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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 28 Dec 2010 09:04:46 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/28/t1293526948wv56qkoy8rt5fhy.htm/, Retrieved Sat, 04 May 2024 20:58:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116237, Retrieved Sat, 04 May 2024 20:58:44 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112

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]
-           [Multiple Regression] [] [2010-12-28 09:04:46] [4cd8ab5c565aadc5ce193fb2faa1c53a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	22	27	27	5	5	26	26	49	49	35	35
1	23	36	36	4	4	25	25	45	45	34	34
1	27	25	25	4	4	17	17	54	54	13	13
1	19	27	27	3	3	37	37	36	36	35	35
1	25	50	50	4	4	27	27	46	46	35	35
1	25	41	41	4	4	36	36	42	42	36	36
1	21	48	48	5	5	25	25	41	41	27	27
1	22	44	44	2	2	29	29	45	45	29	29
1	21	28	28	3	3	26	26	42	42	15	15
1	20	56	56	3	3	24	24	45	45	33	33
1	21	50	50	5	5	29	29	43	43	32	32
1	23	47	47	4	4	26	26	45	45	21	21
1	19	52	52	2	2	21	21	42	42	25	25
1	23	45	45	4	4	21	21	47	47	22	22
1	27			3	3	30	30	41	41	26	26
1	18	52	52	4	4	21	21	44	44	34	34
1	30	46	46	2	2	29	29	51	51	34	34
1	26	58	58	3	3	28	28	46	46	36	36
1	20	54	54	5	5	19	19	47	47	36	36
1	22	29	29	3	3	26	26	46	46	26	26
1	21	43	43	2	2	34	34	50	50	34	34
1	27	45	45	3	3	24	24	51	51	33	33
1	24	46	46	5	5	20	20	47	47	37	37
1	21	25	25	4	4	21	21	46	46	29	29
1	23	47	47	2	2	33	33	43	43	35	35
1	22	41	41	3	3	22	22	55	55	28	28
1	22	29	29	4	4	18	18	52	52	25	25
1	27	45	45	5	5	20	20	56	56	32	32
1	25	54	54	2	2	26	26	46	46	27	27
1	20	28	28	4	4	23	23	51	51	27	27
1	22	37	37	3	3	34	34	39	39	31	31
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1	21	34	34	3	3	46	46	29	29	27	27
1	23	42	42	3	3	24	24	48	48	32	32
1	10	46	46	3	3	42	42	32	32	25	25
1	16	25	25	4	4	30	30	45	45	27	27
1	19	25	25	5	5	19	19	33	33	29	29
1	22	25	25	3	3	37	37	40	40	28	28
1	21	48	48	4	4	23	23	44	44	32	32
1	20	27	27	5	5	34	34	46	46	29	29
1	20	28	28	3	3	38	38	39	39	32	32
1	14	25	25	1	1	26	26	39	39	16	16
1	24	26	26	3	3	36	36	41	41	26	26
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1	19	29	29	3	3	27	27	42	42	24	24
1	19	29	29	4	4	27	27	44	44	26	26
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1	22	44	44	3	3	33	33	46	46	25	25
1	21	25	25	3	3	37	37	45	45	24	24
1	15	51	51	2	2	30	30	40	40	23	23
1	23	42	42	5	5	20	20	48	48	28	28
1	20	25	25	4	4	20	20	53	53	28	28
1	25	51	51	3	3	31	31	45	45	26	26
1	19	46	46	5	5	32	32	46	46	34	34
1	21	29	29			22	22	37	37	30	30
1	24	38	38	3	3	25	25	46	46	30	30
1	7	41	41	1	1	27	27	20	20	15	15
1	27	44	44	4	4	20	20	55	55	31	31
1	19	41	41	5	5	40	40	40	40	33	33
1	24	46	46	4	4	33	33	40	40	27	27
1	24	41	41	4	4	24	24	45	45	31	31
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1	21	55	55	5	5	28	28	39	39	41	41
1	23	21	21	3	3	37	37	45	45	25	25
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1	23	52	52	3	3	30	30	39	39	45	45
1	17			4	4	31	31	35	35	25	25
1	23			3	3	21	21	45	45	29	29
1	15	38	38	5	5	28	28	34	34	25	25
1	20	32	32	5	5	29	29	41	41	27	27
1	16	44	44	4	4	36	36	45	45	24	24
1	21	47	47	5	5	27	27	45	45	35	35
1	22	52	52	1	1	29	29	40	40	32	32
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2	21	52	0	3	0	25	0	46	0	35	0
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2	19	36	0	5	0	35	0	46	0	29	0
2	19	50	0	3	0	23	0	42	0	31	0
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2	29	46	0	3	0	30	0	42	0	37	0
2	24	53	0	5	0	44	0	32	0	38	0
2	25	49	0	2	0	33	0	39	0	28	0
2	19	20	0	3	0	23	0	36	0	21	0
2	23	44	0	3	0	33	0	38	0	35	0
2	22	38	0	4	0	33	0	49	0	31	0
2	24	42	0	4	0	25	0	43	0	30	0
2	19	46	0	4	0	16	0	48	0	24	0
2	21	49	0	2	0	36	0	45	0	27	0
2	18	51	0	3	0	35	0	32	0	26	0
2	24	47	0	3	0	32	0	42	0	28	0
2	24	44	0	3	0	36	0	45	0	34	0
2	23	47	0	3	0	51	0	29	0	29	0
2	24	46	0	3	0	30	0	51	0	26	0
2	20	28	0	3	0	29	0	50	0	28	0
2	20	47	0	4	0	26	0	44	0	33	0
2	22	28	0	4	0	20	0	41	0	32	0
2	18	45	0	4	0	29	0	47	0	31	0
2	14	46	0	4	0	32	0	42	0	37	0
2	29	22	0	3	0	32	0	51	0	19	0
2	25	33	0	3	0	34	0	43	0	27	0
2	20	47	0	5	0	25	0	41	0	38	0
2	25	42	0	3	0	39	0	37	0	35	0
2	21	47	0	3	0	21	0	46	0	35	0
2	21	50	0	3	0	38	0	38	0	30	0
2	13	49	0	4	0	25	0	21	0	21	0
2	23	46	0	4	0	38	0	31	0	33	0
2	24	45	0	3	0	31	0	49	0	29	0
2	16	52	0	3	0	27	0	40	0	31	0
2	20	40	0	4	0	26	0	46	0	31	0
2	24	49	0	4	0	37	0	45	0	31	0
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2	27	32	0	3	0	41	0	45	0	26	0
2	19	41	0	3	0	19	0	48	0	23	0
2	22	43	0	3	0	37	0	43	0	27	0
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2	20	43	0	3	0	37	0	43	0	35	0
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2	19	40	0	2	0	37	0	41	0	28	0
2	23	48	0	3	0	31	0	46	0	29	0
2	30	51	0	3	0	42	0	49	0	38	0
2	22	49	0	4	0	33	0	55	0	24	0
2	23	31	0	4	0	39	0	51	0	25	0
2	22	43	0	3	0	27	0	46	0	37	0
2	22	31	0	3	0	35	0	37	0	33	0
2	23	28	0	4	0	23	0	43	0	30	0
2	27	43	0	4	0	32	0	41	0	22	0
2	23	31	0	3	0	22	0	45	0	28	0
2	18	51	0	3	0	17	0	39	0	24	0
2	24	58	0	4	0	35	0	38	0	33	0
2	19	25	0	5	0	34	0	41	0	37	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time18 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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 & 18 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \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=116237&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]18 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/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=116237&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116237&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 time18 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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
Behoefte_affiliatie[t] = -3.40706072545636 + 0.673472126654755geslacht[t] + 0.168252980774944leeftijd[t] -0.145276934162708leeftijd_man[t] -0.501503813733889opleiding[t] + 0.495292574820152opleiding_man[t] + 0.207046157582967Neuroticisme[t] -0.136851415221506Neuroticisme_man[t] + 0.299625022585822Extraversie[t] + 0.0998485589946164Extraversie_man[t] -0.0326076307539497Openheid[t] + 0.103846821229733Openheid_man[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Behoefte_affiliatie[t] =  -3.40706072545636 +  0.673472126654755geslacht[t] +  0.168252980774944leeftijd[t] -0.145276934162708leeftijd_man[t] -0.501503813733889opleiding[t] +  0.495292574820152opleiding_man[t] +  0.207046157582967Neuroticisme[t] -0.136851415221506Neuroticisme_man[t] +  0.299625022585822Extraversie[t] +  0.0998485589946164Extraversie_man[t] -0.0326076307539497Openheid[t] +  0.103846821229733Openheid_man[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116237&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Behoefte_affiliatie[t] =  -3.40706072545636 +  0.673472126654755geslacht[t] +  0.168252980774944leeftijd[t] -0.145276934162708leeftijd_man[t] -0.501503813733889opleiding[t] +  0.495292574820152opleiding_man[t] +  0.207046157582967Neuroticisme[t] -0.136851415221506Neuroticisme_man[t] +  0.299625022585822Extraversie[t] +  0.0998485589946164Extraversie_man[t] -0.0326076307539497Openheid[t] +  0.103846821229733Openheid_man[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116237&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116237&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
Behoefte_affiliatie[t] = -3.40706072545636 + 0.673472126654755geslacht[t] + 0.168252980774944leeftijd[t] -0.145276934162708leeftijd_man[t] -0.501503813733889opleiding[t] + 0.495292574820152opleiding_man[t] + 0.207046157582967Neuroticisme[t] -0.136851415221506Neuroticisme_man[t] + 0.299625022585822Extraversie[t] + 0.0998485589946164Extraversie_man[t] -0.0326076307539497Openheid[t] + 0.103846821229733Openheid_man[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-3.407060725456362.650164-1.28560.2002060.100103
geslacht0.6734721266547550.04022816.741400
leeftijd0.1682529807749440.0424933.95950.0001075.4e-05
leeftijd_man-0.1452769341627080.065882-2.20510.0286920.014346
opleiding-0.5015038137338890.074938-6.692300
opleiding_man0.4952925748201520.0809946.115200
Neuroticisme0.2070461575829670.0549923.7650.0002240.000112
Neuroticisme_man-0.1368514152215060.075238-1.81890.070560.03528
Extraversie0.2996250225858220.0482146.214400
Extraversie_man0.09984855899461640.0753811.32460.186960.09348
Openheid-0.03260763075394970.069771-0.46740.6408020.320401
Openheid_man0.1038468212297330.0833691.24560.2144920.107246

\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) & -3.40706072545636 & 2.650164 & -1.2856 & 0.200206 & 0.100103 \tabularnewline
geslacht & 0.673472126654755 & 0.040228 & 16.7414 & 0 & 0 \tabularnewline
leeftijd & 0.168252980774944 & 0.042493 & 3.9595 & 0.000107 & 5.4e-05 \tabularnewline
leeftijd_man & -0.145276934162708 & 0.065882 & -2.2051 & 0.028692 & 0.014346 \tabularnewline
opleiding & -0.501503813733889 & 0.074938 & -6.6923 & 0 & 0 \tabularnewline
opleiding_man & 0.495292574820152 & 0.080994 & 6.1152 & 0 & 0 \tabularnewline
Neuroticisme & 0.207046157582967 & 0.054992 & 3.765 & 0.000224 & 0.000112 \tabularnewline
Neuroticisme_man & -0.136851415221506 & 0.075238 & -1.8189 & 0.07056 & 0.03528 \tabularnewline
Extraversie & 0.299625022585822 & 0.048214 & 6.2144 & 0 & 0 \tabularnewline
Extraversie_man & 0.0998485589946164 & 0.075381 & 1.3246 & 0.18696 & 0.09348 \tabularnewline
Openheid & -0.0326076307539497 & 0.069771 & -0.4674 & 0.640802 & 0.320401 \tabularnewline
Openheid_man & 0.103846821229733 & 0.083369 & 1.2456 & 0.214492 & 0.107246 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116237&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]-3.40706072545636[/C][C]2.650164[/C][C]-1.2856[/C][C]0.200206[/C][C]0.100103[/C][/ROW]
[ROW][C]geslacht[/C][C]0.673472126654755[/C][C]0.040228[/C][C]16.7414[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]leeftijd[/C][C]0.168252980774944[/C][C]0.042493[/C][C]3.9595[/C][C]0.000107[/C][C]5.4e-05[/C][/ROW]
[ROW][C]leeftijd_man[/C][C]-0.145276934162708[/C][C]0.065882[/C][C]-2.2051[/C][C]0.028692[/C][C]0.014346[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.501503813733889[/C][C]0.074938[/C][C]-6.6923[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]opleiding_man[/C][C]0.495292574820152[/C][C]0.080994[/C][C]6.1152[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]0.207046157582967[/C][C]0.054992[/C][C]3.765[/C][C]0.000224[/C][C]0.000112[/C][/ROW]
[ROW][C]Neuroticisme_man[/C][C]-0.136851415221506[/C][C]0.075238[/C][C]-1.8189[/C][C]0.07056[/C][C]0.03528[/C][/ROW]
[ROW][C]Extraversie[/C][C]0.299625022585822[/C][C]0.048214[/C][C]6.2144[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Extraversie_man[/C][C]0.0998485589946164[/C][C]0.075381[/C][C]1.3246[/C][C]0.18696[/C][C]0.09348[/C][/ROW]
[ROW][C]Openheid[/C][C]-0.0326076307539497[/C][C]0.069771[/C][C]-0.4674[/C][C]0.640802[/C][C]0.320401[/C][/ROW]
[ROW][C]Openheid_man[/C][C]0.103846821229733[/C][C]0.083369[/C][C]1.2456[/C][C]0.214492[/C][C]0.107246[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116237&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116237&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)-3.407060725456362.650164-1.28560.2002060.100103
geslacht0.6734721266547550.04022816.741400
leeftijd0.1682529807749440.0424933.95950.0001075.4e-05
leeftijd_man-0.1452769341627080.065882-2.20510.0286920.014346
opleiding-0.5015038137338890.074938-6.692300
opleiding_man0.4952925748201520.0809946.115200
Neuroticisme0.2070461575829670.0549923.7650.0002240.000112
Neuroticisme_man-0.1368514152215060.075238-1.81890.070560.03528
Extraversie0.2996250225858220.0482146.214400
Extraversie_man0.09984855899461640.0753811.32460.186960.09348
Openheid-0.03260763075394970.069771-0.46740.6408020.320401
Openheid_man0.1038468212297330.0833691.24560.2144920.107246






Multiple Linear Regression - Regression Statistics
Multiple R0.936114779057777
R-squared0.87631087957039
Adjusted R-squared0.868876014407954
F-TEST (value)117.865066874100
F-TEST (DF numerator)11
F-TEST (DF denominator)183
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.64178670153387
Sum Squared Residuals3942.95163220421

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.936114779057777 \tabularnewline
R-squared & 0.87631087957039 \tabularnewline
Adjusted R-squared & 0.868876014407954 \tabularnewline
F-TEST (value) & 117.865066874100 \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 & 4.64178670153387 \tabularnewline
Sum Squared Residuals & 3942.95163220421 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116237&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.936114779057777[/C][/ROW]
[ROW][C]R-squared[/C][C]0.87631087957039[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.868876014407954[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]117.865066874100[/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]4.64178670153387[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3942.95163220421[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116237&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116237&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 R0.936114779057777
R-squared0.87631087957039
Adjusted R-squared0.868876014407954
F-TEST (value)117.865066874100
F-TEST (DF numerator)11
F-TEST (DF denominator)183
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.64178670153387
Sum Squared Residuals3942.95163220421






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12221.7483489306520.251651069347999
22320.22201632991752.77798367008253
32721.50696111252315.49303888747693
41917.33975701390981.66024298609017
52521.15478323926733.84521676073269
62520.05309636516444.94690363483561
72118.39494899069782.60505100930225
82220.34283019770921.65716980229084
92117.5626485745133.437351425487
102020.5463145682381-0.546314568238074
112119.87682316890781.12317683109217
122319.61883810882773.38116189117226
131918.48170312507090.518296874929094
142320.09209865743262.90790134256737
152712.249935082915614.7500649170844
165251.32842547362850.671574526371483
174647.2679266207769-1.26792662077694
185855.20367195013512.79632804986490
195452.88952507178511.11047492821487
202931.7945137604001-2.79451376040011
214345.2501061251123-2.25010612511227
224546.0413195112632-1.04131951126319
234647.7911635799841-1.79116357998408
242530.5607718821628-5.5607718821628
254747.3390821495466-0.339082149546559
264141.4355709015558-0.435570901555839
272932.5119555182890-3.51195551828896
284546.1674635115992-1.16746351159921
295448.90396764050745.09603235949262
302832.0169455117163-4.01694551171628
313738.5347587524705-1.53475875247047
325645.169638065605910.8303619343941
334339.72725014416813.27274985583187
343434.4515062192942-0.451506219294211
354241.95698982541780.0430101745821530
364641.24272601012164.75727398987844
372529.2203415414979-4.22034154149790
382529.5797919346738-4.57979193467378
392529.4100803347221-4.41008033472211
404846.78469911372381.21530088627617
412731.5384056125168-4.53840561251677
422832.2250573111248-4.22505731112479
432524.88011461191120.119885388088787
442629.6725517431804-3.67255174318038
455149.2584842814791.74151571852099
462930.5008827464202-1.50088274642018
472930.9439613347676-1.94396133476755
484337.5019790027165.49802099728398
494441.45364340624482.5463565937552
502527.5400787928293-2.54007879282926
515145.15118398808695.84881601191314
524241.46836076027280.531639239727157
532530.8665663784858-5.86656637848579
545146.3019559674154.69804403258495
554646.4480132259165-0.448013225916463
562923.90821008057265.09178991942736
5734.92725384568103-1.92725384568103
5814.94552151325091-3.94552151325091
5946.69832203934784-2.69832203934784
6058.92031191402827-3.92031191402827
6147.30864305454018-3.30864305454018
6245.61266300832699-1.61266300832699
6337.1595973397394-4.15959733973940
6457.33154021159766-2.33154021159766
6533.2494488429953-0.249448842995301
662633.0320054432439-7.03200544324394
673019.100544470833710.8994555291663
683543.4343832915794-8.43438329157936
692927.83506930938011.16493069061994
702525.3502114803408-0.350211480340851
712730.2148056939411-3.21480569394109
722423.92430343455960.0756965654403998
733531.31813594036663.6818640596334
743235.2974595728038-3.29745957280375
752431.4607351091177-7.46073510911774
763829.64394458351218.35605541648788
773634.33743572309301.66256427690696
782425.3284257175568-1.32842571755682
791818.9776754681178-0.97767546811778
803429.53712171325334.4628782867467
812324.8483120573274-1.84831205732739
823430.49128827520383.50871172479621
833229.84162667122582.15837332877420
842425.1111628203216-1.11116282032158
853427.95997245982876.04002754017131
863337.9094208330562-4.9094208330562
873314.371415616023018.6285843839770
880-7.105224731006127.10522473100612
8902.06938867088764-2.06938867088764
900-3.335225608443483.33522560844348
9100.204133599192590-0.204133599192590
920-2.405707713476912.40570771347691
930-0.3893288625536550.389328862553655
940-2.487619383049392.48761938304939
950-4.366306180840464.36630618084046
96015.1741635010604-15.1741635010604
9704.45505107693719-4.45505107693719
98015.1490327660078-15.1490327660078
9907.93488129258886-7.93488129258886
10001.23641341591376-1.23641341591376
10106.21966115192624-6.21966115192624
1020-4.944806718055284.94480671805528
10305.72321924406316-5.72321924406316
10403.75774906424738-3.75774906424738
105012.3186109965140-12.3186109965140
1062-2.751584513211124.75158451321112
10720.7423044884133371.25769551158666
108214.9384006508831-12.9384006508831
1092225.4270371949954-3.42703719499536
1101928.3545858820960-9.35458588209604
1111922.5924530319061-3.5924530319061
1122422.27866517142771.7213348285723
1132220.23786853279531.76213146720469
1142621.50118849691754.49881150308245
1152317.34661092909715.6533890709029
1162123.0021649890824-2.00216498908235
1171618.3557139769996-2.35571397699964
1182123.1295240715956-2.12952407159562
1191821.6059475930155-3.60594759301551
1202021.7168958012726-1.71689580127257
1212419.56400640522144.43599359477857
1222424.038289835222-0.0382898352219761
1232321.53326825692191.46673174307814
1242322.84485008404930.155149915950730
1252222.7234868752787-0.723486875278708
1261718.9176817112553-1.91768171125532
1272523.14491553681641.8550844631836
1282518.10674000607726.89325999392276
1292326.0016715322332-3.00167153223319
1302722.67656429620094.32343570379908
1312319.93788599200833.06211400799173
1321921.5732170295688-2.57321702956885
1331921.1834971450390-2.18349714503904
1342623.28754934676512.71245065323492
1351922.4872427258826-3.48724272588259
1362224.8723522021251-2.87235220212512
1372022.8344398790570-2.83443987905705
1382123.4077106878281-2.40771068782806
1392125.2379795171793-4.23797951717931
1402823.93022696126114.06977303873889
1412421.49425977365372.50574022634631
1422921.76416254049637.23583745950366
1432421.80871412800252.19128587199747
1442522.78615737833202.21384262166796
1451914.66423389381534.33576610618471
1462320.91551022286002.08448977714004
1472222.8307942959363-0.830794295936259
1482420.08229445361133.91770554638869
1491920.5856618559172-1.58566185591719
1502125.2376536173498-4.23765361734983
1511821.0030919447211-3.00309194472112
1522422.63997651322281.36002348677723
1532423.66663148446360.333368515536428
1542322.64612058292950.353879417070497
1552424.8194716820622-0.819471682062188
1562021.2190315864365-1.21903158643651
1572021.3324076453930-1.33240764539297
1582216.02705662816775.97294337183228
1591822.5811304858573-4.58113048585735
1601421.6767510419284-7.67675104192838
1612921.42374587390717.57625412609288
1622521.03075975087933.96924024912074
1632019.56194445254890.438055547451104
1642521.52165618422213.47834381577794
1652121.3327154548758-0.332715454875766
1662123.1232970491942-2.12329704919421
1671314.9617834989334-1.96178349893337
1682319.75358326199793.24641673800206
1692424.1611919214367-0.161191921436717
1701621.7489376917492-5.74893769174915
1712020.8191020866479-0.819102086647907
1722424.3112616244492-0.311261624449218
1732722.54210616039064.45789383960938
1742722.94368754911074.05631245088932
1751920.8996468692792-1.89964686927921
1762223.3344280313776-1.33442803137760
1771817.38364637841100.616353621588978
1782424.2907879529218-0.290787952921754
1792023.073566985346-3.073566985346
1802023.3474608443502-3.34746084435016
1812722.34811107609674.6518889239033
1821922.6993152268611-3.69931522686106
1832323.7670757960041-0.767075796004081
1843027.15474886271352.84525113728653
1852226.7075806352532-4.70758063525322
1862323.6901962057048-0.690196205704802
1872221.83676521576590.163234784234105
1882218.90790402687373.09209597312629
1892317.31266040759625.68733959240384
1902721.3614815383275.63851846167302
1912318.77634231275154.22365768724854
1921819.4388515278364-1.43885152783636
1932423.24885571664910.751144283350882
1941917.75640192450081.24359807549921
1952221.74834893065200.251651069347983

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 22 & 21.748348930652 & 0.251651069347999 \tabularnewline
2 & 23 & 20.2220163299175 & 2.77798367008253 \tabularnewline
3 & 27 & 21.5069611125231 & 5.49303888747693 \tabularnewline
4 & 19 & 17.3397570139098 & 1.66024298609017 \tabularnewline
5 & 25 & 21.1547832392673 & 3.84521676073269 \tabularnewline
6 & 25 & 20.0530963651644 & 4.94690363483561 \tabularnewline
7 & 21 & 18.3949489906978 & 2.60505100930225 \tabularnewline
8 & 22 & 20.3428301977092 & 1.65716980229084 \tabularnewline
9 & 21 & 17.562648574513 & 3.437351425487 \tabularnewline
10 & 20 & 20.5463145682381 & -0.546314568238074 \tabularnewline
11 & 21 & 19.8768231689078 & 1.12317683109217 \tabularnewline
12 & 23 & 19.6188381088277 & 3.38116189117226 \tabularnewline
13 & 19 & 18.4817031250709 & 0.518296874929094 \tabularnewline
14 & 23 & 20.0920986574326 & 2.90790134256737 \tabularnewline
15 & 27 & 12.2499350829156 & 14.7500649170844 \tabularnewline
16 & 52 & 51.3284254736285 & 0.671574526371483 \tabularnewline
17 & 46 & 47.2679266207769 & -1.26792662077694 \tabularnewline
18 & 58 & 55.2036719501351 & 2.79632804986490 \tabularnewline
19 & 54 & 52.8895250717851 & 1.11047492821487 \tabularnewline
20 & 29 & 31.7945137604001 & -2.79451376040011 \tabularnewline
21 & 43 & 45.2501061251123 & -2.25010612511227 \tabularnewline
22 & 45 & 46.0413195112632 & -1.04131951126319 \tabularnewline
23 & 46 & 47.7911635799841 & -1.79116357998408 \tabularnewline
24 & 25 & 30.5607718821628 & -5.5607718821628 \tabularnewline
25 & 47 & 47.3390821495466 & -0.339082149546559 \tabularnewline
26 & 41 & 41.4355709015558 & -0.435570901555839 \tabularnewline
27 & 29 & 32.5119555182890 & -3.51195551828896 \tabularnewline
28 & 45 & 46.1674635115992 & -1.16746351159921 \tabularnewline
29 & 54 & 48.9039676405074 & 5.09603235949262 \tabularnewline
30 & 28 & 32.0169455117163 & -4.01694551171628 \tabularnewline
31 & 37 & 38.5347587524705 & -1.53475875247047 \tabularnewline
32 & 56 & 45.1696380656059 & 10.8303619343941 \tabularnewline
33 & 43 & 39.7272501441681 & 3.27274985583187 \tabularnewline
34 & 34 & 34.4515062192942 & -0.451506219294211 \tabularnewline
35 & 42 & 41.9569898254178 & 0.0430101745821530 \tabularnewline
36 & 46 & 41.2427260101216 & 4.75727398987844 \tabularnewline
37 & 25 & 29.2203415414979 & -4.22034154149790 \tabularnewline
38 & 25 & 29.5797919346738 & -4.57979193467378 \tabularnewline
39 & 25 & 29.4100803347221 & -4.41008033472211 \tabularnewline
40 & 48 & 46.7846991137238 & 1.21530088627617 \tabularnewline
41 & 27 & 31.5384056125168 & -4.53840561251677 \tabularnewline
42 & 28 & 32.2250573111248 & -4.22505731112479 \tabularnewline
43 & 25 & 24.8801146119112 & 0.119885388088787 \tabularnewline
44 & 26 & 29.6725517431804 & -3.67255174318038 \tabularnewline
45 & 51 & 49.258484281479 & 1.74151571852099 \tabularnewline
46 & 29 & 30.5008827464202 & -1.50088274642018 \tabularnewline
47 & 29 & 30.9439613347676 & -1.94396133476755 \tabularnewline
48 & 43 & 37.501979002716 & 5.49802099728398 \tabularnewline
49 & 44 & 41.4536434062448 & 2.5463565937552 \tabularnewline
50 & 25 & 27.5400787928293 & -2.54007879282926 \tabularnewline
51 & 51 & 45.1511839880869 & 5.84881601191314 \tabularnewline
52 & 42 & 41.4683607602728 & 0.531639239727157 \tabularnewline
53 & 25 & 30.8665663784858 & -5.86656637848579 \tabularnewline
54 & 51 & 46.301955967415 & 4.69804403258495 \tabularnewline
55 & 46 & 46.4480132259165 & -0.448013225916463 \tabularnewline
56 & 29 & 23.9082100805726 & 5.09178991942736 \tabularnewline
57 & 3 & 4.92725384568103 & -1.92725384568103 \tabularnewline
58 & 1 & 4.94552151325091 & -3.94552151325091 \tabularnewline
59 & 4 & 6.69832203934784 & -2.69832203934784 \tabularnewline
60 & 5 & 8.92031191402827 & -3.92031191402827 \tabularnewline
61 & 4 & 7.30864305454018 & -3.30864305454018 \tabularnewline
62 & 4 & 5.61266300832699 & -1.61266300832699 \tabularnewline
63 & 3 & 7.1595973397394 & -4.15959733973940 \tabularnewline
64 & 5 & 7.33154021159766 & -2.33154021159766 \tabularnewline
65 & 3 & 3.2494488429953 & -0.249448842995301 \tabularnewline
66 & 26 & 33.0320054432439 & -7.03200544324394 \tabularnewline
67 & 30 & 19.1005444708337 & 10.8994555291663 \tabularnewline
68 & 35 & 43.4343832915794 & -8.43438329157936 \tabularnewline
69 & 29 & 27.8350693093801 & 1.16493069061994 \tabularnewline
70 & 25 & 25.3502114803408 & -0.350211480340851 \tabularnewline
71 & 27 & 30.2148056939411 & -3.21480569394109 \tabularnewline
72 & 24 & 23.9243034345596 & 0.0756965654403998 \tabularnewline
73 & 35 & 31.3181359403666 & 3.6818640596334 \tabularnewline
74 & 32 & 35.2974595728038 & -3.29745957280375 \tabularnewline
75 & 24 & 31.4607351091177 & -7.46073510911774 \tabularnewline
76 & 38 & 29.6439445835121 & 8.35605541648788 \tabularnewline
77 & 36 & 34.3374357230930 & 1.66256427690696 \tabularnewline
78 & 24 & 25.3284257175568 & -1.32842571755682 \tabularnewline
79 & 18 & 18.9776754681178 & -0.97767546811778 \tabularnewline
80 & 34 & 29.5371217132533 & 4.4628782867467 \tabularnewline
81 & 23 & 24.8483120573274 & -1.84831205732739 \tabularnewline
82 & 34 & 30.4912882752038 & 3.50871172479621 \tabularnewline
83 & 32 & 29.8416266712258 & 2.15837332877420 \tabularnewline
84 & 24 & 25.1111628203216 & -1.11116282032158 \tabularnewline
85 & 34 & 27.9599724598287 & 6.04002754017131 \tabularnewline
86 & 33 & 37.9094208330562 & -4.9094208330562 \tabularnewline
87 & 33 & 14.3714156160230 & 18.6285843839770 \tabularnewline
88 & 0 & -7.10522473100612 & 7.10522473100612 \tabularnewline
89 & 0 & 2.06938867088764 & -2.06938867088764 \tabularnewline
90 & 0 & -3.33522560844348 & 3.33522560844348 \tabularnewline
91 & 0 & 0.204133599192590 & -0.204133599192590 \tabularnewline
92 & 0 & -2.40570771347691 & 2.40570771347691 \tabularnewline
93 & 0 & -0.389328862553655 & 0.389328862553655 \tabularnewline
94 & 0 & -2.48761938304939 & 2.48761938304939 \tabularnewline
95 & 0 & -4.36630618084046 & 4.36630618084046 \tabularnewline
96 & 0 & 15.1741635010604 & -15.1741635010604 \tabularnewline
97 & 0 & 4.45505107693719 & -4.45505107693719 \tabularnewline
98 & 0 & 15.1490327660078 & -15.1490327660078 \tabularnewline
99 & 0 & 7.93488129258886 & -7.93488129258886 \tabularnewline
100 & 0 & 1.23641341591376 & -1.23641341591376 \tabularnewline
101 & 0 & 6.21966115192624 & -6.21966115192624 \tabularnewline
102 & 0 & -4.94480671805528 & 4.94480671805528 \tabularnewline
103 & 0 & 5.72321924406316 & -5.72321924406316 \tabularnewline
104 & 0 & 3.75774906424738 & -3.75774906424738 \tabularnewline
105 & 0 & 12.3186109965140 & -12.3186109965140 \tabularnewline
106 & 2 & -2.75158451321112 & 4.75158451321112 \tabularnewline
107 & 2 & 0.742304488413337 & 1.25769551158666 \tabularnewline
108 & 2 & 14.9384006508831 & -12.9384006508831 \tabularnewline
109 & 22 & 25.4270371949954 & -3.42703719499536 \tabularnewline
110 & 19 & 28.3545858820960 & -9.35458588209604 \tabularnewline
111 & 19 & 22.5924530319061 & -3.5924530319061 \tabularnewline
112 & 24 & 22.2786651714277 & 1.7213348285723 \tabularnewline
113 & 22 & 20.2378685327953 & 1.76213146720469 \tabularnewline
114 & 26 & 21.5011884969175 & 4.49881150308245 \tabularnewline
115 & 23 & 17.3466109290971 & 5.6533890709029 \tabularnewline
116 & 21 & 23.0021649890824 & -2.00216498908235 \tabularnewline
117 & 16 & 18.3557139769996 & -2.35571397699964 \tabularnewline
118 & 21 & 23.1295240715956 & -2.12952407159562 \tabularnewline
119 & 18 & 21.6059475930155 & -3.60594759301551 \tabularnewline
120 & 20 & 21.7168958012726 & -1.71689580127257 \tabularnewline
121 & 24 & 19.5640064052214 & 4.43599359477857 \tabularnewline
122 & 24 & 24.038289835222 & -0.0382898352219761 \tabularnewline
123 & 23 & 21.5332682569219 & 1.46673174307814 \tabularnewline
124 & 23 & 22.8448500840493 & 0.155149915950730 \tabularnewline
125 & 22 & 22.7234868752787 & -0.723486875278708 \tabularnewline
126 & 17 & 18.9176817112553 & -1.91768171125532 \tabularnewline
127 & 25 & 23.1449155368164 & 1.8550844631836 \tabularnewline
128 & 25 & 18.1067400060772 & 6.89325999392276 \tabularnewline
129 & 23 & 26.0016715322332 & -3.00167153223319 \tabularnewline
130 & 27 & 22.6765642962009 & 4.32343570379908 \tabularnewline
131 & 23 & 19.9378859920083 & 3.06211400799173 \tabularnewline
132 & 19 & 21.5732170295688 & -2.57321702956885 \tabularnewline
133 & 19 & 21.1834971450390 & -2.18349714503904 \tabularnewline
134 & 26 & 23.2875493467651 & 2.71245065323492 \tabularnewline
135 & 19 & 22.4872427258826 & -3.48724272588259 \tabularnewline
136 & 22 & 24.8723522021251 & -2.87235220212512 \tabularnewline
137 & 20 & 22.8344398790570 & -2.83443987905705 \tabularnewline
138 & 21 & 23.4077106878281 & -2.40771068782806 \tabularnewline
139 & 21 & 25.2379795171793 & -4.23797951717931 \tabularnewline
140 & 28 & 23.9302269612611 & 4.06977303873889 \tabularnewline
141 & 24 & 21.4942597736537 & 2.50574022634631 \tabularnewline
142 & 29 & 21.7641625404963 & 7.23583745950366 \tabularnewline
143 & 24 & 21.8087141280025 & 2.19128587199747 \tabularnewline
144 & 25 & 22.7861573783320 & 2.21384262166796 \tabularnewline
145 & 19 & 14.6642338938153 & 4.33576610618471 \tabularnewline
146 & 23 & 20.9155102228600 & 2.08448977714004 \tabularnewline
147 & 22 & 22.8307942959363 & -0.830794295936259 \tabularnewline
148 & 24 & 20.0822944536113 & 3.91770554638869 \tabularnewline
149 & 19 & 20.5856618559172 & -1.58566185591719 \tabularnewline
150 & 21 & 25.2376536173498 & -4.23765361734983 \tabularnewline
151 & 18 & 21.0030919447211 & -3.00309194472112 \tabularnewline
152 & 24 & 22.6399765132228 & 1.36002348677723 \tabularnewline
153 & 24 & 23.6666314844636 & 0.333368515536428 \tabularnewline
154 & 23 & 22.6461205829295 & 0.353879417070497 \tabularnewline
155 & 24 & 24.8194716820622 & -0.819471682062188 \tabularnewline
156 & 20 & 21.2190315864365 & -1.21903158643651 \tabularnewline
157 & 20 & 21.3324076453930 & -1.33240764539297 \tabularnewline
158 & 22 & 16.0270566281677 & 5.97294337183228 \tabularnewline
159 & 18 & 22.5811304858573 & -4.58113048585735 \tabularnewline
160 & 14 & 21.6767510419284 & -7.67675104192838 \tabularnewline
161 & 29 & 21.4237458739071 & 7.57625412609288 \tabularnewline
162 & 25 & 21.0307597508793 & 3.96924024912074 \tabularnewline
163 & 20 & 19.5619444525489 & 0.438055547451104 \tabularnewline
164 & 25 & 21.5216561842221 & 3.47834381577794 \tabularnewline
165 & 21 & 21.3327154548758 & -0.332715454875766 \tabularnewline
166 & 21 & 23.1232970491942 & -2.12329704919421 \tabularnewline
167 & 13 & 14.9617834989334 & -1.96178349893337 \tabularnewline
168 & 23 & 19.7535832619979 & 3.24641673800206 \tabularnewline
169 & 24 & 24.1611919214367 & -0.161191921436717 \tabularnewline
170 & 16 & 21.7489376917492 & -5.74893769174915 \tabularnewline
171 & 20 & 20.8191020866479 & -0.819102086647907 \tabularnewline
172 & 24 & 24.3112616244492 & -0.311261624449218 \tabularnewline
173 & 27 & 22.5421061603906 & 4.45789383960938 \tabularnewline
174 & 27 & 22.9436875491107 & 4.05631245088932 \tabularnewline
175 & 19 & 20.8996468692792 & -1.89964686927921 \tabularnewline
176 & 22 & 23.3344280313776 & -1.33442803137760 \tabularnewline
177 & 18 & 17.3836463784110 & 0.616353621588978 \tabularnewline
178 & 24 & 24.2907879529218 & -0.290787952921754 \tabularnewline
179 & 20 & 23.073566985346 & -3.073566985346 \tabularnewline
180 & 20 & 23.3474608443502 & -3.34746084435016 \tabularnewline
181 & 27 & 22.3481110760967 & 4.6518889239033 \tabularnewline
182 & 19 & 22.6993152268611 & -3.69931522686106 \tabularnewline
183 & 23 & 23.7670757960041 & -0.767075796004081 \tabularnewline
184 & 30 & 27.1547488627135 & 2.84525113728653 \tabularnewline
185 & 22 & 26.7075806352532 & -4.70758063525322 \tabularnewline
186 & 23 & 23.6901962057048 & -0.690196205704802 \tabularnewline
187 & 22 & 21.8367652157659 & 0.163234784234105 \tabularnewline
188 & 22 & 18.9079040268737 & 3.09209597312629 \tabularnewline
189 & 23 & 17.3126604075962 & 5.68733959240384 \tabularnewline
190 & 27 & 21.361481538327 & 5.63851846167302 \tabularnewline
191 & 23 & 18.7763423127515 & 4.22365768724854 \tabularnewline
192 & 18 & 19.4388515278364 & -1.43885152783636 \tabularnewline
193 & 24 & 23.2488557166491 & 0.751144283350882 \tabularnewline
194 & 19 & 17.7564019245008 & 1.24359807549921 \tabularnewline
195 & 22 & 21.7483489306520 & 0.251651069347983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116237&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]22[/C][C]21.748348930652[/C][C]0.251651069347999[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]20.2220163299175[/C][C]2.77798367008253[/C][/ROW]
[ROW][C]3[/C][C]27[/C][C]21.5069611125231[/C][C]5.49303888747693[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]17.3397570139098[/C][C]1.66024298609017[/C][/ROW]
[ROW][C]5[/C][C]25[/C][C]21.1547832392673[/C][C]3.84521676073269[/C][/ROW]
[ROW][C]6[/C][C]25[/C][C]20.0530963651644[/C][C]4.94690363483561[/C][/ROW]
[ROW][C]7[/C][C]21[/C][C]18.3949489906978[/C][C]2.60505100930225[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.3428301977092[/C][C]1.65716980229084[/C][/ROW]
[ROW][C]9[/C][C]21[/C][C]17.562648574513[/C][C]3.437351425487[/C][/ROW]
[ROW][C]10[/C][C]20[/C][C]20.5463145682381[/C][C]-0.546314568238074[/C][/ROW]
[ROW][C]11[/C][C]21[/C][C]19.8768231689078[/C][C]1.12317683109217[/C][/ROW]
[ROW][C]12[/C][C]23[/C][C]19.6188381088277[/C][C]3.38116189117226[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]18.4817031250709[/C][C]0.518296874929094[/C][/ROW]
[ROW][C]14[/C][C]23[/C][C]20.0920986574326[/C][C]2.90790134256737[/C][/ROW]
[ROW][C]15[/C][C]27[/C][C]12.2499350829156[/C][C]14.7500649170844[/C][/ROW]
[ROW][C]16[/C][C]52[/C][C]51.3284254736285[/C][C]0.671574526371483[/C][/ROW]
[ROW][C]17[/C][C]46[/C][C]47.2679266207769[/C][C]-1.26792662077694[/C][/ROW]
[ROW][C]18[/C][C]58[/C][C]55.2036719501351[/C][C]2.79632804986490[/C][/ROW]
[ROW][C]19[/C][C]54[/C][C]52.8895250717851[/C][C]1.11047492821487[/C][/ROW]
[ROW][C]20[/C][C]29[/C][C]31.7945137604001[/C][C]-2.79451376040011[/C][/ROW]
[ROW][C]21[/C][C]43[/C][C]45.2501061251123[/C][C]-2.25010612511227[/C][/ROW]
[ROW][C]22[/C][C]45[/C][C]46.0413195112632[/C][C]-1.04131951126319[/C][/ROW]
[ROW][C]23[/C][C]46[/C][C]47.7911635799841[/C][C]-1.79116357998408[/C][/ROW]
[ROW][C]24[/C][C]25[/C][C]30.5607718821628[/C][C]-5.5607718821628[/C][/ROW]
[ROW][C]25[/C][C]47[/C][C]47.3390821495466[/C][C]-0.339082149546559[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]41.4355709015558[/C][C]-0.435570901555839[/C][/ROW]
[ROW][C]27[/C][C]29[/C][C]32.5119555182890[/C][C]-3.51195551828896[/C][/ROW]
[ROW][C]28[/C][C]45[/C][C]46.1674635115992[/C][C]-1.16746351159921[/C][/ROW]
[ROW][C]29[/C][C]54[/C][C]48.9039676405074[/C][C]5.09603235949262[/C][/ROW]
[ROW][C]30[/C][C]28[/C][C]32.0169455117163[/C][C]-4.01694551171628[/C][/ROW]
[ROW][C]31[/C][C]37[/C][C]38.5347587524705[/C][C]-1.53475875247047[/C][/ROW]
[ROW][C]32[/C][C]56[/C][C]45.1696380656059[/C][C]10.8303619343941[/C][/ROW]
[ROW][C]33[/C][C]43[/C][C]39.7272501441681[/C][C]3.27274985583187[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]34.4515062192942[/C][C]-0.451506219294211[/C][/ROW]
[ROW][C]35[/C][C]42[/C][C]41.9569898254178[/C][C]0.0430101745821530[/C][/ROW]
[ROW][C]36[/C][C]46[/C][C]41.2427260101216[/C][C]4.75727398987844[/C][/ROW]
[ROW][C]37[/C][C]25[/C][C]29.2203415414979[/C][C]-4.22034154149790[/C][/ROW]
[ROW][C]38[/C][C]25[/C][C]29.5797919346738[/C][C]-4.57979193467378[/C][/ROW]
[ROW][C]39[/C][C]25[/C][C]29.4100803347221[/C][C]-4.41008033472211[/C][/ROW]
[ROW][C]40[/C][C]48[/C][C]46.7846991137238[/C][C]1.21530088627617[/C][/ROW]
[ROW][C]41[/C][C]27[/C][C]31.5384056125168[/C][C]-4.53840561251677[/C][/ROW]
[ROW][C]42[/C][C]28[/C][C]32.2250573111248[/C][C]-4.22505731112479[/C][/ROW]
[ROW][C]43[/C][C]25[/C][C]24.8801146119112[/C][C]0.119885388088787[/C][/ROW]
[ROW][C]44[/C][C]26[/C][C]29.6725517431804[/C][C]-3.67255174318038[/C][/ROW]
[ROW][C]45[/C][C]51[/C][C]49.258484281479[/C][C]1.74151571852099[/C][/ROW]
[ROW][C]46[/C][C]29[/C][C]30.5008827464202[/C][C]-1.50088274642018[/C][/ROW]
[ROW][C]47[/C][C]29[/C][C]30.9439613347676[/C][C]-1.94396133476755[/C][/ROW]
[ROW][C]48[/C][C]43[/C][C]37.501979002716[/C][C]5.49802099728398[/C][/ROW]
[ROW][C]49[/C][C]44[/C][C]41.4536434062448[/C][C]2.5463565937552[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]27.5400787928293[/C][C]-2.54007879282926[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]45.1511839880869[/C][C]5.84881601191314[/C][/ROW]
[ROW][C]52[/C][C]42[/C][C]41.4683607602728[/C][C]0.531639239727157[/C][/ROW]
[ROW][C]53[/C][C]25[/C][C]30.8665663784858[/C][C]-5.86656637848579[/C][/ROW]
[ROW][C]54[/C][C]51[/C][C]46.301955967415[/C][C]4.69804403258495[/C][/ROW]
[ROW][C]55[/C][C]46[/C][C]46.4480132259165[/C][C]-0.448013225916463[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]23.9082100805726[/C][C]5.09178991942736[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]4.92725384568103[/C][C]-1.92725384568103[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]4.94552151325091[/C][C]-3.94552151325091[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]6.69832203934784[/C][C]-2.69832203934784[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]8.92031191402827[/C][C]-3.92031191402827[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]7.30864305454018[/C][C]-3.30864305454018[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]5.61266300832699[/C][C]-1.61266300832699[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]7.1595973397394[/C][C]-4.15959733973940[/C][/ROW]
[ROW][C]64[/C][C]5[/C][C]7.33154021159766[/C][C]-2.33154021159766[/C][/ROW]
[ROW][C]65[/C][C]3[/C][C]3.2494488429953[/C][C]-0.249448842995301[/C][/ROW]
[ROW][C]66[/C][C]26[/C][C]33.0320054432439[/C][C]-7.03200544324394[/C][/ROW]
[ROW][C]67[/C][C]30[/C][C]19.1005444708337[/C][C]10.8994555291663[/C][/ROW]
[ROW][C]68[/C][C]35[/C][C]43.4343832915794[/C][C]-8.43438329157936[/C][/ROW]
[ROW][C]69[/C][C]29[/C][C]27.8350693093801[/C][C]1.16493069061994[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]25.3502114803408[/C][C]-0.350211480340851[/C][/ROW]
[ROW][C]71[/C][C]27[/C][C]30.2148056939411[/C][C]-3.21480569394109[/C][/ROW]
[ROW][C]72[/C][C]24[/C][C]23.9243034345596[/C][C]0.0756965654403998[/C][/ROW]
[ROW][C]73[/C][C]35[/C][C]31.3181359403666[/C][C]3.6818640596334[/C][/ROW]
[ROW][C]74[/C][C]32[/C][C]35.2974595728038[/C][C]-3.29745957280375[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]31.4607351091177[/C][C]-7.46073510911774[/C][/ROW]
[ROW][C]76[/C][C]38[/C][C]29.6439445835121[/C][C]8.35605541648788[/C][/ROW]
[ROW][C]77[/C][C]36[/C][C]34.3374357230930[/C][C]1.66256427690696[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]25.3284257175568[/C][C]-1.32842571755682[/C][/ROW]
[ROW][C]79[/C][C]18[/C][C]18.9776754681178[/C][C]-0.97767546811778[/C][/ROW]
[ROW][C]80[/C][C]34[/C][C]29.5371217132533[/C][C]4.4628782867467[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]24.8483120573274[/C][C]-1.84831205732739[/C][/ROW]
[ROW][C]82[/C][C]34[/C][C]30.4912882752038[/C][C]3.50871172479621[/C][/ROW]
[ROW][C]83[/C][C]32[/C][C]29.8416266712258[/C][C]2.15837332877420[/C][/ROW]
[ROW][C]84[/C][C]24[/C][C]25.1111628203216[/C][C]-1.11116282032158[/C][/ROW]
[ROW][C]85[/C][C]34[/C][C]27.9599724598287[/C][C]6.04002754017131[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]37.9094208330562[/C][C]-4.9094208330562[/C][/ROW]
[ROW][C]87[/C][C]33[/C][C]14.3714156160230[/C][C]18.6285843839770[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]-7.10522473100612[/C][C]7.10522473100612[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]2.06938867088764[/C][C]-2.06938867088764[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]-3.33522560844348[/C][C]3.33522560844348[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.204133599192590[/C][C]-0.204133599192590[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]-2.40570771347691[/C][C]2.40570771347691[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]-0.389328862553655[/C][C]0.389328862553655[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]-2.48761938304939[/C][C]2.48761938304939[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]-4.36630618084046[/C][C]4.36630618084046[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]15.1741635010604[/C][C]-15.1741635010604[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]4.45505107693719[/C][C]-4.45505107693719[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]15.1490327660078[/C][C]-15.1490327660078[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]7.93488129258886[/C][C]-7.93488129258886[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]1.23641341591376[/C][C]-1.23641341591376[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]6.21966115192624[/C][C]-6.21966115192624[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]-4.94480671805528[/C][C]4.94480671805528[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]5.72321924406316[/C][C]-5.72321924406316[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]3.75774906424738[/C][C]-3.75774906424738[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]12.3186109965140[/C][C]-12.3186109965140[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]-2.75158451321112[/C][C]4.75158451321112[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]0.742304488413337[/C][C]1.25769551158666[/C][/ROW]
[ROW][C]108[/C][C]2[/C][C]14.9384006508831[/C][C]-12.9384006508831[/C][/ROW]
[ROW][C]109[/C][C]22[/C][C]25.4270371949954[/C][C]-3.42703719499536[/C][/ROW]
[ROW][C]110[/C][C]19[/C][C]28.3545858820960[/C][C]-9.35458588209604[/C][/ROW]
[ROW][C]111[/C][C]19[/C][C]22.5924530319061[/C][C]-3.5924530319061[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]22.2786651714277[/C][C]1.7213348285723[/C][/ROW]
[ROW][C]113[/C][C]22[/C][C]20.2378685327953[/C][C]1.76213146720469[/C][/ROW]
[ROW][C]114[/C][C]26[/C][C]21.5011884969175[/C][C]4.49881150308245[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]17.3466109290971[/C][C]5.6533890709029[/C][/ROW]
[ROW][C]116[/C][C]21[/C][C]23.0021649890824[/C][C]-2.00216498908235[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]18.3557139769996[/C][C]-2.35571397699964[/C][/ROW]
[ROW][C]118[/C][C]21[/C][C]23.1295240715956[/C][C]-2.12952407159562[/C][/ROW]
[ROW][C]119[/C][C]18[/C][C]21.6059475930155[/C][C]-3.60594759301551[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]21.7168958012726[/C][C]-1.71689580127257[/C][/ROW]
[ROW][C]121[/C][C]24[/C][C]19.5640064052214[/C][C]4.43599359477857[/C][/ROW]
[ROW][C]122[/C][C]24[/C][C]24.038289835222[/C][C]-0.0382898352219761[/C][/ROW]
[ROW][C]123[/C][C]23[/C][C]21.5332682569219[/C][C]1.46673174307814[/C][/ROW]
[ROW][C]124[/C][C]23[/C][C]22.8448500840493[/C][C]0.155149915950730[/C][/ROW]
[ROW][C]125[/C][C]22[/C][C]22.7234868752787[/C][C]-0.723486875278708[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]18.9176817112553[/C][C]-1.91768171125532[/C][/ROW]
[ROW][C]127[/C][C]25[/C][C]23.1449155368164[/C][C]1.8550844631836[/C][/ROW]
[ROW][C]128[/C][C]25[/C][C]18.1067400060772[/C][C]6.89325999392276[/C][/ROW]
[ROW][C]129[/C][C]23[/C][C]26.0016715322332[/C][C]-3.00167153223319[/C][/ROW]
[ROW][C]130[/C][C]27[/C][C]22.6765642962009[/C][C]4.32343570379908[/C][/ROW]
[ROW][C]131[/C][C]23[/C][C]19.9378859920083[/C][C]3.06211400799173[/C][/ROW]
[ROW][C]132[/C][C]19[/C][C]21.5732170295688[/C][C]-2.57321702956885[/C][/ROW]
[ROW][C]133[/C][C]19[/C][C]21.1834971450390[/C][C]-2.18349714503904[/C][/ROW]
[ROW][C]134[/C][C]26[/C][C]23.2875493467651[/C][C]2.71245065323492[/C][/ROW]
[ROW][C]135[/C][C]19[/C][C]22.4872427258826[/C][C]-3.48724272588259[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]24.8723522021251[/C][C]-2.87235220212512[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]22.8344398790570[/C][C]-2.83443987905705[/C][/ROW]
[ROW][C]138[/C][C]21[/C][C]23.4077106878281[/C][C]-2.40771068782806[/C][/ROW]
[ROW][C]139[/C][C]21[/C][C]25.2379795171793[/C][C]-4.23797951717931[/C][/ROW]
[ROW][C]140[/C][C]28[/C][C]23.9302269612611[/C][C]4.06977303873889[/C][/ROW]
[ROW][C]141[/C][C]24[/C][C]21.4942597736537[/C][C]2.50574022634631[/C][/ROW]
[ROW][C]142[/C][C]29[/C][C]21.7641625404963[/C][C]7.23583745950366[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]21.8087141280025[/C][C]2.19128587199747[/C][/ROW]
[ROW][C]144[/C][C]25[/C][C]22.7861573783320[/C][C]2.21384262166796[/C][/ROW]
[ROW][C]145[/C][C]19[/C][C]14.6642338938153[/C][C]4.33576610618471[/C][/ROW]
[ROW][C]146[/C][C]23[/C][C]20.9155102228600[/C][C]2.08448977714004[/C][/ROW]
[ROW][C]147[/C][C]22[/C][C]22.8307942959363[/C][C]-0.830794295936259[/C][/ROW]
[ROW][C]148[/C][C]24[/C][C]20.0822944536113[/C][C]3.91770554638869[/C][/ROW]
[ROW][C]149[/C][C]19[/C][C]20.5856618559172[/C][C]-1.58566185591719[/C][/ROW]
[ROW][C]150[/C][C]21[/C][C]25.2376536173498[/C][C]-4.23765361734983[/C][/ROW]
[ROW][C]151[/C][C]18[/C][C]21.0030919447211[/C][C]-3.00309194472112[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]22.6399765132228[/C][C]1.36002348677723[/C][/ROW]
[ROW][C]153[/C][C]24[/C][C]23.6666314844636[/C][C]0.333368515536428[/C][/ROW]
[ROW][C]154[/C][C]23[/C][C]22.6461205829295[/C][C]0.353879417070497[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]24.8194716820622[/C][C]-0.819471682062188[/C][/ROW]
[ROW][C]156[/C][C]20[/C][C]21.2190315864365[/C][C]-1.21903158643651[/C][/ROW]
[ROW][C]157[/C][C]20[/C][C]21.3324076453930[/C][C]-1.33240764539297[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]16.0270566281677[/C][C]5.97294337183228[/C][/ROW]
[ROW][C]159[/C][C]18[/C][C]22.5811304858573[/C][C]-4.58113048585735[/C][/ROW]
[ROW][C]160[/C][C]14[/C][C]21.6767510419284[/C][C]-7.67675104192838[/C][/ROW]
[ROW][C]161[/C][C]29[/C][C]21.4237458739071[/C][C]7.57625412609288[/C][/ROW]
[ROW][C]162[/C][C]25[/C][C]21.0307597508793[/C][C]3.96924024912074[/C][/ROW]
[ROW][C]163[/C][C]20[/C][C]19.5619444525489[/C][C]0.438055547451104[/C][/ROW]
[ROW][C]164[/C][C]25[/C][C]21.5216561842221[/C][C]3.47834381577794[/C][/ROW]
[ROW][C]165[/C][C]21[/C][C]21.3327154548758[/C][C]-0.332715454875766[/C][/ROW]
[ROW][C]166[/C][C]21[/C][C]23.1232970491942[/C][C]-2.12329704919421[/C][/ROW]
[ROW][C]167[/C][C]13[/C][C]14.9617834989334[/C][C]-1.96178349893337[/C][/ROW]
[ROW][C]168[/C][C]23[/C][C]19.7535832619979[/C][C]3.24641673800206[/C][/ROW]
[ROW][C]169[/C][C]24[/C][C]24.1611919214367[/C][C]-0.161191921436717[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]21.7489376917492[/C][C]-5.74893769174915[/C][/ROW]
[ROW][C]171[/C][C]20[/C][C]20.8191020866479[/C][C]-0.819102086647907[/C][/ROW]
[ROW][C]172[/C][C]24[/C][C]24.3112616244492[/C][C]-0.311261624449218[/C][/ROW]
[ROW][C]173[/C][C]27[/C][C]22.5421061603906[/C][C]4.45789383960938[/C][/ROW]
[ROW][C]174[/C][C]27[/C][C]22.9436875491107[/C][C]4.05631245088932[/C][/ROW]
[ROW][C]175[/C][C]19[/C][C]20.8996468692792[/C][C]-1.89964686927921[/C][/ROW]
[ROW][C]176[/C][C]22[/C][C]23.3344280313776[/C][C]-1.33442803137760[/C][/ROW]
[ROW][C]177[/C][C]18[/C][C]17.3836463784110[/C][C]0.616353621588978[/C][/ROW]
[ROW][C]178[/C][C]24[/C][C]24.2907879529218[/C][C]-0.290787952921754[/C][/ROW]
[ROW][C]179[/C][C]20[/C][C]23.073566985346[/C][C]-3.073566985346[/C][/ROW]
[ROW][C]180[/C][C]20[/C][C]23.3474608443502[/C][C]-3.34746084435016[/C][/ROW]
[ROW][C]181[/C][C]27[/C][C]22.3481110760967[/C][C]4.6518889239033[/C][/ROW]
[ROW][C]182[/C][C]19[/C][C]22.6993152268611[/C][C]-3.69931522686106[/C][/ROW]
[ROW][C]183[/C][C]23[/C][C]23.7670757960041[/C][C]-0.767075796004081[/C][/ROW]
[ROW][C]184[/C][C]30[/C][C]27.1547488627135[/C][C]2.84525113728653[/C][/ROW]
[ROW][C]185[/C][C]22[/C][C]26.7075806352532[/C][C]-4.70758063525322[/C][/ROW]
[ROW][C]186[/C][C]23[/C][C]23.6901962057048[/C][C]-0.690196205704802[/C][/ROW]
[ROW][C]187[/C][C]22[/C][C]21.8367652157659[/C][C]0.163234784234105[/C][/ROW]
[ROW][C]188[/C][C]22[/C][C]18.9079040268737[/C][C]3.09209597312629[/C][/ROW]
[ROW][C]189[/C][C]23[/C][C]17.3126604075962[/C][C]5.68733959240384[/C][/ROW]
[ROW][C]190[/C][C]27[/C][C]21.361481538327[/C][C]5.63851846167302[/C][/ROW]
[ROW][C]191[/C][C]23[/C][C]18.7763423127515[/C][C]4.22365768724854[/C][/ROW]
[ROW][C]192[/C][C]18[/C][C]19.4388515278364[/C][C]-1.43885152783636[/C][/ROW]
[ROW][C]193[/C][C]24[/C][C]23.2488557166491[/C][C]0.751144283350882[/C][/ROW]
[ROW][C]194[/C][C]19[/C][C]17.7564019245008[/C][C]1.24359807549921[/C][/ROW]
[ROW][C]195[/C][C]22[/C][C]21.7483489306520[/C][C]0.251651069347983[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116237&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116237&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
12221.7483489306520.251651069347999
22320.22201632991752.77798367008253
32721.50696111252315.49303888747693
41917.33975701390981.66024298609017
52521.15478323926733.84521676073269
62520.05309636516444.94690363483561
72118.39494899069782.60505100930225
82220.34283019770921.65716980229084
92117.5626485745133.437351425487
102020.5463145682381-0.546314568238074
112119.87682316890781.12317683109217
122319.61883810882773.38116189117226
131918.48170312507090.518296874929094
142320.09209865743262.90790134256737
152712.249935082915614.7500649170844
165251.32842547362850.671574526371483
174647.2679266207769-1.26792662077694
185855.20367195013512.79632804986490
195452.88952507178511.11047492821487
202931.7945137604001-2.79451376040011
214345.2501061251123-2.25010612511227
224546.0413195112632-1.04131951126319
234647.7911635799841-1.79116357998408
242530.5607718821628-5.5607718821628
254747.3390821495466-0.339082149546559
264141.4355709015558-0.435570901555839
272932.5119555182890-3.51195551828896
284546.1674635115992-1.16746351159921
295448.90396764050745.09603235949262
302832.0169455117163-4.01694551171628
313738.5347587524705-1.53475875247047
325645.169638065605910.8303619343941
334339.72725014416813.27274985583187
343434.4515062192942-0.451506219294211
354241.95698982541780.0430101745821530
364641.24272601012164.75727398987844
372529.2203415414979-4.22034154149790
382529.5797919346738-4.57979193467378
392529.4100803347221-4.41008033472211
404846.78469911372381.21530088627617
412731.5384056125168-4.53840561251677
422832.2250573111248-4.22505731112479
432524.88011461191120.119885388088787
442629.6725517431804-3.67255174318038
455149.2584842814791.74151571852099
462930.5008827464202-1.50088274642018
472930.9439613347676-1.94396133476755
484337.5019790027165.49802099728398
494441.45364340624482.5463565937552
502527.5400787928293-2.54007879282926
515145.15118398808695.84881601191314
524241.46836076027280.531639239727157
532530.8665663784858-5.86656637848579
545146.3019559674154.69804403258495
554646.4480132259165-0.448013225916463
562923.90821008057265.09178991942736
5734.92725384568103-1.92725384568103
5814.94552151325091-3.94552151325091
5946.69832203934784-2.69832203934784
6058.92031191402827-3.92031191402827
6147.30864305454018-3.30864305454018
6245.61266300832699-1.61266300832699
6337.1595973397394-4.15959733973940
6457.33154021159766-2.33154021159766
6533.2494488429953-0.249448842995301
662633.0320054432439-7.03200544324394
673019.100544470833710.8994555291663
683543.4343832915794-8.43438329157936
692927.83506930938011.16493069061994
702525.3502114803408-0.350211480340851
712730.2148056939411-3.21480569394109
722423.92430343455960.0756965654403998
733531.31813594036663.6818640596334
743235.2974595728038-3.29745957280375
752431.4607351091177-7.46073510911774
763829.64394458351218.35605541648788
773634.33743572309301.66256427690696
782425.3284257175568-1.32842571755682
791818.9776754681178-0.97767546811778
803429.53712171325334.4628782867467
812324.8483120573274-1.84831205732739
823430.49128827520383.50871172479621
833229.84162667122582.15837332877420
842425.1111628203216-1.11116282032158
853427.95997245982876.04002754017131
863337.9094208330562-4.9094208330562
873314.371415616023018.6285843839770
880-7.105224731006127.10522473100612
8902.06938867088764-2.06938867088764
900-3.335225608443483.33522560844348
9100.204133599192590-0.204133599192590
920-2.405707713476912.40570771347691
930-0.3893288625536550.389328862553655
940-2.487619383049392.48761938304939
950-4.366306180840464.36630618084046
96015.1741635010604-15.1741635010604
9704.45505107693719-4.45505107693719
98015.1490327660078-15.1490327660078
9907.93488129258886-7.93488129258886
10001.23641341591376-1.23641341591376
10106.21966115192624-6.21966115192624
1020-4.944806718055284.94480671805528
10305.72321924406316-5.72321924406316
10403.75774906424738-3.75774906424738
105012.3186109965140-12.3186109965140
1062-2.751584513211124.75158451321112
10720.7423044884133371.25769551158666
108214.9384006508831-12.9384006508831
1092225.4270371949954-3.42703719499536
1101928.3545858820960-9.35458588209604
1111922.5924530319061-3.5924530319061
1122422.27866517142771.7213348285723
1132220.23786853279531.76213146720469
1142621.50118849691754.49881150308245
1152317.34661092909715.6533890709029
1162123.0021649890824-2.00216498908235
1171618.3557139769996-2.35571397699964
1182123.1295240715956-2.12952407159562
1191821.6059475930155-3.60594759301551
1202021.7168958012726-1.71689580127257
1212419.56400640522144.43599359477857
1222424.038289835222-0.0382898352219761
1232321.53326825692191.46673174307814
1242322.84485008404930.155149915950730
1252222.7234868752787-0.723486875278708
1261718.9176817112553-1.91768171125532
1272523.14491553681641.8550844631836
1282518.10674000607726.89325999392276
1292326.0016715322332-3.00167153223319
1302722.67656429620094.32343570379908
1312319.93788599200833.06211400799173
1321921.5732170295688-2.57321702956885
1331921.1834971450390-2.18349714503904
1342623.28754934676512.71245065323492
1351922.4872427258826-3.48724272588259
1362224.8723522021251-2.87235220212512
1372022.8344398790570-2.83443987905705
1382123.4077106878281-2.40771068782806
1392125.2379795171793-4.23797951717931
1402823.93022696126114.06977303873889
1412421.49425977365372.50574022634631
1422921.76416254049637.23583745950366
1432421.80871412800252.19128587199747
1442522.78615737833202.21384262166796
1451914.66423389381534.33576610618471
1462320.91551022286002.08448977714004
1472222.8307942959363-0.830794295936259
1482420.08229445361133.91770554638869
1491920.5856618559172-1.58566185591719
1502125.2376536173498-4.23765361734983
1511821.0030919447211-3.00309194472112
1522422.63997651322281.36002348677723
1532423.66663148446360.333368515536428
1542322.64612058292950.353879417070497
1552424.8194716820622-0.819471682062188
1562021.2190315864365-1.21903158643651
1572021.3324076453930-1.33240764539297
1582216.02705662816775.97294337183228
1591822.5811304858573-4.58113048585735
1601421.6767510419284-7.67675104192838
1612921.42374587390717.57625412609288
1622521.03075975087933.96924024912074
1632019.56194445254890.438055547451104
1642521.52165618422213.47834381577794
1652121.3327154548758-0.332715454875766
1662123.1232970491942-2.12329704919421
1671314.9617834989334-1.96178349893337
1682319.75358326199793.24641673800206
1692424.1611919214367-0.161191921436717
1701621.7489376917492-5.74893769174915
1712020.8191020866479-0.819102086647907
1722424.3112616244492-0.311261624449218
1732722.54210616039064.45789383960938
1742722.94368754911074.05631245088932
1751920.8996468692792-1.89964686927921
1762223.3344280313776-1.33442803137760
1771817.38364637841100.616353621588978
1782424.2907879529218-0.290787952921754
1792023.073566985346-3.073566985346
1802023.3474608443502-3.34746084435016
1812722.34811107609674.6518889239033
1821922.6993152268611-3.69931522686106
1832323.7670757960041-0.767075796004081
1843027.15474886271352.84525113728653
1852226.7075806352532-4.70758063525322
1862323.6901962057048-0.690196205704802
1872221.83676521576590.163234784234105
1882218.90790402687373.09209597312629
1892317.31266040759625.68733959240384
1902721.3614815383275.63851846167302
1912318.77634231275154.22365768724854
1921819.4388515278364-1.43885152783636
1932423.24885571664910.751144283350882
1941917.75640192450081.24359807549921
1952221.74834893065200.251651069347983






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.2153625825938490.4307251651876990.78463741740615
160.1003564989052360.2007129978104720.899643501094764
170.07219892048155640.1443978409631130.927801079518444
180.172813490408340.345626980816680.82718650959166
190.1154990375490600.2309980750981190.88450096245094
200.3683625130480280.7367250260960570.631637486951972
210.4089627152369710.8179254304739430.591037284763029
220.3266356060874030.6532712121748060.673364393912597
230.2551138593162520.5102277186325040.744886140683748
240.3843674115520110.7687348231040230.615632588447989
250.3364248783237770.6728497566475550.663575121676223
260.2629092348188690.5258184696377380.737090765181131
270.2056358348825430.4112716697650870.794364165117457
280.1577388370349840.3154776740699680.842261162965016
290.1528355888417290.3056711776834590.847164411158271
300.1626015846102640.3252031692205280.837398415389736
310.1913311218346520.3826622436693040.808668878165348
320.2026132486535970.4052264973071950.797386751346403
330.1616121966847640.3232243933695280.838387803315236
340.1502003795597210.3004007591194420.84979962044028
350.1840245226216230.3680490452432450.815975477378378
360.1469387762609580.2938775525219170.853061223739042
370.1792416384906630.3584832769813250.820758361509337
380.1973412849538680.3946825699077360.802658715046132
390.2104067839390340.4208135678780680.789593216060966
400.1702706396797870.3405412793595740.829729360320213
410.1687783167082470.3375566334164930.831221683291753
420.1930801589957170.3861603179914340.806919841004283
430.1588912967950400.3177825935900810.84110870320496
440.1369695887632470.2739391775264930.863030411236753
450.1099869833521100.2199739667042210.89001301664789
460.09393652512204180.1878730502440840.906063474877958
470.08856705609755040.1771341121951010.91143294390245
480.0791480760278950.158296152055790.920851923972105
490.06405397468012280.1281079493602460.935946025319877
500.05673593549582670.1134718709916530.943264064504173
510.05213593647431990.1042718729486400.94786406352568
520.03970307482883480.07940614965766970.960296925171165
530.03533873242446440.07067746484892880.964661267575536
540.03005893522300900.06011787044601790.96994106477699
550.02267220661313270.04534441322626530.977327793386867
560.02074214349633370.04148428699266750.979257856503666
570.02467554777012170.04935109554024350.975324452229878
580.02966791359630440.05933582719260880.970332086403696
590.02367183177950200.04734366355900410.976328168220498
600.01785200827970170.03570401655940330.982147991720298
610.01403087661203580.02806175322407160.985969123387964
620.01097909144308460.02195818288616930.989020908556915
630.01216359342867080.02432718685734160.98783640657133
640.01107577132504860.02215154265009710.988924228674951
650.009420328834555190.01884065766911040.990579671165445
660.00805074796067820.01610149592135640.991949252039322
670.01436633405506850.0287326681101370.985633665944932
680.01107194213019550.02214388426039100.988928057869805
690.008152477646381720.01630495529276340.991847522353618
700.006078916777469310.01215783355493860.99392108322253
710.004923002440051960.009846004880103930.995076997559948
720.004463376459122490.008926752918244980.995536623540878
730.005462032018239640.01092406403647930.99453796798176
740.00498632831683490.00997265663366980.995013671683165
750.004155953080871960.008311906161743910.995844046919128
760.004335929329119860.008671858658239720.99566407067088
770.004369365003637240.008738730007274480.995630634996363
780.003176460550504050.00635292110100810.996823539449496
790.004062066309886130.008124132619772250.995937933690114
800.005836325904870320.01167265180974060.99416367409513
810.005373294512283760.01074658902456750.994626705487716
820.004211267795889910.008422535591779810.99578873220411
830.003176603677041650.00635320735408330.996823396322958
840.002775725014106270.005551450028212550.997224274985894
850.002323967412086540.004647934824173080.997676032587913
860.001849019018760310.003698038037520620.99815098098124
870.7371381168014990.5257237663970020.262861883198501
880.8962338525226320.2075322949547370.103766147477368
890.9754328066389620.04913438672207570.0245671933610378
900.9721380830515780.05572383389684470.0278619169484223
910.9761659821057440.04766803578851150.0238340178942557
920.9710868771205150.05782624575896910.0289131228794845
930.9678040745626530.06439185087469310.0321959254373465
940.960777940490260.07844411901947930.0392220595097397
950.951427443310610.09714511337878140.0485725566893907
960.997270332208160.00545933558368180.0027296677918409
970.9976440213515740.004711957296851810.00235597864842591
980.9996617391581670.0006765216836656540.000338260841832827
990.999679041530820.000641916938358670.000320958469179335
1000.9995341226781690.0009317546436625720.000465877321831286
1010.9998641831504620.000271633699076310.000135816849538155
1020.9999941105219671.17789560667192e-055.88947803335959e-06
1030.9999994913383011.01732339758802e-065.08661698794012e-07
1040.9999992859514681.42809706335037e-067.14048531675183e-07
1050.9999993739263691.25214726274043e-066.26073631370214e-07
1060.999999425061031.14987793765528e-065.7493896882764e-07
1070.9999989913256062.01734878796480e-061.00867439398240e-06
1080.9999991355918221.72881635581287e-068.64408177906435e-07
1090.9999985676662432.86466751377055e-061.43233375688527e-06
1100.999999045079881.90984024179249e-069.54920120896244e-07
1110.9999985519739132.89605217371246e-061.44802608685623e-06
1120.9999981099259213.78014815759783e-061.89007407879891e-06
1130.9999971277358965.7445282076537e-062.87226410382685e-06
1140.9999977907044834.41859103322388e-062.20929551661194e-06
1150.9999979216406254.15671875069295e-062.07835937534647e-06
1160.9999966143399436.77132011428564e-063.38566005714282e-06
1170.9999956520969568.69580608833472e-064.34790304416736e-06
1180.9999929446370031.41107259943242e-057.05536299716211e-06
1190.9999916726238851.66547522292634e-058.32737611463171e-06
1200.9999915974907651.68050184700253e-058.40250923501267e-06
1210.9999905993179251.88013641510149e-059.40068207550743e-06
1220.99998410141683.17971664004673e-051.58985832002336e-05
1230.9999750282574264.99434851488382e-052.49717425744191e-05
1240.999960099831467.98003370784096e-053.99001685392048e-05
1250.9999350242539630.0001299514920739946.49757460369972e-05
1260.9999305122653270.0001389754693458706.94877346729348e-05
1270.99989626405130.0002074718974000760.000103735948700038
1280.9999115562476760.0001768875046485538.84437523242764e-05
1290.9998771062546770.0002457874906453890.000122893745322694
1300.9998564921827060.0002870156345871060.000143507817293553
1310.9998001308551190.000399738289762560.00019986914488128
1320.9997868160767670.0004263678464660640.000213183923233032
1330.9996871333752830.0006257332494332820.000312866624716641
1340.9996356596016370.0007286807967256570.000364340398362829
1350.99952477435650.0009504512870001530.000475225643500076
1360.9993619370994120.001276125801176580.000638062900588291
1370.9992562056138280.001487588772344920.000743794386172461
1380.9989282882296040.002143423540792050.00107171177039602
1390.9988412849859260.002317430028148730.00115871501407437
1400.9988770383202830.002245923359434910.00112296167971746
1410.9984934559875450.003013088024909640.00150654401245482
1420.99945345807880.001093083842400350.000546541921200173
1430.9992154112804110.001569177439177920.000784588719588961
1440.9991244832942620.001751033411475610.000875516705737805
1450.9987342343760140.002531531247971160.00126576562398558
1460.9982009533172730.003598093365453270.00179904668272663
1470.9974239648767030.005152070246593770.00257603512329689
1480.9970749240805490.005850151838902870.00292507591945143
1490.995575687004230.008848625991541430.00442431299577071
1500.9942883655255020.01142326894899560.0057116344744978
1510.9923012828016440.01539743439671240.00769871719835622
1520.9895072099135680.02098558017286400.0104927900864320
1530.9845275026364020.03094499472719530.0154724973635976
1540.9773980307383070.04520393852338700.0226019692616935
1550.9676481322504610.06470373549907730.0323518677495387
1560.964041140389910.0719177192201820.035958859610091
1570.9501418576534750.0997162846930490.0498581423465245
1580.9429985918084530.1140028163830930.0570014081915467
1590.9447675557633390.1104648884733230.0552324442366614
1600.9811072765434470.03778544691310610.0188927234565530
1610.9874432393432920.02511352131341530.0125567606567076
1620.9846602915805930.03067941683881350.0153397084194067
1630.9784472975521060.04310540489578730.0215527024478936
1640.971788632252880.05642273549424140.0282113677471207
1650.956793026444410.08641394711117840.0432069735555892
1660.9379820036801660.1240359926396670.0620179963198336
1670.9257848249273360.1484303501453280.0742151750726642
1680.8946389020542410.2107221958915180.105361097945759
1690.8531418738545480.2937162522909040.146858126145452
1700.8869129421329820.2261741157340360.113087057867018
1710.8538955529757530.2922088940484930.146104447024247
1720.79539102905450.4092179418910.2046089709455
1730.7842182040996050.431563591800790.215781795900395
1740.8076567732052630.3846864535894740.192343226794737
1750.7382128977228010.5235742045543980.261787102277199
1760.6364457756008330.7271084487983340.363554224399167
1770.5186558596888630.9626882806222730.481344140311137
1780.3914807257270140.7829614514540280.608519274272986
1790.3259616870999640.6519233741999280.674038312900036
1800.2304349755991650.460869951198330.769565024400835

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.215362582593849 & 0.430725165187699 & 0.78463741740615 \tabularnewline
16 & 0.100356498905236 & 0.200712997810472 & 0.899643501094764 \tabularnewline
17 & 0.0721989204815564 & 0.144397840963113 & 0.927801079518444 \tabularnewline
18 & 0.17281349040834 & 0.34562698081668 & 0.82718650959166 \tabularnewline
19 & 0.115499037549060 & 0.230998075098119 & 0.88450096245094 \tabularnewline
20 & 0.368362513048028 & 0.736725026096057 & 0.631637486951972 \tabularnewline
21 & 0.408962715236971 & 0.817925430473943 & 0.591037284763029 \tabularnewline
22 & 0.326635606087403 & 0.653271212174806 & 0.673364393912597 \tabularnewline
23 & 0.255113859316252 & 0.510227718632504 & 0.744886140683748 \tabularnewline
24 & 0.384367411552011 & 0.768734823104023 & 0.615632588447989 \tabularnewline
25 & 0.336424878323777 & 0.672849756647555 & 0.663575121676223 \tabularnewline
26 & 0.262909234818869 & 0.525818469637738 & 0.737090765181131 \tabularnewline
27 & 0.205635834882543 & 0.411271669765087 & 0.794364165117457 \tabularnewline
28 & 0.157738837034984 & 0.315477674069968 & 0.842261162965016 \tabularnewline
29 & 0.152835588841729 & 0.305671177683459 & 0.847164411158271 \tabularnewline
30 & 0.162601584610264 & 0.325203169220528 & 0.837398415389736 \tabularnewline
31 & 0.191331121834652 & 0.382662243669304 & 0.808668878165348 \tabularnewline
32 & 0.202613248653597 & 0.405226497307195 & 0.797386751346403 \tabularnewline
33 & 0.161612196684764 & 0.323224393369528 & 0.838387803315236 \tabularnewline
34 & 0.150200379559721 & 0.300400759119442 & 0.84979962044028 \tabularnewline
35 & 0.184024522621623 & 0.368049045243245 & 0.815975477378378 \tabularnewline
36 & 0.146938776260958 & 0.293877552521917 & 0.853061223739042 \tabularnewline
37 & 0.179241638490663 & 0.358483276981325 & 0.820758361509337 \tabularnewline
38 & 0.197341284953868 & 0.394682569907736 & 0.802658715046132 \tabularnewline
39 & 0.210406783939034 & 0.420813567878068 & 0.789593216060966 \tabularnewline
40 & 0.170270639679787 & 0.340541279359574 & 0.829729360320213 \tabularnewline
41 & 0.168778316708247 & 0.337556633416493 & 0.831221683291753 \tabularnewline
42 & 0.193080158995717 & 0.386160317991434 & 0.806919841004283 \tabularnewline
43 & 0.158891296795040 & 0.317782593590081 & 0.84110870320496 \tabularnewline
44 & 0.136969588763247 & 0.273939177526493 & 0.863030411236753 \tabularnewline
45 & 0.109986983352110 & 0.219973966704221 & 0.89001301664789 \tabularnewline
46 & 0.0939365251220418 & 0.187873050244084 & 0.906063474877958 \tabularnewline
47 & 0.0885670560975504 & 0.177134112195101 & 0.91143294390245 \tabularnewline
48 & 0.079148076027895 & 0.15829615205579 & 0.920851923972105 \tabularnewline
49 & 0.0640539746801228 & 0.128107949360246 & 0.935946025319877 \tabularnewline
50 & 0.0567359354958267 & 0.113471870991653 & 0.943264064504173 \tabularnewline
51 & 0.0521359364743199 & 0.104271872948640 & 0.94786406352568 \tabularnewline
52 & 0.0397030748288348 & 0.0794061496576697 & 0.960296925171165 \tabularnewline
53 & 0.0353387324244644 & 0.0706774648489288 & 0.964661267575536 \tabularnewline
54 & 0.0300589352230090 & 0.0601178704460179 & 0.96994106477699 \tabularnewline
55 & 0.0226722066131327 & 0.0453444132262653 & 0.977327793386867 \tabularnewline
56 & 0.0207421434963337 & 0.0414842869926675 & 0.979257856503666 \tabularnewline
57 & 0.0246755477701217 & 0.0493510955402435 & 0.975324452229878 \tabularnewline
58 & 0.0296679135963044 & 0.0593358271926088 & 0.970332086403696 \tabularnewline
59 & 0.0236718317795020 & 0.0473436635590041 & 0.976328168220498 \tabularnewline
60 & 0.0178520082797017 & 0.0357040165594033 & 0.982147991720298 \tabularnewline
61 & 0.0140308766120358 & 0.0280617532240716 & 0.985969123387964 \tabularnewline
62 & 0.0109790914430846 & 0.0219581828861693 & 0.989020908556915 \tabularnewline
63 & 0.0121635934286708 & 0.0243271868573416 & 0.98783640657133 \tabularnewline
64 & 0.0110757713250486 & 0.0221515426500971 & 0.988924228674951 \tabularnewline
65 & 0.00942032883455519 & 0.0188406576691104 & 0.990579671165445 \tabularnewline
66 & 0.0080507479606782 & 0.0161014959213564 & 0.991949252039322 \tabularnewline
67 & 0.0143663340550685 & 0.028732668110137 & 0.985633665944932 \tabularnewline
68 & 0.0110719421301955 & 0.0221438842603910 & 0.988928057869805 \tabularnewline
69 & 0.00815247764638172 & 0.0163049552927634 & 0.991847522353618 \tabularnewline
70 & 0.00607891677746931 & 0.0121578335549386 & 0.99392108322253 \tabularnewline
71 & 0.00492300244005196 & 0.00984600488010393 & 0.995076997559948 \tabularnewline
72 & 0.00446337645912249 & 0.00892675291824498 & 0.995536623540878 \tabularnewline
73 & 0.00546203201823964 & 0.0109240640364793 & 0.99453796798176 \tabularnewline
74 & 0.0049863283168349 & 0.0099726566336698 & 0.995013671683165 \tabularnewline
75 & 0.00415595308087196 & 0.00831190616174391 & 0.995844046919128 \tabularnewline
76 & 0.00433592932911986 & 0.00867185865823972 & 0.99566407067088 \tabularnewline
77 & 0.00436936500363724 & 0.00873873000727448 & 0.995630634996363 \tabularnewline
78 & 0.00317646055050405 & 0.0063529211010081 & 0.996823539449496 \tabularnewline
79 & 0.00406206630988613 & 0.00812413261977225 & 0.995937933690114 \tabularnewline
80 & 0.00583632590487032 & 0.0116726518097406 & 0.99416367409513 \tabularnewline
81 & 0.00537329451228376 & 0.0107465890245675 & 0.994626705487716 \tabularnewline
82 & 0.00421126779588991 & 0.00842253559177981 & 0.99578873220411 \tabularnewline
83 & 0.00317660367704165 & 0.0063532073540833 & 0.996823396322958 \tabularnewline
84 & 0.00277572501410627 & 0.00555145002821255 & 0.997224274985894 \tabularnewline
85 & 0.00232396741208654 & 0.00464793482417308 & 0.997676032587913 \tabularnewline
86 & 0.00184901901876031 & 0.00369803803752062 & 0.99815098098124 \tabularnewline
87 & 0.737138116801499 & 0.525723766397002 & 0.262861883198501 \tabularnewline
88 & 0.896233852522632 & 0.207532294954737 & 0.103766147477368 \tabularnewline
89 & 0.975432806638962 & 0.0491343867220757 & 0.0245671933610378 \tabularnewline
90 & 0.972138083051578 & 0.0557238338968447 & 0.0278619169484223 \tabularnewline
91 & 0.976165982105744 & 0.0476680357885115 & 0.0238340178942557 \tabularnewline
92 & 0.971086877120515 & 0.0578262457589691 & 0.0289131228794845 \tabularnewline
93 & 0.967804074562653 & 0.0643918508746931 & 0.0321959254373465 \tabularnewline
94 & 0.96077794049026 & 0.0784441190194793 & 0.0392220595097397 \tabularnewline
95 & 0.95142744331061 & 0.0971451133787814 & 0.0485725566893907 \tabularnewline
96 & 0.99727033220816 & 0.0054593355836818 & 0.0027296677918409 \tabularnewline
97 & 0.997644021351574 & 0.00471195729685181 & 0.00235597864842591 \tabularnewline
98 & 0.999661739158167 & 0.000676521683665654 & 0.000338260841832827 \tabularnewline
99 & 0.99967904153082 & 0.00064191693835867 & 0.000320958469179335 \tabularnewline
100 & 0.999534122678169 & 0.000931754643662572 & 0.000465877321831286 \tabularnewline
101 & 0.999864183150462 & 0.00027163369907631 & 0.000135816849538155 \tabularnewline
102 & 0.999994110521967 & 1.17789560667192e-05 & 5.88947803335959e-06 \tabularnewline
103 & 0.999999491338301 & 1.01732339758802e-06 & 5.08661698794012e-07 \tabularnewline
104 & 0.999999285951468 & 1.42809706335037e-06 & 7.14048531675183e-07 \tabularnewline
105 & 0.999999373926369 & 1.25214726274043e-06 & 6.26073631370214e-07 \tabularnewline
106 & 0.99999942506103 & 1.14987793765528e-06 & 5.7493896882764e-07 \tabularnewline
107 & 0.999998991325606 & 2.01734878796480e-06 & 1.00867439398240e-06 \tabularnewline
108 & 0.999999135591822 & 1.72881635581287e-06 & 8.64408177906435e-07 \tabularnewline
109 & 0.999998567666243 & 2.86466751377055e-06 & 1.43233375688527e-06 \tabularnewline
110 & 0.99999904507988 & 1.90984024179249e-06 & 9.54920120896244e-07 \tabularnewline
111 & 0.999998551973913 & 2.89605217371246e-06 & 1.44802608685623e-06 \tabularnewline
112 & 0.999998109925921 & 3.78014815759783e-06 & 1.89007407879891e-06 \tabularnewline
113 & 0.999997127735896 & 5.7445282076537e-06 & 2.87226410382685e-06 \tabularnewline
114 & 0.999997790704483 & 4.41859103322388e-06 & 2.20929551661194e-06 \tabularnewline
115 & 0.999997921640625 & 4.15671875069295e-06 & 2.07835937534647e-06 \tabularnewline
116 & 0.999996614339943 & 6.77132011428564e-06 & 3.38566005714282e-06 \tabularnewline
117 & 0.999995652096956 & 8.69580608833472e-06 & 4.34790304416736e-06 \tabularnewline
118 & 0.999992944637003 & 1.41107259943242e-05 & 7.05536299716211e-06 \tabularnewline
119 & 0.999991672623885 & 1.66547522292634e-05 & 8.32737611463171e-06 \tabularnewline
120 & 0.999991597490765 & 1.68050184700253e-05 & 8.40250923501267e-06 \tabularnewline
121 & 0.999990599317925 & 1.88013641510149e-05 & 9.40068207550743e-06 \tabularnewline
122 & 0.9999841014168 & 3.17971664004673e-05 & 1.58985832002336e-05 \tabularnewline
123 & 0.999975028257426 & 4.99434851488382e-05 & 2.49717425744191e-05 \tabularnewline
124 & 0.99996009983146 & 7.98003370784096e-05 & 3.99001685392048e-05 \tabularnewline
125 & 0.999935024253963 & 0.000129951492073994 & 6.49757460369972e-05 \tabularnewline
126 & 0.999930512265327 & 0.000138975469345870 & 6.94877346729348e-05 \tabularnewline
127 & 0.9998962640513 & 0.000207471897400076 & 0.000103735948700038 \tabularnewline
128 & 0.999911556247676 & 0.000176887504648553 & 8.84437523242764e-05 \tabularnewline
129 & 0.999877106254677 & 0.000245787490645389 & 0.000122893745322694 \tabularnewline
130 & 0.999856492182706 & 0.000287015634587106 & 0.000143507817293553 \tabularnewline
131 & 0.999800130855119 & 0.00039973828976256 & 0.00019986914488128 \tabularnewline
132 & 0.999786816076767 & 0.000426367846466064 & 0.000213183923233032 \tabularnewline
133 & 0.999687133375283 & 0.000625733249433282 & 0.000312866624716641 \tabularnewline
134 & 0.999635659601637 & 0.000728680796725657 & 0.000364340398362829 \tabularnewline
135 & 0.9995247743565 & 0.000950451287000153 & 0.000475225643500076 \tabularnewline
136 & 0.999361937099412 & 0.00127612580117658 & 0.000638062900588291 \tabularnewline
137 & 0.999256205613828 & 0.00148758877234492 & 0.000743794386172461 \tabularnewline
138 & 0.998928288229604 & 0.00214342354079205 & 0.00107171177039602 \tabularnewline
139 & 0.998841284985926 & 0.00231743002814873 & 0.00115871501407437 \tabularnewline
140 & 0.998877038320283 & 0.00224592335943491 & 0.00112296167971746 \tabularnewline
141 & 0.998493455987545 & 0.00301308802490964 & 0.00150654401245482 \tabularnewline
142 & 0.9994534580788 & 0.00109308384240035 & 0.000546541921200173 \tabularnewline
143 & 0.999215411280411 & 0.00156917743917792 & 0.000784588719588961 \tabularnewline
144 & 0.999124483294262 & 0.00175103341147561 & 0.000875516705737805 \tabularnewline
145 & 0.998734234376014 & 0.00253153124797116 & 0.00126576562398558 \tabularnewline
146 & 0.998200953317273 & 0.00359809336545327 & 0.00179904668272663 \tabularnewline
147 & 0.997423964876703 & 0.00515207024659377 & 0.00257603512329689 \tabularnewline
148 & 0.997074924080549 & 0.00585015183890287 & 0.00292507591945143 \tabularnewline
149 & 0.99557568700423 & 0.00884862599154143 & 0.00442431299577071 \tabularnewline
150 & 0.994288365525502 & 0.0114232689489956 & 0.0057116344744978 \tabularnewline
151 & 0.992301282801644 & 0.0153974343967124 & 0.00769871719835622 \tabularnewline
152 & 0.989507209913568 & 0.0209855801728640 & 0.0104927900864320 \tabularnewline
153 & 0.984527502636402 & 0.0309449947271953 & 0.0154724973635976 \tabularnewline
154 & 0.977398030738307 & 0.0452039385233870 & 0.0226019692616935 \tabularnewline
155 & 0.967648132250461 & 0.0647037354990773 & 0.0323518677495387 \tabularnewline
156 & 0.96404114038991 & 0.071917719220182 & 0.035958859610091 \tabularnewline
157 & 0.950141857653475 & 0.099716284693049 & 0.0498581423465245 \tabularnewline
158 & 0.942998591808453 & 0.114002816383093 & 0.0570014081915467 \tabularnewline
159 & 0.944767555763339 & 0.110464888473323 & 0.0552324442366614 \tabularnewline
160 & 0.981107276543447 & 0.0377854469131061 & 0.0188927234565530 \tabularnewline
161 & 0.987443239343292 & 0.0251135213134153 & 0.0125567606567076 \tabularnewline
162 & 0.984660291580593 & 0.0306794168388135 & 0.0153397084194067 \tabularnewline
163 & 0.978447297552106 & 0.0431054048957873 & 0.0215527024478936 \tabularnewline
164 & 0.97178863225288 & 0.0564227354942414 & 0.0282113677471207 \tabularnewline
165 & 0.95679302644441 & 0.0864139471111784 & 0.0432069735555892 \tabularnewline
166 & 0.937982003680166 & 0.124035992639667 & 0.0620179963198336 \tabularnewline
167 & 0.925784824927336 & 0.148430350145328 & 0.0742151750726642 \tabularnewline
168 & 0.894638902054241 & 0.210722195891518 & 0.105361097945759 \tabularnewline
169 & 0.853141873854548 & 0.293716252290904 & 0.146858126145452 \tabularnewline
170 & 0.886912942132982 & 0.226174115734036 & 0.113087057867018 \tabularnewline
171 & 0.853895552975753 & 0.292208894048493 & 0.146104447024247 \tabularnewline
172 & 0.7953910290545 & 0.409217941891 & 0.2046089709455 \tabularnewline
173 & 0.784218204099605 & 0.43156359180079 & 0.215781795900395 \tabularnewline
174 & 0.807656773205263 & 0.384686453589474 & 0.192343226794737 \tabularnewline
175 & 0.738212897722801 & 0.523574204554398 & 0.261787102277199 \tabularnewline
176 & 0.636445775600833 & 0.727108448798334 & 0.363554224399167 \tabularnewline
177 & 0.518655859688863 & 0.962688280622273 & 0.481344140311137 \tabularnewline
178 & 0.391480725727014 & 0.782961451454028 & 0.608519274272986 \tabularnewline
179 & 0.325961687099964 & 0.651923374199928 & 0.674038312900036 \tabularnewline
180 & 0.230434975599165 & 0.46086995119833 & 0.769565024400835 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116237&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]0.215362582593849[/C][C]0.430725165187699[/C][C]0.78463741740615[/C][/ROW]
[ROW][C]16[/C][C]0.100356498905236[/C][C]0.200712997810472[/C][C]0.899643501094764[/C][/ROW]
[ROW][C]17[/C][C]0.0721989204815564[/C][C]0.144397840963113[/C][C]0.927801079518444[/C][/ROW]
[ROW][C]18[/C][C]0.17281349040834[/C][C]0.34562698081668[/C][C]0.82718650959166[/C][/ROW]
[ROW][C]19[/C][C]0.115499037549060[/C][C]0.230998075098119[/C][C]0.88450096245094[/C][/ROW]
[ROW][C]20[/C][C]0.368362513048028[/C][C]0.736725026096057[/C][C]0.631637486951972[/C][/ROW]
[ROW][C]21[/C][C]0.408962715236971[/C][C]0.817925430473943[/C][C]0.591037284763029[/C][/ROW]
[ROW][C]22[/C][C]0.326635606087403[/C][C]0.653271212174806[/C][C]0.673364393912597[/C][/ROW]
[ROW][C]23[/C][C]0.255113859316252[/C][C]0.510227718632504[/C][C]0.744886140683748[/C][/ROW]
[ROW][C]24[/C][C]0.384367411552011[/C][C]0.768734823104023[/C][C]0.615632588447989[/C][/ROW]
[ROW][C]25[/C][C]0.336424878323777[/C][C]0.672849756647555[/C][C]0.663575121676223[/C][/ROW]
[ROW][C]26[/C][C]0.262909234818869[/C][C]0.525818469637738[/C][C]0.737090765181131[/C][/ROW]
[ROW][C]27[/C][C]0.205635834882543[/C][C]0.411271669765087[/C][C]0.794364165117457[/C][/ROW]
[ROW][C]28[/C][C]0.157738837034984[/C][C]0.315477674069968[/C][C]0.842261162965016[/C][/ROW]
[ROW][C]29[/C][C]0.152835588841729[/C][C]0.305671177683459[/C][C]0.847164411158271[/C][/ROW]
[ROW][C]30[/C][C]0.162601584610264[/C][C]0.325203169220528[/C][C]0.837398415389736[/C][/ROW]
[ROW][C]31[/C][C]0.191331121834652[/C][C]0.382662243669304[/C][C]0.808668878165348[/C][/ROW]
[ROW][C]32[/C][C]0.202613248653597[/C][C]0.405226497307195[/C][C]0.797386751346403[/C][/ROW]
[ROW][C]33[/C][C]0.161612196684764[/C][C]0.323224393369528[/C][C]0.838387803315236[/C][/ROW]
[ROW][C]34[/C][C]0.150200379559721[/C][C]0.300400759119442[/C][C]0.84979962044028[/C][/ROW]
[ROW][C]35[/C][C]0.184024522621623[/C][C]0.368049045243245[/C][C]0.815975477378378[/C][/ROW]
[ROW][C]36[/C][C]0.146938776260958[/C][C]0.293877552521917[/C][C]0.853061223739042[/C][/ROW]
[ROW][C]37[/C][C]0.179241638490663[/C][C]0.358483276981325[/C][C]0.820758361509337[/C][/ROW]
[ROW][C]38[/C][C]0.197341284953868[/C][C]0.394682569907736[/C][C]0.802658715046132[/C][/ROW]
[ROW][C]39[/C][C]0.210406783939034[/C][C]0.420813567878068[/C][C]0.789593216060966[/C][/ROW]
[ROW][C]40[/C][C]0.170270639679787[/C][C]0.340541279359574[/C][C]0.829729360320213[/C][/ROW]
[ROW][C]41[/C][C]0.168778316708247[/C][C]0.337556633416493[/C][C]0.831221683291753[/C][/ROW]
[ROW][C]42[/C][C]0.193080158995717[/C][C]0.386160317991434[/C][C]0.806919841004283[/C][/ROW]
[ROW][C]43[/C][C]0.158891296795040[/C][C]0.317782593590081[/C][C]0.84110870320496[/C][/ROW]
[ROW][C]44[/C][C]0.136969588763247[/C][C]0.273939177526493[/C][C]0.863030411236753[/C][/ROW]
[ROW][C]45[/C][C]0.109986983352110[/C][C]0.219973966704221[/C][C]0.89001301664789[/C][/ROW]
[ROW][C]46[/C][C]0.0939365251220418[/C][C]0.187873050244084[/C][C]0.906063474877958[/C][/ROW]
[ROW][C]47[/C][C]0.0885670560975504[/C][C]0.177134112195101[/C][C]0.91143294390245[/C][/ROW]
[ROW][C]48[/C][C]0.079148076027895[/C][C]0.15829615205579[/C][C]0.920851923972105[/C][/ROW]
[ROW][C]49[/C][C]0.0640539746801228[/C][C]0.128107949360246[/C][C]0.935946025319877[/C][/ROW]
[ROW][C]50[/C][C]0.0567359354958267[/C][C]0.113471870991653[/C][C]0.943264064504173[/C][/ROW]
[ROW][C]51[/C][C]0.0521359364743199[/C][C]0.104271872948640[/C][C]0.94786406352568[/C][/ROW]
[ROW][C]52[/C][C]0.0397030748288348[/C][C]0.0794061496576697[/C][C]0.960296925171165[/C][/ROW]
[ROW][C]53[/C][C]0.0353387324244644[/C][C]0.0706774648489288[/C][C]0.964661267575536[/C][/ROW]
[ROW][C]54[/C][C]0.0300589352230090[/C][C]0.0601178704460179[/C][C]0.96994106477699[/C][/ROW]
[ROW][C]55[/C][C]0.0226722066131327[/C][C]0.0453444132262653[/C][C]0.977327793386867[/C][/ROW]
[ROW][C]56[/C][C]0.0207421434963337[/C][C]0.0414842869926675[/C][C]0.979257856503666[/C][/ROW]
[ROW][C]57[/C][C]0.0246755477701217[/C][C]0.0493510955402435[/C][C]0.975324452229878[/C][/ROW]
[ROW][C]58[/C][C]0.0296679135963044[/C][C]0.0593358271926088[/C][C]0.970332086403696[/C][/ROW]
[ROW][C]59[/C][C]0.0236718317795020[/C][C]0.0473436635590041[/C][C]0.976328168220498[/C][/ROW]
[ROW][C]60[/C][C]0.0178520082797017[/C][C]0.0357040165594033[/C][C]0.982147991720298[/C][/ROW]
[ROW][C]61[/C][C]0.0140308766120358[/C][C]0.0280617532240716[/C][C]0.985969123387964[/C][/ROW]
[ROW][C]62[/C][C]0.0109790914430846[/C][C]0.0219581828861693[/C][C]0.989020908556915[/C][/ROW]
[ROW][C]63[/C][C]0.0121635934286708[/C][C]0.0243271868573416[/C][C]0.98783640657133[/C][/ROW]
[ROW][C]64[/C][C]0.0110757713250486[/C][C]0.0221515426500971[/C][C]0.988924228674951[/C][/ROW]
[ROW][C]65[/C][C]0.00942032883455519[/C][C]0.0188406576691104[/C][C]0.990579671165445[/C][/ROW]
[ROW][C]66[/C][C]0.0080507479606782[/C][C]0.0161014959213564[/C][C]0.991949252039322[/C][/ROW]
[ROW][C]67[/C][C]0.0143663340550685[/C][C]0.028732668110137[/C][C]0.985633665944932[/C][/ROW]
[ROW][C]68[/C][C]0.0110719421301955[/C][C]0.0221438842603910[/C][C]0.988928057869805[/C][/ROW]
[ROW][C]69[/C][C]0.00815247764638172[/C][C]0.0163049552927634[/C][C]0.991847522353618[/C][/ROW]
[ROW][C]70[/C][C]0.00607891677746931[/C][C]0.0121578335549386[/C][C]0.99392108322253[/C][/ROW]
[ROW][C]71[/C][C]0.00492300244005196[/C][C]0.00984600488010393[/C][C]0.995076997559948[/C][/ROW]
[ROW][C]72[/C][C]0.00446337645912249[/C][C]0.00892675291824498[/C][C]0.995536623540878[/C][/ROW]
[ROW][C]73[/C][C]0.00546203201823964[/C][C]0.0109240640364793[/C][C]0.99453796798176[/C][/ROW]
[ROW][C]74[/C][C]0.0049863283168349[/C][C]0.0099726566336698[/C][C]0.995013671683165[/C][/ROW]
[ROW][C]75[/C][C]0.00415595308087196[/C][C]0.00831190616174391[/C][C]0.995844046919128[/C][/ROW]
[ROW][C]76[/C][C]0.00433592932911986[/C][C]0.00867185865823972[/C][C]0.99566407067088[/C][/ROW]
[ROW][C]77[/C][C]0.00436936500363724[/C][C]0.00873873000727448[/C][C]0.995630634996363[/C][/ROW]
[ROW][C]78[/C][C]0.00317646055050405[/C][C]0.0063529211010081[/C][C]0.996823539449496[/C][/ROW]
[ROW][C]79[/C][C]0.00406206630988613[/C][C]0.00812413261977225[/C][C]0.995937933690114[/C][/ROW]
[ROW][C]80[/C][C]0.00583632590487032[/C][C]0.0116726518097406[/C][C]0.99416367409513[/C][/ROW]
[ROW][C]81[/C][C]0.00537329451228376[/C][C]0.0107465890245675[/C][C]0.994626705487716[/C][/ROW]
[ROW][C]82[/C][C]0.00421126779588991[/C][C]0.00842253559177981[/C][C]0.99578873220411[/C][/ROW]
[ROW][C]83[/C][C]0.00317660367704165[/C][C]0.0063532073540833[/C][C]0.996823396322958[/C][/ROW]
[ROW][C]84[/C][C]0.00277572501410627[/C][C]0.00555145002821255[/C][C]0.997224274985894[/C][/ROW]
[ROW][C]85[/C][C]0.00232396741208654[/C][C]0.00464793482417308[/C][C]0.997676032587913[/C][/ROW]
[ROW][C]86[/C][C]0.00184901901876031[/C][C]0.00369803803752062[/C][C]0.99815098098124[/C][/ROW]
[ROW][C]87[/C][C]0.737138116801499[/C][C]0.525723766397002[/C][C]0.262861883198501[/C][/ROW]
[ROW][C]88[/C][C]0.896233852522632[/C][C]0.207532294954737[/C][C]0.103766147477368[/C][/ROW]
[ROW][C]89[/C][C]0.975432806638962[/C][C]0.0491343867220757[/C][C]0.0245671933610378[/C][/ROW]
[ROW][C]90[/C][C]0.972138083051578[/C][C]0.0557238338968447[/C][C]0.0278619169484223[/C][/ROW]
[ROW][C]91[/C][C]0.976165982105744[/C][C]0.0476680357885115[/C][C]0.0238340178942557[/C][/ROW]
[ROW][C]92[/C][C]0.971086877120515[/C][C]0.0578262457589691[/C][C]0.0289131228794845[/C][/ROW]
[ROW][C]93[/C][C]0.967804074562653[/C][C]0.0643918508746931[/C][C]0.0321959254373465[/C][/ROW]
[ROW][C]94[/C][C]0.96077794049026[/C][C]0.0784441190194793[/C][C]0.0392220595097397[/C][/ROW]
[ROW][C]95[/C][C]0.95142744331061[/C][C]0.0971451133787814[/C][C]0.0485725566893907[/C][/ROW]
[ROW][C]96[/C][C]0.99727033220816[/C][C]0.0054593355836818[/C][C]0.0027296677918409[/C][/ROW]
[ROW][C]97[/C][C]0.997644021351574[/C][C]0.00471195729685181[/C][C]0.00235597864842591[/C][/ROW]
[ROW][C]98[/C][C]0.999661739158167[/C][C]0.000676521683665654[/C][C]0.000338260841832827[/C][/ROW]
[ROW][C]99[/C][C]0.99967904153082[/C][C]0.00064191693835867[/C][C]0.000320958469179335[/C][/ROW]
[ROW][C]100[/C][C]0.999534122678169[/C][C]0.000931754643662572[/C][C]0.000465877321831286[/C][/ROW]
[ROW][C]101[/C][C]0.999864183150462[/C][C]0.00027163369907631[/C][C]0.000135816849538155[/C][/ROW]
[ROW][C]102[/C][C]0.999994110521967[/C][C]1.17789560667192e-05[/C][C]5.88947803335959e-06[/C][/ROW]
[ROW][C]103[/C][C]0.999999491338301[/C][C]1.01732339758802e-06[/C][C]5.08661698794012e-07[/C][/ROW]
[ROW][C]104[/C][C]0.999999285951468[/C][C]1.42809706335037e-06[/C][C]7.14048531675183e-07[/C][/ROW]
[ROW][C]105[/C][C]0.999999373926369[/C][C]1.25214726274043e-06[/C][C]6.26073631370214e-07[/C][/ROW]
[ROW][C]106[/C][C]0.99999942506103[/C][C]1.14987793765528e-06[/C][C]5.7493896882764e-07[/C][/ROW]
[ROW][C]107[/C][C]0.999998991325606[/C][C]2.01734878796480e-06[/C][C]1.00867439398240e-06[/C][/ROW]
[ROW][C]108[/C][C]0.999999135591822[/C][C]1.72881635581287e-06[/C][C]8.64408177906435e-07[/C][/ROW]
[ROW][C]109[/C][C]0.999998567666243[/C][C]2.86466751377055e-06[/C][C]1.43233375688527e-06[/C][/ROW]
[ROW][C]110[/C][C]0.99999904507988[/C][C]1.90984024179249e-06[/C][C]9.54920120896244e-07[/C][/ROW]
[ROW][C]111[/C][C]0.999998551973913[/C][C]2.89605217371246e-06[/C][C]1.44802608685623e-06[/C][/ROW]
[ROW][C]112[/C][C]0.999998109925921[/C][C]3.78014815759783e-06[/C][C]1.89007407879891e-06[/C][/ROW]
[ROW][C]113[/C][C]0.999997127735896[/C][C]5.7445282076537e-06[/C][C]2.87226410382685e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999997790704483[/C][C]4.41859103322388e-06[/C][C]2.20929551661194e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999997921640625[/C][C]4.15671875069295e-06[/C][C]2.07835937534647e-06[/C][/ROW]
[ROW][C]116[/C][C]0.999996614339943[/C][C]6.77132011428564e-06[/C][C]3.38566005714282e-06[/C][/ROW]
[ROW][C]117[/C][C]0.999995652096956[/C][C]8.69580608833472e-06[/C][C]4.34790304416736e-06[/C][/ROW]
[ROW][C]118[/C][C]0.999992944637003[/C][C]1.41107259943242e-05[/C][C]7.05536299716211e-06[/C][/ROW]
[ROW][C]119[/C][C]0.999991672623885[/C][C]1.66547522292634e-05[/C][C]8.32737611463171e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999991597490765[/C][C]1.68050184700253e-05[/C][C]8.40250923501267e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999990599317925[/C][C]1.88013641510149e-05[/C][C]9.40068207550743e-06[/C][/ROW]
[ROW][C]122[/C][C]0.9999841014168[/C][C]3.17971664004673e-05[/C][C]1.58985832002336e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999975028257426[/C][C]4.99434851488382e-05[/C][C]2.49717425744191e-05[/C][/ROW]
[ROW][C]124[/C][C]0.99996009983146[/C][C]7.98003370784096e-05[/C][C]3.99001685392048e-05[/C][/ROW]
[ROW][C]125[/C][C]0.999935024253963[/C][C]0.000129951492073994[/C][C]6.49757460369972e-05[/C][/ROW]
[ROW][C]126[/C][C]0.999930512265327[/C][C]0.000138975469345870[/C][C]6.94877346729348e-05[/C][/ROW]
[ROW][C]127[/C][C]0.9998962640513[/C][C]0.000207471897400076[/C][C]0.000103735948700038[/C][/ROW]
[ROW][C]128[/C][C]0.999911556247676[/C][C]0.000176887504648553[/C][C]8.84437523242764e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999877106254677[/C][C]0.000245787490645389[/C][C]0.000122893745322694[/C][/ROW]
[ROW][C]130[/C][C]0.999856492182706[/C][C]0.000287015634587106[/C][C]0.000143507817293553[/C][/ROW]
[ROW][C]131[/C][C]0.999800130855119[/C][C]0.00039973828976256[/C][C]0.00019986914488128[/C][/ROW]
[ROW][C]132[/C][C]0.999786816076767[/C][C]0.000426367846466064[/C][C]0.000213183923233032[/C][/ROW]
[ROW][C]133[/C][C]0.999687133375283[/C][C]0.000625733249433282[/C][C]0.000312866624716641[/C][/ROW]
[ROW][C]134[/C][C]0.999635659601637[/C][C]0.000728680796725657[/C][C]0.000364340398362829[/C][/ROW]
[ROW][C]135[/C][C]0.9995247743565[/C][C]0.000950451287000153[/C][C]0.000475225643500076[/C][/ROW]
[ROW][C]136[/C][C]0.999361937099412[/C][C]0.00127612580117658[/C][C]0.000638062900588291[/C][/ROW]
[ROW][C]137[/C][C]0.999256205613828[/C][C]0.00148758877234492[/C][C]0.000743794386172461[/C][/ROW]
[ROW][C]138[/C][C]0.998928288229604[/C][C]0.00214342354079205[/C][C]0.00107171177039602[/C][/ROW]
[ROW][C]139[/C][C]0.998841284985926[/C][C]0.00231743002814873[/C][C]0.00115871501407437[/C][/ROW]
[ROW][C]140[/C][C]0.998877038320283[/C][C]0.00224592335943491[/C][C]0.00112296167971746[/C][/ROW]
[ROW][C]141[/C][C]0.998493455987545[/C][C]0.00301308802490964[/C][C]0.00150654401245482[/C][/ROW]
[ROW][C]142[/C][C]0.9994534580788[/C][C]0.00109308384240035[/C][C]0.000546541921200173[/C][/ROW]
[ROW][C]143[/C][C]0.999215411280411[/C][C]0.00156917743917792[/C][C]0.000784588719588961[/C][/ROW]
[ROW][C]144[/C][C]0.999124483294262[/C][C]0.00175103341147561[/C][C]0.000875516705737805[/C][/ROW]
[ROW][C]145[/C][C]0.998734234376014[/C][C]0.00253153124797116[/C][C]0.00126576562398558[/C][/ROW]
[ROW][C]146[/C][C]0.998200953317273[/C][C]0.00359809336545327[/C][C]0.00179904668272663[/C][/ROW]
[ROW][C]147[/C][C]0.997423964876703[/C][C]0.00515207024659377[/C][C]0.00257603512329689[/C][/ROW]
[ROW][C]148[/C][C]0.997074924080549[/C][C]0.00585015183890287[/C][C]0.00292507591945143[/C][/ROW]
[ROW][C]149[/C][C]0.99557568700423[/C][C]0.00884862599154143[/C][C]0.00442431299577071[/C][/ROW]
[ROW][C]150[/C][C]0.994288365525502[/C][C]0.0114232689489956[/C][C]0.0057116344744978[/C][/ROW]
[ROW][C]151[/C][C]0.992301282801644[/C][C]0.0153974343967124[/C][C]0.00769871719835622[/C][/ROW]
[ROW][C]152[/C][C]0.989507209913568[/C][C]0.0209855801728640[/C][C]0.0104927900864320[/C][/ROW]
[ROW][C]153[/C][C]0.984527502636402[/C][C]0.0309449947271953[/C][C]0.0154724973635976[/C][/ROW]
[ROW][C]154[/C][C]0.977398030738307[/C][C]0.0452039385233870[/C][C]0.0226019692616935[/C][/ROW]
[ROW][C]155[/C][C]0.967648132250461[/C][C]0.0647037354990773[/C][C]0.0323518677495387[/C][/ROW]
[ROW][C]156[/C][C]0.96404114038991[/C][C]0.071917719220182[/C][C]0.035958859610091[/C][/ROW]
[ROW][C]157[/C][C]0.950141857653475[/C][C]0.099716284693049[/C][C]0.0498581423465245[/C][/ROW]
[ROW][C]158[/C][C]0.942998591808453[/C][C]0.114002816383093[/C][C]0.0570014081915467[/C][/ROW]
[ROW][C]159[/C][C]0.944767555763339[/C][C]0.110464888473323[/C][C]0.0552324442366614[/C][/ROW]
[ROW][C]160[/C][C]0.981107276543447[/C][C]0.0377854469131061[/C][C]0.0188927234565530[/C][/ROW]
[ROW][C]161[/C][C]0.987443239343292[/C][C]0.0251135213134153[/C][C]0.0125567606567076[/C][/ROW]
[ROW][C]162[/C][C]0.984660291580593[/C][C]0.0306794168388135[/C][C]0.0153397084194067[/C][/ROW]
[ROW][C]163[/C][C]0.978447297552106[/C][C]0.0431054048957873[/C][C]0.0215527024478936[/C][/ROW]
[ROW][C]164[/C][C]0.97178863225288[/C][C]0.0564227354942414[/C][C]0.0282113677471207[/C][/ROW]
[ROW][C]165[/C][C]0.95679302644441[/C][C]0.0864139471111784[/C][C]0.0432069735555892[/C][/ROW]
[ROW][C]166[/C][C]0.937982003680166[/C][C]0.124035992639667[/C][C]0.0620179963198336[/C][/ROW]
[ROW][C]167[/C][C]0.925784824927336[/C][C]0.148430350145328[/C][C]0.0742151750726642[/C][/ROW]
[ROW][C]168[/C][C]0.894638902054241[/C][C]0.210722195891518[/C][C]0.105361097945759[/C][/ROW]
[ROW][C]169[/C][C]0.853141873854548[/C][C]0.293716252290904[/C][C]0.146858126145452[/C][/ROW]
[ROW][C]170[/C][C]0.886912942132982[/C][C]0.226174115734036[/C][C]0.113087057867018[/C][/ROW]
[ROW][C]171[/C][C]0.853895552975753[/C][C]0.292208894048493[/C][C]0.146104447024247[/C][/ROW]
[ROW][C]172[/C][C]0.7953910290545[/C][C]0.409217941891[/C][C]0.2046089709455[/C][/ROW]
[ROW][C]173[/C][C]0.784218204099605[/C][C]0.43156359180079[/C][C]0.215781795900395[/C][/ROW]
[ROW][C]174[/C][C]0.807656773205263[/C][C]0.384686453589474[/C][C]0.192343226794737[/C][/ROW]
[ROW][C]175[/C][C]0.738212897722801[/C][C]0.523574204554398[/C][C]0.261787102277199[/C][/ROW]
[ROW][C]176[/C][C]0.636445775600833[/C][C]0.727108448798334[/C][C]0.363554224399167[/C][/ROW]
[ROW][C]177[/C][C]0.518655859688863[/C][C]0.962688280622273[/C][C]0.481344140311137[/C][/ROW]
[ROW][C]178[/C][C]0.391480725727014[/C][C]0.782961451454028[/C][C]0.608519274272986[/C][/ROW]
[ROW][C]179[/C][C]0.325961687099964[/C][C]0.651923374199928[/C][C]0.674038312900036[/C][/ROW]
[ROW][C]180[/C][C]0.230434975599165[/C][C]0.46086995119833[/C][C]0.769565024400835[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116237&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116237&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
150.2153625825938490.4307251651876990.78463741740615
160.1003564989052360.2007129978104720.899643501094764
170.07219892048155640.1443978409631130.927801079518444
180.172813490408340.345626980816680.82718650959166
190.1154990375490600.2309980750981190.88450096245094
200.3683625130480280.7367250260960570.631637486951972
210.4089627152369710.8179254304739430.591037284763029
220.3266356060874030.6532712121748060.673364393912597
230.2551138593162520.5102277186325040.744886140683748
240.3843674115520110.7687348231040230.615632588447989
250.3364248783237770.6728497566475550.663575121676223
260.2629092348188690.5258184696377380.737090765181131
270.2056358348825430.4112716697650870.794364165117457
280.1577388370349840.3154776740699680.842261162965016
290.1528355888417290.3056711776834590.847164411158271
300.1626015846102640.3252031692205280.837398415389736
310.1913311218346520.3826622436693040.808668878165348
320.2026132486535970.4052264973071950.797386751346403
330.1616121966847640.3232243933695280.838387803315236
340.1502003795597210.3004007591194420.84979962044028
350.1840245226216230.3680490452432450.815975477378378
360.1469387762609580.2938775525219170.853061223739042
370.1792416384906630.3584832769813250.820758361509337
380.1973412849538680.3946825699077360.802658715046132
390.2104067839390340.4208135678780680.789593216060966
400.1702706396797870.3405412793595740.829729360320213
410.1687783167082470.3375566334164930.831221683291753
420.1930801589957170.3861603179914340.806919841004283
430.1588912967950400.3177825935900810.84110870320496
440.1369695887632470.2739391775264930.863030411236753
450.1099869833521100.2199739667042210.89001301664789
460.09393652512204180.1878730502440840.906063474877958
470.08856705609755040.1771341121951010.91143294390245
480.0791480760278950.158296152055790.920851923972105
490.06405397468012280.1281079493602460.935946025319877
500.05673593549582670.1134718709916530.943264064504173
510.05213593647431990.1042718729486400.94786406352568
520.03970307482883480.07940614965766970.960296925171165
530.03533873242446440.07067746484892880.964661267575536
540.03005893522300900.06011787044601790.96994106477699
550.02267220661313270.04534441322626530.977327793386867
560.02074214349633370.04148428699266750.979257856503666
570.02467554777012170.04935109554024350.975324452229878
580.02966791359630440.05933582719260880.970332086403696
590.02367183177950200.04734366355900410.976328168220498
600.01785200827970170.03570401655940330.982147991720298
610.01403087661203580.02806175322407160.985969123387964
620.01097909144308460.02195818288616930.989020908556915
630.01216359342867080.02432718685734160.98783640657133
640.01107577132504860.02215154265009710.988924228674951
650.009420328834555190.01884065766911040.990579671165445
660.00805074796067820.01610149592135640.991949252039322
670.01436633405506850.0287326681101370.985633665944932
680.01107194213019550.02214388426039100.988928057869805
690.008152477646381720.01630495529276340.991847522353618
700.006078916777469310.01215783355493860.99392108322253
710.004923002440051960.009846004880103930.995076997559948
720.004463376459122490.008926752918244980.995536623540878
730.005462032018239640.01092406403647930.99453796798176
740.00498632831683490.00997265663366980.995013671683165
750.004155953080871960.008311906161743910.995844046919128
760.004335929329119860.008671858658239720.99566407067088
770.004369365003637240.008738730007274480.995630634996363
780.003176460550504050.00635292110100810.996823539449496
790.004062066309886130.008124132619772250.995937933690114
800.005836325904870320.01167265180974060.99416367409513
810.005373294512283760.01074658902456750.994626705487716
820.004211267795889910.008422535591779810.99578873220411
830.003176603677041650.00635320735408330.996823396322958
840.002775725014106270.005551450028212550.997224274985894
850.002323967412086540.004647934824173080.997676032587913
860.001849019018760310.003698038037520620.99815098098124
870.7371381168014990.5257237663970020.262861883198501
880.8962338525226320.2075322949547370.103766147477368
890.9754328066389620.04913438672207570.0245671933610378
900.9721380830515780.05572383389684470.0278619169484223
910.9761659821057440.04766803578851150.0238340178942557
920.9710868771205150.05782624575896910.0289131228794845
930.9678040745626530.06439185087469310.0321959254373465
940.960777940490260.07844411901947930.0392220595097397
950.951427443310610.09714511337878140.0485725566893907
960.997270332208160.00545933558368180.0027296677918409
970.9976440213515740.004711957296851810.00235597864842591
980.9996617391581670.0006765216836656540.000338260841832827
990.999679041530820.000641916938358670.000320958469179335
1000.9995341226781690.0009317546436625720.000465877321831286
1010.9998641831504620.000271633699076310.000135816849538155
1020.9999941105219671.17789560667192e-055.88947803335959e-06
1030.9999994913383011.01732339758802e-065.08661698794012e-07
1040.9999992859514681.42809706335037e-067.14048531675183e-07
1050.9999993739263691.25214726274043e-066.26073631370214e-07
1060.999999425061031.14987793765528e-065.7493896882764e-07
1070.9999989913256062.01734878796480e-061.00867439398240e-06
1080.9999991355918221.72881635581287e-068.64408177906435e-07
1090.9999985676662432.86466751377055e-061.43233375688527e-06
1100.999999045079881.90984024179249e-069.54920120896244e-07
1110.9999985519739132.89605217371246e-061.44802608685623e-06
1120.9999981099259213.78014815759783e-061.89007407879891e-06
1130.9999971277358965.7445282076537e-062.87226410382685e-06
1140.9999977907044834.41859103322388e-062.20929551661194e-06
1150.9999979216406254.15671875069295e-062.07835937534647e-06
1160.9999966143399436.77132011428564e-063.38566005714282e-06
1170.9999956520969568.69580608833472e-064.34790304416736e-06
1180.9999929446370031.41107259943242e-057.05536299716211e-06
1190.9999916726238851.66547522292634e-058.32737611463171e-06
1200.9999915974907651.68050184700253e-058.40250923501267e-06
1210.9999905993179251.88013641510149e-059.40068207550743e-06
1220.99998410141683.17971664004673e-051.58985832002336e-05
1230.9999750282574264.99434851488382e-052.49717425744191e-05
1240.999960099831467.98003370784096e-053.99001685392048e-05
1250.9999350242539630.0001299514920739946.49757460369972e-05
1260.9999305122653270.0001389754693458706.94877346729348e-05
1270.99989626405130.0002074718974000760.000103735948700038
1280.9999115562476760.0001768875046485538.84437523242764e-05
1290.9998771062546770.0002457874906453890.000122893745322694
1300.9998564921827060.0002870156345871060.000143507817293553
1310.9998001308551190.000399738289762560.00019986914488128
1320.9997868160767670.0004263678464660640.000213183923233032
1330.9996871333752830.0006257332494332820.000312866624716641
1340.9996356596016370.0007286807967256570.000364340398362829
1350.99952477435650.0009504512870001530.000475225643500076
1360.9993619370994120.001276125801176580.000638062900588291
1370.9992562056138280.001487588772344920.000743794386172461
1380.9989282882296040.002143423540792050.00107171177039602
1390.9988412849859260.002317430028148730.00115871501407437
1400.9988770383202830.002245923359434910.00112296167971746
1410.9984934559875450.003013088024909640.00150654401245482
1420.99945345807880.001093083842400350.000546541921200173
1430.9992154112804110.001569177439177920.000784588719588961
1440.9991244832942620.001751033411475610.000875516705737805
1450.9987342343760140.002531531247971160.00126576562398558
1460.9982009533172730.003598093365453270.00179904668272663
1470.9974239648767030.005152070246593770.00257603512329689
1480.9970749240805490.005850151838902870.00292507591945143
1490.995575687004230.008848625991541430.00442431299577071
1500.9942883655255020.01142326894899560.0057116344744978
1510.9923012828016440.01539743439671240.00769871719835622
1520.9895072099135680.02098558017286400.0104927900864320
1530.9845275026364020.03094499472719530.0154724973635976
1540.9773980307383070.04520393852338700.0226019692616935
1550.9676481322504610.06470373549907730.0323518677495387
1560.964041140389910.0719177192201820.035958859610091
1570.9501418576534750.0997162846930490.0498581423465245
1580.9429985918084530.1140028163830930.0570014081915467
1590.9447675557633390.1104648884733230.0552324442366614
1600.9811072765434470.03778544691310610.0188927234565530
1610.9874432393432920.02511352131341530.0125567606567076
1620.9846602915805930.03067941683881350.0153397084194067
1630.9784472975521060.04310540489578730.0215527024478936
1640.971788632252880.05642273549424140.0282113677471207
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1660.9379820036801660.1240359926396670.0620179963198336
1670.9257848249273360.1484303501453280.0742151750726642
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1690.8531418738545480.2937162522909040.146858126145452
1700.8869129421329820.2261741157340360.113087057867018
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1720.79539102905450.4092179418910.2046089709455
1730.7842182040996050.431563591800790.215781795900395
1740.8076567732052630.3846864535894740.192343226794737
1750.7382128977228010.5235742045543980.261787102277199
1760.6364457756008330.7271084487983340.363554224399167
1770.5186558596888630.9626882806222730.481344140311137
1780.3914807257270140.7829614514540280.608519274272986
1790.3259616870999640.6519233741999280.674038312900036
1800.2304349755991650.460869951198330.769565024400835






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level670.403614457831325NOK
5% type I error level960.578313253012048NOK
10% type I error level1100.662650602409639NOK

\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 & 67 & 0.403614457831325 & NOK \tabularnewline
5% type I error level & 96 & 0.578313253012048 & NOK \tabularnewline
10% type I error level & 110 & 0.662650602409639 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116237&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]67[/C][C]0.403614457831325[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]96[/C][C]0.578313253012048[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]110[/C][C]0.662650602409639[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116237&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116237&T=6

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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 level670.403614457831325NOK
5% type I error level960.578313253012048NOK
10% type I error level1100.662650602409639NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}