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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 01 Dec 2010 12:47:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/01/t1291207587kfqinpxzbbjlpca.htm/, Retrieved Sat, 04 May 2024 20:15:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103942, Retrieved Sat, 04 May 2024 20:15:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-01 10:13:15] [e45804683e9a4263debf179d74e04a01]
-       [Multiple Regression] [] [2010-12-01 11:48:49] [eab273d59b25a7fddba6574425ce83eb]
-    D      [Multiple Regression] [] [2010-12-01 12:47:37] [33c49f0356cf94839cc9a2df1ea19a67] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4	1	27	5	26	49	35	5
4	1	36	4	25	45	34	4
5	1	25	4	17	54	13	2
2	1	27	3	37	36	35	3
3	2	25	3	35	36	28	1
5	2	44	3	15	53	32	3
4	1	50	4	27	46	35	2
4	1	41	4	36	42	36	2
4	1	48	5	25	41	27	2
4	2	43	4	30	45	29	2
5	2	47	2	27	47	27	4
4	2	41	3	33	42	28	1
3	1	44	2	29	45	29	6
4	2	47	5	30	40	28	2
3	2	40	3	25	45	30	2
3	2	46	3	23	40	25	2
4	1	28	3	26	42	15	2
3	1	56	3	24	45	33	1
4	2	49	4	35	47	31	2
2	2	25	4	39	31	37	4
4	2	41	4	23	46	37	4
3	2	26	3	32	34	34	2
4	1	50	5	29	43	32	2
4	1	47	4	26	45	21	5
3	1	52	2	21	42	25	1
3	2	37	5	35	51	32	2
2	2	41	3	23	44	28	1
4	1	45	4	21	47	22	2
5	2	26	4	28	47	25	2
4	1		3	30	41	26	1
2	1	52	4	21	44	34	4
5	1	46	2	29	51	34	1
4	1	58	3	28	46	36	2
3	1	54	5	19	47	36	2
4	1	29	3	26	46	26	2
2	2	50	3	33	38	26	1
3	1	43	2	34	50	34	4
3	2	30	3	33	48	33	1
3	2	47	2	40	36	31	2
5	1	45	3	24	51	33	2
	2	48	1	35	35	22	1
4	2	48	3	35	49	29	2
4	2	26	4	32	38	24	2
4	1	46	5	20	47	37	2
2	2		3	35	36	32	2
4	2	50	3	35	47	23	2
3	1	25	4	21	46	29	2
4	1	47	2	33	43	35	2
1	2	47	2	40	53	20	4
2	1	41	3	22	55	28	1
2	2	45	2	35	39	26	1
4	2	41	4	20	55	36	2
3	2	45	5	28	41	26	2
4	2	40	3	46	33	33	1
3	1	29	4	18	52	25	2
3	2	34	5	22	42	29	2
5	1	45	5	20	56	32	2
3	2	52	3	25	46	35	2
2	2	41	4	31	33	24	2
1	2	48	3	21	51	31	1
2	2	45	3	23	46	29	4
5	1	54	2	26	46	27	1
4	2	25	3	34	50	29	2
4	2	26	4	31	46	29	3
3	1	28	4	23	51	27	2
4	2	50	4	31	48	34	2
4	2	48	4	26	44	32	3
2	2	51	3	36	38	31	2
3	2	53	3	28	42	31	2
4	1	37	3	34	39	31	2
3	1	56	2	25	45	16	1
2	1	43	3	33	31	25	1
4	1	34	3	46	29	27	4
4	1	42	3	24	48	32	4
3	2	32	3	32	38	28	1
5	2	31	5	33	55	25	4
1	1	46	3	42	32	25	2
3	2	30	5	17	51	36	5
3	2	47	4	36	53	36	1
5	2	33	4	40	47	36	2
2	1	25	4	30	45	27	4
3	1	25	5	19	33	29	6
3	2	21	4	33	49	32	1
4	2	36	5	35	46	29	2
2	2	50	3	23	42	31	1
4	2	48	3	15	56	34	3
3	2	48	2	38	35	27	1
3	1	25	3	37	40	28	3
3	1	48	4	23	44	32	4
2	2	49	5	41	46	33	3
3	1	27	5	34	46	29	5
2	1	28	3	38	39	32	1
4	2	43	2	45	35	35	1
4	2	48	3	27	48	33	2
2	2	48	4	46	42	27	3
1	1	25	1	26	39	16	1
5	2	49	4	44	39	32	2
4	1	26	3	36	41	26	2
4	1	51	3	20	52	32	2
4	2	25	4	44	45	38	2
3	1	29	3	27	42	24	1
3	1	29	4	27	44	26	2
1	1	43	2	41	33	19	3
5	2	46	3	30	42	37	2
3	1	44	3	33	46	25	1
3	1	25	3	37	45	24	1
2	1	51	2	30	40	23	4
4	1	42	5	20	48	28	4
4	2	53	5	44	32	38	2
3	1	25	4	20	53	28	2
4	2	49	2	33	39	28	4
4	1	51	3	31	45	26	1
2	2	20	3	23	36	21	2
3	2	44	3	33	38	35	2
3	2	38	4	33	49	31	2
3	1	46	5	32	46	34	4
4	2	42	4	25	43	30	2
5	1	29		22	37	30	1
3	2	46	4	16	48	24	3
3	2	49	2	36	45	27	2
2	2	51	3	35	32	26	2
3	1	38	3	25	46	30	1
1	1	41	1	27	20	15	1
4	2	47	3	32	42	28	1
4	2	44	3	36	45	34	2
4	2	47	3	51	29	29	2
3	2	46	3	30	51	26	2
5	1	44	4	20	55	31	2
2	2	28	3	29	50	28	2
2	2	47	4	26	44	33	4
3	2	28	4	20	41	32	3
3	1	41	5	40	40	33	2
2	2	45	4	29	47	31	3
1	2	46	4	32	42	37	2
3	1	46	4	33	40	27	4
5	2	22	3	32	51	19	2
4	2	33	3	34	43	27	2
4	1	41	4	24	45	31	3
4	2	47	5	25	41	38	2
3	1	25	3	41	41	22	1
5	2	42	3	39	37	35	2
3	2	47	3	21	46	35	6
3	2	50	3	38	38	30	6
3	1	55	5	28	39	41	6
3	1	21	3	37	45	25	1
4	1		3	26	46	28	1
2	1	52	3	30	39	45	5
2	2	49	4	25	21	21	3
4	2	46	4	38	31	33	2
3	1		4	31	35	25	2
3	2	45	3	31	49	29	1
2	2	52	3	27	40	31	2
3	1		3	21	45	29	3
3	2	40	4	26	46	31	2
4	2	49	4	37	45	31	2
1	1	38	5	28	34	25	5
1	1	32	5	29	41	27	4
5	2	46	4	33	43	26	2
4	2	32	3	41	45	26	2
3	2	41	3	19	48	23	2
3	2	43	3	37	43	27	1
4	1	44	4	36	45	24	2
3	1	47	5	27	45	35	2
2	2	28	3	33	34	24	2
1	1	52	1	29	40	32	1
1	1	27	2	42	40	24	2
5	2	45	5	27	55	24	2
4	1	27	4	47	44	38	3
3	1	25	4	17	44	36	2
4	1	28	4	34	48	24	2
5	1	25	3	32	51	18	1
4	1	52	4	25	49	34	2
4	1	44	3	27	33	23	2
2	2	43	3	37	43	35	2
3	2	47	4	34	44	22	2
4	2	52	4	27	44	34	2
3	2	40	2	37	41	28	1
4	1	42	3	32	45	34	2
3	1	45	5	26	44	32	2
4	1	45	2	29	44	24	4
1	1	50	5	28	40	34	5
2	1	49	3	19	48	33	4
3	1	52	2	46	49	33	1
3	2	48	3	31	46	29	2
5	2	51	3	42	49	38	2
4	2	49	4	33	55	24	2
3	2	31	4	39	51	25	3
3	2	43	3	27	46	37	2
3	2	31	3	35	37	33	2
3	2	28	4	23	43	30	2
4	2	43	4	32	41	22	3
3	2	31	3	22	45	28	2
2	2	51	3	17	39	24	3
4	2	58	4	35	38	33	2
2	2	25	5	34	41	37	3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time23 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 23 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103942&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]23 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103942&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103942&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 time23 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Teamwork33[t] = + 49.6085876250846 -0.139372560558695geslacht[t] -0.308848721560136leeftijd[t] -0.397800126959575opleiding[t] -0.197798722000610Neuroticisme[t] -0.498369913461478Extraversie[t] -0.0686083798311122Openheid[t] -0.327316080922906Beroep[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Teamwork33[t] =  +  49.6085876250846 -0.139372560558695geslacht[t] -0.308848721560136leeftijd[t] -0.397800126959575opleiding[t] -0.197798722000610Neuroticisme[t] -0.498369913461478Extraversie[t] -0.0686083798311122Openheid[t] -0.327316080922906Beroep[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103942&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Teamwork33[t] =  +  49.6085876250846 -0.139372560558695geslacht[t] -0.308848721560136leeftijd[t] -0.397800126959575opleiding[t] -0.197798722000610Neuroticisme[t] -0.498369913461478Extraversie[t] -0.0686083798311122Openheid[t] -0.327316080922906Beroep[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103942&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103942&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
Teamwork33[t] = + 49.6085876250846 -0.139372560558695geslacht[t] -0.308848721560136leeftijd[t] -0.397800126959575opleiding[t] -0.197798722000610Neuroticisme[t] -0.498369913461478Extraversie[t] -0.0686083798311122Openheid[t] -0.327316080922906Beroep[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)49.60858762508465.2723189.409300
geslacht-0.1393725605586950.053863-2.58750.0104260.005213
leeftijd-0.3088487215601360.048803-6.328500
opleiding-0.3978001269595750.052168-7.625400
Neuroticisme-0.1977987220006100.06838-2.89270.0042730.002137
Extraversie-0.4983699134614780.054295-9.178900
Openheid-0.06860837983111220.056159-1.22170.2233690.111684
Beroep-0.3273160809229060.05581-5.864800

\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) & 49.6085876250846 & 5.272318 & 9.4093 & 0 & 0 \tabularnewline
geslacht & -0.139372560558695 & 0.053863 & -2.5875 & 0.010426 & 0.005213 \tabularnewline
leeftijd & -0.308848721560136 & 0.048803 & -6.3285 & 0 & 0 \tabularnewline
opleiding & -0.397800126959575 & 0.052168 & -7.6254 & 0 & 0 \tabularnewline
Neuroticisme & -0.197798722000610 & 0.06838 & -2.8927 & 0.004273 & 0.002137 \tabularnewline
Extraversie & -0.498369913461478 & 0.054295 & -9.1789 & 0 & 0 \tabularnewline
Openheid & -0.0686083798311122 & 0.056159 & -1.2217 & 0.223369 & 0.111684 \tabularnewline
Beroep & -0.327316080922906 & 0.05581 & -5.8648 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103942&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]49.6085876250846[/C][C]5.272318[/C][C]9.4093[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]geslacht[/C][C]-0.139372560558695[/C][C]0.053863[/C][C]-2.5875[/C][C]0.010426[/C][C]0.005213[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.308848721560136[/C][C]0.048803[/C][C]-6.3285[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.397800126959575[/C][C]0.052168[/C][C]-7.6254[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]-0.197798722000610[/C][C]0.06838[/C][C]-2.8927[/C][C]0.004273[/C][C]0.002137[/C][/ROW]
[ROW][C]Extraversie[/C][C]-0.498369913461478[/C][C]0.054295[/C][C]-9.1789[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Openheid[/C][C]-0.0686083798311122[/C][C]0.056159[/C][C]-1.2217[/C][C]0.223369[/C][C]0.111684[/C][/ROW]
[ROW][C]Beroep[/C][C]-0.327316080922906[/C][C]0.05581[/C][C]-5.8648[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103942&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103942&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)49.60858762508465.2723189.409300
geslacht-0.1393725605586950.053863-2.58750.0104260.005213
leeftijd-0.3088487215601360.048803-6.328500
opleiding-0.3978001269595750.052168-7.625400
Neuroticisme-0.1977987220006100.06838-2.89270.0042730.002137
Extraversie-0.4983699134614780.054295-9.178900
Openheid-0.06860837983111220.056159-1.22170.2233690.111684
Beroep-0.3273160809229060.05581-5.864800






Multiple Linear Regression - Regression Statistics
Multiple R0.771539000887454
R-squared0.595272429890411
Adjusted R-squared0.580122199993261
F-TEST (value)39.291313328676
F-TEST (DF numerator)7
F-TEST (DF denominator)187
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.95572041113526
Sum Squared Residuals11835.8821176488

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.771539000887454 \tabularnewline
R-squared & 0.595272429890411 \tabularnewline
Adjusted R-squared & 0.580122199993261 \tabularnewline
F-TEST (value) & 39.291313328676 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 187 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.95572041113526 \tabularnewline
Sum Squared Residuals & 11835.8821176488 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103942&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.771539000887454[/C][/ROW]
[ROW][C]R-squared[/C][C]0.595272429890411[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.580122199993261[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]39.291313328676[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]187[/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]7.95572041113526[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11835.8821176488[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103942&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103942&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 R0.771539000887454
R-squared0.595272429890411
Adjusted R-squared0.580122199993261
F-TEST (value)39.291313328676
F-TEST (DF numerator)7
F-TEST (DF denominator)187
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.95572041113526
Sum Squared Residuals11835.8821176488






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
145.54053271727264-1.54053271727264
245.74589718679147-1.74589718679147
358.33570181710378-3.33570181710378
4211.2937880660300-9.29378806603005
5313.3026012132565-10.3026012132565
651.989095733610633.01090426638937
741.114071509501622.88592849049838
844.03839277955215-0.0383927795521541
944.7702828756198-0.7702828756198
1043.453264026310170.546735973689828
1152.092710330884062.90728966911594
1245.76639963152664-1.76639963152664
1332.967922517536870.0320774824631324
1444.38052696024856-0.380526960248558
1535.69799554812209-2.69799554812209
1637.07539212922545-4.07539212922545
17411.8699894832529-7.86998948325292
1831.215078227148801.78492177285120
194-0.5227784996388694.52277849963887
20213.0060321043531-11.0060321043531
2143.753683409478520.246316590521480
22313.8449221247115-10.8449221247115
2342.021608818418581.97839118158142
2442.715355384510941.28464461548906
2535.48564618538407-2.48564618538407
2630.7235179984461592.27648200155384
2726.74764702460978-4.74764702460978
2844.23864647364891-0.238646473648908
2958.37698342923519-3.37698342923519
30414.8180591973579-10.8180591973579
3115.21328217398476-4.21328217398476
3212.63336413119257-1.63336413119257
3311.30080629405956-0.300806294059556
3414.29568543288673-3.29568543288673
35111.4492260197185-10.4492260197185
3627.06148343418202-5.06148343418202
3711.38177084150096-0.38177084150096
3824.38235803111947-2.38235803111947
3922.18435628371811-0.184356283718110
4015.53728230046518-4.53728230046518
414818.957497887690829.0425021123092
424811.226590021673736.7734099783263
432617.70587471327668.29412528672336
444614.931002961534831.0689970384652
4531.990292063004281.00970793699572
46310.9230375848442-7.92303758484422
4743.096977698703210.90302230129679
4821.390753735475150.609246264524851
4924.43219800826241-2.43219800826241
5032.356329102001130.643670897998866
5126.5941901103881-4.59419011038809
524-1.243495428020175.24349542802017
5357.08161795010098-2.08161795010098
5438.62435464925371-5.6243546492537
5547.93807414448493-3.93807414448493
5654.349262272769390.650737727230611
575-2.252356208202347.25235620820234
5833.04471462734026-0.0447146273402611
5948.80647138782632-4.80647138782632
6032.537696167921650.462303832078353
613-0.3669473463372233.36694734633722
62210.5258592723023-8.52585927230233
6336.85476884474955-3.85476884474955
6448.22282882211235-4.22282882211235
6541.019032601104462.98096739889554
664-1.299370445555905.2993704455559
6741.245480359550892.75451964044911
6831.147443856977651.85255614302235
6935.8343252163428-2.8343252163428
7030.2040003935972962.79599960640270
71210.5235407011182-8.52354070111824
72312.1013460772376-9.1013460772376
7336.89967516579061-3.89967516579061
7435.81166749650087-2.81166749650087
7539.30034715547275-6.30034715547275
7651.662897363102083.33710263689792
77312.0793714459706-9.0793714459706
785-0.8380111879511795.83801118795118
7940.2730929123135193.72690708768648
8045.55314304503774-1.55314304503774
81410.2507998789184-6.25079987891844
82515.5395409497259-10.5395409497259
8343.034227658761200.965772341238795
8451.091945055204613.90805494479539
8533.0599014711538-0.0599014711538007
863-1.239628057637014.23962805763701
87212.8177027760521-10.8177027760521
8833.09116429662244-0.0911642966224442
8942.120431481072171.87956851892782
9055.56521878961001-0.565218789610011
9157.90748360989653-2.90748360989653
9233.13463950510875-0.134639505108745
9320.5644456819451641.43555431805484
9430.6526597505614062.34734024943859
95410.1419236235833-6.1419236235833
9618.70964586416737-7.70964586416737
9747.73358717527648-3.73358717527648
9832.434768955310570.565231044689433
9937.3222029494952-4.32220294949519
10043.010115754850980.989884245149022
101312.0729959484639-9.07299594846386
10246.8762142234261-2.8762142234261
10327.86510020303653-5.86510020303653
10431.375936680469091.62406331953091
105310.9128298956032-7.9128298956032
10633.05014031135404-0.0501403113540434
10727.32350070540654-5.32350070540654
10850.5883506337314354.41164936626856
10958.33441345882437-3.33441345882437
11040.7489687936236063.25103120637639
11122.27938638218655-0.279386382186551
112313.1689655495660-10.1689655495660
113310.5008305852629-7.50083058526286
11434.88410224465903-1.88410224465903
11540.5280465477836993.4719534522163
11650.747253478541894.25274652145811
11748.4615530373401-4.4615530373401
1182228.7331553218017-6.73315532180165
1191628.7063562534502-12.7063562534502
1203628.32899377882097.67100622117911
1213531.64216591690943.35783408309063
1222529.6977597761604-4.69775977616038
1232735.7961286537927-8.79612865379268
1243228.9207240807133.07927591928703
1253626.04588880322329.95411119677684
1265130.086500481794720.9134995182053
1273027.85952346238342.14047653761662
1282027.277866023109-7.27786602310897
1292927.27039969286881.72960030713118
1302626.8725516892917-0.872551689291674
1312027.2764631142216-7.27646311422158
1324027.257098450317312.7429015496827
1332926.63057818473662.36942181526336
1343225.97492125458916.02507874541087
1353329.62650317679563.37349682320437
1363230.80290158090861.19709841909140
1373429.06927908678674.93072091321325
1382425.9200026790982-1.92000267909818
1392527.5735371509640-2.57353715096395
1404130.852607008294110.1473929917059
1413927.049819288133211.9501807118668
1422123.9984405957733-2.99844059577330
1433826.158360540503711.8416394594963
1442825.60896911888712.39103088111292
1453725.212196189027511.7878038109725
146468.4633651647838337.5366348352162
147397.723919792212731.2760802077873
148218.1309591232091512.8690408767908
1493127.42399431973593.57600568026408
1502519.04629611496895.95370388503113
1512921.33020459071407.66979540928596
1523124.366788424476.63321157553001
15335.60917080306307-2.60917080306307
1542-3.523863568376965.52386356837696
15528.58502339424696-6.58502339424696
15659.33856342187934-4.33856342187933
15741.297441070065742.70255892993426
15823.07963806270505-1.07963806270505
159211.3790706800555-9.37907068005548
16020.646589559293651.35341044070635
16111.56356826790288-0.563568267902879
16221.196594056583570.803405943416427
163210.3458673393406-8.34586733934062
16420.6057837459068461.39421625409315
16516.49270797027965-5.49270797027965
16624.31899355966605-2.31899355966605
1672-1.669715374950183.66971537495018
168314.8709870052178-11.8709870052178
16928.7192849866857-6.7192849866857
170212.7259227019734-10.7259227019734
17110.3156419717490250.684358028250975
17227.39731284995304-5.39731284995304
1732-1.832566527530913.83256652753091
17421.920672094622590.0793279053774057
1752-0.6469046775785072.64690467757851
17621.847689340912300.152310659087697
17711.27728608843949-0.277286088439488
17823.54112084656403-1.54112084656403
17925.11856335900598-3.11856335900598
18040.4539268634392243.54607313656078
18155.37173013726864-0.371730137268636
1824-9.287840125459213.2878401254592
18310.7873511039254940.212648896074506
1842-9.3185333139463811.3185333139464
18520.07474485361818141.92525514638182
18624.3314176570819-2.3314176570819
18732.151302745186030.848697254813966
18824.86468470318074-2.86468470318074
189212.4110232873786-10.4110232873786
19024.57506500831849-2.57506500831849
191312.4312069441456-9.4312069441456
19228.82734113549848-6.82734113549848
1933-6.28169838711829.2816983871182
19425.90993265194108-3.90993265194108
19539.23716042935341-6.23716042935341

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 5.54053271727264 & -1.54053271727264 \tabularnewline
2 & 4 & 5.74589718679147 & -1.74589718679147 \tabularnewline
3 & 5 & 8.33570181710378 & -3.33570181710378 \tabularnewline
4 & 2 & 11.2937880660300 & -9.29378806603005 \tabularnewline
5 & 3 & 13.3026012132565 & -10.3026012132565 \tabularnewline
6 & 5 & 1.98909573361063 & 3.01090426638937 \tabularnewline
7 & 4 & 1.11407150950162 & 2.88592849049838 \tabularnewline
8 & 4 & 4.03839277955215 & -0.0383927795521541 \tabularnewline
9 & 4 & 4.7702828756198 & -0.7702828756198 \tabularnewline
10 & 4 & 3.45326402631017 & 0.546735973689828 \tabularnewline
11 & 5 & 2.09271033088406 & 2.90728966911594 \tabularnewline
12 & 4 & 5.76639963152664 & -1.76639963152664 \tabularnewline
13 & 3 & 2.96792251753687 & 0.0320774824631324 \tabularnewline
14 & 4 & 4.38052696024856 & -0.380526960248558 \tabularnewline
15 & 3 & 5.69799554812209 & -2.69799554812209 \tabularnewline
16 & 3 & 7.07539212922545 & -4.07539212922545 \tabularnewline
17 & 4 & 11.8699894832529 & -7.86998948325292 \tabularnewline
18 & 3 & 1.21507822714880 & 1.78492177285120 \tabularnewline
19 & 4 & -0.522778499638869 & 4.52277849963887 \tabularnewline
20 & 2 & 13.0060321043531 & -11.0060321043531 \tabularnewline
21 & 4 & 3.75368340947852 & 0.246316590521480 \tabularnewline
22 & 3 & 13.8449221247115 & -10.8449221247115 \tabularnewline
23 & 4 & 2.02160881841858 & 1.97839118158142 \tabularnewline
24 & 4 & 2.71535538451094 & 1.28464461548906 \tabularnewline
25 & 3 & 5.48564618538407 & -2.48564618538407 \tabularnewline
26 & 3 & 0.723517998446159 & 2.27648200155384 \tabularnewline
27 & 2 & 6.74764702460978 & -4.74764702460978 \tabularnewline
28 & 4 & 4.23864647364891 & -0.238646473648908 \tabularnewline
29 & 5 & 8.37698342923519 & -3.37698342923519 \tabularnewline
30 & 4 & 14.8180591973579 & -10.8180591973579 \tabularnewline
31 & 1 & 5.21328217398476 & -4.21328217398476 \tabularnewline
32 & 1 & 2.63336413119257 & -1.63336413119257 \tabularnewline
33 & 1 & 1.30080629405956 & -0.300806294059556 \tabularnewline
34 & 1 & 4.29568543288673 & -3.29568543288673 \tabularnewline
35 & 1 & 11.4492260197185 & -10.4492260197185 \tabularnewline
36 & 2 & 7.06148343418202 & -5.06148343418202 \tabularnewline
37 & 1 & 1.38177084150096 & -0.38177084150096 \tabularnewline
38 & 2 & 4.38235803111947 & -2.38235803111947 \tabularnewline
39 & 2 & 2.18435628371811 & -0.184356283718110 \tabularnewline
40 & 1 & 5.53728230046518 & -4.53728230046518 \tabularnewline
41 & 48 & 18.9574978876908 & 29.0425021123092 \tabularnewline
42 & 48 & 11.2265900216737 & 36.7734099783263 \tabularnewline
43 & 26 & 17.7058747132766 & 8.29412528672336 \tabularnewline
44 & 46 & 14.9310029615348 & 31.0689970384652 \tabularnewline
45 & 3 & 1.99029206300428 & 1.00970793699572 \tabularnewline
46 & 3 & 10.9230375848442 & -7.92303758484422 \tabularnewline
47 & 4 & 3.09697769870321 & 0.90302230129679 \tabularnewline
48 & 2 & 1.39075373547515 & 0.609246264524851 \tabularnewline
49 & 2 & 4.43219800826241 & -2.43219800826241 \tabularnewline
50 & 3 & 2.35632910200113 & 0.643670897998866 \tabularnewline
51 & 2 & 6.5941901103881 & -4.59419011038809 \tabularnewline
52 & 4 & -1.24349542802017 & 5.24349542802017 \tabularnewline
53 & 5 & 7.08161795010098 & -2.08161795010098 \tabularnewline
54 & 3 & 8.62435464925371 & -5.6243546492537 \tabularnewline
55 & 4 & 7.93807414448493 & -3.93807414448493 \tabularnewline
56 & 5 & 4.34926227276939 & 0.650737727230611 \tabularnewline
57 & 5 & -2.25235620820234 & 7.25235620820234 \tabularnewline
58 & 3 & 3.04471462734026 & -0.0447146273402611 \tabularnewline
59 & 4 & 8.80647138782632 & -4.80647138782632 \tabularnewline
60 & 3 & 2.53769616792165 & 0.462303832078353 \tabularnewline
61 & 3 & -0.366947346337223 & 3.36694734633722 \tabularnewline
62 & 2 & 10.5258592723023 & -8.52585927230233 \tabularnewline
63 & 3 & 6.85476884474955 & -3.85476884474955 \tabularnewline
64 & 4 & 8.22282882211235 & -4.22282882211235 \tabularnewline
65 & 4 & 1.01903260110446 & 2.98096739889554 \tabularnewline
66 & 4 & -1.29937044555590 & 5.2993704455559 \tabularnewline
67 & 4 & 1.24548035955089 & 2.75451964044911 \tabularnewline
68 & 3 & 1.14744385697765 & 1.85255614302235 \tabularnewline
69 & 3 & 5.8343252163428 & -2.8343252163428 \tabularnewline
70 & 3 & 0.204000393597296 & 2.79599960640270 \tabularnewline
71 & 2 & 10.5235407011182 & -8.52354070111824 \tabularnewline
72 & 3 & 12.1013460772376 & -9.1013460772376 \tabularnewline
73 & 3 & 6.89967516579061 & -3.89967516579061 \tabularnewline
74 & 3 & 5.81166749650087 & -2.81166749650087 \tabularnewline
75 & 3 & 9.30034715547275 & -6.30034715547275 \tabularnewline
76 & 5 & 1.66289736310208 & 3.33710263689792 \tabularnewline
77 & 3 & 12.0793714459706 & -9.0793714459706 \tabularnewline
78 & 5 & -0.838011187951179 & 5.83801118795118 \tabularnewline
79 & 4 & 0.273092912313519 & 3.72690708768648 \tabularnewline
80 & 4 & 5.55314304503774 & -1.55314304503774 \tabularnewline
81 & 4 & 10.2507998789184 & -6.25079987891844 \tabularnewline
82 & 5 & 15.5395409497259 & -10.5395409497259 \tabularnewline
83 & 4 & 3.03422765876120 & 0.965772341238795 \tabularnewline
84 & 5 & 1.09194505520461 & 3.90805494479539 \tabularnewline
85 & 3 & 3.0599014711538 & -0.0599014711538007 \tabularnewline
86 & 3 & -1.23962805763701 & 4.23962805763701 \tabularnewline
87 & 2 & 12.8177027760521 & -10.8177027760521 \tabularnewline
88 & 3 & 3.09116429662244 & -0.0911642966224442 \tabularnewline
89 & 4 & 2.12043148107217 & 1.87956851892782 \tabularnewline
90 & 5 & 5.56521878961001 & -0.565218789610011 \tabularnewline
91 & 5 & 7.90748360989653 & -2.90748360989653 \tabularnewline
92 & 3 & 3.13463950510875 & -0.134639505108745 \tabularnewline
93 & 2 & 0.564445681945164 & 1.43555431805484 \tabularnewline
94 & 3 & 0.652659750561406 & 2.34734024943859 \tabularnewline
95 & 4 & 10.1419236235833 & -6.1419236235833 \tabularnewline
96 & 1 & 8.70964586416737 & -7.70964586416737 \tabularnewline
97 & 4 & 7.73358717527648 & -3.73358717527648 \tabularnewline
98 & 3 & 2.43476895531057 & 0.565231044689433 \tabularnewline
99 & 3 & 7.3222029494952 & -4.32220294949519 \tabularnewline
100 & 4 & 3.01011575485098 & 0.989884245149022 \tabularnewline
101 & 3 & 12.0729959484639 & -9.07299594846386 \tabularnewline
102 & 4 & 6.8762142234261 & -2.8762142234261 \tabularnewline
103 & 2 & 7.86510020303653 & -5.86510020303653 \tabularnewline
104 & 3 & 1.37593668046909 & 1.62406331953091 \tabularnewline
105 & 3 & 10.9128298956032 & -7.9128298956032 \tabularnewline
106 & 3 & 3.05014031135404 & -0.0501403113540434 \tabularnewline
107 & 2 & 7.32350070540654 & -5.32350070540654 \tabularnewline
108 & 5 & 0.588350633731435 & 4.41164936626856 \tabularnewline
109 & 5 & 8.33441345882437 & -3.33441345882437 \tabularnewline
110 & 4 & 0.748968793623606 & 3.25103120637639 \tabularnewline
111 & 2 & 2.27938638218655 & -0.279386382186551 \tabularnewline
112 & 3 & 13.1689655495660 & -10.1689655495660 \tabularnewline
113 & 3 & 10.5008305852629 & -7.50083058526286 \tabularnewline
114 & 3 & 4.88410224465903 & -1.88410224465903 \tabularnewline
115 & 4 & 0.528046547783699 & 3.4719534522163 \tabularnewline
116 & 5 & 0.74725347854189 & 4.25274652145811 \tabularnewline
117 & 4 & 8.4615530373401 & -4.4615530373401 \tabularnewline
118 & 22 & 28.7331553218017 & -6.73315532180165 \tabularnewline
119 & 16 & 28.7063562534502 & -12.7063562534502 \tabularnewline
120 & 36 & 28.3289937788209 & 7.67100622117911 \tabularnewline
121 & 35 & 31.6421659169094 & 3.35783408309063 \tabularnewline
122 & 25 & 29.6977597761604 & -4.69775977616038 \tabularnewline
123 & 27 & 35.7961286537927 & -8.79612865379268 \tabularnewline
124 & 32 & 28.920724080713 & 3.07927591928703 \tabularnewline
125 & 36 & 26.0458888032232 & 9.95411119677684 \tabularnewline
126 & 51 & 30.0865004817947 & 20.9134995182053 \tabularnewline
127 & 30 & 27.8595234623834 & 2.14047653761662 \tabularnewline
128 & 20 & 27.277866023109 & -7.27786602310897 \tabularnewline
129 & 29 & 27.2703996928688 & 1.72960030713118 \tabularnewline
130 & 26 & 26.8725516892917 & -0.872551689291674 \tabularnewline
131 & 20 & 27.2764631142216 & -7.27646311422158 \tabularnewline
132 & 40 & 27.2570984503173 & 12.7429015496827 \tabularnewline
133 & 29 & 26.6305781847366 & 2.36942181526336 \tabularnewline
134 & 32 & 25.9749212545891 & 6.02507874541087 \tabularnewline
135 & 33 & 29.6265031767956 & 3.37349682320437 \tabularnewline
136 & 32 & 30.8029015809086 & 1.19709841909140 \tabularnewline
137 & 34 & 29.0692790867867 & 4.93072091321325 \tabularnewline
138 & 24 & 25.9200026790982 & -1.92000267909818 \tabularnewline
139 & 25 & 27.5735371509640 & -2.57353715096395 \tabularnewline
140 & 41 & 30.8526070082941 & 10.1473929917059 \tabularnewline
141 & 39 & 27.0498192881332 & 11.9501807118668 \tabularnewline
142 & 21 & 23.9984405957733 & -2.99844059577330 \tabularnewline
143 & 38 & 26.1583605405037 & 11.8416394594963 \tabularnewline
144 & 28 & 25.6089691188871 & 2.39103088111292 \tabularnewline
145 & 37 & 25.2121961890275 & 11.7878038109725 \tabularnewline
146 & 46 & 8.46336516478383 & 37.5366348352162 \tabularnewline
147 & 39 & 7.7239197922127 & 31.2760802077873 \tabularnewline
148 & 21 & 8.13095912320915 & 12.8690408767908 \tabularnewline
149 & 31 & 27.4239943197359 & 3.57600568026408 \tabularnewline
150 & 25 & 19.0462961149689 & 5.95370388503113 \tabularnewline
151 & 29 & 21.3302045907140 & 7.66979540928596 \tabularnewline
152 & 31 & 24.36678842447 & 6.63321157553001 \tabularnewline
153 & 3 & 5.60917080306307 & -2.60917080306307 \tabularnewline
154 & 2 & -3.52386356837696 & 5.52386356837696 \tabularnewline
155 & 2 & 8.58502339424696 & -6.58502339424696 \tabularnewline
156 & 5 & 9.33856342187934 & -4.33856342187933 \tabularnewline
157 & 4 & 1.29744107006574 & 2.70255892993426 \tabularnewline
158 & 2 & 3.07963806270505 & -1.07963806270505 \tabularnewline
159 & 2 & 11.3790706800555 & -9.37907068005548 \tabularnewline
160 & 2 & 0.64658955929365 & 1.35341044070635 \tabularnewline
161 & 1 & 1.56356826790288 & -0.563568267902879 \tabularnewline
162 & 2 & 1.19659405658357 & 0.803405943416427 \tabularnewline
163 & 2 & 10.3458673393406 & -8.34586733934062 \tabularnewline
164 & 2 & 0.605783745906846 & 1.39421625409315 \tabularnewline
165 & 1 & 6.49270797027965 & -5.49270797027965 \tabularnewline
166 & 2 & 4.31899355966605 & -2.31899355966605 \tabularnewline
167 & 2 & -1.66971537495018 & 3.66971537495018 \tabularnewline
168 & 3 & 14.8709870052178 & -11.8709870052178 \tabularnewline
169 & 2 & 8.7192849866857 & -6.7192849866857 \tabularnewline
170 & 2 & 12.7259227019734 & -10.7259227019734 \tabularnewline
171 & 1 & 0.315641971749025 & 0.684358028250975 \tabularnewline
172 & 2 & 7.39731284995304 & -5.39731284995304 \tabularnewline
173 & 2 & -1.83256652753091 & 3.83256652753091 \tabularnewline
174 & 2 & 1.92067209462259 & 0.0793279053774057 \tabularnewline
175 & 2 & -0.646904677578507 & 2.64690467757851 \tabularnewline
176 & 2 & 1.84768934091230 & 0.152310659087697 \tabularnewline
177 & 1 & 1.27728608843949 & -0.277286088439488 \tabularnewline
178 & 2 & 3.54112084656403 & -1.54112084656403 \tabularnewline
179 & 2 & 5.11856335900598 & -3.11856335900598 \tabularnewline
180 & 4 & 0.453926863439224 & 3.54607313656078 \tabularnewline
181 & 5 & 5.37173013726864 & -0.371730137268636 \tabularnewline
182 & 4 & -9.2878401254592 & 13.2878401254592 \tabularnewline
183 & 1 & 0.787351103925494 & 0.212648896074506 \tabularnewline
184 & 2 & -9.31853331394638 & 11.3185333139464 \tabularnewline
185 & 2 & 0.0747448536181814 & 1.92525514638182 \tabularnewline
186 & 2 & 4.3314176570819 & -2.3314176570819 \tabularnewline
187 & 3 & 2.15130274518603 & 0.848697254813966 \tabularnewline
188 & 2 & 4.86468470318074 & -2.86468470318074 \tabularnewline
189 & 2 & 12.4110232873786 & -10.4110232873786 \tabularnewline
190 & 2 & 4.57506500831849 & -2.57506500831849 \tabularnewline
191 & 3 & 12.4312069441456 & -9.4312069441456 \tabularnewline
192 & 2 & 8.82734113549848 & -6.82734113549848 \tabularnewline
193 & 3 & -6.2816983871182 & 9.2816983871182 \tabularnewline
194 & 2 & 5.90993265194108 & -3.90993265194108 \tabularnewline
195 & 3 & 9.23716042935341 & -6.23716042935341 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103942&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]4[/C][C]5.54053271727264[/C][C]-1.54053271727264[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]5.74589718679147[/C][C]-1.74589718679147[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]8.33570181710378[/C][C]-3.33570181710378[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]11.2937880660300[/C][C]-9.29378806603005[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]13.3026012132565[/C][C]-10.3026012132565[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]1.98909573361063[/C][C]3.01090426638937[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]1.11407150950162[/C][C]2.88592849049838[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.03839277955215[/C][C]-0.0383927795521541[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]4.7702828756198[/C][C]-0.7702828756198[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.45326402631017[/C][C]0.546735973689828[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]2.09271033088406[/C][C]2.90728966911594[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]5.76639963152664[/C][C]-1.76639963152664[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]2.96792251753687[/C][C]0.0320774824631324[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]4.38052696024856[/C][C]-0.380526960248558[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]5.69799554812209[/C][C]-2.69799554812209[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]7.07539212922545[/C][C]-4.07539212922545[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]11.8699894832529[/C][C]-7.86998948325292[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]1.21507822714880[/C][C]1.78492177285120[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]-0.522778499638869[/C][C]4.52277849963887[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]13.0060321043531[/C][C]-11.0060321043531[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.75368340947852[/C][C]0.246316590521480[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]13.8449221247115[/C][C]-10.8449221247115[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]2.02160881841858[/C][C]1.97839118158142[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]2.71535538451094[/C][C]1.28464461548906[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]5.48564618538407[/C][C]-2.48564618538407[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]0.723517998446159[/C][C]2.27648200155384[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]6.74764702460978[/C][C]-4.74764702460978[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]4.23864647364891[/C][C]-0.238646473648908[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]8.37698342923519[/C][C]-3.37698342923519[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]14.8180591973579[/C][C]-10.8180591973579[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]5.21328217398476[/C][C]-4.21328217398476[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]2.63336413119257[/C][C]-1.63336413119257[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.30080629405956[/C][C]-0.300806294059556[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]4.29568543288673[/C][C]-3.29568543288673[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]11.4492260197185[/C][C]-10.4492260197185[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]7.06148343418202[/C][C]-5.06148343418202[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.38177084150096[/C][C]-0.38177084150096[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]4.38235803111947[/C][C]-2.38235803111947[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]2.18435628371811[/C][C]-0.184356283718110[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]5.53728230046518[/C][C]-4.53728230046518[/C][/ROW]
[ROW][C]41[/C][C]48[/C][C]18.9574978876908[/C][C]29.0425021123092[/C][/ROW]
[ROW][C]42[/C][C]48[/C][C]11.2265900216737[/C][C]36.7734099783263[/C][/ROW]
[ROW][C]43[/C][C]26[/C][C]17.7058747132766[/C][C]8.29412528672336[/C][/ROW]
[ROW][C]44[/C][C]46[/C][C]14.9310029615348[/C][C]31.0689970384652[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]1.99029206300428[/C][C]1.00970793699572[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]10.9230375848442[/C][C]-7.92303758484422[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]3.09697769870321[/C][C]0.90302230129679[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.39075373547515[/C][C]0.609246264524851[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]4.43219800826241[/C][C]-2.43219800826241[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]2.35632910200113[/C][C]0.643670897998866[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]6.5941901103881[/C][C]-4.59419011038809[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]-1.24349542802017[/C][C]5.24349542802017[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]7.08161795010098[/C][C]-2.08161795010098[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]8.62435464925371[/C][C]-5.6243546492537[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]7.93807414448493[/C][C]-3.93807414448493[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]4.34926227276939[/C][C]0.650737727230611[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]-2.25235620820234[/C][C]7.25235620820234[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]3.04471462734026[/C][C]-0.0447146273402611[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]8.80647138782632[/C][C]-4.80647138782632[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]2.53769616792165[/C][C]0.462303832078353[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]-0.366947346337223[/C][C]3.36694734633722[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]10.5258592723023[/C][C]-8.52585927230233[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]6.85476884474955[/C][C]-3.85476884474955[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]8.22282882211235[/C][C]-4.22282882211235[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]1.01903260110446[/C][C]2.98096739889554[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]-1.29937044555590[/C][C]5.2993704455559[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]1.24548035955089[/C][C]2.75451964044911[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]1.14744385697765[/C][C]1.85255614302235[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]5.8343252163428[/C][C]-2.8343252163428[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]0.204000393597296[/C][C]2.79599960640270[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]10.5235407011182[/C][C]-8.52354070111824[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]12.1013460772376[/C][C]-9.1013460772376[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]6.89967516579061[/C][C]-3.89967516579061[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]5.81166749650087[/C][C]-2.81166749650087[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]9.30034715547275[/C][C]-6.30034715547275[/C][/ROW]
[ROW][C]76[/C][C]5[/C][C]1.66289736310208[/C][C]3.33710263689792[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]12.0793714459706[/C][C]-9.0793714459706[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]-0.838011187951179[/C][C]5.83801118795118[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]0.273092912313519[/C][C]3.72690708768648[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]5.55314304503774[/C][C]-1.55314304503774[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]10.2507998789184[/C][C]-6.25079987891844[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]15.5395409497259[/C][C]-10.5395409497259[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]3.03422765876120[/C][C]0.965772341238795[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]1.09194505520461[/C][C]3.90805494479539[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]3.0599014711538[/C][C]-0.0599014711538007[/C][/ROW]
[ROW][C]86[/C][C]3[/C][C]-1.23962805763701[/C][C]4.23962805763701[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]12.8177027760521[/C][C]-10.8177027760521[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]3.09116429662244[/C][C]-0.0911642966224442[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]2.12043148107217[/C][C]1.87956851892782[/C][/ROW]
[ROW][C]90[/C][C]5[/C][C]5.56521878961001[/C][C]-0.565218789610011[/C][/ROW]
[ROW][C]91[/C][C]5[/C][C]7.90748360989653[/C][C]-2.90748360989653[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]3.13463950510875[/C][C]-0.134639505108745[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]0.564445681945164[/C][C]1.43555431805484[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]0.652659750561406[/C][C]2.34734024943859[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]10.1419236235833[/C][C]-6.1419236235833[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]8.70964586416737[/C][C]-7.70964586416737[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]7.73358717527648[/C][C]-3.73358717527648[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]2.43476895531057[/C][C]0.565231044689433[/C][/ROW]
[ROW][C]99[/C][C]3[/C][C]7.3222029494952[/C][C]-4.32220294949519[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]3.01011575485098[/C][C]0.989884245149022[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]12.0729959484639[/C][C]-9.07299594846386[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]6.8762142234261[/C][C]-2.8762142234261[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]7.86510020303653[/C][C]-5.86510020303653[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]1.37593668046909[/C][C]1.62406331953091[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]10.9128298956032[/C][C]-7.9128298956032[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]3.05014031135404[/C][C]-0.0501403113540434[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]7.32350070540654[/C][C]-5.32350070540654[/C][/ROW]
[ROW][C]108[/C][C]5[/C][C]0.588350633731435[/C][C]4.41164936626856[/C][/ROW]
[ROW][C]109[/C][C]5[/C][C]8.33441345882437[/C][C]-3.33441345882437[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]0.748968793623606[/C][C]3.25103120637639[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]2.27938638218655[/C][C]-0.279386382186551[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]13.1689655495660[/C][C]-10.1689655495660[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]10.5008305852629[/C][C]-7.50083058526286[/C][/ROW]
[ROW][C]114[/C][C]3[/C][C]4.88410224465903[/C][C]-1.88410224465903[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]0.528046547783699[/C][C]3.4719534522163[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]0.74725347854189[/C][C]4.25274652145811[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]8.4615530373401[/C][C]-4.4615530373401[/C][/ROW]
[ROW][C]118[/C][C]22[/C][C]28.7331553218017[/C][C]-6.73315532180165[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]28.7063562534502[/C][C]-12.7063562534502[/C][/ROW]
[ROW][C]120[/C][C]36[/C][C]28.3289937788209[/C][C]7.67100622117911[/C][/ROW]
[ROW][C]121[/C][C]35[/C][C]31.6421659169094[/C][C]3.35783408309063[/C][/ROW]
[ROW][C]122[/C][C]25[/C][C]29.6977597761604[/C][C]-4.69775977616038[/C][/ROW]
[ROW][C]123[/C][C]27[/C][C]35.7961286537927[/C][C]-8.79612865379268[/C][/ROW]
[ROW][C]124[/C][C]32[/C][C]28.920724080713[/C][C]3.07927591928703[/C][/ROW]
[ROW][C]125[/C][C]36[/C][C]26.0458888032232[/C][C]9.95411119677684[/C][/ROW]
[ROW][C]126[/C][C]51[/C][C]30.0865004817947[/C][C]20.9134995182053[/C][/ROW]
[ROW][C]127[/C][C]30[/C][C]27.8595234623834[/C][C]2.14047653761662[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]27.277866023109[/C][C]-7.27786602310897[/C][/ROW]
[ROW][C]129[/C][C]29[/C][C]27.2703996928688[/C][C]1.72960030713118[/C][/ROW]
[ROW][C]130[/C][C]26[/C][C]26.8725516892917[/C][C]-0.872551689291674[/C][/ROW]
[ROW][C]131[/C][C]20[/C][C]27.2764631142216[/C][C]-7.27646311422158[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]27.2570984503173[/C][C]12.7429015496827[/C][/ROW]
[ROW][C]133[/C][C]29[/C][C]26.6305781847366[/C][C]2.36942181526336[/C][/ROW]
[ROW][C]134[/C][C]32[/C][C]25.9749212545891[/C][C]6.02507874541087[/C][/ROW]
[ROW][C]135[/C][C]33[/C][C]29.6265031767956[/C][C]3.37349682320437[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]30.8029015809086[/C][C]1.19709841909140[/C][/ROW]
[ROW][C]137[/C][C]34[/C][C]29.0692790867867[/C][C]4.93072091321325[/C][/ROW]
[ROW][C]138[/C][C]24[/C][C]25.9200026790982[/C][C]-1.92000267909818[/C][/ROW]
[ROW][C]139[/C][C]25[/C][C]27.5735371509640[/C][C]-2.57353715096395[/C][/ROW]
[ROW][C]140[/C][C]41[/C][C]30.8526070082941[/C][C]10.1473929917059[/C][/ROW]
[ROW][C]141[/C][C]39[/C][C]27.0498192881332[/C][C]11.9501807118668[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]23.9984405957733[/C][C]-2.99844059577330[/C][/ROW]
[ROW][C]143[/C][C]38[/C][C]26.1583605405037[/C][C]11.8416394594963[/C][/ROW]
[ROW][C]144[/C][C]28[/C][C]25.6089691188871[/C][C]2.39103088111292[/C][/ROW]
[ROW][C]145[/C][C]37[/C][C]25.2121961890275[/C][C]11.7878038109725[/C][/ROW]
[ROW][C]146[/C][C]46[/C][C]8.46336516478383[/C][C]37.5366348352162[/C][/ROW]
[ROW][C]147[/C][C]39[/C][C]7.7239197922127[/C][C]31.2760802077873[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]8.13095912320915[/C][C]12.8690408767908[/C][/ROW]
[ROW][C]149[/C][C]31[/C][C]27.4239943197359[/C][C]3.57600568026408[/C][/ROW]
[ROW][C]150[/C][C]25[/C][C]19.0462961149689[/C][C]5.95370388503113[/C][/ROW]
[ROW][C]151[/C][C]29[/C][C]21.3302045907140[/C][C]7.66979540928596[/C][/ROW]
[ROW][C]152[/C][C]31[/C][C]24.36678842447[/C][C]6.63321157553001[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]5.60917080306307[/C][C]-2.60917080306307[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]-3.52386356837696[/C][C]5.52386356837696[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]8.58502339424696[/C][C]-6.58502339424696[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]9.33856342187934[/C][C]-4.33856342187933[/C][/ROW]
[ROW][C]157[/C][C]4[/C][C]1.29744107006574[/C][C]2.70255892993426[/C][/ROW]
[ROW][C]158[/C][C]2[/C][C]3.07963806270505[/C][C]-1.07963806270505[/C][/ROW]
[ROW][C]159[/C][C]2[/C][C]11.3790706800555[/C][C]-9.37907068005548[/C][/ROW]
[ROW][C]160[/C][C]2[/C][C]0.64658955929365[/C][C]1.35341044070635[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]1.56356826790288[/C][C]-0.563568267902879[/C][/ROW]
[ROW][C]162[/C][C]2[/C][C]1.19659405658357[/C][C]0.803405943416427[/C][/ROW]
[ROW][C]163[/C][C]2[/C][C]10.3458673393406[/C][C]-8.34586733934062[/C][/ROW]
[ROW][C]164[/C][C]2[/C][C]0.605783745906846[/C][C]1.39421625409315[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]6.49270797027965[/C][C]-5.49270797027965[/C][/ROW]
[ROW][C]166[/C][C]2[/C][C]4.31899355966605[/C][C]-2.31899355966605[/C][/ROW]
[ROW][C]167[/C][C]2[/C][C]-1.66971537495018[/C][C]3.66971537495018[/C][/ROW]
[ROW][C]168[/C][C]3[/C][C]14.8709870052178[/C][C]-11.8709870052178[/C][/ROW]
[ROW][C]169[/C][C]2[/C][C]8.7192849866857[/C][C]-6.7192849866857[/C][/ROW]
[ROW][C]170[/C][C]2[/C][C]12.7259227019734[/C][C]-10.7259227019734[/C][/ROW]
[ROW][C]171[/C][C]1[/C][C]0.315641971749025[/C][C]0.684358028250975[/C][/ROW]
[ROW][C]172[/C][C]2[/C][C]7.39731284995304[/C][C]-5.39731284995304[/C][/ROW]
[ROW][C]173[/C][C]2[/C][C]-1.83256652753091[/C][C]3.83256652753091[/C][/ROW]
[ROW][C]174[/C][C]2[/C][C]1.92067209462259[/C][C]0.0793279053774057[/C][/ROW]
[ROW][C]175[/C][C]2[/C][C]-0.646904677578507[/C][C]2.64690467757851[/C][/ROW]
[ROW][C]176[/C][C]2[/C][C]1.84768934091230[/C][C]0.152310659087697[/C][/ROW]
[ROW][C]177[/C][C]1[/C][C]1.27728608843949[/C][C]-0.277286088439488[/C][/ROW]
[ROW][C]178[/C][C]2[/C][C]3.54112084656403[/C][C]-1.54112084656403[/C][/ROW]
[ROW][C]179[/C][C]2[/C][C]5.11856335900598[/C][C]-3.11856335900598[/C][/ROW]
[ROW][C]180[/C][C]4[/C][C]0.453926863439224[/C][C]3.54607313656078[/C][/ROW]
[ROW][C]181[/C][C]5[/C][C]5.37173013726864[/C][C]-0.371730137268636[/C][/ROW]
[ROW][C]182[/C][C]4[/C][C]-9.2878401254592[/C][C]13.2878401254592[/C][/ROW]
[ROW][C]183[/C][C]1[/C][C]0.787351103925494[/C][C]0.212648896074506[/C][/ROW]
[ROW][C]184[/C][C]2[/C][C]-9.31853331394638[/C][C]11.3185333139464[/C][/ROW]
[ROW][C]185[/C][C]2[/C][C]0.0747448536181814[/C][C]1.92525514638182[/C][/ROW]
[ROW][C]186[/C][C]2[/C][C]4.3314176570819[/C][C]-2.3314176570819[/C][/ROW]
[ROW][C]187[/C][C]3[/C][C]2.15130274518603[/C][C]0.848697254813966[/C][/ROW]
[ROW][C]188[/C][C]2[/C][C]4.86468470318074[/C][C]-2.86468470318074[/C][/ROW]
[ROW][C]189[/C][C]2[/C][C]12.4110232873786[/C][C]-10.4110232873786[/C][/ROW]
[ROW][C]190[/C][C]2[/C][C]4.57506500831849[/C][C]-2.57506500831849[/C][/ROW]
[ROW][C]191[/C][C]3[/C][C]12.4312069441456[/C][C]-9.4312069441456[/C][/ROW]
[ROW][C]192[/C][C]2[/C][C]8.82734113549848[/C][C]-6.82734113549848[/C][/ROW]
[ROW][C]193[/C][C]3[/C][C]-6.2816983871182[/C][C]9.2816983871182[/C][/ROW]
[ROW][C]194[/C][C]2[/C][C]5.90993265194108[/C][C]-3.90993265194108[/C][/ROW]
[ROW][C]195[/C][C]3[/C][C]9.23716042935341[/C][C]-6.23716042935341[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103942&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103942&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
145.54053271727264-1.54053271727264
245.74589718679147-1.74589718679147
358.33570181710378-3.33570181710378
4211.2937880660300-9.29378806603005
5313.3026012132565-10.3026012132565
651.989095733610633.01090426638937
741.114071509501622.88592849049838
844.03839277955215-0.0383927795521541
944.7702828756198-0.7702828756198
1043.453264026310170.546735973689828
1152.092710330884062.90728966911594
1245.76639963152664-1.76639963152664
1332.967922517536870.0320774824631324
1444.38052696024856-0.380526960248558
1535.69799554812209-2.69799554812209
1637.07539212922545-4.07539212922545
17411.8699894832529-7.86998948325292
1831.215078227148801.78492177285120
194-0.5227784996388694.52277849963887
20213.0060321043531-11.0060321043531
2143.753683409478520.246316590521480
22313.8449221247115-10.8449221247115
2342.021608818418581.97839118158142
2442.715355384510941.28464461548906
2535.48564618538407-2.48564618538407
2630.7235179984461592.27648200155384
2726.74764702460978-4.74764702460978
2844.23864647364891-0.238646473648908
2958.37698342923519-3.37698342923519
30414.8180591973579-10.8180591973579
3115.21328217398476-4.21328217398476
3212.63336413119257-1.63336413119257
3311.30080629405956-0.300806294059556
3414.29568543288673-3.29568543288673
35111.4492260197185-10.4492260197185
3627.06148343418202-5.06148343418202
3711.38177084150096-0.38177084150096
3824.38235803111947-2.38235803111947
3922.18435628371811-0.184356283718110
4015.53728230046518-4.53728230046518
414818.957497887690829.0425021123092
424811.226590021673736.7734099783263
432617.70587471327668.29412528672336
444614.931002961534831.0689970384652
4531.990292063004281.00970793699572
46310.9230375848442-7.92303758484422
4743.096977698703210.90302230129679
4821.390753735475150.609246264524851
4924.43219800826241-2.43219800826241
5032.356329102001130.643670897998866
5126.5941901103881-4.59419011038809
524-1.243495428020175.24349542802017
5357.08161795010098-2.08161795010098
5438.62435464925371-5.6243546492537
5547.93807414448493-3.93807414448493
5654.349262272769390.650737727230611
575-2.252356208202347.25235620820234
5833.04471462734026-0.0447146273402611
5948.80647138782632-4.80647138782632
6032.537696167921650.462303832078353
613-0.3669473463372233.36694734633722
62210.5258592723023-8.52585927230233
6336.85476884474955-3.85476884474955
6448.22282882211235-4.22282882211235
6541.019032601104462.98096739889554
664-1.299370445555905.2993704455559
6741.245480359550892.75451964044911
6831.147443856977651.85255614302235
6935.8343252163428-2.8343252163428
7030.2040003935972962.79599960640270
71210.5235407011182-8.52354070111824
72312.1013460772376-9.1013460772376
7336.89967516579061-3.89967516579061
7435.81166749650087-2.81166749650087
7539.30034715547275-6.30034715547275
7651.662897363102083.33710263689792
77312.0793714459706-9.0793714459706
785-0.8380111879511795.83801118795118
7940.2730929123135193.72690708768648
8045.55314304503774-1.55314304503774
81410.2507998789184-6.25079987891844
82515.5395409497259-10.5395409497259
8343.034227658761200.965772341238795
8451.091945055204613.90805494479539
8533.0599014711538-0.0599014711538007
863-1.239628057637014.23962805763701
87212.8177027760521-10.8177027760521
8833.09116429662244-0.0911642966224442
8942.120431481072171.87956851892782
9055.56521878961001-0.565218789610011
9157.90748360989653-2.90748360989653
9233.13463950510875-0.134639505108745
9320.5644456819451641.43555431805484
9430.6526597505614062.34734024943859
95410.1419236235833-6.1419236235833
9618.70964586416737-7.70964586416737
9747.73358717527648-3.73358717527648
9832.434768955310570.565231044689433
9937.3222029494952-4.32220294949519
10043.010115754850980.989884245149022
101312.0729959484639-9.07299594846386
10246.8762142234261-2.8762142234261
10327.86510020303653-5.86510020303653
10431.375936680469091.62406331953091
105310.9128298956032-7.9128298956032
10633.05014031135404-0.0501403113540434
10727.32350070540654-5.32350070540654
10850.5883506337314354.41164936626856
10958.33441345882437-3.33441345882437
11040.7489687936236063.25103120637639
11122.27938638218655-0.279386382186551
112313.1689655495660-10.1689655495660
113310.5008305852629-7.50083058526286
11434.88410224465903-1.88410224465903
11540.5280465477836993.4719534522163
11650.747253478541894.25274652145811
11748.4615530373401-4.4615530373401
1182228.7331553218017-6.73315532180165
1191628.7063562534502-12.7063562534502
1203628.32899377882097.67100622117911
1213531.64216591690943.35783408309063
1222529.6977597761604-4.69775977616038
1232735.7961286537927-8.79612865379268
1243228.9207240807133.07927591928703
1253626.04588880322329.95411119677684
1265130.086500481794720.9134995182053
1273027.85952346238342.14047653761662
1282027.277866023109-7.27786602310897
1292927.27039969286881.72960030713118
1302626.8725516892917-0.872551689291674
1312027.2764631142216-7.27646311422158
1324027.257098450317312.7429015496827
1332926.63057818473662.36942181526336
1343225.97492125458916.02507874541087
1353329.62650317679563.37349682320437
1363230.80290158090861.19709841909140
1373429.06927908678674.93072091321325
1382425.9200026790982-1.92000267909818
1392527.5735371509640-2.57353715096395
1404130.852607008294110.1473929917059
1413927.049819288133211.9501807118668
1422123.9984405957733-2.99844059577330
1433826.158360540503711.8416394594963
1442825.60896911888712.39103088111292
1453725.212196189027511.7878038109725
146468.4633651647838337.5366348352162
147397.723919792212731.2760802077873
148218.1309591232091512.8690408767908
1493127.42399431973593.57600568026408
1502519.04629611496895.95370388503113
1512921.33020459071407.66979540928596
1523124.366788424476.63321157553001
15335.60917080306307-2.60917080306307
1542-3.523863568376965.52386356837696
15528.58502339424696-6.58502339424696
15659.33856342187934-4.33856342187933
15741.297441070065742.70255892993426
15823.07963806270505-1.07963806270505
159211.3790706800555-9.37907068005548
16020.646589559293651.35341044070635
16111.56356826790288-0.563568267902879
16221.196594056583570.803405943416427
163210.3458673393406-8.34586733934062
16420.6057837459068461.39421625409315
16516.49270797027965-5.49270797027965
16624.31899355966605-2.31899355966605
1672-1.669715374950183.66971537495018
168314.8709870052178-11.8709870052178
16928.7192849866857-6.7192849866857
170212.7259227019734-10.7259227019734
17110.3156419717490250.684358028250975
17227.39731284995304-5.39731284995304
1732-1.832566527530913.83256652753091
17421.920672094622590.0793279053774057
1752-0.6469046775785072.64690467757851
17621.847689340912300.152310659087697
17711.27728608843949-0.277286088439488
17823.54112084656403-1.54112084656403
17925.11856335900598-3.11856335900598
18040.4539268634392243.54607313656078
18155.37173013726864-0.371730137268636
1824-9.287840125459213.2878401254592
18310.7873511039254940.212648896074506
1842-9.3185333139463811.3185333139464
18520.07474485361818141.92525514638182
18624.3314176570819-2.3314176570819
18732.151302745186030.848697254813966
18824.86468470318074-2.86468470318074
189212.4110232873786-10.4110232873786
19024.57506500831849-2.57506500831849
191312.4312069441456-9.4312069441456
19228.82734113549848-6.82734113549848
1933-6.28169838711829.2816983871182
19425.90993265194108-3.90993265194108
19539.23716042935341-6.23716042935341






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.000332875204586140.000665750409172280.999667124795414
121.68402933510352e-053.36805867020705e-050.999983159706649
133.07037544078863e-066.14075088157727e-060.99999692962456
141.75905338270447e-073.51810676540894e-070.999999824094662
157.81530113846575e-081.56306022769315e-070.999999921846989
166.20055851749139e-091.24011170349828e-080.999999993799441
176.37318481244552e-101.27463696248910e-090.999999999362682
188.30729340803286e-111.66145868160657e-100.999999999916927
191.25424226072459e-112.50848452144919e-110.999999999987458
209.65306075663017e-131.93061215132603e-120.999999999999035
216.51834263203616e-141.30366852640723e-130.999999999999935
228.81323916358107e-151.76264783271621e-140.99999999999999
236.00199157128413e-161.20039831425683e-151
243.83528145289316e-177.67056290578633e-171
252.57106945427485e-185.14213890854971e-181
269.59638355367987e-181.91927671073597e-171
271.83285164602989e-173.66570329205979e-171
281.80028824634064e-183.60057649268128e-181
295.18210214275806e-191.03642042855161e-181
309.48919697945464e-201.89783939589093e-191
311.07345389169835e-202.14690778339670e-201
321.40360915249209e-212.80721830498417e-211
331.28116512082332e-222.56233024164665e-221
341.28383369198434e-232.56766738396867e-231
352.79389759195507e-245.58779518391014e-241
368.2392892877798e-251.64785785755596e-241
371.82766597670785e-253.6553319534157e-251
383.18555873555979e-266.37111747111957e-261
393.75587313646106e-277.51174627292212e-271
407.35866422008247e-271.47173284401649e-261
410.0004956163845567950.000991232769113590.999504383615443
420.003179906180548730.006359812361097460.996820093819451
430.01043203455272720.02086406910545440.989567965447273
440.1080558152915300.2161116305830610.89194418470847
450.1479949767074940.2959899534149880.852005023292506
460.2303676895827880.4607353791655760.769632310417212
470.1940962494430780.3881924988861560.805903750556922
480.1702535226919180.3405070453838370.829746477308082
490.1583479938590450.316695987718090.841652006140955
500.1376079605956540.2752159211913090.862392039404346
510.1144609166029130.2289218332058260.885539083397087
520.1178592845601980.2357185691203960.882140715439802
530.09646969782351960.1929393956470390.90353030217648
540.09565624963362370.1913124992672470.904343750366376
550.09451457058386630.1890291411677330.905485429416134
560.07968577632074150.1593715526414830.920314223679259
570.068425219233070.136850438466140.93157478076693
580.06822603763645780.1364520752729160.931773962363542
590.07623577666282570.1524715533256510.923764223337174
600.06568461048321130.1313692209664230.934315389516789
610.06044965559813670.1208993111962730.939550344401863
620.08803649103308420.1760729820661680.911963508966916
630.0907156685908790.1814313371817580.909284331409121
640.08305954327983480.1661190865596700.916940456720165
650.07054550959037150.1410910191807430.929454490409628
660.05822166863400890.1164433372680180.94177833136599
670.04814454170940640.09628908341881290.951855458290594
680.04551145986216290.09102291972432590.954488540137837
690.03809035530778890.07618071061557780.96190964469221
700.03627788535129670.07255577070259350.963722114648703
710.03311729138007110.06623458276014220.966882708619929
720.0269341550267530.0538683100535060.973065844973247
730.02894060192356250.0578812038471250.971059398076437
740.03260670441580620.06521340883161250.967393295584194
750.02700211027178220.05400422054356450.972997889728218
760.02312411332863930.04624822665727860.97687588667136
770.02064824366754680.04129648733509360.979351756332453
780.02109149102837090.04218298205674180.97890850897163
790.01787931076748510.03575862153497010.982120689232515
800.01647810368596080.03295620737192170.98352189631404
810.01451070636706320.02902141273412630.985489293632937
820.01700359297675940.03400718595351890.98299640702324
830.01304904536162240.02609809072324470.986950954638378
840.01188983466761510.02377966933523030.988110165332385
850.009189116997499050.01837823399499810.9908108830025
860.01016962996153580.02033925992307150.989830370038464
870.00952363219743340.01904726439486680.990476367802567
880.008085219333094080.01617043866618820.991914780666906
890.006512406320701690.01302481264140340.993487593679298
900.004835252933948350.00967050586789670.995164747066052
910.003629605601091830.007259211202183660.996370394398908
920.002697684601118920.005395369202237830.997302315398881
930.002124435173431270.004248870346862530.997875564826569
940.001660586662280390.003321173324560770.99833941333772
950.001446518636732610.002893037273465220.998553481363267
960.001415352241726000.002830704483452010.998584647758274
970.001107407593438280.002214815186876570.998892592406562
980.001002091896811380.002004183793622770.998997908103189
990.001330190662346860.002660381324693720.998669809337653
1000.000956640939201760.001913281878403520.999043359060798
1010.0007737510672227320.001547502134445460.999226248932777
1020.0005340335158465630.001068067031693130.999465966484153
1030.000947479766839320.001894959533678640.99905252023316
1040.0007073793453725030.001414758690745010.999292620654628
1050.0006060263740066490.001212052748013300.999393973625993
1060.0005574265055329160.001114853011065830.999442573494467
1070.0004894292011260420.0009788584022520840.999510570798874
1080.0003637011102438760.0007274022204877530.999636298889756
1090.0002890126706139160.0005780253412278320.999710987329386
1100.0002189572913837750.000437914582767550.999781042708616
1110.0001680133604658780.0003360267209317560.999831986639534
1120.0001809020605682070.0003618041211364150.999819097939432
1130.0001535783596852780.0003071567193705560.999846421640315
1140.0001235495392562260.0002470990785124520.999876450460744
1159.01596979440711e-050.0001803193958881420.999909840302056
1166.66064684600807e-050.0001332129369201610.99993339353154
1170.0001039870293271270.0002079740586542530.999896012970673
1180.0003081624740052780.0006163249480105560.999691837525995
1190.000767749054546710.001535498109093420.999232250945453
1200.004066057841070920.008132115682141850.99593394215893
1210.008390559234557590.01678111846911520.991609440765442
1220.008660627079451060.01732125415890210.99133937292055
1230.006731475626455740.01346295125291150.993268524373544
1240.007307205349948020.01461441069989600.992692794650052
1250.01042896654231400.02085793308462800.989571033457686
1260.2878605253311930.5757210506623870.712139474668807
1270.2677380177720900.5354760355441810.73226198222791
1280.4700930157644380.9401860315288770.529906984235562
1290.4546902466290060.9093804932580130.545309753370994
1300.4637055148895230.9274110297790460.536294485110477
1310.5497057103944920.9005885792110160.450294289605508
1320.6781615972932810.6436768054134380.321838402706719
1330.6492758179865750.701448364026850.350724182013425
1340.6140594143603250.771881171279350.385940585639675
1350.6338667905088440.7322664189823110.366133209491156
1360.6858591315304830.6282817369390350.314140868469517
1370.6529902606788530.6940194786422940.347009739321147
1380.7212653970920750.557469205815850.278734602907925
1390.7393642672787290.5212714654425430.260635732721271
1400.7644374876556620.4711250246886760.235562512344338
1410.8556285381690260.2887429236619480.144371461830974
1420.9327070214359480.1345859571281050.0672929785640523
1430.961344660246950.07731067950609850.0386553397530492
1440.9557142529871290.08857149402574210.0442857470128711
1450.9830258607419880.03394827851602330.0169741392580117
1460.9999997006036495.98792702817675e-072.99396351408838e-07
1470.9999999050042321.89991536010069e-079.49957680050347e-08
1480.9999997986572584.02685483950036e-072.01342741975018e-07
1490.999999892957852.14084299996821e-071.07042149998410e-07
1500.999999841971323.16057360029883e-071.58028680014941e-07
1510.9999998320115873.35976826948974e-071.67988413474487e-07
15211.06444353769508e-255.32221768847539e-26
15313.85032867366682e-251.92516433683341e-25
15413.06513006702655e-241.53256503351327e-24
15511.27714000607843e-236.38570003039214e-24
15615.15910326060828e-242.57955163030414e-24
15711.99373826439351e-249.96869132196754e-25
15811.71868711764767e-238.59343558823833e-24
15911.26125181945533e-226.30625909727666e-23
16011.24053009161772e-216.20265045808858e-22
16117.36099897388477e-213.68049948694239e-21
16214.71692695730218e-202.35846347865109e-20
16313.64605909355228e-191.82302954677614e-19
16411.92702546181660e-189.63512730908301e-19
16513.20242167371673e-181.60121083685836e-18
16612.73093426699121e-171.36546713349560e-17
16712.60498225001989e-161.30249112500995e-16
1680.9999999999999992.02742854939359e-151.01371427469680e-15
1690.999999999999991.82470912534778e-149.12354562673888e-15
1700.9999999999999321.36885398848659e-136.84426994243295e-14
1710.999999999999911.80747820577943e-139.03739102889716e-14
1720.9999999999991531.69322386992273e-128.46611934961367e-13
1730.999999999994961.00797412815165e-115.03987064075823e-12
1740.999999999952149.57194822140053e-114.78597411070027e-11
1750.999999999579328.41359193607225e-104.20679596803612e-10
1760.9999999962872667.42546718335215e-093.71273359167607e-09
1770.999999994748941.05021203862190e-085.25106019310952e-09
1780.9999999845041223.09917565628446e-081.54958782814223e-08
1790.9999999471054841.05789032311418e-075.2894516155709e-08
1800.9999994071108771.18577824551565e-065.92889122757827e-07
1810.9999963217761067.35644778822883e-063.67822389411442e-06
1820.9999818194275683.63611448630276e-051.81805724315138e-05
1830.999930016914480.0001399661710399486.99830855199738e-05
1840.9996362917861490.000727416427702490.000363708213851245

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.00033287520458614 & 0.00066575040917228 & 0.999667124795414 \tabularnewline
12 & 1.68402933510352e-05 & 3.36805867020705e-05 & 0.999983159706649 \tabularnewline
13 & 3.07037544078863e-06 & 6.14075088157727e-06 & 0.99999692962456 \tabularnewline
14 & 1.75905338270447e-07 & 3.51810676540894e-07 & 0.999999824094662 \tabularnewline
15 & 7.81530113846575e-08 & 1.56306022769315e-07 & 0.999999921846989 \tabularnewline
16 & 6.20055851749139e-09 & 1.24011170349828e-08 & 0.999999993799441 \tabularnewline
17 & 6.37318481244552e-10 & 1.27463696248910e-09 & 0.999999999362682 \tabularnewline
18 & 8.30729340803286e-11 & 1.66145868160657e-10 & 0.999999999916927 \tabularnewline
19 & 1.25424226072459e-11 & 2.50848452144919e-11 & 0.999999999987458 \tabularnewline
20 & 9.65306075663017e-13 & 1.93061215132603e-12 & 0.999999999999035 \tabularnewline
21 & 6.51834263203616e-14 & 1.30366852640723e-13 & 0.999999999999935 \tabularnewline
22 & 8.81323916358107e-15 & 1.76264783271621e-14 & 0.99999999999999 \tabularnewline
23 & 6.00199157128413e-16 & 1.20039831425683e-15 & 1 \tabularnewline
24 & 3.83528145289316e-17 & 7.67056290578633e-17 & 1 \tabularnewline
25 & 2.57106945427485e-18 & 5.14213890854971e-18 & 1 \tabularnewline
26 & 9.59638355367987e-18 & 1.91927671073597e-17 & 1 \tabularnewline
27 & 1.83285164602989e-17 & 3.66570329205979e-17 & 1 \tabularnewline
28 & 1.80028824634064e-18 & 3.60057649268128e-18 & 1 \tabularnewline
29 & 5.18210214275806e-19 & 1.03642042855161e-18 & 1 \tabularnewline
30 & 9.48919697945464e-20 & 1.89783939589093e-19 & 1 \tabularnewline
31 & 1.07345389169835e-20 & 2.14690778339670e-20 & 1 \tabularnewline
32 & 1.40360915249209e-21 & 2.80721830498417e-21 & 1 \tabularnewline
33 & 1.28116512082332e-22 & 2.56233024164665e-22 & 1 \tabularnewline
34 & 1.28383369198434e-23 & 2.56766738396867e-23 & 1 \tabularnewline
35 & 2.79389759195507e-24 & 5.58779518391014e-24 & 1 \tabularnewline
36 & 8.2392892877798e-25 & 1.64785785755596e-24 & 1 \tabularnewline
37 & 1.82766597670785e-25 & 3.6553319534157e-25 & 1 \tabularnewline
38 & 3.18555873555979e-26 & 6.37111747111957e-26 & 1 \tabularnewline
39 & 3.75587313646106e-27 & 7.51174627292212e-27 & 1 \tabularnewline
40 & 7.35866422008247e-27 & 1.47173284401649e-26 & 1 \tabularnewline
41 & 0.000495616384556795 & 0.00099123276911359 & 0.999504383615443 \tabularnewline
42 & 0.00317990618054873 & 0.00635981236109746 & 0.996820093819451 \tabularnewline
43 & 0.0104320345527272 & 0.0208640691054544 & 0.989567965447273 \tabularnewline
44 & 0.108055815291530 & 0.216111630583061 & 0.89194418470847 \tabularnewline
45 & 0.147994976707494 & 0.295989953414988 & 0.852005023292506 \tabularnewline
46 & 0.230367689582788 & 0.460735379165576 & 0.769632310417212 \tabularnewline
47 & 0.194096249443078 & 0.388192498886156 & 0.805903750556922 \tabularnewline
48 & 0.170253522691918 & 0.340507045383837 & 0.829746477308082 \tabularnewline
49 & 0.158347993859045 & 0.31669598771809 & 0.841652006140955 \tabularnewline
50 & 0.137607960595654 & 0.275215921191309 & 0.862392039404346 \tabularnewline
51 & 0.114460916602913 & 0.228921833205826 & 0.885539083397087 \tabularnewline
52 & 0.117859284560198 & 0.235718569120396 & 0.882140715439802 \tabularnewline
53 & 0.0964696978235196 & 0.192939395647039 & 0.90353030217648 \tabularnewline
54 & 0.0956562496336237 & 0.191312499267247 & 0.904343750366376 \tabularnewline
55 & 0.0945145705838663 & 0.189029141167733 & 0.905485429416134 \tabularnewline
56 & 0.0796857763207415 & 0.159371552641483 & 0.920314223679259 \tabularnewline
57 & 0.06842521923307 & 0.13685043846614 & 0.93157478076693 \tabularnewline
58 & 0.0682260376364578 & 0.136452075272916 & 0.931773962363542 \tabularnewline
59 & 0.0762357766628257 & 0.152471553325651 & 0.923764223337174 \tabularnewline
60 & 0.0656846104832113 & 0.131369220966423 & 0.934315389516789 \tabularnewline
61 & 0.0604496555981367 & 0.120899311196273 & 0.939550344401863 \tabularnewline
62 & 0.0880364910330842 & 0.176072982066168 & 0.911963508966916 \tabularnewline
63 & 0.090715668590879 & 0.181431337181758 & 0.909284331409121 \tabularnewline
64 & 0.0830595432798348 & 0.166119086559670 & 0.916940456720165 \tabularnewline
65 & 0.0705455095903715 & 0.141091019180743 & 0.929454490409628 \tabularnewline
66 & 0.0582216686340089 & 0.116443337268018 & 0.94177833136599 \tabularnewline
67 & 0.0481445417094064 & 0.0962890834188129 & 0.951855458290594 \tabularnewline
68 & 0.0455114598621629 & 0.0910229197243259 & 0.954488540137837 \tabularnewline
69 & 0.0380903553077889 & 0.0761807106155778 & 0.96190964469221 \tabularnewline
70 & 0.0362778853512967 & 0.0725557707025935 & 0.963722114648703 \tabularnewline
71 & 0.0331172913800711 & 0.0662345827601422 & 0.966882708619929 \tabularnewline
72 & 0.026934155026753 & 0.053868310053506 & 0.973065844973247 \tabularnewline
73 & 0.0289406019235625 & 0.057881203847125 & 0.971059398076437 \tabularnewline
74 & 0.0326067044158062 & 0.0652134088316125 & 0.967393295584194 \tabularnewline
75 & 0.0270021102717822 & 0.0540042205435645 & 0.972997889728218 \tabularnewline
76 & 0.0231241133286393 & 0.0462482266572786 & 0.97687588667136 \tabularnewline
77 & 0.0206482436675468 & 0.0412964873350936 & 0.979351756332453 \tabularnewline
78 & 0.0210914910283709 & 0.0421829820567418 & 0.97890850897163 \tabularnewline
79 & 0.0178793107674851 & 0.0357586215349701 & 0.982120689232515 \tabularnewline
80 & 0.0164781036859608 & 0.0329562073719217 & 0.98352189631404 \tabularnewline
81 & 0.0145107063670632 & 0.0290214127341263 & 0.985489293632937 \tabularnewline
82 & 0.0170035929767594 & 0.0340071859535189 & 0.98299640702324 \tabularnewline
83 & 0.0130490453616224 & 0.0260980907232447 & 0.986950954638378 \tabularnewline
84 & 0.0118898346676151 & 0.0237796693352303 & 0.988110165332385 \tabularnewline
85 & 0.00918911699749905 & 0.0183782339949981 & 0.9908108830025 \tabularnewline
86 & 0.0101696299615358 & 0.0203392599230715 & 0.989830370038464 \tabularnewline
87 & 0.0095236321974334 & 0.0190472643948668 & 0.990476367802567 \tabularnewline
88 & 0.00808521933309408 & 0.0161704386661882 & 0.991914780666906 \tabularnewline
89 & 0.00651240632070169 & 0.0130248126414034 & 0.993487593679298 \tabularnewline
90 & 0.00483525293394835 & 0.0096705058678967 & 0.995164747066052 \tabularnewline
91 & 0.00362960560109183 & 0.00725921120218366 & 0.996370394398908 \tabularnewline
92 & 0.00269768460111892 & 0.00539536920223783 & 0.997302315398881 \tabularnewline
93 & 0.00212443517343127 & 0.00424887034686253 & 0.997875564826569 \tabularnewline
94 & 0.00166058666228039 & 0.00332117332456077 & 0.99833941333772 \tabularnewline
95 & 0.00144651863673261 & 0.00289303727346522 & 0.998553481363267 \tabularnewline
96 & 0.00141535224172600 & 0.00283070448345201 & 0.998584647758274 \tabularnewline
97 & 0.00110740759343828 & 0.00221481518687657 & 0.998892592406562 \tabularnewline
98 & 0.00100209189681138 & 0.00200418379362277 & 0.998997908103189 \tabularnewline
99 & 0.00133019066234686 & 0.00266038132469372 & 0.998669809337653 \tabularnewline
100 & 0.00095664093920176 & 0.00191328187840352 & 0.999043359060798 \tabularnewline
101 & 0.000773751067222732 & 0.00154750213444546 & 0.999226248932777 \tabularnewline
102 & 0.000534033515846563 & 0.00106806703169313 & 0.999465966484153 \tabularnewline
103 & 0.00094747976683932 & 0.00189495953367864 & 0.99905252023316 \tabularnewline
104 & 0.000707379345372503 & 0.00141475869074501 & 0.999292620654628 \tabularnewline
105 & 0.000606026374006649 & 0.00121205274801330 & 0.999393973625993 \tabularnewline
106 & 0.000557426505532916 & 0.00111485301106583 & 0.999442573494467 \tabularnewline
107 & 0.000489429201126042 & 0.000978858402252084 & 0.999510570798874 \tabularnewline
108 & 0.000363701110243876 & 0.000727402220487753 & 0.999636298889756 \tabularnewline
109 & 0.000289012670613916 & 0.000578025341227832 & 0.999710987329386 \tabularnewline
110 & 0.000218957291383775 & 0.00043791458276755 & 0.999781042708616 \tabularnewline
111 & 0.000168013360465878 & 0.000336026720931756 & 0.999831986639534 \tabularnewline
112 & 0.000180902060568207 & 0.000361804121136415 & 0.999819097939432 \tabularnewline
113 & 0.000153578359685278 & 0.000307156719370556 & 0.999846421640315 \tabularnewline
114 & 0.000123549539256226 & 0.000247099078512452 & 0.999876450460744 \tabularnewline
115 & 9.01596979440711e-05 & 0.000180319395888142 & 0.999909840302056 \tabularnewline
116 & 6.66064684600807e-05 & 0.000133212936920161 & 0.99993339353154 \tabularnewline
117 & 0.000103987029327127 & 0.000207974058654253 & 0.999896012970673 \tabularnewline
118 & 0.000308162474005278 & 0.000616324948010556 & 0.999691837525995 \tabularnewline
119 & 0.00076774905454671 & 0.00153549810909342 & 0.999232250945453 \tabularnewline
120 & 0.00406605784107092 & 0.00813211568214185 & 0.99593394215893 \tabularnewline
121 & 0.00839055923455759 & 0.0167811184691152 & 0.991609440765442 \tabularnewline
122 & 0.00866062707945106 & 0.0173212541589021 & 0.99133937292055 \tabularnewline
123 & 0.00673147562645574 & 0.0134629512529115 & 0.993268524373544 \tabularnewline
124 & 0.00730720534994802 & 0.0146144106998960 & 0.992692794650052 \tabularnewline
125 & 0.0104289665423140 & 0.0208579330846280 & 0.989571033457686 \tabularnewline
126 & 0.287860525331193 & 0.575721050662387 & 0.712139474668807 \tabularnewline
127 & 0.267738017772090 & 0.535476035544181 & 0.73226198222791 \tabularnewline
128 & 0.470093015764438 & 0.940186031528877 & 0.529906984235562 \tabularnewline
129 & 0.454690246629006 & 0.909380493258013 & 0.545309753370994 \tabularnewline
130 & 0.463705514889523 & 0.927411029779046 & 0.536294485110477 \tabularnewline
131 & 0.549705710394492 & 0.900588579211016 & 0.450294289605508 \tabularnewline
132 & 0.678161597293281 & 0.643676805413438 & 0.321838402706719 \tabularnewline
133 & 0.649275817986575 & 0.70144836402685 & 0.350724182013425 \tabularnewline
134 & 0.614059414360325 & 0.77188117127935 & 0.385940585639675 \tabularnewline
135 & 0.633866790508844 & 0.732266418982311 & 0.366133209491156 \tabularnewline
136 & 0.685859131530483 & 0.628281736939035 & 0.314140868469517 \tabularnewline
137 & 0.652990260678853 & 0.694019478642294 & 0.347009739321147 \tabularnewline
138 & 0.721265397092075 & 0.55746920581585 & 0.278734602907925 \tabularnewline
139 & 0.739364267278729 & 0.521271465442543 & 0.260635732721271 \tabularnewline
140 & 0.764437487655662 & 0.471125024688676 & 0.235562512344338 \tabularnewline
141 & 0.855628538169026 & 0.288742923661948 & 0.144371461830974 \tabularnewline
142 & 0.932707021435948 & 0.134585957128105 & 0.0672929785640523 \tabularnewline
143 & 0.96134466024695 & 0.0773106795060985 & 0.0386553397530492 \tabularnewline
144 & 0.955714252987129 & 0.0885714940257421 & 0.0442857470128711 \tabularnewline
145 & 0.983025860741988 & 0.0339482785160233 & 0.0169741392580117 \tabularnewline
146 & 0.999999700603649 & 5.98792702817675e-07 & 2.99396351408838e-07 \tabularnewline
147 & 0.999999905004232 & 1.89991536010069e-07 & 9.49957680050347e-08 \tabularnewline
148 & 0.999999798657258 & 4.02685483950036e-07 & 2.01342741975018e-07 \tabularnewline
149 & 0.99999989295785 & 2.14084299996821e-07 & 1.07042149998410e-07 \tabularnewline
150 & 0.99999984197132 & 3.16057360029883e-07 & 1.58028680014941e-07 \tabularnewline
151 & 0.999999832011587 & 3.35976826948974e-07 & 1.67988413474487e-07 \tabularnewline
152 & 1 & 1.06444353769508e-25 & 5.32221768847539e-26 \tabularnewline
153 & 1 & 3.85032867366682e-25 & 1.92516433683341e-25 \tabularnewline
154 & 1 & 3.06513006702655e-24 & 1.53256503351327e-24 \tabularnewline
155 & 1 & 1.27714000607843e-23 & 6.38570003039214e-24 \tabularnewline
156 & 1 & 5.15910326060828e-24 & 2.57955163030414e-24 \tabularnewline
157 & 1 & 1.99373826439351e-24 & 9.96869132196754e-25 \tabularnewline
158 & 1 & 1.71868711764767e-23 & 8.59343558823833e-24 \tabularnewline
159 & 1 & 1.26125181945533e-22 & 6.30625909727666e-23 \tabularnewline
160 & 1 & 1.24053009161772e-21 & 6.20265045808858e-22 \tabularnewline
161 & 1 & 7.36099897388477e-21 & 3.68049948694239e-21 \tabularnewline
162 & 1 & 4.71692695730218e-20 & 2.35846347865109e-20 \tabularnewline
163 & 1 & 3.64605909355228e-19 & 1.82302954677614e-19 \tabularnewline
164 & 1 & 1.92702546181660e-18 & 9.63512730908301e-19 \tabularnewline
165 & 1 & 3.20242167371673e-18 & 1.60121083685836e-18 \tabularnewline
166 & 1 & 2.73093426699121e-17 & 1.36546713349560e-17 \tabularnewline
167 & 1 & 2.60498225001989e-16 & 1.30249112500995e-16 \tabularnewline
168 & 0.999999999999999 & 2.02742854939359e-15 & 1.01371427469680e-15 \tabularnewline
169 & 0.99999999999999 & 1.82470912534778e-14 & 9.12354562673888e-15 \tabularnewline
170 & 0.999999999999932 & 1.36885398848659e-13 & 6.84426994243295e-14 \tabularnewline
171 & 0.99999999999991 & 1.80747820577943e-13 & 9.03739102889716e-14 \tabularnewline
172 & 0.999999999999153 & 1.69322386992273e-12 & 8.46611934961367e-13 \tabularnewline
173 & 0.99999999999496 & 1.00797412815165e-11 & 5.03987064075823e-12 \tabularnewline
174 & 0.99999999995214 & 9.57194822140053e-11 & 4.78597411070027e-11 \tabularnewline
175 & 0.99999999957932 & 8.41359193607225e-10 & 4.20679596803612e-10 \tabularnewline
176 & 0.999999996287266 & 7.42546718335215e-09 & 3.71273359167607e-09 \tabularnewline
177 & 0.99999999474894 & 1.05021203862190e-08 & 5.25106019310952e-09 \tabularnewline
178 & 0.999999984504122 & 3.09917565628446e-08 & 1.54958782814223e-08 \tabularnewline
179 & 0.999999947105484 & 1.05789032311418e-07 & 5.2894516155709e-08 \tabularnewline
180 & 0.999999407110877 & 1.18577824551565e-06 & 5.92889122757827e-07 \tabularnewline
181 & 0.999996321776106 & 7.35644778822883e-06 & 3.67822389411442e-06 \tabularnewline
182 & 0.999981819427568 & 3.63611448630276e-05 & 1.81805724315138e-05 \tabularnewline
183 & 0.99993001691448 & 0.000139966171039948 & 6.99830855199738e-05 \tabularnewline
184 & 0.999636291786149 & 0.00072741642770249 & 0.000363708213851245 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103942&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]11[/C][C]0.00033287520458614[/C][C]0.00066575040917228[/C][C]0.999667124795414[/C][/ROW]
[ROW][C]12[/C][C]1.68402933510352e-05[/C][C]3.36805867020705e-05[/C][C]0.999983159706649[/C][/ROW]
[ROW][C]13[/C][C]3.07037544078863e-06[/C][C]6.14075088157727e-06[/C][C]0.99999692962456[/C][/ROW]
[ROW][C]14[/C][C]1.75905338270447e-07[/C][C]3.51810676540894e-07[/C][C]0.999999824094662[/C][/ROW]
[ROW][C]15[/C][C]7.81530113846575e-08[/C][C]1.56306022769315e-07[/C][C]0.999999921846989[/C][/ROW]
[ROW][C]16[/C][C]6.20055851749139e-09[/C][C]1.24011170349828e-08[/C][C]0.999999993799441[/C][/ROW]
[ROW][C]17[/C][C]6.37318481244552e-10[/C][C]1.27463696248910e-09[/C][C]0.999999999362682[/C][/ROW]
[ROW][C]18[/C][C]8.30729340803286e-11[/C][C]1.66145868160657e-10[/C][C]0.999999999916927[/C][/ROW]
[ROW][C]19[/C][C]1.25424226072459e-11[/C][C]2.50848452144919e-11[/C][C]0.999999999987458[/C][/ROW]
[ROW][C]20[/C][C]9.65306075663017e-13[/C][C]1.93061215132603e-12[/C][C]0.999999999999035[/C][/ROW]
[ROW][C]21[/C][C]6.51834263203616e-14[/C][C]1.30366852640723e-13[/C][C]0.999999999999935[/C][/ROW]
[ROW][C]22[/C][C]8.81323916358107e-15[/C][C]1.76264783271621e-14[/C][C]0.99999999999999[/C][/ROW]
[ROW][C]23[/C][C]6.00199157128413e-16[/C][C]1.20039831425683e-15[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]3.83528145289316e-17[/C][C]7.67056290578633e-17[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]2.57106945427485e-18[/C][C]5.14213890854971e-18[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]9.59638355367987e-18[/C][C]1.91927671073597e-17[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]1.83285164602989e-17[/C][C]3.66570329205979e-17[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.80028824634064e-18[/C][C]3.60057649268128e-18[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]5.18210214275806e-19[/C][C]1.03642042855161e-18[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]9.48919697945464e-20[/C][C]1.89783939589093e-19[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.07345389169835e-20[/C][C]2.14690778339670e-20[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]1.40360915249209e-21[/C][C]2.80721830498417e-21[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]1.28116512082332e-22[/C][C]2.56233024164665e-22[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]1.28383369198434e-23[/C][C]2.56766738396867e-23[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]2.79389759195507e-24[/C][C]5.58779518391014e-24[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]8.2392892877798e-25[/C][C]1.64785785755596e-24[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]1.82766597670785e-25[/C][C]3.6553319534157e-25[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]3.18555873555979e-26[/C][C]6.37111747111957e-26[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]3.75587313646106e-27[/C][C]7.51174627292212e-27[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]7.35866422008247e-27[/C][C]1.47173284401649e-26[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0.000495616384556795[/C][C]0.00099123276911359[/C][C]0.999504383615443[/C][/ROW]
[ROW][C]42[/C][C]0.00317990618054873[/C][C]0.00635981236109746[/C][C]0.996820093819451[/C][/ROW]
[ROW][C]43[/C][C]0.0104320345527272[/C][C]0.0208640691054544[/C][C]0.989567965447273[/C][/ROW]
[ROW][C]44[/C][C]0.108055815291530[/C][C]0.216111630583061[/C][C]0.89194418470847[/C][/ROW]
[ROW][C]45[/C][C]0.147994976707494[/C][C]0.295989953414988[/C][C]0.852005023292506[/C][/ROW]
[ROW][C]46[/C][C]0.230367689582788[/C][C]0.460735379165576[/C][C]0.769632310417212[/C][/ROW]
[ROW][C]47[/C][C]0.194096249443078[/C][C]0.388192498886156[/C][C]0.805903750556922[/C][/ROW]
[ROW][C]48[/C][C]0.170253522691918[/C][C]0.340507045383837[/C][C]0.829746477308082[/C][/ROW]
[ROW][C]49[/C][C]0.158347993859045[/C][C]0.31669598771809[/C][C]0.841652006140955[/C][/ROW]
[ROW][C]50[/C][C]0.137607960595654[/C][C]0.275215921191309[/C][C]0.862392039404346[/C][/ROW]
[ROW][C]51[/C][C]0.114460916602913[/C][C]0.228921833205826[/C][C]0.885539083397087[/C][/ROW]
[ROW][C]52[/C][C]0.117859284560198[/C][C]0.235718569120396[/C][C]0.882140715439802[/C][/ROW]
[ROW][C]53[/C][C]0.0964696978235196[/C][C]0.192939395647039[/C][C]0.90353030217648[/C][/ROW]
[ROW][C]54[/C][C]0.0956562496336237[/C][C]0.191312499267247[/C][C]0.904343750366376[/C][/ROW]
[ROW][C]55[/C][C]0.0945145705838663[/C][C]0.189029141167733[/C][C]0.905485429416134[/C][/ROW]
[ROW][C]56[/C][C]0.0796857763207415[/C][C]0.159371552641483[/C][C]0.920314223679259[/C][/ROW]
[ROW][C]57[/C][C]0.06842521923307[/C][C]0.13685043846614[/C][C]0.93157478076693[/C][/ROW]
[ROW][C]58[/C][C]0.0682260376364578[/C][C]0.136452075272916[/C][C]0.931773962363542[/C][/ROW]
[ROW][C]59[/C][C]0.0762357766628257[/C][C]0.152471553325651[/C][C]0.923764223337174[/C][/ROW]
[ROW][C]60[/C][C]0.0656846104832113[/C][C]0.131369220966423[/C][C]0.934315389516789[/C][/ROW]
[ROW][C]61[/C][C]0.0604496555981367[/C][C]0.120899311196273[/C][C]0.939550344401863[/C][/ROW]
[ROW][C]62[/C][C]0.0880364910330842[/C][C]0.176072982066168[/C][C]0.911963508966916[/C][/ROW]
[ROW][C]63[/C][C]0.090715668590879[/C][C]0.181431337181758[/C][C]0.909284331409121[/C][/ROW]
[ROW][C]64[/C][C]0.0830595432798348[/C][C]0.166119086559670[/C][C]0.916940456720165[/C][/ROW]
[ROW][C]65[/C][C]0.0705455095903715[/C][C]0.141091019180743[/C][C]0.929454490409628[/C][/ROW]
[ROW][C]66[/C][C]0.0582216686340089[/C][C]0.116443337268018[/C][C]0.94177833136599[/C][/ROW]
[ROW][C]67[/C][C]0.0481445417094064[/C][C]0.0962890834188129[/C][C]0.951855458290594[/C][/ROW]
[ROW][C]68[/C][C]0.0455114598621629[/C][C]0.0910229197243259[/C][C]0.954488540137837[/C][/ROW]
[ROW][C]69[/C][C]0.0380903553077889[/C][C]0.0761807106155778[/C][C]0.96190964469221[/C][/ROW]
[ROW][C]70[/C][C]0.0362778853512967[/C][C]0.0725557707025935[/C][C]0.963722114648703[/C][/ROW]
[ROW][C]71[/C][C]0.0331172913800711[/C][C]0.0662345827601422[/C][C]0.966882708619929[/C][/ROW]
[ROW][C]72[/C][C]0.026934155026753[/C][C]0.053868310053506[/C][C]0.973065844973247[/C][/ROW]
[ROW][C]73[/C][C]0.0289406019235625[/C][C]0.057881203847125[/C][C]0.971059398076437[/C][/ROW]
[ROW][C]74[/C][C]0.0326067044158062[/C][C]0.0652134088316125[/C][C]0.967393295584194[/C][/ROW]
[ROW][C]75[/C][C]0.0270021102717822[/C][C]0.0540042205435645[/C][C]0.972997889728218[/C][/ROW]
[ROW][C]76[/C][C]0.0231241133286393[/C][C]0.0462482266572786[/C][C]0.97687588667136[/C][/ROW]
[ROW][C]77[/C][C]0.0206482436675468[/C][C]0.0412964873350936[/C][C]0.979351756332453[/C][/ROW]
[ROW][C]78[/C][C]0.0210914910283709[/C][C]0.0421829820567418[/C][C]0.97890850897163[/C][/ROW]
[ROW][C]79[/C][C]0.0178793107674851[/C][C]0.0357586215349701[/C][C]0.982120689232515[/C][/ROW]
[ROW][C]80[/C][C]0.0164781036859608[/C][C]0.0329562073719217[/C][C]0.98352189631404[/C][/ROW]
[ROW][C]81[/C][C]0.0145107063670632[/C][C]0.0290214127341263[/C][C]0.985489293632937[/C][/ROW]
[ROW][C]82[/C][C]0.0170035929767594[/C][C]0.0340071859535189[/C][C]0.98299640702324[/C][/ROW]
[ROW][C]83[/C][C]0.0130490453616224[/C][C]0.0260980907232447[/C][C]0.986950954638378[/C][/ROW]
[ROW][C]84[/C][C]0.0118898346676151[/C][C]0.0237796693352303[/C][C]0.988110165332385[/C][/ROW]
[ROW][C]85[/C][C]0.00918911699749905[/C][C]0.0183782339949981[/C][C]0.9908108830025[/C][/ROW]
[ROW][C]86[/C][C]0.0101696299615358[/C][C]0.0203392599230715[/C][C]0.989830370038464[/C][/ROW]
[ROW][C]87[/C][C]0.0095236321974334[/C][C]0.0190472643948668[/C][C]0.990476367802567[/C][/ROW]
[ROW][C]88[/C][C]0.00808521933309408[/C][C]0.0161704386661882[/C][C]0.991914780666906[/C][/ROW]
[ROW][C]89[/C][C]0.00651240632070169[/C][C]0.0130248126414034[/C][C]0.993487593679298[/C][/ROW]
[ROW][C]90[/C][C]0.00483525293394835[/C][C]0.0096705058678967[/C][C]0.995164747066052[/C][/ROW]
[ROW][C]91[/C][C]0.00362960560109183[/C][C]0.00725921120218366[/C][C]0.996370394398908[/C][/ROW]
[ROW][C]92[/C][C]0.00269768460111892[/C][C]0.00539536920223783[/C][C]0.997302315398881[/C][/ROW]
[ROW][C]93[/C][C]0.00212443517343127[/C][C]0.00424887034686253[/C][C]0.997875564826569[/C][/ROW]
[ROW][C]94[/C][C]0.00166058666228039[/C][C]0.00332117332456077[/C][C]0.99833941333772[/C][/ROW]
[ROW][C]95[/C][C]0.00144651863673261[/C][C]0.00289303727346522[/C][C]0.998553481363267[/C][/ROW]
[ROW][C]96[/C][C]0.00141535224172600[/C][C]0.00283070448345201[/C][C]0.998584647758274[/C][/ROW]
[ROW][C]97[/C][C]0.00110740759343828[/C][C]0.00221481518687657[/C][C]0.998892592406562[/C][/ROW]
[ROW][C]98[/C][C]0.00100209189681138[/C][C]0.00200418379362277[/C][C]0.998997908103189[/C][/ROW]
[ROW][C]99[/C][C]0.00133019066234686[/C][C]0.00266038132469372[/C][C]0.998669809337653[/C][/ROW]
[ROW][C]100[/C][C]0.00095664093920176[/C][C]0.00191328187840352[/C][C]0.999043359060798[/C][/ROW]
[ROW][C]101[/C][C]0.000773751067222732[/C][C]0.00154750213444546[/C][C]0.999226248932777[/C][/ROW]
[ROW][C]102[/C][C]0.000534033515846563[/C][C]0.00106806703169313[/C][C]0.999465966484153[/C][/ROW]
[ROW][C]103[/C][C]0.00094747976683932[/C][C]0.00189495953367864[/C][C]0.99905252023316[/C][/ROW]
[ROW][C]104[/C][C]0.000707379345372503[/C][C]0.00141475869074501[/C][C]0.999292620654628[/C][/ROW]
[ROW][C]105[/C][C]0.000606026374006649[/C][C]0.00121205274801330[/C][C]0.999393973625993[/C][/ROW]
[ROW][C]106[/C][C]0.000557426505532916[/C][C]0.00111485301106583[/C][C]0.999442573494467[/C][/ROW]
[ROW][C]107[/C][C]0.000489429201126042[/C][C]0.000978858402252084[/C][C]0.999510570798874[/C][/ROW]
[ROW][C]108[/C][C]0.000363701110243876[/C][C]0.000727402220487753[/C][C]0.999636298889756[/C][/ROW]
[ROW][C]109[/C][C]0.000289012670613916[/C][C]0.000578025341227832[/C][C]0.999710987329386[/C][/ROW]
[ROW][C]110[/C][C]0.000218957291383775[/C][C]0.00043791458276755[/C][C]0.999781042708616[/C][/ROW]
[ROW][C]111[/C][C]0.000168013360465878[/C][C]0.000336026720931756[/C][C]0.999831986639534[/C][/ROW]
[ROW][C]112[/C][C]0.000180902060568207[/C][C]0.000361804121136415[/C][C]0.999819097939432[/C][/ROW]
[ROW][C]113[/C][C]0.000153578359685278[/C][C]0.000307156719370556[/C][C]0.999846421640315[/C][/ROW]
[ROW][C]114[/C][C]0.000123549539256226[/C][C]0.000247099078512452[/C][C]0.999876450460744[/C][/ROW]
[ROW][C]115[/C][C]9.01596979440711e-05[/C][C]0.000180319395888142[/C][C]0.999909840302056[/C][/ROW]
[ROW][C]116[/C][C]6.66064684600807e-05[/C][C]0.000133212936920161[/C][C]0.99993339353154[/C][/ROW]
[ROW][C]117[/C][C]0.000103987029327127[/C][C]0.000207974058654253[/C][C]0.999896012970673[/C][/ROW]
[ROW][C]118[/C][C]0.000308162474005278[/C][C]0.000616324948010556[/C][C]0.999691837525995[/C][/ROW]
[ROW][C]119[/C][C]0.00076774905454671[/C][C]0.00153549810909342[/C][C]0.999232250945453[/C][/ROW]
[ROW][C]120[/C][C]0.00406605784107092[/C][C]0.00813211568214185[/C][C]0.99593394215893[/C][/ROW]
[ROW][C]121[/C][C]0.00839055923455759[/C][C]0.0167811184691152[/C][C]0.991609440765442[/C][/ROW]
[ROW][C]122[/C][C]0.00866062707945106[/C][C]0.0173212541589021[/C][C]0.99133937292055[/C][/ROW]
[ROW][C]123[/C][C]0.00673147562645574[/C][C]0.0134629512529115[/C][C]0.993268524373544[/C][/ROW]
[ROW][C]124[/C][C]0.00730720534994802[/C][C]0.0146144106998960[/C][C]0.992692794650052[/C][/ROW]
[ROW][C]125[/C][C]0.0104289665423140[/C][C]0.0208579330846280[/C][C]0.989571033457686[/C][/ROW]
[ROW][C]126[/C][C]0.287860525331193[/C][C]0.575721050662387[/C][C]0.712139474668807[/C][/ROW]
[ROW][C]127[/C][C]0.267738017772090[/C][C]0.535476035544181[/C][C]0.73226198222791[/C][/ROW]
[ROW][C]128[/C][C]0.470093015764438[/C][C]0.940186031528877[/C][C]0.529906984235562[/C][/ROW]
[ROW][C]129[/C][C]0.454690246629006[/C][C]0.909380493258013[/C][C]0.545309753370994[/C][/ROW]
[ROW][C]130[/C][C]0.463705514889523[/C][C]0.927411029779046[/C][C]0.536294485110477[/C][/ROW]
[ROW][C]131[/C][C]0.549705710394492[/C][C]0.900588579211016[/C][C]0.450294289605508[/C][/ROW]
[ROW][C]132[/C][C]0.678161597293281[/C][C]0.643676805413438[/C][C]0.321838402706719[/C][/ROW]
[ROW][C]133[/C][C]0.649275817986575[/C][C]0.70144836402685[/C][C]0.350724182013425[/C][/ROW]
[ROW][C]134[/C][C]0.614059414360325[/C][C]0.77188117127935[/C][C]0.385940585639675[/C][/ROW]
[ROW][C]135[/C][C]0.633866790508844[/C][C]0.732266418982311[/C][C]0.366133209491156[/C][/ROW]
[ROW][C]136[/C][C]0.685859131530483[/C][C]0.628281736939035[/C][C]0.314140868469517[/C][/ROW]
[ROW][C]137[/C][C]0.652990260678853[/C][C]0.694019478642294[/C][C]0.347009739321147[/C][/ROW]
[ROW][C]138[/C][C]0.721265397092075[/C][C]0.55746920581585[/C][C]0.278734602907925[/C][/ROW]
[ROW][C]139[/C][C]0.739364267278729[/C][C]0.521271465442543[/C][C]0.260635732721271[/C][/ROW]
[ROW][C]140[/C][C]0.764437487655662[/C][C]0.471125024688676[/C][C]0.235562512344338[/C][/ROW]
[ROW][C]141[/C][C]0.855628538169026[/C][C]0.288742923661948[/C][C]0.144371461830974[/C][/ROW]
[ROW][C]142[/C][C]0.932707021435948[/C][C]0.134585957128105[/C][C]0.0672929785640523[/C][/ROW]
[ROW][C]143[/C][C]0.96134466024695[/C][C]0.0773106795060985[/C][C]0.0386553397530492[/C][/ROW]
[ROW][C]144[/C][C]0.955714252987129[/C][C]0.0885714940257421[/C][C]0.0442857470128711[/C][/ROW]
[ROW][C]145[/C][C]0.983025860741988[/C][C]0.0339482785160233[/C][C]0.0169741392580117[/C][/ROW]
[ROW][C]146[/C][C]0.999999700603649[/C][C]5.98792702817675e-07[/C][C]2.99396351408838e-07[/C][/ROW]
[ROW][C]147[/C][C]0.999999905004232[/C][C]1.89991536010069e-07[/C][C]9.49957680050347e-08[/C][/ROW]
[ROW][C]148[/C][C]0.999999798657258[/C][C]4.02685483950036e-07[/C][C]2.01342741975018e-07[/C][/ROW]
[ROW][C]149[/C][C]0.99999989295785[/C][C]2.14084299996821e-07[/C][C]1.07042149998410e-07[/C][/ROW]
[ROW][C]150[/C][C]0.99999984197132[/C][C]3.16057360029883e-07[/C][C]1.58028680014941e-07[/C][/ROW]
[ROW][C]151[/C][C]0.999999832011587[/C][C]3.35976826948974e-07[/C][C]1.67988413474487e-07[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]1.06444353769508e-25[/C][C]5.32221768847539e-26[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]3.85032867366682e-25[/C][C]1.92516433683341e-25[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]3.06513006702655e-24[/C][C]1.53256503351327e-24[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]1.27714000607843e-23[/C][C]6.38570003039214e-24[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]5.15910326060828e-24[/C][C]2.57955163030414e-24[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]1.99373826439351e-24[/C][C]9.96869132196754e-25[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]1.71868711764767e-23[/C][C]8.59343558823833e-24[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]1.26125181945533e-22[/C][C]6.30625909727666e-23[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]1.24053009161772e-21[/C][C]6.20265045808858e-22[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]7.36099897388477e-21[/C][C]3.68049948694239e-21[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]4.71692695730218e-20[/C][C]2.35846347865109e-20[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]3.64605909355228e-19[/C][C]1.82302954677614e-19[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]1.92702546181660e-18[/C][C]9.63512730908301e-19[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]3.20242167371673e-18[/C][C]1.60121083685836e-18[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]2.73093426699121e-17[/C][C]1.36546713349560e-17[/C][/ROW]
[ROW][C]167[/C][C]1[/C][C]2.60498225001989e-16[/C][C]1.30249112500995e-16[/C][/ROW]
[ROW][C]168[/C][C]0.999999999999999[/C][C]2.02742854939359e-15[/C][C]1.01371427469680e-15[/C][/ROW]
[ROW][C]169[/C][C]0.99999999999999[/C][C]1.82470912534778e-14[/C][C]9.12354562673888e-15[/C][/ROW]
[ROW][C]170[/C][C]0.999999999999932[/C][C]1.36885398848659e-13[/C][C]6.84426994243295e-14[/C][/ROW]
[ROW][C]171[/C][C]0.99999999999991[/C][C]1.80747820577943e-13[/C][C]9.03739102889716e-14[/C][/ROW]
[ROW][C]172[/C][C]0.999999999999153[/C][C]1.69322386992273e-12[/C][C]8.46611934961367e-13[/C][/ROW]
[ROW][C]173[/C][C]0.99999999999496[/C][C]1.00797412815165e-11[/C][C]5.03987064075823e-12[/C][/ROW]
[ROW][C]174[/C][C]0.99999999995214[/C][C]9.57194822140053e-11[/C][C]4.78597411070027e-11[/C][/ROW]
[ROW][C]175[/C][C]0.99999999957932[/C][C]8.41359193607225e-10[/C][C]4.20679596803612e-10[/C][/ROW]
[ROW][C]176[/C][C]0.999999996287266[/C][C]7.42546718335215e-09[/C][C]3.71273359167607e-09[/C][/ROW]
[ROW][C]177[/C][C]0.99999999474894[/C][C]1.05021203862190e-08[/C][C]5.25106019310952e-09[/C][/ROW]
[ROW][C]178[/C][C]0.999999984504122[/C][C]3.09917565628446e-08[/C][C]1.54958782814223e-08[/C][/ROW]
[ROW][C]179[/C][C]0.999999947105484[/C][C]1.05789032311418e-07[/C][C]5.2894516155709e-08[/C][/ROW]
[ROW][C]180[/C][C]0.999999407110877[/C][C]1.18577824551565e-06[/C][C]5.92889122757827e-07[/C][/ROW]
[ROW][C]181[/C][C]0.999996321776106[/C][C]7.35644778822883e-06[/C][C]3.67822389411442e-06[/C][/ROW]
[ROW][C]182[/C][C]0.999981819427568[/C][C]3.63611448630276e-05[/C][C]1.81805724315138e-05[/C][/ROW]
[ROW][C]183[/C][C]0.99993001691448[/C][C]0.000139966171039948[/C][C]6.99830855199738e-05[/C][/ROW]
[ROW][C]184[/C][C]0.999636291786149[/C][C]0.00072741642770249[/C][C]0.000363708213851245[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103942&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103942&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
110.000332875204586140.000665750409172280.999667124795414
121.68402933510352e-053.36805867020705e-050.999983159706649
133.07037544078863e-066.14075088157727e-060.99999692962456
141.75905338270447e-073.51810676540894e-070.999999824094662
157.81530113846575e-081.56306022769315e-070.999999921846989
166.20055851749139e-091.24011170349828e-080.999999993799441
176.37318481244552e-101.27463696248910e-090.999999999362682
188.30729340803286e-111.66145868160657e-100.999999999916927
191.25424226072459e-112.50848452144919e-110.999999999987458
209.65306075663017e-131.93061215132603e-120.999999999999035
216.51834263203616e-141.30366852640723e-130.999999999999935
228.81323916358107e-151.76264783271621e-140.99999999999999
236.00199157128413e-161.20039831425683e-151
243.83528145289316e-177.67056290578633e-171
252.57106945427485e-185.14213890854971e-181
269.59638355367987e-181.91927671073597e-171
271.83285164602989e-173.66570329205979e-171
281.80028824634064e-183.60057649268128e-181
295.18210214275806e-191.03642042855161e-181
309.48919697945464e-201.89783939589093e-191
311.07345389169835e-202.14690778339670e-201
321.40360915249209e-212.80721830498417e-211
331.28116512082332e-222.56233024164665e-221
341.28383369198434e-232.56766738396867e-231
352.79389759195507e-245.58779518391014e-241
368.2392892877798e-251.64785785755596e-241
371.82766597670785e-253.6553319534157e-251
383.18555873555979e-266.37111747111957e-261
393.75587313646106e-277.51174627292212e-271
407.35866422008247e-271.47173284401649e-261
410.0004956163845567950.000991232769113590.999504383615443
420.003179906180548730.006359812361097460.996820093819451
430.01043203455272720.02086406910545440.989567965447273
440.1080558152915300.2161116305830610.89194418470847
450.1479949767074940.2959899534149880.852005023292506
460.2303676895827880.4607353791655760.769632310417212
470.1940962494430780.3881924988861560.805903750556922
480.1702535226919180.3405070453838370.829746477308082
490.1583479938590450.316695987718090.841652006140955
500.1376079605956540.2752159211913090.862392039404346
510.1144609166029130.2289218332058260.885539083397087
520.1178592845601980.2357185691203960.882140715439802
530.09646969782351960.1929393956470390.90353030217648
540.09565624963362370.1913124992672470.904343750366376
550.09451457058386630.1890291411677330.905485429416134
560.07968577632074150.1593715526414830.920314223679259
570.068425219233070.136850438466140.93157478076693
580.06822603763645780.1364520752729160.931773962363542
590.07623577666282570.1524715533256510.923764223337174
600.06568461048321130.1313692209664230.934315389516789
610.06044965559813670.1208993111962730.939550344401863
620.08803649103308420.1760729820661680.911963508966916
630.0907156685908790.1814313371817580.909284331409121
640.08305954327983480.1661190865596700.916940456720165
650.07054550959037150.1410910191807430.929454490409628
660.05822166863400890.1164433372680180.94177833136599
670.04814454170940640.09628908341881290.951855458290594
680.04551145986216290.09102291972432590.954488540137837
690.03809035530778890.07618071061557780.96190964469221
700.03627788535129670.07255577070259350.963722114648703
710.03311729138007110.06623458276014220.966882708619929
720.0269341550267530.0538683100535060.973065844973247
730.02894060192356250.0578812038471250.971059398076437
740.03260670441580620.06521340883161250.967393295584194
750.02700211027178220.05400422054356450.972997889728218
760.02312411332863930.04624822665727860.97687588667136
770.02064824366754680.04129648733509360.979351756332453
780.02109149102837090.04218298205674180.97890850897163
790.01787931076748510.03575862153497010.982120689232515
800.01647810368596080.03295620737192170.98352189631404
810.01451070636706320.02902141273412630.985489293632937
820.01700359297675940.03400718595351890.98299640702324
830.01304904536162240.02609809072324470.986950954638378
840.01188983466761510.02377966933523030.988110165332385
850.009189116997499050.01837823399499810.9908108830025
860.01016962996153580.02033925992307150.989830370038464
870.00952363219743340.01904726439486680.990476367802567
880.008085219333094080.01617043866618820.991914780666906
890.006512406320701690.01302481264140340.993487593679298
900.004835252933948350.00967050586789670.995164747066052
910.003629605601091830.007259211202183660.996370394398908
920.002697684601118920.005395369202237830.997302315398881
930.002124435173431270.004248870346862530.997875564826569
940.001660586662280390.003321173324560770.99833941333772
950.001446518636732610.002893037273465220.998553481363267
960.001415352241726000.002830704483452010.998584647758274
970.001107407593438280.002214815186876570.998892592406562
980.001002091896811380.002004183793622770.998997908103189
990.001330190662346860.002660381324693720.998669809337653
1000.000956640939201760.001913281878403520.999043359060798
1010.0007737510672227320.001547502134445460.999226248932777
1020.0005340335158465630.001068067031693130.999465966484153
1030.000947479766839320.001894959533678640.99905252023316
1040.0007073793453725030.001414758690745010.999292620654628
1050.0006060263740066490.001212052748013300.999393973625993
1060.0005574265055329160.001114853011065830.999442573494467
1070.0004894292011260420.0009788584022520840.999510570798874
1080.0003637011102438760.0007274022204877530.999636298889756
1090.0002890126706139160.0005780253412278320.999710987329386
1100.0002189572913837750.000437914582767550.999781042708616
1110.0001680133604658780.0003360267209317560.999831986639534
1120.0001809020605682070.0003618041211364150.999819097939432
1130.0001535783596852780.0003071567193705560.999846421640315
1140.0001235495392562260.0002470990785124520.999876450460744
1159.01596979440711e-050.0001803193958881420.999909840302056
1166.66064684600807e-050.0001332129369201610.99993339353154
1170.0001039870293271270.0002079740586542530.999896012970673
1180.0003081624740052780.0006163249480105560.999691837525995
1190.000767749054546710.001535498109093420.999232250945453
1200.004066057841070920.008132115682141850.99593394215893
1210.008390559234557590.01678111846911520.991609440765442
1220.008660627079451060.01732125415890210.99133937292055
1230.006731475626455740.01346295125291150.993268524373544
1240.007307205349948020.01461441069989600.992692794650052
1250.01042896654231400.02085793308462800.989571033457686
1260.2878605253311930.5757210506623870.712139474668807
1270.2677380177720900.5354760355441810.73226198222791
1280.4700930157644380.9401860315288770.529906984235562
1290.4546902466290060.9093804932580130.545309753370994
1300.4637055148895230.9274110297790460.536294485110477
1310.5497057103944920.9005885792110160.450294289605508
1320.6781615972932810.6436768054134380.321838402706719
1330.6492758179865750.701448364026850.350724182013425
1340.6140594143603250.771881171279350.385940585639675
1350.6338667905088440.7322664189823110.366133209491156
1360.6858591315304830.6282817369390350.314140868469517
1370.6529902606788530.6940194786422940.347009739321147
1380.7212653970920750.557469205815850.278734602907925
1390.7393642672787290.5212714654425430.260635732721271
1400.7644374876556620.4711250246886760.235562512344338
1410.8556285381690260.2887429236619480.144371461830974
1420.9327070214359480.1345859571281050.0672929785640523
1430.961344660246950.07731067950609850.0386553397530492
1440.9557142529871290.08857149402574210.0442857470128711
1450.9830258607419880.03394827851602330.0169741392580117
1460.9999997006036495.98792702817675e-072.99396351408838e-07
1470.9999999050042321.89991536010069e-079.49957680050347e-08
1480.9999997986572584.02685483950036e-072.01342741975018e-07
1490.999999892957852.14084299996821e-071.07042149998410e-07
1500.999999841971323.16057360029883e-071.58028680014941e-07
1510.9999998320115873.35976826948974e-071.67988413474487e-07
15211.06444353769508e-255.32221768847539e-26
15313.85032867366682e-251.92516433683341e-25
15413.06513006702655e-241.53256503351327e-24
15511.27714000607843e-236.38570003039214e-24
15615.15910326060828e-242.57955163030414e-24
15711.99373826439351e-249.96869132196754e-25
15811.71868711764767e-238.59343558823833e-24
15911.26125181945533e-226.30625909727666e-23
16011.24053009161772e-216.20265045808858e-22
16117.36099897388477e-213.68049948694239e-21
16214.71692695730218e-202.35846347865109e-20
16313.64605909355228e-191.82302954677614e-19
16411.92702546181660e-189.63512730908301e-19
16513.20242167371673e-181.60121083685836e-18
16612.73093426699121e-171.36546713349560e-17
16712.60498225001989e-161.30249112500995e-16
1680.9999999999999992.02742854939359e-151.01371427469680e-15
1690.999999999999991.82470912534778e-149.12354562673888e-15
1700.9999999999999321.36885398848659e-136.84426994243295e-14
1710.999999999999911.80747820577943e-139.03739102889716e-14
1720.9999999999991531.69322386992273e-128.46611934961367e-13
1730.999999999994961.00797412815165e-115.03987064075823e-12
1740.999999999952149.57194822140053e-114.78597411070027e-11
1750.999999999579328.41359193607225e-104.20679596803612e-10
1760.9999999962872667.42546718335215e-093.71273359167607e-09
1770.999999994748941.05021203862190e-085.25106019310952e-09
1780.9999999845041223.09917565628446e-081.54958782814223e-08
1790.9999999471054841.05789032311418e-075.2894516155709e-08
1800.9999994071108771.18577824551565e-065.92889122757827e-07
1810.9999963217761067.35644778822883e-063.67822389411442e-06
1820.9999818194275683.63611448630276e-051.81805724315138e-05
1830.999930016914480.0001399661710399486.99830855199738e-05
1840.9996362917861490.000727416427702490.000363708213851245






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1020.586206896551724NOK
5% type I error level1230.706896551724138NOK
10% type I error level1340.770114942528736NOK

\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 & 102 & 0.586206896551724 & NOK \tabularnewline
5% type I error level & 123 & 0.706896551724138 & NOK \tabularnewline
10% type I error level & 134 & 0.770114942528736 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103942&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]102[/C][C]0.586206896551724[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]123[/C][C]0.706896551724138[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]134[/C][C]0.770114942528736[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103942&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103942&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1020.586206896551724NOK
5% type I error level1230.706896551724138NOK
10% type I error level1340.770114942528736NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Input Parameters & R Code

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