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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 31 Oct 2012 09:05:14 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Oct/31/t135168878644ts9evr3ol6fg8.htm/, Retrieved Mon, 29 Apr 2024 02:33:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185438, Retrieved Mon, 29 Apr 2024 02:33:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
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Histogram
Boxplots 
14	501	11	20	91,81	77585	1303,2	2000	183620
14	485	11	19	91,98	77585	-58,7	2000	183960
15	464	11	18	91,72	77585	-378,9	2000	183440
13	460	11	13	90,27	78302	175,6	2001	180630,27
8	467	11	17	91,89	78302	233,7	2001	183871,89
7	460	9	17	92,07	78302	706,8	2001	184232,07
3	448	8	13	92,92	78224	-23,6	2001	185932,92
3	443	6	14	93,34	78224	420,9	2001	186773,34
4	436	7	13	93,6	78224	722,1	2001	187293,6
4	431	8	17	92,41	78178	1401,3	2001	184912,41
0	484	6	17	93,6	78178	-94,9	2001	187293,6
-4	510	5	15	93,77	78178	1043,6	2001	187633,77
-14	513	2	9	93,6	77988	1300,1	2001	187293,6
-18	503	3	10	93,6	77988	721,1	2001	187293,6
-8	471	3	9	93,51	77988	-45,6	2001	187113,51
-1	471	7	14	92,66	77876	787,5	2002	185505,32
1	476	8	18	94,2	77876	694,3	2002	188588,4
2	475	7	18	94,37	77876	1054,7	2002	188928,74
0	470	7	12	94,45	78432	821,9	2002	189088,9
1	461	6	16	94,62	78432	1100,7	2002	189429,24
0	455	6	12	94,37	78432	862,4	2002	188928,74
-1	456	7	19	93,43	79025	1656,1	2002	187046,86
-3	517	5	13	94,79	79025	-174	2002	189769,58
-3	525	5	12	94,88	79025	1337,6	2002	189949,76
-3	523	5	13	94,79	79407	1394,9	2002	189769,58
-4	519	4	11	94,62	79407	915,7	2002	189429,24
-8	509	4	10	94,71	79407	-481,1	2002	189609,42
-9	512	4	16	93,77	79644	167,9	2003	187821,31
-13	519	1	12	95,73	79644	208,2	2003	191747,19
-18	517	-1	6	95,99	79644	382,2	2003	192267,97
-11	510	3	8	95,82	79381	1004	2003	191927,46
-9	509	4	6	95,47	79381	864,7	2003	191226,41
-10	501	3	8	95,82	79381	1052,9	2003	191927,46
-13	507	2	8	94,71	79536	1417,6	2003	189704,13
-11	569	1	9	96,33	79536	-197,7	2003	192948,99
-5	580	4	13	96,5	79536	1262,1	2003	193289,5
-15	578	3	8	96,16	79813	1147,2	2003	192608,48
-6	565	5	11	96,33	79813	700,2	2003	192948,99
-6	547	6	8	96,33	79813	45,3	2003	192948,99
-3	555	6	10	95,05	80332	458,5	2004	190480,2
-1	562	6	15	96,84	80332	610,2	2004	194067,36
-3	561	6	12	96,92	80332	786,4	2004	194227,68
-4	555	6	13	97,44	81434	787,2	2004	195269,76
-6	544	5	12	97,78	81434	1040	2004	195951,12
0	537	6	15	97,69	81434	324,1	2004	195770,76
-4	543	5	13	96,67	82167	1343	2004	193726,68
-2	594	6	13	98,29	82167	-501,2	2004	196973,16
-2	611	5	16	98,2	82167	800,4	2004	196792,8
-6	613	7	14	98,71	82816	916,7	2004	197814,84
-7	611	4	12	98,54	82816	695,8	2004	197474,16
-6	594	5	15	98,2	82816	28	2004	196792,8
-6	595	6	14	96,92	83000	495,6	2005	194324,6
-3	591	6	19	99,06	83000	366,2	2005	198615,3
-2	589	5	16	99,65	83000	633	2005	199798,25
-5	584	3	16	99,82	83251	848,3	2005	200139,1
-11	573	2	11	99,99	83251	472,2	2005	200479,95
-11	567	3	13	100,33	83251	357,8	2005	201161,65
-11	569	3	12	99,31	83591	824,3	2005	199116,55
-10	621	2	11	101,1	83591	-880,1	2005	202705,5
-14	629	0	6	101,1	83591	1066,8	2005	202705,5
-8	628	4	9	100,93	83910	1052,8	2005	202364,65
-9	612	4	6	100,85	83910	-32,1	2005	202204,25
-5	595	5	15	100,93	83910	-1331,4	2005	202364,65
-1	597	6	17	99,6	84599	-767,1	2006	199797,6
-2	593	6	13	101,88	84599	-236,7	2006	204371,28
-5	590	5	12	101,81	84599	-184,9	2006	204230,86
-4	580	5	13	102,38	85275	-143,4	2006	205374,28
-6	574	3	10	102,74	85275	493,9	2006	206096,44
-2	573	5	14	102,82	85275	549,7	2006	206256,92
-2	573	5	13	101,72	85608	982,7	2006	204050,32
-2	620	5	10	103,47	85608	-856,3	2006	207560,82
-2	626	3	11	102,98	85608	967	2006	206577,88
2	620	6	12	102,68	86303	659,4	2006	205976,08
1	588	6	7	102,9	86303	577,2	2006	206417,4
-8	566	4	11	103,03	86303	-213,1	2006	206678,18
-1	557	6	9	101,29	87115	17,7	2007	203289,03
1	561	5	13	103,69	87115	390,1	2007	208105,83
-1	549	4	12	103,68	87115	509,3	2007	208085,76
2	532	5	5	104,2	87931	410	2007	209129,4
2	526	5	13	104,08	87931	212,5	2007	208888,56
1	511	4	11	104,16	87931	818	2007	209049,12
-1	499	3	8	103,05	88164	422,7	2007	206821,35
-2	555	2	8	104,66	88164	-158	2007	210052,62
-2	565	3	8	104,46	88164	427,2	2007	209651,22
-1	542	2	8	104,95	88792	243,4	2007	210634,65
-8	527	-1	0	105,85	88792	-419,3	2007	212440,95
-4	510	0	3	106,23	88792	-1459,8	2007	213203,61
-6	514	-2	0	104,86	89263	-1389,8	2008	210558,88
-3	517	1	-1	107,44	89263	-2,1	2008	215739,52
-3	508	-2	-1	108,23	89263	-938,6	2008	217325,84
-7	493	-2	-4	108,45	89881	-839,9	2008	217767,6
-9	490	-2	1	109,39	89881	-297,6	2008	219655,12
-11	469	-6	-1	110,15	89881	-376,3	2008	221181,2
-13	478	-4	0	109,13	90120	-79,4	2008	219133,04
-11	528	-2	-1	110,28	90120	-2091,3	2008	221442,24
-9	534	0	6	110,17	90120	-1023	2008	221221,36
-17	518	-5	0	109,99	89703	-765,6	2008	220859,92
-22	506	-4	-3	109,26	89703	-1592,3	2008	219394,08
-25	502	-5	-3	109,11	89703	-1588,8	2008	219092,88
-20	516	-1	4	107,06	87818	-1318	2009	215083,54
-24	528	-2	1	109,53	87818	-402,4	2009	220045,77
-24	533	-4	0	108,92	87818	-814,5	2009	218820,28
-22	536	-1	-4	109,24	86273	-98,4	2009	219463,16
-19	537	1	-2	109,12	86273	-305,9	2009	219222,08
-18	524	1	3	109	86273	-18,4	2009	218981
-17	536	-2	2	107,23	86316	610,3	2009	215425,07
-11	587	1	5	109,49	86316	-917,3	2009	219965,41
-11	597	1	6	109,04	86316	88,4	2009	219061,36
-12	581	3	6	109,02	87234	-740,2	2009	219021,18
-10	564	3	3	109,23	87234	29,3	2009	219443,07
-15	558	1	4	109,46	87234	-893,2	2009	219905,14
-15	575	1	7	107,9	87885	-1030,2	2010	216879
-15	580	0	5	110,42	87885	-403,4	2010	221944,2
-13	575	2	6	110,98	87885	-46,9	2010	223069,8
-8	563	2	1	111,48	88003	-321,2	2010	224074,8
-13	552	-1	3	111,88	88003	-239,9	2010	224878,8
-9	537	1	6	111,89	88003	640,9	2010	224898,9
-7	545	0	0	109,85	88910	511,6	2010	220798,5
-4	601	1	3	112,1	88910	-665,1	2010	225321
-4	604	1	4	112,24	88910	657,7	2010	225602,4
-2	586	3	7	112,39	89397	-207,7	2010	225903,9
0	564	2	6	112,52	89397	-885,2	2010	226165,2
-2	549	0	6	113,16	89397	-1595,8	2010	227451,6
-3	551	0	6	111,84	89813	-1374,9	2011	224910,24
1	556	3	6	114,33	89813	-316,6	2011	229917,63
-2	548	-2	2	114,82	89813	-283,4	2011	230903,02
-1	540	0	2	115,2	90539	-175,8	2011	231667,2
1	531	1	2	115,4	90539	-694,2	2011	232069,4
-3	521	-1	3	115,74	90539	-249,9	2011	232753,14
-4	519	-2	-1	114,19	90688	268,2	2011	229636,09
-9	572	-1	-4	115,94	90688	-2105,1	2011	233155,34
-9	581	-1	4	116,03	90688	-762,8	2011	233336,33
-7	563	1	5	116,24	90691	-117,1	2011	233758,64
-14	548	-2	3	116,66	90691	-1094,4	2011	234603,26
-12	539	-5	-1	116,79	90691	-2095,2	2011	234864,69
-16	541	-5	-4	115,48	90645	-1587,6	2012	232345,76
-20	562	-6	0	118,16	90645	-528	2012	237737,92
-12	559	-4	-1	118,38	90645	-324,2	2012	238180,56
-12	546	-3	-1	118,51	90861	-276,1	2012	238442,12
-10	536	-3	3	118,42	90861	-139,1	2012	238261,04
-10	528	-1	2	118,24	90861	268	2012	237898,88
-13	530	-2	-4	116,47	90401	570,5	2012	234337,64
-16	582	-3	-3	118,96	90401	-316,5	2012	239347,52

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185438&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185438&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185438&T=0

As an alternative you can also use a QR Code:  
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 time10 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
i[t] = + 23773.76439707 -0.0209239116112628w[t] + 1.9590546518839f[t] + 0.236538405727251s[t] -199.865165374833c[t] + 0.00238681589629259b[t] + 0.000371803972631377h[t] -11.9475643654165t + 0.0995683309043856c_t[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
i[t] =  +  23773.76439707 -0.0209239116112628w[t] +  1.9590546518839f[t] +  0.236538405727251s[t] -199.865165374833c[t] +  0.00238681589629259b[t] +  0.000371803972631377h[t] -11.9475643654165t +  0.0995683309043856c_t[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185438&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]i[t] =  +  23773.76439707 -0.0209239116112628w[t] +  1.9590546518839f[t] +  0.236538405727251s[t] -199.865165374833c[t] +  0.00238681589629259b[t] +  0.000371803972631377h[t] -11.9475643654165t +  0.0995683309043856c_t[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185438&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185438&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
i[t] = + 23773.76439707 -0.0209239116112628w[t] + 1.9590546518839f[t] + 0.236538405727251s[t] -199.865165374833c[t] + 0.00238681589629259b[t] + 0.000371803972631377h[t] -11.9475643654165t + 0.0995683309043856c_t[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)23773.764397074449.547775.34300
w-0.02092391161126280.009077-2.30520.022690.011345
f1.95905465188390.17193111.394400
s0.2365384057272510.1091222.16770.0319520.015976
c-199.86516537483344.90775-4.45061.8e-059e-06
b0.002386815896292590.0003087.757300
h0.0003718039726313770.0004550.8180.4147990.2074
t-11.94756436541652.220275-5.381100
c_t0.09956833090438560.0222734.47031.6e-058e-06

\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) & 23773.76439707 & 4449.54777 & 5.343 & 0 & 0 \tabularnewline
w & -0.0209239116112628 & 0.009077 & -2.3052 & 0.02269 & 0.011345 \tabularnewline
f & 1.9590546518839 & 0.171931 & 11.3944 & 0 & 0 \tabularnewline
s & 0.236538405727251 & 0.109122 & 2.1677 & 0.031952 & 0.015976 \tabularnewline
c & -199.865165374833 & 44.90775 & -4.4506 & 1.8e-05 & 9e-06 \tabularnewline
b & 0.00238681589629259 & 0.000308 & 7.7573 & 0 & 0 \tabularnewline
h & 0.000371803972631377 & 0.000455 & 0.818 & 0.414799 & 0.2074 \tabularnewline
t & -11.9475643654165 & 2.220275 & -5.3811 & 0 & 0 \tabularnewline
c_t & 0.0995683309043856 & 0.022273 & 4.4703 & 1.6e-05 & 8e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185438&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]23773.76439707[/C][C]4449.54777[/C][C]5.343[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]w[/C][C]-0.0209239116112628[/C][C]0.009077[/C][C]-2.3052[/C][C]0.02269[/C][C]0.011345[/C][/ROW]
[ROW][C]f[/C][C]1.9590546518839[/C][C]0.171931[/C][C]11.3944[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]s[/C][C]0.236538405727251[/C][C]0.109122[/C][C]2.1677[/C][C]0.031952[/C][C]0.015976[/C][/ROW]
[ROW][C]c[/C][C]-199.865165374833[/C][C]44.90775[/C][C]-4.4506[/C][C]1.8e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]b[/C][C]0.00238681589629259[/C][C]0.000308[/C][C]7.7573[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]h[/C][C]0.000371803972631377[/C][C]0.000455[/C][C]0.818[/C][C]0.414799[/C][C]0.2074[/C][/ROW]
[ROW][C]t[/C][C]-11.9475643654165[/C][C]2.220275[/C][C]-5.3811[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]c_t[/C][C]0.0995683309043856[/C][C]0.022273[/C][C]4.4703[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185438&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185438&T=2

As an alternative you can also use a QR Code:  
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)23773.764397074449.547775.34300
w-0.02092391161126280.009077-2.30520.022690.011345
f1.95905465188390.17193111.394400
s0.2365384057272510.1091222.16770.0319520.015976
c-199.86516537483344.90775-4.45061.8e-059e-06
b0.002386815896292590.0003087.757300
h0.0003718039726313770.0004550.8180.4147990.2074
t-11.94756436541652.220275-5.381100
c_t0.09956833090438560.0222734.47031.6e-058e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.897669391820704
R-squared0.805810337011753
Adjusted R-squared0.794216924296037
F-TEST (value)69.5058786201395
F-TEST (DF numerator)8
F-TEST (DF denominator)134
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.40040393526871
Sum Squared Residuals1549.40808768078

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.897669391820704 \tabularnewline
R-squared & 0.805810337011753 \tabularnewline
Adjusted R-squared & 0.794216924296037 \tabularnewline
F-TEST (value) & 69.5058786201395 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 134 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.40040393526871 \tabularnewline
Sum Squared Residuals & 1549.40808768078 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185438&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.897669391820704[/C][/ROW]
[ROW][C]R-squared[/C][C]0.805810337011753[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.794216924296037[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]69.5058786201395[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]134[/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]3.40040393526871[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1549.40808768078[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185438&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185438&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.897669391820704
R-squared0.805810337011753
Adjusted R-squared0.794216924296037
F-TEST (value)69.5058786201395
F-TEST (DF numerator)8
F-TEST (DF denominator)134
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.40040393526871
Sum Squared Residuals1549.40808768078






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.21488965585080.78511034414919
21412.68292839937651.3170716006235
31512.95615143262812.04384856737194
41311.87146638701231.1285336129877
5811.6738793584976-3.67387935849764
677.96492955313435-0.964929553134352
734.31847600627082-1.31847600627082
830.6426387333530012.357361266647
942.460086556206941.53991344379306
1046.36108304587449-2.36108304587449
1100.0292803930221151-0.0292803930221151
12-4-2.63049293934712-1.36950706065288
13-14-10.2408673755328-3.75913262446724
14-18-8.0513097019627-9.9486902980373
15-8-7.84674087078365-0.153259129216347
16-1-0.972371599328174-0.027628400671826
1710.9783399546070590.0216600453929405
182-0.9157850076530362.91578500765304
190-1.032231562789571.03223156278957
201-1.843150813417842.84315081341784
210-2.620020127273552.62002012727355
22-13.18196698266572-4.18196698266572
23-3-4.832108803849311.83210880384931
24-3-4.721662638815391.72166263881539
25-3-3.462565348471860.462565348471863
26-4-5.89917725532651.8991772553265
27-8-6.49345535529084-1.50654464470916
28-9-6.44345759661827-2.55654240338173
29-13-14.24066406012591.24066406012594
30-18-19.58320971286531.58320971286528
31-11-11.45092602567070.450926025670673
32-9-9.845387066345030.845387066345034
33-10-11.24442960690761.24442960690755
34-13-12.3463979392966-0.653602060703422
35-11-16.66304535117055.66304535117045
36-5-9.600197118554744.60019711855474
37-15-11.9355367340941-3.06446326590587
38-6-7.275063495436161.27506349543616
39-6-5.89248807340762-0.107511926592378
40-3-6.127868042241953.12786804224195
41-1-5.62635286621574.6263528662157
42-3-6.275950731202763.27595073120276
43-4-3.45502002098428-0.544979979015724
44-6-5.4387362890235-0.561263710976504
450-2.860052780863652.86005278086365
46-4-2.95252574688236-1.04747425311764
47-2-3.2812444662591.281244466259
48-2-4.372729625756432.37272962575643
49-60.454321938401292-6.45432193840129
50-7-5.88006336182281-1.11993663817719
51-6-2.99169940585881-3.00830059414119
52-6-2.55178443262607-3.44821556737393
53-3-1.82712468230033-1.17287531769967
54-2-4.490839956053612.49083995605361
55-5-7.664402041447022.66440204144702
56-11-10.7550336933112-0.244966306688804
57-11-8.31831818470051-2.68168181529949
58-11-7.37646530590869-3.62353469409131
59-10-11.7066892799121.70668927991204
60-14-16.251016750892.25101675088999
61-8-6.88885747430228-1.11114252569772
62-9-7.64860728268537-1.35139271731463
63-5-3.70653833643108-1.29346166356892
64-1-1.185707827001540.18570782700154
65-2-2.149854340231760.149854340231758
66-5-4.25423966658496-0.745760333415038
67-4-2.2542680749468-1.7457319250532
68-6-6.56669214349920.566692143499198
69-2-1.67124612998777-0.328753870012228
70-2-0.807780783402777-1.19221921659722
71-2-3.41398111809491.4139811180949
72-2-6.478949447994294.47894944799429
7321.344094603182760.655905396817243
7410.77156487181770.2284351281823
75-8-2.05094359864606-5.94905640135394
76-11.97212566503566-2.97212566503566
7710.9383281896694220.0616718103305777
78-1-0.961543642571265-0.0384563574287346
7921.591777173645030.408222826354966
8023.53997963450434-1.53997963450434
8111.64431213078502-0.644312130785019
82-1-0.329123778287979-0.670876221712021
83-2-3.726579699377451.72657969937745
84-2-1.75287942887491-0.247120571125087
85-1-1.815548673368580.815548673368583
86-8-9.545928418086621.54592841808663
87-4-6.92039368005482.9203936800548
88-6-11.94523687685985.94523687685983
89-3-5.675879539522462.67587953952246
90-3-11.65916867691628.65916867691623
91-7-10.52820646606223.52820646606221
92-9-9.017152911931970.0171529119319727
93-11-16.86459441803965.86459441803962
94-13-12.2868282571527-0.713171742847335
95-11-10.3212358088656-0.678764191134436
96-9-4.48318768964229-4.51681231035771
97-17-16.2747564384509-0.725243561549104
98-22-15.1312718578489-6.86872814215111
99-25-17.0155360115571-7.98446398844289
100-20-13.6422139504095-6.35778604959047
101-24-15.8075468536387-8.19245314636133
102-24-20.322277499678-3.67772250032199
103-22-18.8217849848731-3.17821501512693
104-19-14.5487854750357-4.45121452496428
105-18-13.0073023227688-4.99269767723115
106-17-19.33237758172682.3323775817268
107-11-14.00188385836213.0018838583621
108-11-13.67608644891112.67608644891114
109-12-7.54352656652768-4.45647343347232
110-10-7.57613373284204-2.42386626715796
111-15-11.4365997011871-3.56340029881295
112-15-11.0454262408388-3.9545737591612
113-15-12.6758375798714-2.3241624201286
114-13-8.13440154000935-4.86559845999065
115-8-8.752758311749570.752758311749567
116-13-13.78958286805380.78958286805381
117-9-8.51784293641245-0.482157063587552
118-7-10.49179822695543.49179822695541
119-4-8.831214721005884.83121472100588
120-4-8.128220591098724.12822059109872
121-2-2.243168516120890.243168516120886
1220-4.195599343151794.19559934315179
123-2-7.893058840194945.89305884019494
124-3-8.024379250814465.02437925081446
1251-0.945152005003861.94515200500386
126-2-11.32713714634739.32713714634725
127-1-5.329437833448034.32943783344803
1281-3.30146154170054.3014615417005
129-3-6.483906473462683.48390647346268
130-4-9.367459232791275.36745923279127
131-9-9.467580352430590.467580352430589
132-9-7.23150851200271-1.76849148799729
133-7-2.37597902516822-4.62402097483178
134-14-8.62169094952703-5.37830905047298
135-12-15.5811174897933.58111748979296
136-16-17.18349985428721.18349985428717
137-20-16.9921115728573-3.00788842714269
138-12-13.06940568130781.06940568130777
139-12-10.2442830411739-1.75571695882608
140-10-9.07992163433123-0.920078365668768
141-10-5.16353499900298-4.83646500099702
142-13-10.3945125555264-2.60548744447363
143-16-12.3737344812655-3.62626551873453

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.2148896558508 & 0.78511034414919 \tabularnewline
2 & 14 & 12.6829283993765 & 1.3170716006235 \tabularnewline
3 & 15 & 12.9561514326281 & 2.04384856737194 \tabularnewline
4 & 13 & 11.8714663870123 & 1.1285336129877 \tabularnewline
5 & 8 & 11.6738793584976 & -3.67387935849764 \tabularnewline
6 & 7 & 7.96492955313435 & -0.964929553134352 \tabularnewline
7 & 3 & 4.31847600627082 & -1.31847600627082 \tabularnewline
8 & 3 & 0.642638733353001 & 2.357361266647 \tabularnewline
9 & 4 & 2.46008655620694 & 1.53991344379306 \tabularnewline
10 & 4 & 6.36108304587449 & -2.36108304587449 \tabularnewline
11 & 0 & 0.0292803930221151 & -0.0292803930221151 \tabularnewline
12 & -4 & -2.63049293934712 & -1.36950706065288 \tabularnewline
13 & -14 & -10.2408673755328 & -3.75913262446724 \tabularnewline
14 & -18 & -8.0513097019627 & -9.9486902980373 \tabularnewline
15 & -8 & -7.84674087078365 & -0.153259129216347 \tabularnewline
16 & -1 & -0.972371599328174 & -0.027628400671826 \tabularnewline
17 & 1 & 0.978339954607059 & 0.0216600453929405 \tabularnewline
18 & 2 & -0.915785007653036 & 2.91578500765304 \tabularnewline
19 & 0 & -1.03223156278957 & 1.03223156278957 \tabularnewline
20 & 1 & -1.84315081341784 & 2.84315081341784 \tabularnewline
21 & 0 & -2.62002012727355 & 2.62002012727355 \tabularnewline
22 & -1 & 3.18196698266572 & -4.18196698266572 \tabularnewline
23 & -3 & -4.83210880384931 & 1.83210880384931 \tabularnewline
24 & -3 & -4.72166263881539 & 1.72166263881539 \tabularnewline
25 & -3 & -3.46256534847186 & 0.462565348471863 \tabularnewline
26 & -4 & -5.8991772553265 & 1.8991772553265 \tabularnewline
27 & -8 & -6.49345535529084 & -1.50654464470916 \tabularnewline
28 & -9 & -6.44345759661827 & -2.55654240338173 \tabularnewline
29 & -13 & -14.2406640601259 & 1.24066406012594 \tabularnewline
30 & -18 & -19.5832097128653 & 1.58320971286528 \tabularnewline
31 & -11 & -11.4509260256707 & 0.450926025670673 \tabularnewline
32 & -9 & -9.84538706634503 & 0.845387066345034 \tabularnewline
33 & -10 & -11.2444296069076 & 1.24442960690755 \tabularnewline
34 & -13 & -12.3463979392966 & -0.653602060703422 \tabularnewline
35 & -11 & -16.6630453511705 & 5.66304535117045 \tabularnewline
36 & -5 & -9.60019711855474 & 4.60019711855474 \tabularnewline
37 & -15 & -11.9355367340941 & -3.06446326590587 \tabularnewline
38 & -6 & -7.27506349543616 & 1.27506349543616 \tabularnewline
39 & -6 & -5.89248807340762 & -0.107511926592378 \tabularnewline
40 & -3 & -6.12786804224195 & 3.12786804224195 \tabularnewline
41 & -1 & -5.6263528662157 & 4.6263528662157 \tabularnewline
42 & -3 & -6.27595073120276 & 3.27595073120276 \tabularnewline
43 & -4 & -3.45502002098428 & -0.544979979015724 \tabularnewline
44 & -6 & -5.4387362890235 & -0.561263710976504 \tabularnewline
45 & 0 & -2.86005278086365 & 2.86005278086365 \tabularnewline
46 & -4 & -2.95252574688236 & -1.04747425311764 \tabularnewline
47 & -2 & -3.281244466259 & 1.281244466259 \tabularnewline
48 & -2 & -4.37272962575643 & 2.37272962575643 \tabularnewline
49 & -6 & 0.454321938401292 & -6.45432193840129 \tabularnewline
50 & -7 & -5.88006336182281 & -1.11993663817719 \tabularnewline
51 & -6 & -2.99169940585881 & -3.00830059414119 \tabularnewline
52 & -6 & -2.55178443262607 & -3.44821556737393 \tabularnewline
53 & -3 & -1.82712468230033 & -1.17287531769967 \tabularnewline
54 & -2 & -4.49083995605361 & 2.49083995605361 \tabularnewline
55 & -5 & -7.66440204144702 & 2.66440204144702 \tabularnewline
56 & -11 & -10.7550336933112 & -0.244966306688804 \tabularnewline
57 & -11 & -8.31831818470051 & -2.68168181529949 \tabularnewline
58 & -11 & -7.37646530590869 & -3.62353469409131 \tabularnewline
59 & -10 & -11.706689279912 & 1.70668927991204 \tabularnewline
60 & -14 & -16.25101675089 & 2.25101675088999 \tabularnewline
61 & -8 & -6.88885747430228 & -1.11114252569772 \tabularnewline
62 & -9 & -7.64860728268537 & -1.35139271731463 \tabularnewline
63 & -5 & -3.70653833643108 & -1.29346166356892 \tabularnewline
64 & -1 & -1.18570782700154 & 0.18570782700154 \tabularnewline
65 & -2 & -2.14985434023176 & 0.149854340231758 \tabularnewline
66 & -5 & -4.25423966658496 & -0.745760333415038 \tabularnewline
67 & -4 & -2.2542680749468 & -1.7457319250532 \tabularnewline
68 & -6 & -6.5666921434992 & 0.566692143499198 \tabularnewline
69 & -2 & -1.67124612998777 & -0.328753870012228 \tabularnewline
70 & -2 & -0.807780783402777 & -1.19221921659722 \tabularnewline
71 & -2 & -3.4139811180949 & 1.4139811180949 \tabularnewline
72 & -2 & -6.47894944799429 & 4.47894944799429 \tabularnewline
73 & 2 & 1.34409460318276 & 0.655905396817243 \tabularnewline
74 & 1 & 0.7715648718177 & 0.2284351281823 \tabularnewline
75 & -8 & -2.05094359864606 & -5.94905640135394 \tabularnewline
76 & -1 & 1.97212566503566 & -2.97212566503566 \tabularnewline
77 & 1 & 0.938328189669422 & 0.0616718103305777 \tabularnewline
78 & -1 & -0.961543642571265 & -0.0384563574287346 \tabularnewline
79 & 2 & 1.59177717364503 & 0.408222826354966 \tabularnewline
80 & 2 & 3.53997963450434 & -1.53997963450434 \tabularnewline
81 & 1 & 1.64431213078502 & -0.644312130785019 \tabularnewline
82 & -1 & -0.329123778287979 & -0.670876221712021 \tabularnewline
83 & -2 & -3.72657969937745 & 1.72657969937745 \tabularnewline
84 & -2 & -1.75287942887491 & -0.247120571125087 \tabularnewline
85 & -1 & -1.81554867336858 & 0.815548673368583 \tabularnewline
86 & -8 & -9.54592841808662 & 1.54592841808663 \tabularnewline
87 & -4 & -6.9203936800548 & 2.9203936800548 \tabularnewline
88 & -6 & -11.9452368768598 & 5.94523687685983 \tabularnewline
89 & -3 & -5.67587953952246 & 2.67587953952246 \tabularnewline
90 & -3 & -11.6591686769162 & 8.65916867691623 \tabularnewline
91 & -7 & -10.5282064660622 & 3.52820646606221 \tabularnewline
92 & -9 & -9.01715291193197 & 0.0171529119319727 \tabularnewline
93 & -11 & -16.8645944180396 & 5.86459441803962 \tabularnewline
94 & -13 & -12.2868282571527 & -0.713171742847335 \tabularnewline
95 & -11 & -10.3212358088656 & -0.678764191134436 \tabularnewline
96 & -9 & -4.48318768964229 & -4.51681231035771 \tabularnewline
97 & -17 & -16.2747564384509 & -0.725243561549104 \tabularnewline
98 & -22 & -15.1312718578489 & -6.86872814215111 \tabularnewline
99 & -25 & -17.0155360115571 & -7.98446398844289 \tabularnewline
100 & -20 & -13.6422139504095 & -6.35778604959047 \tabularnewline
101 & -24 & -15.8075468536387 & -8.19245314636133 \tabularnewline
102 & -24 & -20.322277499678 & -3.67772250032199 \tabularnewline
103 & -22 & -18.8217849848731 & -3.17821501512693 \tabularnewline
104 & -19 & -14.5487854750357 & -4.45121452496428 \tabularnewline
105 & -18 & -13.0073023227688 & -4.99269767723115 \tabularnewline
106 & -17 & -19.3323775817268 & 2.3323775817268 \tabularnewline
107 & -11 & -14.0018838583621 & 3.0018838583621 \tabularnewline
108 & -11 & -13.6760864489111 & 2.67608644891114 \tabularnewline
109 & -12 & -7.54352656652768 & -4.45647343347232 \tabularnewline
110 & -10 & -7.57613373284204 & -2.42386626715796 \tabularnewline
111 & -15 & -11.4365997011871 & -3.56340029881295 \tabularnewline
112 & -15 & -11.0454262408388 & -3.9545737591612 \tabularnewline
113 & -15 & -12.6758375798714 & -2.3241624201286 \tabularnewline
114 & -13 & -8.13440154000935 & -4.86559845999065 \tabularnewline
115 & -8 & -8.75275831174957 & 0.752758311749567 \tabularnewline
116 & -13 & -13.7895828680538 & 0.78958286805381 \tabularnewline
117 & -9 & -8.51784293641245 & -0.482157063587552 \tabularnewline
118 & -7 & -10.4917982269554 & 3.49179822695541 \tabularnewline
119 & -4 & -8.83121472100588 & 4.83121472100588 \tabularnewline
120 & -4 & -8.12822059109872 & 4.12822059109872 \tabularnewline
121 & -2 & -2.24316851612089 & 0.243168516120886 \tabularnewline
122 & 0 & -4.19559934315179 & 4.19559934315179 \tabularnewline
123 & -2 & -7.89305884019494 & 5.89305884019494 \tabularnewline
124 & -3 & -8.02437925081446 & 5.02437925081446 \tabularnewline
125 & 1 & -0.94515200500386 & 1.94515200500386 \tabularnewline
126 & -2 & -11.3271371463473 & 9.32713714634725 \tabularnewline
127 & -1 & -5.32943783344803 & 4.32943783344803 \tabularnewline
128 & 1 & -3.3014615417005 & 4.3014615417005 \tabularnewline
129 & -3 & -6.48390647346268 & 3.48390647346268 \tabularnewline
130 & -4 & -9.36745923279127 & 5.36745923279127 \tabularnewline
131 & -9 & -9.46758035243059 & 0.467580352430589 \tabularnewline
132 & -9 & -7.23150851200271 & -1.76849148799729 \tabularnewline
133 & -7 & -2.37597902516822 & -4.62402097483178 \tabularnewline
134 & -14 & -8.62169094952703 & -5.37830905047298 \tabularnewline
135 & -12 & -15.581117489793 & 3.58111748979296 \tabularnewline
136 & -16 & -17.1834998542872 & 1.18349985428717 \tabularnewline
137 & -20 & -16.9921115728573 & -3.00788842714269 \tabularnewline
138 & -12 & -13.0694056813078 & 1.06940568130777 \tabularnewline
139 & -12 & -10.2442830411739 & -1.75571695882608 \tabularnewline
140 & -10 & -9.07992163433123 & -0.920078365668768 \tabularnewline
141 & -10 & -5.16353499900298 & -4.83646500099702 \tabularnewline
142 & -13 & -10.3945125555264 & -2.60548744447363 \tabularnewline
143 & -16 & -12.3737344812655 & -3.62626551873453 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185438&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]14[/C][C]13.2148896558508[/C][C]0.78511034414919[/C][/ROW]
[ROW][C]2[/C][C]14[/C][C]12.6829283993765[/C][C]1.3170716006235[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]12.9561514326281[/C][C]2.04384856737194[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]11.8714663870123[/C][C]1.1285336129877[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]11.6738793584976[/C][C]-3.67387935849764[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]7.96492955313435[/C][C]-0.964929553134352[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]4.31847600627082[/C][C]-1.31847600627082[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]0.642638733353001[/C][C]2.357361266647[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]2.46008655620694[/C][C]1.53991344379306[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]6.36108304587449[/C][C]-2.36108304587449[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.0292803930221151[/C][C]-0.0292803930221151[/C][/ROW]
[ROW][C]12[/C][C]-4[/C][C]-2.63049293934712[/C][C]-1.36950706065288[/C][/ROW]
[ROW][C]13[/C][C]-14[/C][C]-10.2408673755328[/C][C]-3.75913262446724[/C][/ROW]
[ROW][C]14[/C][C]-18[/C][C]-8.0513097019627[/C][C]-9.9486902980373[/C][/ROW]
[ROW][C]15[/C][C]-8[/C][C]-7.84674087078365[/C][C]-0.153259129216347[/C][/ROW]
[ROW][C]16[/C][C]-1[/C][C]-0.972371599328174[/C][C]-0.027628400671826[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.978339954607059[/C][C]0.0216600453929405[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]-0.915785007653036[/C][C]2.91578500765304[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-1.03223156278957[/C][C]1.03223156278957[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]-1.84315081341784[/C][C]2.84315081341784[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]-2.62002012727355[/C][C]2.62002012727355[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]3.18196698266572[/C][C]-4.18196698266572[/C][/ROW]
[ROW][C]23[/C][C]-3[/C][C]-4.83210880384931[/C][C]1.83210880384931[/C][/ROW]
[ROW][C]24[/C][C]-3[/C][C]-4.72166263881539[/C][C]1.72166263881539[/C][/ROW]
[ROW][C]25[/C][C]-3[/C][C]-3.46256534847186[/C][C]0.462565348471863[/C][/ROW]
[ROW][C]26[/C][C]-4[/C][C]-5.8991772553265[/C][C]1.8991772553265[/C][/ROW]
[ROW][C]27[/C][C]-8[/C][C]-6.49345535529084[/C][C]-1.50654464470916[/C][/ROW]
[ROW][C]28[/C][C]-9[/C][C]-6.44345759661827[/C][C]-2.55654240338173[/C][/ROW]
[ROW][C]29[/C][C]-13[/C][C]-14.2406640601259[/C][C]1.24066406012594[/C][/ROW]
[ROW][C]30[/C][C]-18[/C][C]-19.5832097128653[/C][C]1.58320971286528[/C][/ROW]
[ROW][C]31[/C][C]-11[/C][C]-11.4509260256707[/C][C]0.450926025670673[/C][/ROW]
[ROW][C]32[/C][C]-9[/C][C]-9.84538706634503[/C][C]0.845387066345034[/C][/ROW]
[ROW][C]33[/C][C]-10[/C][C]-11.2444296069076[/C][C]1.24442960690755[/C][/ROW]
[ROW][C]34[/C][C]-13[/C][C]-12.3463979392966[/C][C]-0.653602060703422[/C][/ROW]
[ROW][C]35[/C][C]-11[/C][C]-16.6630453511705[/C][C]5.66304535117045[/C][/ROW]
[ROW][C]36[/C][C]-5[/C][C]-9.60019711855474[/C][C]4.60019711855474[/C][/ROW]
[ROW][C]37[/C][C]-15[/C][C]-11.9355367340941[/C][C]-3.06446326590587[/C][/ROW]
[ROW][C]38[/C][C]-6[/C][C]-7.27506349543616[/C][C]1.27506349543616[/C][/ROW]
[ROW][C]39[/C][C]-6[/C][C]-5.89248807340762[/C][C]-0.107511926592378[/C][/ROW]
[ROW][C]40[/C][C]-3[/C][C]-6.12786804224195[/C][C]3.12786804224195[/C][/ROW]
[ROW][C]41[/C][C]-1[/C][C]-5.6263528662157[/C][C]4.6263528662157[/C][/ROW]
[ROW][C]42[/C][C]-3[/C][C]-6.27595073120276[/C][C]3.27595073120276[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-3.45502002098428[/C][C]-0.544979979015724[/C][/ROW]
[ROW][C]44[/C][C]-6[/C][C]-5.4387362890235[/C][C]-0.561263710976504[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]-2.86005278086365[/C][C]2.86005278086365[/C][/ROW]
[ROW][C]46[/C][C]-4[/C][C]-2.95252574688236[/C][C]-1.04747425311764[/C][/ROW]
[ROW][C]47[/C][C]-2[/C][C]-3.281244466259[/C][C]1.281244466259[/C][/ROW]
[ROW][C]48[/C][C]-2[/C][C]-4.37272962575643[/C][C]2.37272962575643[/C][/ROW]
[ROW][C]49[/C][C]-6[/C][C]0.454321938401292[/C][C]-6.45432193840129[/C][/ROW]
[ROW][C]50[/C][C]-7[/C][C]-5.88006336182281[/C][C]-1.11993663817719[/C][/ROW]
[ROW][C]51[/C][C]-6[/C][C]-2.99169940585881[/C][C]-3.00830059414119[/C][/ROW]
[ROW][C]52[/C][C]-6[/C][C]-2.55178443262607[/C][C]-3.44821556737393[/C][/ROW]
[ROW][C]53[/C][C]-3[/C][C]-1.82712468230033[/C][C]-1.17287531769967[/C][/ROW]
[ROW][C]54[/C][C]-2[/C][C]-4.49083995605361[/C][C]2.49083995605361[/C][/ROW]
[ROW][C]55[/C][C]-5[/C][C]-7.66440204144702[/C][C]2.66440204144702[/C][/ROW]
[ROW][C]56[/C][C]-11[/C][C]-10.7550336933112[/C][C]-0.244966306688804[/C][/ROW]
[ROW][C]57[/C][C]-11[/C][C]-8.31831818470051[/C][C]-2.68168181529949[/C][/ROW]
[ROW][C]58[/C][C]-11[/C][C]-7.37646530590869[/C][C]-3.62353469409131[/C][/ROW]
[ROW][C]59[/C][C]-10[/C][C]-11.706689279912[/C][C]1.70668927991204[/C][/ROW]
[ROW][C]60[/C][C]-14[/C][C]-16.25101675089[/C][C]2.25101675088999[/C][/ROW]
[ROW][C]61[/C][C]-8[/C][C]-6.88885747430228[/C][C]-1.11114252569772[/C][/ROW]
[ROW][C]62[/C][C]-9[/C][C]-7.64860728268537[/C][C]-1.35139271731463[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-3.70653833643108[/C][C]-1.29346166356892[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]-1.18570782700154[/C][C]0.18570782700154[/C][/ROW]
[ROW][C]65[/C][C]-2[/C][C]-2.14985434023176[/C][C]0.149854340231758[/C][/ROW]
[ROW][C]66[/C][C]-5[/C][C]-4.25423966658496[/C][C]-0.745760333415038[/C][/ROW]
[ROW][C]67[/C][C]-4[/C][C]-2.2542680749468[/C][C]-1.7457319250532[/C][/ROW]
[ROW][C]68[/C][C]-6[/C][C]-6.5666921434992[/C][C]0.566692143499198[/C][/ROW]
[ROW][C]69[/C][C]-2[/C][C]-1.67124612998777[/C][C]-0.328753870012228[/C][/ROW]
[ROW][C]70[/C][C]-2[/C][C]-0.807780783402777[/C][C]-1.19221921659722[/C][/ROW]
[ROW][C]71[/C][C]-2[/C][C]-3.4139811180949[/C][C]1.4139811180949[/C][/ROW]
[ROW][C]72[/C][C]-2[/C][C]-6.47894944799429[/C][C]4.47894944799429[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.34409460318276[/C][C]0.655905396817243[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.7715648718177[/C][C]0.2284351281823[/C][/ROW]
[ROW][C]75[/C][C]-8[/C][C]-2.05094359864606[/C][C]-5.94905640135394[/C][/ROW]
[ROW][C]76[/C][C]-1[/C][C]1.97212566503566[/C][C]-2.97212566503566[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]0.938328189669422[/C][C]0.0616718103305777[/C][/ROW]
[ROW][C]78[/C][C]-1[/C][C]-0.961543642571265[/C][C]-0.0384563574287346[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.59177717364503[/C][C]0.408222826354966[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]3.53997963450434[/C][C]-1.53997963450434[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.64431213078502[/C][C]-0.644312130785019[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]-0.329123778287979[/C][C]-0.670876221712021[/C][/ROW]
[ROW][C]83[/C][C]-2[/C][C]-3.72657969937745[/C][C]1.72657969937745[/C][/ROW]
[ROW][C]84[/C][C]-2[/C][C]-1.75287942887491[/C][C]-0.247120571125087[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-1.81554867336858[/C][C]0.815548673368583[/C][/ROW]
[ROW][C]86[/C][C]-8[/C][C]-9.54592841808662[/C][C]1.54592841808663[/C][/ROW]
[ROW][C]87[/C][C]-4[/C][C]-6.9203936800548[/C][C]2.9203936800548[/C][/ROW]
[ROW][C]88[/C][C]-6[/C][C]-11.9452368768598[/C][C]5.94523687685983[/C][/ROW]
[ROW][C]89[/C][C]-3[/C][C]-5.67587953952246[/C][C]2.67587953952246[/C][/ROW]
[ROW][C]90[/C][C]-3[/C][C]-11.6591686769162[/C][C]8.65916867691623[/C][/ROW]
[ROW][C]91[/C][C]-7[/C][C]-10.5282064660622[/C][C]3.52820646606221[/C][/ROW]
[ROW][C]92[/C][C]-9[/C][C]-9.01715291193197[/C][C]0.0171529119319727[/C][/ROW]
[ROW][C]93[/C][C]-11[/C][C]-16.8645944180396[/C][C]5.86459441803962[/C][/ROW]
[ROW][C]94[/C][C]-13[/C][C]-12.2868282571527[/C][C]-0.713171742847335[/C][/ROW]
[ROW][C]95[/C][C]-11[/C][C]-10.3212358088656[/C][C]-0.678764191134436[/C][/ROW]
[ROW][C]96[/C][C]-9[/C][C]-4.48318768964229[/C][C]-4.51681231035771[/C][/ROW]
[ROW][C]97[/C][C]-17[/C][C]-16.2747564384509[/C][C]-0.725243561549104[/C][/ROW]
[ROW][C]98[/C][C]-22[/C][C]-15.1312718578489[/C][C]-6.86872814215111[/C][/ROW]
[ROW][C]99[/C][C]-25[/C][C]-17.0155360115571[/C][C]-7.98446398844289[/C][/ROW]
[ROW][C]100[/C][C]-20[/C][C]-13.6422139504095[/C][C]-6.35778604959047[/C][/ROW]
[ROW][C]101[/C][C]-24[/C][C]-15.8075468536387[/C][C]-8.19245314636133[/C][/ROW]
[ROW][C]102[/C][C]-24[/C][C]-20.322277499678[/C][C]-3.67772250032199[/C][/ROW]
[ROW][C]103[/C][C]-22[/C][C]-18.8217849848731[/C][C]-3.17821501512693[/C][/ROW]
[ROW][C]104[/C][C]-19[/C][C]-14.5487854750357[/C][C]-4.45121452496428[/C][/ROW]
[ROW][C]105[/C][C]-18[/C][C]-13.0073023227688[/C][C]-4.99269767723115[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-19.3323775817268[/C][C]2.3323775817268[/C][/ROW]
[ROW][C]107[/C][C]-11[/C][C]-14.0018838583621[/C][C]3.0018838583621[/C][/ROW]
[ROW][C]108[/C][C]-11[/C][C]-13.6760864489111[/C][C]2.67608644891114[/C][/ROW]
[ROW][C]109[/C][C]-12[/C][C]-7.54352656652768[/C][C]-4.45647343347232[/C][/ROW]
[ROW][C]110[/C][C]-10[/C][C]-7.57613373284204[/C][C]-2.42386626715796[/C][/ROW]
[ROW][C]111[/C][C]-15[/C][C]-11.4365997011871[/C][C]-3.56340029881295[/C][/ROW]
[ROW][C]112[/C][C]-15[/C][C]-11.0454262408388[/C][C]-3.9545737591612[/C][/ROW]
[ROW][C]113[/C][C]-15[/C][C]-12.6758375798714[/C][C]-2.3241624201286[/C][/ROW]
[ROW][C]114[/C][C]-13[/C][C]-8.13440154000935[/C][C]-4.86559845999065[/C][/ROW]
[ROW][C]115[/C][C]-8[/C][C]-8.75275831174957[/C][C]0.752758311749567[/C][/ROW]
[ROW][C]116[/C][C]-13[/C][C]-13.7895828680538[/C][C]0.78958286805381[/C][/ROW]
[ROW][C]117[/C][C]-9[/C][C]-8.51784293641245[/C][C]-0.482157063587552[/C][/ROW]
[ROW][C]118[/C][C]-7[/C][C]-10.4917982269554[/C][C]3.49179822695541[/C][/ROW]
[ROW][C]119[/C][C]-4[/C][C]-8.83121472100588[/C][C]4.83121472100588[/C][/ROW]
[ROW][C]120[/C][C]-4[/C][C]-8.12822059109872[/C][C]4.12822059109872[/C][/ROW]
[ROW][C]121[/C][C]-2[/C][C]-2.24316851612089[/C][C]0.243168516120886[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-4.19559934315179[/C][C]4.19559934315179[/C][/ROW]
[ROW][C]123[/C][C]-2[/C][C]-7.89305884019494[/C][C]5.89305884019494[/C][/ROW]
[ROW][C]124[/C][C]-3[/C][C]-8.02437925081446[/C][C]5.02437925081446[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]-0.94515200500386[/C][C]1.94515200500386[/C][/ROW]
[ROW][C]126[/C][C]-2[/C][C]-11.3271371463473[/C][C]9.32713714634725[/C][/ROW]
[ROW][C]127[/C][C]-1[/C][C]-5.32943783344803[/C][C]4.32943783344803[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]-3.3014615417005[/C][C]4.3014615417005[/C][/ROW]
[ROW][C]129[/C][C]-3[/C][C]-6.48390647346268[/C][C]3.48390647346268[/C][/ROW]
[ROW][C]130[/C][C]-4[/C][C]-9.36745923279127[/C][C]5.36745923279127[/C][/ROW]
[ROW][C]131[/C][C]-9[/C][C]-9.46758035243059[/C][C]0.467580352430589[/C][/ROW]
[ROW][C]132[/C][C]-9[/C][C]-7.23150851200271[/C][C]-1.76849148799729[/C][/ROW]
[ROW][C]133[/C][C]-7[/C][C]-2.37597902516822[/C][C]-4.62402097483178[/C][/ROW]
[ROW][C]134[/C][C]-14[/C][C]-8.62169094952703[/C][C]-5.37830905047298[/C][/ROW]
[ROW][C]135[/C][C]-12[/C][C]-15.581117489793[/C][C]3.58111748979296[/C][/ROW]
[ROW][C]136[/C][C]-16[/C][C]-17.1834998542872[/C][C]1.18349985428717[/C][/ROW]
[ROW][C]137[/C][C]-20[/C][C]-16.9921115728573[/C][C]-3.00788842714269[/C][/ROW]
[ROW][C]138[/C][C]-12[/C][C]-13.0694056813078[/C][C]1.06940568130777[/C][/ROW]
[ROW][C]139[/C][C]-12[/C][C]-10.2442830411739[/C][C]-1.75571695882608[/C][/ROW]
[ROW][C]140[/C][C]-10[/C][C]-9.07992163433123[/C][C]-0.920078365668768[/C][/ROW]
[ROW][C]141[/C][C]-10[/C][C]-5.16353499900298[/C][C]-4.83646500099702[/C][/ROW]
[ROW][C]142[/C][C]-13[/C][C]-10.3945125555264[/C][C]-2.60548744447363[/C][/ROW]
[ROW][C]143[/C][C]-16[/C][C]-12.3737344812655[/C][C]-3.62626551873453[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185438&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185438&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.21488965585080.78511034414919
21412.68292839937651.3170716006235
31512.95615143262812.04384856737194
41311.87146638701231.1285336129877
5811.6738793584976-3.67387935849764
677.96492955313435-0.964929553134352
734.31847600627082-1.31847600627082
830.6426387333530012.357361266647
942.460086556206941.53991344379306
1046.36108304587449-2.36108304587449
1100.0292803930221151-0.0292803930221151
12-4-2.63049293934712-1.36950706065288
13-14-10.2408673755328-3.75913262446724
14-18-8.0513097019627-9.9486902980373
15-8-7.84674087078365-0.153259129216347
16-1-0.972371599328174-0.027628400671826
1710.9783399546070590.0216600453929405
182-0.9157850076530362.91578500765304
190-1.032231562789571.03223156278957
201-1.843150813417842.84315081341784
210-2.620020127273552.62002012727355
22-13.18196698266572-4.18196698266572
23-3-4.832108803849311.83210880384931
24-3-4.721662638815391.72166263881539
25-3-3.462565348471860.462565348471863
26-4-5.89917725532651.8991772553265
27-8-6.49345535529084-1.50654464470916
28-9-6.44345759661827-2.55654240338173
29-13-14.24066406012591.24066406012594
30-18-19.58320971286531.58320971286528
31-11-11.45092602567070.450926025670673
32-9-9.845387066345030.845387066345034
33-10-11.24442960690761.24442960690755
34-13-12.3463979392966-0.653602060703422
35-11-16.66304535117055.66304535117045
36-5-9.600197118554744.60019711855474
37-15-11.9355367340941-3.06446326590587
38-6-7.275063495436161.27506349543616
39-6-5.89248807340762-0.107511926592378
40-3-6.127868042241953.12786804224195
41-1-5.62635286621574.6263528662157
42-3-6.275950731202763.27595073120276
43-4-3.45502002098428-0.544979979015724
44-6-5.4387362890235-0.561263710976504
450-2.860052780863652.86005278086365
46-4-2.95252574688236-1.04747425311764
47-2-3.2812444662591.281244466259
48-2-4.372729625756432.37272962575643
49-60.454321938401292-6.45432193840129
50-7-5.88006336182281-1.11993663817719
51-6-2.99169940585881-3.00830059414119
52-6-2.55178443262607-3.44821556737393
53-3-1.82712468230033-1.17287531769967
54-2-4.490839956053612.49083995605361
55-5-7.664402041447022.66440204144702
56-11-10.7550336933112-0.244966306688804
57-11-8.31831818470051-2.68168181529949
58-11-7.37646530590869-3.62353469409131
59-10-11.7066892799121.70668927991204
60-14-16.251016750892.25101675088999
61-8-6.88885747430228-1.11114252569772
62-9-7.64860728268537-1.35139271731463
63-5-3.70653833643108-1.29346166356892
64-1-1.185707827001540.18570782700154
65-2-2.149854340231760.149854340231758
66-5-4.25423966658496-0.745760333415038
67-4-2.2542680749468-1.7457319250532
68-6-6.56669214349920.566692143499198
69-2-1.67124612998777-0.328753870012228
70-2-0.807780783402777-1.19221921659722
71-2-3.41398111809491.4139811180949
72-2-6.478949447994294.47894944799429
7321.344094603182760.655905396817243
7410.77156487181770.2284351281823
75-8-2.05094359864606-5.94905640135394
76-11.97212566503566-2.97212566503566
7710.9383281896694220.0616718103305777
78-1-0.961543642571265-0.0384563574287346
7921.591777173645030.408222826354966
8023.53997963450434-1.53997963450434
8111.64431213078502-0.644312130785019
82-1-0.329123778287979-0.670876221712021
83-2-3.726579699377451.72657969937745
84-2-1.75287942887491-0.247120571125087
85-1-1.815548673368580.815548673368583
86-8-9.545928418086621.54592841808663
87-4-6.92039368005482.9203936800548
88-6-11.94523687685985.94523687685983
89-3-5.675879539522462.67587953952246
90-3-11.65916867691628.65916867691623
91-7-10.52820646606223.52820646606221
92-9-9.017152911931970.0171529119319727
93-11-16.86459441803965.86459441803962
94-13-12.2868282571527-0.713171742847335
95-11-10.3212358088656-0.678764191134436
96-9-4.48318768964229-4.51681231035771
97-17-16.2747564384509-0.725243561549104
98-22-15.1312718578489-6.86872814215111
99-25-17.0155360115571-7.98446398844289
100-20-13.6422139504095-6.35778604959047
101-24-15.8075468536387-8.19245314636133
102-24-20.322277499678-3.67772250032199
103-22-18.8217849848731-3.17821501512693
104-19-14.5487854750357-4.45121452496428
105-18-13.0073023227688-4.99269767723115
106-17-19.33237758172682.3323775817268
107-11-14.00188385836213.0018838583621
108-11-13.67608644891112.67608644891114
109-12-7.54352656652768-4.45647343347232
110-10-7.57613373284204-2.42386626715796
111-15-11.4365997011871-3.56340029881295
112-15-11.0454262408388-3.9545737591612
113-15-12.6758375798714-2.3241624201286
114-13-8.13440154000935-4.86559845999065
115-8-8.752758311749570.752758311749567
116-13-13.78958286805380.78958286805381
117-9-8.51784293641245-0.482157063587552
118-7-10.49179822695543.49179822695541
119-4-8.831214721005884.83121472100588
120-4-8.128220591098724.12822059109872
121-2-2.243168516120890.243168516120886
1220-4.195599343151794.19559934315179
123-2-7.893058840194945.89305884019494
124-3-8.024379250814465.02437925081446
1251-0.945152005003861.94515200500386
126-2-11.32713714634739.32713714634725
127-1-5.329437833448034.32943783344803
1281-3.30146154170054.3014615417005
129-3-6.483906473462683.48390647346268
130-4-9.367459232791275.36745923279127
131-9-9.467580352430590.467580352430589
132-9-7.23150851200271-1.76849148799729
133-7-2.37597902516822-4.62402097483178
134-14-8.62169094952703-5.37830905047298
135-12-15.5811174897933.58111748979296
136-16-17.18349985428721.18349985428717
137-20-16.9921115728573-3.00788842714269
138-12-13.06940568130781.06940568130777
139-12-10.2442830411739-1.75571695882608
140-10-9.07992163433123-0.920078365668768
141-10-5.16353499900298-4.83646500099702
142-13-10.3945125555264-2.60548744447363
143-16-12.3737344812655-3.62626551873453






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.0858067842191560.1716135684383120.914193215780844
130.06093765287186770.1218753057437350.939062347128132
140.1735369505866890.3470739011733790.826463049413311
150.2431140625376360.4862281250752720.756885937462364
160.517106278692570.9657874426148610.482893721307431
170.4740604798772810.9481209597545610.525939520122719
180.3981351879851240.7962703759702480.601864812014876
190.3059537506116860.6119075012233710.694046249388314
200.2291162190550730.4582324381101460.770883780944927
210.1692270387752930.3384540775505860.830772961224707
220.1668238670870470.3336477341740940.833176132912953
230.1324900359635870.2649800719271740.867509964036413
240.1127204525764010.2254409051528020.887279547423599
250.07942654067355160.1588530813471030.920573459326448
260.06019131450657470.1203826290131490.939808685493425
270.06264974553414720.1252994910682940.937350254465853
280.04574405028851220.09148810057702440.954255949711488
290.0385892573631150.07717851472622990.961410742636885
300.02612695111699880.05225390223399760.973873048883001
310.02709291750446090.05418583500892170.972907082495539
320.02027990077480770.04055980154961540.979720099225192
330.01386563610203010.02773127220406010.98613436389797
340.009188525737803430.01837705147560690.990811474262197
350.01031653853850330.02063307707700660.989683461461497
360.00748598421687140.01497196843374280.992514015783129
370.01586559269972530.03173118539945050.984134407300275
380.01258248065167410.02516496130334820.987417519348326
390.01431650409250740.02863300818501480.985683495907493
400.01182263503659290.02364527007318580.988177364963407
410.01101388533542840.02202777067085680.988986114664572
420.01034893137078430.02069786274156850.989651068629216
430.01428586398698260.02857172797396510.985714136013017
440.01244979705059880.02489959410119760.987550202949401
450.01111969820611650.02223939641223290.988880301793884
460.007726380218179860.01545276043635970.99227361978182
470.006388964812301660.01277792962460330.993611035187698
480.005258077758235960.01051615551647190.994741922241764
490.0156062962053750.03121259241074990.984393703794625
500.01097765272582570.02195530545165140.989022347274174
510.00856417424647820.01712834849295640.991435825753522
520.007315093551354870.01463018710270970.992684906448645
530.006185150089119740.01237030017823950.99381484991088
540.005177842101649180.01035568420329840.994822157898351
550.004697655833032920.009395311666065830.995302344166967
560.003507174600817030.007014349201634070.996492825399183
570.003873496932850540.007746993865701080.996126503067149
580.002862420779051090.005724841558102180.997137579220949
590.002111752000991120.004223504001982250.997888247999009
600.002047659177970130.004095318355940260.99795234082203
610.001355950550537490.002711901101074970.998644049449463
620.000930656857427760.001861313714855520.999069343142572
630.0008022609379184710.001604521875836940.999197739062082
640.0005340503304381460.001068100660876290.999465949669562
650.0004115869530832810.0008231739061665610.999588413046917
660.0003043633965369330.0006087267930738660.999695636603463
670.0001967684181118940.0003935368362237880.999803231581888
680.000153026109938460.000306052219876920.999846973890061
690.0001019031239999190.0002038062479998380.999898096876
706.62922730710258e-050.0001325845461420520.999933707726929
715.10222210292054e-050.0001020444420584110.999948977778971
720.000155856075075950.00031171215015190.999844143924924
730.0001291770358611020.0002583540717222050.999870822964139
740.0001053074065702830.0002106148131405660.99989469259343
750.0001198723956620210.0002397447913240410.999880127604338
768.28298058386992e-050.0001656596116773980.999917170194161
775.07721194290908e-050.0001015442388581820.999949227880571
783.13251041210864e-056.26502082421728e-050.999968674895879
792.31660251220388e-054.63320502440776e-050.999976833974878
801.35380166627405e-052.70760333254811e-050.999986461983337
818.38469330998443e-061.67693866199689e-050.99999161530669
827.33797241471842e-061.46759448294368e-050.999992662027585
836.04933899353886e-061.20986779870777e-050.999993950661006
843.464665398911e-066.929330797822e-060.999996535334601
852.57149504029102e-065.14299008058204e-060.99999742850496
861.72105791024022e-063.44211582048043e-060.99999827894209
872.26343558158058e-064.52687116316116e-060.999997736564418
888.73868119680953e-061.74773623936191e-050.999991261318803
891.01951926380952e-052.03903852761904e-050.999989804807362
900.000223229185296750.0004464583705935010.999776770814703
910.0007716340073357930.001543268014671590.999228365992664
920.001393686204232460.002787372408464920.998606313795768
930.002292166334348830.004584332668697650.997707833665651
940.003297682695276860.006595365390553710.996702317304723
950.006378330098560920.01275666019712180.993621669901439
960.0118386981989690.0236773963979380.988161301801031
970.0122398310444360.0244796620888720.987760168955564
980.02900662505688150.0580132501137630.970993374943118
990.05458447224588020.109168944491760.94541552775412
1000.07136259719799780.1427251943959960.928637402802002
1010.1664708416213130.3329416832426250.833529158378687
1020.1950715823798840.3901431647597690.804928417620116
1030.1731409452560080.3462818905120160.826859054743992
1040.1579876880054360.3159753760108720.842012311994564
1050.1647728783490810.3295457566981610.835227121650919
1060.1714422541102150.3428845082204310.828557745889785
1070.2516030653009270.5032061306018550.748396934699073
1080.4057169948063770.8114339896127530.594283005193623
1090.3529865757880420.7059731515760840.647013424211958
1100.3014381542362820.6028763084725630.698561845763718
1110.2500868280565010.5001736561130020.749913171943499
1120.2455062404450650.4910124808901310.754493759554935
1130.2431510614457170.4863021228914330.756848938554283
1140.3424147570117480.6848295140234970.657585242988252
1150.3079154731169990.6158309462339990.692084526883001
1160.3170709740518570.6341419481037150.682929025948143
1170.6380416220694960.7239167558610070.361958377930504
1180.690015192930120.619969614139760.30998480706988
1190.6573706362162140.6852587275675720.342629363783786
1200.5999265610582980.8001468778834040.400073438941702
1210.5524180283629780.8951639432740430.447581971637022
1220.5096831320734780.9806337358530430.490316867926522
1230.7019345279580090.5961309440839820.298065472041991
1240.828122457896810.3437550842063790.171877542103189
1250.9133691347859140.1732617304281710.0866308652140857
1260.8775512431378280.2448975137243440.122448756862172
1270.8309383499736770.3381233000526460.169061650026323
1280.7441836296631840.5116327406736320.255816370336816
1290.7053629306584710.5892741386830580.294637069341529
1300.5810803797615140.8378392404769710.418919620238486
1310.6294064161834170.7411871676331660.370593583816583

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.085806784219156 & 0.171613568438312 & 0.914193215780844 \tabularnewline
13 & 0.0609376528718677 & 0.121875305743735 & 0.939062347128132 \tabularnewline
14 & 0.173536950586689 & 0.347073901173379 & 0.826463049413311 \tabularnewline
15 & 0.243114062537636 & 0.486228125075272 & 0.756885937462364 \tabularnewline
16 & 0.51710627869257 & 0.965787442614861 & 0.482893721307431 \tabularnewline
17 & 0.474060479877281 & 0.948120959754561 & 0.525939520122719 \tabularnewline
18 & 0.398135187985124 & 0.796270375970248 & 0.601864812014876 \tabularnewline
19 & 0.305953750611686 & 0.611907501223371 & 0.694046249388314 \tabularnewline
20 & 0.229116219055073 & 0.458232438110146 & 0.770883780944927 \tabularnewline
21 & 0.169227038775293 & 0.338454077550586 & 0.830772961224707 \tabularnewline
22 & 0.166823867087047 & 0.333647734174094 & 0.833176132912953 \tabularnewline
23 & 0.132490035963587 & 0.264980071927174 & 0.867509964036413 \tabularnewline
24 & 0.112720452576401 & 0.225440905152802 & 0.887279547423599 \tabularnewline
25 & 0.0794265406735516 & 0.158853081347103 & 0.920573459326448 \tabularnewline
26 & 0.0601913145065747 & 0.120382629013149 & 0.939808685493425 \tabularnewline
27 & 0.0626497455341472 & 0.125299491068294 & 0.937350254465853 \tabularnewline
28 & 0.0457440502885122 & 0.0914881005770244 & 0.954255949711488 \tabularnewline
29 & 0.038589257363115 & 0.0771785147262299 & 0.961410742636885 \tabularnewline
30 & 0.0261269511169988 & 0.0522539022339976 & 0.973873048883001 \tabularnewline
31 & 0.0270929175044609 & 0.0541858350089217 & 0.972907082495539 \tabularnewline
32 & 0.0202799007748077 & 0.0405598015496154 & 0.979720099225192 \tabularnewline
33 & 0.0138656361020301 & 0.0277312722040601 & 0.98613436389797 \tabularnewline
34 & 0.00918852573780343 & 0.0183770514756069 & 0.990811474262197 \tabularnewline
35 & 0.0103165385385033 & 0.0206330770770066 & 0.989683461461497 \tabularnewline
36 & 0.0074859842168714 & 0.0149719684337428 & 0.992514015783129 \tabularnewline
37 & 0.0158655926997253 & 0.0317311853994505 & 0.984134407300275 \tabularnewline
38 & 0.0125824806516741 & 0.0251649613033482 & 0.987417519348326 \tabularnewline
39 & 0.0143165040925074 & 0.0286330081850148 & 0.985683495907493 \tabularnewline
40 & 0.0118226350365929 & 0.0236452700731858 & 0.988177364963407 \tabularnewline
41 & 0.0110138853354284 & 0.0220277706708568 & 0.988986114664572 \tabularnewline
42 & 0.0103489313707843 & 0.0206978627415685 & 0.989651068629216 \tabularnewline
43 & 0.0142858639869826 & 0.0285717279739651 & 0.985714136013017 \tabularnewline
44 & 0.0124497970505988 & 0.0248995941011976 & 0.987550202949401 \tabularnewline
45 & 0.0111196982061165 & 0.0222393964122329 & 0.988880301793884 \tabularnewline
46 & 0.00772638021817986 & 0.0154527604363597 & 0.99227361978182 \tabularnewline
47 & 0.00638896481230166 & 0.0127779296246033 & 0.993611035187698 \tabularnewline
48 & 0.00525807775823596 & 0.0105161555164719 & 0.994741922241764 \tabularnewline
49 & 0.015606296205375 & 0.0312125924107499 & 0.984393703794625 \tabularnewline
50 & 0.0109776527258257 & 0.0219553054516514 & 0.989022347274174 \tabularnewline
51 & 0.0085641742464782 & 0.0171283484929564 & 0.991435825753522 \tabularnewline
52 & 0.00731509355135487 & 0.0146301871027097 & 0.992684906448645 \tabularnewline
53 & 0.00618515008911974 & 0.0123703001782395 & 0.99381484991088 \tabularnewline
54 & 0.00517784210164918 & 0.0103556842032984 & 0.994822157898351 \tabularnewline
55 & 0.00469765583303292 & 0.00939531166606583 & 0.995302344166967 \tabularnewline
56 & 0.00350717460081703 & 0.00701434920163407 & 0.996492825399183 \tabularnewline
57 & 0.00387349693285054 & 0.00774699386570108 & 0.996126503067149 \tabularnewline
58 & 0.00286242077905109 & 0.00572484155810218 & 0.997137579220949 \tabularnewline
59 & 0.00211175200099112 & 0.00422350400198225 & 0.997888247999009 \tabularnewline
60 & 0.00204765917797013 & 0.00409531835594026 & 0.99795234082203 \tabularnewline
61 & 0.00135595055053749 & 0.00271190110107497 & 0.998644049449463 \tabularnewline
62 & 0.00093065685742776 & 0.00186131371485552 & 0.999069343142572 \tabularnewline
63 & 0.000802260937918471 & 0.00160452187583694 & 0.999197739062082 \tabularnewline
64 & 0.000534050330438146 & 0.00106810066087629 & 0.999465949669562 \tabularnewline
65 & 0.000411586953083281 & 0.000823173906166561 & 0.999588413046917 \tabularnewline
66 & 0.000304363396536933 & 0.000608726793073866 & 0.999695636603463 \tabularnewline
67 & 0.000196768418111894 & 0.000393536836223788 & 0.999803231581888 \tabularnewline
68 & 0.00015302610993846 & 0.00030605221987692 & 0.999846973890061 \tabularnewline
69 & 0.000101903123999919 & 0.000203806247999838 & 0.999898096876 \tabularnewline
70 & 6.62922730710258e-05 & 0.000132584546142052 & 0.999933707726929 \tabularnewline
71 & 5.10222210292054e-05 & 0.000102044442058411 & 0.999948977778971 \tabularnewline
72 & 0.00015585607507595 & 0.0003117121501519 & 0.999844143924924 \tabularnewline
73 & 0.000129177035861102 & 0.000258354071722205 & 0.999870822964139 \tabularnewline
74 & 0.000105307406570283 & 0.000210614813140566 & 0.99989469259343 \tabularnewline
75 & 0.000119872395662021 & 0.000239744791324041 & 0.999880127604338 \tabularnewline
76 & 8.28298058386992e-05 & 0.000165659611677398 & 0.999917170194161 \tabularnewline
77 & 5.07721194290908e-05 & 0.000101544238858182 & 0.999949227880571 \tabularnewline
78 & 3.13251041210864e-05 & 6.26502082421728e-05 & 0.999968674895879 \tabularnewline
79 & 2.31660251220388e-05 & 4.63320502440776e-05 & 0.999976833974878 \tabularnewline
80 & 1.35380166627405e-05 & 2.70760333254811e-05 & 0.999986461983337 \tabularnewline
81 & 8.38469330998443e-06 & 1.67693866199689e-05 & 0.99999161530669 \tabularnewline
82 & 7.33797241471842e-06 & 1.46759448294368e-05 & 0.999992662027585 \tabularnewline
83 & 6.04933899353886e-06 & 1.20986779870777e-05 & 0.999993950661006 \tabularnewline
84 & 3.464665398911e-06 & 6.929330797822e-06 & 0.999996535334601 \tabularnewline
85 & 2.57149504029102e-06 & 5.14299008058204e-06 & 0.99999742850496 \tabularnewline
86 & 1.72105791024022e-06 & 3.44211582048043e-06 & 0.99999827894209 \tabularnewline
87 & 2.26343558158058e-06 & 4.52687116316116e-06 & 0.999997736564418 \tabularnewline
88 & 8.73868119680953e-06 & 1.74773623936191e-05 & 0.999991261318803 \tabularnewline
89 & 1.01951926380952e-05 & 2.03903852761904e-05 & 0.999989804807362 \tabularnewline
90 & 0.00022322918529675 & 0.000446458370593501 & 0.999776770814703 \tabularnewline
91 & 0.000771634007335793 & 0.00154326801467159 & 0.999228365992664 \tabularnewline
92 & 0.00139368620423246 & 0.00278737240846492 & 0.998606313795768 \tabularnewline
93 & 0.00229216633434883 & 0.00458433266869765 & 0.997707833665651 \tabularnewline
94 & 0.00329768269527686 & 0.00659536539055371 & 0.996702317304723 \tabularnewline
95 & 0.00637833009856092 & 0.0127566601971218 & 0.993621669901439 \tabularnewline
96 & 0.011838698198969 & 0.023677396397938 & 0.988161301801031 \tabularnewline
97 & 0.012239831044436 & 0.024479662088872 & 0.987760168955564 \tabularnewline
98 & 0.0290066250568815 & 0.058013250113763 & 0.970993374943118 \tabularnewline
99 & 0.0545844722458802 & 0.10916894449176 & 0.94541552775412 \tabularnewline
100 & 0.0713625971979978 & 0.142725194395996 & 0.928637402802002 \tabularnewline
101 & 0.166470841621313 & 0.332941683242625 & 0.833529158378687 \tabularnewline
102 & 0.195071582379884 & 0.390143164759769 & 0.804928417620116 \tabularnewline
103 & 0.173140945256008 & 0.346281890512016 & 0.826859054743992 \tabularnewline
104 & 0.157987688005436 & 0.315975376010872 & 0.842012311994564 \tabularnewline
105 & 0.164772878349081 & 0.329545756698161 & 0.835227121650919 \tabularnewline
106 & 0.171442254110215 & 0.342884508220431 & 0.828557745889785 \tabularnewline
107 & 0.251603065300927 & 0.503206130601855 & 0.748396934699073 \tabularnewline
108 & 0.405716994806377 & 0.811433989612753 & 0.594283005193623 \tabularnewline
109 & 0.352986575788042 & 0.705973151576084 & 0.647013424211958 \tabularnewline
110 & 0.301438154236282 & 0.602876308472563 & 0.698561845763718 \tabularnewline
111 & 0.250086828056501 & 0.500173656113002 & 0.749913171943499 \tabularnewline
112 & 0.245506240445065 & 0.491012480890131 & 0.754493759554935 \tabularnewline
113 & 0.243151061445717 & 0.486302122891433 & 0.756848938554283 \tabularnewline
114 & 0.342414757011748 & 0.684829514023497 & 0.657585242988252 \tabularnewline
115 & 0.307915473116999 & 0.615830946233999 & 0.692084526883001 \tabularnewline
116 & 0.317070974051857 & 0.634141948103715 & 0.682929025948143 \tabularnewline
117 & 0.638041622069496 & 0.723916755861007 & 0.361958377930504 \tabularnewline
118 & 0.69001519293012 & 0.61996961413976 & 0.30998480706988 \tabularnewline
119 & 0.657370636216214 & 0.685258727567572 & 0.342629363783786 \tabularnewline
120 & 0.599926561058298 & 0.800146877883404 & 0.400073438941702 \tabularnewline
121 & 0.552418028362978 & 0.895163943274043 & 0.447581971637022 \tabularnewline
122 & 0.509683132073478 & 0.980633735853043 & 0.490316867926522 \tabularnewline
123 & 0.701934527958009 & 0.596130944083982 & 0.298065472041991 \tabularnewline
124 & 0.82812245789681 & 0.343755084206379 & 0.171877542103189 \tabularnewline
125 & 0.913369134785914 & 0.173261730428171 & 0.0866308652140857 \tabularnewline
126 & 0.877551243137828 & 0.244897513724344 & 0.122448756862172 \tabularnewline
127 & 0.830938349973677 & 0.338123300052646 & 0.169061650026323 \tabularnewline
128 & 0.744183629663184 & 0.511632740673632 & 0.255816370336816 \tabularnewline
129 & 0.705362930658471 & 0.589274138683058 & 0.294637069341529 \tabularnewline
130 & 0.581080379761514 & 0.837839240476971 & 0.418919620238486 \tabularnewline
131 & 0.629406416183417 & 0.741187167633166 & 0.370593583816583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185438&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]12[/C][C]0.085806784219156[/C][C]0.171613568438312[/C][C]0.914193215780844[/C][/ROW]
[ROW][C]13[/C][C]0.0609376528718677[/C][C]0.121875305743735[/C][C]0.939062347128132[/C][/ROW]
[ROW][C]14[/C][C]0.173536950586689[/C][C]0.347073901173379[/C][C]0.826463049413311[/C][/ROW]
[ROW][C]15[/C][C]0.243114062537636[/C][C]0.486228125075272[/C][C]0.756885937462364[/C][/ROW]
[ROW][C]16[/C][C]0.51710627869257[/C][C]0.965787442614861[/C][C]0.482893721307431[/C][/ROW]
[ROW][C]17[/C][C]0.474060479877281[/C][C]0.948120959754561[/C][C]0.525939520122719[/C][/ROW]
[ROW][C]18[/C][C]0.398135187985124[/C][C]0.796270375970248[/C][C]0.601864812014876[/C][/ROW]
[ROW][C]19[/C][C]0.305953750611686[/C][C]0.611907501223371[/C][C]0.694046249388314[/C][/ROW]
[ROW][C]20[/C][C]0.229116219055073[/C][C]0.458232438110146[/C][C]0.770883780944927[/C][/ROW]
[ROW][C]21[/C][C]0.169227038775293[/C][C]0.338454077550586[/C][C]0.830772961224707[/C][/ROW]
[ROW][C]22[/C][C]0.166823867087047[/C][C]0.333647734174094[/C][C]0.833176132912953[/C][/ROW]
[ROW][C]23[/C][C]0.132490035963587[/C][C]0.264980071927174[/C][C]0.867509964036413[/C][/ROW]
[ROW][C]24[/C][C]0.112720452576401[/C][C]0.225440905152802[/C][C]0.887279547423599[/C][/ROW]
[ROW][C]25[/C][C]0.0794265406735516[/C][C]0.158853081347103[/C][C]0.920573459326448[/C][/ROW]
[ROW][C]26[/C][C]0.0601913145065747[/C][C]0.120382629013149[/C][C]0.939808685493425[/C][/ROW]
[ROW][C]27[/C][C]0.0626497455341472[/C][C]0.125299491068294[/C][C]0.937350254465853[/C][/ROW]
[ROW][C]28[/C][C]0.0457440502885122[/C][C]0.0914881005770244[/C][C]0.954255949711488[/C][/ROW]
[ROW][C]29[/C][C]0.038589257363115[/C][C]0.0771785147262299[/C][C]0.961410742636885[/C][/ROW]
[ROW][C]30[/C][C]0.0261269511169988[/C][C]0.0522539022339976[/C][C]0.973873048883001[/C][/ROW]
[ROW][C]31[/C][C]0.0270929175044609[/C][C]0.0541858350089217[/C][C]0.972907082495539[/C][/ROW]
[ROW][C]32[/C][C]0.0202799007748077[/C][C]0.0405598015496154[/C][C]0.979720099225192[/C][/ROW]
[ROW][C]33[/C][C]0.0138656361020301[/C][C]0.0277312722040601[/C][C]0.98613436389797[/C][/ROW]
[ROW][C]34[/C][C]0.00918852573780343[/C][C]0.0183770514756069[/C][C]0.990811474262197[/C][/ROW]
[ROW][C]35[/C][C]0.0103165385385033[/C][C]0.0206330770770066[/C][C]0.989683461461497[/C][/ROW]
[ROW][C]36[/C][C]0.0074859842168714[/C][C]0.0149719684337428[/C][C]0.992514015783129[/C][/ROW]
[ROW][C]37[/C][C]0.0158655926997253[/C][C]0.0317311853994505[/C][C]0.984134407300275[/C][/ROW]
[ROW][C]38[/C][C]0.0125824806516741[/C][C]0.0251649613033482[/C][C]0.987417519348326[/C][/ROW]
[ROW][C]39[/C][C]0.0143165040925074[/C][C]0.0286330081850148[/C][C]0.985683495907493[/C][/ROW]
[ROW][C]40[/C][C]0.0118226350365929[/C][C]0.0236452700731858[/C][C]0.988177364963407[/C][/ROW]
[ROW][C]41[/C][C]0.0110138853354284[/C][C]0.0220277706708568[/C][C]0.988986114664572[/C][/ROW]
[ROW][C]42[/C][C]0.0103489313707843[/C][C]0.0206978627415685[/C][C]0.989651068629216[/C][/ROW]
[ROW][C]43[/C][C]0.0142858639869826[/C][C]0.0285717279739651[/C][C]0.985714136013017[/C][/ROW]
[ROW][C]44[/C][C]0.0124497970505988[/C][C]0.0248995941011976[/C][C]0.987550202949401[/C][/ROW]
[ROW][C]45[/C][C]0.0111196982061165[/C][C]0.0222393964122329[/C][C]0.988880301793884[/C][/ROW]
[ROW][C]46[/C][C]0.00772638021817986[/C][C]0.0154527604363597[/C][C]0.99227361978182[/C][/ROW]
[ROW][C]47[/C][C]0.00638896481230166[/C][C]0.0127779296246033[/C][C]0.993611035187698[/C][/ROW]
[ROW][C]48[/C][C]0.00525807775823596[/C][C]0.0105161555164719[/C][C]0.994741922241764[/C][/ROW]
[ROW][C]49[/C][C]0.015606296205375[/C][C]0.0312125924107499[/C][C]0.984393703794625[/C][/ROW]
[ROW][C]50[/C][C]0.0109776527258257[/C][C]0.0219553054516514[/C][C]0.989022347274174[/C][/ROW]
[ROW][C]51[/C][C]0.0085641742464782[/C][C]0.0171283484929564[/C][C]0.991435825753522[/C][/ROW]
[ROW][C]52[/C][C]0.00731509355135487[/C][C]0.0146301871027097[/C][C]0.992684906448645[/C][/ROW]
[ROW][C]53[/C][C]0.00618515008911974[/C][C]0.0123703001782395[/C][C]0.99381484991088[/C][/ROW]
[ROW][C]54[/C][C]0.00517784210164918[/C][C]0.0103556842032984[/C][C]0.994822157898351[/C][/ROW]
[ROW][C]55[/C][C]0.00469765583303292[/C][C]0.00939531166606583[/C][C]0.995302344166967[/C][/ROW]
[ROW][C]56[/C][C]0.00350717460081703[/C][C]0.00701434920163407[/C][C]0.996492825399183[/C][/ROW]
[ROW][C]57[/C][C]0.00387349693285054[/C][C]0.00774699386570108[/C][C]0.996126503067149[/C][/ROW]
[ROW][C]58[/C][C]0.00286242077905109[/C][C]0.00572484155810218[/C][C]0.997137579220949[/C][/ROW]
[ROW][C]59[/C][C]0.00211175200099112[/C][C]0.00422350400198225[/C][C]0.997888247999009[/C][/ROW]
[ROW][C]60[/C][C]0.00204765917797013[/C][C]0.00409531835594026[/C][C]0.99795234082203[/C][/ROW]
[ROW][C]61[/C][C]0.00135595055053749[/C][C]0.00271190110107497[/C][C]0.998644049449463[/C][/ROW]
[ROW][C]62[/C][C]0.00093065685742776[/C][C]0.00186131371485552[/C][C]0.999069343142572[/C][/ROW]
[ROW][C]63[/C][C]0.000802260937918471[/C][C]0.00160452187583694[/C][C]0.999197739062082[/C][/ROW]
[ROW][C]64[/C][C]0.000534050330438146[/C][C]0.00106810066087629[/C][C]0.999465949669562[/C][/ROW]
[ROW][C]65[/C][C]0.000411586953083281[/C][C]0.000823173906166561[/C][C]0.999588413046917[/C][/ROW]
[ROW][C]66[/C][C]0.000304363396536933[/C][C]0.000608726793073866[/C][C]0.999695636603463[/C][/ROW]
[ROW][C]67[/C][C]0.000196768418111894[/C][C]0.000393536836223788[/C][C]0.999803231581888[/C][/ROW]
[ROW][C]68[/C][C]0.00015302610993846[/C][C]0.00030605221987692[/C][C]0.999846973890061[/C][/ROW]
[ROW][C]69[/C][C]0.000101903123999919[/C][C]0.000203806247999838[/C][C]0.999898096876[/C][/ROW]
[ROW][C]70[/C][C]6.62922730710258e-05[/C][C]0.000132584546142052[/C][C]0.999933707726929[/C][/ROW]
[ROW][C]71[/C][C]5.10222210292054e-05[/C][C]0.000102044442058411[/C][C]0.999948977778971[/C][/ROW]
[ROW][C]72[/C][C]0.00015585607507595[/C][C]0.0003117121501519[/C][C]0.999844143924924[/C][/ROW]
[ROW][C]73[/C][C]0.000129177035861102[/C][C]0.000258354071722205[/C][C]0.999870822964139[/C][/ROW]
[ROW][C]74[/C][C]0.000105307406570283[/C][C]0.000210614813140566[/C][C]0.99989469259343[/C][/ROW]
[ROW][C]75[/C][C]0.000119872395662021[/C][C]0.000239744791324041[/C][C]0.999880127604338[/C][/ROW]
[ROW][C]76[/C][C]8.28298058386992e-05[/C][C]0.000165659611677398[/C][C]0.999917170194161[/C][/ROW]
[ROW][C]77[/C][C]5.07721194290908e-05[/C][C]0.000101544238858182[/C][C]0.999949227880571[/C][/ROW]
[ROW][C]78[/C][C]3.13251041210864e-05[/C][C]6.26502082421728e-05[/C][C]0.999968674895879[/C][/ROW]
[ROW][C]79[/C][C]2.31660251220388e-05[/C][C]4.63320502440776e-05[/C][C]0.999976833974878[/C][/ROW]
[ROW][C]80[/C][C]1.35380166627405e-05[/C][C]2.70760333254811e-05[/C][C]0.999986461983337[/C][/ROW]
[ROW][C]81[/C][C]8.38469330998443e-06[/C][C]1.67693866199689e-05[/C][C]0.99999161530669[/C][/ROW]
[ROW][C]82[/C][C]7.33797241471842e-06[/C][C]1.46759448294368e-05[/C][C]0.999992662027585[/C][/ROW]
[ROW][C]83[/C][C]6.04933899353886e-06[/C][C]1.20986779870777e-05[/C][C]0.999993950661006[/C][/ROW]
[ROW][C]84[/C][C]3.464665398911e-06[/C][C]6.929330797822e-06[/C][C]0.999996535334601[/C][/ROW]
[ROW][C]85[/C][C]2.57149504029102e-06[/C][C]5.14299008058204e-06[/C][C]0.99999742850496[/C][/ROW]
[ROW][C]86[/C][C]1.72105791024022e-06[/C][C]3.44211582048043e-06[/C][C]0.99999827894209[/C][/ROW]
[ROW][C]87[/C][C]2.26343558158058e-06[/C][C]4.52687116316116e-06[/C][C]0.999997736564418[/C][/ROW]
[ROW][C]88[/C][C]8.73868119680953e-06[/C][C]1.74773623936191e-05[/C][C]0.999991261318803[/C][/ROW]
[ROW][C]89[/C][C]1.01951926380952e-05[/C][C]2.03903852761904e-05[/C][C]0.999989804807362[/C][/ROW]
[ROW][C]90[/C][C]0.00022322918529675[/C][C]0.000446458370593501[/C][C]0.999776770814703[/C][/ROW]
[ROW][C]91[/C][C]0.000771634007335793[/C][C]0.00154326801467159[/C][C]0.999228365992664[/C][/ROW]
[ROW][C]92[/C][C]0.00139368620423246[/C][C]0.00278737240846492[/C][C]0.998606313795768[/C][/ROW]
[ROW][C]93[/C][C]0.00229216633434883[/C][C]0.00458433266869765[/C][C]0.997707833665651[/C][/ROW]
[ROW][C]94[/C][C]0.00329768269527686[/C][C]0.00659536539055371[/C][C]0.996702317304723[/C][/ROW]
[ROW][C]95[/C][C]0.00637833009856092[/C][C]0.0127566601971218[/C][C]0.993621669901439[/C][/ROW]
[ROW][C]96[/C][C]0.011838698198969[/C][C]0.023677396397938[/C][C]0.988161301801031[/C][/ROW]
[ROW][C]97[/C][C]0.012239831044436[/C][C]0.024479662088872[/C][C]0.987760168955564[/C][/ROW]
[ROW][C]98[/C][C]0.0290066250568815[/C][C]0.058013250113763[/C][C]0.970993374943118[/C][/ROW]
[ROW][C]99[/C][C]0.0545844722458802[/C][C]0.10916894449176[/C][C]0.94541552775412[/C][/ROW]
[ROW][C]100[/C][C]0.0713625971979978[/C][C]0.142725194395996[/C][C]0.928637402802002[/C][/ROW]
[ROW][C]101[/C][C]0.166470841621313[/C][C]0.332941683242625[/C][C]0.833529158378687[/C][/ROW]
[ROW][C]102[/C][C]0.195071582379884[/C][C]0.390143164759769[/C][C]0.804928417620116[/C][/ROW]
[ROW][C]103[/C][C]0.173140945256008[/C][C]0.346281890512016[/C][C]0.826859054743992[/C][/ROW]
[ROW][C]104[/C][C]0.157987688005436[/C][C]0.315975376010872[/C][C]0.842012311994564[/C][/ROW]
[ROW][C]105[/C][C]0.164772878349081[/C][C]0.329545756698161[/C][C]0.835227121650919[/C][/ROW]
[ROW][C]106[/C][C]0.171442254110215[/C][C]0.342884508220431[/C][C]0.828557745889785[/C][/ROW]
[ROW][C]107[/C][C]0.251603065300927[/C][C]0.503206130601855[/C][C]0.748396934699073[/C][/ROW]
[ROW][C]108[/C][C]0.405716994806377[/C][C]0.811433989612753[/C][C]0.594283005193623[/C][/ROW]
[ROW][C]109[/C][C]0.352986575788042[/C][C]0.705973151576084[/C][C]0.647013424211958[/C][/ROW]
[ROW][C]110[/C][C]0.301438154236282[/C][C]0.602876308472563[/C][C]0.698561845763718[/C][/ROW]
[ROW][C]111[/C][C]0.250086828056501[/C][C]0.500173656113002[/C][C]0.749913171943499[/C][/ROW]
[ROW][C]112[/C][C]0.245506240445065[/C][C]0.491012480890131[/C][C]0.754493759554935[/C][/ROW]
[ROW][C]113[/C][C]0.243151061445717[/C][C]0.486302122891433[/C][C]0.756848938554283[/C][/ROW]
[ROW][C]114[/C][C]0.342414757011748[/C][C]0.684829514023497[/C][C]0.657585242988252[/C][/ROW]
[ROW][C]115[/C][C]0.307915473116999[/C][C]0.615830946233999[/C][C]0.692084526883001[/C][/ROW]
[ROW][C]116[/C][C]0.317070974051857[/C][C]0.634141948103715[/C][C]0.682929025948143[/C][/ROW]
[ROW][C]117[/C][C]0.638041622069496[/C][C]0.723916755861007[/C][C]0.361958377930504[/C][/ROW]
[ROW][C]118[/C][C]0.69001519293012[/C][C]0.61996961413976[/C][C]0.30998480706988[/C][/ROW]
[ROW][C]119[/C][C]0.657370636216214[/C][C]0.685258727567572[/C][C]0.342629363783786[/C][/ROW]
[ROW][C]120[/C][C]0.599926561058298[/C][C]0.800146877883404[/C][C]0.400073438941702[/C][/ROW]
[ROW][C]121[/C][C]0.552418028362978[/C][C]0.895163943274043[/C][C]0.447581971637022[/C][/ROW]
[ROW][C]122[/C][C]0.509683132073478[/C][C]0.980633735853043[/C][C]0.490316867926522[/C][/ROW]
[ROW][C]123[/C][C]0.701934527958009[/C][C]0.596130944083982[/C][C]0.298065472041991[/C][/ROW]
[ROW][C]124[/C][C]0.82812245789681[/C][C]0.343755084206379[/C][C]0.171877542103189[/C][/ROW]
[ROW][C]125[/C][C]0.913369134785914[/C][C]0.173261730428171[/C][C]0.0866308652140857[/C][/ROW]
[ROW][C]126[/C][C]0.877551243137828[/C][C]0.244897513724344[/C][C]0.122448756862172[/C][/ROW]
[ROW][C]127[/C][C]0.830938349973677[/C][C]0.338123300052646[/C][C]0.169061650026323[/C][/ROW]
[ROW][C]128[/C][C]0.744183629663184[/C][C]0.511632740673632[/C][C]0.255816370336816[/C][/ROW]
[ROW][C]129[/C][C]0.705362930658471[/C][C]0.589274138683058[/C][C]0.294637069341529[/C][/ROW]
[ROW][C]130[/C][C]0.581080379761514[/C][C]0.837839240476971[/C][C]0.418919620238486[/C][/ROW]
[ROW][C]131[/C][C]0.629406416183417[/C][C]0.741187167633166[/C][C]0.370593583816583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185438&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185438&T=5

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.0858067842191560.1716135684383120.914193215780844
130.06093765287186770.1218753057437350.939062347128132
140.1735369505866890.3470739011733790.826463049413311
150.2431140625376360.4862281250752720.756885937462364
160.517106278692570.9657874426148610.482893721307431
170.4740604798772810.9481209597545610.525939520122719
180.3981351879851240.7962703759702480.601864812014876
190.3059537506116860.6119075012233710.694046249388314
200.2291162190550730.4582324381101460.770883780944927
210.1692270387752930.3384540775505860.830772961224707
220.1668238670870470.3336477341740940.833176132912953
230.1324900359635870.2649800719271740.867509964036413
240.1127204525764010.2254409051528020.887279547423599
250.07942654067355160.1588530813471030.920573459326448
260.06019131450657470.1203826290131490.939808685493425
270.06264974553414720.1252994910682940.937350254465853
280.04574405028851220.09148810057702440.954255949711488
290.0385892573631150.07717851472622990.961410742636885
300.02612695111699880.05225390223399760.973873048883001
310.02709291750446090.05418583500892170.972907082495539
320.02027990077480770.04055980154961540.979720099225192
330.01386563610203010.02773127220406010.98613436389797
340.009188525737803430.01837705147560690.990811474262197
350.01031653853850330.02063307707700660.989683461461497
360.00748598421687140.01497196843374280.992514015783129
370.01586559269972530.03173118539945050.984134407300275
380.01258248065167410.02516496130334820.987417519348326
390.01431650409250740.02863300818501480.985683495907493
400.01182263503659290.02364527007318580.988177364963407
410.01101388533542840.02202777067085680.988986114664572
420.01034893137078430.02069786274156850.989651068629216
430.01428586398698260.02857172797396510.985714136013017
440.01244979705059880.02489959410119760.987550202949401
450.01111969820611650.02223939641223290.988880301793884
460.007726380218179860.01545276043635970.99227361978182
470.006388964812301660.01277792962460330.993611035187698
480.005258077758235960.01051615551647190.994741922241764
490.0156062962053750.03121259241074990.984393703794625
500.01097765272582570.02195530545165140.989022347274174
510.00856417424647820.01712834849295640.991435825753522
520.007315093551354870.01463018710270970.992684906448645
530.006185150089119740.01237030017823950.99381484991088
540.005177842101649180.01035568420329840.994822157898351
550.004697655833032920.009395311666065830.995302344166967
560.003507174600817030.007014349201634070.996492825399183
570.003873496932850540.007746993865701080.996126503067149
580.002862420779051090.005724841558102180.997137579220949
590.002111752000991120.004223504001982250.997888247999009
600.002047659177970130.004095318355940260.99795234082203
610.001355950550537490.002711901101074970.998644049449463
620.000930656857427760.001861313714855520.999069343142572
630.0008022609379184710.001604521875836940.999197739062082
640.0005340503304381460.001068100660876290.999465949669562
650.0004115869530832810.0008231739061665610.999588413046917
660.0003043633965369330.0006087267930738660.999695636603463
670.0001967684181118940.0003935368362237880.999803231581888
680.000153026109938460.000306052219876920.999846973890061
690.0001019031239999190.0002038062479998380.999898096876
706.62922730710258e-050.0001325845461420520.999933707726929
715.10222210292054e-050.0001020444420584110.999948977778971
720.000155856075075950.00031171215015190.999844143924924
730.0001291770358611020.0002583540717222050.999870822964139
740.0001053074065702830.0002106148131405660.99989469259343
750.0001198723956620210.0002397447913240410.999880127604338
768.28298058386992e-050.0001656596116773980.999917170194161
775.07721194290908e-050.0001015442388581820.999949227880571
783.13251041210864e-056.26502082421728e-050.999968674895879
792.31660251220388e-054.63320502440776e-050.999976833974878
801.35380166627405e-052.70760333254811e-050.999986461983337
818.38469330998443e-061.67693866199689e-050.99999161530669
827.33797241471842e-061.46759448294368e-050.999992662027585
836.04933899353886e-061.20986779870777e-050.999993950661006
843.464665398911e-066.929330797822e-060.999996535334601
852.57149504029102e-065.14299008058204e-060.99999742850496
861.72105791024022e-063.44211582048043e-060.99999827894209
872.26343558158058e-064.52687116316116e-060.999997736564418
888.73868119680953e-061.74773623936191e-050.999991261318803
891.01951926380952e-052.03903852761904e-050.999989804807362
900.000223229185296750.0004464583705935010.999776770814703
910.0007716340073357930.001543268014671590.999228365992664
920.001393686204232460.002787372408464920.998606313795768
930.002292166334348830.004584332668697650.997707833665651
940.003297682695276860.006595365390553710.996702317304723
950.006378330098560920.01275666019712180.993621669901439
960.0118386981989690.0236773963979380.988161301801031
970.0122398310444360.0244796620888720.987760168955564
980.02900662505688150.0580132501137630.970993374943118
990.05458447224588020.109168944491760.94541552775412
1000.07136259719799780.1427251943959960.928637402802002
1010.1664708416213130.3329416832426250.833529158378687
1020.1950715823798840.3901431647597690.804928417620116
1030.1731409452560080.3462818905120160.826859054743992
1040.1579876880054360.3159753760108720.842012311994564
1050.1647728783490810.3295457566981610.835227121650919
1060.1714422541102150.3428845082204310.828557745889785
1070.2516030653009270.5032061306018550.748396934699073
1080.4057169948063770.8114339896127530.594283005193623
1090.3529865757880420.7059731515760840.647013424211958
1100.3014381542362820.6028763084725630.698561845763718
1110.2500868280565010.5001736561130020.749913171943499
1120.2455062404450650.4910124808901310.754493759554935
1130.2431510614457170.4863021228914330.756848938554283
1140.3424147570117480.6848295140234970.657585242988252
1150.3079154731169990.6158309462339990.692084526883001
1160.3170709740518570.6341419481037150.682929025948143
1170.6380416220694960.7239167558610070.361958377930504
1180.690015192930120.619969614139760.30998480706988
1190.6573706362162140.6852587275675720.342629363783786
1200.5999265610582980.8001468778834040.400073438941702
1210.5524180283629780.8951639432740430.447581971637022
1220.5096831320734780.9806337358530430.490316867926522
1230.7019345279580090.5961309440839820.298065472041991
1240.828122457896810.3437550842063790.171877542103189
1250.9133691347859140.1732617304281710.0866308652140857
1260.8775512431378280.2448975137243440.122448756862172
1270.8309383499736770.3381233000526460.169061650026323
1280.7441836296631840.5116327406736320.255816370336816
1290.7053629306584710.5892741386830580.294637069341529
1300.5810803797615140.8378392404769710.418919620238486
1310.6294064161834170.7411871676331660.370593583816583






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level400.333333333333333NOK
5% type I error level660.55NOK
10% type I error level710.591666666666667NOK

\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 & 40 & 0.333333333333333 & NOK \tabularnewline
5% type I error level & 66 & 0.55 & NOK \tabularnewline
10% type I error level & 71 & 0.591666666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185438&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]40[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]66[/C][C]0.55[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]71[/C][C]0.591666666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185438&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185438&T=6

As an alternative you can also use a QR Code:  
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 level400.333333333333333NOK
5% type I error level660.55NOK
10% type I error level710.591666666666667NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}