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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 computationTue, 28 Dec 2010 11:55:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t1293537197kg7xves9gae3tnz.htm/, Retrieved Sun, 05 May 2024 03:39:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116301, Retrieved Sun, 05 May 2024 03:39:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2010-12-26 21:43:16] [dcd1a35a8985187cb1e9de87792355b2]
-   PD    [Multiple Regression] [] [2010-12-26 22:18:54] [dcd1a35a8985187cb1e9de87792355b2]
-             [Multiple Regression] [] [2010-12-28 11:55:37] [82d760768aff8bf374d9817688c406af] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	27	27	5	5	26	26	49	49	35	35	18
1	36	36	4	4	25	25	45	45	34	34	10
1	25	25	4	4	17	17	54	54	13	13	23
1	27	27	3	3	37	37	36	36	35	35	14
1	50	50	4	4	27	27	46	46	35	35	18
1	41	41	4	4	36	36	42	42	36	36	19
1	48	48	5	5	25	25	41	41	27	27	19
1	44	44	2	2	29	29	45	45	29	29	16
1	28	28	3	3	26	26	42	42	15	15	23
1	56	56	3	3	24	24	45	45	33	33	17
1	50	50	5	5	29	29	43	43	32	32	20
1	47	47	4	4	26	26	45	45	21	21	18
1	52	52	2	2	21	21	42	42	25	25	13
1	45	45	4	4	21	21	47	47	22	22	17
1			3	3	30	30	41	41	26	26	17
1	52	52	4	4	21	21	44	44	34	34	16
1	46	46	2	2	29	29	51	51	34	34	20
1	58	58	3	3	28	28	46	46	36	36	18
1	54	54	5	5	19	19	47	47	36	36	9
1	29	29	3	3	26	26	46	46	26	26	14
1	43	43	2	2	34	34	50	50	34	34	21
1	45	45	3	3	24	24	51	51	33	33	20
1	46	46	5	5	20	20	47	47	37	37	16
1	25	25	4	4	21	21	46	46	29	29	21
1	47	47	2	2	33	33	43	43	35	35	18
1	41	41	3	3	22	22	55	55	28	28	20
1	29	29	4	4	18	18	52	52	25	25	15
1	45	45	5	5	20	20	56	56	32	32	11
1	54	54	2	2	26	26	46	46	27	27	20
1	28	28	4	4	23	23	51	51	27	27	20
1	37	37	3	3	34	34	39	39	31	31	20
1	56	56	2	2	25	25	45	45	16	16	14
1	43	43	3	3	33	33	31	31	25	25	20
1	34	34	3	3	46	46	29	29	27	27	20
1	42	42	3	3	24	24	48	48	32	32	15
1	46	46	3	3	42	42	32	32	25	25	11
1	25	25	4	4	30	30	45	45	27	27	17
1	25	25	5	5	19	19	33	33	29	29	15
1	25	25	3	3	37	37	40	40	28	28	13
1	48	48	4	4	23	23	44	44	32	32	12
1	27	27	5	5	34	34	46	46	29	29	21
1	28	28	3	3	38	38	39	39	32	32	19
1	25	25	1	1	26	26	39	39	16	16	9
1	26	26	3	3	36	36	41	41	26	26	18
1	51	51	3	3	20	20	52	52	32	32	14
1	29	29	3	3	27	27	42	42	24	24	23
1	29	29	4	4	27	27	44	44	26	26	19
1	43	43	2	2	41	41	33	33	19	19	24
1	44	44	3	3	33	33	46	46	25	25	20
1	25	25	3	3	37	37	45	45	24	24	21
1	51	51	2	2	30	30	40	40	23	23	18
1	42	42	5	5	20	20	48	48	28	28	20
1	25	25	4	4	20	20	53	53	28	28	18
1	51	51	3	3	31	31	45	45	26	26	18
1	46	46	5	5	32	32	46	46	34	34	14
1	29	29			22	22	37	37	30	30	22
1	38	38	3	3	25	25	46	46	30	30	19
1	41	41	1	1	27	27	20	20	15	15	14
1	44	44	4	4	20	20	55	55	31	31	17
1	41	41	5	5	40	40	40	40	33	33	19
1	46	46	4	4	33	33	40	40	27	27	17
1	41	41	4	4	24	24	45	45	31	31	12
1	25	25	3	3	41	41	41	41	22	22	15
1	55	55	5	5	28	28	39	39	41	41	13
1	21	21	3	3	37	37	45	45	25	25	17
1			3	3	26	26	46	46	28	28	16
1	52	52	3	3	30	30	39	39	45	45	14
1			4	4	31	31	35	35	25	25	14
1			3	3	21	21	45	45	29	29	13
1	38	38	5	5	28	28	34	34	25	25	13
1	32	32	5	5	29	29	41	41	27	27	23
1	44	44	4	4	36	36	45	45	24	24	17
1	47	47	5	5	27	27	45	45	35	35	11
1	52	52	1	1	29	29	40	40	32	32	14
1	27	27	2	2	42	42	40	40	24	24	19
1	27	27	4	4	47	47	44	44	38	38	14
1	25	25	4	4	17	17	44	44	36	36	18
1	28	28	4	4	34	34	48	48	24	24	25
1	25	25	3	3	32	32	51	51	18	18	22
1	52	52	4	4	25	25	49	49	34	34	15
1	44	44	3	3	27	27	33	33	23	23	18
1	42	42	3	3	32	32	45	45	34	34	22
1	45	45	5	5	26	26	44	44	32	32	15
1	45	45	2	2	29	29	44	44	24	24	16
1	50	50	5	5	28	28	40	40	34	34	11
1	49	49	3	3	19	19	48	48	33	33	20
1	52	52	2	2	46	46	49	49	33	33	14
2	25	0	3	0	35	0	36	0	28	0	20
2	44	0	3	0	15	0	53	0	32	0	15
2	43	0	4	0	30	0	45	0	29	0	14
2	47	0	2	0	27	0	47	0	27	0	15
2	41	0	3	0	33	0	42	0	28	0	14
2	47	0	5	0	30	0	40	0	28	0	13
2	40	0	3	0	25	0	45	0	30	0	13
2	46	0	3	0	23	0	40	0	25	0	14
2	49	0	4	0	35	0	47	0	31	0	14
2	25	0	4	0	39	0	31	0	37	0	21
2	41	0	4	0	23	0	46	0	37	0	15
2	26	0	3	0	32	0	34	0	34	0	19
2	37	0	5	0	35	0	51	0	32	0	20
2	41	0	3	0	23	0	44	0	28	0	12
2	26	0	4	0	28	0	47	0	25	0	13
2	50	0	3	0	33	0	38	0	26	0	12
2	30	0	3	0	33	0	48	0	33	0	16
2	47	0	2	0	40	0	36	0	31	0	12
2	48	0	1	0	35	0	35	0	22	0	18
2	48	0	3	0	35	0	49	0	29	0	22
2	26	0	4	0	32	0	38	0	24	0	17
2		0	3	0	35	0	36	0	32	0	14
2	50	0	3	0	35	0	47	0	23	0	19
2	47	0	2	0	40	0	53	0	20	0	23
2	45	0	2	0	35	0	39	0	26	0	10
2	41	0	4	0	20	0	55	0	36	0	16
2	45	0	5	0	28	0	41	0	26	0	18
2	40	0	3	0	46	0	33	0	33	0	12
2	34	0	5	0	22	0	42	0	29	0	19
2	52	0	3	0	25	0	46	0	35	0	16
2	41	0	4	0	31	0	33	0	24	0	12
2	48	0	3	0	21	0	51	0	31	0	18
2	45	0	3	0	23	0	46	0	29	0	14
2	25	0	3	0	34	0	50	0	29	0	15
2	26	0	4	0	31	0	46	0	29	0	17
2	50	0	4	0	31	0	48	0	34	0	14
2	48	0	4	0	26	0	44	0	32	0	16
2	51	0	3	0	36	0	38	0	31	0	15
2	53	0	3	0	28	0	42	0	31	0	17
2	32	0	3	0	32	0	38	0	28	0	21
2	31	0	5	0	33	0	55	0	25	0	22
2	30	0	5	0	17	0	51	0	36	0	20
2	47	0	4	0	36	0	53	0	36	0	17
2	33	0	4	0	40	0	47	0	36	0	19
2	21	0	4	0	33	0	49	0	32	0	20
2	36	0	5	0	35	0	46	0	29	0	12
2	50	0	3	0	23	0	42	0	31	0	13
2	48	0	3	0	15	0	56	0	34	0	18
2	48	0	2	0	38	0	35	0	27	0	19
2	49	0	5	0	41	0	46	0	33	0	16
2	43	0	2	0	45	0	35	0	35	0	19
2	48	0	3	0	27	0	48	0	33	0	12
2	48	0	4	0	46	0	42	0	27	0	22
2	49	0	4	0	44	0	39	0	32	0	9
2	25	0	4	0	44	0	45	0	38	0	14
2	46	0	3	0	30	0	42	0	37	0	12
2	53	0	5	0	44	0	32	0	38	0	18
2	49	0	2	0	33	0	39	0	28	0	17
2	20	0	3	0	23	0	36	0	21	0	14
2	44	0	3	0	33	0	38	0	35	0	23
2	38	0	4	0	33	0	49	0	31	0	19
2	42	0	4	0	25	0	43	0	30	0	17
2	46	0	4	0	16	0	48	0	24	0	10
2	49	0	2	0	36	0	45	0	27	0	16
2	51	0	3	0	35	0	32	0	26	0	14
2	47	0	3	0	32	0	42	0	28	0	18
2	44	0	3	0	36	0	45	0	34	0	19
2	47	0	3	0	51	0	29	0	29	0	21
2	46	0	3	0	30	0	51	0	26	0	13
2	28	0	3	0	29	0	50	0	28	0	11
2	47	0	4	0	26	0	44	0	33	0	16
2	28	0	4	0	20	0	41	0	32	0	22
2	45	0	4	0	29	0	47	0	31	0	17
2	46	0	4	0	32	0	42	0	37	0	25
2	22	0	3	0	32	0	51	0	19	0	23
2	33	0	3	0	34	0	43	0	27	0	21
2	47	0	5	0	25	0	41	0	38	0	18
2	42	0	3	0	39	0	37	0	35	0	17
2	47	0	3	0	21	0	46	0	35	0	11
2	50	0	3	0	38	0	38	0	30	0	17
2	49	0	4	0	25	0	21	0	21	0	15
2	46	0	4	0	38	0	31	0	33	0	20
2	45	0	3	0	31	0	49	0	29	0	16
2	52	0	3	0	27	0	40	0	31	0	14
2	40	0	4	0	26	0	46	0	31	0	15
2	49	0	4	0	37	0	45	0	31	0	13
2	46	0	4	0	33	0	43	0	26	0	18
2	32	0	3	0	41	0	45	0	26	0	21
2	41	0	3	0	19	0	48	0	23	0	14
2	43	0	3	0	37	0	43	0	27	0	12
2	28	0	3	0	33	0	34	0	24	0	15
2	45	0	5	0	27	0	55	0	24	0	12
2	43	0	3	0	37	0	43	0	35	0	18
2	47	0	4	0	34	0	44	0	22	0	12
2	52	0	4	0	27	0	44	0	34	0	12
2	40	0	2	0	37	0	41	0	28	0	16
2	48	0	3	0	31	0	46	0	29	0	20
2	51	0	3	0	42	0	49	0	38	0	15
2	49	0	4	0	33	0	55	0	24	0	12
2	31	0	4	0	39	0	51	0	25	0	18
2	43	0	3	0	27	0	46	0	37	0	18
2	31	0	3	0	35	0	37	0	33	0	11
2	28	0	4	0	23	0	43	0	30	0	13
2	43	0	4	0	32	0	41	0	22	0	15
2	31	0	3	0	22	0	45	0	28	0	19
2	51	0	3	0	17	0	39	0	24	0	13
2	58	0	4	0	35	0	38	0	33	0	19
2	25	0	5	0	34	0	41	0	37	0	18

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 9 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116301&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116301&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 time9 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Extrinsieke_waarden[t] = + 9.17265069352998 + 0.160207666432521geslacht[t] -0.125996872123757leeftijd[t] -0.0992089240642966leeftijd_man[t] -0.00941326692962691opleiding[t] + 0.178373242926735opleiding_man[t] + 0.11009600111611Neuroticisme[t] -0.0726361395252345Neuroticisme_man[t] + 0.224060126846621Extraversie[t] + 0.170531472270368Extraversie_man[t] + 0.367687899652953Openheid[t] -0.428294055693013Openheid_man[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Extrinsieke_waarden[t] =  +  9.17265069352998 +  0.160207666432521geslacht[t] -0.125996872123757leeftijd[t] -0.0992089240642966leeftijd_man[t] -0.00941326692962691opleiding[t] +  0.178373242926735opleiding_man[t] +  0.11009600111611Neuroticisme[t] -0.0726361395252345Neuroticisme_man[t] +  0.224060126846621Extraversie[t] +  0.170531472270368Extraversie_man[t] +  0.367687899652953Openheid[t] -0.428294055693013Openheid_man[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116301&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Extrinsieke_waarden[t] =  +  9.17265069352998 +  0.160207666432521geslacht[t] -0.125996872123757leeftijd[t] -0.0992089240642966leeftijd_man[t] -0.00941326692962691opleiding[t] +  0.178373242926735opleiding_man[t] +  0.11009600111611Neuroticisme[t] -0.0726361395252345Neuroticisme_man[t] +  0.224060126846621Extraversie[t] +  0.170531472270368Extraversie_man[t] +  0.367687899652953Openheid[t] -0.428294055693013Openheid_man[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116301&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
Extrinsieke_waarden[t] = + 9.17265069352998 + 0.160207666432521geslacht[t] -0.125996872123757leeftijd[t] -0.0992089240642966leeftijd_man[t] -0.00941326692962691opleiding[t] + 0.178373242926735opleiding_man[t] + 0.11009600111611Neuroticisme[t] -0.0726361395252345Neuroticisme_man[t] + 0.224060126846621Extraversie[t] + 0.170531472270368Extraversie_man[t] + 0.367687899652953Openheid[t] -0.428294055693013Openheid_man[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.172650693529984.287562.13940.0337310.016866
geslacht0.1602076664325210.0605372.64640.0088430.004422
leeftijd-0.1259968721237570.099548-1.26570.2072350.103618
leeftijd_man-0.09920892406429660.062473-1.5880.1140040.057002
opleiding-0.009413266929626910.134696-0.06990.9443610.472181
opleiding_man0.1783732429267350.1173051.52060.1300890.065045
Neuroticisme0.110096001116110.1046461.05210.2941490.147074
Neuroticisme_man-0.07263613952523450.089828-0.80860.4197850.209893
Extraversie0.2240601268466210.0978982.28870.0232390.011619
Extraversie_man0.1705314722703680.0738472.30930.0220460.011023
Openheid0.3676878996529530.1105793.32510.0010680.000534
Openheid_man-0.4282940556930130.097796-4.37952e-051e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 9.17265069352998 & 4.28756 & 2.1394 & 0.033731 & 0.016866 \tabularnewline
geslacht & 0.160207666432521 & 0.060537 & 2.6464 & 0.008843 & 0.004422 \tabularnewline
leeftijd & -0.125996872123757 & 0.099548 & -1.2657 & 0.207235 & 0.103618 \tabularnewline
leeftijd_man & -0.0992089240642966 & 0.062473 & -1.588 & 0.114004 & 0.057002 \tabularnewline
opleiding & -0.00941326692962691 & 0.134696 & -0.0699 & 0.944361 & 0.472181 \tabularnewline
opleiding_man & 0.178373242926735 & 0.117305 & 1.5206 & 0.130089 & 0.065045 \tabularnewline
Neuroticisme & 0.11009600111611 & 0.104646 & 1.0521 & 0.294149 & 0.147074 \tabularnewline
Neuroticisme_man & -0.0726361395252345 & 0.089828 & -0.8086 & 0.419785 & 0.209893 \tabularnewline
Extraversie & 0.224060126846621 & 0.097898 & 2.2887 & 0.023239 & 0.011619 \tabularnewline
Extraversie_man & 0.170531472270368 & 0.073847 & 2.3093 & 0.022046 & 0.011023 \tabularnewline
Openheid & 0.367687899652953 & 0.110579 & 3.3251 & 0.001068 & 0.000534 \tabularnewline
Openheid_man & -0.428294055693013 & 0.097796 & -4.3795 & 2e-05 & 1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116301&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]9.17265069352998[/C][C]4.28756[/C][C]2.1394[/C][C]0.033731[/C][C]0.016866[/C][/ROW]
[ROW][C]geslacht[/C][C]0.160207666432521[/C][C]0.060537[/C][C]2.6464[/C][C]0.008843[/C][C]0.004422[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.125996872123757[/C][C]0.099548[/C][C]-1.2657[/C][C]0.207235[/C][C]0.103618[/C][/ROW]
[ROW][C]leeftijd_man[/C][C]-0.0992089240642966[/C][C]0.062473[/C][C]-1.588[/C][C]0.114004[/C][C]0.057002[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.00941326692962691[/C][C]0.134696[/C][C]-0.0699[/C][C]0.944361[/C][C]0.472181[/C][/ROW]
[ROW][C]opleiding_man[/C][C]0.178373242926735[/C][C]0.117305[/C][C]1.5206[/C][C]0.130089[/C][C]0.065045[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]0.11009600111611[/C][C]0.104646[/C][C]1.0521[/C][C]0.294149[/C][C]0.147074[/C][/ROW]
[ROW][C]Neuroticisme_man[/C][C]-0.0726361395252345[/C][C]0.089828[/C][C]-0.8086[/C][C]0.419785[/C][C]0.209893[/C][/ROW]
[ROW][C]Extraversie[/C][C]0.224060126846621[/C][C]0.097898[/C][C]2.2887[/C][C]0.023239[/C][C]0.011619[/C][/ROW]
[ROW][C]Extraversie_man[/C][C]0.170531472270368[/C][C]0.073847[/C][C]2.3093[/C][C]0.022046[/C][C]0.011023[/C][/ROW]
[ROW][C]Openheid[/C][C]0.367687899652953[/C][C]0.110579[/C][C]3.3251[/C][C]0.001068[/C][C]0.000534[/C][/ROW]
[ROW][C]Openheid_man[/C][C]-0.428294055693013[/C][C]0.097796[/C][C]-4.3795[/C][C]2e-05[/C][C]1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116301&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116301&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)9.172650693529984.287562.13940.0337310.016866
geslacht0.1602076664325210.0605372.64640.0088430.004422
leeftijd-0.1259968721237570.099548-1.26570.2072350.103618
leeftijd_man-0.09920892406429660.062473-1.5880.1140040.057002
opleiding-0.009413266929626910.134696-0.06990.9443610.472181
opleiding_man0.1783732429267350.1173051.52060.1300890.065045
Neuroticisme0.110096001116110.1046461.05210.2941490.147074
Neuroticisme_man-0.07263613952523450.089828-0.80860.4197850.209893
Extraversie0.2240601268466210.0978982.28870.0232390.011619
Extraversie_man0.1705314722703680.0738472.30930.0220460.011023
Openheid0.3676878996529530.1105793.32510.0010680.000534
Openheid_man-0.4282940556930130.097796-4.37952e-051e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.923270312835714
R-squared0.852428070563757
Adjusted R-squared0.843557626717863
F-TEST (value)96.097566860569
F-TEST (DF numerator)11
F-TEST (DF denominator)183
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.89989753876335
Sum Squared Residuals8712.37124631416

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.923270312835714 \tabularnewline
R-squared & 0.852428070563757 \tabularnewline
Adjusted R-squared & 0.843557626717863 \tabularnewline
F-TEST (value) & 96.097566860569 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 183 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.89989753876335 \tabularnewline
Sum Squared Residuals & 8712.37124631416 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116301&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.923270312835714[/C][/ROW]
[ROW][C]R-squared[/C][C]0.852428070563757[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.843557626717863[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]96.097566860569[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]183[/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]6.89989753876335[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8712.37124631416[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116301&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116301&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 R0.923270312835714
R-squared0.852428070563757
Adjusted R-squared0.843557626717863
F-TEST (value)96.097566860569
F-TEST (DF numerator)11
F-TEST (DF denominator)183
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.89989753876335
Sum Squared Residuals8712.37124631416






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11822.2848310395637-4.28483103956371
21018.5337987958554-8.53379879585538
32325.5354373300911-2.53543733009113
41417.2292787765483-3.22927877654829
51815.78982281548132.21017718451868
61916.51484118298372.48515881701634
71914.84616591340834.15383408659167
81616.8471027009205-0.847102700920547
92320.17168721836382.82831278163624
101713.88386919054643.1161308094536
112015.03174618542944.96825381457061
121816.88187492789851.11812507210155
131313.8044272654984-0.804427265498392
141717.8735642545141-0.873564254514106
155231.343676391986420.6563236080136
164640.66984627793855.33015372206149
175843.243662309444314.7563376905557
185444.63849662542319.36150337457692
192938.7548833480572-9.75488334805723
204334.23788675710418.76211324289594
214543.93806722819431.06193277180565
224641.61885736772124.3811426322788
232540.6105888892818-15.6105888892818
244736.28464051012210.715359489878
254143.4392467869493-2.43924678694933
262938.8169882007755-9.81698820077546
274532.858856623020412.1411433769796
285436.976122483317517.0238775166825
292841.0690032111398-13.0690032111398
303736.13361167148120.866388328518753
315640.788094258907615.2119057410924
324334.63636371821238.3636362817877
333438.9131517940767-4.91315179407669
344240.38202595919861.61797404080137
354638.79282368626937.20717631373074
362537.6426833420625-12.6426833420625
372535.5078856356593-10.5078856356593
382533.0284091633406-8.02840916334065
394835.652351956377912.3476480436221
402738.1070007673569-11.1070007673569
412838.5763297347614-10.5763297347614
422540.0489688644674-15.0489688644674
432628.0008931631793-2.00089316317929
445136.73031580843514.269684191565
452939.3410043268841-10.3410043268841
462936.7770151853803-7.77701518538026
474335.94516071199617.05483928800388
484439.66815912533324.33184087466677
492539.6352573843724-14.6352573843723
505137.20278806508813.797211934912
514238.72571875051163.27428124948841
522537.9266452917015-12.9266452917015
535134.880244267185216.1197557328148
544640.0405470356265.95945296437401
552940.6814981429994-11.6814981429994
56319.0362807969289-16.0362807969289
57114.8619050164331-13.8619050164331
5847.83410168905075-3.83410168905075
59517.2581210556775-12.2581210556775
60410.5998223550671-6.59982235506705
61411.6462066189127-7.6462066189127
62314.5170959733621-11.5170959733621
6358.3694068127644-3.36940681276439
64313.6038039659754-10.6038039659754
652613.658089854054112.3419101459459
663029.73530728833990.264692711660101
673513.967336990367821.0326630096322
682914.867887764305514.1321122356945
692527.6772386241581-2.67723862415806
702725.99299660928191.00700339071811
712429.5632196409425-5.56321964094247
723526.83159327191678.16840672808326
733231.13703324747330.862966752526679
742431.2215708819885-7.22157088198854
753831.1153782845166.88462171548396
763621.812602049686214.1873979503138
772427.9607117171461-3.96071171714605
781823.6409001591532-5.64090015915315
793425.01609510870338.98390489129673
802328.8311379017971-5.83113790179711
813427.59862712577816.40137287422189
823227.1317801805524.86821981944798
832428.7647381651316-4.76473816513161
843428.16209245134915.83790754865089
853326.11410013674096.88589986325915
863335.8827077077136-2.88270770771362
872833.6709547851877-5.67095478518767
883229.21876024567682.7812397543232
892930.387652579817-1.38765257981702
902730.3189998007561-3.31899980075609
912829.8654997941412-1.86549979414117
922828.7476528870736-0.74765288707362
933029.43748948950420.562510510495799
942527.0944501359684-2.09445013596844
953132.312846283902-1.31284628390203
963727.55199880315239.44800119684768
973728.44976489266038.55023510733966
983426.84113547401537.15886452598466
993233.3766687261667-1.37666872616669
1002827.73028996436210.269710035637918
1012531.2129242796046-6.21292427960461
1022628.5620225374102-2.56202253741023
1033332.55316374465610.446836255343948
1043128.93522080933222.06477919066782
1052227.8317104958954-5.83171049589541
1062932.4435518605264-3.44355186052642
1072427.540004968877-3.54000496887696
1080-2.741135915748652.74113591574865
10902.36018883728793-2.36018883728793
11005.22537100179177-5.22537100179177
11101.47059526733338-1.47059526733338
1120-0.3234171219044590.323417121904459
11301.73377725230331-1.73377725230331
1140-3.476269959831893.47626995983189
11501.50571089362323-1.50571089362323
1160-1.620889424012241.62088942401224
11701.22666762028114-1.22666762028114
11800.510870749442132-0.510870749442132
11901.62840199121386-1.62840199121386
12002.8548781610736-2.8548781610736
12102.63003267586279-2.63003267586279
1220-1.230973502739621.23097350273962
1230-0.9755357339778560.975535733977856
1240-2.452376589413982.45237658941398
1250-1.547787098691751.54778709869175
12601.16822735942015-1.16822735942015
12706.37629449121807-6.37629449121807
12802.40452881241402-2.40452881241402
1290-0.819835178909520.81983517890952
1300-1.225266633030081.22526663303008
13102.84834793259595-2.84834793259595
13202.32681045220822-2.32681045220822
1330-1.367602628170241.36760262817024
13400.674670447298861-0.674670447298861
1350-0.9563982003723050.956398200372305
1360-1.272106286983611.27210628698361
1370-4.715574335136614.71557433513661
1380-0.1716749508787640.171674950878764
1390-0.948266523423260.94826652342326
1400-1.953191199388051.95319119938805
1410-3.201452186003573.20145218600357
1420-3.728568909882743.72856890988274
1430-7.537214957094367.53721495709436
1440-0.4385945934219690.438594593421969
14505.66945514922917-5.66945514922917
1460-3.57359725802533.5735972580253
14702.23092094492601-2.23092094492601
14801.17944892121142-1.17944892121142
14904.53834484332622-4.53834484332622
1500-0.4866814541426170.486681454142617
1510-1.261459005045891.26145900504589
1520-0.1184036117603850.118403611760385
1530-1.528660649285331.52866064928533
1540-3.342655125785043.34265512578504
15503.63890261820136-3.63890261820136
15603.18851847576738-3.18851847576738
1570-1.785243864904141.78524386490414
15801.25328050051341-1.25328050051341
15901.32573242360361-1.32573242360361
1600-3.214844946708913.21484494670891
16109.5135335722752-9.5135335722752
16203.16594357194537-3.16594357194537
1630-2.585222513403132.58522251340313
1640-3.340697053588393.34069705358839
1650-1.154715578454861.15471557845486
1660-2.871184220869742.87118422086974
16700.267525231185318-0.267525231185318
1680-4.133747088927364.13374708892736
16901.87932262468813-1.87932262468813
1700-1.556797313210431.55679731321043
17100.587492658770266-0.587492658770266
1720-1.114708998423831.11470899842383
17300.953454332929285-0.953454332929285
17402.72501818740417-2.72501818740417
17505.70723252356836-5.70723252356836
17600.51407691483401-0.51407691483401
17701.72243891768008-1.72243891768008
17804.89023444856721-4.89023444856721
1790-3.232690863575143.23269086357514
18003.46635529654478-3.46635529654478
1810-2.622011014011342.62201101401134
1820-0.4564418889986260.456441888998626
18300.429270769954054-0.429270769954054
1840-3.369574955712353.36957495571235
18503.87920867223188-3.87920867223188
18603.63810952416576-3.63810952416576
1870-1.890077164302591.89007716430259
1880-1.102266219394171.10226621939417
18901.43198480570187-1.43198480570187
19002.69583052287873-2.69583052287873
19102.86710265176433-2.86710265176433
19202.5769229229845-2.5769229229845
1930-4.233040786095674.23304078609567
1940-0.9484727168877470.948472716887747
1953525.75941982591329.2405801740868

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18 & 22.2848310395637 & -4.28483103956371 \tabularnewline
2 & 10 & 18.5337987958554 & -8.53379879585538 \tabularnewline
3 & 23 & 25.5354373300911 & -2.53543733009113 \tabularnewline
4 & 14 & 17.2292787765483 & -3.22927877654829 \tabularnewline
5 & 18 & 15.7898228154813 & 2.21017718451868 \tabularnewline
6 & 19 & 16.5148411829837 & 2.48515881701634 \tabularnewline
7 & 19 & 14.8461659134083 & 4.15383408659167 \tabularnewline
8 & 16 & 16.8471027009205 & -0.847102700920547 \tabularnewline
9 & 23 & 20.1716872183638 & 2.82831278163624 \tabularnewline
10 & 17 & 13.8838691905464 & 3.1161308094536 \tabularnewline
11 & 20 & 15.0317461854294 & 4.96825381457061 \tabularnewline
12 & 18 & 16.8818749278985 & 1.11812507210155 \tabularnewline
13 & 13 & 13.8044272654984 & -0.804427265498392 \tabularnewline
14 & 17 & 17.8735642545141 & -0.873564254514106 \tabularnewline
15 & 52 & 31.3436763919864 & 20.6563236080136 \tabularnewline
16 & 46 & 40.6698462779385 & 5.33015372206149 \tabularnewline
17 & 58 & 43.2436623094443 & 14.7563376905557 \tabularnewline
18 & 54 & 44.6384966254231 & 9.36150337457692 \tabularnewline
19 & 29 & 38.7548833480572 & -9.75488334805723 \tabularnewline
20 & 43 & 34.2378867571041 & 8.76211324289594 \tabularnewline
21 & 45 & 43.9380672281943 & 1.06193277180565 \tabularnewline
22 & 46 & 41.6188573677212 & 4.3811426322788 \tabularnewline
23 & 25 & 40.6105888892818 & -15.6105888892818 \tabularnewline
24 & 47 & 36.284640510122 & 10.715359489878 \tabularnewline
25 & 41 & 43.4392467869493 & -2.43924678694933 \tabularnewline
26 & 29 & 38.8169882007755 & -9.81698820077546 \tabularnewline
27 & 45 & 32.8588566230204 & 12.1411433769796 \tabularnewline
28 & 54 & 36.9761224833175 & 17.0238775166825 \tabularnewline
29 & 28 & 41.0690032111398 & -13.0690032111398 \tabularnewline
30 & 37 & 36.1336116714812 & 0.866388328518753 \tabularnewline
31 & 56 & 40.7880942589076 & 15.2119057410924 \tabularnewline
32 & 43 & 34.6363637182123 & 8.3636362817877 \tabularnewline
33 & 34 & 38.9131517940767 & -4.91315179407669 \tabularnewline
34 & 42 & 40.3820259591986 & 1.61797404080137 \tabularnewline
35 & 46 & 38.7928236862693 & 7.20717631373074 \tabularnewline
36 & 25 & 37.6426833420625 & -12.6426833420625 \tabularnewline
37 & 25 & 35.5078856356593 & -10.5078856356593 \tabularnewline
38 & 25 & 33.0284091633406 & -8.02840916334065 \tabularnewline
39 & 48 & 35.6523519563779 & 12.3476480436221 \tabularnewline
40 & 27 & 38.1070007673569 & -11.1070007673569 \tabularnewline
41 & 28 & 38.5763297347614 & -10.5763297347614 \tabularnewline
42 & 25 & 40.0489688644674 & -15.0489688644674 \tabularnewline
43 & 26 & 28.0008931631793 & -2.00089316317929 \tabularnewline
44 & 51 & 36.730315808435 & 14.269684191565 \tabularnewline
45 & 29 & 39.3410043268841 & -10.3410043268841 \tabularnewline
46 & 29 & 36.7770151853803 & -7.77701518538026 \tabularnewline
47 & 43 & 35.9451607119961 & 7.05483928800388 \tabularnewline
48 & 44 & 39.6681591253332 & 4.33184087466677 \tabularnewline
49 & 25 & 39.6352573843724 & -14.6352573843723 \tabularnewline
50 & 51 & 37.202788065088 & 13.797211934912 \tabularnewline
51 & 42 & 38.7257187505116 & 3.27428124948841 \tabularnewline
52 & 25 & 37.9266452917015 & -12.9266452917015 \tabularnewline
53 & 51 & 34.8802442671852 & 16.1197557328148 \tabularnewline
54 & 46 & 40.040547035626 & 5.95945296437401 \tabularnewline
55 & 29 & 40.6814981429994 & -11.6814981429994 \tabularnewline
56 & 3 & 19.0362807969289 & -16.0362807969289 \tabularnewline
57 & 1 & 14.8619050164331 & -13.8619050164331 \tabularnewline
58 & 4 & 7.83410168905075 & -3.83410168905075 \tabularnewline
59 & 5 & 17.2581210556775 & -12.2581210556775 \tabularnewline
60 & 4 & 10.5998223550671 & -6.59982235506705 \tabularnewline
61 & 4 & 11.6462066189127 & -7.6462066189127 \tabularnewline
62 & 3 & 14.5170959733621 & -11.5170959733621 \tabularnewline
63 & 5 & 8.3694068127644 & -3.36940681276439 \tabularnewline
64 & 3 & 13.6038039659754 & -10.6038039659754 \tabularnewline
65 & 26 & 13.6580898540541 & 12.3419101459459 \tabularnewline
66 & 30 & 29.7353072883399 & 0.264692711660101 \tabularnewline
67 & 35 & 13.9673369903678 & 21.0326630096322 \tabularnewline
68 & 29 & 14.8678877643055 & 14.1321122356945 \tabularnewline
69 & 25 & 27.6772386241581 & -2.67723862415806 \tabularnewline
70 & 27 & 25.9929966092819 & 1.00700339071811 \tabularnewline
71 & 24 & 29.5632196409425 & -5.56321964094247 \tabularnewline
72 & 35 & 26.8315932719167 & 8.16840672808326 \tabularnewline
73 & 32 & 31.1370332474733 & 0.862966752526679 \tabularnewline
74 & 24 & 31.2215708819885 & -7.22157088198854 \tabularnewline
75 & 38 & 31.115378284516 & 6.88462171548396 \tabularnewline
76 & 36 & 21.8126020496862 & 14.1873979503138 \tabularnewline
77 & 24 & 27.9607117171461 & -3.96071171714605 \tabularnewline
78 & 18 & 23.6409001591532 & -5.64090015915315 \tabularnewline
79 & 34 & 25.0160951087033 & 8.98390489129673 \tabularnewline
80 & 23 & 28.8311379017971 & -5.83113790179711 \tabularnewline
81 & 34 & 27.5986271257781 & 6.40137287422189 \tabularnewline
82 & 32 & 27.131780180552 & 4.86821981944798 \tabularnewline
83 & 24 & 28.7647381651316 & -4.76473816513161 \tabularnewline
84 & 34 & 28.1620924513491 & 5.83790754865089 \tabularnewline
85 & 33 & 26.1141001367409 & 6.88589986325915 \tabularnewline
86 & 33 & 35.8827077077136 & -2.88270770771362 \tabularnewline
87 & 28 & 33.6709547851877 & -5.67095478518767 \tabularnewline
88 & 32 & 29.2187602456768 & 2.7812397543232 \tabularnewline
89 & 29 & 30.387652579817 & -1.38765257981702 \tabularnewline
90 & 27 & 30.3189998007561 & -3.31899980075609 \tabularnewline
91 & 28 & 29.8654997941412 & -1.86549979414117 \tabularnewline
92 & 28 & 28.7476528870736 & -0.74765288707362 \tabularnewline
93 & 30 & 29.4374894895042 & 0.562510510495799 \tabularnewline
94 & 25 & 27.0944501359684 & -2.09445013596844 \tabularnewline
95 & 31 & 32.312846283902 & -1.31284628390203 \tabularnewline
96 & 37 & 27.5519988031523 & 9.44800119684768 \tabularnewline
97 & 37 & 28.4497648926603 & 8.55023510733966 \tabularnewline
98 & 34 & 26.8411354740153 & 7.15886452598466 \tabularnewline
99 & 32 & 33.3766687261667 & -1.37666872616669 \tabularnewline
100 & 28 & 27.7302899643621 & 0.269710035637918 \tabularnewline
101 & 25 & 31.2129242796046 & -6.21292427960461 \tabularnewline
102 & 26 & 28.5620225374102 & -2.56202253741023 \tabularnewline
103 & 33 & 32.5531637446561 & 0.446836255343948 \tabularnewline
104 & 31 & 28.9352208093322 & 2.06477919066782 \tabularnewline
105 & 22 & 27.8317104958954 & -5.83171049589541 \tabularnewline
106 & 29 & 32.4435518605264 & -3.44355186052642 \tabularnewline
107 & 24 & 27.540004968877 & -3.54000496887696 \tabularnewline
108 & 0 & -2.74113591574865 & 2.74113591574865 \tabularnewline
109 & 0 & 2.36018883728793 & -2.36018883728793 \tabularnewline
110 & 0 & 5.22537100179177 & -5.22537100179177 \tabularnewline
111 & 0 & 1.47059526733338 & -1.47059526733338 \tabularnewline
112 & 0 & -0.323417121904459 & 0.323417121904459 \tabularnewline
113 & 0 & 1.73377725230331 & -1.73377725230331 \tabularnewline
114 & 0 & -3.47626995983189 & 3.47626995983189 \tabularnewline
115 & 0 & 1.50571089362323 & -1.50571089362323 \tabularnewline
116 & 0 & -1.62088942401224 & 1.62088942401224 \tabularnewline
117 & 0 & 1.22666762028114 & -1.22666762028114 \tabularnewline
118 & 0 & 0.510870749442132 & -0.510870749442132 \tabularnewline
119 & 0 & 1.62840199121386 & -1.62840199121386 \tabularnewline
120 & 0 & 2.8548781610736 & -2.8548781610736 \tabularnewline
121 & 0 & 2.63003267586279 & -2.63003267586279 \tabularnewline
122 & 0 & -1.23097350273962 & 1.23097350273962 \tabularnewline
123 & 0 & -0.975535733977856 & 0.975535733977856 \tabularnewline
124 & 0 & -2.45237658941398 & 2.45237658941398 \tabularnewline
125 & 0 & -1.54778709869175 & 1.54778709869175 \tabularnewline
126 & 0 & 1.16822735942015 & -1.16822735942015 \tabularnewline
127 & 0 & 6.37629449121807 & -6.37629449121807 \tabularnewline
128 & 0 & 2.40452881241402 & -2.40452881241402 \tabularnewline
129 & 0 & -0.81983517890952 & 0.81983517890952 \tabularnewline
130 & 0 & -1.22526663303008 & 1.22526663303008 \tabularnewline
131 & 0 & 2.84834793259595 & -2.84834793259595 \tabularnewline
132 & 0 & 2.32681045220822 & -2.32681045220822 \tabularnewline
133 & 0 & -1.36760262817024 & 1.36760262817024 \tabularnewline
134 & 0 & 0.674670447298861 & -0.674670447298861 \tabularnewline
135 & 0 & -0.956398200372305 & 0.956398200372305 \tabularnewline
136 & 0 & -1.27210628698361 & 1.27210628698361 \tabularnewline
137 & 0 & -4.71557433513661 & 4.71557433513661 \tabularnewline
138 & 0 & -0.171674950878764 & 0.171674950878764 \tabularnewline
139 & 0 & -0.94826652342326 & 0.94826652342326 \tabularnewline
140 & 0 & -1.95319119938805 & 1.95319119938805 \tabularnewline
141 & 0 & -3.20145218600357 & 3.20145218600357 \tabularnewline
142 & 0 & -3.72856890988274 & 3.72856890988274 \tabularnewline
143 & 0 & -7.53721495709436 & 7.53721495709436 \tabularnewline
144 & 0 & -0.438594593421969 & 0.438594593421969 \tabularnewline
145 & 0 & 5.66945514922917 & -5.66945514922917 \tabularnewline
146 & 0 & -3.5735972580253 & 3.5735972580253 \tabularnewline
147 & 0 & 2.23092094492601 & -2.23092094492601 \tabularnewline
148 & 0 & 1.17944892121142 & -1.17944892121142 \tabularnewline
149 & 0 & 4.53834484332622 & -4.53834484332622 \tabularnewline
150 & 0 & -0.486681454142617 & 0.486681454142617 \tabularnewline
151 & 0 & -1.26145900504589 & 1.26145900504589 \tabularnewline
152 & 0 & -0.118403611760385 & 0.118403611760385 \tabularnewline
153 & 0 & -1.52866064928533 & 1.52866064928533 \tabularnewline
154 & 0 & -3.34265512578504 & 3.34265512578504 \tabularnewline
155 & 0 & 3.63890261820136 & -3.63890261820136 \tabularnewline
156 & 0 & 3.18851847576738 & -3.18851847576738 \tabularnewline
157 & 0 & -1.78524386490414 & 1.78524386490414 \tabularnewline
158 & 0 & 1.25328050051341 & -1.25328050051341 \tabularnewline
159 & 0 & 1.32573242360361 & -1.32573242360361 \tabularnewline
160 & 0 & -3.21484494670891 & 3.21484494670891 \tabularnewline
161 & 0 & 9.5135335722752 & -9.5135335722752 \tabularnewline
162 & 0 & 3.16594357194537 & -3.16594357194537 \tabularnewline
163 & 0 & -2.58522251340313 & 2.58522251340313 \tabularnewline
164 & 0 & -3.34069705358839 & 3.34069705358839 \tabularnewline
165 & 0 & -1.15471557845486 & 1.15471557845486 \tabularnewline
166 & 0 & -2.87118422086974 & 2.87118422086974 \tabularnewline
167 & 0 & 0.267525231185318 & -0.267525231185318 \tabularnewline
168 & 0 & -4.13374708892736 & 4.13374708892736 \tabularnewline
169 & 0 & 1.87932262468813 & -1.87932262468813 \tabularnewline
170 & 0 & -1.55679731321043 & 1.55679731321043 \tabularnewline
171 & 0 & 0.587492658770266 & -0.587492658770266 \tabularnewline
172 & 0 & -1.11470899842383 & 1.11470899842383 \tabularnewline
173 & 0 & 0.953454332929285 & -0.953454332929285 \tabularnewline
174 & 0 & 2.72501818740417 & -2.72501818740417 \tabularnewline
175 & 0 & 5.70723252356836 & -5.70723252356836 \tabularnewline
176 & 0 & 0.51407691483401 & -0.51407691483401 \tabularnewline
177 & 0 & 1.72243891768008 & -1.72243891768008 \tabularnewline
178 & 0 & 4.89023444856721 & -4.89023444856721 \tabularnewline
179 & 0 & -3.23269086357514 & 3.23269086357514 \tabularnewline
180 & 0 & 3.46635529654478 & -3.46635529654478 \tabularnewline
181 & 0 & -2.62201101401134 & 2.62201101401134 \tabularnewline
182 & 0 & -0.456441888998626 & 0.456441888998626 \tabularnewline
183 & 0 & 0.429270769954054 & -0.429270769954054 \tabularnewline
184 & 0 & -3.36957495571235 & 3.36957495571235 \tabularnewline
185 & 0 & 3.87920867223188 & -3.87920867223188 \tabularnewline
186 & 0 & 3.63810952416576 & -3.63810952416576 \tabularnewline
187 & 0 & -1.89007716430259 & 1.89007716430259 \tabularnewline
188 & 0 & -1.10226621939417 & 1.10226621939417 \tabularnewline
189 & 0 & 1.43198480570187 & -1.43198480570187 \tabularnewline
190 & 0 & 2.69583052287873 & -2.69583052287873 \tabularnewline
191 & 0 & 2.86710265176433 & -2.86710265176433 \tabularnewline
192 & 0 & 2.5769229229845 & -2.5769229229845 \tabularnewline
193 & 0 & -4.23304078609567 & 4.23304078609567 \tabularnewline
194 & 0 & -0.948472716887747 & 0.948472716887747 \tabularnewline
195 & 35 & 25.7594198259132 & 9.2405801740868 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116301&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]18[/C][C]22.2848310395637[/C][C]-4.28483103956371[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]18.5337987958554[/C][C]-8.53379879585538[/C][/ROW]
[ROW][C]3[/C][C]23[/C][C]25.5354373300911[/C][C]-2.53543733009113[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]17.2292787765483[/C][C]-3.22927877654829[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]15.7898228154813[/C][C]2.21017718451868[/C][/ROW]
[ROW][C]6[/C][C]19[/C][C]16.5148411829837[/C][C]2.48515881701634[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.8461659134083[/C][C]4.15383408659167[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]16.8471027009205[/C][C]-0.847102700920547[/C][/ROW]
[ROW][C]9[/C][C]23[/C][C]20.1716872183638[/C][C]2.82831278163624[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]13.8838691905464[/C][C]3.1161308094536[/C][/ROW]
[ROW][C]11[/C][C]20[/C][C]15.0317461854294[/C][C]4.96825381457061[/C][/ROW]
[ROW][C]12[/C][C]18[/C][C]16.8818749278985[/C][C]1.11812507210155[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]13.8044272654984[/C][C]-0.804427265498392[/C][/ROW]
[ROW][C]14[/C][C]17[/C][C]17.8735642545141[/C][C]-0.873564254514106[/C][/ROW]
[ROW][C]15[/C][C]52[/C][C]31.3436763919864[/C][C]20.6563236080136[/C][/ROW]
[ROW][C]16[/C][C]46[/C][C]40.6698462779385[/C][C]5.33015372206149[/C][/ROW]
[ROW][C]17[/C][C]58[/C][C]43.2436623094443[/C][C]14.7563376905557[/C][/ROW]
[ROW][C]18[/C][C]54[/C][C]44.6384966254231[/C][C]9.36150337457692[/C][/ROW]
[ROW][C]19[/C][C]29[/C][C]38.7548833480572[/C][C]-9.75488334805723[/C][/ROW]
[ROW][C]20[/C][C]43[/C][C]34.2378867571041[/C][C]8.76211324289594[/C][/ROW]
[ROW][C]21[/C][C]45[/C][C]43.9380672281943[/C][C]1.06193277180565[/C][/ROW]
[ROW][C]22[/C][C]46[/C][C]41.6188573677212[/C][C]4.3811426322788[/C][/ROW]
[ROW][C]23[/C][C]25[/C][C]40.6105888892818[/C][C]-15.6105888892818[/C][/ROW]
[ROW][C]24[/C][C]47[/C][C]36.284640510122[/C][C]10.715359489878[/C][/ROW]
[ROW][C]25[/C][C]41[/C][C]43.4392467869493[/C][C]-2.43924678694933[/C][/ROW]
[ROW][C]26[/C][C]29[/C][C]38.8169882007755[/C][C]-9.81698820077546[/C][/ROW]
[ROW][C]27[/C][C]45[/C][C]32.8588566230204[/C][C]12.1411433769796[/C][/ROW]
[ROW][C]28[/C][C]54[/C][C]36.9761224833175[/C][C]17.0238775166825[/C][/ROW]
[ROW][C]29[/C][C]28[/C][C]41.0690032111398[/C][C]-13.0690032111398[/C][/ROW]
[ROW][C]30[/C][C]37[/C][C]36.1336116714812[/C][C]0.866388328518753[/C][/ROW]
[ROW][C]31[/C][C]56[/C][C]40.7880942589076[/C][C]15.2119057410924[/C][/ROW]
[ROW][C]32[/C][C]43[/C][C]34.6363637182123[/C][C]8.3636362817877[/C][/ROW]
[ROW][C]33[/C][C]34[/C][C]38.9131517940767[/C][C]-4.91315179407669[/C][/ROW]
[ROW][C]34[/C][C]42[/C][C]40.3820259591986[/C][C]1.61797404080137[/C][/ROW]
[ROW][C]35[/C][C]46[/C][C]38.7928236862693[/C][C]7.20717631373074[/C][/ROW]
[ROW][C]36[/C][C]25[/C][C]37.6426833420625[/C][C]-12.6426833420625[/C][/ROW]
[ROW][C]37[/C][C]25[/C][C]35.5078856356593[/C][C]-10.5078856356593[/C][/ROW]
[ROW][C]38[/C][C]25[/C][C]33.0284091633406[/C][C]-8.02840916334065[/C][/ROW]
[ROW][C]39[/C][C]48[/C][C]35.6523519563779[/C][C]12.3476480436221[/C][/ROW]
[ROW][C]40[/C][C]27[/C][C]38.1070007673569[/C][C]-11.1070007673569[/C][/ROW]
[ROW][C]41[/C][C]28[/C][C]38.5763297347614[/C][C]-10.5763297347614[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]40.0489688644674[/C][C]-15.0489688644674[/C][/ROW]
[ROW][C]43[/C][C]26[/C][C]28.0008931631793[/C][C]-2.00089316317929[/C][/ROW]
[ROW][C]44[/C][C]51[/C][C]36.730315808435[/C][C]14.269684191565[/C][/ROW]
[ROW][C]45[/C][C]29[/C][C]39.3410043268841[/C][C]-10.3410043268841[/C][/ROW]
[ROW][C]46[/C][C]29[/C][C]36.7770151853803[/C][C]-7.77701518538026[/C][/ROW]
[ROW][C]47[/C][C]43[/C][C]35.9451607119961[/C][C]7.05483928800388[/C][/ROW]
[ROW][C]48[/C][C]44[/C][C]39.6681591253332[/C][C]4.33184087466677[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]39.6352573843724[/C][C]-14.6352573843723[/C][/ROW]
[ROW][C]50[/C][C]51[/C][C]37.202788065088[/C][C]13.797211934912[/C][/ROW]
[ROW][C]51[/C][C]42[/C][C]38.7257187505116[/C][C]3.27428124948841[/C][/ROW]
[ROW][C]52[/C][C]25[/C][C]37.9266452917015[/C][C]-12.9266452917015[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]34.8802442671852[/C][C]16.1197557328148[/C][/ROW]
[ROW][C]54[/C][C]46[/C][C]40.040547035626[/C][C]5.95945296437401[/C][/ROW]
[ROW][C]55[/C][C]29[/C][C]40.6814981429994[/C][C]-11.6814981429994[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]19.0362807969289[/C][C]-16.0362807969289[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]14.8619050164331[/C][C]-13.8619050164331[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]7.83410168905075[/C][C]-3.83410168905075[/C][/ROW]
[ROW][C]59[/C][C]5[/C][C]17.2581210556775[/C][C]-12.2581210556775[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]10.5998223550671[/C][C]-6.59982235506705[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]11.6462066189127[/C][C]-7.6462066189127[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]14.5170959733621[/C][C]-11.5170959733621[/C][/ROW]
[ROW][C]63[/C][C]5[/C][C]8.3694068127644[/C][C]-3.36940681276439[/C][/ROW]
[ROW][C]64[/C][C]3[/C][C]13.6038039659754[/C][C]-10.6038039659754[/C][/ROW]
[ROW][C]65[/C][C]26[/C][C]13.6580898540541[/C][C]12.3419101459459[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]29.7353072883399[/C][C]0.264692711660101[/C][/ROW]
[ROW][C]67[/C][C]35[/C][C]13.9673369903678[/C][C]21.0326630096322[/C][/ROW]
[ROW][C]68[/C][C]29[/C][C]14.8678877643055[/C][C]14.1321122356945[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]27.6772386241581[/C][C]-2.67723862415806[/C][/ROW]
[ROW][C]70[/C][C]27[/C][C]25.9929966092819[/C][C]1.00700339071811[/C][/ROW]
[ROW][C]71[/C][C]24[/C][C]29.5632196409425[/C][C]-5.56321964094247[/C][/ROW]
[ROW][C]72[/C][C]35[/C][C]26.8315932719167[/C][C]8.16840672808326[/C][/ROW]
[ROW][C]73[/C][C]32[/C][C]31.1370332474733[/C][C]0.862966752526679[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]31.2215708819885[/C][C]-7.22157088198854[/C][/ROW]
[ROW][C]75[/C][C]38[/C][C]31.115378284516[/C][C]6.88462171548396[/C][/ROW]
[ROW][C]76[/C][C]36[/C][C]21.8126020496862[/C][C]14.1873979503138[/C][/ROW]
[ROW][C]77[/C][C]24[/C][C]27.9607117171461[/C][C]-3.96071171714605[/C][/ROW]
[ROW][C]78[/C][C]18[/C][C]23.6409001591532[/C][C]-5.64090015915315[/C][/ROW]
[ROW][C]79[/C][C]34[/C][C]25.0160951087033[/C][C]8.98390489129673[/C][/ROW]
[ROW][C]80[/C][C]23[/C][C]28.8311379017971[/C][C]-5.83113790179711[/C][/ROW]
[ROW][C]81[/C][C]34[/C][C]27.5986271257781[/C][C]6.40137287422189[/C][/ROW]
[ROW][C]82[/C][C]32[/C][C]27.131780180552[/C][C]4.86821981944798[/C][/ROW]
[ROW][C]83[/C][C]24[/C][C]28.7647381651316[/C][C]-4.76473816513161[/C][/ROW]
[ROW][C]84[/C][C]34[/C][C]28.1620924513491[/C][C]5.83790754865089[/C][/ROW]
[ROW][C]85[/C][C]33[/C][C]26.1141001367409[/C][C]6.88589986325915[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]35.8827077077136[/C][C]-2.88270770771362[/C][/ROW]
[ROW][C]87[/C][C]28[/C][C]33.6709547851877[/C][C]-5.67095478518767[/C][/ROW]
[ROW][C]88[/C][C]32[/C][C]29.2187602456768[/C][C]2.7812397543232[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]30.387652579817[/C][C]-1.38765257981702[/C][/ROW]
[ROW][C]90[/C][C]27[/C][C]30.3189998007561[/C][C]-3.31899980075609[/C][/ROW]
[ROW][C]91[/C][C]28[/C][C]29.8654997941412[/C][C]-1.86549979414117[/C][/ROW]
[ROW][C]92[/C][C]28[/C][C]28.7476528870736[/C][C]-0.74765288707362[/C][/ROW]
[ROW][C]93[/C][C]30[/C][C]29.4374894895042[/C][C]0.562510510495799[/C][/ROW]
[ROW][C]94[/C][C]25[/C][C]27.0944501359684[/C][C]-2.09445013596844[/C][/ROW]
[ROW][C]95[/C][C]31[/C][C]32.312846283902[/C][C]-1.31284628390203[/C][/ROW]
[ROW][C]96[/C][C]37[/C][C]27.5519988031523[/C][C]9.44800119684768[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]28.4497648926603[/C][C]8.55023510733966[/C][/ROW]
[ROW][C]98[/C][C]34[/C][C]26.8411354740153[/C][C]7.15886452598466[/C][/ROW]
[ROW][C]99[/C][C]32[/C][C]33.3766687261667[/C][C]-1.37666872616669[/C][/ROW]
[ROW][C]100[/C][C]28[/C][C]27.7302899643621[/C][C]0.269710035637918[/C][/ROW]
[ROW][C]101[/C][C]25[/C][C]31.2129242796046[/C][C]-6.21292427960461[/C][/ROW]
[ROW][C]102[/C][C]26[/C][C]28.5620225374102[/C][C]-2.56202253741023[/C][/ROW]
[ROW][C]103[/C][C]33[/C][C]32.5531637446561[/C][C]0.446836255343948[/C][/ROW]
[ROW][C]104[/C][C]31[/C][C]28.9352208093322[/C][C]2.06477919066782[/C][/ROW]
[ROW][C]105[/C][C]22[/C][C]27.8317104958954[/C][C]-5.83171049589541[/C][/ROW]
[ROW][C]106[/C][C]29[/C][C]32.4435518605264[/C][C]-3.44355186052642[/C][/ROW]
[ROW][C]107[/C][C]24[/C][C]27.540004968877[/C][C]-3.54000496887696[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]-2.74113591574865[/C][C]2.74113591574865[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]2.36018883728793[/C][C]-2.36018883728793[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]5.22537100179177[/C][C]-5.22537100179177[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]1.47059526733338[/C][C]-1.47059526733338[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]-0.323417121904459[/C][C]0.323417121904459[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]1.73377725230331[/C][C]-1.73377725230331[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]-3.47626995983189[/C][C]3.47626995983189[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]1.50571089362323[/C][C]-1.50571089362323[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-1.62088942401224[/C][C]1.62088942401224[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]1.22666762028114[/C][C]-1.22666762028114[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.510870749442132[/C][C]-0.510870749442132[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]1.62840199121386[/C][C]-1.62840199121386[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]2.8548781610736[/C][C]-2.8548781610736[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]2.63003267586279[/C][C]-2.63003267586279[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-1.23097350273962[/C][C]1.23097350273962[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]-0.975535733977856[/C][C]0.975535733977856[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]-2.45237658941398[/C][C]2.45237658941398[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]-1.54778709869175[/C][C]1.54778709869175[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]1.16822735942015[/C][C]-1.16822735942015[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]6.37629449121807[/C][C]-6.37629449121807[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]2.40452881241402[/C][C]-2.40452881241402[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]-0.81983517890952[/C][C]0.81983517890952[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]-1.22526663303008[/C][C]1.22526663303008[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]2.84834793259595[/C][C]-2.84834793259595[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]2.32681045220822[/C][C]-2.32681045220822[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]-1.36760262817024[/C][C]1.36760262817024[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.674670447298861[/C][C]-0.674670447298861[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]-0.956398200372305[/C][C]0.956398200372305[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]-1.27210628698361[/C][C]1.27210628698361[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]-4.71557433513661[/C][C]4.71557433513661[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]-0.171674950878764[/C][C]0.171674950878764[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]-0.94826652342326[/C][C]0.94826652342326[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]-1.95319119938805[/C][C]1.95319119938805[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]-3.20145218600357[/C][C]3.20145218600357[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]-3.72856890988274[/C][C]3.72856890988274[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]-7.53721495709436[/C][C]7.53721495709436[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]-0.438594593421969[/C][C]0.438594593421969[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]5.66945514922917[/C][C]-5.66945514922917[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]-3.5735972580253[/C][C]3.5735972580253[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]2.23092094492601[/C][C]-2.23092094492601[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]1.17944892121142[/C][C]-1.17944892121142[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]4.53834484332622[/C][C]-4.53834484332622[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]-0.486681454142617[/C][C]0.486681454142617[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]-1.26145900504589[/C][C]1.26145900504589[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]-0.118403611760385[/C][C]0.118403611760385[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]-1.52866064928533[/C][C]1.52866064928533[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]-3.34265512578504[/C][C]3.34265512578504[/C][/ROW]
[ROW][C]155[/C][C]0[/C][C]3.63890261820136[/C][C]-3.63890261820136[/C][/ROW]
[ROW][C]156[/C][C]0[/C][C]3.18851847576738[/C][C]-3.18851847576738[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]-1.78524386490414[/C][C]1.78524386490414[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]1.25328050051341[/C][C]-1.25328050051341[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]1.32573242360361[/C][C]-1.32573242360361[/C][/ROW]
[ROW][C]160[/C][C]0[/C][C]-3.21484494670891[/C][C]3.21484494670891[/C][/ROW]
[ROW][C]161[/C][C]0[/C][C]9.5135335722752[/C][C]-9.5135335722752[/C][/ROW]
[ROW][C]162[/C][C]0[/C][C]3.16594357194537[/C][C]-3.16594357194537[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]-2.58522251340313[/C][C]2.58522251340313[/C][/ROW]
[ROW][C]164[/C][C]0[/C][C]-3.34069705358839[/C][C]3.34069705358839[/C][/ROW]
[ROW][C]165[/C][C]0[/C][C]-1.15471557845486[/C][C]1.15471557845486[/C][/ROW]
[ROW][C]166[/C][C]0[/C][C]-2.87118422086974[/C][C]2.87118422086974[/C][/ROW]
[ROW][C]167[/C][C]0[/C][C]0.267525231185318[/C][C]-0.267525231185318[/C][/ROW]
[ROW][C]168[/C][C]0[/C][C]-4.13374708892736[/C][C]4.13374708892736[/C][/ROW]
[ROW][C]169[/C][C]0[/C][C]1.87932262468813[/C][C]-1.87932262468813[/C][/ROW]
[ROW][C]170[/C][C]0[/C][C]-1.55679731321043[/C][C]1.55679731321043[/C][/ROW]
[ROW][C]171[/C][C]0[/C][C]0.587492658770266[/C][C]-0.587492658770266[/C][/ROW]
[ROW][C]172[/C][C]0[/C][C]-1.11470899842383[/C][C]1.11470899842383[/C][/ROW]
[ROW][C]173[/C][C]0[/C][C]0.953454332929285[/C][C]-0.953454332929285[/C][/ROW]
[ROW][C]174[/C][C]0[/C][C]2.72501818740417[/C][C]-2.72501818740417[/C][/ROW]
[ROW][C]175[/C][C]0[/C][C]5.70723252356836[/C][C]-5.70723252356836[/C][/ROW]
[ROW][C]176[/C][C]0[/C][C]0.51407691483401[/C][C]-0.51407691483401[/C][/ROW]
[ROW][C]177[/C][C]0[/C][C]1.72243891768008[/C][C]-1.72243891768008[/C][/ROW]
[ROW][C]178[/C][C]0[/C][C]4.89023444856721[/C][C]-4.89023444856721[/C][/ROW]
[ROW][C]179[/C][C]0[/C][C]-3.23269086357514[/C][C]3.23269086357514[/C][/ROW]
[ROW][C]180[/C][C]0[/C][C]3.46635529654478[/C][C]-3.46635529654478[/C][/ROW]
[ROW][C]181[/C][C]0[/C][C]-2.62201101401134[/C][C]2.62201101401134[/C][/ROW]
[ROW][C]182[/C][C]0[/C][C]-0.456441888998626[/C][C]0.456441888998626[/C][/ROW]
[ROW][C]183[/C][C]0[/C][C]0.429270769954054[/C][C]-0.429270769954054[/C][/ROW]
[ROW][C]184[/C][C]0[/C][C]-3.36957495571235[/C][C]3.36957495571235[/C][/ROW]
[ROW][C]185[/C][C]0[/C][C]3.87920867223188[/C][C]-3.87920867223188[/C][/ROW]
[ROW][C]186[/C][C]0[/C][C]3.63810952416576[/C][C]-3.63810952416576[/C][/ROW]
[ROW][C]187[/C][C]0[/C][C]-1.89007716430259[/C][C]1.89007716430259[/C][/ROW]
[ROW][C]188[/C][C]0[/C][C]-1.10226621939417[/C][C]1.10226621939417[/C][/ROW]
[ROW][C]189[/C][C]0[/C][C]1.43198480570187[/C][C]-1.43198480570187[/C][/ROW]
[ROW][C]190[/C][C]0[/C][C]2.69583052287873[/C][C]-2.69583052287873[/C][/ROW]
[ROW][C]191[/C][C]0[/C][C]2.86710265176433[/C][C]-2.86710265176433[/C][/ROW]
[ROW][C]192[/C][C]0[/C][C]2.5769229229845[/C][C]-2.5769229229845[/C][/ROW]
[ROW][C]193[/C][C]0[/C][C]-4.23304078609567[/C][C]4.23304078609567[/C][/ROW]
[ROW][C]194[/C][C]0[/C][C]-0.948472716887747[/C][C]0.948472716887747[/C][/ROW]
[ROW][C]195[/C][C]35[/C][C]25.7594198259132[/C][C]9.2405801740868[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116301&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116301&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
11822.2848310395637-4.28483103956371
21018.5337987958554-8.53379879585538
32325.5354373300911-2.53543733009113
41417.2292787765483-3.22927877654829
51815.78982281548132.21017718451868
61916.51484118298372.48515881701634
71914.84616591340834.15383408659167
81616.8471027009205-0.847102700920547
92320.17168721836382.82831278163624
101713.88386919054643.1161308094536
112015.03174618542944.96825381457061
121816.88187492789851.11812507210155
131313.8044272654984-0.804427265498392
141717.8735642545141-0.873564254514106
155231.343676391986420.6563236080136
164640.66984627793855.33015372206149
175843.243662309444314.7563376905557
185444.63849662542319.36150337457692
192938.7548833480572-9.75488334805723
204334.23788675710418.76211324289594
214543.93806722819431.06193277180565
224641.61885736772124.3811426322788
232540.6105888892818-15.6105888892818
244736.28464051012210.715359489878
254143.4392467869493-2.43924678694933
262938.8169882007755-9.81698820077546
274532.858856623020412.1411433769796
285436.976122483317517.0238775166825
292841.0690032111398-13.0690032111398
303736.13361167148120.866388328518753
315640.788094258907615.2119057410924
324334.63636371821238.3636362817877
333438.9131517940767-4.91315179407669
344240.38202595919861.61797404080137
354638.79282368626937.20717631373074
362537.6426833420625-12.6426833420625
372535.5078856356593-10.5078856356593
382533.0284091633406-8.02840916334065
394835.652351956377912.3476480436221
402738.1070007673569-11.1070007673569
412838.5763297347614-10.5763297347614
422540.0489688644674-15.0489688644674
432628.0008931631793-2.00089316317929
445136.73031580843514.269684191565
452939.3410043268841-10.3410043268841
462936.7770151853803-7.77701518538026
474335.94516071199617.05483928800388
484439.66815912533324.33184087466677
492539.6352573843724-14.6352573843723
505137.20278806508813.797211934912
514238.72571875051163.27428124948841
522537.9266452917015-12.9266452917015
535134.880244267185216.1197557328148
544640.0405470356265.95945296437401
552940.6814981429994-11.6814981429994
56319.0362807969289-16.0362807969289
57114.8619050164331-13.8619050164331
5847.83410168905075-3.83410168905075
59517.2581210556775-12.2581210556775
60410.5998223550671-6.59982235506705
61411.6462066189127-7.6462066189127
62314.5170959733621-11.5170959733621
6358.3694068127644-3.36940681276439
64313.6038039659754-10.6038039659754
652613.658089854054112.3419101459459
663029.73530728833990.264692711660101
673513.967336990367821.0326630096322
682914.867887764305514.1321122356945
692527.6772386241581-2.67723862415806
702725.99299660928191.00700339071811
712429.5632196409425-5.56321964094247
723526.83159327191678.16840672808326
733231.13703324747330.862966752526679
742431.2215708819885-7.22157088198854
753831.1153782845166.88462171548396
763621.812602049686214.1873979503138
772427.9607117171461-3.96071171714605
781823.6409001591532-5.64090015915315
793425.01609510870338.98390489129673
802328.8311379017971-5.83113790179711
813427.59862712577816.40137287422189
823227.1317801805524.86821981944798
832428.7647381651316-4.76473816513161
843428.16209245134915.83790754865089
853326.11410013674096.88589986325915
863335.8827077077136-2.88270770771362
872833.6709547851877-5.67095478518767
883229.21876024567682.7812397543232
892930.387652579817-1.38765257981702
902730.3189998007561-3.31899980075609
912829.8654997941412-1.86549979414117
922828.7476528870736-0.74765288707362
933029.43748948950420.562510510495799
942527.0944501359684-2.09445013596844
953132.312846283902-1.31284628390203
963727.55199880315239.44800119684768
973728.44976489266038.55023510733966
983426.84113547401537.15886452598466
993233.3766687261667-1.37666872616669
1002827.73028996436210.269710035637918
1012531.2129242796046-6.21292427960461
1022628.5620225374102-2.56202253741023
1033332.55316374465610.446836255343948
1043128.93522080933222.06477919066782
1052227.8317104958954-5.83171049589541
1062932.4435518605264-3.44355186052642
1072427.540004968877-3.54000496887696
1080-2.741135915748652.74113591574865
10902.36018883728793-2.36018883728793
11005.22537100179177-5.22537100179177
11101.47059526733338-1.47059526733338
1120-0.3234171219044590.323417121904459
11301.73377725230331-1.73377725230331
1140-3.476269959831893.47626995983189
11501.50571089362323-1.50571089362323
1160-1.620889424012241.62088942401224
11701.22666762028114-1.22666762028114
11800.510870749442132-0.510870749442132
11901.62840199121386-1.62840199121386
12002.8548781610736-2.8548781610736
12102.63003267586279-2.63003267586279
1220-1.230973502739621.23097350273962
1230-0.9755357339778560.975535733977856
1240-2.452376589413982.45237658941398
1250-1.547787098691751.54778709869175
12601.16822735942015-1.16822735942015
12706.37629449121807-6.37629449121807
12802.40452881241402-2.40452881241402
1290-0.819835178909520.81983517890952
1300-1.225266633030081.22526663303008
13102.84834793259595-2.84834793259595
13202.32681045220822-2.32681045220822
1330-1.367602628170241.36760262817024
13400.674670447298861-0.674670447298861
1350-0.9563982003723050.956398200372305
1360-1.272106286983611.27210628698361
1370-4.715574335136614.71557433513661
1380-0.1716749508787640.171674950878764
1390-0.948266523423260.94826652342326
1400-1.953191199388051.95319119938805
1410-3.201452186003573.20145218600357
1420-3.728568909882743.72856890988274
1430-7.537214957094367.53721495709436
1440-0.4385945934219690.438594593421969
14505.66945514922917-5.66945514922917
1460-3.57359725802533.5735972580253
14702.23092094492601-2.23092094492601
14801.17944892121142-1.17944892121142
14904.53834484332622-4.53834484332622
1500-0.4866814541426170.486681454142617
1510-1.261459005045891.26145900504589
1520-0.1184036117603850.118403611760385
1530-1.528660649285331.52866064928533
1540-3.342655125785043.34265512578504
15503.63890261820136-3.63890261820136
15603.18851847576738-3.18851847576738
1570-1.785243864904141.78524386490414
15801.25328050051341-1.25328050051341
15901.32573242360361-1.32573242360361
1600-3.214844946708913.21484494670891
16109.5135335722752-9.5135335722752
16203.16594357194537-3.16594357194537
1630-2.585222513403132.58522251340313
1640-3.340697053588393.34069705358839
1650-1.154715578454861.15471557845486
1660-2.871184220869742.87118422086974
16700.267525231185318-0.267525231185318
1680-4.133747088927364.13374708892736
16901.87932262468813-1.87932262468813
1700-1.556797313210431.55679731321043
17100.587492658770266-0.587492658770266
1720-1.114708998423831.11470899842383
17300.953454332929285-0.953454332929285
17402.72501818740417-2.72501818740417
17505.70723252356836-5.70723252356836
17600.51407691483401-0.51407691483401
17701.72243891768008-1.72243891768008
17804.89023444856721-4.89023444856721
1790-3.232690863575143.23269086357514
18003.46635529654478-3.46635529654478
1810-2.622011014011342.62201101401134
1820-0.4564418889986260.456441888998626
18300.429270769954054-0.429270769954054
1840-3.369574955712353.36957495571235
18503.87920867223188-3.87920867223188
18603.63810952416576-3.63810952416576
1870-1.890077164302591.89007716430259
1880-1.102266219394171.10226621939417
18901.43198480570187-1.43198480570187
19002.69583052287873-2.69583052287873
19102.86710265176433-2.86710265176433
19202.5769229229845-2.5769229229845
1930-4.233040786095674.23304078609567
1940-0.9484727168877470.948472716887747
1953525.75941982591329.2405801740868






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.2466177297250250.493235459450050.753382270274975
160.1217003836918530.2434007673837070.878299616308147
170.05822643497393090.1164528699478620.94177356502607
180.02708169008355460.05416338016710930.972918309916445
190.06792226940687260.1358445388137450.932077730593127
200.03689862139560620.07379724279121230.963101378604394
210.2505359723556280.5010719447112550.749464027644372
220.2707952729728750.5415905459457510.729204727027125
230.6993705617764680.6012588764470650.300629438223532
240.6429868743450280.7140262513099430.357013125654972
250.6696683872902440.6606632254195110.330331612709755
260.8176284713743630.3647430572512730.182371528625637
270.8444486373211890.3111027253576220.155551362678811
280.9496605193716260.1006789612567480.0503394806283739
290.9621172672011820.07576546559763560.0378827327988178
300.9584773237613640.08304535247727170.0415226762386359
310.974923052906890.05015389418622010.02507694709311
320.9743384209298690.05132315814026210.0256615790701311
330.9724550936812160.05508981263756740.0275449063187837
340.9757247401307780.04855051973844380.0242752598692219
350.972616906468140.05476618706371920.0273830935318596
360.993385737236450.01322852552710120.00661426276355059
370.9989455686393580.002108862721284670.00105443136064234
380.9988203605844140.002359278831172530.00117963941558627
390.9991179809706570.001764038058686080.000882019029343039
400.9993293749620050.001341250075989970.000670625037994983
410.9998740039615020.0002519920769970880.000125996038498544
420.9999787916112674.24167774667623e-052.12083887333812e-05
430.9999686248942516.275021149752e-053.137510574876e-05
440.999991690495941.66190081197845e-058.30950405989223e-06
450.9999941618553921.16762892167348e-055.8381446083674e-06
460.9999939397484021.21205031961448e-056.0602515980724e-06
470.9999943616337711.12767324578514e-055.63836622892571e-06
480.9999940392486271.19215027469465e-055.96075137347325e-06
490.999998777677412.4446451822038e-061.2223225911019e-06
500.999999803703443.92593121770571e-071.96296560885285e-07
510.9999997835052014.32989597765453e-072.16494798882727e-07
520.999999894168752.11662499316891e-071.05831249658445e-07
530.9999999997901674.19666523447919e-102.09833261723959e-10
540.9999999999977764.44897445320659e-122.2244872266033e-12
550.9999999999999764.83808561873652e-142.41904280936826e-14
560.999999999999975.84966432520433e-142.92483216260217e-14
570.9999999999999754.92189723378582e-142.46094861689291e-14
580.9999999999999823.53177787671232e-141.76588893835616e-14
590.9999999999999745.11684409543827e-142.55842204771913e-14
600.9999999999999725.50850908788608e-142.75425454394304e-14
610.9999999999999882.4584255477258e-141.2292127738629e-14
620.9999999999999967.80747256958248e-153.90373628479124e-15
6318.82148914651204e-194.41074457325602e-19
6415.71620990319575e-262.85810495159787e-26
6513.05722695213605e-281.52861347606802e-28
6611.05345096206424e-275.26725481032118e-28
6716.10630921653569e-283.05315460826784e-28
6811.55019299027588e-287.75096495137939e-29
6911.3212728548135e-286.60636427406751e-29
7019.95904993257364e-294.97952496628682e-29
7117.7650038564738e-303.8825019282369e-30
7211.42352342178936e-297.1176171089468e-30
7314.99244323820919e-302.4962216191046e-30
7411.3544125794079e-296.77206289703948e-30
7511.16127670689808e-305.8063835344904e-31
7611.06423167456317e-335.32115837281585e-34
7715.26486290962052e-342.63243145481026e-34
7812.956778528429e-391.4783892642145e-39
7911.22470458142289e-386.12352290711446e-39
8011.64361799601427e-388.21808998007136e-39
8112.38354754115572e-381.19177377057786e-38
8219.13787367551462e-384.56893683775731e-38
8312.36365826778499e-381.1818291338925e-38
8419.66946858421996e-384.83473429210998e-38
8513.2289330404449e-381.61446652022245e-38
8611.42935514035281e-377.14677570176404e-38
8715.9795998579272e-372.9897999289636e-37
8813.82001323656094e-391.91000661828047e-39
8911.24929761588215e-386.24648807941075e-39
9015.7718804174234e-382.8859402087117e-38
9111.7737388310603e-378.8686941553015e-38
9211.08913337957252e-375.44566689786261e-38
9311.19917427281663e-375.99587136408313e-38
9415.04364677504854e-372.52182338752427e-37
9514.25264414244025e-372.12632207122013e-37
9616.6615144864725e-403.33075724323625e-40
9717.06305833523294e-473.53152916761647e-47
9816.17307316677699e-603.08653658338849e-60
9913.54038354986394e-591.77019177493197e-59
10011.18106028451604e-635.9053014225802e-64
10112.03361924347023e-681.01680962173511e-68
10211.06832489929691e-675.34162449648455e-68
10319.47527338312545e-904.73763669156273e-90
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
131100
132100
133100
134100
135100
136100
137100
138100
139100
140100
141100
142100
143100
144100
145100
146100
147100
148100
149100
150100
151100
152100
153100
154100
155100
156100
157100
158100
159100
160100
16116.66000490594e-3213.33000245297e-321
16215.50595820030843e-3062.75297910015422e-306
16312.71903202677565e-2871.35951601338783e-287
16414.37347046209018e-2832.18673523104509e-283
16511.20398719743895e-2626.01993598719476e-263
16614.36474152466535e-2582.18237076233267e-258
16715.66087578249251e-2342.83043789124626e-234
16813.24312064090454e-2341.62156032045227e-234
16911.03029583107778e-2115.1514791553889e-212
17012.37468883211131e-1911.18734441605565e-191
17111.9008642112229e-1739.50432105611452e-174
17215.91626305705264e-1622.95813152852632e-162
17312.24215812175224e-1441.12107906087612e-144
17414.46924776160545e-1322.23462388080272e-132
17512.51912870449472e-1191.25956435224736e-119
17616.61603380491438e-1033.30801690245719e-103
17719.7335861958709e-884.86679309793545e-88
17811.16533330733994e-725.82666653669971e-73
17911.1299722156146e-615.649861078073e-62
18012.78421770513857e-451.39210885256928e-45

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.246617729725025 & 0.49323545945005 & 0.753382270274975 \tabularnewline
16 & 0.121700383691853 & 0.243400767383707 & 0.878299616308147 \tabularnewline
17 & 0.0582264349739309 & 0.116452869947862 & 0.94177356502607 \tabularnewline
18 & 0.0270816900835546 & 0.0541633801671093 & 0.972918309916445 \tabularnewline
19 & 0.0679222694068726 & 0.135844538813745 & 0.932077730593127 \tabularnewline
20 & 0.0368986213956062 & 0.0737972427912123 & 0.963101378604394 \tabularnewline
21 & 0.250535972355628 & 0.501071944711255 & 0.749464027644372 \tabularnewline
22 & 0.270795272972875 & 0.541590545945751 & 0.729204727027125 \tabularnewline
23 & 0.699370561776468 & 0.601258876447065 & 0.300629438223532 \tabularnewline
24 & 0.642986874345028 & 0.714026251309943 & 0.357013125654972 \tabularnewline
25 & 0.669668387290244 & 0.660663225419511 & 0.330331612709755 \tabularnewline
26 & 0.817628471374363 & 0.364743057251273 & 0.182371528625637 \tabularnewline
27 & 0.844448637321189 & 0.311102725357622 & 0.155551362678811 \tabularnewline
28 & 0.949660519371626 & 0.100678961256748 & 0.0503394806283739 \tabularnewline
29 & 0.962117267201182 & 0.0757654655976356 & 0.0378827327988178 \tabularnewline
30 & 0.958477323761364 & 0.0830453524772717 & 0.0415226762386359 \tabularnewline
31 & 0.97492305290689 & 0.0501538941862201 & 0.02507694709311 \tabularnewline
32 & 0.974338420929869 & 0.0513231581402621 & 0.0256615790701311 \tabularnewline
33 & 0.972455093681216 & 0.0550898126375674 & 0.0275449063187837 \tabularnewline
34 & 0.975724740130778 & 0.0485505197384438 & 0.0242752598692219 \tabularnewline
35 & 0.97261690646814 & 0.0547661870637192 & 0.0273830935318596 \tabularnewline
36 & 0.99338573723645 & 0.0132285255271012 & 0.00661426276355059 \tabularnewline
37 & 0.998945568639358 & 0.00210886272128467 & 0.00105443136064234 \tabularnewline
38 & 0.998820360584414 & 0.00235927883117253 & 0.00117963941558627 \tabularnewline
39 & 0.999117980970657 & 0.00176403805868608 & 0.000882019029343039 \tabularnewline
40 & 0.999329374962005 & 0.00134125007598997 & 0.000670625037994983 \tabularnewline
41 & 0.999874003961502 & 0.000251992076997088 & 0.000125996038498544 \tabularnewline
42 & 0.999978791611267 & 4.24167774667623e-05 & 2.12083887333812e-05 \tabularnewline
43 & 0.999968624894251 & 6.275021149752e-05 & 3.137510574876e-05 \tabularnewline
44 & 0.99999169049594 & 1.66190081197845e-05 & 8.30950405989223e-06 \tabularnewline
45 & 0.999994161855392 & 1.16762892167348e-05 & 5.8381446083674e-06 \tabularnewline
46 & 0.999993939748402 & 1.21205031961448e-05 & 6.0602515980724e-06 \tabularnewline
47 & 0.999994361633771 & 1.12767324578514e-05 & 5.63836622892571e-06 \tabularnewline
48 & 0.999994039248627 & 1.19215027469465e-05 & 5.96075137347325e-06 \tabularnewline
49 & 0.99999877767741 & 2.4446451822038e-06 & 1.2223225911019e-06 \tabularnewline
50 & 0.99999980370344 & 3.92593121770571e-07 & 1.96296560885285e-07 \tabularnewline
51 & 0.999999783505201 & 4.32989597765453e-07 & 2.16494798882727e-07 \tabularnewline
52 & 0.99999989416875 & 2.11662499316891e-07 & 1.05831249658445e-07 \tabularnewline
53 & 0.999999999790167 & 4.19666523447919e-10 & 2.09833261723959e-10 \tabularnewline
54 & 0.999999999997776 & 4.44897445320659e-12 & 2.2244872266033e-12 \tabularnewline
55 & 0.999999999999976 & 4.83808561873652e-14 & 2.41904280936826e-14 \tabularnewline
56 & 0.99999999999997 & 5.84966432520433e-14 & 2.92483216260217e-14 \tabularnewline
57 & 0.999999999999975 & 4.92189723378582e-14 & 2.46094861689291e-14 \tabularnewline
58 & 0.999999999999982 & 3.53177787671232e-14 & 1.76588893835616e-14 \tabularnewline
59 & 0.999999999999974 & 5.11684409543827e-14 & 2.55842204771913e-14 \tabularnewline
60 & 0.999999999999972 & 5.50850908788608e-14 & 2.75425454394304e-14 \tabularnewline
61 & 0.999999999999988 & 2.4584255477258e-14 & 1.2292127738629e-14 \tabularnewline
62 & 0.999999999999996 & 7.80747256958248e-15 & 3.90373628479124e-15 \tabularnewline
63 & 1 & 8.82148914651204e-19 & 4.41074457325602e-19 \tabularnewline
64 & 1 & 5.71620990319575e-26 & 2.85810495159787e-26 \tabularnewline
65 & 1 & 3.05722695213605e-28 & 1.52861347606802e-28 \tabularnewline
66 & 1 & 1.05345096206424e-27 & 5.26725481032118e-28 \tabularnewline
67 & 1 & 6.10630921653569e-28 & 3.05315460826784e-28 \tabularnewline
68 & 1 & 1.55019299027588e-28 & 7.75096495137939e-29 \tabularnewline
69 & 1 & 1.3212728548135e-28 & 6.60636427406751e-29 \tabularnewline
70 & 1 & 9.95904993257364e-29 & 4.97952496628682e-29 \tabularnewline
71 & 1 & 7.7650038564738e-30 & 3.8825019282369e-30 \tabularnewline
72 & 1 & 1.42352342178936e-29 & 7.1176171089468e-30 \tabularnewline
73 & 1 & 4.99244323820919e-30 & 2.4962216191046e-30 \tabularnewline
74 & 1 & 1.3544125794079e-29 & 6.77206289703948e-30 \tabularnewline
75 & 1 & 1.16127670689808e-30 & 5.8063835344904e-31 \tabularnewline
76 & 1 & 1.06423167456317e-33 & 5.32115837281585e-34 \tabularnewline
77 & 1 & 5.26486290962052e-34 & 2.63243145481026e-34 \tabularnewline
78 & 1 & 2.956778528429e-39 & 1.4783892642145e-39 \tabularnewline
79 & 1 & 1.22470458142289e-38 & 6.12352290711446e-39 \tabularnewline
80 & 1 & 1.64361799601427e-38 & 8.21808998007136e-39 \tabularnewline
81 & 1 & 2.38354754115572e-38 & 1.19177377057786e-38 \tabularnewline
82 & 1 & 9.13787367551462e-38 & 4.56893683775731e-38 \tabularnewline
83 & 1 & 2.36365826778499e-38 & 1.1818291338925e-38 \tabularnewline
84 & 1 & 9.66946858421996e-38 & 4.83473429210998e-38 \tabularnewline
85 & 1 & 3.2289330404449e-38 & 1.61446652022245e-38 \tabularnewline
86 & 1 & 1.42935514035281e-37 & 7.14677570176404e-38 \tabularnewline
87 & 1 & 5.9795998579272e-37 & 2.9897999289636e-37 \tabularnewline
88 & 1 & 3.82001323656094e-39 & 1.91000661828047e-39 \tabularnewline
89 & 1 & 1.24929761588215e-38 & 6.24648807941075e-39 \tabularnewline
90 & 1 & 5.7718804174234e-38 & 2.8859402087117e-38 \tabularnewline
91 & 1 & 1.7737388310603e-37 & 8.8686941553015e-38 \tabularnewline
92 & 1 & 1.08913337957252e-37 & 5.44566689786261e-38 \tabularnewline
93 & 1 & 1.19917427281663e-37 & 5.99587136408313e-38 \tabularnewline
94 & 1 & 5.04364677504854e-37 & 2.52182338752427e-37 \tabularnewline
95 & 1 & 4.25264414244025e-37 & 2.12632207122013e-37 \tabularnewline
96 & 1 & 6.6615144864725e-40 & 3.33075724323625e-40 \tabularnewline
97 & 1 & 7.06305833523294e-47 & 3.53152916761647e-47 \tabularnewline
98 & 1 & 6.17307316677699e-60 & 3.08653658338849e-60 \tabularnewline
99 & 1 & 3.54038354986394e-59 & 1.77019177493197e-59 \tabularnewline
100 & 1 & 1.18106028451604e-63 & 5.9053014225802e-64 \tabularnewline
101 & 1 & 2.03361924347023e-68 & 1.01680962173511e-68 \tabularnewline
102 & 1 & 1.06832489929691e-67 & 5.34162449648455e-68 \tabularnewline
103 & 1 & 9.47527338312545e-90 & 4.73763669156273e-90 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 0 & 0 \tabularnewline
126 & 1 & 0 & 0 \tabularnewline
127 & 1 & 0 & 0 \tabularnewline
128 & 1 & 0 & 0 \tabularnewline
129 & 1 & 0 & 0 \tabularnewline
130 & 1 & 0 & 0 \tabularnewline
131 & 1 & 0 & 0 \tabularnewline
132 & 1 & 0 & 0 \tabularnewline
133 & 1 & 0 & 0 \tabularnewline
134 & 1 & 0 & 0 \tabularnewline
135 & 1 & 0 & 0 \tabularnewline
136 & 1 & 0 & 0 \tabularnewline
137 & 1 & 0 & 0 \tabularnewline
138 & 1 & 0 & 0 \tabularnewline
139 & 1 & 0 & 0 \tabularnewline
140 & 1 & 0 & 0 \tabularnewline
141 & 1 & 0 & 0 \tabularnewline
142 & 1 & 0 & 0 \tabularnewline
143 & 1 & 0 & 0 \tabularnewline
144 & 1 & 0 & 0 \tabularnewline
145 & 1 & 0 & 0 \tabularnewline
146 & 1 & 0 & 0 \tabularnewline
147 & 1 & 0 & 0 \tabularnewline
148 & 1 & 0 & 0 \tabularnewline
149 & 1 & 0 & 0 \tabularnewline
150 & 1 & 0 & 0 \tabularnewline
151 & 1 & 0 & 0 \tabularnewline
152 & 1 & 0 & 0 \tabularnewline
153 & 1 & 0 & 0 \tabularnewline
154 & 1 & 0 & 0 \tabularnewline
155 & 1 & 0 & 0 \tabularnewline
156 & 1 & 0 & 0 \tabularnewline
157 & 1 & 0 & 0 \tabularnewline
158 & 1 & 0 & 0 \tabularnewline
159 & 1 & 0 & 0 \tabularnewline
160 & 1 & 0 & 0 \tabularnewline
161 & 1 & 6.66000490594e-321 & 3.33000245297e-321 \tabularnewline
162 & 1 & 5.50595820030843e-306 & 2.75297910015422e-306 \tabularnewline
163 & 1 & 2.71903202677565e-287 & 1.35951601338783e-287 \tabularnewline
164 & 1 & 4.37347046209018e-283 & 2.18673523104509e-283 \tabularnewline
165 & 1 & 1.20398719743895e-262 & 6.01993598719476e-263 \tabularnewline
166 & 1 & 4.36474152466535e-258 & 2.18237076233267e-258 \tabularnewline
167 & 1 & 5.66087578249251e-234 & 2.83043789124626e-234 \tabularnewline
168 & 1 & 3.24312064090454e-234 & 1.62156032045227e-234 \tabularnewline
169 & 1 & 1.03029583107778e-211 & 5.1514791553889e-212 \tabularnewline
170 & 1 & 2.37468883211131e-191 & 1.18734441605565e-191 \tabularnewline
171 & 1 & 1.9008642112229e-173 & 9.50432105611452e-174 \tabularnewline
172 & 1 & 5.91626305705264e-162 & 2.95813152852632e-162 \tabularnewline
173 & 1 & 2.24215812175224e-144 & 1.12107906087612e-144 \tabularnewline
174 & 1 & 4.46924776160545e-132 & 2.23462388080272e-132 \tabularnewline
175 & 1 & 2.51912870449472e-119 & 1.25956435224736e-119 \tabularnewline
176 & 1 & 6.61603380491438e-103 & 3.30801690245719e-103 \tabularnewline
177 & 1 & 9.7335861958709e-88 & 4.86679309793545e-88 \tabularnewline
178 & 1 & 1.16533330733994e-72 & 5.82666653669971e-73 \tabularnewline
179 & 1 & 1.1299722156146e-61 & 5.649861078073e-62 \tabularnewline
180 & 1 & 2.78421770513857e-45 & 1.39210885256928e-45 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116301&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]15[/C][C]0.246617729725025[/C][C]0.49323545945005[/C][C]0.753382270274975[/C][/ROW]
[ROW][C]16[/C][C]0.121700383691853[/C][C]0.243400767383707[/C][C]0.878299616308147[/C][/ROW]
[ROW][C]17[/C][C]0.0582264349739309[/C][C]0.116452869947862[/C][C]0.94177356502607[/C][/ROW]
[ROW][C]18[/C][C]0.0270816900835546[/C][C]0.0541633801671093[/C][C]0.972918309916445[/C][/ROW]
[ROW][C]19[/C][C]0.0679222694068726[/C][C]0.135844538813745[/C][C]0.932077730593127[/C][/ROW]
[ROW][C]20[/C][C]0.0368986213956062[/C][C]0.0737972427912123[/C][C]0.963101378604394[/C][/ROW]
[ROW][C]21[/C][C]0.250535972355628[/C][C]0.501071944711255[/C][C]0.749464027644372[/C][/ROW]
[ROW][C]22[/C][C]0.270795272972875[/C][C]0.541590545945751[/C][C]0.729204727027125[/C][/ROW]
[ROW][C]23[/C][C]0.699370561776468[/C][C]0.601258876447065[/C][C]0.300629438223532[/C][/ROW]
[ROW][C]24[/C][C]0.642986874345028[/C][C]0.714026251309943[/C][C]0.357013125654972[/C][/ROW]
[ROW][C]25[/C][C]0.669668387290244[/C][C]0.660663225419511[/C][C]0.330331612709755[/C][/ROW]
[ROW][C]26[/C][C]0.817628471374363[/C][C]0.364743057251273[/C][C]0.182371528625637[/C][/ROW]
[ROW][C]27[/C][C]0.844448637321189[/C][C]0.311102725357622[/C][C]0.155551362678811[/C][/ROW]
[ROW][C]28[/C][C]0.949660519371626[/C][C]0.100678961256748[/C][C]0.0503394806283739[/C][/ROW]
[ROW][C]29[/C][C]0.962117267201182[/C][C]0.0757654655976356[/C][C]0.0378827327988178[/C][/ROW]
[ROW][C]30[/C][C]0.958477323761364[/C][C]0.0830453524772717[/C][C]0.0415226762386359[/C][/ROW]
[ROW][C]31[/C][C]0.97492305290689[/C][C]0.0501538941862201[/C][C]0.02507694709311[/C][/ROW]
[ROW][C]32[/C][C]0.974338420929869[/C][C]0.0513231581402621[/C][C]0.0256615790701311[/C][/ROW]
[ROW][C]33[/C][C]0.972455093681216[/C][C]0.0550898126375674[/C][C]0.0275449063187837[/C][/ROW]
[ROW][C]34[/C][C]0.975724740130778[/C][C]0.0485505197384438[/C][C]0.0242752598692219[/C][/ROW]
[ROW][C]35[/C][C]0.97261690646814[/C][C]0.0547661870637192[/C][C]0.0273830935318596[/C][/ROW]
[ROW][C]36[/C][C]0.99338573723645[/C][C]0.0132285255271012[/C][C]0.00661426276355059[/C][/ROW]
[ROW][C]37[/C][C]0.998945568639358[/C][C]0.00210886272128467[/C][C]0.00105443136064234[/C][/ROW]
[ROW][C]38[/C][C]0.998820360584414[/C][C]0.00235927883117253[/C][C]0.00117963941558627[/C][/ROW]
[ROW][C]39[/C][C]0.999117980970657[/C][C]0.00176403805868608[/C][C]0.000882019029343039[/C][/ROW]
[ROW][C]40[/C][C]0.999329374962005[/C][C]0.00134125007598997[/C][C]0.000670625037994983[/C][/ROW]
[ROW][C]41[/C][C]0.999874003961502[/C][C]0.000251992076997088[/C][C]0.000125996038498544[/C][/ROW]
[ROW][C]42[/C][C]0.999978791611267[/C][C]4.24167774667623e-05[/C][C]2.12083887333812e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999968624894251[/C][C]6.275021149752e-05[/C][C]3.137510574876e-05[/C][/ROW]
[ROW][C]44[/C][C]0.99999169049594[/C][C]1.66190081197845e-05[/C][C]8.30950405989223e-06[/C][/ROW]
[ROW][C]45[/C][C]0.999994161855392[/C][C]1.16762892167348e-05[/C][C]5.8381446083674e-06[/C][/ROW]
[ROW][C]46[/C][C]0.999993939748402[/C][C]1.21205031961448e-05[/C][C]6.0602515980724e-06[/C][/ROW]
[ROW][C]47[/C][C]0.999994361633771[/C][C]1.12767324578514e-05[/C][C]5.63836622892571e-06[/C][/ROW]
[ROW][C]48[/C][C]0.999994039248627[/C][C]1.19215027469465e-05[/C][C]5.96075137347325e-06[/C][/ROW]
[ROW][C]49[/C][C]0.99999877767741[/C][C]2.4446451822038e-06[/C][C]1.2223225911019e-06[/C][/ROW]
[ROW][C]50[/C][C]0.99999980370344[/C][C]3.92593121770571e-07[/C][C]1.96296560885285e-07[/C][/ROW]
[ROW][C]51[/C][C]0.999999783505201[/C][C]4.32989597765453e-07[/C][C]2.16494798882727e-07[/C][/ROW]
[ROW][C]52[/C][C]0.99999989416875[/C][C]2.11662499316891e-07[/C][C]1.05831249658445e-07[/C][/ROW]
[ROW][C]53[/C][C]0.999999999790167[/C][C]4.19666523447919e-10[/C][C]2.09833261723959e-10[/C][/ROW]
[ROW][C]54[/C][C]0.999999999997776[/C][C]4.44897445320659e-12[/C][C]2.2244872266033e-12[/C][/ROW]
[ROW][C]55[/C][C]0.999999999999976[/C][C]4.83808561873652e-14[/C][C]2.41904280936826e-14[/C][/ROW]
[ROW][C]56[/C][C]0.99999999999997[/C][C]5.84966432520433e-14[/C][C]2.92483216260217e-14[/C][/ROW]
[ROW][C]57[/C][C]0.999999999999975[/C][C]4.92189723378582e-14[/C][C]2.46094861689291e-14[/C][/ROW]
[ROW][C]58[/C][C]0.999999999999982[/C][C]3.53177787671232e-14[/C][C]1.76588893835616e-14[/C][/ROW]
[ROW][C]59[/C][C]0.999999999999974[/C][C]5.11684409543827e-14[/C][C]2.55842204771913e-14[/C][/ROW]
[ROW][C]60[/C][C]0.999999999999972[/C][C]5.50850908788608e-14[/C][C]2.75425454394304e-14[/C][/ROW]
[ROW][C]61[/C][C]0.999999999999988[/C][C]2.4584255477258e-14[/C][C]1.2292127738629e-14[/C][/ROW]
[ROW][C]62[/C][C]0.999999999999996[/C][C]7.80747256958248e-15[/C][C]3.90373628479124e-15[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]8.82148914651204e-19[/C][C]4.41074457325602e-19[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]5.71620990319575e-26[/C][C]2.85810495159787e-26[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]3.05722695213605e-28[/C][C]1.52861347606802e-28[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.05345096206424e-27[/C][C]5.26725481032118e-28[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]6.10630921653569e-28[/C][C]3.05315460826784e-28[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.55019299027588e-28[/C][C]7.75096495137939e-29[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.3212728548135e-28[/C][C]6.60636427406751e-29[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]9.95904993257364e-29[/C][C]4.97952496628682e-29[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]7.7650038564738e-30[/C][C]3.8825019282369e-30[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]1.42352342178936e-29[/C][C]7.1176171089468e-30[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]4.99244323820919e-30[/C][C]2.4962216191046e-30[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.3544125794079e-29[/C][C]6.77206289703948e-30[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.16127670689808e-30[/C][C]5.8063835344904e-31[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.06423167456317e-33[/C][C]5.32115837281585e-34[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]5.26486290962052e-34[/C][C]2.63243145481026e-34[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]2.956778528429e-39[/C][C]1.4783892642145e-39[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.22470458142289e-38[/C][C]6.12352290711446e-39[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.64361799601427e-38[/C][C]8.21808998007136e-39[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]2.38354754115572e-38[/C][C]1.19177377057786e-38[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]9.13787367551462e-38[/C][C]4.56893683775731e-38[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]2.36365826778499e-38[/C][C]1.1818291338925e-38[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]9.66946858421996e-38[/C][C]4.83473429210998e-38[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]3.2289330404449e-38[/C][C]1.61446652022245e-38[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.42935514035281e-37[/C][C]7.14677570176404e-38[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]5.9795998579272e-37[/C][C]2.9897999289636e-37[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]3.82001323656094e-39[/C][C]1.91000661828047e-39[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.24929761588215e-38[/C][C]6.24648807941075e-39[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]5.7718804174234e-38[/C][C]2.8859402087117e-38[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.7737388310603e-37[/C][C]8.8686941553015e-38[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]1.08913337957252e-37[/C][C]5.44566689786261e-38[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.19917427281663e-37[/C][C]5.99587136408313e-38[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]5.04364677504854e-37[/C][C]2.52182338752427e-37[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]4.25264414244025e-37[/C][C]2.12632207122013e-37[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]6.6615144864725e-40[/C][C]3.33075724323625e-40[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]7.06305833523294e-47[/C][C]3.53152916761647e-47[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]6.17307316677699e-60[/C][C]3.08653658338849e-60[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]3.54038354986394e-59[/C][C]1.77019177493197e-59[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]1.18106028451604e-63[/C][C]5.9053014225802e-64[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]2.03361924347023e-68[/C][C]1.01680962173511e-68[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.06832489929691e-67[/C][C]5.34162449648455e-68[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]9.47527338312545e-90[/C][C]4.73763669156273e-90[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]6.66000490594e-321[/C][C]3.33000245297e-321[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]5.50595820030843e-306[/C][C]2.75297910015422e-306[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]2.71903202677565e-287[/C][C]1.35951601338783e-287[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]4.37347046209018e-283[/C][C]2.18673523104509e-283[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]1.20398719743895e-262[/C][C]6.01993598719476e-263[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]4.36474152466535e-258[/C][C]2.18237076233267e-258[/C][/ROW]
[ROW][C]167[/C][C]1[/C][C]5.66087578249251e-234[/C][C]2.83043789124626e-234[/C][/ROW]
[ROW][C]168[/C][C]1[/C][C]3.24312064090454e-234[/C][C]1.62156032045227e-234[/C][/ROW]
[ROW][C]169[/C][C]1[/C][C]1.03029583107778e-211[/C][C]5.1514791553889e-212[/C][/ROW]
[ROW][C]170[/C][C]1[/C][C]2.37468883211131e-191[/C][C]1.18734441605565e-191[/C][/ROW]
[ROW][C]171[/C][C]1[/C][C]1.9008642112229e-173[/C][C]9.50432105611452e-174[/C][/ROW]
[ROW][C]172[/C][C]1[/C][C]5.91626305705264e-162[/C][C]2.95813152852632e-162[/C][/ROW]
[ROW][C]173[/C][C]1[/C][C]2.24215812175224e-144[/C][C]1.12107906087612e-144[/C][/ROW]
[ROW][C]174[/C][C]1[/C][C]4.46924776160545e-132[/C][C]2.23462388080272e-132[/C][/ROW]
[ROW][C]175[/C][C]1[/C][C]2.51912870449472e-119[/C][C]1.25956435224736e-119[/C][/ROW]
[ROW][C]176[/C][C]1[/C][C]6.61603380491438e-103[/C][C]3.30801690245719e-103[/C][/ROW]
[ROW][C]177[/C][C]1[/C][C]9.7335861958709e-88[/C][C]4.86679309793545e-88[/C][/ROW]
[ROW][C]178[/C][C]1[/C][C]1.16533330733994e-72[/C][C]5.82666653669971e-73[/C][/ROW]
[ROW][C]179[/C][C]1[/C][C]1.1299722156146e-61[/C][C]5.649861078073e-62[/C][/ROW]
[ROW][C]180[/C][C]1[/C][C]2.78421770513857e-45[/C][C]1.39210885256928e-45[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116301&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116301&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.2466177297250250.493235459450050.753382270274975
160.1217003836918530.2434007673837070.878299616308147
170.05822643497393090.1164528699478620.94177356502607
180.02708169008355460.05416338016710930.972918309916445
190.06792226940687260.1358445388137450.932077730593127
200.03689862139560620.07379724279121230.963101378604394
210.2505359723556280.5010719447112550.749464027644372
220.2707952729728750.5415905459457510.729204727027125
230.6993705617764680.6012588764470650.300629438223532
240.6429868743450280.7140262513099430.357013125654972
250.6696683872902440.6606632254195110.330331612709755
260.8176284713743630.3647430572512730.182371528625637
270.8444486373211890.3111027253576220.155551362678811
280.9496605193716260.1006789612567480.0503394806283739
290.9621172672011820.07576546559763560.0378827327988178
300.9584773237613640.08304535247727170.0415226762386359
310.974923052906890.05015389418622010.02507694709311
320.9743384209298690.05132315814026210.0256615790701311
330.9724550936812160.05508981263756740.0275449063187837
340.9757247401307780.04855051973844380.0242752598692219
350.972616906468140.05476618706371920.0273830935318596
360.993385737236450.01322852552710120.00661426276355059
370.9989455686393580.002108862721284670.00105443136064234
380.9988203605844140.002359278831172530.00117963941558627
390.9991179809706570.001764038058686080.000882019029343039
400.9993293749620050.001341250075989970.000670625037994983
410.9998740039615020.0002519920769970880.000125996038498544
420.9999787916112674.24167774667623e-052.12083887333812e-05
430.9999686248942516.275021149752e-053.137510574876e-05
440.999991690495941.66190081197845e-058.30950405989223e-06
450.9999941618553921.16762892167348e-055.8381446083674e-06
460.9999939397484021.21205031961448e-056.0602515980724e-06
470.9999943616337711.12767324578514e-055.63836622892571e-06
480.9999940392486271.19215027469465e-055.96075137347325e-06
490.999998777677412.4446451822038e-061.2223225911019e-06
500.999999803703443.92593121770571e-071.96296560885285e-07
510.9999997835052014.32989597765453e-072.16494798882727e-07
520.999999894168752.11662499316891e-071.05831249658445e-07
530.9999999997901674.19666523447919e-102.09833261723959e-10
540.9999999999977764.44897445320659e-122.2244872266033e-12
550.9999999999999764.83808561873652e-142.41904280936826e-14
560.999999999999975.84966432520433e-142.92483216260217e-14
570.9999999999999754.92189723378582e-142.46094861689291e-14
580.9999999999999823.53177787671232e-141.76588893835616e-14
590.9999999999999745.11684409543827e-142.55842204771913e-14
600.9999999999999725.50850908788608e-142.75425454394304e-14
610.9999999999999882.4584255477258e-141.2292127738629e-14
620.9999999999999967.80747256958248e-153.90373628479124e-15
6318.82148914651204e-194.41074457325602e-19
6415.71620990319575e-262.85810495159787e-26
6513.05722695213605e-281.52861347606802e-28
6611.05345096206424e-275.26725481032118e-28
6716.10630921653569e-283.05315460826784e-28
6811.55019299027588e-287.75096495137939e-29
6911.3212728548135e-286.60636427406751e-29
7019.95904993257364e-294.97952496628682e-29
7117.7650038564738e-303.8825019282369e-30
7211.42352342178936e-297.1176171089468e-30
7314.99244323820919e-302.4962216191046e-30
7411.3544125794079e-296.77206289703948e-30
7511.16127670689808e-305.8063835344904e-31
7611.06423167456317e-335.32115837281585e-34
7715.26486290962052e-342.63243145481026e-34
7812.956778528429e-391.4783892642145e-39
7911.22470458142289e-386.12352290711446e-39
8011.64361799601427e-388.21808998007136e-39
8112.38354754115572e-381.19177377057786e-38
8219.13787367551462e-384.56893683775731e-38
8312.36365826778499e-381.1818291338925e-38
8419.66946858421996e-384.83473429210998e-38
8513.2289330404449e-381.61446652022245e-38
8611.42935514035281e-377.14677570176404e-38
8715.9795998579272e-372.9897999289636e-37
8813.82001323656094e-391.91000661828047e-39
8911.24929761588215e-386.24648807941075e-39
9015.7718804174234e-382.8859402087117e-38
9111.7737388310603e-378.8686941553015e-38
9211.08913337957252e-375.44566689786261e-38
9311.19917427281663e-375.99587136408313e-38
9415.04364677504854e-372.52182338752427e-37
9514.25264414244025e-372.12632207122013e-37
9616.6615144864725e-403.33075724323625e-40
9717.06305833523294e-473.53152916761647e-47
9816.17307316677699e-603.08653658338849e-60
9913.54038354986394e-591.77019177493197e-59
10011.18106028451604e-635.9053014225802e-64
10112.03361924347023e-681.01680962173511e-68
10211.06832489929691e-675.34162449648455e-68
10319.47527338312545e-904.73763669156273e-90
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
125100
126100
127100
128100
129100
130100
131100
132100
133100
134100
135100
136100
137100
138100
139100
140100
141100
142100
143100
144100
145100
146100
147100
148100
149100
150100
151100
152100
153100
154100
155100
156100
157100
158100
159100
160100
16116.66000490594e-3213.33000245297e-321
16215.50595820030843e-3062.75297910015422e-306
16312.71903202677565e-2871.35951601338783e-287
16414.37347046209018e-2832.18673523104509e-283
16511.20398719743895e-2626.01993598719476e-263
16614.36474152466535e-2582.18237076233267e-258
16715.66087578249251e-2342.83043789124626e-234
16813.24312064090454e-2341.62156032045227e-234
16911.03029583107778e-2115.1514791553889e-212
17012.37468883211131e-1911.18734441605565e-191
17111.9008642112229e-1739.50432105611452e-174
17215.91626305705264e-1622.95813152852632e-162
17312.24215812175224e-1441.12107906087612e-144
17414.46924776160545e-1322.23462388080272e-132
17512.51912870449472e-1191.25956435224736e-119
17616.61603380491438e-1033.30801690245719e-103
17719.7335861958709e-884.86679309793545e-88
17811.16533330733994e-725.82666653669971e-73
17911.1299722156146e-615.649861078073e-62
18012.78421770513857e-451.39210885256928e-45






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1440.867469879518072NOK
5% type I error level1460.879518072289157NOK
10% type I error level1540.927710843373494NOK

\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 & 144 & 0.867469879518072 & NOK \tabularnewline
5% type I error level & 146 & 0.879518072289157 & NOK \tabularnewline
10% type I error level & 154 & 0.927710843373494 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116301&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]144[/C][C]0.867469879518072[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]146[/C][C]0.879518072289157[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]154[/C][C]0.927710843373494[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116301&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116301&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 level1440.867469879518072NOK
5% type I error level1460.879518072289157NOK
10% type I error level1540.927710843373494NOK


Figures (Output of Computation)

Figure 1
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 12 ; 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')
}