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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 11:45:38 -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/21/t1321894062666bezxd8yu1p6x.htm/, Retrieved Fri, 29 Mar 2024 10:43:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145816, Retrieved Fri, 29 Mar 2024 10:43:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS 7] [2011-11-21 16:45:38] [84449ea5bbe6e767918d59f07903f9b5] [Current]
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Dataseries X:
11	65	146455	1	95556	114468	127
11	54	84944	4	54565	88594	90
10	58	113337	9	63016	74151	68
9	75	128655	2	79774	77921	111
9	41	74398	1	31258	53212	51
10	0	35523	2	52491	34956	33
10	111	293403	0	91256	149703	123
10	1	32750	0	22807	6853	5
9	36	106539	5	77411	58907	63
9	60	130539	0	48821	67067	66
11	63	154991	0	52295	110563	99
11	71	126683	7	63262	58126	72
9	38	100672	6	50466	57113	55
9	76	179562	3	62932	77993	116
9	61	125971	4	38439	68091	71
9	125	234509	0	70817	124676	125
9	84	158980	4	105965	109522	123
9	69	184217	3	73795	75865	74
11	77	107342	0	82043	79746	116
9	95	141371	5	74349	77844	117
9	78	154730	0	82204	98681	98
9	76	264020	1	55709	105531	101
9	40	90938	3	37137	51428	43
9	81	101324	5	70780	65703	103
10	102	130232	0	55027	72562	107
9	70	137793	0	56699	81728	77
9	75	161678	4	65911	95580	87
10	93	151503	0	56316	98278	99
10	42	105324	0	26982	46629	46
10	95	175914	0	54628	115189	96
10	87	181853	3	96750	124865	92
11	44	114928	4	53009	59392	96
11	84	190410	1	64664	127818	96
11	28	61499	4	36990	17821	15
9	87	223004	1	85224	154076	147
9	71	167131	0	37048	64881	56
9	68	233482	0	59635	136506	81
10	50	121185	2	42051	66524	69
9	30	78776	1	26998	45988	34
10	86	188967	2	63717	107445	98
10	75	199512	8	55071	102772	82
9	46	102531	5	40001	46657	64
10	52	118958	3	54506	97563	61
10	31	68948	4	35838	36663	45
10	30	93125	1	50838	55369	37
11	70	277108	2	86997	77921	64
11	20	78800	2	33032	56968	21
10	84	157250	0	61704	77519	104
11	81	210554	6	117986	129805	126
11	79	127324	3	56733	72761	104
10	70	114397	0	55064	81278	87
9	8	24188	0	5950	15049	7
9	67	246209	6	84607	113935	130
10	21	65029	5	32551	25109	21
10	30	98030	3	31701	45824	35
9	70	173587	1	71170	89644	97
9	87	172684	5	101773	109011	103
9	87	191381	5	101653	134245	210
9	112	191276	0	81493	136692	151
11	54	134043	9	55901	50741	57
11	96	233406	6	109104	149510	117
11	93	195304	6	114425	147888	152
11	49	127619	5	36311	54987	52
9	49	162810	6	70027	74467	83
9	38	129100	2	73713	100033	87
9	64	108715	0	40671	85505	80
9	62	106469	3	89041	62426	88
9	66	142069	8	57231	82932	83
10	98	143937	2	78792	79169	140
10	97	84256	5	59155	65469	76
10	56	118807	11	55827	63572	70
10	22	69471	6	22618	23824	26
9	51	122433	5	58425	73831	66
10	56	131122	1	65724	63551	89
10	94	94763	0	56979	56756	100
10	98	188780	3	72369	81399	98
10	76	191467	3	79194	117881	109
10	57	105615	6	202316	70711	51
10	75	89318	1	44970	50495	82
11	48	107335	0	49319	53845	65
11	48	98599	1	36252	51390	46
11	109	260646	0	75741	104953	104
11	27	131876	5	38417	65983	36
11	83	119291	2	64102	76839	123
11	49	80953	0	56622	55792	59
11	24	99768	0	15430	25155	27
10	43	84572	5	72571	55291	84
10	44	202373	1	67271	84279	61
10	49	166790	0	43460	99692	46
10	106	99946	1	99501	59633	125
10	42	116900	1	28340	63249	58
9	108	142146	2	76013	82928	152
9	27	99246	4	37361	50000	52
11	79	156833	1	48204	69455	85
11	49	175078	4	76168	84068	95
10	64	130533	0	85168	76195	78
9	75	142339	2	125410	114634	144
9	115	176789	0	123328	139357	149
9	92	181379	7	83038	110044	101
9	106	228548	7	120087	155118	205
11	73	142141	6	91939	83061	61
10	105	167845	0	103646	127122	145
10	30	103012	0	29467	45653	28
10	13	43287	4	43750	19630	49
11	69	125366	4	34497	67229	68
10	72	118372	0	66477	86060	142
10	80	135171	0	71181	88003	82
10	106	175568	0	74482	95815	105
10	28	74112	0	174949	85499	52
11	70	88817	0	46765	27220	56
9	51	164767	4	90257	109882	81
9	90	141933	0	51370	72579	100
9	12	22938	0	1168	5841	11
9	84	115199	0	51360	68369	87
10	23	61857	4	25162	24610	31
10	57	91185	0	21067	30995	67
10	84	213765	1	58233	150662	150
11	4	21054	0	855	6622	4
10	56	167105	5	85903	93694	75
11	18	31414	0	14116	13155	39
11	86	178863	1	57637	111908	88
11	39	126681	7	94137	57550	67
10	16	64320	5	62147	16356	24
9	18	67746	2	62832	40174	58
9	16	38214	0	8773	13983	16
9	42	90961	1	63785	52316	49
9	75	181510	0	65196	99585	109
10	30	116775	0	73087	86271	124
10	104	223914	2	72631	131012	115
10	121	185139	0	86281	130274	128
10	106	242879	2	162365	159051	159
9	57	139144	0	56530	76506	75
10	28	75812	0	35606	49145	30
10	56	178218	4	70111	66398	83
10	81	246834	4	92046	127546	135
9	2	50999	8	63989	6802	8
11	88	223842	0	104911	99509	115
11	41	93577	4	43448	43106	60
11	83	155383	0	60029	108303	99
11	55	111664	1	38650	64167	98
11	3	75426	0	47261	8579	36
11	54	243551	9	73586	97811	93
10	89	136548	0	83042	84365	158
9	41	173260	3	37238	10901	16
9	94	185039	7	63958	91346	100
9	101	67507	5	78956	33660	49
10	70	139350	2	99518	93634	89
10	111	172964	1	111436	109348	153
11	0	0	9	0	0	0
11	4	14688	0	6023	7953	5
11	0	98	0	0	0	0
10	0	455	0	0	0	0
9	0	0	1	0	0	0
11	0	0	0	0	0	0
10	42	128066	2	42564	63538	80
9	97	176460	1	38885	108281	122
9	0	0	0	0	0	0
9	0	203	0	0	0	0
10	7	7199	0	1644	4245	6
9	12	46660	0	6179	21509	13
11	0	17547	0	3926	7670	3
11	37	73567	0	23238	10641	18
10	0	969	0	0	0	0
9	39	101060	2	49288	41243	49




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145816&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145816&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145816&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
BloggedComputations[t] = + 9.83144812675628 -0.50806541813393Month[t] + 0.000157570808754214TotalTime[t] -0.8600128055572Shared[t] + 3.18067381031166e-05Characters[t] -2.72926341112233e-06Writing[t] + 0.442706516754504hyperlinks[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BloggedComputations[t] =  +  9.83144812675628 -0.50806541813393Month[t] +  0.000157570808754214TotalTime[t] -0.8600128055572Shared[t] +  3.18067381031166e-05Characters[t] -2.72926341112233e-06Writing[t] +  0.442706516754504hyperlinks[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145816&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BloggedComputations[t] =  +  9.83144812675628 -0.50806541813393Month[t] +  0.000157570808754214TotalTime[t] -0.8600128055572Shared[t] +  3.18067381031166e-05Characters[t] -2.72926341112233e-06Writing[t] +  0.442706516754504hyperlinks[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145816&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145816&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
BloggedComputations[t] = + 9.83144812675628 -0.50806541813393Month[t] + 0.000157570808754214TotalTime[t] -0.8600128055572Shared[t] + 3.18067381031166e-05Characters[t] -2.72926341112233e-06Writing[t] + 0.442706516754504hyperlinks[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.8314481267562815.7975640.62230.5346210.267311
Month-0.508065418133931.546857-0.32850.7430090.371504
TotalTime0.0001575708087542144e-053.95790.0001145.7e-05
Shared-0.86001280555720.490114-1.75470.0812580.040629
Characters3.18067381031166e-055.6e-050.56630.5719950.285998
Writing-2.72926341112233e-068.6e-05-0.03190.9746180.487309
hyperlinks0.4427065167545040.0564567.841600

\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) & 9.83144812675628 & 15.797564 & 0.6223 & 0.534621 & 0.267311 \tabularnewline
Month & -0.50806541813393 & 1.546857 & -0.3285 & 0.743009 & 0.371504 \tabularnewline
TotalTime & 0.000157570808754214 & 4e-05 & 3.9579 & 0.000114 & 5.7e-05 \tabularnewline
Shared & -0.8600128055572 & 0.490114 & -1.7547 & 0.081258 & 0.040629 \tabularnewline
Characters & 3.18067381031166e-05 & 5.6e-05 & 0.5663 & 0.571995 & 0.285998 \tabularnewline
Writing & -2.72926341112233e-06 & 8.6e-05 & -0.0319 & 0.974618 & 0.487309 \tabularnewline
hyperlinks & 0.442706516754504 & 0.056456 & 7.8416 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145816&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]9.83144812675628[/C][C]15.797564[/C][C]0.6223[/C][C]0.534621[/C][C]0.267311[/C][/ROW]
[ROW][C]Month[/C][C]-0.50806541813393[/C][C]1.546857[/C][C]-0.3285[/C][C]0.743009[/C][C]0.371504[/C][/ROW]
[ROW][C]TotalTime[/C][C]0.000157570808754214[/C][C]4e-05[/C][C]3.9579[/C][C]0.000114[/C][C]5.7e-05[/C][/ROW]
[ROW][C]Shared[/C][C]-0.8600128055572[/C][C]0.490114[/C][C]-1.7547[/C][C]0.081258[/C][C]0.040629[/C][/ROW]
[ROW][C]Characters[/C][C]3.18067381031166e-05[/C][C]5.6e-05[/C][C]0.5663[/C][C]0.571995[/C][C]0.285998[/C][/ROW]
[ROW][C]Writing[/C][C]-2.72926341112233e-06[/C][C]8.6e-05[/C][C]-0.0319[/C][C]0.974618[/C][C]0.487309[/C][/ROW]
[ROW][C]hyperlinks[/C][C]0.442706516754504[/C][C]0.056456[/C][C]7.8416[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145816&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145816&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)9.8314481267562815.7975640.62230.5346210.267311
Month-0.508065418133931.546857-0.32850.7430090.371504
TotalTime0.0001575708087542144e-053.95790.0001145.7e-05
Shared-0.86001280555720.490114-1.75470.0812580.040629
Characters3.18067381031166e-055.6e-050.56630.5719950.285998
Writing-2.72926341112233e-068.6e-05-0.03190.9746180.487309
hyperlinks0.4427065167545040.0564567.841600







Multiple Linear Regression - Regression Statistics
Multiple R0.882335960891695
R-squared0.778516747882671
Adjusted R-squared0.770052419776276
F-TEST (value)91.9762015480597
F-TEST (DF numerator)6
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.4610814095291
Sum Squared Residuals37530.0710212774

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.882335960891695 \tabularnewline
R-squared & 0.778516747882671 \tabularnewline
Adjusted R-squared & 0.770052419776276 \tabularnewline
F-TEST (value) & 91.9762015480597 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 15.4610814095291 \tabularnewline
Sum Squared Residuals & 37530.0710212774 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145816&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.882335960891695[/C][/ROW]
[ROW][C]R-squared[/C][C]0.778516747882671[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.770052419776276[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]91.9762015480597[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/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]15.4610814095291[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]37530.0710212774[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145816&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145816&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 R0.882335960891695
R-squared0.778516747882671
Adjusted R-squared0.770052419776276
F-TEST (value)91.9762015480597
F-TEST (DF numerator)6
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation15.4610814095291
Sum Squared Residuals37530.0710212774







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16585.4103874876833-20.4103874876833
25455.5246968937292-1.52469689372916
35846.775280383592711.2247196164073
47575.2762133036398-0.276213303639818
54139.5488173971641.45118260283598
6024.8116345845487-24.8116345845487
7111107.9294202790363.0705797209636
8112.8314831496514-11.8314831496514
93647.9381609687052-11.9381609687053
106056.4164185250533.58358147494696
116373.8543087241819-10.8543087241819
127151.912567560961519.0874324390385
133841.7598918345221-3.75989183452212
147684.1153046530076-8.11530465300763
156154.1371041114886.86289588851204
1612599.461130875209225.5388691247908
178484.3938034936549-0.393803493654858
186966.60644753262242.39355246737765
197774.9025225983112.09747740168901
209577.183742990687117.8162570093129
217875.37034390038752.62965609961252
227692.1979493534336-16.1979493534336
234037.08522164804192.91477835195806
248164.595231316728416.4047686832716
2510274.1933413697927.8066586302099
267062.63976860796157.36023139203847
277567.64555923500727.35444076499278
289373.974191056299419.0258089437006
294242.4422281612541-0.442228161254078
309576.392688171071518.6073118289285
318774.290991790186612.7090082098134
324462.9357477895857-18.9357477895857
338477.59350094706526.40649905293477
342818.26161526313199.7383847368681
3587106.905808615098-19.9058086150985
367157.386689833570913.6133101664291
376879.4322687857978-11.4322687857978
385053.8286800740554-3.82868007405544
393032.5968711076261-2.59687110762606
408677.92507421860988.07492578139022
417567.08103008578837.91896991421167
424646.5928670883215-0.592867088321521
435249.38754425942312.61245574057693
443133.1365549948239-2.13655499482393
453036.4105781908884-6.41057819088842
467077.0744755212184-7.07447552121844
472025.1312789928626-5.13127899286264
488477.32131456203356.67868543796653
498191.4393146361957-10.4393146361957
507969.37262124464899.62737875535114
517062.82146586749557.17853413250454
52812.3173051096189-4.31730510961893
536798.8260950248037-31.8260950248037
542120.9610509489580.0389490510419622
553033.9953896353843-3.99538963538426
567076.7126461239705-6.71264612397051
578776.707071523650110.2929284763499
5887126.950083186171-39.950083186171
59112104.4660154427967.5339845572038
605444.49769156207479.50230843792534
619690.71947652370845.28052347629156
6293100.384112173663-7.38411217366253
634944.07729287321024.92270712678981
644964.521617184816-15.521617184816
653864.4682457992376-26.4682457992376
666458.86593735504945.13406264495055
676261.0751276282650.92487237173504
686659.10331319378786.89668680621215
699889.97999363321418.02000636678594
709749.075556699595447.9244433004046
715646.60279436727589.39720563272421
722222.7020710335646-0.702071033564606
735151.1260966965349-0.126096696534878
745665.8696793525297-9.86967935252969
759465.610742227058828.3892577729412
769877.381873964689820.6181260353102
777682.7925504119011-6.79255041190112
785745.052613514136111.9473864858639
797555.559159866590519.4408401334095
804851.3532342020951-3.3532342020951
814840.29634068780647.70365931219361
8210993.47703705618315.522962943817
832737.7017415479813-10.7017415479813
848377.60154547871035.39845452128966
854944.766932757524.23306724248
862432.3384522752694-8.3384522752694
874353.1214988505876-10.1214988505876
884464.6936074298099-20.6936074298099
894952.5067640182194-3.50676401821937
9010677.979715868924228.0202841310758
914248.7165664313409-6.71656643134088
9210895.419677707572312.5803222924277
932741.5296878698915-14.5296878698915
947967.068723308511511.9312766914885
954972.6401903631944-23.6401903631944
966462.35105267653791.64894732346212
977593.3930601559924-18.3930601559924
98115102.62123550441812.3787644955819
999274.87299249367917.1270075063209
100106129.402316735264-23.4023167352644
1017351.182605889368521.8173941106315
10210598.3404030242586.65959697574196
1033034.1909106551088-4.19091065510877
1041331.1620989939536-18.1620989939536
1056951.574493849118217.4255061508818
1067288.1466272181301-16.1466272181301
1078064.375584166351115.6244158336489
10810681.006895049659424.9931049503406
1092844.7806283270581-16.7806283270581
1107044.442411543999825.5575884560002
1115166.2113892832746-15.2113892832746
1129073.329833565154416.6701664348456
1131213.7641989015747-1.76419890157469
1148463.373322977690520.6266770223095
1152325.5146562312901-2.51465623129013
1165749.36570379641247.63429620358762
11784105.420888082292-21.4208880822922
11849.340171980582-5.340171980582
1195662.4611672883221-6.46116728832205
1201826.8712925218039-8.87129252180387
1218672.052295316564613.9477046834354
1223950.68232493023-11.68232493023
1231623.1426942593522-7.1426942593522
1241841.7794392742773-23.7794392742773
1251618.6044517404574-2.60445174045743
1264242.3102728593475-0.310272859347486
1277583.9164255873434-8.91642558734341
1283080.1359359992504-50.1359359992504
12910491.176916769611112.8230832303889
13012192.978495160594428.0215048394056
131106116.421953914994-10.4219539149936
1325761.9761106118729-4.97611061187292
1332830.9761286678865-2.97612866788654
1345668.1861225915465-12.1861225915464
13581102.549531877488-21.5495318774884
136211.9730796435434-9.97307964354338
1378893.4902333555705-5.49023335557049
1384143.3743634096219-2.37436340962195
1398374.16823792901228.83176207098781
1405565.4171429347818-10.4171429347818
141333.5449028502279-30.5449028502279
1425478.1244260248854-24.1244260248854
1438998.6254432242787-9.6254432242787
1444138.21751115274572.78248884725428
1459474.451154341218719.5488456587813
14610135.708013050557665.2919869494424
1477067.29893163766212.70106836233791
148111102.1249317404428.87506825955774
1490-3.497386722731753.49738672273175
15048.94052730172388-4.94052730172388
15104.25817046654097-4.25817046654097
15204.82248866340015-4.82248866340015
15304.39884655799372-4.39884655799372
15404.24272852728305-4.24272852728305
1554259.8071629305852-17.8071629305852
1569787.15526415453179.84473584546824
15705.25885936355091-5.25885936355091
15805.29084623772802-5.29084623772802
15978.5820898524269-1.5820898524269
1601218.5041281258604-6.50412812586042
16108.43968286218628-8.43968286218628
1623724.513540404567912.4864595954321
16304.90348005909982-4.90348005909982
1643942.6106865028696-3.61068650286957

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 65 & 85.4103874876833 & -20.4103874876833 \tabularnewline
2 & 54 & 55.5246968937292 & -1.52469689372916 \tabularnewline
3 & 58 & 46.7752803835927 & 11.2247196164073 \tabularnewline
4 & 75 & 75.2762133036398 & -0.276213303639818 \tabularnewline
5 & 41 & 39.548817397164 & 1.45118260283598 \tabularnewline
6 & 0 & 24.8116345845487 & -24.8116345845487 \tabularnewline
7 & 111 & 107.929420279036 & 3.0705797209636 \tabularnewline
8 & 1 & 12.8314831496514 & -11.8314831496514 \tabularnewline
9 & 36 & 47.9381609687052 & -11.9381609687053 \tabularnewline
10 & 60 & 56.416418525053 & 3.58358147494696 \tabularnewline
11 & 63 & 73.8543087241819 & -10.8543087241819 \tabularnewline
12 & 71 & 51.9125675609615 & 19.0874324390385 \tabularnewline
13 & 38 & 41.7598918345221 & -3.75989183452212 \tabularnewline
14 & 76 & 84.1153046530076 & -8.11530465300763 \tabularnewline
15 & 61 & 54.137104111488 & 6.86289588851204 \tabularnewline
16 & 125 & 99.4611308752092 & 25.5388691247908 \tabularnewline
17 & 84 & 84.3938034936549 & -0.393803493654858 \tabularnewline
18 & 69 & 66.6064475326224 & 2.39355246737765 \tabularnewline
19 & 77 & 74.902522598311 & 2.09747740168901 \tabularnewline
20 & 95 & 77.1837429906871 & 17.8162570093129 \tabularnewline
21 & 78 & 75.3703439003875 & 2.62965609961252 \tabularnewline
22 & 76 & 92.1979493534336 & -16.1979493534336 \tabularnewline
23 & 40 & 37.0852216480419 & 2.91477835195806 \tabularnewline
24 & 81 & 64.5952313167284 & 16.4047686832716 \tabularnewline
25 & 102 & 74.19334136979 & 27.8066586302099 \tabularnewline
26 & 70 & 62.6397686079615 & 7.36023139203847 \tabularnewline
27 & 75 & 67.6455592350072 & 7.35444076499278 \tabularnewline
28 & 93 & 73.9741910562994 & 19.0258089437006 \tabularnewline
29 & 42 & 42.4422281612541 & -0.442228161254078 \tabularnewline
30 & 95 & 76.3926881710715 & 18.6073118289285 \tabularnewline
31 & 87 & 74.2909917901866 & 12.7090082098134 \tabularnewline
32 & 44 & 62.9357477895857 & -18.9357477895857 \tabularnewline
33 & 84 & 77.5935009470652 & 6.40649905293477 \tabularnewline
34 & 28 & 18.2616152631319 & 9.7383847368681 \tabularnewline
35 & 87 & 106.905808615098 & -19.9058086150985 \tabularnewline
36 & 71 & 57.3866898335709 & 13.6133101664291 \tabularnewline
37 & 68 & 79.4322687857978 & -11.4322687857978 \tabularnewline
38 & 50 & 53.8286800740554 & -3.82868007405544 \tabularnewline
39 & 30 & 32.5968711076261 & -2.59687110762606 \tabularnewline
40 & 86 & 77.9250742186098 & 8.07492578139022 \tabularnewline
41 & 75 & 67.0810300857883 & 7.91896991421167 \tabularnewline
42 & 46 & 46.5928670883215 & -0.592867088321521 \tabularnewline
43 & 52 & 49.3875442594231 & 2.61245574057693 \tabularnewline
44 & 31 & 33.1365549948239 & -2.13655499482393 \tabularnewline
45 & 30 & 36.4105781908884 & -6.41057819088842 \tabularnewline
46 & 70 & 77.0744755212184 & -7.07447552121844 \tabularnewline
47 & 20 & 25.1312789928626 & -5.13127899286264 \tabularnewline
48 & 84 & 77.3213145620335 & 6.67868543796653 \tabularnewline
49 & 81 & 91.4393146361957 & -10.4393146361957 \tabularnewline
50 & 79 & 69.3726212446489 & 9.62737875535114 \tabularnewline
51 & 70 & 62.8214658674955 & 7.17853413250454 \tabularnewline
52 & 8 & 12.3173051096189 & -4.31730510961893 \tabularnewline
53 & 67 & 98.8260950248037 & -31.8260950248037 \tabularnewline
54 & 21 & 20.961050948958 & 0.0389490510419622 \tabularnewline
55 & 30 & 33.9953896353843 & -3.99538963538426 \tabularnewline
56 & 70 & 76.7126461239705 & -6.71264612397051 \tabularnewline
57 & 87 & 76.7070715236501 & 10.2929284763499 \tabularnewline
58 & 87 & 126.950083186171 & -39.950083186171 \tabularnewline
59 & 112 & 104.466015442796 & 7.5339845572038 \tabularnewline
60 & 54 & 44.4976915620747 & 9.50230843792534 \tabularnewline
61 & 96 & 90.7194765237084 & 5.28052347629156 \tabularnewline
62 & 93 & 100.384112173663 & -7.38411217366253 \tabularnewline
63 & 49 & 44.0772928732102 & 4.92270712678981 \tabularnewline
64 & 49 & 64.521617184816 & -15.521617184816 \tabularnewline
65 & 38 & 64.4682457992376 & -26.4682457992376 \tabularnewline
66 & 64 & 58.8659373550494 & 5.13406264495055 \tabularnewline
67 & 62 & 61.075127628265 & 0.92487237173504 \tabularnewline
68 & 66 & 59.1033131937878 & 6.89668680621215 \tabularnewline
69 & 98 & 89.9799936332141 & 8.02000636678594 \tabularnewline
70 & 97 & 49.0755566995954 & 47.9244433004046 \tabularnewline
71 & 56 & 46.6027943672758 & 9.39720563272421 \tabularnewline
72 & 22 & 22.7020710335646 & -0.702071033564606 \tabularnewline
73 & 51 & 51.1260966965349 & -0.126096696534878 \tabularnewline
74 & 56 & 65.8696793525297 & -9.86967935252969 \tabularnewline
75 & 94 & 65.6107422270588 & 28.3892577729412 \tabularnewline
76 & 98 & 77.3818739646898 & 20.6181260353102 \tabularnewline
77 & 76 & 82.7925504119011 & -6.79255041190112 \tabularnewline
78 & 57 & 45.0526135141361 & 11.9473864858639 \tabularnewline
79 & 75 & 55.5591598665905 & 19.4408401334095 \tabularnewline
80 & 48 & 51.3532342020951 & -3.3532342020951 \tabularnewline
81 & 48 & 40.2963406878064 & 7.70365931219361 \tabularnewline
82 & 109 & 93.477037056183 & 15.522962943817 \tabularnewline
83 & 27 & 37.7017415479813 & -10.7017415479813 \tabularnewline
84 & 83 & 77.6015454787103 & 5.39845452128966 \tabularnewline
85 & 49 & 44.76693275752 & 4.23306724248 \tabularnewline
86 & 24 & 32.3384522752694 & -8.3384522752694 \tabularnewline
87 & 43 & 53.1214988505876 & -10.1214988505876 \tabularnewline
88 & 44 & 64.6936074298099 & -20.6936074298099 \tabularnewline
89 & 49 & 52.5067640182194 & -3.50676401821937 \tabularnewline
90 & 106 & 77.9797158689242 & 28.0202841310758 \tabularnewline
91 & 42 & 48.7165664313409 & -6.71656643134088 \tabularnewline
92 & 108 & 95.4196777075723 & 12.5803222924277 \tabularnewline
93 & 27 & 41.5296878698915 & -14.5296878698915 \tabularnewline
94 & 79 & 67.0687233085115 & 11.9312766914885 \tabularnewline
95 & 49 & 72.6401903631944 & -23.6401903631944 \tabularnewline
96 & 64 & 62.3510526765379 & 1.64894732346212 \tabularnewline
97 & 75 & 93.3930601559924 & -18.3930601559924 \tabularnewline
98 & 115 & 102.621235504418 & 12.3787644955819 \tabularnewline
99 & 92 & 74.872992493679 & 17.1270075063209 \tabularnewline
100 & 106 & 129.402316735264 & -23.4023167352644 \tabularnewline
101 & 73 & 51.1826058893685 & 21.8173941106315 \tabularnewline
102 & 105 & 98.340403024258 & 6.65959697574196 \tabularnewline
103 & 30 & 34.1909106551088 & -4.19091065510877 \tabularnewline
104 & 13 & 31.1620989939536 & -18.1620989939536 \tabularnewline
105 & 69 & 51.5744938491182 & 17.4255061508818 \tabularnewline
106 & 72 & 88.1466272181301 & -16.1466272181301 \tabularnewline
107 & 80 & 64.3755841663511 & 15.6244158336489 \tabularnewline
108 & 106 & 81.0068950496594 & 24.9931049503406 \tabularnewline
109 & 28 & 44.7806283270581 & -16.7806283270581 \tabularnewline
110 & 70 & 44.4424115439998 & 25.5575884560002 \tabularnewline
111 & 51 & 66.2113892832746 & -15.2113892832746 \tabularnewline
112 & 90 & 73.3298335651544 & 16.6701664348456 \tabularnewline
113 & 12 & 13.7641989015747 & -1.76419890157469 \tabularnewline
114 & 84 & 63.3733229776905 & 20.6266770223095 \tabularnewline
115 & 23 & 25.5146562312901 & -2.51465623129013 \tabularnewline
116 & 57 & 49.3657037964124 & 7.63429620358762 \tabularnewline
117 & 84 & 105.420888082292 & -21.4208880822922 \tabularnewline
118 & 4 & 9.340171980582 & -5.340171980582 \tabularnewline
119 & 56 & 62.4611672883221 & -6.46116728832205 \tabularnewline
120 & 18 & 26.8712925218039 & -8.87129252180387 \tabularnewline
121 & 86 & 72.0522953165646 & 13.9477046834354 \tabularnewline
122 & 39 & 50.68232493023 & -11.68232493023 \tabularnewline
123 & 16 & 23.1426942593522 & -7.1426942593522 \tabularnewline
124 & 18 & 41.7794392742773 & -23.7794392742773 \tabularnewline
125 & 16 & 18.6044517404574 & -2.60445174045743 \tabularnewline
126 & 42 & 42.3102728593475 & -0.310272859347486 \tabularnewline
127 & 75 & 83.9164255873434 & -8.91642558734341 \tabularnewline
128 & 30 & 80.1359359992504 & -50.1359359992504 \tabularnewline
129 & 104 & 91.1769167696111 & 12.8230832303889 \tabularnewline
130 & 121 & 92.9784951605944 & 28.0215048394056 \tabularnewline
131 & 106 & 116.421953914994 & -10.4219539149936 \tabularnewline
132 & 57 & 61.9761106118729 & -4.97611061187292 \tabularnewline
133 & 28 & 30.9761286678865 & -2.97612866788654 \tabularnewline
134 & 56 & 68.1861225915465 & -12.1861225915464 \tabularnewline
135 & 81 & 102.549531877488 & -21.5495318774884 \tabularnewline
136 & 2 & 11.9730796435434 & -9.97307964354338 \tabularnewline
137 & 88 & 93.4902333555705 & -5.49023335557049 \tabularnewline
138 & 41 & 43.3743634096219 & -2.37436340962195 \tabularnewline
139 & 83 & 74.1682379290122 & 8.83176207098781 \tabularnewline
140 & 55 & 65.4171429347818 & -10.4171429347818 \tabularnewline
141 & 3 & 33.5449028502279 & -30.5449028502279 \tabularnewline
142 & 54 & 78.1244260248854 & -24.1244260248854 \tabularnewline
143 & 89 & 98.6254432242787 & -9.6254432242787 \tabularnewline
144 & 41 & 38.2175111527457 & 2.78248884725428 \tabularnewline
145 & 94 & 74.4511543412187 & 19.5488456587813 \tabularnewline
146 & 101 & 35.7080130505576 & 65.2919869494424 \tabularnewline
147 & 70 & 67.2989316376621 & 2.70106836233791 \tabularnewline
148 & 111 & 102.124931740442 & 8.87506825955774 \tabularnewline
149 & 0 & -3.49738672273175 & 3.49738672273175 \tabularnewline
150 & 4 & 8.94052730172388 & -4.94052730172388 \tabularnewline
151 & 0 & 4.25817046654097 & -4.25817046654097 \tabularnewline
152 & 0 & 4.82248866340015 & -4.82248866340015 \tabularnewline
153 & 0 & 4.39884655799372 & -4.39884655799372 \tabularnewline
154 & 0 & 4.24272852728305 & -4.24272852728305 \tabularnewline
155 & 42 & 59.8071629305852 & -17.8071629305852 \tabularnewline
156 & 97 & 87.1552641545317 & 9.84473584546824 \tabularnewline
157 & 0 & 5.25885936355091 & -5.25885936355091 \tabularnewline
158 & 0 & 5.29084623772802 & -5.29084623772802 \tabularnewline
159 & 7 & 8.5820898524269 & -1.5820898524269 \tabularnewline
160 & 12 & 18.5041281258604 & -6.50412812586042 \tabularnewline
161 & 0 & 8.43968286218628 & -8.43968286218628 \tabularnewline
162 & 37 & 24.5135404045679 & 12.4864595954321 \tabularnewline
163 & 0 & 4.90348005909982 & -4.90348005909982 \tabularnewline
164 & 39 & 42.6106865028696 & -3.61068650286957 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145816&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]65[/C][C]85.4103874876833[/C][C]-20.4103874876833[/C][/ROW]
[ROW][C]2[/C][C]54[/C][C]55.5246968937292[/C][C]-1.52469689372916[/C][/ROW]
[ROW][C]3[/C][C]58[/C][C]46.7752803835927[/C][C]11.2247196164073[/C][/ROW]
[ROW][C]4[/C][C]75[/C][C]75.2762133036398[/C][C]-0.276213303639818[/C][/ROW]
[ROW][C]5[/C][C]41[/C][C]39.548817397164[/C][C]1.45118260283598[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]24.8116345845487[/C][C]-24.8116345845487[/C][/ROW]
[ROW][C]7[/C][C]111[/C][C]107.929420279036[/C][C]3.0705797209636[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]12.8314831496514[/C][C]-11.8314831496514[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]47.9381609687052[/C][C]-11.9381609687053[/C][/ROW]
[ROW][C]10[/C][C]60[/C][C]56.416418525053[/C][C]3.58358147494696[/C][/ROW]
[ROW][C]11[/C][C]63[/C][C]73.8543087241819[/C][C]-10.8543087241819[/C][/ROW]
[ROW][C]12[/C][C]71[/C][C]51.9125675609615[/C][C]19.0874324390385[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]41.7598918345221[/C][C]-3.75989183452212[/C][/ROW]
[ROW][C]14[/C][C]76[/C][C]84.1153046530076[/C][C]-8.11530465300763[/C][/ROW]
[ROW][C]15[/C][C]61[/C][C]54.137104111488[/C][C]6.86289588851204[/C][/ROW]
[ROW][C]16[/C][C]125[/C][C]99.4611308752092[/C][C]25.5388691247908[/C][/ROW]
[ROW][C]17[/C][C]84[/C][C]84.3938034936549[/C][C]-0.393803493654858[/C][/ROW]
[ROW][C]18[/C][C]69[/C][C]66.6064475326224[/C][C]2.39355246737765[/C][/ROW]
[ROW][C]19[/C][C]77[/C][C]74.902522598311[/C][C]2.09747740168901[/C][/ROW]
[ROW][C]20[/C][C]95[/C][C]77.1837429906871[/C][C]17.8162570093129[/C][/ROW]
[ROW][C]21[/C][C]78[/C][C]75.3703439003875[/C][C]2.62965609961252[/C][/ROW]
[ROW][C]22[/C][C]76[/C][C]92.1979493534336[/C][C]-16.1979493534336[/C][/ROW]
[ROW][C]23[/C][C]40[/C][C]37.0852216480419[/C][C]2.91477835195806[/C][/ROW]
[ROW][C]24[/C][C]81[/C][C]64.5952313167284[/C][C]16.4047686832716[/C][/ROW]
[ROW][C]25[/C][C]102[/C][C]74.19334136979[/C][C]27.8066586302099[/C][/ROW]
[ROW][C]26[/C][C]70[/C][C]62.6397686079615[/C][C]7.36023139203847[/C][/ROW]
[ROW][C]27[/C][C]75[/C][C]67.6455592350072[/C][C]7.35444076499278[/C][/ROW]
[ROW][C]28[/C][C]93[/C][C]73.9741910562994[/C][C]19.0258089437006[/C][/ROW]
[ROW][C]29[/C][C]42[/C][C]42.4422281612541[/C][C]-0.442228161254078[/C][/ROW]
[ROW][C]30[/C][C]95[/C][C]76.3926881710715[/C][C]18.6073118289285[/C][/ROW]
[ROW][C]31[/C][C]87[/C][C]74.2909917901866[/C][C]12.7090082098134[/C][/ROW]
[ROW][C]32[/C][C]44[/C][C]62.9357477895857[/C][C]-18.9357477895857[/C][/ROW]
[ROW][C]33[/C][C]84[/C][C]77.5935009470652[/C][C]6.40649905293477[/C][/ROW]
[ROW][C]34[/C][C]28[/C][C]18.2616152631319[/C][C]9.7383847368681[/C][/ROW]
[ROW][C]35[/C][C]87[/C][C]106.905808615098[/C][C]-19.9058086150985[/C][/ROW]
[ROW][C]36[/C][C]71[/C][C]57.3866898335709[/C][C]13.6133101664291[/C][/ROW]
[ROW][C]37[/C][C]68[/C][C]79.4322687857978[/C][C]-11.4322687857978[/C][/ROW]
[ROW][C]38[/C][C]50[/C][C]53.8286800740554[/C][C]-3.82868007405544[/C][/ROW]
[ROW][C]39[/C][C]30[/C][C]32.5968711076261[/C][C]-2.59687110762606[/C][/ROW]
[ROW][C]40[/C][C]86[/C][C]77.9250742186098[/C][C]8.07492578139022[/C][/ROW]
[ROW][C]41[/C][C]75[/C][C]67.0810300857883[/C][C]7.91896991421167[/C][/ROW]
[ROW][C]42[/C][C]46[/C][C]46.5928670883215[/C][C]-0.592867088321521[/C][/ROW]
[ROW][C]43[/C][C]52[/C][C]49.3875442594231[/C][C]2.61245574057693[/C][/ROW]
[ROW][C]44[/C][C]31[/C][C]33.1365549948239[/C][C]-2.13655499482393[/C][/ROW]
[ROW][C]45[/C][C]30[/C][C]36.4105781908884[/C][C]-6.41057819088842[/C][/ROW]
[ROW][C]46[/C][C]70[/C][C]77.0744755212184[/C][C]-7.07447552121844[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]25.1312789928626[/C][C]-5.13127899286264[/C][/ROW]
[ROW][C]48[/C][C]84[/C][C]77.3213145620335[/C][C]6.67868543796653[/C][/ROW]
[ROW][C]49[/C][C]81[/C][C]91.4393146361957[/C][C]-10.4393146361957[/C][/ROW]
[ROW][C]50[/C][C]79[/C][C]69.3726212446489[/C][C]9.62737875535114[/C][/ROW]
[ROW][C]51[/C][C]70[/C][C]62.8214658674955[/C][C]7.17853413250454[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]12.3173051096189[/C][C]-4.31730510961893[/C][/ROW]
[ROW][C]53[/C][C]67[/C][C]98.8260950248037[/C][C]-31.8260950248037[/C][/ROW]
[ROW][C]54[/C][C]21[/C][C]20.961050948958[/C][C]0.0389490510419622[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]33.9953896353843[/C][C]-3.99538963538426[/C][/ROW]
[ROW][C]56[/C][C]70[/C][C]76.7126461239705[/C][C]-6.71264612397051[/C][/ROW]
[ROW][C]57[/C][C]87[/C][C]76.7070715236501[/C][C]10.2929284763499[/C][/ROW]
[ROW][C]58[/C][C]87[/C][C]126.950083186171[/C][C]-39.950083186171[/C][/ROW]
[ROW][C]59[/C][C]112[/C][C]104.466015442796[/C][C]7.5339845572038[/C][/ROW]
[ROW][C]60[/C][C]54[/C][C]44.4976915620747[/C][C]9.50230843792534[/C][/ROW]
[ROW][C]61[/C][C]96[/C][C]90.7194765237084[/C][C]5.28052347629156[/C][/ROW]
[ROW][C]62[/C][C]93[/C][C]100.384112173663[/C][C]-7.38411217366253[/C][/ROW]
[ROW][C]63[/C][C]49[/C][C]44.0772928732102[/C][C]4.92270712678981[/C][/ROW]
[ROW][C]64[/C][C]49[/C][C]64.521617184816[/C][C]-15.521617184816[/C][/ROW]
[ROW][C]65[/C][C]38[/C][C]64.4682457992376[/C][C]-26.4682457992376[/C][/ROW]
[ROW][C]66[/C][C]64[/C][C]58.8659373550494[/C][C]5.13406264495055[/C][/ROW]
[ROW][C]67[/C][C]62[/C][C]61.075127628265[/C][C]0.92487237173504[/C][/ROW]
[ROW][C]68[/C][C]66[/C][C]59.1033131937878[/C][C]6.89668680621215[/C][/ROW]
[ROW][C]69[/C][C]98[/C][C]89.9799936332141[/C][C]8.02000636678594[/C][/ROW]
[ROW][C]70[/C][C]97[/C][C]49.0755566995954[/C][C]47.9244433004046[/C][/ROW]
[ROW][C]71[/C][C]56[/C][C]46.6027943672758[/C][C]9.39720563272421[/C][/ROW]
[ROW][C]72[/C][C]22[/C][C]22.7020710335646[/C][C]-0.702071033564606[/C][/ROW]
[ROW][C]73[/C][C]51[/C][C]51.1260966965349[/C][C]-0.126096696534878[/C][/ROW]
[ROW][C]74[/C][C]56[/C][C]65.8696793525297[/C][C]-9.86967935252969[/C][/ROW]
[ROW][C]75[/C][C]94[/C][C]65.6107422270588[/C][C]28.3892577729412[/C][/ROW]
[ROW][C]76[/C][C]98[/C][C]77.3818739646898[/C][C]20.6181260353102[/C][/ROW]
[ROW][C]77[/C][C]76[/C][C]82.7925504119011[/C][C]-6.79255041190112[/C][/ROW]
[ROW][C]78[/C][C]57[/C][C]45.0526135141361[/C][C]11.9473864858639[/C][/ROW]
[ROW][C]79[/C][C]75[/C][C]55.5591598665905[/C][C]19.4408401334095[/C][/ROW]
[ROW][C]80[/C][C]48[/C][C]51.3532342020951[/C][C]-3.3532342020951[/C][/ROW]
[ROW][C]81[/C][C]48[/C][C]40.2963406878064[/C][C]7.70365931219361[/C][/ROW]
[ROW][C]82[/C][C]109[/C][C]93.477037056183[/C][C]15.522962943817[/C][/ROW]
[ROW][C]83[/C][C]27[/C][C]37.7017415479813[/C][C]-10.7017415479813[/C][/ROW]
[ROW][C]84[/C][C]83[/C][C]77.6015454787103[/C][C]5.39845452128966[/C][/ROW]
[ROW][C]85[/C][C]49[/C][C]44.76693275752[/C][C]4.23306724248[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]32.3384522752694[/C][C]-8.3384522752694[/C][/ROW]
[ROW][C]87[/C][C]43[/C][C]53.1214988505876[/C][C]-10.1214988505876[/C][/ROW]
[ROW][C]88[/C][C]44[/C][C]64.6936074298099[/C][C]-20.6936074298099[/C][/ROW]
[ROW][C]89[/C][C]49[/C][C]52.5067640182194[/C][C]-3.50676401821937[/C][/ROW]
[ROW][C]90[/C][C]106[/C][C]77.9797158689242[/C][C]28.0202841310758[/C][/ROW]
[ROW][C]91[/C][C]42[/C][C]48.7165664313409[/C][C]-6.71656643134088[/C][/ROW]
[ROW][C]92[/C][C]108[/C][C]95.4196777075723[/C][C]12.5803222924277[/C][/ROW]
[ROW][C]93[/C][C]27[/C][C]41.5296878698915[/C][C]-14.5296878698915[/C][/ROW]
[ROW][C]94[/C][C]79[/C][C]67.0687233085115[/C][C]11.9312766914885[/C][/ROW]
[ROW][C]95[/C][C]49[/C][C]72.6401903631944[/C][C]-23.6401903631944[/C][/ROW]
[ROW][C]96[/C][C]64[/C][C]62.3510526765379[/C][C]1.64894732346212[/C][/ROW]
[ROW][C]97[/C][C]75[/C][C]93.3930601559924[/C][C]-18.3930601559924[/C][/ROW]
[ROW][C]98[/C][C]115[/C][C]102.621235504418[/C][C]12.3787644955819[/C][/ROW]
[ROW][C]99[/C][C]92[/C][C]74.872992493679[/C][C]17.1270075063209[/C][/ROW]
[ROW][C]100[/C][C]106[/C][C]129.402316735264[/C][C]-23.4023167352644[/C][/ROW]
[ROW][C]101[/C][C]73[/C][C]51.1826058893685[/C][C]21.8173941106315[/C][/ROW]
[ROW][C]102[/C][C]105[/C][C]98.340403024258[/C][C]6.65959697574196[/C][/ROW]
[ROW][C]103[/C][C]30[/C][C]34.1909106551088[/C][C]-4.19091065510877[/C][/ROW]
[ROW][C]104[/C][C]13[/C][C]31.1620989939536[/C][C]-18.1620989939536[/C][/ROW]
[ROW][C]105[/C][C]69[/C][C]51.5744938491182[/C][C]17.4255061508818[/C][/ROW]
[ROW][C]106[/C][C]72[/C][C]88.1466272181301[/C][C]-16.1466272181301[/C][/ROW]
[ROW][C]107[/C][C]80[/C][C]64.3755841663511[/C][C]15.6244158336489[/C][/ROW]
[ROW][C]108[/C][C]106[/C][C]81.0068950496594[/C][C]24.9931049503406[/C][/ROW]
[ROW][C]109[/C][C]28[/C][C]44.7806283270581[/C][C]-16.7806283270581[/C][/ROW]
[ROW][C]110[/C][C]70[/C][C]44.4424115439998[/C][C]25.5575884560002[/C][/ROW]
[ROW][C]111[/C][C]51[/C][C]66.2113892832746[/C][C]-15.2113892832746[/C][/ROW]
[ROW][C]112[/C][C]90[/C][C]73.3298335651544[/C][C]16.6701664348456[/C][/ROW]
[ROW][C]113[/C][C]12[/C][C]13.7641989015747[/C][C]-1.76419890157469[/C][/ROW]
[ROW][C]114[/C][C]84[/C][C]63.3733229776905[/C][C]20.6266770223095[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]25.5146562312901[/C][C]-2.51465623129013[/C][/ROW]
[ROW][C]116[/C][C]57[/C][C]49.3657037964124[/C][C]7.63429620358762[/C][/ROW]
[ROW][C]117[/C][C]84[/C][C]105.420888082292[/C][C]-21.4208880822922[/C][/ROW]
[ROW][C]118[/C][C]4[/C][C]9.340171980582[/C][C]-5.340171980582[/C][/ROW]
[ROW][C]119[/C][C]56[/C][C]62.4611672883221[/C][C]-6.46116728832205[/C][/ROW]
[ROW][C]120[/C][C]18[/C][C]26.8712925218039[/C][C]-8.87129252180387[/C][/ROW]
[ROW][C]121[/C][C]86[/C][C]72.0522953165646[/C][C]13.9477046834354[/C][/ROW]
[ROW][C]122[/C][C]39[/C][C]50.68232493023[/C][C]-11.68232493023[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]23.1426942593522[/C][C]-7.1426942593522[/C][/ROW]
[ROW][C]124[/C][C]18[/C][C]41.7794392742773[/C][C]-23.7794392742773[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]18.6044517404574[/C][C]-2.60445174045743[/C][/ROW]
[ROW][C]126[/C][C]42[/C][C]42.3102728593475[/C][C]-0.310272859347486[/C][/ROW]
[ROW][C]127[/C][C]75[/C][C]83.9164255873434[/C][C]-8.91642558734341[/C][/ROW]
[ROW][C]128[/C][C]30[/C][C]80.1359359992504[/C][C]-50.1359359992504[/C][/ROW]
[ROW][C]129[/C][C]104[/C][C]91.1769167696111[/C][C]12.8230832303889[/C][/ROW]
[ROW][C]130[/C][C]121[/C][C]92.9784951605944[/C][C]28.0215048394056[/C][/ROW]
[ROW][C]131[/C][C]106[/C][C]116.421953914994[/C][C]-10.4219539149936[/C][/ROW]
[ROW][C]132[/C][C]57[/C][C]61.9761106118729[/C][C]-4.97611061187292[/C][/ROW]
[ROW][C]133[/C][C]28[/C][C]30.9761286678865[/C][C]-2.97612866788654[/C][/ROW]
[ROW][C]134[/C][C]56[/C][C]68.1861225915465[/C][C]-12.1861225915464[/C][/ROW]
[ROW][C]135[/C][C]81[/C][C]102.549531877488[/C][C]-21.5495318774884[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]11.9730796435434[/C][C]-9.97307964354338[/C][/ROW]
[ROW][C]137[/C][C]88[/C][C]93.4902333555705[/C][C]-5.49023335557049[/C][/ROW]
[ROW][C]138[/C][C]41[/C][C]43.3743634096219[/C][C]-2.37436340962195[/C][/ROW]
[ROW][C]139[/C][C]83[/C][C]74.1682379290122[/C][C]8.83176207098781[/C][/ROW]
[ROW][C]140[/C][C]55[/C][C]65.4171429347818[/C][C]-10.4171429347818[/C][/ROW]
[ROW][C]141[/C][C]3[/C][C]33.5449028502279[/C][C]-30.5449028502279[/C][/ROW]
[ROW][C]142[/C][C]54[/C][C]78.1244260248854[/C][C]-24.1244260248854[/C][/ROW]
[ROW][C]143[/C][C]89[/C][C]98.6254432242787[/C][C]-9.6254432242787[/C][/ROW]
[ROW][C]144[/C][C]41[/C][C]38.2175111527457[/C][C]2.78248884725428[/C][/ROW]
[ROW][C]145[/C][C]94[/C][C]74.4511543412187[/C][C]19.5488456587813[/C][/ROW]
[ROW][C]146[/C][C]101[/C][C]35.7080130505576[/C][C]65.2919869494424[/C][/ROW]
[ROW][C]147[/C][C]70[/C][C]67.2989316376621[/C][C]2.70106836233791[/C][/ROW]
[ROW][C]148[/C][C]111[/C][C]102.124931740442[/C][C]8.87506825955774[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-3.49738672273175[/C][C]3.49738672273175[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]8.94052730172388[/C][C]-4.94052730172388[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]4.25817046654097[/C][C]-4.25817046654097[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]4.82248866340015[/C][C]-4.82248866340015[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]4.39884655799372[/C][C]-4.39884655799372[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]4.24272852728305[/C][C]-4.24272852728305[/C][/ROW]
[ROW][C]155[/C][C]42[/C][C]59.8071629305852[/C][C]-17.8071629305852[/C][/ROW]
[ROW][C]156[/C][C]97[/C][C]87.1552641545317[/C][C]9.84473584546824[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]5.25885936355091[/C][C]-5.25885936355091[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]5.29084623772802[/C][C]-5.29084623772802[/C][/ROW]
[ROW][C]159[/C][C]7[/C][C]8.5820898524269[/C][C]-1.5820898524269[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]18.5041281258604[/C][C]-6.50412812586042[/C][/ROW]
[ROW][C]161[/C][C]0[/C][C]8.43968286218628[/C][C]-8.43968286218628[/C][/ROW]
[ROW][C]162[/C][C]37[/C][C]24.5135404045679[/C][C]12.4864595954321[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]4.90348005909982[/C][C]-4.90348005909982[/C][/ROW]
[ROW][C]164[/C][C]39[/C][C]42.6106865028696[/C][C]-3.61068650286957[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145816&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145816&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
16585.4103874876833-20.4103874876833
25455.5246968937292-1.52469689372916
35846.775280383592711.2247196164073
47575.2762133036398-0.276213303639818
54139.5488173971641.45118260283598
6024.8116345845487-24.8116345845487
7111107.9294202790363.0705797209636
8112.8314831496514-11.8314831496514
93647.9381609687052-11.9381609687053
106056.4164185250533.58358147494696
116373.8543087241819-10.8543087241819
127151.912567560961519.0874324390385
133841.7598918345221-3.75989183452212
147684.1153046530076-8.11530465300763
156154.1371041114886.86289588851204
1612599.461130875209225.5388691247908
178484.3938034936549-0.393803493654858
186966.60644753262242.39355246737765
197774.9025225983112.09747740168901
209577.183742990687117.8162570093129
217875.37034390038752.62965609961252
227692.1979493534336-16.1979493534336
234037.08522164804192.91477835195806
248164.595231316728416.4047686832716
2510274.1933413697927.8066586302099
267062.63976860796157.36023139203847
277567.64555923500727.35444076499278
289373.974191056299419.0258089437006
294242.4422281612541-0.442228161254078
309576.392688171071518.6073118289285
318774.290991790186612.7090082098134
324462.9357477895857-18.9357477895857
338477.59350094706526.40649905293477
342818.26161526313199.7383847368681
3587106.905808615098-19.9058086150985
367157.386689833570913.6133101664291
376879.4322687857978-11.4322687857978
385053.8286800740554-3.82868007405544
393032.5968711076261-2.59687110762606
408677.92507421860988.07492578139022
417567.08103008578837.91896991421167
424646.5928670883215-0.592867088321521
435249.38754425942312.61245574057693
443133.1365549948239-2.13655499482393
453036.4105781908884-6.41057819088842
467077.0744755212184-7.07447552121844
472025.1312789928626-5.13127899286264
488477.32131456203356.67868543796653
498191.4393146361957-10.4393146361957
507969.37262124464899.62737875535114
517062.82146586749557.17853413250454
52812.3173051096189-4.31730510961893
536798.8260950248037-31.8260950248037
542120.9610509489580.0389490510419622
553033.9953896353843-3.99538963538426
567076.7126461239705-6.71264612397051
578776.707071523650110.2929284763499
5887126.950083186171-39.950083186171
59112104.4660154427967.5339845572038
605444.49769156207479.50230843792534
619690.71947652370845.28052347629156
6293100.384112173663-7.38411217366253
634944.07729287321024.92270712678981
644964.521617184816-15.521617184816
653864.4682457992376-26.4682457992376
666458.86593735504945.13406264495055
676261.0751276282650.92487237173504
686659.10331319378786.89668680621215
699889.97999363321418.02000636678594
709749.075556699595447.9244433004046
715646.60279436727589.39720563272421
722222.7020710335646-0.702071033564606
735151.1260966965349-0.126096696534878
745665.8696793525297-9.86967935252969
759465.610742227058828.3892577729412
769877.381873964689820.6181260353102
777682.7925504119011-6.79255041190112
785745.052613514136111.9473864858639
797555.559159866590519.4408401334095
804851.3532342020951-3.3532342020951
814840.29634068780647.70365931219361
8210993.47703705618315.522962943817
832737.7017415479813-10.7017415479813
848377.60154547871035.39845452128966
854944.766932757524.23306724248
862432.3384522752694-8.3384522752694
874353.1214988505876-10.1214988505876
884464.6936074298099-20.6936074298099
894952.5067640182194-3.50676401821937
9010677.979715868924228.0202841310758
914248.7165664313409-6.71656643134088
9210895.419677707572312.5803222924277
932741.5296878698915-14.5296878698915
947967.068723308511511.9312766914885
954972.6401903631944-23.6401903631944
966462.35105267653791.64894732346212
977593.3930601559924-18.3930601559924
98115102.62123550441812.3787644955819
999274.87299249367917.1270075063209
100106129.402316735264-23.4023167352644
1017351.182605889368521.8173941106315
10210598.3404030242586.65959697574196
1033034.1909106551088-4.19091065510877
1041331.1620989939536-18.1620989939536
1056951.574493849118217.4255061508818
1067288.1466272181301-16.1466272181301
1078064.375584166351115.6244158336489
10810681.006895049659424.9931049503406
1092844.7806283270581-16.7806283270581
1107044.442411543999825.5575884560002
1115166.2113892832746-15.2113892832746
1129073.329833565154416.6701664348456
1131213.7641989015747-1.76419890157469
1148463.373322977690520.6266770223095
1152325.5146562312901-2.51465623129013
1165749.36570379641247.63429620358762
11784105.420888082292-21.4208880822922
11849.340171980582-5.340171980582
1195662.4611672883221-6.46116728832205
1201826.8712925218039-8.87129252180387
1218672.052295316564613.9477046834354
1223950.68232493023-11.68232493023
1231623.1426942593522-7.1426942593522
1241841.7794392742773-23.7794392742773
1251618.6044517404574-2.60445174045743
1264242.3102728593475-0.310272859347486
1277583.9164255873434-8.91642558734341
1283080.1359359992504-50.1359359992504
12910491.176916769611112.8230832303889
13012192.978495160594428.0215048394056
131106116.421953914994-10.4219539149936
1325761.9761106118729-4.97611061187292
1332830.9761286678865-2.97612866788654
1345668.1861225915465-12.1861225915464
13581102.549531877488-21.5495318774884
136211.9730796435434-9.97307964354338
1378893.4902333555705-5.49023335557049
1384143.3743634096219-2.37436340962195
1398374.16823792901228.83176207098781
1405565.4171429347818-10.4171429347818
141333.5449028502279-30.5449028502279
1425478.1244260248854-24.1244260248854
1438998.6254432242787-9.6254432242787
1444138.21751115274572.78248884725428
1459474.451154341218719.5488456587813
14610135.708013050557665.2919869494424
1477067.29893163766212.70106836233791
148111102.1249317404428.87506825955774
1490-3.497386722731753.49738672273175
15048.94052730172388-4.94052730172388
15104.25817046654097-4.25817046654097
15204.82248866340015-4.82248866340015
15304.39884655799372-4.39884655799372
15404.24272852728305-4.24272852728305
1554259.8071629305852-17.8071629305852
1569787.15526415453179.84473584546824
15705.25885936355091-5.25885936355091
15805.29084623772802-5.29084623772802
15978.5820898524269-1.5820898524269
1601218.5041281258604-6.50412812586042
16108.43968286218628-8.43968286218628
1623724.513540404567912.4864595954321
16304.90348005909982-4.90348005909982
1643942.6106865028696-3.61068650286957







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.001632569153483230.003265138306966450.998367430846517
110.01813054638779750.0362610927755950.981869453612202
120.00593297243760470.01186594487520940.994067027562395
130.01501616449785770.03003232899571530.984983835502142
140.1457772138783890.2915544277567780.854222786121611
150.08662561428940120.1732512285788020.913374385710599
160.173923485828510.347846971657020.82607651417149
170.1293000592010180.2586001184020360.870699940798982
180.08341348155763160.1668269631152630.916586518442368
190.100094355975850.2001887119516990.899905644024151
200.08063536219545680.1612707243909140.919364637804543
210.06639134630708120.1327826926141620.933608653692919
220.1728578609327820.3457157218655640.827142139067218
230.1301733071664170.2603466143328350.869826692833582
240.1090794606792220.2181589213584440.890920539320778
250.1894131460159390.3788262920318780.810586853984061
260.1636412908598810.3272825817197620.836358709140119
270.1262426221146230.2524852442292470.873757377885377
280.1379790591165280.2759581182330570.862020940883472
290.1029401314648830.2058802629297660.897059868535117
300.1059912080725820.2119824161451640.894008791927418
310.12012825628090.2402565125617990.8798717437191
320.1586615531359190.3173231062718380.841338446864081
330.1244116686568940.2488233373137880.875588331343106
340.1544415718412350.3088831436824710.845558428158765
350.2611551033979130.5223102067958260.738844896602087
360.2387255033775610.4774510067551220.761274496622439
370.2287570054124380.4575140108248760.771242994587562
380.1931826150983790.3863652301967580.806817384901621
390.1563466326884620.3126932653769250.843653367311538
400.1284971758538620.2569943517077230.871502824146138
410.1027164272063070.2054328544126140.897283572793693
420.08233848721253070.1646769744250610.917661512787469
430.06374897323395310.1274979464679060.936251026766047
440.0487451605682990.09749032113659790.951254839431701
450.03684840047098120.07369680094196250.963151599529019
460.02828460635450310.05656921270900630.971715393645497
470.02066285129480010.04132570258960020.9793371487052
480.01534847879908720.03069695759817440.984651521200913
490.01266371105225990.02532742210451980.98733628894774
500.009476393366482410.01895278673296480.990523606633518
510.007078384997492990.0141567699949860.992921615002507
520.004993078790699440.009986157581398870.995006921209301
530.02815867516038780.05631735032077560.971841324839612
540.02073082989043390.04146165978086780.979269170109566
550.01550729116909310.03101458233818620.984492708830907
560.01193156259250030.02386312518500060.9880684374075
570.01087333718017620.02174667436035240.989126662819824
580.06727075985684930.1345415197136990.932729240143151
590.05679465475348480.113589309506970.943205345246515
600.04846139461644680.09692278923289350.951538605383553
610.03858447093187240.07716894186374470.961415529068128
620.03064603780630410.06129207561260830.969353962193696
630.023393017772370.04678603554473990.97660698222763
640.02313382447543310.04626764895086610.976866175524567
650.03838896039969320.07677792079938630.961611039600307
660.03002812576710850.06005625153421690.969971874232892
670.02350782579710730.04701565159421450.976492174202893
680.01867654434869540.03735308869739080.981323455651305
690.0155601000228360.03112020004567190.984439899977164
700.1307299141056190.2614598282112390.86927008589438
710.1148046950450850.2296093900901690.885195304954915
720.09551635200678430.1910327040135690.904483647993216
730.07709351718884010.154187034377680.92290648281116
740.06737132446333250.1347426489266650.932628675536668
750.1079719635253080.2159439270506160.892028036474692
760.1243562640650660.2487125281301310.875643735934934
770.1054874486411290.2109748972822580.894512551358871
780.102273523504060.204547047008120.89772647649594
790.1097838634734670.2195677269469340.890216136526533
800.09259516091946120.1851903218389220.907404839080539
810.0778923484578670.1557846969157340.922107651542133
820.07554467818090090.1510893563618020.924455321819099
830.06876608178352450.1375321635670490.931233918216475
840.05637444683280970.1127488936656190.94362555316719
850.04534658737914850.0906931747582970.954653412620852
860.04009651915185760.08019303830371510.959903480848142
870.03516934236850720.07033868473701430.964830657631493
880.04356329680187390.08712659360374790.956436703198126
890.03448810651412170.06897621302824330.965511893485878
900.06079749848275340.1215949969655070.939202501517247
910.05102228690894940.1020445738178990.948977713091051
920.05052271755402830.1010454351080570.949477282445972
930.04942268147469750.0988453629493950.950577318525302
940.04456368939300260.08912737878600520.955436310606997
950.06053564858407510.121071297168150.939464351415925
960.04793063290719440.09586126581438880.952069367092806
970.05032915745385550.1006583149077110.949670842546144
980.04651795937883220.09303591875766430.953482040621168
990.04935051900069140.09870103800138280.950649480999309
1000.05776688909050740.1155337781810150.942233110909493
1010.07278882519578910.1455776503915780.927211174804211
1020.06139213216508490.122784264330170.938607867834915
1030.04990895766464110.09981791532928210.950091042335359
1040.05279746195135760.1055949239027150.947202538048642
1050.05752524609785260.1150504921957050.942474753902147
1060.0561117471940090.1122234943880180.943888252805991
1070.05612287949335370.1122457589867070.943877120506646
1080.08155780796554660.1631156159310930.918442192034453
1090.08121689038271260.1624337807654250.918783109617287
1100.1313071024653710.2626142049307420.868692897534629
1110.1415905714684130.2831811429368250.858409428531587
1120.1494602928061550.298920585612310.850539707193845
1130.1231870962263470.2463741924526950.876812903773653
1140.1459068326546540.2918136653093070.854093167345346
1150.1199028435245860.2398056870491720.880097156475414
1160.120310988120740.2406219762414790.87968901187926
1170.1421752001564240.2843504003128480.857824799843576
1180.1192763953211740.2385527906423480.880723604678826
1190.1053442419071920.2106884838143840.894655758092808
1200.0902173397289270.1804346794578540.909782660271073
1210.08248766036841790.1649753207368360.917512339631582
1220.07166113762044660.1433222752408930.928338862379553
1230.05814037908516990.116280758170340.94185962091483
1240.08408200725141780.1681640145028360.915917992748582
1250.06538971866821160.1307794373364230.934610281331788
1260.05109257675797030.1021851535159410.94890742324203
1270.04133881605709690.08267763211419380.958661183942903
1280.2920047244754380.5840094489508760.707995275524562
1290.2774708484083560.5549416968167120.722529151591644
1300.3929170568539360.7858341137078720.607082943146064
1310.4092068958271770.8184137916543550.590793104172822
1320.3626890010560050.7253780021120110.637310998943995
1330.310334027841740.620668055683480.68966597215826
1340.2728572578850440.5457145157700890.727142742114956
1350.3219154223322510.6438308446645020.678084577667749
1360.5021915198608240.9956169602783520.497808480139176
1370.4368838770946060.8737677541892120.563116122905394
1380.3712680022913140.7425360045826290.628731997708686
1390.390313793201910.7806275864038190.60968620679809
1400.3411481323980980.6822962647961960.658851867601902
1410.4869252811046450.9738505622092910.513074718895355
1420.6262624942102470.7474750115795070.373737505789753
1430.6313661459996840.7372677080006310.368633854000316
1440.5972255616751320.8055488766497360.402774438324868
1450.5279075718159290.9441848563681420.472092428184071
1460.9940402489466380.01191950210672370.00595975105336183
1470.9891016348158230.02179673036835480.0108983651841774
1480.9934122077653740.01317558446925130.00658779223462563
1490.9959164506513420.008167098697316590.00408354934865829
1500.9893601130246860.02127977395062850.0106398869753142
1510.9746271495470470.05074570090590560.0253728504529528
1520.9404136855084140.1191726289831720.0595863144915859
1530.9992021417885980.001595716422803690.000797858211401843
1540.9973727676808580.005254464638284350.00262723231914217

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.00163256915348323 & 0.00326513830696645 & 0.998367430846517 \tabularnewline
11 & 0.0181305463877975 & 0.036261092775595 & 0.981869453612202 \tabularnewline
12 & 0.0059329724376047 & 0.0118659448752094 & 0.994067027562395 \tabularnewline
13 & 0.0150161644978577 & 0.0300323289957153 & 0.984983835502142 \tabularnewline
14 & 0.145777213878389 & 0.291554427756778 & 0.854222786121611 \tabularnewline
15 & 0.0866256142894012 & 0.173251228578802 & 0.913374385710599 \tabularnewline
16 & 0.17392348582851 & 0.34784697165702 & 0.82607651417149 \tabularnewline
17 & 0.129300059201018 & 0.258600118402036 & 0.870699940798982 \tabularnewline
18 & 0.0834134815576316 & 0.166826963115263 & 0.916586518442368 \tabularnewline
19 & 0.10009435597585 & 0.200188711951699 & 0.899905644024151 \tabularnewline
20 & 0.0806353621954568 & 0.161270724390914 & 0.919364637804543 \tabularnewline
21 & 0.0663913463070812 & 0.132782692614162 & 0.933608653692919 \tabularnewline
22 & 0.172857860932782 & 0.345715721865564 & 0.827142139067218 \tabularnewline
23 & 0.130173307166417 & 0.260346614332835 & 0.869826692833582 \tabularnewline
24 & 0.109079460679222 & 0.218158921358444 & 0.890920539320778 \tabularnewline
25 & 0.189413146015939 & 0.378826292031878 & 0.810586853984061 \tabularnewline
26 & 0.163641290859881 & 0.327282581719762 & 0.836358709140119 \tabularnewline
27 & 0.126242622114623 & 0.252485244229247 & 0.873757377885377 \tabularnewline
28 & 0.137979059116528 & 0.275958118233057 & 0.862020940883472 \tabularnewline
29 & 0.102940131464883 & 0.205880262929766 & 0.897059868535117 \tabularnewline
30 & 0.105991208072582 & 0.211982416145164 & 0.894008791927418 \tabularnewline
31 & 0.1201282562809 & 0.240256512561799 & 0.8798717437191 \tabularnewline
32 & 0.158661553135919 & 0.317323106271838 & 0.841338446864081 \tabularnewline
33 & 0.124411668656894 & 0.248823337313788 & 0.875588331343106 \tabularnewline
34 & 0.154441571841235 & 0.308883143682471 & 0.845558428158765 \tabularnewline
35 & 0.261155103397913 & 0.522310206795826 & 0.738844896602087 \tabularnewline
36 & 0.238725503377561 & 0.477451006755122 & 0.761274496622439 \tabularnewline
37 & 0.228757005412438 & 0.457514010824876 & 0.771242994587562 \tabularnewline
38 & 0.193182615098379 & 0.386365230196758 & 0.806817384901621 \tabularnewline
39 & 0.156346632688462 & 0.312693265376925 & 0.843653367311538 \tabularnewline
40 & 0.128497175853862 & 0.256994351707723 & 0.871502824146138 \tabularnewline
41 & 0.102716427206307 & 0.205432854412614 & 0.897283572793693 \tabularnewline
42 & 0.0823384872125307 & 0.164676974425061 & 0.917661512787469 \tabularnewline
43 & 0.0637489732339531 & 0.127497946467906 & 0.936251026766047 \tabularnewline
44 & 0.048745160568299 & 0.0974903211365979 & 0.951254839431701 \tabularnewline
45 & 0.0368484004709812 & 0.0736968009419625 & 0.963151599529019 \tabularnewline
46 & 0.0282846063545031 & 0.0565692127090063 & 0.971715393645497 \tabularnewline
47 & 0.0206628512948001 & 0.0413257025896002 & 0.9793371487052 \tabularnewline
48 & 0.0153484787990872 & 0.0306969575981744 & 0.984651521200913 \tabularnewline
49 & 0.0126637110522599 & 0.0253274221045198 & 0.98733628894774 \tabularnewline
50 & 0.00947639336648241 & 0.0189527867329648 & 0.990523606633518 \tabularnewline
51 & 0.00707838499749299 & 0.014156769994986 & 0.992921615002507 \tabularnewline
52 & 0.00499307879069944 & 0.00998615758139887 & 0.995006921209301 \tabularnewline
53 & 0.0281586751603878 & 0.0563173503207756 & 0.971841324839612 \tabularnewline
54 & 0.0207308298904339 & 0.0414616597808678 & 0.979269170109566 \tabularnewline
55 & 0.0155072911690931 & 0.0310145823381862 & 0.984492708830907 \tabularnewline
56 & 0.0119315625925003 & 0.0238631251850006 & 0.9880684374075 \tabularnewline
57 & 0.0108733371801762 & 0.0217466743603524 & 0.989126662819824 \tabularnewline
58 & 0.0672707598568493 & 0.134541519713699 & 0.932729240143151 \tabularnewline
59 & 0.0567946547534848 & 0.11358930950697 & 0.943205345246515 \tabularnewline
60 & 0.0484613946164468 & 0.0969227892328935 & 0.951538605383553 \tabularnewline
61 & 0.0385844709318724 & 0.0771689418637447 & 0.961415529068128 \tabularnewline
62 & 0.0306460378063041 & 0.0612920756126083 & 0.969353962193696 \tabularnewline
63 & 0.02339301777237 & 0.0467860355447399 & 0.97660698222763 \tabularnewline
64 & 0.0231338244754331 & 0.0462676489508661 & 0.976866175524567 \tabularnewline
65 & 0.0383889603996932 & 0.0767779207993863 & 0.961611039600307 \tabularnewline
66 & 0.0300281257671085 & 0.0600562515342169 & 0.969971874232892 \tabularnewline
67 & 0.0235078257971073 & 0.0470156515942145 & 0.976492174202893 \tabularnewline
68 & 0.0186765443486954 & 0.0373530886973908 & 0.981323455651305 \tabularnewline
69 & 0.015560100022836 & 0.0311202000456719 & 0.984439899977164 \tabularnewline
70 & 0.130729914105619 & 0.261459828211239 & 0.86927008589438 \tabularnewline
71 & 0.114804695045085 & 0.229609390090169 & 0.885195304954915 \tabularnewline
72 & 0.0955163520067843 & 0.191032704013569 & 0.904483647993216 \tabularnewline
73 & 0.0770935171888401 & 0.15418703437768 & 0.92290648281116 \tabularnewline
74 & 0.0673713244633325 & 0.134742648926665 & 0.932628675536668 \tabularnewline
75 & 0.107971963525308 & 0.215943927050616 & 0.892028036474692 \tabularnewline
76 & 0.124356264065066 & 0.248712528130131 & 0.875643735934934 \tabularnewline
77 & 0.105487448641129 & 0.210974897282258 & 0.894512551358871 \tabularnewline
78 & 0.10227352350406 & 0.20454704700812 & 0.89772647649594 \tabularnewline
79 & 0.109783863473467 & 0.219567726946934 & 0.890216136526533 \tabularnewline
80 & 0.0925951609194612 & 0.185190321838922 & 0.907404839080539 \tabularnewline
81 & 0.077892348457867 & 0.155784696915734 & 0.922107651542133 \tabularnewline
82 & 0.0755446781809009 & 0.151089356361802 & 0.924455321819099 \tabularnewline
83 & 0.0687660817835245 & 0.137532163567049 & 0.931233918216475 \tabularnewline
84 & 0.0563744468328097 & 0.112748893665619 & 0.94362555316719 \tabularnewline
85 & 0.0453465873791485 & 0.090693174758297 & 0.954653412620852 \tabularnewline
86 & 0.0400965191518576 & 0.0801930383037151 & 0.959903480848142 \tabularnewline
87 & 0.0351693423685072 & 0.0703386847370143 & 0.964830657631493 \tabularnewline
88 & 0.0435632968018739 & 0.0871265936037479 & 0.956436703198126 \tabularnewline
89 & 0.0344881065141217 & 0.0689762130282433 & 0.965511893485878 \tabularnewline
90 & 0.0607974984827534 & 0.121594996965507 & 0.939202501517247 \tabularnewline
91 & 0.0510222869089494 & 0.102044573817899 & 0.948977713091051 \tabularnewline
92 & 0.0505227175540283 & 0.101045435108057 & 0.949477282445972 \tabularnewline
93 & 0.0494226814746975 & 0.098845362949395 & 0.950577318525302 \tabularnewline
94 & 0.0445636893930026 & 0.0891273787860052 & 0.955436310606997 \tabularnewline
95 & 0.0605356485840751 & 0.12107129716815 & 0.939464351415925 \tabularnewline
96 & 0.0479306329071944 & 0.0958612658143888 & 0.952069367092806 \tabularnewline
97 & 0.0503291574538555 & 0.100658314907711 & 0.949670842546144 \tabularnewline
98 & 0.0465179593788322 & 0.0930359187576643 & 0.953482040621168 \tabularnewline
99 & 0.0493505190006914 & 0.0987010380013828 & 0.950649480999309 \tabularnewline
100 & 0.0577668890905074 & 0.115533778181015 & 0.942233110909493 \tabularnewline
101 & 0.0727888251957891 & 0.145577650391578 & 0.927211174804211 \tabularnewline
102 & 0.0613921321650849 & 0.12278426433017 & 0.938607867834915 \tabularnewline
103 & 0.0499089576646411 & 0.0998179153292821 & 0.950091042335359 \tabularnewline
104 & 0.0527974619513576 & 0.105594923902715 & 0.947202538048642 \tabularnewline
105 & 0.0575252460978526 & 0.115050492195705 & 0.942474753902147 \tabularnewline
106 & 0.056111747194009 & 0.112223494388018 & 0.943888252805991 \tabularnewline
107 & 0.0561228794933537 & 0.112245758986707 & 0.943877120506646 \tabularnewline
108 & 0.0815578079655466 & 0.163115615931093 & 0.918442192034453 \tabularnewline
109 & 0.0812168903827126 & 0.162433780765425 & 0.918783109617287 \tabularnewline
110 & 0.131307102465371 & 0.262614204930742 & 0.868692897534629 \tabularnewline
111 & 0.141590571468413 & 0.283181142936825 & 0.858409428531587 \tabularnewline
112 & 0.149460292806155 & 0.29892058561231 & 0.850539707193845 \tabularnewline
113 & 0.123187096226347 & 0.246374192452695 & 0.876812903773653 \tabularnewline
114 & 0.145906832654654 & 0.291813665309307 & 0.854093167345346 \tabularnewline
115 & 0.119902843524586 & 0.239805687049172 & 0.880097156475414 \tabularnewline
116 & 0.12031098812074 & 0.240621976241479 & 0.87968901187926 \tabularnewline
117 & 0.142175200156424 & 0.284350400312848 & 0.857824799843576 \tabularnewline
118 & 0.119276395321174 & 0.238552790642348 & 0.880723604678826 \tabularnewline
119 & 0.105344241907192 & 0.210688483814384 & 0.894655758092808 \tabularnewline
120 & 0.090217339728927 & 0.180434679457854 & 0.909782660271073 \tabularnewline
121 & 0.0824876603684179 & 0.164975320736836 & 0.917512339631582 \tabularnewline
122 & 0.0716611376204466 & 0.143322275240893 & 0.928338862379553 \tabularnewline
123 & 0.0581403790851699 & 0.11628075817034 & 0.94185962091483 \tabularnewline
124 & 0.0840820072514178 & 0.168164014502836 & 0.915917992748582 \tabularnewline
125 & 0.0653897186682116 & 0.130779437336423 & 0.934610281331788 \tabularnewline
126 & 0.0510925767579703 & 0.102185153515941 & 0.94890742324203 \tabularnewline
127 & 0.0413388160570969 & 0.0826776321141938 & 0.958661183942903 \tabularnewline
128 & 0.292004724475438 & 0.584009448950876 & 0.707995275524562 \tabularnewline
129 & 0.277470848408356 & 0.554941696816712 & 0.722529151591644 \tabularnewline
130 & 0.392917056853936 & 0.785834113707872 & 0.607082943146064 \tabularnewline
131 & 0.409206895827177 & 0.818413791654355 & 0.590793104172822 \tabularnewline
132 & 0.362689001056005 & 0.725378002112011 & 0.637310998943995 \tabularnewline
133 & 0.31033402784174 & 0.62066805568348 & 0.68966597215826 \tabularnewline
134 & 0.272857257885044 & 0.545714515770089 & 0.727142742114956 \tabularnewline
135 & 0.321915422332251 & 0.643830844664502 & 0.678084577667749 \tabularnewline
136 & 0.502191519860824 & 0.995616960278352 & 0.497808480139176 \tabularnewline
137 & 0.436883877094606 & 0.873767754189212 & 0.563116122905394 \tabularnewline
138 & 0.371268002291314 & 0.742536004582629 & 0.628731997708686 \tabularnewline
139 & 0.39031379320191 & 0.780627586403819 & 0.60968620679809 \tabularnewline
140 & 0.341148132398098 & 0.682296264796196 & 0.658851867601902 \tabularnewline
141 & 0.486925281104645 & 0.973850562209291 & 0.513074718895355 \tabularnewline
142 & 0.626262494210247 & 0.747475011579507 & 0.373737505789753 \tabularnewline
143 & 0.631366145999684 & 0.737267708000631 & 0.368633854000316 \tabularnewline
144 & 0.597225561675132 & 0.805548876649736 & 0.402774438324868 \tabularnewline
145 & 0.527907571815929 & 0.944184856368142 & 0.472092428184071 \tabularnewline
146 & 0.994040248946638 & 0.0119195021067237 & 0.00595975105336183 \tabularnewline
147 & 0.989101634815823 & 0.0217967303683548 & 0.0108983651841774 \tabularnewline
148 & 0.993412207765374 & 0.0131755844692513 & 0.00658779223462563 \tabularnewline
149 & 0.995916450651342 & 0.00816709869731659 & 0.00408354934865829 \tabularnewline
150 & 0.989360113024686 & 0.0212797739506285 & 0.0106398869753142 \tabularnewline
151 & 0.974627149547047 & 0.0507457009059056 & 0.0253728504529528 \tabularnewline
152 & 0.940413685508414 & 0.119172628983172 & 0.0595863144915859 \tabularnewline
153 & 0.999202141788598 & 0.00159571642280369 & 0.000797858211401843 \tabularnewline
154 & 0.997372767680858 & 0.00525446463828435 & 0.00262723231914217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145816&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]0.00163256915348323[/C][C]0.00326513830696645[/C][C]0.998367430846517[/C][/ROW]
[ROW][C]11[/C][C]0.0181305463877975[/C][C]0.036261092775595[/C][C]0.981869453612202[/C][/ROW]
[ROW][C]12[/C][C]0.0059329724376047[/C][C]0.0118659448752094[/C][C]0.994067027562395[/C][/ROW]
[ROW][C]13[/C][C]0.0150161644978577[/C][C]0.0300323289957153[/C][C]0.984983835502142[/C][/ROW]
[ROW][C]14[/C][C]0.145777213878389[/C][C]0.291554427756778[/C][C]0.854222786121611[/C][/ROW]
[ROW][C]15[/C][C]0.0866256142894012[/C][C]0.173251228578802[/C][C]0.913374385710599[/C][/ROW]
[ROW][C]16[/C][C]0.17392348582851[/C][C]0.34784697165702[/C][C]0.82607651417149[/C][/ROW]
[ROW][C]17[/C][C]0.129300059201018[/C][C]0.258600118402036[/C][C]0.870699940798982[/C][/ROW]
[ROW][C]18[/C][C]0.0834134815576316[/C][C]0.166826963115263[/C][C]0.916586518442368[/C][/ROW]
[ROW][C]19[/C][C]0.10009435597585[/C][C]0.200188711951699[/C][C]0.899905644024151[/C][/ROW]
[ROW][C]20[/C][C]0.0806353621954568[/C][C]0.161270724390914[/C][C]0.919364637804543[/C][/ROW]
[ROW][C]21[/C][C]0.0663913463070812[/C][C]0.132782692614162[/C][C]0.933608653692919[/C][/ROW]
[ROW][C]22[/C][C]0.172857860932782[/C][C]0.345715721865564[/C][C]0.827142139067218[/C][/ROW]
[ROW][C]23[/C][C]0.130173307166417[/C][C]0.260346614332835[/C][C]0.869826692833582[/C][/ROW]
[ROW][C]24[/C][C]0.109079460679222[/C][C]0.218158921358444[/C][C]0.890920539320778[/C][/ROW]
[ROW][C]25[/C][C]0.189413146015939[/C][C]0.378826292031878[/C][C]0.810586853984061[/C][/ROW]
[ROW][C]26[/C][C]0.163641290859881[/C][C]0.327282581719762[/C][C]0.836358709140119[/C][/ROW]
[ROW][C]27[/C][C]0.126242622114623[/C][C]0.252485244229247[/C][C]0.873757377885377[/C][/ROW]
[ROW][C]28[/C][C]0.137979059116528[/C][C]0.275958118233057[/C][C]0.862020940883472[/C][/ROW]
[ROW][C]29[/C][C]0.102940131464883[/C][C]0.205880262929766[/C][C]0.897059868535117[/C][/ROW]
[ROW][C]30[/C][C]0.105991208072582[/C][C]0.211982416145164[/C][C]0.894008791927418[/C][/ROW]
[ROW][C]31[/C][C]0.1201282562809[/C][C]0.240256512561799[/C][C]0.8798717437191[/C][/ROW]
[ROW][C]32[/C][C]0.158661553135919[/C][C]0.317323106271838[/C][C]0.841338446864081[/C][/ROW]
[ROW][C]33[/C][C]0.124411668656894[/C][C]0.248823337313788[/C][C]0.875588331343106[/C][/ROW]
[ROW][C]34[/C][C]0.154441571841235[/C][C]0.308883143682471[/C][C]0.845558428158765[/C][/ROW]
[ROW][C]35[/C][C]0.261155103397913[/C][C]0.522310206795826[/C][C]0.738844896602087[/C][/ROW]
[ROW][C]36[/C][C]0.238725503377561[/C][C]0.477451006755122[/C][C]0.761274496622439[/C][/ROW]
[ROW][C]37[/C][C]0.228757005412438[/C][C]0.457514010824876[/C][C]0.771242994587562[/C][/ROW]
[ROW][C]38[/C][C]0.193182615098379[/C][C]0.386365230196758[/C][C]0.806817384901621[/C][/ROW]
[ROW][C]39[/C][C]0.156346632688462[/C][C]0.312693265376925[/C][C]0.843653367311538[/C][/ROW]
[ROW][C]40[/C][C]0.128497175853862[/C][C]0.256994351707723[/C][C]0.871502824146138[/C][/ROW]
[ROW][C]41[/C][C]0.102716427206307[/C][C]0.205432854412614[/C][C]0.897283572793693[/C][/ROW]
[ROW][C]42[/C][C]0.0823384872125307[/C][C]0.164676974425061[/C][C]0.917661512787469[/C][/ROW]
[ROW][C]43[/C][C]0.0637489732339531[/C][C]0.127497946467906[/C][C]0.936251026766047[/C][/ROW]
[ROW][C]44[/C][C]0.048745160568299[/C][C]0.0974903211365979[/C][C]0.951254839431701[/C][/ROW]
[ROW][C]45[/C][C]0.0368484004709812[/C][C]0.0736968009419625[/C][C]0.963151599529019[/C][/ROW]
[ROW][C]46[/C][C]0.0282846063545031[/C][C]0.0565692127090063[/C][C]0.971715393645497[/C][/ROW]
[ROW][C]47[/C][C]0.0206628512948001[/C][C]0.0413257025896002[/C][C]0.9793371487052[/C][/ROW]
[ROW][C]48[/C][C]0.0153484787990872[/C][C]0.0306969575981744[/C][C]0.984651521200913[/C][/ROW]
[ROW][C]49[/C][C]0.0126637110522599[/C][C]0.0253274221045198[/C][C]0.98733628894774[/C][/ROW]
[ROW][C]50[/C][C]0.00947639336648241[/C][C]0.0189527867329648[/C][C]0.990523606633518[/C][/ROW]
[ROW][C]51[/C][C]0.00707838499749299[/C][C]0.014156769994986[/C][C]0.992921615002507[/C][/ROW]
[ROW][C]52[/C][C]0.00499307879069944[/C][C]0.00998615758139887[/C][C]0.995006921209301[/C][/ROW]
[ROW][C]53[/C][C]0.0281586751603878[/C][C]0.0563173503207756[/C][C]0.971841324839612[/C][/ROW]
[ROW][C]54[/C][C]0.0207308298904339[/C][C]0.0414616597808678[/C][C]0.979269170109566[/C][/ROW]
[ROW][C]55[/C][C]0.0155072911690931[/C][C]0.0310145823381862[/C][C]0.984492708830907[/C][/ROW]
[ROW][C]56[/C][C]0.0119315625925003[/C][C]0.0238631251850006[/C][C]0.9880684374075[/C][/ROW]
[ROW][C]57[/C][C]0.0108733371801762[/C][C]0.0217466743603524[/C][C]0.989126662819824[/C][/ROW]
[ROW][C]58[/C][C]0.0672707598568493[/C][C]0.134541519713699[/C][C]0.932729240143151[/C][/ROW]
[ROW][C]59[/C][C]0.0567946547534848[/C][C]0.11358930950697[/C][C]0.943205345246515[/C][/ROW]
[ROW][C]60[/C][C]0.0484613946164468[/C][C]0.0969227892328935[/C][C]0.951538605383553[/C][/ROW]
[ROW][C]61[/C][C]0.0385844709318724[/C][C]0.0771689418637447[/C][C]0.961415529068128[/C][/ROW]
[ROW][C]62[/C][C]0.0306460378063041[/C][C]0.0612920756126083[/C][C]0.969353962193696[/C][/ROW]
[ROW][C]63[/C][C]0.02339301777237[/C][C]0.0467860355447399[/C][C]0.97660698222763[/C][/ROW]
[ROW][C]64[/C][C]0.0231338244754331[/C][C]0.0462676489508661[/C][C]0.976866175524567[/C][/ROW]
[ROW][C]65[/C][C]0.0383889603996932[/C][C]0.0767779207993863[/C][C]0.961611039600307[/C][/ROW]
[ROW][C]66[/C][C]0.0300281257671085[/C][C]0.0600562515342169[/C][C]0.969971874232892[/C][/ROW]
[ROW][C]67[/C][C]0.0235078257971073[/C][C]0.0470156515942145[/C][C]0.976492174202893[/C][/ROW]
[ROW][C]68[/C][C]0.0186765443486954[/C][C]0.0373530886973908[/C][C]0.981323455651305[/C][/ROW]
[ROW][C]69[/C][C]0.015560100022836[/C][C]0.0311202000456719[/C][C]0.984439899977164[/C][/ROW]
[ROW][C]70[/C][C]0.130729914105619[/C][C]0.261459828211239[/C][C]0.86927008589438[/C][/ROW]
[ROW][C]71[/C][C]0.114804695045085[/C][C]0.229609390090169[/C][C]0.885195304954915[/C][/ROW]
[ROW][C]72[/C][C]0.0955163520067843[/C][C]0.191032704013569[/C][C]0.904483647993216[/C][/ROW]
[ROW][C]73[/C][C]0.0770935171888401[/C][C]0.15418703437768[/C][C]0.92290648281116[/C][/ROW]
[ROW][C]74[/C][C]0.0673713244633325[/C][C]0.134742648926665[/C][C]0.932628675536668[/C][/ROW]
[ROW][C]75[/C][C]0.107971963525308[/C][C]0.215943927050616[/C][C]0.892028036474692[/C][/ROW]
[ROW][C]76[/C][C]0.124356264065066[/C][C]0.248712528130131[/C][C]0.875643735934934[/C][/ROW]
[ROW][C]77[/C][C]0.105487448641129[/C][C]0.210974897282258[/C][C]0.894512551358871[/C][/ROW]
[ROW][C]78[/C][C]0.10227352350406[/C][C]0.20454704700812[/C][C]0.89772647649594[/C][/ROW]
[ROW][C]79[/C][C]0.109783863473467[/C][C]0.219567726946934[/C][C]0.890216136526533[/C][/ROW]
[ROW][C]80[/C][C]0.0925951609194612[/C][C]0.185190321838922[/C][C]0.907404839080539[/C][/ROW]
[ROW][C]81[/C][C]0.077892348457867[/C][C]0.155784696915734[/C][C]0.922107651542133[/C][/ROW]
[ROW][C]82[/C][C]0.0755446781809009[/C][C]0.151089356361802[/C][C]0.924455321819099[/C][/ROW]
[ROW][C]83[/C][C]0.0687660817835245[/C][C]0.137532163567049[/C][C]0.931233918216475[/C][/ROW]
[ROW][C]84[/C][C]0.0563744468328097[/C][C]0.112748893665619[/C][C]0.94362555316719[/C][/ROW]
[ROW][C]85[/C][C]0.0453465873791485[/C][C]0.090693174758297[/C][C]0.954653412620852[/C][/ROW]
[ROW][C]86[/C][C]0.0400965191518576[/C][C]0.0801930383037151[/C][C]0.959903480848142[/C][/ROW]
[ROW][C]87[/C][C]0.0351693423685072[/C][C]0.0703386847370143[/C][C]0.964830657631493[/C][/ROW]
[ROW][C]88[/C][C]0.0435632968018739[/C][C]0.0871265936037479[/C][C]0.956436703198126[/C][/ROW]
[ROW][C]89[/C][C]0.0344881065141217[/C][C]0.0689762130282433[/C][C]0.965511893485878[/C][/ROW]
[ROW][C]90[/C][C]0.0607974984827534[/C][C]0.121594996965507[/C][C]0.939202501517247[/C][/ROW]
[ROW][C]91[/C][C]0.0510222869089494[/C][C]0.102044573817899[/C][C]0.948977713091051[/C][/ROW]
[ROW][C]92[/C][C]0.0505227175540283[/C][C]0.101045435108057[/C][C]0.949477282445972[/C][/ROW]
[ROW][C]93[/C][C]0.0494226814746975[/C][C]0.098845362949395[/C][C]0.950577318525302[/C][/ROW]
[ROW][C]94[/C][C]0.0445636893930026[/C][C]0.0891273787860052[/C][C]0.955436310606997[/C][/ROW]
[ROW][C]95[/C][C]0.0605356485840751[/C][C]0.12107129716815[/C][C]0.939464351415925[/C][/ROW]
[ROW][C]96[/C][C]0.0479306329071944[/C][C]0.0958612658143888[/C][C]0.952069367092806[/C][/ROW]
[ROW][C]97[/C][C]0.0503291574538555[/C][C]0.100658314907711[/C][C]0.949670842546144[/C][/ROW]
[ROW][C]98[/C][C]0.0465179593788322[/C][C]0.0930359187576643[/C][C]0.953482040621168[/C][/ROW]
[ROW][C]99[/C][C]0.0493505190006914[/C][C]0.0987010380013828[/C][C]0.950649480999309[/C][/ROW]
[ROW][C]100[/C][C]0.0577668890905074[/C][C]0.115533778181015[/C][C]0.942233110909493[/C][/ROW]
[ROW][C]101[/C][C]0.0727888251957891[/C][C]0.145577650391578[/C][C]0.927211174804211[/C][/ROW]
[ROW][C]102[/C][C]0.0613921321650849[/C][C]0.12278426433017[/C][C]0.938607867834915[/C][/ROW]
[ROW][C]103[/C][C]0.0499089576646411[/C][C]0.0998179153292821[/C][C]0.950091042335359[/C][/ROW]
[ROW][C]104[/C][C]0.0527974619513576[/C][C]0.105594923902715[/C][C]0.947202538048642[/C][/ROW]
[ROW][C]105[/C][C]0.0575252460978526[/C][C]0.115050492195705[/C][C]0.942474753902147[/C][/ROW]
[ROW][C]106[/C][C]0.056111747194009[/C][C]0.112223494388018[/C][C]0.943888252805991[/C][/ROW]
[ROW][C]107[/C][C]0.0561228794933537[/C][C]0.112245758986707[/C][C]0.943877120506646[/C][/ROW]
[ROW][C]108[/C][C]0.0815578079655466[/C][C]0.163115615931093[/C][C]0.918442192034453[/C][/ROW]
[ROW][C]109[/C][C]0.0812168903827126[/C][C]0.162433780765425[/C][C]0.918783109617287[/C][/ROW]
[ROW][C]110[/C][C]0.131307102465371[/C][C]0.262614204930742[/C][C]0.868692897534629[/C][/ROW]
[ROW][C]111[/C][C]0.141590571468413[/C][C]0.283181142936825[/C][C]0.858409428531587[/C][/ROW]
[ROW][C]112[/C][C]0.149460292806155[/C][C]0.29892058561231[/C][C]0.850539707193845[/C][/ROW]
[ROW][C]113[/C][C]0.123187096226347[/C][C]0.246374192452695[/C][C]0.876812903773653[/C][/ROW]
[ROW][C]114[/C][C]0.145906832654654[/C][C]0.291813665309307[/C][C]0.854093167345346[/C][/ROW]
[ROW][C]115[/C][C]0.119902843524586[/C][C]0.239805687049172[/C][C]0.880097156475414[/C][/ROW]
[ROW][C]116[/C][C]0.12031098812074[/C][C]0.240621976241479[/C][C]0.87968901187926[/C][/ROW]
[ROW][C]117[/C][C]0.142175200156424[/C][C]0.284350400312848[/C][C]0.857824799843576[/C][/ROW]
[ROW][C]118[/C][C]0.119276395321174[/C][C]0.238552790642348[/C][C]0.880723604678826[/C][/ROW]
[ROW][C]119[/C][C]0.105344241907192[/C][C]0.210688483814384[/C][C]0.894655758092808[/C][/ROW]
[ROW][C]120[/C][C]0.090217339728927[/C][C]0.180434679457854[/C][C]0.909782660271073[/C][/ROW]
[ROW][C]121[/C][C]0.0824876603684179[/C][C]0.164975320736836[/C][C]0.917512339631582[/C][/ROW]
[ROW][C]122[/C][C]0.0716611376204466[/C][C]0.143322275240893[/C][C]0.928338862379553[/C][/ROW]
[ROW][C]123[/C][C]0.0581403790851699[/C][C]0.11628075817034[/C][C]0.94185962091483[/C][/ROW]
[ROW][C]124[/C][C]0.0840820072514178[/C][C]0.168164014502836[/C][C]0.915917992748582[/C][/ROW]
[ROW][C]125[/C][C]0.0653897186682116[/C][C]0.130779437336423[/C][C]0.934610281331788[/C][/ROW]
[ROW][C]126[/C][C]0.0510925767579703[/C][C]0.102185153515941[/C][C]0.94890742324203[/C][/ROW]
[ROW][C]127[/C][C]0.0413388160570969[/C][C]0.0826776321141938[/C][C]0.958661183942903[/C][/ROW]
[ROW][C]128[/C][C]0.292004724475438[/C][C]0.584009448950876[/C][C]0.707995275524562[/C][/ROW]
[ROW][C]129[/C][C]0.277470848408356[/C][C]0.554941696816712[/C][C]0.722529151591644[/C][/ROW]
[ROW][C]130[/C][C]0.392917056853936[/C][C]0.785834113707872[/C][C]0.607082943146064[/C][/ROW]
[ROW][C]131[/C][C]0.409206895827177[/C][C]0.818413791654355[/C][C]0.590793104172822[/C][/ROW]
[ROW][C]132[/C][C]0.362689001056005[/C][C]0.725378002112011[/C][C]0.637310998943995[/C][/ROW]
[ROW][C]133[/C][C]0.31033402784174[/C][C]0.62066805568348[/C][C]0.68966597215826[/C][/ROW]
[ROW][C]134[/C][C]0.272857257885044[/C][C]0.545714515770089[/C][C]0.727142742114956[/C][/ROW]
[ROW][C]135[/C][C]0.321915422332251[/C][C]0.643830844664502[/C][C]0.678084577667749[/C][/ROW]
[ROW][C]136[/C][C]0.502191519860824[/C][C]0.995616960278352[/C][C]0.497808480139176[/C][/ROW]
[ROW][C]137[/C][C]0.436883877094606[/C][C]0.873767754189212[/C][C]0.563116122905394[/C][/ROW]
[ROW][C]138[/C][C]0.371268002291314[/C][C]0.742536004582629[/C][C]0.628731997708686[/C][/ROW]
[ROW][C]139[/C][C]0.39031379320191[/C][C]0.780627586403819[/C][C]0.60968620679809[/C][/ROW]
[ROW][C]140[/C][C]0.341148132398098[/C][C]0.682296264796196[/C][C]0.658851867601902[/C][/ROW]
[ROW][C]141[/C][C]0.486925281104645[/C][C]0.973850562209291[/C][C]0.513074718895355[/C][/ROW]
[ROW][C]142[/C][C]0.626262494210247[/C][C]0.747475011579507[/C][C]0.373737505789753[/C][/ROW]
[ROW][C]143[/C][C]0.631366145999684[/C][C]0.737267708000631[/C][C]0.368633854000316[/C][/ROW]
[ROW][C]144[/C][C]0.597225561675132[/C][C]0.805548876649736[/C][C]0.402774438324868[/C][/ROW]
[ROW][C]145[/C][C]0.527907571815929[/C][C]0.944184856368142[/C][C]0.472092428184071[/C][/ROW]
[ROW][C]146[/C][C]0.994040248946638[/C][C]0.0119195021067237[/C][C]0.00595975105336183[/C][/ROW]
[ROW][C]147[/C][C]0.989101634815823[/C][C]0.0217967303683548[/C][C]0.0108983651841774[/C][/ROW]
[ROW][C]148[/C][C]0.993412207765374[/C][C]0.0131755844692513[/C][C]0.00658779223462563[/C][/ROW]
[ROW][C]149[/C][C]0.995916450651342[/C][C]0.00816709869731659[/C][C]0.00408354934865829[/C][/ROW]
[ROW][C]150[/C][C]0.989360113024686[/C][C]0.0212797739506285[/C][C]0.0106398869753142[/C][/ROW]
[ROW][C]151[/C][C]0.974627149547047[/C][C]0.0507457009059056[/C][C]0.0253728504529528[/C][/ROW]
[ROW][C]152[/C][C]0.940413685508414[/C][C]0.119172628983172[/C][C]0.0595863144915859[/C][/ROW]
[ROW][C]153[/C][C]0.999202141788598[/C][C]0.00159571642280369[/C][C]0.000797858211401843[/C][/ROW]
[ROW][C]154[/C][C]0.997372767680858[/C][C]0.00525446463828435[/C][C]0.00262723231914217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145816&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145816&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
100.001632569153483230.003265138306966450.998367430846517
110.01813054638779750.0362610927755950.981869453612202
120.00593297243760470.01186594487520940.994067027562395
130.01501616449785770.03003232899571530.984983835502142
140.1457772138783890.2915544277567780.854222786121611
150.08662561428940120.1732512285788020.913374385710599
160.173923485828510.347846971657020.82607651417149
170.1293000592010180.2586001184020360.870699940798982
180.08341348155763160.1668269631152630.916586518442368
190.100094355975850.2001887119516990.899905644024151
200.08063536219545680.1612707243909140.919364637804543
210.06639134630708120.1327826926141620.933608653692919
220.1728578609327820.3457157218655640.827142139067218
230.1301733071664170.2603466143328350.869826692833582
240.1090794606792220.2181589213584440.890920539320778
250.1894131460159390.3788262920318780.810586853984061
260.1636412908598810.3272825817197620.836358709140119
270.1262426221146230.2524852442292470.873757377885377
280.1379790591165280.2759581182330570.862020940883472
290.1029401314648830.2058802629297660.897059868535117
300.1059912080725820.2119824161451640.894008791927418
310.12012825628090.2402565125617990.8798717437191
320.1586615531359190.3173231062718380.841338446864081
330.1244116686568940.2488233373137880.875588331343106
340.1544415718412350.3088831436824710.845558428158765
350.2611551033979130.5223102067958260.738844896602087
360.2387255033775610.4774510067551220.761274496622439
370.2287570054124380.4575140108248760.771242994587562
380.1931826150983790.3863652301967580.806817384901621
390.1563466326884620.3126932653769250.843653367311538
400.1284971758538620.2569943517077230.871502824146138
410.1027164272063070.2054328544126140.897283572793693
420.08233848721253070.1646769744250610.917661512787469
430.06374897323395310.1274979464679060.936251026766047
440.0487451605682990.09749032113659790.951254839431701
450.03684840047098120.07369680094196250.963151599529019
460.02828460635450310.05656921270900630.971715393645497
470.02066285129480010.04132570258960020.9793371487052
480.01534847879908720.03069695759817440.984651521200913
490.01266371105225990.02532742210451980.98733628894774
500.009476393366482410.01895278673296480.990523606633518
510.007078384997492990.0141567699949860.992921615002507
520.004993078790699440.009986157581398870.995006921209301
530.02815867516038780.05631735032077560.971841324839612
540.02073082989043390.04146165978086780.979269170109566
550.01550729116909310.03101458233818620.984492708830907
560.01193156259250030.02386312518500060.9880684374075
570.01087333718017620.02174667436035240.989126662819824
580.06727075985684930.1345415197136990.932729240143151
590.05679465475348480.113589309506970.943205345246515
600.04846139461644680.09692278923289350.951538605383553
610.03858447093187240.07716894186374470.961415529068128
620.03064603780630410.06129207561260830.969353962193696
630.023393017772370.04678603554473990.97660698222763
640.02313382447543310.04626764895086610.976866175524567
650.03838896039969320.07677792079938630.961611039600307
660.03002812576710850.06005625153421690.969971874232892
670.02350782579710730.04701565159421450.976492174202893
680.01867654434869540.03735308869739080.981323455651305
690.0155601000228360.03112020004567190.984439899977164
700.1307299141056190.2614598282112390.86927008589438
710.1148046950450850.2296093900901690.885195304954915
720.09551635200678430.1910327040135690.904483647993216
730.07709351718884010.154187034377680.92290648281116
740.06737132446333250.1347426489266650.932628675536668
750.1079719635253080.2159439270506160.892028036474692
760.1243562640650660.2487125281301310.875643735934934
770.1054874486411290.2109748972822580.894512551358871
780.102273523504060.204547047008120.89772647649594
790.1097838634734670.2195677269469340.890216136526533
800.09259516091946120.1851903218389220.907404839080539
810.0778923484578670.1557846969157340.922107651542133
820.07554467818090090.1510893563618020.924455321819099
830.06876608178352450.1375321635670490.931233918216475
840.05637444683280970.1127488936656190.94362555316719
850.04534658737914850.0906931747582970.954653412620852
860.04009651915185760.08019303830371510.959903480848142
870.03516934236850720.07033868473701430.964830657631493
880.04356329680187390.08712659360374790.956436703198126
890.03448810651412170.06897621302824330.965511893485878
900.06079749848275340.1215949969655070.939202501517247
910.05102228690894940.1020445738178990.948977713091051
920.05052271755402830.1010454351080570.949477282445972
930.04942268147469750.0988453629493950.950577318525302
940.04456368939300260.08912737878600520.955436310606997
950.06053564858407510.121071297168150.939464351415925
960.04793063290719440.09586126581438880.952069367092806
970.05032915745385550.1006583149077110.949670842546144
980.04651795937883220.09303591875766430.953482040621168
990.04935051900069140.09870103800138280.950649480999309
1000.05776688909050740.1155337781810150.942233110909493
1010.07278882519578910.1455776503915780.927211174804211
1020.06139213216508490.122784264330170.938607867834915
1030.04990895766464110.09981791532928210.950091042335359
1040.05279746195135760.1055949239027150.947202538048642
1050.05752524609785260.1150504921957050.942474753902147
1060.0561117471940090.1122234943880180.943888252805991
1070.05612287949335370.1122457589867070.943877120506646
1080.08155780796554660.1631156159310930.918442192034453
1090.08121689038271260.1624337807654250.918783109617287
1100.1313071024653710.2626142049307420.868692897534629
1110.1415905714684130.2831811429368250.858409428531587
1120.1494602928061550.298920585612310.850539707193845
1130.1231870962263470.2463741924526950.876812903773653
1140.1459068326546540.2918136653093070.854093167345346
1150.1199028435245860.2398056870491720.880097156475414
1160.120310988120740.2406219762414790.87968901187926
1170.1421752001564240.2843504003128480.857824799843576
1180.1192763953211740.2385527906423480.880723604678826
1190.1053442419071920.2106884838143840.894655758092808
1200.0902173397289270.1804346794578540.909782660271073
1210.08248766036841790.1649753207368360.917512339631582
1220.07166113762044660.1433222752408930.928338862379553
1230.05814037908516990.116280758170340.94185962091483
1240.08408200725141780.1681640145028360.915917992748582
1250.06538971866821160.1307794373364230.934610281331788
1260.05109257675797030.1021851535159410.94890742324203
1270.04133881605709690.08267763211419380.958661183942903
1280.2920047244754380.5840094489508760.707995275524562
1290.2774708484083560.5549416968167120.722529151591644
1300.3929170568539360.7858341137078720.607082943146064
1310.4092068958271770.8184137916543550.590793104172822
1320.3626890010560050.7253780021120110.637310998943995
1330.310334027841740.620668055683480.68966597215826
1340.2728572578850440.5457145157700890.727142742114956
1350.3219154223322510.6438308446645020.678084577667749
1360.5021915198608240.9956169602783520.497808480139176
1370.4368838770946060.8737677541892120.563116122905394
1380.3712680022913140.7425360045826290.628731997708686
1390.390313793201910.7806275864038190.60968620679809
1400.3411481323980980.6822962647961960.658851867601902
1410.4869252811046450.9738505622092910.513074718895355
1420.6262624942102470.7474750115795070.373737505789753
1430.6313661459996840.7372677080006310.368633854000316
1440.5972255616751320.8055488766497360.402774438324868
1450.5279075718159290.9441848563681420.472092428184071
1460.9940402489466380.01191950210672370.00595975105336183
1470.9891016348158230.02179673036835480.0108983651841774
1480.9934122077653740.01317558446925130.00658779223462563
1490.9959164506513420.008167098697316590.00408354934865829
1500.9893601130246860.02127977395062850.0106398869753142
1510.9746271495470470.05074570090590560.0253728504529528
1520.9404136855084140.1191726289831720.0595863144915859
1530.9992021417885980.001595716422803690.000797858211401843
1540.9973727676808580.005254464638284350.00262723231914217







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0344827586206897NOK
5% type I error level260.179310344827586NOK
10% type I error level480.331034482758621NOK

\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 & 5 & 0.0344827586206897 & NOK \tabularnewline
5% type I error level & 26 & 0.179310344827586 & NOK \tabularnewline
10% type I error level & 48 & 0.331034482758621 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145816&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]5[/C][C]0.0344827586206897[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]26[/C][C]0.179310344827586[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.331034482758621[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145816&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145816&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 level50.0344827586206897NOK
5% type I error level260.179310344827586NOK
10% type I error level480.331034482758621NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}