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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 computationMon, 27 Dec 2010 21:44:47 +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/27/t129348615166ngfzpnipvuf0q.htm/, Retrieved Mon, 06 May 2024 16:35:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116152, Retrieved Mon, 06 May 2024 16:35:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119

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] [] [2010-12-27 21:44:47] [4cd8ab5c565aadc5ce193fb2faa1c53a] [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
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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
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1	52	52	1	1	29	29	40	40	32	32	14
1	27	27	2	2	42	42	40	40	24	24	19
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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
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2	43	0	4	0	30	0	45	0	29	0	14
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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
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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
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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
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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 time12 seconds
R Server'George Udny Yule' @ 72.249.76.132
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 & 12 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \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=116152&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/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=116152&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116152&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 time12 seconds
R Server'George Udny Yule' @ 72.249.76.132
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] -0.099208924064297leeftijd_man[t] -0.00941326692962698opleiding[t] + 0.178373242926735opleiding_man[t] + 0.11009600111611Neuroticisme[t] -0.0726361395252346Neuroticisme_man[t] + 0.224060126846621Extraversie[t] + 0.170531472270368Extraversie_man[t] + 0.367687899652952Openheid[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] -0.099208924064297leeftijd_man[t] -0.00941326692962698opleiding[t] +  0.178373242926735opleiding_man[t] +  0.11009600111611Neuroticisme[t] -0.0726361395252346Neuroticisme_man[t] +  0.224060126846621Extraversie[t] +  0.170531472270368Extraversie_man[t] +  0.367687899652952Openheid[t] -0.428294055693013Openheid_man[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116152&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] -0.099208924064297leeftijd_man[t] -0.00941326692962698opleiding[t] +  0.178373242926735opleiding_man[t] +  0.11009600111611Neuroticisme[t] -0.0726361395252346Neuroticisme_man[t] +  0.224060126846621Extraversie[t] +  0.170531472270368Extraversie_man[t] +  0.367687899652952Openheid[t] -0.428294055693013Openheid_man[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116152&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116152&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] -0.099208924064297leeftijd_man[t] -0.00941326692962698opleiding[t] + 0.178373242926735opleiding_man[t] + 0.11009600111611Neuroticisme[t] -0.0726361395252346Neuroticisme_man[t] + 0.224060126846621Extraversie[t] + 0.170531472270368Extraversie_man[t] + 0.367687899652952Openheid[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-0.0992089240642970.062473-1.5880.1140040.057002
opleiding-0.009413266929626980.134696-0.06990.9443610.472181
opleiding_man0.1783732429267350.1173051.52060.1300890.065045
Neuroticisme0.110096001116110.1046461.05210.2941490.147074
Neuroticisme_man-0.07263613952523460.089828-0.80860.4197850.209893
Extraversie0.2240601268466210.0978982.28870.0232390.011619
Extraversie_man0.1705314722703680.0738472.30930.0220460.011023
Openheid0.3676878996529520.1105793.32510.0010680.000534
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 & -0.099208924064297 & 0.062473 & -1.588 & 0.114004 & 0.057002 \tabularnewline
opleiding & -0.00941326692962698 & 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 & -0.0726361395252346 & 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 & 0.367687899652952 & 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=116152&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]-0.099208924064297[/C][C]0.062473[/C][C]-1.588[/C][C]0.114004[/C][C]0.057002[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.00941326692962698[/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]-0.0726361395252346[/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]0.367687899652952[/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=116152&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116152&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-0.0992089240642970.062473-1.5880.1140040.057002
opleiding-0.009413266929626980.134696-0.06990.9443610.472181
opleiding_man0.1783732429267350.1173051.52060.1300890.065045
Neuroticisme0.110096001116110.1046461.05210.2941490.147074
Neuroticisme_man-0.07263613952523460.089828-0.80860.4197850.209893
Extraversie0.2240601268466210.0978982.28870.0232390.011619
Extraversie_man0.1705314722703680.0738472.30930.0220460.011023
Openheid0.3676878996529520.1105793.32510.0010680.000534
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=116152&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=116152&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116152&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
11822.2848310395639-4.28483103956387
21018.5337987958555-8.53379879585552
32325.5354373300911-2.53543733009115
41417.2292787765482-3.22927877654822
51815.78982281548132.21017718451872
61916.51484118298362.48515881701636
71914.84616591340844.15383408659163
81616.8471027009205-0.847102700920534
92320.17168721836372.82831278163628
101713.88386919054643.11613080945362
112015.03174618542944.96825381457063
121816.88187492789851.11812507210154
131313.8044272654984-0.804427265498376
141717.8735642545141-0.873564254514086
155231.343676391986420.6563236080136
164640.66984627793855.33015372206149
175843.243662309444314.7563376905557
185444.63849662542319.36150337457693
192938.7548833480572-9.75488334805722
204334.23788675710418.76211324289594
214543.93806722819431.06193277180565
224641.61885736772124.38114263227881
232540.6105888892818-15.6105888892818
244736.28464051012210.7153594898780
254143.4392467869493-2.43924678694932
262938.8169882007755-9.81698820077546
274532.858856623020412.1411433769796
285436.976122483317517.0238775166825
292841.0690032111398-13.0690032111398
303736.13361167148120.866388328518751
315640.788094258907615.2119057410924
324334.63636371821238.3636362817877
333438.9131517940767-4.91315179407669
344240.38202595919861.61797404080136
354638.79282368626937.20717631373075
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
432628.0008931631793-2.00089316317930
445136.73031580843514.269684191565
452939.3410043268841-10.3410043268841
462936.7770151853803-7.77701518538026
474335.94516071199617.05483928800387
484439.66815912533324.33184087466676
492539.6352573843724-14.6352573843723
505137.20278806508813.7972119349120
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
6358.3694068127644-3.3694068127644
64313.6038039659754-10.6038039659754
652613.658089854054112.3419101459459
663029.73530728833990.264692711660112
673513.967336990367821.0326630096322
682914.867887764305514.1321122356945
692527.6772386241581-2.67723862415805
702725.99299660928191.00700339071811
712429.5632196409425-5.56321964094246
723526.83159327191678.16840672808326
733231.13703324747330.862966752526678
742431.2215708819885-7.22157088198852
753831.1153782845166.88462171548397
763621.812602049686214.1873979503138
772427.9607117171460-3.96071171714604
781823.6409001591531-5.64090015915314
793425.01609510870338.98390489129673
802328.8311379017971-5.83113790179712
813427.59862712577816.40137287422188
823227.1317801805524.86821981944798
832428.7647381651316-4.76473816513161
843428.16209245134915.83790754865089
853326.11410013674096.88589986325915
863335.8827077077136-2.88270770771361
872833.6709547851877-5.67095478518766
883229.21876024567682.78123975432319
892930.387652579817-1.38765257981702
902730.3189998007561-3.31899980075609
912829.8654997941412-1.86549979414117
922828.7476528870736-0.747652887073618
933029.43748948950420.5625105104958
942527.0944501359684-2.09445013596844
953132.312846283902-1.31284628390203
963727.55199880315239.44800119684769
973728.44976489266038.55023510733966
983426.84113547401537.15886452598467
993233.3766687261667-1.37666872616668
1002827.73028996436210.269710035637917
1012531.2129242796046-6.21292427960461
1022628.5620225374102-2.56202253741023
1033332.55316374465600.446836255343951
1043128.93522080933222.06477919066783
1052227.8317104958954-5.83171049589541
1062932.4435518605264-3.44355186052642
1072427.5400049688770-3.54000496887695
1080-2.741135915748642.74113591574864
10902.36018883728793-2.36018883728793
11005.22537100179176-5.22537100179176
11101.47059526733338-1.47059526733338
1120-0.3234171219044620.323417121904462
11301.73377725230331-1.73377725230331
1140-3.476269959831893.47626995983189
11501.50571089362323-1.50571089362323
1160-1.620889424012241.62088942401224
11701.22666762028114-1.22666762028114
11800.51087074944213-0.51087074944213
11901.62840199121386-1.62840199121386
12002.85487816107360-2.85487816107360
12102.63003267586279-2.63003267586279
1220-1.230973502739621.23097350273962
1230-0.9755357339778570.975535733977857
1240-2.452376589413982.45237658941398
1250-1.547787098691751.54778709869175
12601.16822735942015-1.16822735942015
12706.37629449121806-6.37629449121806
12802.40452881241401-2.40452881241401
1290-0.8198351789095250.819835178909525
1300-1.225266633030081.22526663303008
13102.84834793259595-2.84834793259595
13202.32681045220822-2.32681045220822
1330-1.367602628170251.36760262817025
13400.674670447298857-0.674670447298857
1350-0.9563982003723050.956398200372305
1360-1.272106286983611.27210628698361
1370-4.715574335136614.71557433513661
1380-0.1716749508787650.171674950878765
1390-0.9482665234232570.948266523423257
1400-1.953191199388051.95319119938805
1410-3.201452186003583.20145218600358
1420-3.728568909882743.72856890988274
1430-7.537214957094377.53721495709437
1440-0.4385945934219690.438594593421969
14505.66945514922918-5.66945514922918
1460-3.573597258025303.57359725802530
14702.23092094492601-2.23092094492601
14801.17944892121142-1.17944892121142
14904.53834484332622-4.53834484332622
1500-0.4866814541426150.486681454142615
1510-1.261459005045891.26145900504589
1520-0.1184036117603860.118403611760386
1530-1.528660649285331.52866064928533
1540-3.342655125785033.34265512578503
15503.63890261820136-3.63890261820136
15603.18851847576737-3.18851847576737
1570-1.785243864904141.78524386490414
15801.25328050051342-1.25328050051342
15901.32573242360361-1.32573242360361
1600-3.214844946708913.21484494670891
16109.51353357227519-9.51353357227519
16203.16594357194537-3.16594357194537
1630-2.585222513403132.58522251340313
1640-3.340697053588403.34069705358840
1650-1.154715578454861.15471557845486
1660-2.871184220869742.87118422086974
16700.267525231185326-0.267525231185326
1680-4.133747088927354.13374708892735
16901.87932262468813-1.87932262468813
1700-1.556797313210431.55679731321043
17100.587492658770264-0.587492658770264
1720-1.114708998423831.11470899842383
17300.953454332929287-0.953454332929287
17402.72501818740416-2.72501818740416
17505.70723252356836-5.70723252356836
17600.51407691483401-0.51407691483401
17701.72243891768008-1.72243891768008
17804.89023444856721-4.89023444856721
1790-3.232690863575143.23269086357514
18003.46635529654478-3.46635529654478
1810-2.622011014011352.62201101401135
1820-0.4564418889986270.456441888998627
18300.429270769954051-0.429270769954051
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
1930-4.233040786095684.23304078609568
1940-0.9484727168877480.948472716887748
1953525.75941982591329.24058017408682

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18 & 22.2848310395639 & -4.28483103956387 \tabularnewline
2 & 10 & 18.5337987958555 & -8.53379879585552 \tabularnewline
3 & 23 & 25.5354373300911 & -2.53543733009115 \tabularnewline
4 & 14 & 17.2292787765482 & -3.22927877654822 \tabularnewline
5 & 18 & 15.7898228154813 & 2.21017718451872 \tabularnewline
6 & 19 & 16.5148411829836 & 2.48515881701636 \tabularnewline
7 & 19 & 14.8461659134084 & 4.15383408659163 \tabularnewline
8 & 16 & 16.8471027009205 & -0.847102700920534 \tabularnewline
9 & 23 & 20.1716872183637 & 2.82831278163628 \tabularnewline
10 & 17 & 13.8838691905464 & 3.11613080945362 \tabularnewline
11 & 20 & 15.0317461854294 & 4.96825381457063 \tabularnewline
12 & 18 & 16.8818749278985 & 1.11812507210154 \tabularnewline
13 & 13 & 13.8044272654984 & -0.804427265498376 \tabularnewline
14 & 17 & 17.8735642545141 & -0.873564254514086 \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 & 9.36150337457693 \tabularnewline
19 & 29 & 38.7548833480572 & -9.75488334805722 \tabularnewline
20 & 43 & 34.2378867571041 & 8.76211324289594 \tabularnewline
21 & 45 & 43.9380672281943 & 1.06193277180565 \tabularnewline
22 & 46 & 41.6188573677212 & 4.38114263227881 \tabularnewline
23 & 25 & 40.6105888892818 & -15.6105888892818 \tabularnewline
24 & 47 & 36.284640510122 & 10.7153594898780 \tabularnewline
25 & 41 & 43.4392467869493 & -2.43924678694932 \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 & 0.866388328518751 \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 & 1.61797404080136 \tabularnewline
35 & 46 & 38.7928236862693 & 7.20717631373075 \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 & -2.00089316317930 \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 & 7.05483928800387 \tabularnewline
48 & 44 & 39.6681591253332 & 4.33184087466676 \tabularnewline
49 & 25 & 39.6352573843724 & -14.6352573843723 \tabularnewline
50 & 51 & 37.202788065088 & 13.7972119349120 \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 & -3.3694068127644 \tabularnewline
64 & 3 & 13.6038039659754 & -10.6038039659754 \tabularnewline
65 & 26 & 13.6580898540541 & 12.3419101459459 \tabularnewline
66 & 30 & 29.7353072883399 & 0.264692711660112 \tabularnewline
67 & 35 & 13.9673369903678 & 21.0326630096322 \tabularnewline
68 & 29 & 14.8678877643055 & 14.1321122356945 \tabularnewline
69 & 25 & 27.6772386241581 & -2.67723862415805 \tabularnewline
70 & 27 & 25.9929966092819 & 1.00700339071811 \tabularnewline
71 & 24 & 29.5632196409425 & -5.56321964094246 \tabularnewline
72 & 35 & 26.8315932719167 & 8.16840672808326 \tabularnewline
73 & 32 & 31.1370332474733 & 0.862966752526678 \tabularnewline
74 & 24 & 31.2215708819885 & -7.22157088198852 \tabularnewline
75 & 38 & 31.115378284516 & 6.88462171548397 \tabularnewline
76 & 36 & 21.8126020496862 & 14.1873979503138 \tabularnewline
77 & 24 & 27.9607117171460 & -3.96071171714604 \tabularnewline
78 & 18 & 23.6409001591531 & -5.64090015915314 \tabularnewline
79 & 34 & 25.0160951087033 & 8.98390489129673 \tabularnewline
80 & 23 & 28.8311379017971 & -5.83113790179712 \tabularnewline
81 & 34 & 27.5986271257781 & 6.40137287422188 \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 & -2.88270770771361 \tabularnewline
87 & 28 & 33.6709547851877 & -5.67095478518766 \tabularnewline
88 & 32 & 29.2187602456768 & 2.78123975432319 \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 & -0.747652887073618 \tabularnewline
93 & 30 & 29.4374894895042 & 0.5625105104958 \tabularnewline
94 & 25 & 27.0944501359684 & -2.09445013596844 \tabularnewline
95 & 31 & 32.312846283902 & -1.31284628390203 \tabularnewline
96 & 37 & 27.5519988031523 & 9.44800119684769 \tabularnewline
97 & 37 & 28.4497648926603 & 8.55023510733966 \tabularnewline
98 & 34 & 26.8411354740153 & 7.15886452598467 \tabularnewline
99 & 32 & 33.3766687261667 & -1.37666872616668 \tabularnewline
100 & 28 & 27.7302899643621 & 0.269710035637917 \tabularnewline
101 & 25 & 31.2129242796046 & -6.21292427960461 \tabularnewline
102 & 26 & 28.5620225374102 & -2.56202253741023 \tabularnewline
103 & 33 & 32.5531637446560 & 0.446836255343951 \tabularnewline
104 & 31 & 28.9352208093322 & 2.06477919066783 \tabularnewline
105 & 22 & 27.8317104958954 & -5.83171049589541 \tabularnewline
106 & 29 & 32.4435518605264 & -3.44355186052642 \tabularnewline
107 & 24 & 27.5400049688770 & -3.54000496887695 \tabularnewline
108 & 0 & -2.74113591574864 & 2.74113591574864 \tabularnewline
109 & 0 & 2.36018883728793 & -2.36018883728793 \tabularnewline
110 & 0 & 5.22537100179176 & -5.22537100179176 \tabularnewline
111 & 0 & 1.47059526733338 & -1.47059526733338 \tabularnewline
112 & 0 & -0.323417121904462 & 0.323417121904462 \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 & 0.51087074944213 & -0.51087074944213 \tabularnewline
119 & 0 & 1.62840199121386 & -1.62840199121386 \tabularnewline
120 & 0 & 2.85487816107360 & -2.85487816107360 \tabularnewline
121 & 0 & 2.63003267586279 & -2.63003267586279 \tabularnewline
122 & 0 & -1.23097350273962 & 1.23097350273962 \tabularnewline
123 & 0 & -0.975535733977857 & 0.975535733977857 \tabularnewline
124 & 0 & -2.45237658941398 & 2.45237658941398 \tabularnewline
125 & 0 & -1.54778709869175 & 1.54778709869175 \tabularnewline
126 & 0 & 1.16822735942015 & -1.16822735942015 \tabularnewline
127 & 0 & 6.37629449121806 & -6.37629449121806 \tabularnewline
128 & 0 & 2.40452881241401 & -2.40452881241401 \tabularnewline
129 & 0 & -0.819835178909525 & 0.819835178909525 \tabularnewline
130 & 0 & -1.22526663303008 & 1.22526663303008 \tabularnewline
131 & 0 & 2.84834793259595 & -2.84834793259595 \tabularnewline
132 & 0 & 2.32681045220822 & -2.32681045220822 \tabularnewline
133 & 0 & -1.36760262817025 & 1.36760262817025 \tabularnewline
134 & 0 & 0.674670447298857 & -0.674670447298857 \tabularnewline
135 & 0 & -0.956398200372305 & 0.956398200372305 \tabularnewline
136 & 0 & -1.27210628698361 & 1.27210628698361 \tabularnewline
137 & 0 & -4.71557433513661 & 4.71557433513661 \tabularnewline
138 & 0 & -0.171674950878765 & 0.171674950878765 \tabularnewline
139 & 0 & -0.948266523423257 & 0.948266523423257 \tabularnewline
140 & 0 & -1.95319119938805 & 1.95319119938805 \tabularnewline
141 & 0 & -3.20145218600358 & 3.20145218600358 \tabularnewline
142 & 0 & -3.72856890988274 & 3.72856890988274 \tabularnewline
143 & 0 & -7.53721495709437 & 7.53721495709437 \tabularnewline
144 & 0 & -0.438594593421969 & 0.438594593421969 \tabularnewline
145 & 0 & 5.66945514922918 & -5.66945514922918 \tabularnewline
146 & 0 & -3.57359725802530 & 3.57359725802530 \tabularnewline
147 & 0 & 2.23092094492601 & -2.23092094492601 \tabularnewline
148 & 0 & 1.17944892121142 & -1.17944892121142 \tabularnewline
149 & 0 & 4.53834484332622 & -4.53834484332622 \tabularnewline
150 & 0 & -0.486681454142615 & 0.486681454142615 \tabularnewline
151 & 0 & -1.26145900504589 & 1.26145900504589 \tabularnewline
152 & 0 & -0.118403611760386 & 0.118403611760386 \tabularnewline
153 & 0 & -1.52866064928533 & 1.52866064928533 \tabularnewline
154 & 0 & -3.34265512578503 & 3.34265512578503 \tabularnewline
155 & 0 & 3.63890261820136 & -3.63890261820136 \tabularnewline
156 & 0 & 3.18851847576737 & -3.18851847576737 \tabularnewline
157 & 0 & -1.78524386490414 & 1.78524386490414 \tabularnewline
158 & 0 & 1.25328050051342 & -1.25328050051342 \tabularnewline
159 & 0 & 1.32573242360361 & -1.32573242360361 \tabularnewline
160 & 0 & -3.21484494670891 & 3.21484494670891 \tabularnewline
161 & 0 & 9.51353357227519 & -9.51353357227519 \tabularnewline
162 & 0 & 3.16594357194537 & -3.16594357194537 \tabularnewline
163 & 0 & -2.58522251340313 & 2.58522251340313 \tabularnewline
164 & 0 & -3.34069705358840 & 3.34069705358840 \tabularnewline
165 & 0 & -1.15471557845486 & 1.15471557845486 \tabularnewline
166 & 0 & -2.87118422086974 & 2.87118422086974 \tabularnewline
167 & 0 & 0.267525231185326 & -0.267525231185326 \tabularnewline
168 & 0 & -4.13374708892735 & 4.13374708892735 \tabularnewline
169 & 0 & 1.87932262468813 & -1.87932262468813 \tabularnewline
170 & 0 & -1.55679731321043 & 1.55679731321043 \tabularnewline
171 & 0 & 0.587492658770264 & -0.587492658770264 \tabularnewline
172 & 0 & -1.11470899842383 & 1.11470899842383 \tabularnewline
173 & 0 & 0.953454332929287 & -0.953454332929287 \tabularnewline
174 & 0 & 2.72501818740416 & -2.72501818740416 \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 & -2.62201101401135 & 2.62201101401135 \tabularnewline
182 & 0 & -0.456441888998627 & 0.456441888998627 \tabularnewline
183 & 0 & 0.429270769954051 & -0.429270769954051 \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 & -4.23304078609568 & 4.23304078609568 \tabularnewline
194 & 0 & -0.948472716887748 & 0.948472716887748 \tabularnewline
195 & 35 & 25.7594198259132 & 9.24058017408682 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116152&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]22.2848310395639[/C][C]-4.28483103956387[/C][/ROW]
[ROW][C]2[/C][C]10[/C][C]18.5337987958555[/C][C]-8.53379879585552[/C][/ROW]
[ROW][C]3[/C][C]23[/C][C]25.5354373300911[/C][C]-2.53543733009115[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]17.2292787765482[/C][C]-3.22927877654822[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]15.7898228154813[/C][C]2.21017718451872[/C][/ROW]
[ROW][C]6[/C][C]19[/C][C]16.5148411829836[/C][C]2.48515881701636[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.8461659134084[/C][C]4.15383408659163[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]16.8471027009205[/C][C]-0.847102700920534[/C][/ROW]
[ROW][C]9[/C][C]23[/C][C]20.1716872183637[/C][C]2.82831278163628[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]13.8838691905464[/C][C]3.11613080945362[/C][/ROW]
[ROW][C]11[/C][C]20[/C][C]15.0317461854294[/C][C]4.96825381457063[/C][/ROW]
[ROW][C]12[/C][C]18[/C][C]16.8818749278985[/C][C]1.11812507210154[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]13.8044272654984[/C][C]-0.804427265498376[/C][/ROW]
[ROW][C]14[/C][C]17[/C][C]17.8735642545141[/C][C]-0.873564254514086[/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]9.36150337457693[/C][/ROW]
[ROW][C]19[/C][C]29[/C][C]38.7548833480572[/C][C]-9.75488334805722[/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]4.38114263227881[/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]10.7153594898780[/C][/ROW]
[ROW][C]25[/C][C]41[/C][C]43.4392467869493[/C][C]-2.43924678694932[/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]0.866388328518751[/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]1.61797404080136[/C][/ROW]
[ROW][C]35[/C][C]46[/C][C]38.7928236862693[/C][C]7.20717631373075[/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]-2.00089316317930[/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]7.05483928800387[/C][/ROW]
[ROW][C]48[/C][C]44[/C][C]39.6681591253332[/C][C]4.33184087466676[/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]13.7972119349120[/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]-3.3694068127644[/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]0.264692711660112[/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]-2.67723862415805[/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]-5.56321964094246[/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]0.862966752526678[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]31.2215708819885[/C][C]-7.22157088198852[/C][/ROW]
[ROW][C]75[/C][C]38[/C][C]31.115378284516[/C][C]6.88462171548397[/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]27.9607117171460[/C][C]-3.96071171714604[/C][/ROW]
[ROW][C]78[/C][C]18[/C][C]23.6409001591531[/C][C]-5.64090015915314[/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]-5.83113790179712[/C][/ROW]
[ROW][C]81[/C][C]34[/C][C]27.5986271257781[/C][C]6.40137287422188[/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]-2.88270770771361[/C][/ROW]
[ROW][C]87[/C][C]28[/C][C]33.6709547851877[/C][C]-5.67095478518766[/C][/ROW]
[ROW][C]88[/C][C]32[/C][C]29.2187602456768[/C][C]2.78123975432319[/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]-0.747652887073618[/C][/ROW]
[ROW][C]93[/C][C]30[/C][C]29.4374894895042[/C][C]0.5625105104958[/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]9.44800119684769[/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]7.15886452598467[/C][/ROW]
[ROW][C]99[/C][C]32[/C][C]33.3766687261667[/C][C]-1.37666872616668[/C][/ROW]
[ROW][C]100[/C][C]28[/C][C]27.7302899643621[/C][C]0.269710035637917[/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]32.5531637446560[/C][C]0.446836255343951[/C][/ROW]
[ROW][C]104[/C][C]31[/C][C]28.9352208093322[/C][C]2.06477919066783[/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]27.5400049688770[/C][C]-3.54000496887695[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]-2.74113591574864[/C][C]2.74113591574864[/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]5.22537100179176[/C][C]-5.22537100179176[/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]-0.323417121904462[/C][C]0.323417121904462[/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]0.51087074944213[/C][C]-0.51087074944213[/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]2.85487816107360[/C][C]-2.85487816107360[/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]-0.975535733977857[/C][C]0.975535733977857[/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]6.37629449121806[/C][C]-6.37629449121806[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]2.40452881241401[/C][C]-2.40452881241401[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]-0.819835178909525[/C][C]0.819835178909525[/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]-1.36760262817025[/C][C]1.36760262817025[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.674670447298857[/C][C]-0.674670447298857[/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]-0.171674950878765[/C][C]0.171674950878765[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]-0.948266523423257[/C][C]0.948266523423257[/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]-3.20145218600358[/C][C]3.20145218600358[/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]-7.53721495709437[/C][C]7.53721495709437[/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]5.66945514922918[/C][C]-5.66945514922918[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]-3.57359725802530[/C][C]3.57359725802530[/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]-0.486681454142615[/C][C]0.486681454142615[/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]-0.118403611760386[/C][C]0.118403611760386[/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]-3.34265512578503[/C][C]3.34265512578503[/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]3.18851847576737[/C][C]-3.18851847576737[/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]1.25328050051342[/C][C]-1.25328050051342[/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]9.51353357227519[/C][C]-9.51353357227519[/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]-3.34069705358840[/C][C]3.34069705358840[/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]0.267525231185326[/C][C]-0.267525231185326[/C][/ROW]
[ROW][C]168[/C][C]0[/C][C]-4.13374708892735[/C][C]4.13374708892735[/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]0.587492658770264[/C][C]-0.587492658770264[/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]0.953454332929287[/C][C]-0.953454332929287[/C][/ROW]
[ROW][C]174[/C][C]0[/C][C]2.72501818740416[/C][C]-2.72501818740416[/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]-2.62201101401135[/C][C]2.62201101401135[/C][/ROW]
[ROW][C]182[/C][C]0[/C][C]-0.456441888998627[/C][C]0.456441888998627[/C][/ROW]
[ROW][C]183[/C][C]0[/C][C]0.429270769954051[/C][C]-0.429270769954051[/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]-4.23304078609568[/C][C]4.23304078609568[/C][/ROW]
[ROW][C]194[/C][C]0[/C][C]-0.948472716887748[/C][C]0.948472716887748[/C][/ROW]
[ROW][C]195[/C][C]35[/C][C]25.7594198259132[/C][C]9.24058017408682[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116152&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116152&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
11822.2848310395639-4.28483103956387
21018.5337987958555-8.53379879585552
32325.5354373300911-2.53543733009115
41417.2292787765482-3.22927877654822
51815.78982281548132.21017718451872
61916.51484118298362.48515881701636
71914.84616591340844.15383408659163
81616.8471027009205-0.847102700920534
92320.17168721836372.82831278163628
101713.88386919054643.11613080945362
112015.03174618542944.96825381457063
121816.88187492789851.11812507210154
131313.8044272654984-0.804427265498376
141717.8735642545141-0.873564254514086
155231.343676391986420.6563236080136
164640.66984627793855.33015372206149
175843.243662309444314.7563376905557
185444.63849662542319.36150337457693
192938.7548833480572-9.75488334805722
204334.23788675710418.76211324289594
214543.93806722819431.06193277180565
224641.61885736772124.38114263227881
232540.6105888892818-15.6105888892818
244736.28464051012210.7153594898780
254143.4392467869493-2.43924678694932
262938.8169882007755-9.81698820077546
274532.858856623020412.1411433769796
285436.976122483317517.0238775166825
292841.0690032111398-13.0690032111398
303736.13361167148120.866388328518751
315640.788094258907615.2119057410924
324334.63636371821238.3636362817877
333438.9131517940767-4.91315179407669
344240.38202595919861.61797404080136
354638.79282368626937.20717631373075
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
432628.0008931631793-2.00089316317930
445136.73031580843514.269684191565
452939.3410043268841-10.3410043268841
462936.7770151853803-7.77701518538026
474335.94516071199617.05483928800387
484439.66815912533324.33184087466676
492539.6352573843724-14.6352573843723
505137.20278806508813.7972119349120
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
6358.3694068127644-3.3694068127644
64313.6038039659754-10.6038039659754
652613.658089854054112.3419101459459
663029.73530728833990.264692711660112
673513.967336990367821.0326630096322
682914.867887764305514.1321122356945
692527.6772386241581-2.67723862415805
702725.99299660928191.00700339071811
712429.5632196409425-5.56321964094246
723526.83159327191678.16840672808326
733231.13703324747330.862966752526678
742431.2215708819885-7.22157088198852
753831.1153782845166.88462171548397
763621.812602049686214.1873979503138
772427.9607117171460-3.96071171714604
781823.6409001591531-5.64090015915314
793425.01609510870338.98390489129673
802328.8311379017971-5.83113790179712
813427.59862712577816.40137287422188
823227.1317801805524.86821981944798
832428.7647381651316-4.76473816513161
843428.16209245134915.83790754865089
853326.11410013674096.88589986325915
863335.8827077077136-2.88270770771361
872833.6709547851877-5.67095478518766
883229.21876024567682.78123975432319
892930.387652579817-1.38765257981702
902730.3189998007561-3.31899980075609
912829.8654997941412-1.86549979414117
922828.7476528870736-0.747652887073618
933029.43748948950420.5625105104958
942527.0944501359684-2.09445013596844
953132.312846283902-1.31284628390203
963727.55199880315239.44800119684769
973728.44976489266038.55023510733966
983426.84113547401537.15886452598467
993233.3766687261667-1.37666872616668
1002827.73028996436210.269710035637917
1012531.2129242796046-6.21292427960461
1022628.5620225374102-2.56202253741023
1033332.55316374465600.446836255343951
1043128.93522080933222.06477919066783
1052227.8317104958954-5.83171049589541
1062932.4435518605264-3.44355186052642
1072427.5400049688770-3.54000496887695
1080-2.741135915748642.74113591574864
10902.36018883728793-2.36018883728793
11005.22537100179176-5.22537100179176
11101.47059526733338-1.47059526733338
1120-0.3234171219044620.323417121904462
11301.73377725230331-1.73377725230331
1140-3.476269959831893.47626995983189
11501.50571089362323-1.50571089362323
1160-1.620889424012241.62088942401224
11701.22666762028114-1.22666762028114
11800.51087074944213-0.51087074944213
11901.62840199121386-1.62840199121386
12002.85487816107360-2.85487816107360
12102.63003267586279-2.63003267586279
1220-1.230973502739621.23097350273962
1230-0.9755357339778570.975535733977857
1240-2.452376589413982.45237658941398
1250-1.547787098691751.54778709869175
12601.16822735942015-1.16822735942015
12706.37629449121806-6.37629449121806
12802.40452881241401-2.40452881241401
1290-0.8198351789095250.819835178909525
1300-1.225266633030081.22526663303008
13102.84834793259595-2.84834793259595
13202.32681045220822-2.32681045220822
1330-1.367602628170251.36760262817025
13400.674670447298857-0.674670447298857
1350-0.9563982003723050.956398200372305
1360-1.272106286983611.27210628698361
1370-4.715574335136614.71557433513661
1380-0.1716749508787650.171674950878765
1390-0.9482665234232570.948266523423257
1400-1.953191199388051.95319119938805
1410-3.201452186003583.20145218600358
1420-3.728568909882743.72856890988274
1430-7.537214957094377.53721495709437
1440-0.4385945934219690.438594593421969
14505.66945514922918-5.66945514922918
1460-3.573597258025303.57359725802530
14702.23092094492601-2.23092094492601
14801.17944892121142-1.17944892121142
14904.53834484332622-4.53834484332622
1500-0.4866814541426150.486681454142615
1510-1.261459005045891.26145900504589
1520-0.1184036117603860.118403611760386
1530-1.528660649285331.52866064928533
1540-3.342655125785033.34265512578503
15503.63890261820136-3.63890261820136
15603.18851847576737-3.18851847576737
1570-1.785243864904141.78524386490414
15801.25328050051342-1.25328050051342
15901.32573242360361-1.32573242360361
1600-3.214844946708913.21484494670891
16109.51353357227519-9.51353357227519
16203.16594357194537-3.16594357194537
1630-2.585222513403132.58522251340313
1640-3.340697053588403.34069705358840
1650-1.154715578454861.15471557845486
1660-2.871184220869742.87118422086974
16700.267525231185326-0.267525231185326
1680-4.133747088927354.13374708892735
16901.87932262468813-1.87932262468813
1700-1.556797313210431.55679731321043
17100.587492658770264-0.587492658770264
1720-1.114708998423831.11470899842383
17300.953454332929287-0.953454332929287
17402.72501818740416-2.72501818740416
17505.70723252356836-5.70723252356836
17600.51407691483401-0.51407691483401
17701.72243891768008-1.72243891768008
17804.89023444856721-4.89023444856721
1790-3.232690863575143.23269086357514
18003.46635529654478-3.46635529654478
1810-2.622011014011352.62201101401135
1820-0.4564418889986270.456441888998627
18300.429270769954051-0.429270769954051
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
1930-4.233040786095684.23304078609568
1940-0.9484727168877480.948472716887748
1953525.75941982591329.24058017408682






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.2466177297250260.4932354594500510.753382270274974
160.1217003836918540.2434007673837070.878299616308146
170.0582264349739310.1164528699478620.941773565026069
180.02708169008355450.0541633801671090.972918309916446
190.06792226940687290.1358445388137460.932077730593127
200.03689862139560620.07379724279121240.963101378604394
210.2505359723556290.5010719447112570.749464027644371
220.2707952729728760.5415905459457510.729204727027124
230.6993705617764680.6012588764470640.300629438223532
240.6429868743450290.7140262513099420.357013125654971
250.6696683872902450.660663225419510.330331612709755
260.8176284713743640.3647430572512730.182371528625636
270.8444486373211880.3111027253576230.155551362678812
280.9496605193716270.1006789612567470.0503394806283735
290.9621172672011820.07576546559763620.0378827327988181
300.9584773237613640.08304535247727170.0415226762386359
310.974923052906890.05015389418621990.0250769470931100
320.974338420929870.05132315814026140.0256615790701307
330.9724550936812160.05508981263756760.0275449063187838
340.9757247401307780.04855051973844440.0242752598692222
350.972616906468140.05476618706371860.0273830935318593
360.993385737236450.01322852552710120.0066142627635506
370.9989455686393580.002108862721284660.00105443136064233
380.9988203605844140.00235927883117250.00117963941558625
390.9991179809706570.001764038058686090.000882019029343045
400.9993293749620050.001341250075989950.000670625037994975
410.9998740039615020.0002519920769970800.000125996038498540
420.9999787916112674.24167774667612e-052.12083887333806e-05
430.9999686248942516.27502114975207e-053.13751057487604e-05
440.999991690495941.66190081197847e-058.30950405989235e-06
450.9999941618553921.16762892167345e-055.83814460836724e-06
460.9999939397484021.21205031961449e-056.06025159807245e-06
470.9999943616337711.12767324578515e-055.63836622892576e-06
480.9999940392486271.19215027469466e-055.9607513734733e-06
490.999998777677412.44464518220382e-061.22232259110191e-06
500.999999803703443.92593121770561e-071.96296560885281e-07
510.9999997835052014.32989597765437e-072.16494798882718e-07
520.999999894168752.11662499316887e-071.05831249658443e-07
530.9999999997901674.19666523447922e-102.09833261723961e-10
540.9999999999977764.44897445320645e-122.22448722660322e-12
550.9999999999999764.83808561873634e-142.41904280936817e-14
560.999999999999975.84966432520396e-142.92483216260198e-14
570.9999999999999754.92189723378593e-142.46094861689296e-14
580.9999999999999823.53177787671232e-141.76588893835616e-14
590.9999999999999745.11684409543834e-142.55842204771917e-14
600.9999999999999725.50850908788616e-142.75425454394308e-14
610.9999999999999882.45842554772582e-141.22921277386291e-14
620.9999999999999967.80747256958238e-153.90373628479119e-15
6318.82148914651184e-194.41074457325592e-19
6415.71620990319554e-262.85810495159777e-26
6513.05722695213601e-281.52861347606800e-28
6611.05345096206424e-275.26725481032118e-28
6716.10630921653535e-283.05315460826768e-28
6811.55019299027620e-287.75096495138099e-29
6911.32127285481353e-286.60636427406767e-29
7019.95904993257379e-294.97952496628689e-29
7117.76500385647358e-303.88250192823679e-30
7211.42352342178936e-297.1176171089468e-30
7314.9924432382082e-302.4962216191041e-30
7411.35441257940772e-296.77206289703858e-30
7511.16127670689806e-305.80638353449031e-31
7611.06423167456321e-335.32115837281605e-34
7715.26486290961968e-342.63243145480984e-34
7812.95677852842909e-391.47838926421454e-39
7911.22470458142286e-386.1235229071143e-39
8011.64361799601463e-388.21808998007313e-39
8112.38354754115565e-381.19177377057782e-38
8219.13787367551537e-384.56893683775768e-38
8312.36365826778492e-381.18182913389246e-38
8419.66946858421973e-384.83473429210987e-38
8513.22893304044534e-381.61446652022267e-38
8611.42935514035281e-377.14677570176403e-38
8715.97959985792719e-372.98979992896360e-37
8813.82001323656094e-391.91000661828047e-39
8911.24929761588208e-386.24648807941042e-39
9015.77188041742308e-382.88594020871154e-38
9111.77373883106025e-378.86869415530124e-38
9211.08913337957254e-375.44566689786269e-38
9311.19917427281683e-375.99587136408415e-38
9415.0436467750484e-372.5218233875242e-37
9514.25264414244152e-372.12632207122076e-37
9616.66151448647259e-403.33075724323629e-40
9717.06305833523062e-473.53152916761531e-47
9816.17307316677717e-603.08653658338858e-60
9913.54038354986394e-591.77019177493197e-59
10011.18106028451611e-635.90530142258054e-64
10112.03361924347046e-681.01680962173523e-68
10211.06832489929679e-675.34162449648394e-68
10319.47527338314565e-904.73763669157283e-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
16112.06306594313406e-3031.03153297156703e-303
16214.22037968764819e-2932.11018984382410e-293
16312.56412044123400e-2781.28206022061700e-278
16411.32550135769629e-2666.62750678848146e-267
16512.21484478214879e-2531.10742239107440e-253
16612.39648134391879e-2391.19824067195940e-239
16711.09840493274958e-2255.49202466374789e-226
16812.03074275064428e-2141.01537137532214e-214
16912.19942912386138e-1991.09971456193069e-199
17014.54765939029581e-1832.27382969514790e-183
17116.56795221966862e-1703.28397610983431e-170
17214.13678390651332e-1552.06839195325666e-155
17311.39176692054964e-1406.95883460274819e-141
17414.36701850766019e-1262.18350925383010e-126
17519.15979356583168e-1134.57989678291584e-113
17612.76454965421674e-1001.38227482710837e-100
17718.26388317915576e-874.13194158957788e-87
17811.43508578994265e-717.17542894971326e-72
17918.92697361782171e-624.46348680891086e-62
18017.05854210507196e-443.52927105253598e-44

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.246617729725026 & 0.493235459450051 & 0.753382270274974 \tabularnewline
16 & 0.121700383691854 & 0.243400767383707 & 0.878299616308146 \tabularnewline
17 & 0.058226434973931 & 0.116452869947862 & 0.941773565026069 \tabularnewline
18 & 0.0270816900835545 & 0.054163380167109 & 0.972918309916446 \tabularnewline
19 & 0.0679222694068729 & 0.135844538813746 & 0.932077730593127 \tabularnewline
20 & 0.0368986213956062 & 0.0737972427912124 & 0.963101378604394 \tabularnewline
21 & 0.250535972355629 & 0.501071944711257 & 0.749464027644371 \tabularnewline
22 & 0.270795272972876 & 0.541590545945751 & 0.729204727027124 \tabularnewline
23 & 0.699370561776468 & 0.601258876447064 & 0.300629438223532 \tabularnewline
24 & 0.642986874345029 & 0.714026251309942 & 0.357013125654971 \tabularnewline
25 & 0.669668387290245 & 0.66066322541951 & 0.330331612709755 \tabularnewline
26 & 0.817628471374364 & 0.364743057251273 & 0.182371528625636 \tabularnewline
27 & 0.844448637321188 & 0.311102725357623 & 0.155551362678812 \tabularnewline
28 & 0.949660519371627 & 0.100678961256747 & 0.0503394806283735 \tabularnewline
29 & 0.962117267201182 & 0.0757654655976362 & 0.0378827327988181 \tabularnewline
30 & 0.958477323761364 & 0.0830453524772717 & 0.0415226762386359 \tabularnewline
31 & 0.97492305290689 & 0.0501538941862199 & 0.0250769470931100 \tabularnewline
32 & 0.97433842092987 & 0.0513231581402614 & 0.0256615790701307 \tabularnewline
33 & 0.972455093681216 & 0.0550898126375676 & 0.0275449063187838 \tabularnewline
34 & 0.975724740130778 & 0.0485505197384444 & 0.0242752598692222 \tabularnewline
35 & 0.97261690646814 & 0.0547661870637186 & 0.0273830935318593 \tabularnewline
36 & 0.99338573723645 & 0.0132285255271012 & 0.0066142627635506 \tabularnewline
37 & 0.998945568639358 & 0.00210886272128466 & 0.00105443136064233 \tabularnewline
38 & 0.998820360584414 & 0.0023592788311725 & 0.00117963941558625 \tabularnewline
39 & 0.999117980970657 & 0.00176403805868609 & 0.000882019029343045 \tabularnewline
40 & 0.999329374962005 & 0.00134125007598995 & 0.000670625037994975 \tabularnewline
41 & 0.999874003961502 & 0.000251992076997080 & 0.000125996038498540 \tabularnewline
42 & 0.999978791611267 & 4.24167774667612e-05 & 2.12083887333806e-05 \tabularnewline
43 & 0.999968624894251 & 6.27502114975207e-05 & 3.13751057487604e-05 \tabularnewline
44 & 0.99999169049594 & 1.66190081197847e-05 & 8.30950405989235e-06 \tabularnewline
45 & 0.999994161855392 & 1.16762892167345e-05 & 5.83814460836724e-06 \tabularnewline
46 & 0.999993939748402 & 1.21205031961449e-05 & 6.06025159807245e-06 \tabularnewline
47 & 0.999994361633771 & 1.12767324578515e-05 & 5.63836622892576e-06 \tabularnewline
48 & 0.999994039248627 & 1.19215027469466e-05 & 5.9607513734733e-06 \tabularnewline
49 & 0.99999877767741 & 2.44464518220382e-06 & 1.22232259110191e-06 \tabularnewline
50 & 0.99999980370344 & 3.92593121770561e-07 & 1.96296560885281e-07 \tabularnewline
51 & 0.999999783505201 & 4.32989597765437e-07 & 2.16494798882718e-07 \tabularnewline
52 & 0.99999989416875 & 2.11662499316887e-07 & 1.05831249658443e-07 \tabularnewline
53 & 0.999999999790167 & 4.19666523447922e-10 & 2.09833261723961e-10 \tabularnewline
54 & 0.999999999997776 & 4.44897445320645e-12 & 2.22448722660322e-12 \tabularnewline
55 & 0.999999999999976 & 4.83808561873634e-14 & 2.41904280936817e-14 \tabularnewline
56 & 0.99999999999997 & 5.84966432520396e-14 & 2.92483216260198e-14 \tabularnewline
57 & 0.999999999999975 & 4.92189723378593e-14 & 2.46094861689296e-14 \tabularnewline
58 & 0.999999999999982 & 3.53177787671232e-14 & 1.76588893835616e-14 \tabularnewline
59 & 0.999999999999974 & 5.11684409543834e-14 & 2.55842204771917e-14 \tabularnewline
60 & 0.999999999999972 & 5.50850908788616e-14 & 2.75425454394308e-14 \tabularnewline
61 & 0.999999999999988 & 2.45842554772582e-14 & 1.22921277386291e-14 \tabularnewline
62 & 0.999999999999996 & 7.80747256958238e-15 & 3.90373628479119e-15 \tabularnewline
63 & 1 & 8.82148914651184e-19 & 4.41074457325592e-19 \tabularnewline
64 & 1 & 5.71620990319554e-26 & 2.85810495159777e-26 \tabularnewline
65 & 1 & 3.05722695213601e-28 & 1.52861347606800e-28 \tabularnewline
66 & 1 & 1.05345096206424e-27 & 5.26725481032118e-28 \tabularnewline
67 & 1 & 6.10630921653535e-28 & 3.05315460826768e-28 \tabularnewline
68 & 1 & 1.55019299027620e-28 & 7.75096495138099e-29 \tabularnewline
69 & 1 & 1.32127285481353e-28 & 6.60636427406767e-29 \tabularnewline
70 & 1 & 9.95904993257379e-29 & 4.97952496628689e-29 \tabularnewline
71 & 1 & 7.76500385647358e-30 & 3.88250192823679e-30 \tabularnewline
72 & 1 & 1.42352342178936e-29 & 7.1176171089468e-30 \tabularnewline
73 & 1 & 4.9924432382082e-30 & 2.4962216191041e-30 \tabularnewline
74 & 1 & 1.35441257940772e-29 & 6.77206289703858e-30 \tabularnewline
75 & 1 & 1.16127670689806e-30 & 5.80638353449031e-31 \tabularnewline
76 & 1 & 1.06423167456321e-33 & 5.32115837281605e-34 \tabularnewline
77 & 1 & 5.26486290961968e-34 & 2.63243145480984e-34 \tabularnewline
78 & 1 & 2.95677852842909e-39 & 1.47838926421454e-39 \tabularnewline
79 & 1 & 1.22470458142286e-38 & 6.1235229071143e-39 \tabularnewline
80 & 1 & 1.64361799601463e-38 & 8.21808998007313e-39 \tabularnewline
81 & 1 & 2.38354754115565e-38 & 1.19177377057782e-38 \tabularnewline
82 & 1 & 9.13787367551537e-38 & 4.56893683775768e-38 \tabularnewline
83 & 1 & 2.36365826778492e-38 & 1.18182913389246e-38 \tabularnewline
84 & 1 & 9.66946858421973e-38 & 4.83473429210987e-38 \tabularnewline
85 & 1 & 3.22893304044534e-38 & 1.61446652022267e-38 \tabularnewline
86 & 1 & 1.42935514035281e-37 & 7.14677570176403e-38 \tabularnewline
87 & 1 & 5.97959985792719e-37 & 2.98979992896360e-37 \tabularnewline
88 & 1 & 3.82001323656094e-39 & 1.91000661828047e-39 \tabularnewline
89 & 1 & 1.24929761588208e-38 & 6.24648807941042e-39 \tabularnewline
90 & 1 & 5.77188041742308e-38 & 2.88594020871154e-38 \tabularnewline
91 & 1 & 1.77373883106025e-37 & 8.86869415530124e-38 \tabularnewline
92 & 1 & 1.08913337957254e-37 & 5.44566689786269e-38 \tabularnewline
93 & 1 & 1.19917427281683e-37 & 5.99587136408415e-38 \tabularnewline
94 & 1 & 5.0436467750484e-37 & 2.5218233875242e-37 \tabularnewline
95 & 1 & 4.25264414244152e-37 & 2.12632207122076e-37 \tabularnewline
96 & 1 & 6.66151448647259e-40 & 3.33075724323629e-40 \tabularnewline
97 & 1 & 7.06305833523062e-47 & 3.53152916761531e-47 \tabularnewline
98 & 1 & 6.17307316677717e-60 & 3.08653658338858e-60 \tabularnewline
99 & 1 & 3.54038354986394e-59 & 1.77019177493197e-59 \tabularnewline
100 & 1 & 1.18106028451611e-63 & 5.90530142258054e-64 \tabularnewline
101 & 1 & 2.03361924347046e-68 & 1.01680962173523e-68 \tabularnewline
102 & 1 & 1.06832489929679e-67 & 5.34162449648394e-68 \tabularnewline
103 & 1 & 9.47527338314565e-90 & 4.73763669157283e-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 & 2.06306594313406e-303 & 1.03153297156703e-303 \tabularnewline
162 & 1 & 4.22037968764819e-293 & 2.11018984382410e-293 \tabularnewline
163 & 1 & 2.56412044123400e-278 & 1.28206022061700e-278 \tabularnewline
164 & 1 & 1.32550135769629e-266 & 6.62750678848146e-267 \tabularnewline
165 & 1 & 2.21484478214879e-253 & 1.10742239107440e-253 \tabularnewline
166 & 1 & 2.39648134391879e-239 & 1.19824067195940e-239 \tabularnewline
167 & 1 & 1.09840493274958e-225 & 5.49202466374789e-226 \tabularnewline
168 & 1 & 2.03074275064428e-214 & 1.01537137532214e-214 \tabularnewline
169 & 1 & 2.19942912386138e-199 & 1.09971456193069e-199 \tabularnewline
170 & 1 & 4.54765939029581e-183 & 2.27382969514790e-183 \tabularnewline
171 & 1 & 6.56795221966862e-170 & 3.28397610983431e-170 \tabularnewline
172 & 1 & 4.13678390651332e-155 & 2.06839195325666e-155 \tabularnewline
173 & 1 & 1.39176692054964e-140 & 6.95883460274819e-141 \tabularnewline
174 & 1 & 4.36701850766019e-126 & 2.18350925383010e-126 \tabularnewline
175 & 1 & 9.15979356583168e-113 & 4.57989678291584e-113 \tabularnewline
176 & 1 & 2.76454965421674e-100 & 1.38227482710837e-100 \tabularnewline
177 & 1 & 8.26388317915576e-87 & 4.13194158957788e-87 \tabularnewline
178 & 1 & 1.43508578994265e-71 & 7.17542894971326e-72 \tabularnewline
179 & 1 & 8.92697361782171e-62 & 4.46348680891086e-62 \tabularnewline
180 & 1 & 7.05854210507196e-44 & 3.52927105253598e-44 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116152&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]0.246617729725026[/C][C]0.493235459450051[/C][C]0.753382270274974[/C][/ROW]
[ROW][C]16[/C][C]0.121700383691854[/C][C]0.243400767383707[/C][C]0.878299616308146[/C][/ROW]
[ROW][C]17[/C][C]0.058226434973931[/C][C]0.116452869947862[/C][C]0.941773565026069[/C][/ROW]
[ROW][C]18[/C][C]0.0270816900835545[/C][C]0.054163380167109[/C][C]0.972918309916446[/C][/ROW]
[ROW][C]19[/C][C]0.0679222694068729[/C][C]0.135844538813746[/C][C]0.932077730593127[/C][/ROW]
[ROW][C]20[/C][C]0.0368986213956062[/C][C]0.0737972427912124[/C][C]0.963101378604394[/C][/ROW]
[ROW][C]21[/C][C]0.250535972355629[/C][C]0.501071944711257[/C][C]0.749464027644371[/C][/ROW]
[ROW][C]22[/C][C]0.270795272972876[/C][C]0.541590545945751[/C][C]0.729204727027124[/C][/ROW]
[ROW][C]23[/C][C]0.699370561776468[/C][C]0.601258876447064[/C][C]0.300629438223532[/C][/ROW]
[ROW][C]24[/C][C]0.642986874345029[/C][C]0.714026251309942[/C][C]0.357013125654971[/C][/ROW]
[ROW][C]25[/C][C]0.669668387290245[/C][C]0.66066322541951[/C][C]0.330331612709755[/C][/ROW]
[ROW][C]26[/C][C]0.817628471374364[/C][C]0.364743057251273[/C][C]0.182371528625636[/C][/ROW]
[ROW][C]27[/C][C]0.844448637321188[/C][C]0.311102725357623[/C][C]0.155551362678812[/C][/ROW]
[ROW][C]28[/C][C]0.949660519371627[/C][C]0.100678961256747[/C][C]0.0503394806283735[/C][/ROW]
[ROW][C]29[/C][C]0.962117267201182[/C][C]0.0757654655976362[/C][C]0.0378827327988181[/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]0.0501538941862199[/C][C]0.0250769470931100[/C][/ROW]
[ROW][C]32[/C][C]0.97433842092987[/C][C]0.0513231581402614[/C][C]0.0256615790701307[/C][/ROW]
[ROW][C]33[/C][C]0.972455093681216[/C][C]0.0550898126375676[/C][C]0.0275449063187838[/C][/ROW]
[ROW][C]34[/C][C]0.975724740130778[/C][C]0.0485505197384444[/C][C]0.0242752598692222[/C][/ROW]
[ROW][C]35[/C][C]0.97261690646814[/C][C]0.0547661870637186[/C][C]0.0273830935318593[/C][/ROW]
[ROW][C]36[/C][C]0.99338573723645[/C][C]0.0132285255271012[/C][C]0.0066142627635506[/C][/ROW]
[ROW][C]37[/C][C]0.998945568639358[/C][C]0.00210886272128466[/C][C]0.00105443136064233[/C][/ROW]
[ROW][C]38[/C][C]0.998820360584414[/C][C]0.0023592788311725[/C][C]0.00117963941558625[/C][/ROW]
[ROW][C]39[/C][C]0.999117980970657[/C][C]0.00176403805868609[/C][C]0.000882019029343045[/C][/ROW]
[ROW][C]40[/C][C]0.999329374962005[/C][C]0.00134125007598995[/C][C]0.000670625037994975[/C][/ROW]
[ROW][C]41[/C][C]0.999874003961502[/C][C]0.000251992076997080[/C][C]0.000125996038498540[/C][/ROW]
[ROW][C]42[/C][C]0.999978791611267[/C][C]4.24167774667612e-05[/C][C]2.12083887333806e-05[/C][/ROW]
[ROW][C]43[/C][C]0.999968624894251[/C][C]6.27502114975207e-05[/C][C]3.13751057487604e-05[/C][/ROW]
[ROW][C]44[/C][C]0.99999169049594[/C][C]1.66190081197847e-05[/C][C]8.30950405989235e-06[/C][/ROW]
[ROW][C]45[/C][C]0.999994161855392[/C][C]1.16762892167345e-05[/C][C]5.83814460836724e-06[/C][/ROW]
[ROW][C]46[/C][C]0.999993939748402[/C][C]1.21205031961449e-05[/C][C]6.06025159807245e-06[/C][/ROW]
[ROW][C]47[/C][C]0.999994361633771[/C][C]1.12767324578515e-05[/C][C]5.63836622892576e-06[/C][/ROW]
[ROW][C]48[/C][C]0.999994039248627[/C][C]1.19215027469466e-05[/C][C]5.9607513734733e-06[/C][/ROW]
[ROW][C]49[/C][C]0.99999877767741[/C][C]2.44464518220382e-06[/C][C]1.22232259110191e-06[/C][/ROW]
[ROW][C]50[/C][C]0.99999980370344[/C][C]3.92593121770561e-07[/C][C]1.96296560885281e-07[/C][/ROW]
[ROW][C]51[/C][C]0.999999783505201[/C][C]4.32989597765437e-07[/C][C]2.16494798882718e-07[/C][/ROW]
[ROW][C]52[/C][C]0.99999989416875[/C][C]2.11662499316887e-07[/C][C]1.05831249658443e-07[/C][/ROW]
[ROW][C]53[/C][C]0.999999999790167[/C][C]4.19666523447922e-10[/C][C]2.09833261723961e-10[/C][/ROW]
[ROW][C]54[/C][C]0.999999999997776[/C][C]4.44897445320645e-12[/C][C]2.22448722660322e-12[/C][/ROW]
[ROW][C]55[/C][C]0.999999999999976[/C][C]4.83808561873634e-14[/C][C]2.41904280936817e-14[/C][/ROW]
[ROW][C]56[/C][C]0.99999999999997[/C][C]5.84966432520396e-14[/C][C]2.92483216260198e-14[/C][/ROW]
[ROW][C]57[/C][C]0.999999999999975[/C][C]4.92189723378593e-14[/C][C]2.46094861689296e-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]5.11684409543834e-14[/C][C]2.55842204771917e-14[/C][/ROW]
[ROW][C]60[/C][C]0.999999999999972[/C][C]5.50850908788616e-14[/C][C]2.75425454394308e-14[/C][/ROW]
[ROW][C]61[/C][C]0.999999999999988[/C][C]2.45842554772582e-14[/C][C]1.22921277386291e-14[/C][/ROW]
[ROW][C]62[/C][C]0.999999999999996[/C][C]7.80747256958238e-15[/C][C]3.90373628479119e-15[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]8.82148914651184e-19[/C][C]4.41074457325592e-19[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]5.71620990319554e-26[/C][C]2.85810495159777e-26[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]3.05722695213601e-28[/C][C]1.52861347606800e-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]6.10630921653535e-28[/C][C]3.05315460826768e-28[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.55019299027620e-28[/C][C]7.75096495138099e-29[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.32127285481353e-28[/C][C]6.60636427406767e-29[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]9.95904993257379e-29[/C][C]4.97952496628689e-29[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]7.76500385647358e-30[/C][C]3.88250192823679e-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]4.9924432382082e-30[/C][C]2.4962216191041e-30[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.35441257940772e-29[/C][C]6.77206289703858e-30[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.16127670689806e-30[/C][C]5.80638353449031e-31[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.06423167456321e-33[/C][C]5.32115837281605e-34[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]5.26486290961968e-34[/C][C]2.63243145480984e-34[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]2.95677852842909e-39[/C][C]1.47838926421454e-39[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.22470458142286e-38[/C][C]6.1235229071143e-39[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.64361799601463e-38[/C][C]8.21808998007313e-39[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]2.38354754115565e-38[/C][C]1.19177377057782e-38[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]9.13787367551537e-38[/C][C]4.56893683775768e-38[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]2.36365826778492e-38[/C][C]1.18182913389246e-38[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]9.66946858421973e-38[/C][C]4.83473429210987e-38[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]3.22893304044534e-38[/C][C]1.61446652022267e-38[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.42935514035281e-37[/C][C]7.14677570176403e-38[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]5.97959985792719e-37[/C][C]2.98979992896360e-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]1.24929761588208e-38[/C][C]6.24648807941042e-39[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]5.77188041742308e-38[/C][C]2.88594020871154e-38[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.77373883106025e-37[/C][C]8.86869415530124e-38[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]1.08913337957254e-37[/C][C]5.44566689786269e-38[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.19917427281683e-37[/C][C]5.99587136408415e-38[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]5.0436467750484e-37[/C][C]2.5218233875242e-37[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]4.25264414244152e-37[/C][C]2.12632207122076e-37[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]6.66151448647259e-40[/C][C]3.33075724323629e-40[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]7.06305833523062e-47[/C][C]3.53152916761531e-47[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]6.17307316677717e-60[/C][C]3.08653658338858e-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]1.18106028451611e-63[/C][C]5.90530142258054e-64[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]2.03361924347046e-68[/C][C]1.01680962173523e-68[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.06832489929679e-67[/C][C]5.34162449648394e-68[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]9.47527338314565e-90[/C][C]4.73763669157283e-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]2.06306594313406e-303[/C][C]1.03153297156703e-303[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]4.22037968764819e-293[/C][C]2.11018984382410e-293[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]2.56412044123400e-278[/C][C]1.28206022061700e-278[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]1.32550135769629e-266[/C][C]6.62750678848146e-267[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]2.21484478214879e-253[/C][C]1.10742239107440e-253[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]2.39648134391879e-239[/C][C]1.19824067195940e-239[/C][/ROW]
[ROW][C]167[/C][C]1[/C][C]1.09840493274958e-225[/C][C]5.49202466374789e-226[/C][/ROW]
[ROW][C]168[/C][C]1[/C][C]2.03074275064428e-214[/C][C]1.01537137532214e-214[/C][/ROW]
[ROW][C]169[/C][C]1[/C][C]2.19942912386138e-199[/C][C]1.09971456193069e-199[/C][/ROW]
[ROW][C]170[/C][C]1[/C][C]4.54765939029581e-183[/C][C]2.27382969514790e-183[/C][/ROW]
[ROW][C]171[/C][C]1[/C][C]6.56795221966862e-170[/C][C]3.28397610983431e-170[/C][/ROW]
[ROW][C]172[/C][C]1[/C][C]4.13678390651332e-155[/C][C]2.06839195325666e-155[/C][/ROW]
[ROW][C]173[/C][C]1[/C][C]1.39176692054964e-140[/C][C]6.95883460274819e-141[/C][/ROW]
[ROW][C]174[/C][C]1[/C][C]4.36701850766019e-126[/C][C]2.18350925383010e-126[/C][/ROW]
[ROW][C]175[/C][C]1[/C][C]9.15979356583168e-113[/C][C]4.57989678291584e-113[/C][/ROW]
[ROW][C]176[/C][C]1[/C][C]2.76454965421674e-100[/C][C]1.38227482710837e-100[/C][/ROW]
[ROW][C]177[/C][C]1[/C][C]8.26388317915576e-87[/C][C]4.13194158957788e-87[/C][/ROW]
[ROW][C]178[/C][C]1[/C][C]1.43508578994265e-71[/C][C]7.17542894971326e-72[/C][/ROW]
[ROW][C]179[/C][C]1[/C][C]8.92697361782171e-62[/C][C]4.46348680891086e-62[/C][/ROW]
[ROW][C]180[/C][C]1[/C][C]7.05854210507196e-44[/C][C]3.52927105253598e-44[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116152&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116152&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
150.2466177297250260.4932354594500510.753382270274974
160.1217003836918540.2434007673837070.878299616308146
170.0582264349739310.1164528699478620.941773565026069
180.02708169008355450.0541633801671090.972918309916446
190.06792226940687290.1358445388137460.932077730593127
200.03689862139560620.07379724279121240.963101378604394
210.2505359723556290.5010719447112570.749464027644371
220.2707952729728760.5415905459457510.729204727027124
230.6993705617764680.6012588764470640.300629438223532
240.6429868743450290.7140262513099420.357013125654971
250.6696683872902450.660663225419510.330331612709755
260.8176284713743640.3647430572512730.182371528625636
270.8444486373211880.3111027253576230.155551362678812
280.9496605193716270.1006789612567470.0503394806283735
290.9621172672011820.07576546559763620.0378827327988181
300.9584773237613640.08304535247727170.0415226762386359
310.974923052906890.05015389418621990.0250769470931100
320.974338420929870.05132315814026140.0256615790701307
330.9724550936812160.05508981263756760.0275449063187838
340.9757247401307780.04855051973844440.0242752598692222
350.972616906468140.05476618706371860.0273830935318593
360.993385737236450.01322852552710120.0066142627635506
370.9989455686393580.002108862721284660.00105443136064233
380.9988203605844140.00235927883117250.00117963941558625
390.9991179809706570.001764038058686090.000882019029343045
400.9993293749620050.001341250075989950.000670625037994975
410.9998740039615020.0002519920769970800.000125996038498540
420.9999787916112674.24167774667612e-052.12083887333806e-05
430.9999686248942516.27502114975207e-053.13751057487604e-05
440.999991690495941.66190081197847e-058.30950405989235e-06
450.9999941618553921.16762892167345e-055.83814460836724e-06
460.9999939397484021.21205031961449e-056.06025159807245e-06
470.9999943616337711.12767324578515e-055.63836622892576e-06
480.9999940392486271.19215027469466e-055.9607513734733e-06
490.999998777677412.44464518220382e-061.22232259110191e-06
500.999999803703443.92593121770561e-071.96296560885281e-07
510.9999997835052014.32989597765437e-072.16494798882718e-07
520.999999894168752.11662499316887e-071.05831249658443e-07
530.9999999997901674.19666523447922e-102.09833261723961e-10
540.9999999999977764.44897445320645e-122.22448722660322e-12
550.9999999999999764.83808561873634e-142.41904280936817e-14
560.999999999999975.84966432520396e-142.92483216260198e-14
570.9999999999999754.92189723378593e-142.46094861689296e-14
580.9999999999999823.53177787671232e-141.76588893835616e-14
590.9999999999999745.11684409543834e-142.55842204771917e-14
600.9999999999999725.50850908788616e-142.75425454394308e-14
610.9999999999999882.45842554772582e-141.22921277386291e-14
620.9999999999999967.80747256958238e-153.90373628479119e-15
6318.82148914651184e-194.41074457325592e-19
6415.71620990319554e-262.85810495159777e-26
6513.05722695213601e-281.52861347606800e-28
6611.05345096206424e-275.26725481032118e-28
6716.10630921653535e-283.05315460826768e-28
6811.55019299027620e-287.75096495138099e-29
6911.32127285481353e-286.60636427406767e-29
7019.95904993257379e-294.97952496628689e-29
7117.76500385647358e-303.88250192823679e-30
7211.42352342178936e-297.1176171089468e-30
7314.9924432382082e-302.4962216191041e-30
7411.35441257940772e-296.77206289703858e-30
7511.16127670689806e-305.80638353449031e-31
7611.06423167456321e-335.32115837281605e-34
7715.26486290961968e-342.63243145480984e-34
7812.95677852842909e-391.47838926421454e-39
7911.22470458142286e-386.1235229071143e-39
8011.64361799601463e-388.21808998007313e-39
8112.38354754115565e-381.19177377057782e-38
8219.13787367551537e-384.56893683775768e-38
8312.36365826778492e-381.18182913389246e-38
8419.66946858421973e-384.83473429210987e-38
8513.22893304044534e-381.61446652022267e-38
8611.42935514035281e-377.14677570176403e-38
8715.97959985792719e-372.98979992896360e-37
8813.82001323656094e-391.91000661828047e-39
8911.24929761588208e-386.24648807941042e-39
9015.77188041742308e-382.88594020871154e-38
9111.77373883106025e-378.86869415530124e-38
9211.08913337957254e-375.44566689786269e-38
9311.19917427281683e-375.99587136408415e-38
9415.0436467750484e-372.5218233875242e-37
9514.25264414244152e-372.12632207122076e-37
9616.66151448647259e-403.33075724323629e-40
9717.06305833523062e-473.53152916761531e-47
9816.17307316677717e-603.08653658338858e-60
9913.54038354986394e-591.77019177493197e-59
10011.18106028451611e-635.90530142258054e-64
10112.03361924347046e-681.01680962173523e-68
10211.06832489929679e-675.34162449648394e-68
10319.47527338314565e-904.73763669157283e-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
16112.06306594313406e-3031.03153297156703e-303
16214.22037968764819e-2932.11018984382410e-293
16312.56412044123400e-2781.28206022061700e-278
16411.32550135769629e-2666.62750678848146e-267
16512.21484478214879e-2531.10742239107440e-253
16612.39648134391879e-2391.19824067195940e-239
16711.09840493274958e-2255.49202466374789e-226
16812.03074275064428e-2141.01537137532214e-214
16912.19942912386138e-1991.09971456193069e-199
17014.54765939029581e-1832.27382969514790e-183
17116.56795221966862e-1703.28397610983431e-170
17214.13678390651332e-1552.06839195325666e-155
17311.39176692054964e-1406.95883460274819e-141
17414.36701850766019e-1262.18350925383010e-126
17519.15979356583168e-1134.57989678291584e-113
17612.76454965421674e-1001.38227482710837e-100
17718.26388317915576e-874.13194158957788e-87
17811.43508578994265e-717.17542894971326e-72
17918.92697361782171e-624.46348680891086e-62
18017.05854210507196e-443.52927105253598e-44






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=116152&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=116152&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116152&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')
}