] -1782.2058980465review[t] +  886.630562666766fbm[t] +  0.965385019668044charac[t] -52.6499459504649t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147101&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]time[t] =  +  11489.3212413663 +  158.305304711761comp[t] +  998.640799783652blog[t] -1782.2058980465review[t] +  886.630562666766fbm[t] +  0.965385019668044charac[t] -52.6499459504649t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147101&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147101&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
time[t] = + 11489.3212413663 + 158.305304711761comp[t] + 998.640799783652blog[t] -1782.2058980465review[t] + 886.630562666766fbm[t] + 0.965385019668044charac[t] -52.6499459504649t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11489.32124136636404.9953751.79380.075050.037525
comp158.30530471176160.8326242.60230.0102790.005139
blog998.640799783652161.4131086.186900
review-1782.2058980465803.985513-2.21670.0282920.014146
fbm886.630562666766228.8360113.87450.0001658.2e-05
charac0.9653850196680440.184985.21891e-060
t-52.649945950464946.511184-1.1320.2596180.129809

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 11489.3212413663 & 6404.995375 & 1.7938 & 0.07505 & 0.037525 \tabularnewline
comp & 158.305304711761 & 60.832624 & 2.6023 & 0.010279 & 0.005139 \tabularnewline
blog & 998.640799783652 & 161.413108 & 6.1869 & 0 & 0 \tabularnewline
review & -1782.2058980465 & 803.985513 & -2.2167 & 0.028292 & 0.014146 \tabularnewline
fbm & 886.630562666766 & 228.836011 & 3.8745 & 0.000165 & 8.2e-05 \tabularnewline
charac & 0.965385019668044 & 0.18498 & 5.2189 & 1e-06 & 0 \tabularnewline
t & -52.6499459504649 & 46.511184 & -1.132 & 0.259618 & 0.129809 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147101&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]11489.3212413663[/C][C]6404.995375[/C][C]1.7938[/C][C]0.07505[/C][C]0.037525[/C][/ROW]
[ROW][C]comp[/C][C]158.305304711761[/C][C]60.832624[/C][C]2.6023[/C][C]0.010279[/C][C]0.005139[/C][/ROW]
[ROW][C]blog[/C][C]998.640799783652[/C][C]161.413108[/C][C]6.1869[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]review[/C][C]-1782.2058980465[/C][C]803.985513[/C][C]-2.2167[/C][C]0.028292[/C][C]0.014146[/C][/ROW]
[ROW][C]fbm[/C][C]886.630562666766[/C][C]228.836011[/C][C]3.8745[/C][C]0.000165[/C][C]8.2e-05[/C][/ROW]
[ROW][C]charac[/C][C]0.965385019668044[/C][C]0.18498[/C][C]5.2189[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-52.6499459504649[/C][C]46.511184[/C][C]-1.132[/C][C]0.259618[/C][C]0.129809[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147101&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147101&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11489.32124136636404.9953751.79380.075050.037525
comp158.30530471176160.8326242.60230.0102790.005139
blog998.640799783652161.4131086.186900
review-1782.2058980465803.985513-2.21670.0282920.014146
fbm886.630562666766228.8360113.87450.0001658.2e-05
charac0.9653850196680440.184985.21891e-060
t-52.649945950464946.511184-1.1320.2596180.129809







Multiple Linear Regression - Regression Statistics
Multiple R0.856216044194739
R-squared0.733105914336488
Adjusted R-squared0.721417122263633
F-TEST (value)62.7187060704943
F-TEST (DF numerator)6
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19992.1039782133
Sum Squared Residuals54756738342.1699

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.856216044194739 \tabularnewline
R-squared & 0.733105914336488 \tabularnewline
Adjusted R-squared & 0.721417122263633 \tabularnewline
F-TEST (value) & 62.7187060704943 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 19992.1039782133 \tabularnewline
Sum Squared Residuals & 54756738342.1699 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147101&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.856216044194739[/C][/ROW]
[ROW][C]R-squared[/C][C]0.733105914336488[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.721417122263633[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]62.7187060704943[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]19992.1039782133[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]54756738342.1699[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147101&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147101&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.856216044194739
R-squared0.733105914336488
Adjusted R-squared0.721417122263633
F-TEST (value)62.7187060704943
F-TEST (DF numerator)6
F-TEST (DF denominator)137
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation19992.1039782133
Sum Squared Residuals54756738342.1699







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16303153992.25220457179038.74779542831
26675163181.20689491213569.79310508793
3717612863.4195912143-5687.41959121431
47830689070.390409156-10764.390409156
5137944119257.05481290318686.9451870969
6261308228129.06767500833178.9323249922
76926666476.19200380062789.80799619941
88022687919.8402354819-7693.84023548188
97322673492.4087720934-266.408772093367
10178519113403.6688925265115.3311074795
116647689746.2033774511-23270.2033774511
1298606102256.534977582-3650.53497758203
135000148429.99033027221571.00966972783
149109399354.9230277774-8261.9230277774
157388466260.16188548517623.83811451489
167296171936.68525190441024.3147480956
1769388131279.242165411-61891.2421654114
181562924702.1977552815-9073.19775528155
1971693108244.917632334-36551.9176323342
201992059651.7864294824-39731.7864294824
213940338163.02186714971239.97813285027
2299933123339.188993024-23406.1889930238
235608840990.607242356515097.3927576435
246200673719.9754998732-11713.9754998732
258166572679.82364164848985.17635835164
266522359796.96952973465426.03047026538
278879485657.56051412193136.43948587812
289064280791.196885419850.80311459003
29203699135316.21396792768382.7860320725
309934059218.646534643640121.3534653564
315669561696.1942235063-5001.1942235063
3210814384418.157551507323724.8424484927
335831369194.9730478232-10881.9730478232
342910143325.281020385-14224.281020385
3511306076943.785591659836116.2144083402
3609593.92318714956-9593.92318714956
376577341800.772651475423972.2273485246
386704782555.6803698381-15508.6803698381
394195384397.6054984044-42444.6054984044
40109835130297.110681854-20462.1106818541
418658490014.9331590008-3430.93315900078
425958858061.98378637411526.01621362591
434006454822.947137437-14758.947137437
447022778328.4000409084-8101.40004090839
456043786462.9365714865-26025.9365714865
464700035705.59362085311294.406379147
474029536292.17301398924002.82698601075
4810339782617.898344401320779.1016555987
497898277835.62726198761146.37273801243
506020666152.7336717985-5946.73367179854
513988729666.5798459110220.42015409
524979146499.20954735983291.79045264016
5312928381769.697306921447513.3026930786
5410481677090.645928771927725.3540712281
5510139596955.66143125544439.3385687446
567282452514.153054786920309.8469452131
577601865111.435416243810906.5645837562
583389138955.4727517325-5064.47275173246
596216473976.9876906732-11812.9876906732
602826641661.7493060307-13395.7493060307
613509341405.0618580813-6312.06185808134
623525248139.0798084682-12887.0798084682
633697754345.546913134-17368.546913134
644240646442.7048737424-4036.70487374237
655635341622.891438370714730.1085616293
665881772098.7316619135-13281.7316619135
677605393077.361278796-17024.361278796
687087286089.6544838365-15217.6544838365
694237258108.9043300536-15736.9043300536
701914419361.2552443755-217.255244375533
7111417783123.973614589231053.0263854108
725354455117.8808251584-1573.88082515843
735137981031.1352583095-29652.1352583095
744075656342.6507128495-15586.6507128495
754695675240.1923741913-28284.1923741913
761779922637.8871080703-4838.88710807035
777115469615.29274172071538.70725827928
785830578392.2708172123-20087.2708172123
792745441271.7911277122-13817.7911277122
803432358974.8505558128-24651.8505558128
814476168604.2745807003-23843.2745807003
8211386251319.090332632362542.9096673677
833502726320.15824290558706.84175709452
846239665727.801980106-3331.80198010599
852961343557.2190133282-13944.2190133282
866555951738.615935730313820.3840642697
8711081189567.602986507121243.3970134929
882788342743.002293892-14860.002293892
894018148574.2063403702-8393.20634037015
905339857784.791275072-4386.79127507196
915643556061.3351462598373.664853740196
927728388374.3583791358-11091.3583791358
937173855083.432608504516654.5673914955
944850349829.5806849795-1326.58068497953
952521431567.8245639877-6353.82456398767
9611942469983.387933710549440.6120662895
977920195871.3685606863-16670.3685606863
981934923664.5052596452-4315.50525964516
997876054747.74052922324012.259470777
1005413339786.230992009914346.7690079901
1012162316617.88506696935005.11493303065
1022549752615.4835178412-27118.4835178412
1036953554721.956676282814813.0433237172
1043070928248.88182019032460.11817980975
1053704329685.31778657187357.68221342818
106247165717.3279067352718998.6720932647
1075486579918.3015703765-25053.3015703765
1082724618155.07955671029090.92044328982
10905750.47713276562-5750.47713276562
1103881449482.4071251334-10668.4071251334
1112764650956.5053366955-23310.5053366955
1126537365276.439190593696.5608094063615
1134302167725.767646466-24704.767646466
1144311649335.2429621308-6219.24296213082
11530585434.57745706284-2376.57745706284
11605381.92751111237-5381.92751111237
1179634775133.392976975721213.6070230243
1184862655789.2050098966-7163.20500989662
1197307342283.769321128830789.2306788712
1204526661677.0267291209-16411.0267291209
1214341025644.819106337217765.1808936628
1228384289023.0873550641-5181.08735506412
1233929639336.4372973149-40.4372973149055
1243849044686.8272046868-6196.82720468678
1253984129002.979064055610838.0209359444
1261976423256.2457190565-3492.2457190565
1275997569498.2065821565-9523.20658215648
1286458948060.705010542316528.2949894577
1296333977906.3743466647-14567.3743466647
130117966448.404537822015347.59546217799
13176275700.315454837721926.68454516228
1326899842255.061024801226742.9389751988
13368363496.199055466773339.80094453323
1343336531756.71923091571608.2807690843
13551189533.08784168356-4415.08784168356
1362089813235.9893643617662.01063563896
13704276.27864615261-4276.27864615261
1384269044145.5474679492-1455.54746794923
1391450715396.5005432571-889.500543257135
14071314118.328808301213012.67119169879
14141944698.90008119779-504.900081197792
1422141632508.2776465339-11092.2776465339
1433059131672.5502514645-1081.5502514645
1444241932702.88968077629716.11031922377

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 63031 & 53992.2522045717 & 9038.74779542831 \tabularnewline
2 & 66751 & 63181.2068949121 & 3569.79310508793 \tabularnewline
3 & 7176 & 12863.4195912143 & -5687.41959121431 \tabularnewline
4 & 78306 & 89070.390409156 & -10764.390409156 \tabularnewline
5 & 137944 & 119257.054812903 & 18686.9451870969 \tabularnewline
6 & 261308 & 228129.067675008 & 33178.9323249922 \tabularnewline
7 & 69266 & 66476.1920038006 & 2789.80799619941 \tabularnewline
8 & 80226 & 87919.8402354819 & -7693.84023548188 \tabularnewline
9 & 73226 & 73492.4087720934 & -266.408772093367 \tabularnewline
10 & 178519 & 113403.66889252 & 65115.3311074795 \tabularnewline
11 & 66476 & 89746.2033774511 & -23270.2033774511 \tabularnewline
12 & 98606 & 102256.534977582 & -3650.53497758203 \tabularnewline
13 & 50001 & 48429.9903302722 & 1571.00966972783 \tabularnewline
14 & 91093 & 99354.9230277774 & -8261.9230277774 \tabularnewline
15 & 73884 & 66260.1618854851 & 7623.83811451489 \tabularnewline
16 & 72961 & 71936.6852519044 & 1024.3147480956 \tabularnewline
17 & 69388 & 131279.242165411 & -61891.2421654114 \tabularnewline
18 & 15629 & 24702.1977552815 & -9073.19775528155 \tabularnewline
19 & 71693 & 108244.917632334 & -36551.9176323342 \tabularnewline
20 & 19920 & 59651.7864294824 & -39731.7864294824 \tabularnewline
21 & 39403 & 38163.0218671497 & 1239.97813285027 \tabularnewline
22 & 99933 & 123339.188993024 & -23406.1889930238 \tabularnewline
23 & 56088 & 40990.6072423565 & 15097.3927576435 \tabularnewline
24 & 62006 & 73719.9754998732 & -11713.9754998732 \tabularnewline
25 & 81665 & 72679.8236416484 & 8985.17635835164 \tabularnewline
26 & 65223 & 59796.9695297346 & 5426.03047026538 \tabularnewline
27 & 88794 & 85657.5605141219 & 3136.43948587812 \tabularnewline
28 & 90642 & 80791.19688541 & 9850.80311459003 \tabularnewline
29 & 203699 & 135316.213967927 & 68382.7860320725 \tabularnewline
30 & 99340 & 59218.6465346436 & 40121.3534653564 \tabularnewline
31 & 56695 & 61696.1942235063 & -5001.1942235063 \tabularnewline
32 & 108143 & 84418.1575515073 & 23724.8424484927 \tabularnewline
33 & 58313 & 69194.9730478232 & -10881.9730478232 \tabularnewline
34 & 29101 & 43325.281020385 & -14224.281020385 \tabularnewline
35 & 113060 & 76943.7855916598 & 36116.2144083402 \tabularnewline
36 & 0 & 9593.92318714956 & -9593.92318714956 \tabularnewline
37 & 65773 & 41800.7726514754 & 23972.2273485246 \tabularnewline
38 & 67047 & 82555.6803698381 & -15508.6803698381 \tabularnewline
39 & 41953 & 84397.6054984044 & -42444.6054984044 \tabularnewline
40 & 109835 & 130297.110681854 & -20462.1106818541 \tabularnewline
41 & 86584 & 90014.9331590008 & -3430.93315900078 \tabularnewline
42 & 59588 & 58061.9837863741 & 1526.01621362591 \tabularnewline
43 & 40064 & 54822.947137437 & -14758.947137437 \tabularnewline
44 & 70227 & 78328.4000409084 & -8101.40004090839 \tabularnewline
45 & 60437 & 86462.9365714865 & -26025.9365714865 \tabularnewline
46 & 47000 & 35705.593620853 & 11294.406379147 \tabularnewline
47 & 40295 & 36292.1730139892 & 4002.82698601075 \tabularnewline
48 & 103397 & 82617.8983444013 & 20779.1016555987 \tabularnewline
49 & 78982 & 77835.6272619876 & 1146.37273801243 \tabularnewline
50 & 60206 & 66152.7336717985 & -5946.73367179854 \tabularnewline
51 & 39887 & 29666.57984591 & 10220.42015409 \tabularnewline
52 & 49791 & 46499.2095473598 & 3291.79045264016 \tabularnewline
53 & 129283 & 81769.6973069214 & 47513.3026930786 \tabularnewline
54 & 104816 & 77090.6459287719 & 27725.3540712281 \tabularnewline
55 & 101395 & 96955.6614312554 & 4439.3385687446 \tabularnewline
56 & 72824 & 52514.1530547869 & 20309.8469452131 \tabularnewline
57 & 76018 & 65111.4354162438 & 10906.5645837562 \tabularnewline
58 & 33891 & 38955.4727517325 & -5064.47275173246 \tabularnewline
59 & 62164 & 73976.9876906732 & -11812.9876906732 \tabularnewline
60 & 28266 & 41661.7493060307 & -13395.7493060307 \tabularnewline
61 & 35093 & 41405.0618580813 & -6312.06185808134 \tabularnewline
62 & 35252 & 48139.0798084682 & -12887.0798084682 \tabularnewline
63 & 36977 & 54345.546913134 & -17368.546913134 \tabularnewline
64 & 42406 & 46442.7048737424 & -4036.70487374237 \tabularnewline
65 & 56353 & 41622.8914383707 & 14730.1085616293 \tabularnewline
66 & 58817 & 72098.7316619135 & -13281.7316619135 \tabularnewline
67 & 76053 & 93077.361278796 & -17024.361278796 \tabularnewline
68 & 70872 & 86089.6544838365 & -15217.6544838365 \tabularnewline
69 & 42372 & 58108.9043300536 & -15736.9043300536 \tabularnewline
70 & 19144 & 19361.2552443755 & -217.255244375533 \tabularnewline
71 & 114177 & 83123.9736145892 & 31053.0263854108 \tabularnewline
72 & 53544 & 55117.8808251584 & -1573.88082515843 \tabularnewline
73 & 51379 & 81031.1352583095 & -29652.1352583095 \tabularnewline
74 & 40756 & 56342.6507128495 & -15586.6507128495 \tabularnewline
75 & 46956 & 75240.1923741913 & -28284.1923741913 \tabularnewline
76 & 17799 & 22637.8871080703 & -4838.88710807035 \tabularnewline
77 & 71154 & 69615.2927417207 & 1538.70725827928 \tabularnewline
78 & 58305 & 78392.2708172123 & -20087.2708172123 \tabularnewline
79 & 27454 & 41271.7911277122 & -13817.7911277122 \tabularnewline
80 & 34323 & 58974.8505558128 & -24651.8505558128 \tabularnewline
81 & 44761 & 68604.2745807003 & -23843.2745807003 \tabularnewline
82 & 113862 & 51319.0903326323 & 62542.9096673677 \tabularnewline
83 & 35027 & 26320.1582429055 & 8706.84175709452 \tabularnewline
84 & 62396 & 65727.801980106 & -3331.80198010599 \tabularnewline
85 & 29613 & 43557.2190133282 & -13944.2190133282 \tabularnewline
86 & 65559 & 51738.6159357303 & 13820.3840642697 \tabularnewline
87 & 110811 & 89567.6029865071 & 21243.3970134929 \tabularnewline
88 & 27883 & 42743.002293892 & -14860.002293892 \tabularnewline
89 & 40181 & 48574.2063403702 & -8393.20634037015 \tabularnewline
90 & 53398 & 57784.791275072 & -4386.79127507196 \tabularnewline
91 & 56435 & 56061.3351462598 & 373.664853740196 \tabularnewline
92 & 77283 & 88374.3583791358 & -11091.3583791358 \tabularnewline
93 & 71738 & 55083.4326085045 & 16654.5673914955 \tabularnewline
94 & 48503 & 49829.5806849795 & -1326.58068497953 \tabularnewline
95 & 25214 & 31567.8245639877 & -6353.82456398767 \tabularnewline
96 & 119424 & 69983.3879337105 & 49440.6120662895 \tabularnewline
97 & 79201 & 95871.3685606863 & -16670.3685606863 \tabularnewline
98 & 19349 & 23664.5052596452 & -4315.50525964516 \tabularnewline
99 & 78760 & 54747.740529223 & 24012.259470777 \tabularnewline
100 & 54133 & 39786.2309920099 & 14346.7690079901 \tabularnewline
101 & 21623 & 16617.8850669693 & 5005.11493303065 \tabularnewline
102 & 25497 & 52615.4835178412 & -27118.4835178412 \tabularnewline
103 & 69535 & 54721.9566762828 & 14813.0433237172 \tabularnewline
104 & 30709 & 28248.8818201903 & 2460.11817980975 \tabularnewline
105 & 37043 & 29685.3177865718 & 7357.68221342818 \tabularnewline
106 & 24716 & 5717.32790673527 & 18998.6720932647 \tabularnewline
107 & 54865 & 79918.3015703765 & -25053.3015703765 \tabularnewline
108 & 27246 & 18155.0795567102 & 9090.92044328982 \tabularnewline
109 & 0 & 5750.47713276562 & -5750.47713276562 \tabularnewline
110 & 38814 & 49482.4071251334 & -10668.4071251334 \tabularnewline
111 & 27646 & 50956.5053366955 & -23310.5053366955 \tabularnewline
112 & 65373 & 65276.4391905936 & 96.5608094063615 \tabularnewline
113 & 43021 & 67725.767646466 & -24704.767646466 \tabularnewline
114 & 43116 & 49335.2429621308 & -6219.24296213082 \tabularnewline
115 & 3058 & 5434.57745706284 & -2376.57745706284 \tabularnewline
116 & 0 & 5381.92751111237 & -5381.92751111237 \tabularnewline
117 & 96347 & 75133.3929769757 & 21213.6070230243 \tabularnewline
118 & 48626 & 55789.2050098966 & -7163.20500989662 \tabularnewline
119 & 73073 & 42283.7693211288 & 30789.2306788712 \tabularnewline
120 & 45266 & 61677.0267291209 & -16411.0267291209 \tabularnewline
121 & 43410 & 25644.8191063372 & 17765.1808936628 \tabularnewline
122 & 83842 & 89023.0873550641 & -5181.08735506412 \tabularnewline
123 & 39296 & 39336.4372973149 & -40.4372973149055 \tabularnewline
124 & 38490 & 44686.8272046868 & -6196.82720468678 \tabularnewline
125 & 39841 & 29002.9790640556 & 10838.0209359444 \tabularnewline
126 & 19764 & 23256.2457190565 & -3492.2457190565 \tabularnewline
127 & 59975 & 69498.2065821565 & -9523.20658215648 \tabularnewline
128 & 64589 & 48060.7050105423 & 16528.2949894577 \tabularnewline
129 & 63339 & 77906.3743466647 & -14567.3743466647 \tabularnewline
130 & 11796 & 6448.40453782201 & 5347.59546217799 \tabularnewline
131 & 7627 & 5700.31545483772 & 1926.68454516228 \tabularnewline
132 & 68998 & 42255.0610248012 & 26742.9389751988 \tabularnewline
133 & 6836 & 3496.19905546677 & 3339.80094453323 \tabularnewline
134 & 33365 & 31756.7192309157 & 1608.2807690843 \tabularnewline
135 & 5118 & 9533.08784168356 & -4415.08784168356 \tabularnewline
136 & 20898 & 13235.989364361 & 7662.01063563896 \tabularnewline
137 & 0 & 4276.27864615261 & -4276.27864615261 \tabularnewline
138 & 42690 & 44145.5474679492 & -1455.54746794923 \tabularnewline
139 & 14507 & 15396.5005432571 & -889.500543257135 \tabularnewline
140 & 7131 & 4118.32880830121 & 3012.67119169879 \tabularnewline
141 & 4194 & 4698.90008119779 & -504.900081197792 \tabularnewline
142 & 21416 & 32508.2776465339 & -11092.2776465339 \tabularnewline
143 & 30591 & 31672.5502514645 & -1081.5502514645 \tabularnewline
144 & 42419 & 32702.8896807762 & 9716.11031922377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147101&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]63031[/C][C]53992.2522045717[/C][C]9038.74779542831[/C][/ROW]
[ROW][C]2[/C][C]66751[/C][C]63181.2068949121[/C][C]3569.79310508793[/C][/ROW]
[ROW][C]3[/C][C]7176[/C][C]12863.4195912143[/C][C]-5687.41959121431[/C][/ROW]
[ROW][C]4[/C][C]78306[/C][C]89070.390409156[/C][C]-10764.390409156[/C][/ROW]
[ROW][C]5[/C][C]137944[/C][C]119257.054812903[/C][C]18686.9451870969[/C][/ROW]
[ROW][C]6[/C][C]261308[/C][C]228129.067675008[/C][C]33178.9323249922[/C][/ROW]
[ROW][C]7[/C][C]69266[/C][C]66476.1920038006[/C][C]2789.80799619941[/C][/ROW]
[ROW][C]8[/C][C]80226[/C][C]87919.8402354819[/C][C]-7693.84023548188[/C][/ROW]
[ROW][C]9[/C][C]73226[/C][C]73492.4087720934[/C][C]-266.408772093367[/C][/ROW]
[ROW][C]10[/C][C]178519[/C][C]113403.66889252[/C][C]65115.3311074795[/C][/ROW]
[ROW][C]11[/C][C]66476[/C][C]89746.2033774511[/C][C]-23270.2033774511[/C][/ROW]
[ROW][C]12[/C][C]98606[/C][C]102256.534977582[/C][C]-3650.53497758203[/C][/ROW]
[ROW][C]13[/C][C]50001[/C][C]48429.9903302722[/C][C]1571.00966972783[/C][/ROW]
[ROW][C]14[/C][C]91093[/C][C]99354.9230277774[/C][C]-8261.9230277774[/C][/ROW]
[ROW][C]15[/C][C]73884[/C][C]66260.1618854851[/C][C]7623.83811451489[/C][/ROW]
[ROW][C]16[/C][C]72961[/C][C]71936.6852519044[/C][C]1024.3147480956[/C][/ROW]
[ROW][C]17[/C][C]69388[/C][C]131279.242165411[/C][C]-61891.2421654114[/C][/ROW]
[ROW][C]18[/C][C]15629[/C][C]24702.1977552815[/C][C]-9073.19775528155[/C][/ROW]
[ROW][C]19[/C][C]71693[/C][C]108244.917632334[/C][C]-36551.9176323342[/C][/ROW]
[ROW][C]20[/C][C]19920[/C][C]59651.7864294824[/C][C]-39731.7864294824[/C][/ROW]
[ROW][C]21[/C][C]39403[/C][C]38163.0218671497[/C][C]1239.97813285027[/C][/ROW]
[ROW][C]22[/C][C]99933[/C][C]123339.188993024[/C][C]-23406.1889930238[/C][/ROW]
[ROW][C]23[/C][C]56088[/C][C]40990.6072423565[/C][C]15097.3927576435[/C][/ROW]
[ROW][C]24[/C][C]62006[/C][C]73719.9754998732[/C][C]-11713.9754998732[/C][/ROW]
[ROW][C]25[/C][C]81665[/C][C]72679.8236416484[/C][C]8985.17635835164[/C][/ROW]
[ROW][C]26[/C][C]65223[/C][C]59796.9695297346[/C][C]5426.03047026538[/C][/ROW]
[ROW][C]27[/C][C]88794[/C][C]85657.5605141219[/C][C]3136.43948587812[/C][/ROW]
[ROW][C]28[/C][C]90642[/C][C]80791.19688541[/C][C]9850.80311459003[/C][/ROW]
[ROW][C]29[/C][C]203699[/C][C]135316.213967927[/C][C]68382.7860320725[/C][/ROW]
[ROW][C]30[/C][C]99340[/C][C]59218.6465346436[/C][C]40121.3534653564[/C][/ROW]
[ROW][C]31[/C][C]56695[/C][C]61696.1942235063[/C][C]-5001.1942235063[/C][/ROW]
[ROW][C]32[/C][C]108143[/C][C]84418.1575515073[/C][C]23724.8424484927[/C][/ROW]
[ROW][C]33[/C][C]58313[/C][C]69194.9730478232[/C][C]-10881.9730478232[/C][/ROW]
[ROW][C]34[/C][C]29101[/C][C]43325.281020385[/C][C]-14224.281020385[/C][/ROW]
[ROW][C]35[/C][C]113060[/C][C]76943.7855916598[/C][C]36116.2144083402[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]9593.92318714956[/C][C]-9593.92318714956[/C][/ROW]
[ROW][C]37[/C][C]65773[/C][C]41800.7726514754[/C][C]23972.2273485246[/C][/ROW]
[ROW][C]38[/C][C]67047[/C][C]82555.6803698381[/C][C]-15508.6803698381[/C][/ROW]
[ROW][C]39[/C][C]41953[/C][C]84397.6054984044[/C][C]-42444.6054984044[/C][/ROW]
[ROW][C]40[/C][C]109835[/C][C]130297.110681854[/C][C]-20462.1106818541[/C][/ROW]
[ROW][C]41[/C][C]86584[/C][C]90014.9331590008[/C][C]-3430.93315900078[/C][/ROW]
[ROW][C]42[/C][C]59588[/C][C]58061.9837863741[/C][C]1526.01621362591[/C][/ROW]
[ROW][C]43[/C][C]40064[/C][C]54822.947137437[/C][C]-14758.947137437[/C][/ROW]
[ROW][C]44[/C][C]70227[/C][C]78328.4000409084[/C][C]-8101.40004090839[/C][/ROW]
[ROW][C]45[/C][C]60437[/C][C]86462.9365714865[/C][C]-26025.9365714865[/C][/ROW]
[ROW][C]46[/C][C]47000[/C][C]35705.593620853[/C][C]11294.406379147[/C][/ROW]
[ROW][C]47[/C][C]40295[/C][C]36292.1730139892[/C][C]4002.82698601075[/C][/ROW]
[ROW][C]48[/C][C]103397[/C][C]82617.8983444013[/C][C]20779.1016555987[/C][/ROW]
[ROW][C]49[/C][C]78982[/C][C]77835.6272619876[/C][C]1146.37273801243[/C][/ROW]
[ROW][C]50[/C][C]60206[/C][C]66152.7336717985[/C][C]-5946.73367179854[/C][/ROW]
[ROW][C]51[/C][C]39887[/C][C]29666.57984591[/C][C]10220.42015409[/C][/ROW]
[ROW][C]52[/C][C]49791[/C][C]46499.2095473598[/C][C]3291.79045264016[/C][/ROW]
[ROW][C]53[/C][C]129283[/C][C]81769.6973069214[/C][C]47513.3026930786[/C][/ROW]
[ROW][C]54[/C][C]104816[/C][C]77090.6459287719[/C][C]27725.3540712281[/C][/ROW]
[ROW][C]55[/C][C]101395[/C][C]96955.6614312554[/C][C]4439.3385687446[/C][/ROW]
[ROW][C]56[/C][C]72824[/C][C]52514.1530547869[/C][C]20309.8469452131[/C][/ROW]
[ROW][C]57[/C][C]76018[/C][C]65111.4354162438[/C][C]10906.5645837562[/C][/ROW]
[ROW][C]58[/C][C]33891[/C][C]38955.4727517325[/C][C]-5064.47275173246[/C][/ROW]
[ROW][C]59[/C][C]62164[/C][C]73976.9876906732[/C][C]-11812.9876906732[/C][/ROW]
[ROW][C]60[/C][C]28266[/C][C]41661.7493060307[/C][C]-13395.7493060307[/C][/ROW]
[ROW][C]61[/C][C]35093[/C][C]41405.0618580813[/C][C]-6312.06185808134[/C][/ROW]
[ROW][C]62[/C][C]35252[/C][C]48139.0798084682[/C][C]-12887.0798084682[/C][/ROW]
[ROW][C]63[/C][C]36977[/C][C]54345.546913134[/C][C]-17368.546913134[/C][/ROW]
[ROW][C]64[/C][C]42406[/C][C]46442.7048737424[/C][C]-4036.70487374237[/C][/ROW]
[ROW][C]65[/C][C]56353[/C][C]41622.8914383707[/C][C]14730.1085616293[/C][/ROW]
[ROW][C]66[/C][C]58817[/C][C]72098.7316619135[/C][C]-13281.7316619135[/C][/ROW]
[ROW][C]67[/C][C]76053[/C][C]93077.361278796[/C][C]-17024.361278796[/C][/ROW]
[ROW][C]68[/C][C]70872[/C][C]86089.6544838365[/C][C]-15217.6544838365[/C][/ROW]
[ROW][C]69[/C][C]42372[/C][C]58108.9043300536[/C][C]-15736.9043300536[/C][/ROW]
[ROW][C]70[/C][C]19144[/C][C]19361.2552443755[/C][C]-217.255244375533[/C][/ROW]
[ROW][C]71[/C][C]114177[/C][C]83123.9736145892[/C][C]31053.0263854108[/C][/ROW]
[ROW][C]72[/C][C]53544[/C][C]55117.8808251584[/C][C]-1573.88082515843[/C][/ROW]
[ROW][C]73[/C][C]51379[/C][C]81031.1352583095[/C][C]-29652.1352583095[/C][/ROW]
[ROW][C]74[/C][C]40756[/C][C]56342.6507128495[/C][C]-15586.6507128495[/C][/ROW]
[ROW][C]75[/C][C]46956[/C][C]75240.1923741913[/C][C]-28284.1923741913[/C][/ROW]
[ROW][C]76[/C][C]17799[/C][C]22637.8871080703[/C][C]-4838.88710807035[/C][/ROW]
[ROW][C]77[/C][C]71154[/C][C]69615.2927417207[/C][C]1538.70725827928[/C][/ROW]
[ROW][C]78[/C][C]58305[/C][C]78392.2708172123[/C][C]-20087.2708172123[/C][/ROW]
[ROW][C]79[/C][C]27454[/C][C]41271.7911277122[/C][C]-13817.7911277122[/C][/ROW]
[ROW][C]80[/C][C]34323[/C][C]58974.8505558128[/C][C]-24651.8505558128[/C][/ROW]
[ROW][C]81[/C][C]44761[/C][C]68604.2745807003[/C][C]-23843.2745807003[/C][/ROW]
[ROW][C]82[/C][C]113862[/C][C]51319.0903326323[/C][C]62542.9096673677[/C][/ROW]
[ROW][C]83[/C][C]35027[/C][C]26320.1582429055[/C][C]8706.84175709452[/C][/ROW]
[ROW][C]84[/C][C]62396[/C][C]65727.801980106[/C][C]-3331.80198010599[/C][/ROW]
[ROW][C]85[/C][C]29613[/C][C]43557.2190133282[/C][C]-13944.2190133282[/C][/ROW]
[ROW][C]86[/C][C]65559[/C][C]51738.6159357303[/C][C]13820.3840642697[/C][/ROW]
[ROW][C]87[/C][C]110811[/C][C]89567.6029865071[/C][C]21243.3970134929[/C][/ROW]
[ROW][C]88[/C][C]27883[/C][C]42743.002293892[/C][C]-14860.002293892[/C][/ROW]
[ROW][C]89[/C][C]40181[/C][C]48574.2063403702[/C][C]-8393.20634037015[/C][/ROW]
[ROW][C]90[/C][C]53398[/C][C]57784.791275072[/C][C]-4386.79127507196[/C][/ROW]
[ROW][C]91[/C][C]56435[/C][C]56061.3351462598[/C][C]373.664853740196[/C][/ROW]
[ROW][C]92[/C][C]77283[/C][C]88374.3583791358[/C][C]-11091.3583791358[/C][/ROW]
[ROW][C]93[/C][C]71738[/C][C]55083.4326085045[/C][C]16654.5673914955[/C][/ROW]
[ROW][C]94[/C][C]48503[/C][C]49829.5806849795[/C][C]-1326.58068497953[/C][/ROW]
[ROW][C]95[/C][C]25214[/C][C]31567.8245639877[/C][C]-6353.82456398767[/C][/ROW]
[ROW][C]96[/C][C]119424[/C][C]69983.3879337105[/C][C]49440.6120662895[/C][/ROW]
[ROW][C]97[/C][C]79201[/C][C]95871.3685606863[/C][C]-16670.3685606863[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]23664.5052596452[/C][C]-4315.50525964516[/C][/ROW]
[ROW][C]99[/C][C]78760[/C][C]54747.740529223[/C][C]24012.259470777[/C][/ROW]
[ROW][C]100[/C][C]54133[/C][C]39786.2309920099[/C][C]14346.7690079901[/C][/ROW]
[ROW][C]101[/C][C]21623[/C][C]16617.8850669693[/C][C]5005.11493303065[/C][/ROW]
[ROW][C]102[/C][C]25497[/C][C]52615.4835178412[/C][C]-27118.4835178412[/C][/ROW]
[ROW][C]103[/C][C]69535[/C][C]54721.9566762828[/C][C]14813.0433237172[/C][/ROW]
[ROW][C]104[/C][C]30709[/C][C]28248.8818201903[/C][C]2460.11817980975[/C][/ROW]
[ROW][C]105[/C][C]37043[/C][C]29685.3177865718[/C][C]7357.68221342818[/C][/ROW]
[ROW][C]106[/C][C]24716[/C][C]5717.32790673527[/C][C]18998.6720932647[/C][/ROW]
[ROW][C]107[/C][C]54865[/C][C]79918.3015703765[/C][C]-25053.3015703765[/C][/ROW]
[ROW][C]108[/C][C]27246[/C][C]18155.0795567102[/C][C]9090.92044328982[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]5750.47713276562[/C][C]-5750.47713276562[/C][/ROW]
[ROW][C]110[/C][C]38814[/C][C]49482.4071251334[/C][C]-10668.4071251334[/C][/ROW]
[ROW][C]111[/C][C]27646[/C][C]50956.5053366955[/C][C]-23310.5053366955[/C][/ROW]
[ROW][C]112[/C][C]65373[/C][C]65276.4391905936[/C][C]96.5608094063615[/C][/ROW]
[ROW][C]113[/C][C]43021[/C][C]67725.767646466[/C][C]-24704.767646466[/C][/ROW]
[ROW][C]114[/C][C]43116[/C][C]49335.2429621308[/C][C]-6219.24296213082[/C][/ROW]
[ROW][C]115[/C][C]3058[/C][C]5434.57745706284[/C][C]-2376.57745706284[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]5381.92751111237[/C][C]-5381.92751111237[/C][/ROW]
[ROW][C]117[/C][C]96347[/C][C]75133.3929769757[/C][C]21213.6070230243[/C][/ROW]
[ROW][C]118[/C][C]48626[/C][C]55789.2050098966[/C][C]-7163.20500989662[/C][/ROW]
[ROW][C]119[/C][C]73073[/C][C]42283.7693211288[/C][C]30789.2306788712[/C][/ROW]
[ROW][C]120[/C][C]45266[/C][C]61677.0267291209[/C][C]-16411.0267291209[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]25644.8191063372[/C][C]17765.1808936628[/C][/ROW]
[ROW][C]122[/C][C]83842[/C][C]89023.0873550641[/C][C]-5181.08735506412[/C][/ROW]
[ROW][C]123[/C][C]39296[/C][C]39336.4372973149[/C][C]-40.4372973149055[/C][/ROW]
[ROW][C]124[/C][C]38490[/C][C]44686.8272046868[/C][C]-6196.82720468678[/C][/ROW]
[ROW][C]125[/C][C]39841[/C][C]29002.9790640556[/C][C]10838.0209359444[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]23256.2457190565[/C][C]-3492.2457190565[/C][/ROW]
[ROW][C]127[/C][C]59975[/C][C]69498.2065821565[/C][C]-9523.20658215648[/C][/ROW]
[ROW][C]128[/C][C]64589[/C][C]48060.7050105423[/C][C]16528.2949894577[/C][/ROW]
[ROW][C]129[/C][C]63339[/C][C]77906.3743466647[/C][C]-14567.3743466647[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]6448.40453782201[/C][C]5347.59546217799[/C][/ROW]
[ROW][C]131[/C][C]7627[/C][C]5700.31545483772[/C][C]1926.68454516228[/C][/ROW]
[ROW][C]132[/C][C]68998[/C][C]42255.0610248012[/C][C]26742.9389751988[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]3496.19905546677[/C][C]3339.80094453323[/C][/ROW]
[ROW][C]134[/C][C]33365[/C][C]31756.7192309157[/C][C]1608.2807690843[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]9533.08784168356[/C][C]-4415.08784168356[/C][/ROW]
[ROW][C]136[/C][C]20898[/C][C]13235.989364361[/C][C]7662.01063563896[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]4276.27864615261[/C][C]-4276.27864615261[/C][/ROW]
[ROW][C]138[/C][C]42690[/C][C]44145.5474679492[/C][C]-1455.54746794923[/C][/ROW]
[ROW][C]139[/C][C]14507[/C][C]15396.5005432571[/C][C]-889.500543257135[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]4118.32880830121[/C][C]3012.67119169879[/C][/ROW]
[ROW][C]141[/C][C]4194[/C][C]4698.90008119779[/C][C]-504.900081197792[/C][/ROW]
[ROW][C]142[/C][C]21416[/C][C]32508.2776465339[/C][C]-11092.2776465339[/C][/ROW]
[ROW][C]143[/C][C]30591[/C][C]31672.5502514645[/C][C]-1081.5502514645[/C][/ROW]
[ROW][C]144[/C][C]42419[/C][C]32702.8896807762[/C][C]9716.11031922377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147101&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147101&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16303153992.25220457179038.74779542831
26675163181.20689491213569.79310508793
3717612863.4195912143-5687.41959121431
47830689070.390409156-10764.390409156
5137944119257.05481290318686.9451870969
6261308228129.06767500833178.9323249922
76926666476.19200380062789.80799619941
88022687919.8402354819-7693.84023548188
97322673492.4087720934-266.408772093367
10178519113403.6688925265115.3311074795
116647689746.2033774511-23270.2033774511
1298606102256.534977582-3650.53497758203
135000148429.99033027221571.00966972783
149109399354.9230277774-8261.9230277774
157388466260.16188548517623.83811451489
167296171936.68525190441024.3147480956
1769388131279.242165411-61891.2421654114
181562924702.1977552815-9073.19775528155
1971693108244.917632334-36551.9176323342
201992059651.7864294824-39731.7864294824
213940338163.02186714971239.97813285027
2299933123339.188993024-23406.1889930238
235608840990.607242356515097.3927576435
246200673719.9754998732-11713.9754998732
258166572679.82364164848985.17635835164
266522359796.96952973465426.03047026538
278879485657.56051412193136.43948587812
289064280791.196885419850.80311459003
29203699135316.21396792768382.7860320725
309934059218.646534643640121.3534653564
315669561696.1942235063-5001.1942235063
3210814384418.157551507323724.8424484927
335831369194.9730478232-10881.9730478232
342910143325.281020385-14224.281020385
3511306076943.785591659836116.2144083402
3609593.92318714956-9593.92318714956
376577341800.772651475423972.2273485246
386704782555.6803698381-15508.6803698381
394195384397.6054984044-42444.6054984044
40109835130297.110681854-20462.1106818541
418658490014.9331590008-3430.93315900078
425958858061.98378637411526.01621362591
434006454822.947137437-14758.947137437
447022778328.4000409084-8101.40004090839
456043786462.9365714865-26025.9365714865
464700035705.59362085311294.406379147
474029536292.17301398924002.82698601075
4810339782617.898344401320779.1016555987
497898277835.62726198761146.37273801243
506020666152.7336717985-5946.73367179854
513988729666.5798459110220.42015409
524979146499.20954735983291.79045264016
5312928381769.697306921447513.3026930786
5410481677090.645928771927725.3540712281
5510139596955.66143125544439.3385687446
567282452514.153054786920309.8469452131
577601865111.435416243810906.5645837562
583389138955.4727517325-5064.47275173246
596216473976.9876906732-11812.9876906732
602826641661.7493060307-13395.7493060307
613509341405.0618580813-6312.06185808134
623525248139.0798084682-12887.0798084682
633697754345.546913134-17368.546913134
644240646442.7048737424-4036.70487374237
655635341622.891438370714730.1085616293
665881772098.7316619135-13281.7316619135
677605393077.361278796-17024.361278796
687087286089.6544838365-15217.6544838365
694237258108.9043300536-15736.9043300536
701914419361.2552443755-217.255244375533
7111417783123.973614589231053.0263854108
725354455117.8808251584-1573.88082515843
735137981031.1352583095-29652.1352583095
744075656342.6507128495-15586.6507128495
754695675240.1923741913-28284.1923741913
761779922637.8871080703-4838.88710807035
777115469615.29274172071538.70725827928
785830578392.2708172123-20087.2708172123
792745441271.7911277122-13817.7911277122
803432358974.8505558128-24651.8505558128
814476168604.2745807003-23843.2745807003
8211386251319.090332632362542.9096673677
833502726320.15824290558706.84175709452
846239665727.801980106-3331.80198010599
852961343557.2190133282-13944.2190133282
866555951738.615935730313820.3840642697
8711081189567.602986507121243.3970134929
882788342743.002293892-14860.002293892
894018148574.2063403702-8393.20634037015
905339857784.791275072-4386.79127507196
915643556061.3351462598373.664853740196
927728388374.3583791358-11091.3583791358
937173855083.432608504516654.5673914955
944850349829.5806849795-1326.58068497953
952521431567.8245639877-6353.82456398767
9611942469983.387933710549440.6120662895
977920195871.3685606863-16670.3685606863
981934923664.5052596452-4315.50525964516
997876054747.74052922324012.259470777
1005413339786.230992009914346.7690079901
1012162316617.88506696935005.11493303065
1022549752615.4835178412-27118.4835178412
1036953554721.956676282814813.0433237172
1043070928248.88182019032460.11817980975
1053704329685.31778657187357.68221342818
106247165717.3279067352718998.6720932647
1075486579918.3015703765-25053.3015703765
1082724618155.07955671029090.92044328982
10905750.47713276562-5750.47713276562
1103881449482.4071251334-10668.4071251334
1112764650956.5053366955-23310.5053366955
1126537365276.439190593696.5608094063615
1134302167725.767646466-24704.767646466
1144311649335.2429621308-6219.24296213082
11530585434.57745706284-2376.57745706284
11605381.92751111237-5381.92751111237
1179634775133.392976975721213.6070230243
1184862655789.2050098966-7163.20500989662
1197307342283.769321128830789.2306788712
1204526661677.0267291209-16411.0267291209
1214341025644.819106337217765.1808936628
1228384289023.0873550641-5181.08735506412
1233929639336.4372973149-40.4372973149055
1243849044686.8272046868-6196.82720468678
1253984129002.979064055610838.0209359444
1261976423256.2457190565-3492.2457190565
1275997569498.2065821565-9523.20658215648
1286458948060.705010542316528.2949894577
1296333977906.3743466647-14567.3743466647
130117966448.404537822015347.59546217799
13176275700.315454837721926.68454516228
1326899842255.061024801226742.9389751988
13368363496.199055466773339.80094453323
1343336531756.71923091571608.2807690843
13551189533.08784168356-4415.08784168356
1362089813235.9893643617662.01063563896
13704276.27864615261-4276.27864615261
1384269044145.5474679492-1455.54746794923
1391450715396.5005432571-889.500543257135
14071314118.328808301213012.67119169879
14141944698.90008119779-504.900081197792
1422141632508.2776465339-11092.2776465339
1433059131672.5502514645-1081.5502514645
1444241932702.88968077629716.11031922377







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.7481459844866090.5037080310267820.251854015513391
110.898291727829340.203416544341320.10170827217066
120.8279831815974810.3440336368050380.172016818402519
130.7523984897698430.4952030204603140.247601510230157
140.8391378717602190.3217242564795620.160862128239781
150.7691564654125380.4616870691749240.230843534587462
160.7065266943198370.5869466113603270.293473305680163
170.9812351239398620.03752975212027580.0187648760601379
180.9709317819150660.05813643616986850.0290682180849343
190.9745145252779990.05097094944400290.0254854747220014
200.9775673782436150.04486524351276950.0224326217563847
210.9807994828086340.03840103438273170.0192005171913658
220.9740189937838470.05196201243230550.0259810062161527
230.9806225223042690.03875495539146280.0193774776957314
240.9726796694841430.05464066103171320.0273203305158566
250.9813901156056720.03721976878865590.018609884394328
260.9761553968097830.04768920638043330.0238446031902166
270.9703234849431560.0593530301136880.029676515056844
280.9698482371635140.06030352567297270.0301517628364864
290.9993959552264050.001208089547189450.000604044773594725
300.9998205010264680.0003589979470630780.000179498973531539
310.9996983154102190.0006033691795613110.000301684589780655
320.9996787506058670.0006424987882669760.000321249394133488
330.9996550548510050.0006898902979900510.000344945148995026
340.9995575597811520.0008848804376950570.000442440218847528
350.9997980882516770.0004038234966460960.000201911748323048
360.9997155200152150.0005689599695704140.000284479984785207
370.9997308541101190.0005382917797619140.000269145889880957
380.999686994853980.0006260102920404850.000313005146020243
390.9999537300916639.25398166743852e-054.62699083371926e-05
400.9999600743660387.98512679231146e-053.99256339615573e-05
410.999933190389530.000133619220939636.68096104698152e-05
420.9998884742404210.0002230515191573890.000111525759578695
430.9998522266117090.0002955467765819930.000147773388290997
440.9997658849271750.0004682301456500980.000234115072825049
450.9997857350402990.0004285299194012070.000214264959700604
460.9997257420857260.000548515828547820.00027425791427391
470.9995843086824370.0008313826351257130.000415691317562857
480.9996450588532460.0007098822935076130.000354941146753807
490.9994410960135020.00111780797299620.000558903986498098
500.9991865291968340.001626941606330930.000813470803165465
510.9989130647531750.002173870493650610.00108693524682531
520.9983657955383740.003268408923251060.00163420446162553
530.9998423696553990.000315260689202890.000157630344601445
540.9999453364146860.0001093271706281975.46635853140987e-05
550.999937394187160.0001252116256790696.26058128395346e-05
560.9999492922636570.0001014154726856455.07077363428226e-05
570.9999592155284258.15689431496465e-054.07844715748232e-05
580.9999350865381460.0001298269237085786.49134618542892e-05
590.9999155896303830.0001688207392345938.44103696172967e-05
600.999884074062060.0002318518758793920.000115925937939696
610.999838530121370.0003229397572590550.000161469878629528
620.9998170340189360.0003659319621280350.000182965981064017
630.9998458867966050.0003082264067898250.000154113203394912
640.9997572723634830.0004854552730330590.00024272763651653
650.9997512592315320.0004974815369359290.000248740768467964
660.9996557933340990.0006884133318015580.000344206665900779
670.9995558762275710.0008882475448575380.000444123772428769
680.9997780634410680.0004438731178641120.000221936558932056
690.9997072037254350.0005855925491297670.000292796274564883
700.9995673483780330.0008653032439334410.00043265162196672
710.9996102565993980.0007794868012038070.000389743400601904
720.9994277606295990.001144478740801420.000572239370400711
730.9995143949878810.0009712100242376720.000485605012118836
740.9995283321107930.0009433357784145370.000471667889207269
750.9998303355296650.0003393289406703760.000169664470335188
760.9998674001540540.000265199691892710.000132599845946355
770.999832820676910.000334358646179440.00016717932308972
780.9997909883550620.0004180232898750970.000209011644937548
790.9998429533436050.0003140933127906840.000157046656395342
800.9998194266990170.0003611466019658590.000180573300982929
810.999828442542910.0003431149141807660.000171557457090383
820.9999999178380081.64323984406658e-078.21619922033288e-08
830.9999998688263892.62347221016578e-071.31173610508289e-07
840.9999998360997723.27800456604794e-071.63900228302397e-07
850.9999998272351233.45529754689068e-071.72764877344534e-07
860.9999997099666555.80066689136708e-072.90033344568354e-07
870.9999998109442863.78111427256323e-071.89055713628161e-07
880.9999998820562762.35887447975915e-071.17943723987957e-07
890.9999997668384694.66323062677581e-072.3316153133879e-07
900.9999997478020395.0439592210111e-072.52197961050555e-07
910.999999579820318.40359380168547e-074.20179690084274e-07
920.9999992653586651.46928266972103e-067.34641334860515e-07
930.9999992241390411.55172191906271e-067.75860959531356e-07
940.9999984145599973.17088000646141e-061.58544000323071e-06
950.9999968824201996.23515960222308e-063.11757980111154e-06
960.9999993218986781.3562026438032e-066.78101321901598e-07
970.9999987357446932.52851061470941e-061.2642553073547e-06
980.9999974158002675.16839946669951e-062.58419973334975e-06
990.9999964691036347.06179273211056e-063.53089636605528e-06
1000.9999954130071219.17398575786925e-064.58699287893462e-06
1010.9999925063567161.498728656737e-057.49364328368501e-06
1020.9999929456928891.41086142227926e-057.05430711139629e-06
1030.9999892741080472.14517839053427e-051.07258919526714e-05
1040.9999787285414464.25429171075635e-052.12714585537817e-05
1050.9999644728647277.10542705466741e-053.5527135273337e-05
1060.9999389667827810.0001220664344378546.10332172189269e-05
1070.9999582167040618.35665918774764e-054.17832959387382e-05
1080.9999494685364390.00010106292712195.05314635609499e-05
1090.9998991856704430.0002016286591135970.000100814329556799
1100.9998094575170360.0003810849659271660.000190542482963583
1110.9998586281811270.0002827436377449530.000141371818872477
1120.9998108224836180.0003783550327637290.000189177516381865
1130.9998841267082940.0002317465834128710.000115873291706435
1140.9997693493245180.0004613013509633920.000230650675481696
1150.9995535657112790.0008928685774420060.000446434288721003
1160.99931572377320.001368552453600260.000684276226800129
1170.9997836188008280.0004327623983435170.000216381199171759
1180.9998730254957740.000253949008452110.000126974504226055
1190.9998693127149390.0002613745701214470.000130687285060723
1200.9999510143777949.79712444124171e-054.89856222062086e-05
1210.9999032973239960.0001934053520079149.67026760039571e-05
1220.9997555569553890.0004888860892211880.000244443044610594
1230.9994019145056420.001196170988716670.000598085494358334
1240.9985901938220160.002819612355968790.0014098061779844
1250.996887996520470.006224006959059360.00311200347952968
1260.9946590200460920.01068195990781570.00534097995390783
1270.9894962152316710.02100756953665860.0105037847683293
1280.9893966332229320.02120673355413560.0106033667770678
1290.9915479925944540.0169040148110920.00845200740554598
1300.9797946701617910.04041065967641820.0202053298382091
1310.9557883749202160.08842325015956740.0442116250797837
1320.9972612742768130.005477451446373590.00273872572318679
1330.9884464561548150.02310708769036990.0115535438451849
1340.9927038397177150.01459232056457030.00729616028228513

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.748145984486609 & 0.503708031026782 & 0.251854015513391 \tabularnewline
11 & 0.89829172782934 & 0.20341654434132 & 0.10170827217066 \tabularnewline
12 & 0.827983181597481 & 0.344033636805038 & 0.172016818402519 \tabularnewline
13 & 0.752398489769843 & 0.495203020460314 & 0.247601510230157 \tabularnewline
14 & 0.839137871760219 & 0.321724256479562 & 0.160862128239781 \tabularnewline
15 & 0.769156465412538 & 0.461687069174924 & 0.230843534587462 \tabularnewline
16 & 0.706526694319837 & 0.586946611360327 & 0.293473305680163 \tabularnewline
17 & 0.981235123939862 & 0.0375297521202758 & 0.0187648760601379 \tabularnewline
18 & 0.970931781915066 & 0.0581364361698685 & 0.0290682180849343 \tabularnewline
19 & 0.974514525277999 & 0.0509709494440029 & 0.0254854747220014 \tabularnewline
20 & 0.977567378243615 & 0.0448652435127695 & 0.0224326217563847 \tabularnewline
21 & 0.980799482808634 & 0.0384010343827317 & 0.0192005171913658 \tabularnewline
22 & 0.974018993783847 & 0.0519620124323055 & 0.0259810062161527 \tabularnewline
23 & 0.980622522304269 & 0.0387549553914628 & 0.0193774776957314 \tabularnewline
24 & 0.972679669484143 & 0.0546406610317132 & 0.0273203305158566 \tabularnewline
25 & 0.981390115605672 & 0.0372197687886559 & 0.018609884394328 \tabularnewline
26 & 0.976155396809783 & 0.0476892063804333 & 0.0238446031902166 \tabularnewline
27 & 0.970323484943156 & 0.059353030113688 & 0.029676515056844 \tabularnewline
28 & 0.969848237163514 & 0.0603035256729727 & 0.0301517628364864 \tabularnewline
29 & 0.999395955226405 & 0.00120808954718945 & 0.000604044773594725 \tabularnewline
30 & 0.999820501026468 & 0.000358997947063078 & 0.000179498973531539 \tabularnewline
31 & 0.999698315410219 & 0.000603369179561311 & 0.000301684589780655 \tabularnewline
32 & 0.999678750605867 & 0.000642498788266976 & 0.000321249394133488 \tabularnewline
33 & 0.999655054851005 & 0.000689890297990051 & 0.000344945148995026 \tabularnewline
34 & 0.999557559781152 & 0.000884880437695057 & 0.000442440218847528 \tabularnewline
35 & 0.999798088251677 & 0.000403823496646096 & 0.000201911748323048 \tabularnewline
36 & 0.999715520015215 & 0.000568959969570414 & 0.000284479984785207 \tabularnewline
37 & 0.999730854110119 & 0.000538291779761914 & 0.000269145889880957 \tabularnewline
38 & 0.99968699485398 & 0.000626010292040485 & 0.000313005146020243 \tabularnewline
39 & 0.999953730091663 & 9.25398166743852e-05 & 4.62699083371926e-05 \tabularnewline
40 & 0.999960074366038 & 7.98512679231146e-05 & 3.99256339615573e-05 \tabularnewline
41 & 0.99993319038953 & 0.00013361922093963 & 6.68096104698152e-05 \tabularnewline
42 & 0.999888474240421 & 0.000223051519157389 & 0.000111525759578695 \tabularnewline
43 & 0.999852226611709 & 0.000295546776581993 & 0.000147773388290997 \tabularnewline
44 & 0.999765884927175 & 0.000468230145650098 & 0.000234115072825049 \tabularnewline
45 & 0.999785735040299 & 0.000428529919401207 & 0.000214264959700604 \tabularnewline
46 & 0.999725742085726 & 0.00054851582854782 & 0.00027425791427391 \tabularnewline
47 & 0.999584308682437 & 0.000831382635125713 & 0.000415691317562857 \tabularnewline
48 & 0.999645058853246 & 0.000709882293507613 & 0.000354941146753807 \tabularnewline
49 & 0.999441096013502 & 0.0011178079729962 & 0.000558903986498098 \tabularnewline
50 & 0.999186529196834 & 0.00162694160633093 & 0.000813470803165465 \tabularnewline
51 & 0.998913064753175 & 0.00217387049365061 & 0.00108693524682531 \tabularnewline
52 & 0.998365795538374 & 0.00326840892325106 & 0.00163420446162553 \tabularnewline
53 & 0.999842369655399 & 0.00031526068920289 & 0.000157630344601445 \tabularnewline
54 & 0.999945336414686 & 0.000109327170628197 & 5.46635853140987e-05 \tabularnewline
55 & 0.99993739418716 & 0.000125211625679069 & 6.26058128395346e-05 \tabularnewline
56 & 0.999949292263657 & 0.000101415472685645 & 5.07077363428226e-05 \tabularnewline
57 & 0.999959215528425 & 8.15689431496465e-05 & 4.07844715748232e-05 \tabularnewline
58 & 0.999935086538146 & 0.000129826923708578 & 6.49134618542892e-05 \tabularnewline
59 & 0.999915589630383 & 0.000168820739234593 & 8.44103696172967e-05 \tabularnewline
60 & 0.99988407406206 & 0.000231851875879392 & 0.000115925937939696 \tabularnewline
61 & 0.99983853012137 & 0.000322939757259055 & 0.000161469878629528 \tabularnewline
62 & 0.999817034018936 & 0.000365931962128035 & 0.000182965981064017 \tabularnewline
63 & 0.999845886796605 & 0.000308226406789825 & 0.000154113203394912 \tabularnewline
64 & 0.999757272363483 & 0.000485455273033059 & 0.00024272763651653 \tabularnewline
65 & 0.999751259231532 & 0.000497481536935929 & 0.000248740768467964 \tabularnewline
66 & 0.999655793334099 & 0.000688413331801558 & 0.000344206665900779 \tabularnewline
67 & 0.999555876227571 & 0.000888247544857538 & 0.000444123772428769 \tabularnewline
68 & 0.999778063441068 & 0.000443873117864112 & 0.000221936558932056 \tabularnewline
69 & 0.999707203725435 & 0.000585592549129767 & 0.000292796274564883 \tabularnewline
70 & 0.999567348378033 & 0.000865303243933441 & 0.00043265162196672 \tabularnewline
71 & 0.999610256599398 & 0.000779486801203807 & 0.000389743400601904 \tabularnewline
72 & 0.999427760629599 & 0.00114447874080142 & 0.000572239370400711 \tabularnewline
73 & 0.999514394987881 & 0.000971210024237672 & 0.000485605012118836 \tabularnewline
74 & 0.999528332110793 & 0.000943335778414537 & 0.000471667889207269 \tabularnewline
75 & 0.999830335529665 & 0.000339328940670376 & 0.000169664470335188 \tabularnewline
76 & 0.999867400154054 & 0.00026519969189271 & 0.000132599845946355 \tabularnewline
77 & 0.99983282067691 & 0.00033435864617944 & 0.00016717932308972 \tabularnewline
78 & 0.999790988355062 & 0.000418023289875097 & 0.000209011644937548 \tabularnewline
79 & 0.999842953343605 & 0.000314093312790684 & 0.000157046656395342 \tabularnewline
80 & 0.999819426699017 & 0.000361146601965859 & 0.000180573300982929 \tabularnewline
81 & 0.99982844254291 & 0.000343114914180766 & 0.000171557457090383 \tabularnewline
82 & 0.999999917838008 & 1.64323984406658e-07 & 8.21619922033288e-08 \tabularnewline
83 & 0.999999868826389 & 2.62347221016578e-07 & 1.31173610508289e-07 \tabularnewline
84 & 0.999999836099772 & 3.27800456604794e-07 & 1.63900228302397e-07 \tabularnewline
85 & 0.999999827235123 & 3.45529754689068e-07 & 1.72764877344534e-07 \tabularnewline
86 & 0.999999709966655 & 5.80066689136708e-07 & 2.90033344568354e-07 \tabularnewline
87 & 0.999999810944286 & 3.78111427256323e-07 & 1.89055713628161e-07 \tabularnewline
88 & 0.999999882056276 & 2.35887447975915e-07 & 1.17943723987957e-07 \tabularnewline
89 & 0.999999766838469 & 4.66323062677581e-07 & 2.3316153133879e-07 \tabularnewline
90 & 0.999999747802039 & 5.0439592210111e-07 & 2.52197961050555e-07 \tabularnewline
91 & 0.99999957982031 & 8.40359380168547e-07 & 4.20179690084274e-07 \tabularnewline
92 & 0.999999265358665 & 1.46928266972103e-06 & 7.34641334860515e-07 \tabularnewline
93 & 0.999999224139041 & 1.55172191906271e-06 & 7.75860959531356e-07 \tabularnewline
94 & 0.999998414559997 & 3.17088000646141e-06 & 1.58544000323071e-06 \tabularnewline
95 & 0.999996882420199 & 6.23515960222308e-06 & 3.11757980111154e-06 \tabularnewline
96 & 0.999999321898678 & 1.3562026438032e-06 & 6.78101321901598e-07 \tabularnewline
97 & 0.999998735744693 & 2.52851061470941e-06 & 1.2642553073547e-06 \tabularnewline
98 & 0.999997415800267 & 5.16839946669951e-06 & 2.58419973334975e-06 \tabularnewline
99 & 0.999996469103634 & 7.06179273211056e-06 & 3.53089636605528e-06 \tabularnewline
100 & 0.999995413007121 & 9.17398575786925e-06 & 4.58699287893462e-06 \tabularnewline
101 & 0.999992506356716 & 1.498728656737e-05 & 7.49364328368501e-06 \tabularnewline
102 & 0.999992945692889 & 1.41086142227926e-05 & 7.05430711139629e-06 \tabularnewline
103 & 0.999989274108047 & 2.14517839053427e-05 & 1.07258919526714e-05 \tabularnewline
104 & 0.999978728541446 & 4.25429171075635e-05 & 2.12714585537817e-05 \tabularnewline
105 & 0.999964472864727 & 7.10542705466741e-05 & 3.5527135273337e-05 \tabularnewline
106 & 0.999938966782781 & 0.000122066434437854 & 6.10332172189269e-05 \tabularnewline
107 & 0.999958216704061 & 8.35665918774764e-05 & 4.17832959387382e-05 \tabularnewline
108 & 0.999949468536439 & 0.0001010629271219 & 5.05314635609499e-05 \tabularnewline
109 & 0.999899185670443 & 0.000201628659113597 & 0.000100814329556799 \tabularnewline
110 & 0.999809457517036 & 0.000381084965927166 & 0.000190542482963583 \tabularnewline
111 & 0.999858628181127 & 0.000282743637744953 & 0.000141371818872477 \tabularnewline
112 & 0.999810822483618 & 0.000378355032763729 & 0.000189177516381865 \tabularnewline
113 & 0.999884126708294 & 0.000231746583412871 & 0.000115873291706435 \tabularnewline
114 & 0.999769349324518 & 0.000461301350963392 & 0.000230650675481696 \tabularnewline
115 & 0.999553565711279 & 0.000892868577442006 & 0.000446434288721003 \tabularnewline
116 & 0.9993157237732 & 0.00136855245360026 & 0.000684276226800129 \tabularnewline
117 & 0.999783618800828 & 0.000432762398343517 & 0.000216381199171759 \tabularnewline
118 & 0.999873025495774 & 0.00025394900845211 & 0.000126974504226055 \tabularnewline
119 & 0.999869312714939 & 0.000261374570121447 & 0.000130687285060723 \tabularnewline
120 & 0.999951014377794 & 9.79712444124171e-05 & 4.89856222062086e-05 \tabularnewline
121 & 0.999903297323996 & 0.000193405352007914 & 9.67026760039571e-05 \tabularnewline
122 & 0.999755556955389 & 0.000488886089221188 & 0.000244443044610594 \tabularnewline
123 & 0.999401914505642 & 0.00119617098871667 & 0.000598085494358334 \tabularnewline
124 & 0.998590193822016 & 0.00281961235596879 & 0.0014098061779844 \tabularnewline
125 & 0.99688799652047 & 0.00622400695905936 & 0.00311200347952968 \tabularnewline
126 & 0.994659020046092 & 0.0106819599078157 & 0.00534097995390783 \tabularnewline
127 & 0.989496215231671 & 0.0210075695366586 & 0.0105037847683293 \tabularnewline
128 & 0.989396633222932 & 0.0212067335541356 & 0.0106033667770678 \tabularnewline
129 & 0.991547992594454 & 0.016904014811092 & 0.00845200740554598 \tabularnewline
130 & 0.979794670161791 & 0.0404106596764182 & 0.0202053298382091 \tabularnewline
131 & 0.955788374920216 & 0.0884232501595674 & 0.0442116250797837 \tabularnewline
132 & 0.997261274276813 & 0.00547745144637359 & 0.00273872572318679 \tabularnewline
133 & 0.988446456154815 & 0.0231070876903699 & 0.0115535438451849 \tabularnewline
134 & 0.992703839717715 & 0.0145923205645703 & 0.00729616028228513 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147101&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.748145984486609[/C][C]0.503708031026782[/C][C]0.251854015513391[/C][/ROW]
[ROW][C]11[/C][C]0.89829172782934[/C][C]0.20341654434132[/C][C]0.10170827217066[/C][/ROW]
[ROW][C]12[/C][C]0.827983181597481[/C][C]0.344033636805038[/C][C]0.172016818402519[/C][/ROW]
[ROW][C]13[/C][C]0.752398489769843[/C][C]0.495203020460314[/C][C]0.247601510230157[/C][/ROW]
[ROW][C]14[/C][C]0.839137871760219[/C][C]0.321724256479562[/C][C]0.160862128239781[/C][/ROW]
[ROW][C]15[/C][C]0.769156465412538[/C][C]0.461687069174924[/C][C]0.230843534587462[/C][/ROW]
[ROW][C]16[/C][C]0.706526694319837[/C][C]0.586946611360327[/C][C]0.293473305680163[/C][/ROW]
[ROW][C]17[/C][C]0.981235123939862[/C][C]0.0375297521202758[/C][C]0.0187648760601379[/C][/ROW]
[ROW][C]18[/C][C]0.970931781915066[/C][C]0.0581364361698685[/C][C]0.0290682180849343[/C][/ROW]
[ROW][C]19[/C][C]0.974514525277999[/C][C]0.0509709494440029[/C][C]0.0254854747220014[/C][/ROW]
[ROW][C]20[/C][C]0.977567378243615[/C][C]0.0448652435127695[/C][C]0.0224326217563847[/C][/ROW]
[ROW][C]21[/C][C]0.980799482808634[/C][C]0.0384010343827317[/C][C]0.0192005171913658[/C][/ROW]
[ROW][C]22[/C][C]0.974018993783847[/C][C]0.0519620124323055[/C][C]0.0259810062161527[/C][/ROW]
[ROW][C]23[/C][C]0.980622522304269[/C][C]0.0387549553914628[/C][C]0.0193774776957314[/C][/ROW]
[ROW][C]24[/C][C]0.972679669484143[/C][C]0.0546406610317132[/C][C]0.0273203305158566[/C][/ROW]
[ROW][C]25[/C][C]0.981390115605672[/C][C]0.0372197687886559[/C][C]0.018609884394328[/C][/ROW]
[ROW][C]26[/C][C]0.976155396809783[/C][C]0.0476892063804333[/C][C]0.0238446031902166[/C][/ROW]
[ROW][C]27[/C][C]0.970323484943156[/C][C]0.059353030113688[/C][C]0.029676515056844[/C][/ROW]
[ROW][C]28[/C][C]0.969848237163514[/C][C]0.0603035256729727[/C][C]0.0301517628364864[/C][/ROW]
[ROW][C]29[/C][C]0.999395955226405[/C][C]0.00120808954718945[/C][C]0.000604044773594725[/C][/ROW]
[ROW][C]30[/C][C]0.999820501026468[/C][C]0.000358997947063078[/C][C]0.000179498973531539[/C][/ROW]
[ROW][C]31[/C][C]0.999698315410219[/C][C]0.000603369179561311[/C][C]0.000301684589780655[/C][/ROW]
[ROW][C]32[/C][C]0.999678750605867[/C][C]0.000642498788266976[/C][C]0.000321249394133488[/C][/ROW]
[ROW][C]33[/C][C]0.999655054851005[/C][C]0.000689890297990051[/C][C]0.000344945148995026[/C][/ROW]
[ROW][C]34[/C][C]0.999557559781152[/C][C]0.000884880437695057[/C][C]0.000442440218847528[/C][/ROW]
[ROW][C]35[/C][C]0.999798088251677[/C][C]0.000403823496646096[/C][C]0.000201911748323048[/C][/ROW]
[ROW][C]36[/C][C]0.999715520015215[/C][C]0.000568959969570414[/C][C]0.000284479984785207[/C][/ROW]
[ROW][C]37[/C][C]0.999730854110119[/C][C]0.000538291779761914[/C][C]0.000269145889880957[/C][/ROW]
[ROW][C]38[/C][C]0.99968699485398[/C][C]0.000626010292040485[/C][C]0.000313005146020243[/C][/ROW]
[ROW][C]39[/C][C]0.999953730091663[/C][C]9.25398166743852e-05[/C][C]4.62699083371926e-05[/C][/ROW]
[ROW][C]40[/C][C]0.999960074366038[/C][C]7.98512679231146e-05[/C][C]3.99256339615573e-05[/C][/ROW]
[ROW][C]41[/C][C]0.99993319038953[/C][C]0.00013361922093963[/C][C]6.68096104698152e-05[/C][/ROW]
[ROW][C]42[/C][C]0.999888474240421[/C][C]0.000223051519157389[/C][C]0.000111525759578695[/C][/ROW]
[ROW][C]43[/C][C]0.999852226611709[/C][C]0.000295546776581993[/C][C]0.000147773388290997[/C][/ROW]
[ROW][C]44[/C][C]0.999765884927175[/C][C]0.000468230145650098[/C][C]0.000234115072825049[/C][/ROW]
[ROW][C]45[/C][C]0.999785735040299[/C][C]0.000428529919401207[/C][C]0.000214264959700604[/C][/ROW]
[ROW][C]46[/C][C]0.999725742085726[/C][C]0.00054851582854782[/C][C]0.00027425791427391[/C][/ROW]
[ROW][C]47[/C][C]0.999584308682437[/C][C]0.000831382635125713[/C][C]0.000415691317562857[/C][/ROW]
[ROW][C]48[/C][C]0.999645058853246[/C][C]0.000709882293507613[/C][C]0.000354941146753807[/C][/ROW]
[ROW][C]49[/C][C]0.999441096013502[/C][C]0.0011178079729962[/C][C]0.000558903986498098[/C][/ROW]
[ROW][C]50[/C][C]0.999186529196834[/C][C]0.00162694160633093[/C][C]0.000813470803165465[/C][/ROW]
[ROW][C]51[/C][C]0.998913064753175[/C][C]0.00217387049365061[/C][C]0.00108693524682531[/C][/ROW]
[ROW][C]52[/C][C]0.998365795538374[/C][C]0.00326840892325106[/C][C]0.00163420446162553[/C][/ROW]
[ROW][C]53[/C][C]0.999842369655399[/C][C]0.00031526068920289[/C][C]0.000157630344601445[/C][/ROW]
[ROW][C]54[/C][C]0.999945336414686[/C][C]0.000109327170628197[/C][C]5.46635853140987e-05[/C][/ROW]
[ROW][C]55[/C][C]0.99993739418716[/C][C]0.000125211625679069[/C][C]6.26058128395346e-05[/C][/ROW]
[ROW][C]56[/C][C]0.999949292263657[/C][C]0.000101415472685645[/C][C]5.07077363428226e-05[/C][/ROW]
[ROW][C]57[/C][C]0.999959215528425[/C][C]8.15689431496465e-05[/C][C]4.07844715748232e-05[/C][/ROW]
[ROW][C]58[/C][C]0.999935086538146[/C][C]0.000129826923708578[/C][C]6.49134618542892e-05[/C][/ROW]
[ROW][C]59[/C][C]0.999915589630383[/C][C]0.000168820739234593[/C][C]8.44103696172967e-05[/C][/ROW]
[ROW][C]60[/C][C]0.99988407406206[/C][C]0.000231851875879392[/C][C]0.000115925937939696[/C][/ROW]
[ROW][C]61[/C][C]0.99983853012137[/C][C]0.000322939757259055[/C][C]0.000161469878629528[/C][/ROW]
[ROW][C]62[/C][C]0.999817034018936[/C][C]0.000365931962128035[/C][C]0.000182965981064017[/C][/ROW]
[ROW][C]63[/C][C]0.999845886796605[/C][C]0.000308226406789825[/C][C]0.000154113203394912[/C][/ROW]
[ROW][C]64[/C][C]0.999757272363483[/C][C]0.000485455273033059[/C][C]0.00024272763651653[/C][/ROW]
[ROW][C]65[/C][C]0.999751259231532[/C][C]0.000497481536935929[/C][C]0.000248740768467964[/C][/ROW]
[ROW][C]66[/C][C]0.999655793334099[/C][C]0.000688413331801558[/C][C]0.000344206665900779[/C][/ROW]
[ROW][C]67[/C][C]0.999555876227571[/C][C]0.000888247544857538[/C][C]0.000444123772428769[/C][/ROW]
[ROW][C]68[/C][C]0.999778063441068[/C][C]0.000443873117864112[/C][C]0.000221936558932056[/C][/ROW]
[ROW][C]69[/C][C]0.999707203725435[/C][C]0.000585592549129767[/C][C]0.000292796274564883[/C][/ROW]
[ROW][C]70[/C][C]0.999567348378033[/C][C]0.000865303243933441[/C][C]0.00043265162196672[/C][/ROW]
[ROW][C]71[/C][C]0.999610256599398[/C][C]0.000779486801203807[/C][C]0.000389743400601904[/C][/ROW]
[ROW][C]72[/C][C]0.999427760629599[/C][C]0.00114447874080142[/C][C]0.000572239370400711[/C][/ROW]
[ROW][C]73[/C][C]0.999514394987881[/C][C]0.000971210024237672[/C][C]0.000485605012118836[/C][/ROW]
[ROW][C]74[/C][C]0.999528332110793[/C][C]0.000943335778414537[/C][C]0.000471667889207269[/C][/ROW]
[ROW][C]75[/C][C]0.999830335529665[/C][C]0.000339328940670376[/C][C]0.000169664470335188[/C][/ROW]
[ROW][C]76[/C][C]0.999867400154054[/C][C]0.00026519969189271[/C][C]0.000132599845946355[/C][/ROW]
[ROW][C]77[/C][C]0.99983282067691[/C][C]0.00033435864617944[/C][C]0.00016717932308972[/C][/ROW]
[ROW][C]78[/C][C]0.999790988355062[/C][C]0.000418023289875097[/C][C]0.000209011644937548[/C][/ROW]
[ROW][C]79[/C][C]0.999842953343605[/C][C]0.000314093312790684[/C][C]0.000157046656395342[/C][/ROW]
[ROW][C]80[/C][C]0.999819426699017[/C][C]0.000361146601965859[/C][C]0.000180573300982929[/C][/ROW]
[ROW][C]81[/C][C]0.99982844254291[/C][C]0.000343114914180766[/C][C]0.000171557457090383[/C][/ROW]
[ROW][C]82[/C][C]0.999999917838008[/C][C]1.64323984406658e-07[/C][C]8.21619922033288e-08[/C][/ROW]
[ROW][C]83[/C][C]0.999999868826389[/C][C]2.62347221016578e-07[/C][C]1.31173610508289e-07[/C][/ROW]
[ROW][C]84[/C][C]0.999999836099772[/C][C]3.27800456604794e-07[/C][C]1.63900228302397e-07[/C][/ROW]
[ROW][C]85[/C][C]0.999999827235123[/C][C]3.45529754689068e-07[/C][C]1.72764877344534e-07[/C][/ROW]
[ROW][C]86[/C][C]0.999999709966655[/C][C]5.80066689136708e-07[/C][C]2.90033344568354e-07[/C][/ROW]
[ROW][C]87[/C][C]0.999999810944286[/C][C]3.78111427256323e-07[/C][C]1.89055713628161e-07[/C][/ROW]
[ROW][C]88[/C][C]0.999999882056276[/C][C]2.35887447975915e-07[/C][C]1.17943723987957e-07[/C][/ROW]
[ROW][C]89[/C][C]0.999999766838469[/C][C]4.66323062677581e-07[/C][C]2.3316153133879e-07[/C][/ROW]
[ROW][C]90[/C][C]0.999999747802039[/C][C]5.0439592210111e-07[/C][C]2.52197961050555e-07[/C][/ROW]
[ROW][C]91[/C][C]0.99999957982031[/C][C]8.40359380168547e-07[/C][C]4.20179690084274e-07[/C][/ROW]
[ROW][C]92[/C][C]0.999999265358665[/C][C]1.46928266972103e-06[/C][C]7.34641334860515e-07[/C][/ROW]
[ROW][C]93[/C][C]0.999999224139041[/C][C]1.55172191906271e-06[/C][C]7.75860959531356e-07[/C][/ROW]
[ROW][C]94[/C][C]0.999998414559997[/C][C]3.17088000646141e-06[/C][C]1.58544000323071e-06[/C][/ROW]
[ROW][C]95[/C][C]0.999996882420199[/C][C]6.23515960222308e-06[/C][C]3.11757980111154e-06[/C][/ROW]
[ROW][C]96[/C][C]0.999999321898678[/C][C]1.3562026438032e-06[/C][C]6.78101321901598e-07[/C][/ROW]
[ROW][C]97[/C][C]0.999998735744693[/C][C]2.52851061470941e-06[/C][C]1.2642553073547e-06[/C][/ROW]
[ROW][C]98[/C][C]0.999997415800267[/C][C]5.16839946669951e-06[/C][C]2.58419973334975e-06[/C][/ROW]
[ROW][C]99[/C][C]0.999996469103634[/C][C]7.06179273211056e-06[/C][C]3.53089636605528e-06[/C][/ROW]
[ROW][C]100[/C][C]0.999995413007121[/C][C]9.17398575786925e-06[/C][C]4.58699287893462e-06[/C][/ROW]
[ROW][C]101[/C][C]0.999992506356716[/C][C]1.498728656737e-05[/C][C]7.49364328368501e-06[/C][/ROW]
[ROW][C]102[/C][C]0.999992945692889[/C][C]1.41086142227926e-05[/C][C]7.05430711139629e-06[/C][/ROW]
[ROW][C]103[/C][C]0.999989274108047[/C][C]2.14517839053427e-05[/C][C]1.07258919526714e-05[/C][/ROW]
[ROW][C]104[/C][C]0.999978728541446[/C][C]4.25429171075635e-05[/C][C]2.12714585537817e-05[/C][/ROW]
[ROW][C]105[/C][C]0.999964472864727[/C][C]7.10542705466741e-05[/C][C]3.5527135273337e-05[/C][/ROW]
[ROW][C]106[/C][C]0.999938966782781[/C][C]0.000122066434437854[/C][C]6.10332172189269e-05[/C][/ROW]
[ROW][C]107[/C][C]0.999958216704061[/C][C]8.35665918774764e-05[/C][C]4.17832959387382e-05[/C][/ROW]
[ROW][C]108[/C][C]0.999949468536439[/C][C]0.0001010629271219[/C][C]5.05314635609499e-05[/C][/ROW]
[ROW][C]109[/C][C]0.999899185670443[/C][C]0.000201628659113597[/C][C]0.000100814329556799[/C][/ROW]
[ROW][C]110[/C][C]0.999809457517036[/C][C]0.000381084965927166[/C][C]0.000190542482963583[/C][/ROW]
[ROW][C]111[/C][C]0.999858628181127[/C][C]0.000282743637744953[/C][C]0.000141371818872477[/C][/ROW]
[ROW][C]112[/C][C]0.999810822483618[/C][C]0.000378355032763729[/C][C]0.000189177516381865[/C][/ROW]
[ROW][C]113[/C][C]0.999884126708294[/C][C]0.000231746583412871[/C][C]0.000115873291706435[/C][/ROW]
[ROW][C]114[/C][C]0.999769349324518[/C][C]0.000461301350963392[/C][C]0.000230650675481696[/C][/ROW]
[ROW][C]115[/C][C]0.999553565711279[/C][C]0.000892868577442006[/C][C]0.000446434288721003[/C][/ROW]
[ROW][C]116[/C][C]0.9993157237732[/C][C]0.00136855245360026[/C][C]0.000684276226800129[/C][/ROW]
[ROW][C]117[/C][C]0.999783618800828[/C][C]0.000432762398343517[/C][C]0.000216381199171759[/C][/ROW]
[ROW][C]118[/C][C]0.999873025495774[/C][C]0.00025394900845211[/C][C]0.000126974504226055[/C][/ROW]
[ROW][C]119[/C][C]0.999869312714939[/C][C]0.000261374570121447[/C][C]0.000130687285060723[/C][/ROW]
[ROW][C]120[/C][C]0.999951014377794[/C][C]9.79712444124171e-05[/C][C]4.89856222062086e-05[/C][/ROW]
[ROW][C]121[/C][C]0.999903297323996[/C][C]0.000193405352007914[/C][C]9.67026760039571e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999755556955389[/C][C]0.000488886089221188[/C][C]0.000244443044610594[/C][/ROW]
[ROW][C]123[/C][C]0.999401914505642[/C][C]0.00119617098871667[/C][C]0.000598085494358334[/C][/ROW]
[ROW][C]124[/C][C]0.998590193822016[/C][C]0.00281961235596879[/C][C]0.0014098061779844[/C][/ROW]
[ROW][C]125[/C][C]0.99688799652047[/C][C]0.00622400695905936[/C][C]0.00311200347952968[/C][/ROW]
[ROW][C]126[/C][C]0.994659020046092[/C][C]0.0106819599078157[/C][C]0.00534097995390783[/C][/ROW]
[ROW][C]127[/C][C]0.989496215231671[/C][C]0.0210075695366586[/C][C]0.0105037847683293[/C][/ROW]
[ROW][C]128[/C][C]0.989396633222932[/C][C]0.0212067335541356[/C][C]0.0106033667770678[/C][/ROW]
[ROW][C]129[/C][C]0.991547992594454[/C][C]0.016904014811092[/C][C]0.00845200740554598[/C][/ROW]
[ROW][C]130[/C][C]0.979794670161791[/C][C]0.0404106596764182[/C][C]0.0202053298382091[/C][/ROW]
[ROW][C]131[/C][C]0.955788374920216[/C][C]0.0884232501595674[/C][C]0.0442116250797837[/C][/ROW]
[ROW][C]132[/C][C]0.997261274276813[/C][C]0.00547745144637359[/C][C]0.00273872572318679[/C][/ROW]
[ROW][C]133[/C][C]0.988446456154815[/C][C]0.0231070876903699[/C][C]0.0115535438451849[/C][/ROW]
[ROW][C]134[/C][C]0.992703839717715[/C][C]0.0145923205645703[/C][C]0.00729616028228513[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147101&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147101&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.7481459844866090.5037080310267820.251854015513391
110.898291727829340.203416544341320.10170827217066
120.8279831815974810.3440336368050380.172016818402519
130.7523984897698430.4952030204603140.247601510230157
140.8391378717602190.3217242564795620.160862128239781
150.7691564654125380.4616870691749240.230843534587462
160.7065266943198370.5869466113603270.293473305680163
170.9812351239398620.03752975212027580.0187648760601379
180.9709317819150660.05813643616986850.0290682180849343
190.9745145252779990.05097094944400290.0254854747220014
200.9775673782436150.04486524351276950.0224326217563847
210.9807994828086340.03840103438273170.0192005171913658
220.9740189937838470.05196201243230550.0259810062161527
230.9806225223042690.03875495539146280.0193774776957314
240.9726796694841430.05464066103171320.0273203305158566
250.9813901156056720.03721976878865590.018609884394328
260.9761553968097830.04768920638043330.0238446031902166
270.9703234849431560.0593530301136880.029676515056844
280.9698482371635140.06030352567297270.0301517628364864
290.9993959552264050.001208089547189450.000604044773594725
300.9998205010264680.0003589979470630780.000179498973531539
310.9996983154102190.0006033691795613110.000301684589780655
320.9996787506058670.0006424987882669760.000321249394133488
330.9996550548510050.0006898902979900510.000344945148995026
340.9995575597811520.0008848804376950570.000442440218847528
350.9997980882516770.0004038234966460960.000201911748323048
360.9997155200152150.0005689599695704140.000284479984785207
370.9997308541101190.0005382917797619140.000269145889880957
380.999686994853980.0006260102920404850.000313005146020243
390.9999537300916639.25398166743852e-054.62699083371926e-05
400.9999600743660387.98512679231146e-053.99256339615573e-05
410.999933190389530.000133619220939636.68096104698152e-05
420.9998884742404210.0002230515191573890.000111525759578695
430.9998522266117090.0002955467765819930.000147773388290997
440.9997658849271750.0004682301456500980.000234115072825049
450.9997857350402990.0004285299194012070.000214264959700604
460.9997257420857260.000548515828547820.00027425791427391
470.9995843086824370.0008313826351257130.000415691317562857
480.9996450588532460.0007098822935076130.000354941146753807
490.9994410960135020.00111780797299620.000558903986498098
500.9991865291968340.001626941606330930.000813470803165465
510.9989130647531750.002173870493650610.00108693524682531
520.9983657955383740.003268408923251060.00163420446162553
530.9998423696553990.000315260689202890.000157630344601445
540.9999453364146860.0001093271706281975.46635853140987e-05
550.999937394187160.0001252116256790696.26058128395346e-05
560.9999492922636570.0001014154726856455.07077363428226e-05
570.9999592155284258.15689431496465e-054.07844715748232e-05
580.9999350865381460.0001298269237085786.49134618542892e-05
590.9999155896303830.0001688207392345938.44103696172967e-05
600.999884074062060.0002318518758793920.000115925937939696
610.999838530121370.0003229397572590550.000161469878629528
620.9998170340189360.0003659319621280350.000182965981064017
630.9998458867966050.0003082264067898250.000154113203394912
640.9997572723634830.0004854552730330590.00024272763651653
650.9997512592315320.0004974815369359290.000248740768467964
660.9996557933340990.0006884133318015580.000344206665900779
670.9995558762275710.0008882475448575380.000444123772428769
680.9997780634410680.0004438731178641120.000221936558932056
690.9997072037254350.0005855925491297670.000292796274564883
700.9995673483780330.0008653032439334410.00043265162196672
710.9996102565993980.0007794868012038070.000389743400601904
720.9994277606295990.001144478740801420.000572239370400711
730.9995143949878810.0009712100242376720.000485605012118836
740.9995283321107930.0009433357784145370.000471667889207269
750.9998303355296650.0003393289406703760.000169664470335188
760.9998674001540540.000265199691892710.000132599845946355
770.999832820676910.000334358646179440.00016717932308972
780.9997909883550620.0004180232898750970.000209011644937548
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800.9998194266990170.0003611466019658590.000180573300982929
810.999828442542910.0003431149141807660.000171557457090383
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830.9999998688263892.62347221016578e-071.31173610508289e-07
840.9999998360997723.27800456604794e-071.63900228302397e-07
850.9999998272351233.45529754689068e-071.72764877344534e-07
860.9999997099666555.80066689136708e-072.90033344568354e-07
870.9999998109442863.78111427256323e-071.89055713628161e-07
880.9999998820562762.35887447975915e-071.17943723987957e-07
890.9999997668384694.66323062677581e-072.3316153133879e-07
900.9999997478020395.0439592210111e-072.52197961050555e-07
910.999999579820318.40359380168547e-074.20179690084274e-07
920.9999992653586651.46928266972103e-067.34641334860515e-07
930.9999992241390411.55172191906271e-067.75860959531356e-07
940.9999984145599973.17088000646141e-061.58544000323071e-06
950.9999968824201996.23515960222308e-063.11757980111154e-06
960.9999993218986781.3562026438032e-066.78101321901598e-07
970.9999987357446932.52851061470941e-061.2642553073547e-06
980.9999974158002675.16839946669951e-062.58419973334975e-06
990.9999964691036347.06179273211056e-063.53089636605528e-06
1000.9999954130071219.17398575786925e-064.58699287893462e-06
1010.9999925063567161.498728656737e-057.49364328368501e-06
1020.9999929456928891.41086142227926e-057.05430711139629e-06
1030.9999892741080472.14517839053427e-051.07258919526714e-05
1040.9999787285414464.25429171075635e-052.12714585537817e-05
1050.9999644728647277.10542705466741e-053.5527135273337e-05
1060.9999389667827810.0001220664344378546.10332172189269e-05
1070.9999582167040618.35665918774764e-054.17832959387382e-05
1080.9999494685364390.00010106292712195.05314635609499e-05
1090.9998991856704430.0002016286591135970.000100814329556799
1100.9998094575170360.0003810849659271660.000190542482963583
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1120.9998108224836180.0003783550327637290.000189177516381865
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1150.9995535657112790.0008928685774420060.000446434288721003
1160.99931572377320.001368552453600260.000684276226800129
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1200.9999510143777949.79712444124171e-054.89856222062086e-05
1210.9999032973239960.0001934053520079149.67026760039571e-05
1220.9997555569553890.0004888860892211880.000244443044610594
1230.9994019145056420.001196170988716670.000598085494358334
1240.9985901938220160.002819612355968790.0014098061779844
1250.996887996520470.006224006959059360.00311200347952968
1260.9946590200460920.01068195990781570.00534097995390783
1270.9894962152316710.02100756953665860.0105037847683293
1280.9893966332229320.02120673355413560.0106033667770678
1290.9915479925944540.0169040148110920.00845200740554598
1300.9797946701617910.04041065967641820.0202053298382091
1310.9557883749202160.08842325015956740.0442116250797837
1320.9972612742768130.005477451446373590.00273872572318679
1330.9884464561548150.02310708769036990.0115535438451849
1340.9927038397177150.01459232056457030.00729616028228513







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level980.784NOK
5% type I error level1110.888NOK
10% type I error level1180.944NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 98 & 0.784 & NOK \tabularnewline
5% type I error level & 111 & 0.888 & NOK \tabularnewline
10% type I error level & 118 & 0.944 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147101&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]98[/C][C]0.784[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]111[/C][C]0.888[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]118[/C][C]0.944[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147101&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147101&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level980.784NOK
5% type I error level1110.888NOK
10% type I error level1180.944NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}