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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 10:12:58 +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/t12911982696yug1ymsupa7ejg.htm/, Retrieved Sun, 05 May 2024 00:21:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103904, Retrieved Sun, 05 May 2024 00:21:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Workshop 4] [2010-12-01 10:12:58] [4cd8ab5c565aadc5ce193fb2faa1c53a] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 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 & 11 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=103904&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]11 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=103904&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103904&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 time11 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
Teamwork33rec[t] = + 56.3578676483602 -0.181121611496331geslacht[t] -0.217858586723552leeftijd[t] -0.403102859125285opleiding[t] -0.309347564888897Openheid[t] -0.308371926580031Neuroticisme[t] -0.526092314808072`Extraversie `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Teamwork33rec[t] =  +  56.3578676483602 -0.181121611496331geslacht[t] -0.217858586723552leeftijd[t] -0.403102859125285opleiding[t] -0.309347564888897Openheid[t] -0.308371926580031Neuroticisme[t] -0.526092314808072`Extraversie
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103904&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Teamwork33rec[t] =  +  56.3578676483602 -0.181121611496331geslacht[t] -0.217858586723552leeftijd[t] -0.403102859125285opleiding[t] -0.309347564888897Openheid[t] -0.308371926580031Neuroticisme[t] -0.526092314808072`Extraversie
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103904&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103904&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
Teamwork33rec[t] = + 56.3578676483602 -0.181121611496331geslacht[t] -0.217858586723552leeftijd[t] -0.403102859125285opleiding[t] -0.309347564888897Openheid[t] -0.308371926580031Neuroticisme[t] -0.526092314808072`Extraversie `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)56.35786764836024.90695611.485300
geslacht-0.1811216114963310.064002-2.82990.0051620.002581
leeftijd-0.2178585867235520.057284-3.80310.0001939.7e-05
opleiding-0.4031028591252850.059104-6.820200
Openheid-0.3093475648888970.060651-5.10041e-060
Neuroticisme-0.3083719265800310.057734-5.341300
`Extraversie `-0.5260923148080720.047006-11.191900

\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) & 56.3578676483602 & 4.906956 & 11.4853 & 0 & 0 \tabularnewline
geslacht & -0.181121611496331 & 0.064002 & -2.8299 & 0.005162 & 0.002581 \tabularnewline
leeftijd & -0.217858586723552 & 0.057284 & -3.8031 & 0.000193 & 9.7e-05 \tabularnewline
opleiding & -0.403102859125285 & 0.059104 & -6.8202 & 0 & 0 \tabularnewline
Openheid & -0.309347564888897 & 0.060651 & -5.1004 & 1e-06 & 0 \tabularnewline
Neuroticisme & -0.308371926580031 & 0.057734 & -5.3413 & 0 & 0 \tabularnewline
`Extraversie
` & -0.526092314808072 & 0.047006 & -11.1919 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103904&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]56.3578676483602[/C][C]4.906956[/C][C]11.4853[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]geslacht[/C][C]-0.181121611496331[/C][C]0.064002[/C][C]-2.8299[/C][C]0.005162[/C][C]0.002581[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.217858586723552[/C][C]0.057284[/C][C]-3.8031[/C][C]0.000193[/C][C]9.7e-05[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.403102859125285[/C][C]0.059104[/C][C]-6.8202[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Openheid[/C][C]-0.309347564888897[/C][C]0.060651[/C][C]-5.1004[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]-0.308371926580031[/C][C]0.057734[/C][C]-5.3413[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Extraversie
`[/C][C]-0.526092314808072[/C][C]0.047006[/C][C]-11.1919[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103904&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103904&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)56.35786764836024.90695611.485300
geslacht-0.1811216114963310.064002-2.82990.0051620.002581
leeftijd-0.2178585867235520.057284-3.80310.0001939.7e-05
opleiding-0.4031028591252850.059104-6.820200
Openheid-0.3093475648888970.060651-5.10041e-060
Neuroticisme-0.3083719265800310.057734-5.341300
`Extraversie `-0.5260923148080720.047006-11.191900






Multiple Linear Regression - Regression Statistics
Multiple R0.751173541373145
R-squared0.564261689259072
Adjusted R-squared0.550355147426915
F-TEST (value)40.5752699750474
F-TEST (DF numerator)6
F-TEST (DF denominator)188
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.09104652000884
Sum Squared Residuals12307.4263523221

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.751173541373145 \tabularnewline
R-squared & 0.564261689259072 \tabularnewline
Adjusted R-squared & 0.550355147426915 \tabularnewline
F-TEST (value) & 40.5752699750474 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 188 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.09104652000884 \tabularnewline
Sum Squared Residuals & 12307.4263523221 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103904&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.751173541373145[/C][/ROW]
[ROW][C]R-squared[/C][C]0.564261689259072[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.550355147426915[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]40.5752699750474[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]188[/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]8.09104652000884[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12307.4263523221[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103904&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103904&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 R0.751173541373145
R-squared0.564261689259072
Adjusted R-squared0.550355147426915
F-TEST (value)40.5752699750474
F-TEST (DF numerator)6
F-TEST (DF denominator)188
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.09104652000884
Sum Squared Residuals12307.4263523221






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
123.65569161191305-1.65569161191305
224.82015594122822-2.82015594122822
3111.4450438372218-10.4450438372218
447.90900623028884-3.90900623028884
5310.9457785996221-7.94577859962208
612.79294437218239-1.79294437218239
720.3179519942415231.68204800575848
821.298353629876630.70164637012337
926.07255225487497-4.07255225487497
1023.11890241421145-1.11890241421145
1113.54530006546956-2.54530006546956
1224.92023117635685-2.92023117635685
1334.19674308381483-1.19674308381483
1424.78417434712121-2.78417434712121
1535.40809310151865-2.40809310151865
1638.89488483282224-5.89488483282224
17214.1136362448752-12.1136362448752
1831.483806557351491.51619344264851
192-1.400988498423953.40098849842395
2049.15552152421692-5.15552152421692
2122.71235023979952-0.712350239799518
22310.8491350329213-7.84913503292135
2321.804424921047120.195575078952883
2426.13685790424487-4.13685790424487
2537.73651700664647-4.73651700664647
263-1.603505140987804.6035051409878
2746.95176581254102-2.95176581254102
2826.75290251608709-4.75290251608709
2917.62444787161133-6.62444787161133
30221.0144507437553-19.0144507437553
31111.7718563464008-10.7718563464008
3218.13482686884638-7.13482686884638
3316.26241810889727-5.26241810889727
3419.5530358536636-8.5530358536636
35115.6385762485134-14.6385762485134
36212.6626571803165-10.6626571803165
3716.91373349066285-5.91373349066285
38210.3796501305658-8.37965013056584
39210.9118618344566-8.91186183445657
40110.0479305771889-9.04793057718893
414824.779163805673123.2208361943269
424818.561044567171029.438955432829
432624.60743999525421.39256000474576
444622.504426813886923.4955731861131
4530.8851678933015582.11483210669844
46311.2324631782833-8.23246317828333
4742.334173221671671.66582677832833
482-1.393965531859803.3939655318598
492-0.4589066774151862.45890667741519
503-1.205371912424794.20537191242479
5125.49741981817633-3.49741981817633
524-1.909280066059455.90928006605945
5356.7423165217987-1.74231652179870
54310.5638733778826-7.56387337788265
5547.51509887411194-3.51509887411194
5655.0902582659543-0.090258265954297
575-5.270542682855910.2705426828559
5832.605496038290080.394503961709919
5944.53902564449172-0.539025644491719
6030.08153327631090332.91846672368910
613-1.474842590543034.47484259054303
62212.8727825102441-10.8727825102441
6338.62916684215383-5.62916684215383
6449.84199377090062-5.84199377090062
654-1.641463895413445.64146389541344
664-2.390690662684176.39069066268417
674-1.523715143774625.52371514377462
683-1.845399309489344.84539930948934
6937.320254476196-4.320254476196
703-3.082690013011716.08269001301171
7125.70609681280954-3.70609681280954
72313.3300996063586-10.3300996063586
7336.73316195574574-3.73316195574574
7437.6046921394136-4.60469213941360
75311.7621259274492-8.76212592744922
765-3.585519485011818.5855194850118
77312.4529192344847-9.45291923448468
785-0.6954774990037925.69547749900379
7942.342991172088961.65700882791104
8047.47924173032437-3.47924173032437
81412.4034753841663-8.40347538416633
82521.0709082572884-16.0709082572884
8343.445840305432960.554159694567039
845-3.221190993240098.2211909932401
8532.314160020344940.685839979655059
863-2.439123712998525.43912371299852
87214.6916352816308-12.6916352816308
8830.6127347206459152.38726527935408
8940.1820452460121763.81795475398782
9057.46501377248785-2.46501377248785
9158.87907044584107-3.87907044584107
9232.704929759411550.295070240588451
932-0.3814953196815232.38149531968152
943-1.956829834071434.95682983407143
9549.50835144418757-5.50835144418757
9615.36997226007593-4.36997226007593
97410.6497195173875-6.64971951738749
983-0.4791957082984743.47919570829847
99310.8557088183521-7.85570881835206
10045.25674818434468-1.25674818434468
101312.7053552969696-9.70535529696955
10243.552918818635520.447081181364483
10325.55562272390959-3.55562272390959
10431.805813865225511.19418613477449
105311.7090399878631-8.70903998786305
1063-2.565917638308295.5659176383083
10726.50925433892976-4.50925433892976
1085-1.537978113787486.53797811378748
109512.6014546122057-7.60145461220566
1104-1.449123150181615.44912315018161
11120.618342547130091.38165745286991
112314.3794804915105-11.3794804915105
11338.33901498440335-5.33901498440335
11435.97507472681706-2.97507472681706
1154-1.634936869460295.63493686946029
11651.354210357691273.64578964230873
117412.2699350720912-8.26993507209117
1183026.19494289760213.80505710239792
1192425.0122969624971-1.01229696249706
1202720.49750146564776.50249853435233
1212628.2320701741048-2.23207017410476
1223026.31412920656383.68587079343617
1231529.6137540617741-14.6137540617741
1242824.84037287611443.15962712388556
1253422.537194890218411.4628051097816
1262923.21137717280505.78862282719503
1272623.42824692780122.57175307219876
1283128.30941569369422.69058430630583
1292821.38345520401636.61654479598372
1303329.49614102299253.50385897700755
1313227.01086665668154.98913334331848
1323322.186447197952210.8135528020478
1333121.94230003164179.05769996835832
1343723.603781413909913.3962185860901
1352732.2822536819512-5.28225368195118
1361926.2717367879828-7.27173678798282
1372724.96864209621912.03135790378086
1383123.65846959855827.3415304014418
1393832.09139405409655.90860594590349
1402224.4479836716564-2.44798367165635
1413523.333595890392511.6664041096075
1423523.707941837074411.2920581629256
1433020.08704643779789.91295356220222
1444133.21723409937477.78276590062526
1452523.08147774293051.91852225706950
146266.0663929931834519.9336070068165
1473020.17702773801299.82297226198715
1482518.48786883536376.51213116463625
1493824.430868889571413.5691311104286
1503511.059466655248423.9405333447516
151499.5442504359473339.4557495640527
1524015.266291643449824.7337083565502
15333.86117843611636-0.86117843611636
1542-0.9655477219678732.96554772196787
155511.6273636758047-6.62736367580469
15658.32480193613167-3.32480193613167
15713.52043819858354-2.52043819858354
15823.45440122958215-1.45440122958215
15937.62762208407335-4.62762208407335
16033.03428154667045-0.0342815466704533
16122.77867170394853-0.778671703948527
16231.094517210095001.905482789905
163413.1985215817832-9.19852158178318
16454.559396128525370.440603871474633
16558.06870341105964-3.06870341105965
1661-0.5089871622570751.50898716225707
1672-0.7145971077679272.71459710776793
16839.5909729928579-6.5909729928579
16925.3028760002612-3.3028760002612
17017.25410691762637-6.25410691762637
1712-0.7699507055808022.7699507055808
172212.5795772448798-10.5795772448798
17340.5595010275592753.44049897244073
17433.70550563002747-0.705505630027472
17521.062645403803160.937354596196836
17634.83379723069364-1.83379723069364
17721.757503793952070.242496206047933
17833.29274131959690-0.292741319596895
17926.05171463634383-4.05171463634383
18053.072378662273571.92762133772643
18142.972399352892291.02760064710771
1823-6.130207880621989.13020788062198
18331.598248098330871.40175190166913
1841-6.809823882644417.80982388264441
1852-2.827550209506194.82755020950619
18631.038694486380951.96130551361905
18731.446248219157581.55375178084242
18837.56579694002818-4.56579694002818
18939.28822176585219-6.28822176585219
19026.77196077450595-4.77196077450595
19138.9126312915485-5.9126312915485
192410.4912633383816-6.49126333838164
19320.7544199245589331.24558007544107
19445.03335514991107-1.03335514991107
19523.65569161191377-1.65569161191377

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 3.65569161191305 & -1.65569161191305 \tabularnewline
2 & 2 & 4.82015594122822 & -2.82015594122822 \tabularnewline
3 & 1 & 11.4450438372218 & -10.4450438372218 \tabularnewline
4 & 4 & 7.90900623028884 & -3.90900623028884 \tabularnewline
5 & 3 & 10.9457785996221 & -7.94577859962208 \tabularnewline
6 & 1 & 2.79294437218239 & -1.79294437218239 \tabularnewline
7 & 2 & 0.317951994241523 & 1.68204800575848 \tabularnewline
8 & 2 & 1.29835362987663 & 0.70164637012337 \tabularnewline
9 & 2 & 6.07255225487497 & -4.07255225487497 \tabularnewline
10 & 2 & 3.11890241421145 & -1.11890241421145 \tabularnewline
11 & 1 & 3.54530006546956 & -2.54530006546956 \tabularnewline
12 & 2 & 4.92023117635685 & -2.92023117635685 \tabularnewline
13 & 3 & 4.19674308381483 & -1.19674308381483 \tabularnewline
14 & 2 & 4.78417434712121 & -2.78417434712121 \tabularnewline
15 & 3 & 5.40809310151865 & -2.40809310151865 \tabularnewline
16 & 3 & 8.89488483282224 & -5.89488483282224 \tabularnewline
17 & 2 & 14.1136362448752 & -12.1136362448752 \tabularnewline
18 & 3 & 1.48380655735149 & 1.51619344264851 \tabularnewline
19 & 2 & -1.40098849842395 & 3.40098849842395 \tabularnewline
20 & 4 & 9.15552152421692 & -5.15552152421692 \tabularnewline
21 & 2 & 2.71235023979952 & -0.712350239799518 \tabularnewline
22 & 3 & 10.8491350329213 & -7.84913503292135 \tabularnewline
23 & 2 & 1.80442492104712 & 0.195575078952883 \tabularnewline
24 & 2 & 6.13685790424487 & -4.13685790424487 \tabularnewline
25 & 3 & 7.73651700664647 & -4.73651700664647 \tabularnewline
26 & 3 & -1.60350514098780 & 4.6035051409878 \tabularnewline
27 & 4 & 6.95176581254102 & -2.95176581254102 \tabularnewline
28 & 2 & 6.75290251608709 & -4.75290251608709 \tabularnewline
29 & 1 & 7.62444787161133 & -6.62444787161133 \tabularnewline
30 & 2 & 21.0144507437553 & -19.0144507437553 \tabularnewline
31 & 1 & 11.7718563464008 & -10.7718563464008 \tabularnewline
32 & 1 & 8.13482686884638 & -7.13482686884638 \tabularnewline
33 & 1 & 6.26241810889727 & -5.26241810889727 \tabularnewline
34 & 1 & 9.5530358536636 & -8.5530358536636 \tabularnewline
35 & 1 & 15.6385762485134 & -14.6385762485134 \tabularnewline
36 & 2 & 12.6626571803165 & -10.6626571803165 \tabularnewline
37 & 1 & 6.91373349066285 & -5.91373349066285 \tabularnewline
38 & 2 & 10.3796501305658 & -8.37965013056584 \tabularnewline
39 & 2 & 10.9118618344566 & -8.91186183445657 \tabularnewline
40 & 1 & 10.0479305771889 & -9.04793057718893 \tabularnewline
41 & 48 & 24.7791638056731 & 23.2208361943269 \tabularnewline
42 & 48 & 18.5610445671710 & 29.438955432829 \tabularnewline
43 & 26 & 24.6074399952542 & 1.39256000474576 \tabularnewline
44 & 46 & 22.5044268138869 & 23.4955731861131 \tabularnewline
45 & 3 & 0.885167893301558 & 2.11483210669844 \tabularnewline
46 & 3 & 11.2324631782833 & -8.23246317828333 \tabularnewline
47 & 4 & 2.33417322167167 & 1.66582677832833 \tabularnewline
48 & 2 & -1.39396553185980 & 3.3939655318598 \tabularnewline
49 & 2 & -0.458906677415186 & 2.45890667741519 \tabularnewline
50 & 3 & -1.20537191242479 & 4.20537191242479 \tabularnewline
51 & 2 & 5.49741981817633 & -3.49741981817633 \tabularnewline
52 & 4 & -1.90928006605945 & 5.90928006605945 \tabularnewline
53 & 5 & 6.7423165217987 & -1.74231652179870 \tabularnewline
54 & 3 & 10.5638733778826 & -7.56387337788265 \tabularnewline
55 & 4 & 7.51509887411194 & -3.51509887411194 \tabularnewline
56 & 5 & 5.0902582659543 & -0.090258265954297 \tabularnewline
57 & 5 & -5.2705426828559 & 10.2705426828559 \tabularnewline
58 & 3 & 2.60549603829008 & 0.394503961709919 \tabularnewline
59 & 4 & 4.53902564449172 & -0.539025644491719 \tabularnewline
60 & 3 & 0.0815332763109033 & 2.91846672368910 \tabularnewline
61 & 3 & -1.47484259054303 & 4.47484259054303 \tabularnewline
62 & 2 & 12.8727825102441 & -10.8727825102441 \tabularnewline
63 & 3 & 8.62916684215383 & -5.62916684215383 \tabularnewline
64 & 4 & 9.84199377090062 & -5.84199377090062 \tabularnewline
65 & 4 & -1.64146389541344 & 5.64146389541344 \tabularnewline
66 & 4 & -2.39069066268417 & 6.39069066268417 \tabularnewline
67 & 4 & -1.52371514377462 & 5.52371514377462 \tabularnewline
68 & 3 & -1.84539930948934 & 4.84539930948934 \tabularnewline
69 & 3 & 7.320254476196 & -4.320254476196 \tabularnewline
70 & 3 & -3.08269001301171 & 6.08269001301171 \tabularnewline
71 & 2 & 5.70609681280954 & -3.70609681280954 \tabularnewline
72 & 3 & 13.3300996063586 & -10.3300996063586 \tabularnewline
73 & 3 & 6.73316195574574 & -3.73316195574574 \tabularnewline
74 & 3 & 7.6046921394136 & -4.60469213941360 \tabularnewline
75 & 3 & 11.7621259274492 & -8.76212592744922 \tabularnewline
76 & 5 & -3.58551948501181 & 8.5855194850118 \tabularnewline
77 & 3 & 12.4529192344847 & -9.45291923448468 \tabularnewline
78 & 5 & -0.695477499003792 & 5.69547749900379 \tabularnewline
79 & 4 & 2.34299117208896 & 1.65700882791104 \tabularnewline
80 & 4 & 7.47924173032437 & -3.47924173032437 \tabularnewline
81 & 4 & 12.4034753841663 & -8.40347538416633 \tabularnewline
82 & 5 & 21.0709082572884 & -16.0709082572884 \tabularnewline
83 & 4 & 3.44584030543296 & 0.554159694567039 \tabularnewline
84 & 5 & -3.22119099324009 & 8.2211909932401 \tabularnewline
85 & 3 & 2.31416002034494 & 0.685839979655059 \tabularnewline
86 & 3 & -2.43912371299852 & 5.43912371299852 \tabularnewline
87 & 2 & 14.6916352816308 & -12.6916352816308 \tabularnewline
88 & 3 & 0.612734720645915 & 2.38726527935408 \tabularnewline
89 & 4 & 0.182045246012176 & 3.81795475398782 \tabularnewline
90 & 5 & 7.46501377248785 & -2.46501377248785 \tabularnewline
91 & 5 & 8.87907044584107 & -3.87907044584107 \tabularnewline
92 & 3 & 2.70492975941155 & 0.295070240588451 \tabularnewline
93 & 2 & -0.381495319681523 & 2.38149531968152 \tabularnewline
94 & 3 & -1.95682983407143 & 4.95682983407143 \tabularnewline
95 & 4 & 9.50835144418757 & -5.50835144418757 \tabularnewline
96 & 1 & 5.36997226007593 & -4.36997226007593 \tabularnewline
97 & 4 & 10.6497195173875 & -6.64971951738749 \tabularnewline
98 & 3 & -0.479195708298474 & 3.47919570829847 \tabularnewline
99 & 3 & 10.8557088183521 & -7.85570881835206 \tabularnewline
100 & 4 & 5.25674818434468 & -1.25674818434468 \tabularnewline
101 & 3 & 12.7053552969696 & -9.70535529696955 \tabularnewline
102 & 4 & 3.55291881863552 & 0.447081181364483 \tabularnewline
103 & 2 & 5.55562272390959 & -3.55562272390959 \tabularnewline
104 & 3 & 1.80581386522551 & 1.19418613477449 \tabularnewline
105 & 3 & 11.7090399878631 & -8.70903998786305 \tabularnewline
106 & 3 & -2.56591763830829 & 5.5659176383083 \tabularnewline
107 & 2 & 6.50925433892976 & -4.50925433892976 \tabularnewline
108 & 5 & -1.53797811378748 & 6.53797811378748 \tabularnewline
109 & 5 & 12.6014546122057 & -7.60145461220566 \tabularnewline
110 & 4 & -1.44912315018161 & 5.44912315018161 \tabularnewline
111 & 2 & 0.61834254713009 & 1.38165745286991 \tabularnewline
112 & 3 & 14.3794804915105 & -11.3794804915105 \tabularnewline
113 & 3 & 8.33901498440335 & -5.33901498440335 \tabularnewline
114 & 3 & 5.97507472681706 & -2.97507472681706 \tabularnewline
115 & 4 & -1.63493686946029 & 5.63493686946029 \tabularnewline
116 & 5 & 1.35421035769127 & 3.64578964230873 \tabularnewline
117 & 4 & 12.2699350720912 & -8.26993507209117 \tabularnewline
118 & 30 & 26.1949428976021 & 3.80505710239792 \tabularnewline
119 & 24 & 25.0122969624971 & -1.01229696249706 \tabularnewline
120 & 27 & 20.4975014656477 & 6.50249853435233 \tabularnewline
121 & 26 & 28.2320701741048 & -2.23207017410476 \tabularnewline
122 & 30 & 26.3141292065638 & 3.68587079343617 \tabularnewline
123 & 15 & 29.6137540617741 & -14.6137540617741 \tabularnewline
124 & 28 & 24.8403728761144 & 3.15962712388556 \tabularnewline
125 & 34 & 22.5371948902184 & 11.4628051097816 \tabularnewline
126 & 29 & 23.2113771728050 & 5.78862282719503 \tabularnewline
127 & 26 & 23.4282469278012 & 2.57175307219876 \tabularnewline
128 & 31 & 28.3094156936942 & 2.69058430630583 \tabularnewline
129 & 28 & 21.3834552040163 & 6.61654479598372 \tabularnewline
130 & 33 & 29.4961410229925 & 3.50385897700755 \tabularnewline
131 & 32 & 27.0108666566815 & 4.98913334331848 \tabularnewline
132 & 33 & 22.1864471979522 & 10.8135528020478 \tabularnewline
133 & 31 & 21.9423000316417 & 9.05769996835832 \tabularnewline
134 & 37 & 23.6037814139099 & 13.3962185860901 \tabularnewline
135 & 27 & 32.2822536819512 & -5.28225368195118 \tabularnewline
136 & 19 & 26.2717367879828 & -7.27173678798282 \tabularnewline
137 & 27 & 24.9686420962191 & 2.03135790378086 \tabularnewline
138 & 31 & 23.6584695985582 & 7.3415304014418 \tabularnewline
139 & 38 & 32.0913940540965 & 5.90860594590349 \tabularnewline
140 & 22 & 24.4479836716564 & -2.44798367165635 \tabularnewline
141 & 35 & 23.3335958903925 & 11.6664041096075 \tabularnewline
142 & 35 & 23.7079418370744 & 11.2920581629256 \tabularnewline
143 & 30 & 20.0870464377978 & 9.91295356220222 \tabularnewline
144 & 41 & 33.2172340993747 & 7.78276590062526 \tabularnewline
145 & 25 & 23.0814777429305 & 1.91852225706950 \tabularnewline
146 & 26 & 6.06639299318345 & 19.9336070068165 \tabularnewline
147 & 30 & 20.1770277380129 & 9.82297226198715 \tabularnewline
148 & 25 & 18.4878688353637 & 6.51213116463625 \tabularnewline
149 & 38 & 24.4308688895714 & 13.5691311104286 \tabularnewline
150 & 35 & 11.0594666552484 & 23.9405333447516 \tabularnewline
151 & 49 & 9.54425043594733 & 39.4557495640527 \tabularnewline
152 & 40 & 15.2662916434498 & 24.7337083565502 \tabularnewline
153 & 3 & 3.86117843611636 & -0.86117843611636 \tabularnewline
154 & 2 & -0.965547721967873 & 2.96554772196787 \tabularnewline
155 & 5 & 11.6273636758047 & -6.62736367580469 \tabularnewline
156 & 5 & 8.32480193613167 & -3.32480193613167 \tabularnewline
157 & 1 & 3.52043819858354 & -2.52043819858354 \tabularnewline
158 & 2 & 3.45440122958215 & -1.45440122958215 \tabularnewline
159 & 3 & 7.62762208407335 & -4.62762208407335 \tabularnewline
160 & 3 & 3.03428154667045 & -0.0342815466704533 \tabularnewline
161 & 2 & 2.77867170394853 & -0.778671703948527 \tabularnewline
162 & 3 & 1.09451721009500 & 1.905482789905 \tabularnewline
163 & 4 & 13.1985215817832 & -9.19852158178318 \tabularnewline
164 & 5 & 4.55939612852537 & 0.440603871474633 \tabularnewline
165 & 5 & 8.06870341105964 & -3.06870341105965 \tabularnewline
166 & 1 & -0.508987162257075 & 1.50898716225707 \tabularnewline
167 & 2 & -0.714597107767927 & 2.71459710776793 \tabularnewline
168 & 3 & 9.5909729928579 & -6.5909729928579 \tabularnewline
169 & 2 & 5.3028760002612 & -3.3028760002612 \tabularnewline
170 & 1 & 7.25410691762637 & -6.25410691762637 \tabularnewline
171 & 2 & -0.769950705580802 & 2.7699507055808 \tabularnewline
172 & 2 & 12.5795772448798 & -10.5795772448798 \tabularnewline
173 & 4 & 0.559501027559275 & 3.44049897244073 \tabularnewline
174 & 3 & 3.70550563002747 & -0.705505630027472 \tabularnewline
175 & 2 & 1.06264540380316 & 0.937354596196836 \tabularnewline
176 & 3 & 4.83379723069364 & -1.83379723069364 \tabularnewline
177 & 2 & 1.75750379395207 & 0.242496206047933 \tabularnewline
178 & 3 & 3.29274131959690 & -0.292741319596895 \tabularnewline
179 & 2 & 6.05171463634383 & -4.05171463634383 \tabularnewline
180 & 5 & 3.07237866227357 & 1.92762133772643 \tabularnewline
181 & 4 & 2.97239935289229 & 1.02760064710771 \tabularnewline
182 & 3 & -6.13020788062198 & 9.13020788062198 \tabularnewline
183 & 3 & 1.59824809833087 & 1.40175190166913 \tabularnewline
184 & 1 & -6.80982388264441 & 7.80982388264441 \tabularnewline
185 & 2 & -2.82755020950619 & 4.82755020950619 \tabularnewline
186 & 3 & 1.03869448638095 & 1.96130551361905 \tabularnewline
187 & 3 & 1.44624821915758 & 1.55375178084242 \tabularnewline
188 & 3 & 7.56579694002818 & -4.56579694002818 \tabularnewline
189 & 3 & 9.28822176585219 & -6.28822176585219 \tabularnewline
190 & 2 & 6.77196077450595 & -4.77196077450595 \tabularnewline
191 & 3 & 8.9126312915485 & -5.9126312915485 \tabularnewline
192 & 4 & 10.4912633383816 & -6.49126333838164 \tabularnewline
193 & 2 & 0.754419924558933 & 1.24558007544107 \tabularnewline
194 & 4 & 5.03335514991107 & -1.03335514991107 \tabularnewline
195 & 2 & 3.65569161191377 & -1.65569161191377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103904&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]2[/C][C]3.65569161191305[/C][C]-1.65569161191305[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]4.82015594122822[/C][C]-2.82015594122822[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]11.4450438372218[/C][C]-10.4450438372218[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]7.90900623028884[/C][C]-3.90900623028884[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]10.9457785996221[/C][C]-7.94577859962208[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]2.79294437218239[/C][C]-1.79294437218239[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]0.317951994241523[/C][C]1.68204800575848[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.29835362987663[/C][C]0.70164637012337[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]6.07255225487497[/C][C]-4.07255225487497[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]3.11890241421145[/C][C]-1.11890241421145[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]3.54530006546956[/C][C]-2.54530006546956[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]4.92023117635685[/C][C]-2.92023117635685[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]4.19674308381483[/C][C]-1.19674308381483[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]4.78417434712121[/C][C]-2.78417434712121[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]5.40809310151865[/C][C]-2.40809310151865[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]8.89488483282224[/C][C]-5.89488483282224[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]14.1136362448752[/C][C]-12.1136362448752[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]1.48380655735149[/C][C]1.51619344264851[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]-1.40098849842395[/C][C]3.40098849842395[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]9.15552152421692[/C][C]-5.15552152421692[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]2.71235023979952[/C][C]-0.712350239799518[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]10.8491350329213[/C][C]-7.84913503292135[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.80442492104712[/C][C]0.195575078952883[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]6.13685790424487[/C][C]-4.13685790424487[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]7.73651700664647[/C][C]-4.73651700664647[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]-1.60350514098780[/C][C]4.6035051409878[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]6.95176581254102[/C][C]-2.95176581254102[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]6.75290251608709[/C][C]-4.75290251608709[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]7.62444787161133[/C][C]-6.62444787161133[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]21.0144507437553[/C][C]-19.0144507437553[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]11.7718563464008[/C][C]-10.7718563464008[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]8.13482686884638[/C][C]-7.13482686884638[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]6.26241810889727[/C][C]-5.26241810889727[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]9.5530358536636[/C][C]-8.5530358536636[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]15.6385762485134[/C][C]-14.6385762485134[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]12.6626571803165[/C][C]-10.6626571803165[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]6.91373349066285[/C][C]-5.91373349066285[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]10.3796501305658[/C][C]-8.37965013056584[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]10.9118618344566[/C][C]-8.91186183445657[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]10.0479305771889[/C][C]-9.04793057718893[/C][/ROW]
[ROW][C]41[/C][C]48[/C][C]24.7791638056731[/C][C]23.2208361943269[/C][/ROW]
[ROW][C]42[/C][C]48[/C][C]18.5610445671710[/C][C]29.438955432829[/C][/ROW]
[ROW][C]43[/C][C]26[/C][C]24.6074399952542[/C][C]1.39256000474576[/C][/ROW]
[ROW][C]44[/C][C]46[/C][C]22.5044268138869[/C][C]23.4955731861131[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]0.885167893301558[/C][C]2.11483210669844[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]11.2324631782833[/C][C]-8.23246317828333[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]2.33417322167167[/C][C]1.66582677832833[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]-1.39396553185980[/C][C]3.3939655318598[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]-0.458906677415186[/C][C]2.45890667741519[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]-1.20537191242479[/C][C]4.20537191242479[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]5.49741981817633[/C][C]-3.49741981817633[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]-1.90928006605945[/C][C]5.90928006605945[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]6.7423165217987[/C][C]-1.74231652179870[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]10.5638733778826[/C][C]-7.56387337788265[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]7.51509887411194[/C][C]-3.51509887411194[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]5.0902582659543[/C][C]-0.090258265954297[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]-5.2705426828559[/C][C]10.2705426828559[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]2.60549603829008[/C][C]0.394503961709919[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]4.53902564449172[/C][C]-0.539025644491719[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]0.0815332763109033[/C][C]2.91846672368910[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]-1.47484259054303[/C][C]4.47484259054303[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]12.8727825102441[/C][C]-10.8727825102441[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]8.62916684215383[/C][C]-5.62916684215383[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]9.84199377090062[/C][C]-5.84199377090062[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]-1.64146389541344[/C][C]5.64146389541344[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]-2.39069066268417[/C][C]6.39069066268417[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]-1.52371514377462[/C][C]5.52371514377462[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]-1.84539930948934[/C][C]4.84539930948934[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]7.320254476196[/C][C]-4.320254476196[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]-3.08269001301171[/C][C]6.08269001301171[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]5.70609681280954[/C][C]-3.70609681280954[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]13.3300996063586[/C][C]-10.3300996063586[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]6.73316195574574[/C][C]-3.73316195574574[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]7.6046921394136[/C][C]-4.60469213941360[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]11.7621259274492[/C][C]-8.76212592744922[/C][/ROW]
[ROW][C]76[/C][C]5[/C][C]-3.58551948501181[/C][C]8.5855194850118[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]12.4529192344847[/C][C]-9.45291923448468[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]-0.695477499003792[/C][C]5.69547749900379[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]2.34299117208896[/C][C]1.65700882791104[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]7.47924173032437[/C][C]-3.47924173032437[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]12.4034753841663[/C][C]-8.40347538416633[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]21.0709082572884[/C][C]-16.0709082572884[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]3.44584030543296[/C][C]0.554159694567039[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]-3.22119099324009[/C][C]8.2211909932401[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]2.31416002034494[/C][C]0.685839979655059[/C][/ROW]
[ROW][C]86[/C][C]3[/C][C]-2.43912371299852[/C][C]5.43912371299852[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]14.6916352816308[/C][C]-12.6916352816308[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]0.612734720645915[/C][C]2.38726527935408[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]0.182045246012176[/C][C]3.81795475398782[/C][/ROW]
[ROW][C]90[/C][C]5[/C][C]7.46501377248785[/C][C]-2.46501377248785[/C][/ROW]
[ROW][C]91[/C][C]5[/C][C]8.87907044584107[/C][C]-3.87907044584107[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]2.70492975941155[/C][C]0.295070240588451[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]-0.381495319681523[/C][C]2.38149531968152[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]-1.95682983407143[/C][C]4.95682983407143[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]9.50835144418757[/C][C]-5.50835144418757[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]5.36997226007593[/C][C]-4.36997226007593[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]10.6497195173875[/C][C]-6.64971951738749[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]-0.479195708298474[/C][C]3.47919570829847[/C][/ROW]
[ROW][C]99[/C][C]3[/C][C]10.8557088183521[/C][C]-7.85570881835206[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]5.25674818434468[/C][C]-1.25674818434468[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]12.7053552969696[/C][C]-9.70535529696955[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.55291881863552[/C][C]0.447081181364483[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]5.55562272390959[/C][C]-3.55562272390959[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]1.80581386522551[/C][C]1.19418613477449[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]11.7090399878631[/C][C]-8.70903998786305[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]-2.56591763830829[/C][C]5.5659176383083[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]6.50925433892976[/C][C]-4.50925433892976[/C][/ROW]
[ROW][C]108[/C][C]5[/C][C]-1.53797811378748[/C][C]6.53797811378748[/C][/ROW]
[ROW][C]109[/C][C]5[/C][C]12.6014546122057[/C][C]-7.60145461220566[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]-1.44912315018161[/C][C]5.44912315018161[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]0.61834254713009[/C][C]1.38165745286991[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]14.3794804915105[/C][C]-11.3794804915105[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]8.33901498440335[/C][C]-5.33901498440335[/C][/ROW]
[ROW][C]114[/C][C]3[/C][C]5.97507472681706[/C][C]-2.97507472681706[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]-1.63493686946029[/C][C]5.63493686946029[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]1.35421035769127[/C][C]3.64578964230873[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]12.2699350720912[/C][C]-8.26993507209117[/C][/ROW]
[ROW][C]118[/C][C]30[/C][C]26.1949428976021[/C][C]3.80505710239792[/C][/ROW]
[ROW][C]119[/C][C]24[/C][C]25.0122969624971[/C][C]-1.01229696249706[/C][/ROW]
[ROW][C]120[/C][C]27[/C][C]20.4975014656477[/C][C]6.50249853435233[/C][/ROW]
[ROW][C]121[/C][C]26[/C][C]28.2320701741048[/C][C]-2.23207017410476[/C][/ROW]
[ROW][C]122[/C][C]30[/C][C]26.3141292065638[/C][C]3.68587079343617[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]29.6137540617741[/C][C]-14.6137540617741[/C][/ROW]
[ROW][C]124[/C][C]28[/C][C]24.8403728761144[/C][C]3.15962712388556[/C][/ROW]
[ROW][C]125[/C][C]34[/C][C]22.5371948902184[/C][C]11.4628051097816[/C][/ROW]
[ROW][C]126[/C][C]29[/C][C]23.2113771728050[/C][C]5.78862282719503[/C][/ROW]
[ROW][C]127[/C][C]26[/C][C]23.4282469278012[/C][C]2.57175307219876[/C][/ROW]
[ROW][C]128[/C][C]31[/C][C]28.3094156936942[/C][C]2.69058430630583[/C][/ROW]
[ROW][C]129[/C][C]28[/C][C]21.3834552040163[/C][C]6.61654479598372[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]29.4961410229925[/C][C]3.50385897700755[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]27.0108666566815[/C][C]4.98913334331848[/C][/ROW]
[ROW][C]132[/C][C]33[/C][C]22.1864471979522[/C][C]10.8135528020478[/C][/ROW]
[ROW][C]133[/C][C]31[/C][C]21.9423000316417[/C][C]9.05769996835832[/C][/ROW]
[ROW][C]134[/C][C]37[/C][C]23.6037814139099[/C][C]13.3962185860901[/C][/ROW]
[ROW][C]135[/C][C]27[/C][C]32.2822536819512[/C][C]-5.28225368195118[/C][/ROW]
[ROW][C]136[/C][C]19[/C][C]26.2717367879828[/C][C]-7.27173678798282[/C][/ROW]
[ROW][C]137[/C][C]27[/C][C]24.9686420962191[/C][C]2.03135790378086[/C][/ROW]
[ROW][C]138[/C][C]31[/C][C]23.6584695985582[/C][C]7.3415304014418[/C][/ROW]
[ROW][C]139[/C][C]38[/C][C]32.0913940540965[/C][C]5.90860594590349[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]24.4479836716564[/C][C]-2.44798367165635[/C][/ROW]
[ROW][C]141[/C][C]35[/C][C]23.3335958903925[/C][C]11.6664041096075[/C][/ROW]
[ROW][C]142[/C][C]35[/C][C]23.7079418370744[/C][C]11.2920581629256[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]20.0870464377978[/C][C]9.91295356220222[/C][/ROW]
[ROW][C]144[/C][C]41[/C][C]33.2172340993747[/C][C]7.78276590062526[/C][/ROW]
[ROW][C]145[/C][C]25[/C][C]23.0814777429305[/C][C]1.91852225706950[/C][/ROW]
[ROW][C]146[/C][C]26[/C][C]6.06639299318345[/C][C]19.9336070068165[/C][/ROW]
[ROW][C]147[/C][C]30[/C][C]20.1770277380129[/C][C]9.82297226198715[/C][/ROW]
[ROW][C]148[/C][C]25[/C][C]18.4878688353637[/C][C]6.51213116463625[/C][/ROW]
[ROW][C]149[/C][C]38[/C][C]24.4308688895714[/C][C]13.5691311104286[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]11.0594666552484[/C][C]23.9405333447516[/C][/ROW]
[ROW][C]151[/C][C]49[/C][C]9.54425043594733[/C][C]39.4557495640527[/C][/ROW]
[ROW][C]152[/C][C]40[/C][C]15.2662916434498[/C][C]24.7337083565502[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]3.86117843611636[/C][C]-0.86117843611636[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]-0.965547721967873[/C][C]2.96554772196787[/C][/ROW]
[ROW][C]155[/C][C]5[/C][C]11.6273636758047[/C][C]-6.62736367580469[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]8.32480193613167[/C][C]-3.32480193613167[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]3.52043819858354[/C][C]-2.52043819858354[/C][/ROW]
[ROW][C]158[/C][C]2[/C][C]3.45440122958215[/C][C]-1.45440122958215[/C][/ROW]
[ROW][C]159[/C][C]3[/C][C]7.62762208407335[/C][C]-4.62762208407335[/C][/ROW]
[ROW][C]160[/C][C]3[/C][C]3.03428154667045[/C][C]-0.0342815466704533[/C][/ROW]
[ROW][C]161[/C][C]2[/C][C]2.77867170394853[/C][C]-0.778671703948527[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]1.09451721009500[/C][C]1.905482789905[/C][/ROW]
[ROW][C]163[/C][C]4[/C][C]13.1985215817832[/C][C]-9.19852158178318[/C][/ROW]
[ROW][C]164[/C][C]5[/C][C]4.55939612852537[/C][C]0.440603871474633[/C][/ROW]
[ROW][C]165[/C][C]5[/C][C]8.06870341105964[/C][C]-3.06870341105965[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]-0.508987162257075[/C][C]1.50898716225707[/C][/ROW]
[ROW][C]167[/C][C]2[/C][C]-0.714597107767927[/C][C]2.71459710776793[/C][/ROW]
[ROW][C]168[/C][C]3[/C][C]9.5909729928579[/C][C]-6.5909729928579[/C][/ROW]
[ROW][C]169[/C][C]2[/C][C]5.3028760002612[/C][C]-3.3028760002612[/C][/ROW]
[ROW][C]170[/C][C]1[/C][C]7.25410691762637[/C][C]-6.25410691762637[/C][/ROW]
[ROW][C]171[/C][C]2[/C][C]-0.769950705580802[/C][C]2.7699507055808[/C][/ROW]
[ROW][C]172[/C][C]2[/C][C]12.5795772448798[/C][C]-10.5795772448798[/C][/ROW]
[ROW][C]173[/C][C]4[/C][C]0.559501027559275[/C][C]3.44049897244073[/C][/ROW]
[ROW][C]174[/C][C]3[/C][C]3.70550563002747[/C][C]-0.705505630027472[/C][/ROW]
[ROW][C]175[/C][C]2[/C][C]1.06264540380316[/C][C]0.937354596196836[/C][/ROW]
[ROW][C]176[/C][C]3[/C][C]4.83379723069364[/C][C]-1.83379723069364[/C][/ROW]
[ROW][C]177[/C][C]2[/C][C]1.75750379395207[/C][C]0.242496206047933[/C][/ROW]
[ROW][C]178[/C][C]3[/C][C]3.29274131959690[/C][C]-0.292741319596895[/C][/ROW]
[ROW][C]179[/C][C]2[/C][C]6.05171463634383[/C][C]-4.05171463634383[/C][/ROW]
[ROW][C]180[/C][C]5[/C][C]3.07237866227357[/C][C]1.92762133772643[/C][/ROW]
[ROW][C]181[/C][C]4[/C][C]2.97239935289229[/C][C]1.02760064710771[/C][/ROW]
[ROW][C]182[/C][C]3[/C][C]-6.13020788062198[/C][C]9.13020788062198[/C][/ROW]
[ROW][C]183[/C][C]3[/C][C]1.59824809833087[/C][C]1.40175190166913[/C][/ROW]
[ROW][C]184[/C][C]1[/C][C]-6.80982388264441[/C][C]7.80982388264441[/C][/ROW]
[ROW][C]185[/C][C]2[/C][C]-2.82755020950619[/C][C]4.82755020950619[/C][/ROW]
[ROW][C]186[/C][C]3[/C][C]1.03869448638095[/C][C]1.96130551361905[/C][/ROW]
[ROW][C]187[/C][C]3[/C][C]1.44624821915758[/C][C]1.55375178084242[/C][/ROW]
[ROW][C]188[/C][C]3[/C][C]7.56579694002818[/C][C]-4.56579694002818[/C][/ROW]
[ROW][C]189[/C][C]3[/C][C]9.28822176585219[/C][C]-6.28822176585219[/C][/ROW]
[ROW][C]190[/C][C]2[/C][C]6.77196077450595[/C][C]-4.77196077450595[/C][/ROW]
[ROW][C]191[/C][C]3[/C][C]8.9126312915485[/C][C]-5.9126312915485[/C][/ROW]
[ROW][C]192[/C][C]4[/C][C]10.4912633383816[/C][C]-6.49126333838164[/C][/ROW]
[ROW][C]193[/C][C]2[/C][C]0.754419924558933[/C][C]1.24558007544107[/C][/ROW]
[ROW][C]194[/C][C]4[/C][C]5.03335514991107[/C][C]-1.03335514991107[/C][/ROW]
[ROW][C]195[/C][C]2[/C][C]3.65569161191377[/C][C]-1.65569161191377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103904&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103904&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
123.65569161191305-1.65569161191305
224.82015594122822-2.82015594122822
3111.4450438372218-10.4450438372218
447.90900623028884-3.90900623028884
5310.9457785996221-7.94577859962208
612.79294437218239-1.79294437218239
720.3179519942415231.68204800575848
821.298353629876630.70164637012337
926.07255225487497-4.07255225487497
1023.11890241421145-1.11890241421145
1113.54530006546956-2.54530006546956
1224.92023117635685-2.92023117635685
1334.19674308381483-1.19674308381483
1424.78417434712121-2.78417434712121
1535.40809310151865-2.40809310151865
1638.89488483282224-5.89488483282224
17214.1136362448752-12.1136362448752
1831.483806557351491.51619344264851
192-1.400988498423953.40098849842395
2049.15552152421692-5.15552152421692
2122.71235023979952-0.712350239799518
22310.8491350329213-7.84913503292135
2321.804424921047120.195575078952883
2426.13685790424487-4.13685790424487
2537.73651700664647-4.73651700664647
263-1.603505140987804.6035051409878
2746.95176581254102-2.95176581254102
2826.75290251608709-4.75290251608709
2917.62444787161133-6.62444787161133
30221.0144507437553-19.0144507437553
31111.7718563464008-10.7718563464008
3218.13482686884638-7.13482686884638
3316.26241810889727-5.26241810889727
3419.5530358536636-8.5530358536636
35115.6385762485134-14.6385762485134
36212.6626571803165-10.6626571803165
3716.91373349066285-5.91373349066285
38210.3796501305658-8.37965013056584
39210.9118618344566-8.91186183445657
40110.0479305771889-9.04793057718893
414824.779163805673123.2208361943269
424818.561044567171029.438955432829
432624.60743999525421.39256000474576
444622.504426813886923.4955731861131
4530.8851678933015582.11483210669844
46311.2324631782833-8.23246317828333
4742.334173221671671.66582677832833
482-1.393965531859803.3939655318598
492-0.4589066774151862.45890667741519
503-1.205371912424794.20537191242479
5125.49741981817633-3.49741981817633
524-1.909280066059455.90928006605945
5356.7423165217987-1.74231652179870
54310.5638733778826-7.56387337788265
5547.51509887411194-3.51509887411194
5655.0902582659543-0.090258265954297
575-5.270542682855910.2705426828559
5832.605496038290080.394503961709919
5944.53902564449172-0.539025644491719
6030.08153327631090332.91846672368910
613-1.474842590543034.47484259054303
62212.8727825102441-10.8727825102441
6338.62916684215383-5.62916684215383
6449.84199377090062-5.84199377090062
654-1.641463895413445.64146389541344
664-2.390690662684176.39069066268417
674-1.523715143774625.52371514377462
683-1.845399309489344.84539930948934
6937.320254476196-4.320254476196
703-3.082690013011716.08269001301171
7125.70609681280954-3.70609681280954
72313.3300996063586-10.3300996063586
7336.73316195574574-3.73316195574574
7437.6046921394136-4.60469213941360
75311.7621259274492-8.76212592744922
765-3.585519485011818.5855194850118
77312.4529192344847-9.45291923448468
785-0.6954774990037925.69547749900379
7942.342991172088961.65700882791104
8047.47924173032437-3.47924173032437
81412.4034753841663-8.40347538416633
82521.0709082572884-16.0709082572884
8343.445840305432960.554159694567039
845-3.221190993240098.2211909932401
8532.314160020344940.685839979655059
863-2.439123712998525.43912371299852
87214.6916352816308-12.6916352816308
8830.6127347206459152.38726527935408
8940.1820452460121763.81795475398782
9057.46501377248785-2.46501377248785
9158.87907044584107-3.87907044584107
9232.704929759411550.295070240588451
932-0.3814953196815232.38149531968152
943-1.956829834071434.95682983407143
9549.50835144418757-5.50835144418757
9615.36997226007593-4.36997226007593
97410.6497195173875-6.64971951738749
983-0.4791957082984743.47919570829847
99310.8557088183521-7.85570881835206
10045.25674818434468-1.25674818434468
101312.7053552969696-9.70535529696955
10243.552918818635520.447081181364483
10325.55562272390959-3.55562272390959
10431.805813865225511.19418613477449
105311.7090399878631-8.70903998786305
1063-2.565917638308295.5659176383083
10726.50925433892976-4.50925433892976
1085-1.537978113787486.53797811378748
109512.6014546122057-7.60145461220566
1104-1.449123150181615.44912315018161
11120.618342547130091.38165745286991
112314.3794804915105-11.3794804915105
11338.33901498440335-5.33901498440335
11435.97507472681706-2.97507472681706
1154-1.634936869460295.63493686946029
11651.354210357691273.64578964230873
117412.2699350720912-8.26993507209117
1183026.19494289760213.80505710239792
1192425.0122969624971-1.01229696249706
1202720.49750146564776.50249853435233
1212628.2320701741048-2.23207017410476
1223026.31412920656383.68587079343617
1231529.6137540617741-14.6137540617741
1242824.84037287611443.15962712388556
1253422.537194890218411.4628051097816
1262923.21137717280505.78862282719503
1272623.42824692780122.57175307219876
1283128.30941569369422.69058430630583
1292821.38345520401636.61654479598372
1303329.49614102299253.50385897700755
1313227.01086665668154.98913334331848
1323322.186447197952210.8135528020478
1333121.94230003164179.05769996835832
1343723.603781413909913.3962185860901
1352732.2822536819512-5.28225368195118
1361926.2717367879828-7.27173678798282
1372724.96864209621912.03135790378086
1383123.65846959855827.3415304014418
1393832.09139405409655.90860594590349
1402224.4479836716564-2.44798367165635
1413523.333595890392511.6664041096075
1423523.707941837074411.2920581629256
1433020.08704643779789.91295356220222
1444133.21723409937477.78276590062526
1452523.08147774293051.91852225706950
146266.0663929931834519.9336070068165
1473020.17702773801299.82297226198715
1482518.48786883536376.51213116463625
1493824.430868889571413.5691311104286
1503511.059466655248423.9405333447516
151499.5442504359473339.4557495640527
1524015.266291643449824.7337083565502
15333.86117843611636-0.86117843611636
1542-0.9655477219678732.96554772196787
155511.6273636758047-6.62736367580469
15658.32480193613167-3.32480193613167
15713.52043819858354-2.52043819858354
15823.45440122958215-1.45440122958215
15937.62762208407335-4.62762208407335
16033.03428154667045-0.0342815466704533
16122.77867170394853-0.778671703948527
16231.094517210095001.905482789905
163413.1985215817832-9.19852158178318
16454.559396128525370.440603871474633
16558.06870341105964-3.06870341105965
1661-0.5089871622570751.50898716225707
1672-0.7145971077679272.71459710776793
16839.5909729928579-6.5909729928579
16925.3028760002612-3.3028760002612
17017.25410691762637-6.25410691762637
1712-0.7699507055808022.7699507055808
172212.5795772448798-10.5795772448798
17340.5595010275592753.44049897244073
17433.70550563002747-0.705505630027472
17521.062645403803160.937354596196836
17634.83379723069364-1.83379723069364
17721.757503793952070.242496206047933
17833.29274131959690-0.292741319596895
17926.05171463634383-4.05171463634383
18053.072378662273571.92762133772643
18142.972399352892291.02760064710771
1823-6.130207880621989.13020788062198
18331.598248098330871.40175190166913
1841-6.809823882644417.80982388264441
1852-2.827550209506194.82755020950619
18631.038694486380951.96130551361905
18731.446248219157581.55375178084242
18837.56579694002818-4.56579694002818
18939.28822176585219-6.28822176585219
19026.77196077450595-4.77196077450595
19138.9126312915485-5.9126312915485
192410.4912633383816-6.49126333838164
19320.7544199245589331.24558007544107
19445.03335514991107-1.03335514991107
19523.65569161191377-1.65569161191377






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.0002468158761472570.0004936317522945150.999753184123853
111.75749591610434e-053.51499183220868e-050.999982425040839
128.1197712034293e-071.62395424068586e-060.99999918802288
131.87550414495736e-073.75100828991472e-070.999999812449585
141.20708398580669e-082.41416797161337e-080.99999998792916
155.01273743468723e-091.00254748693745e-080.999999994987263
163.89466743549885e-107.7893348709977e-100.999999999610533
174.22568707590942e-118.45137415181883e-110.999999999957743
184.95454520985085e-129.9090904197017e-120.999999999995046
197.71489264802238e-131.54297852960448e-120.999999999999229
205.22455248481107e-141.04491049696221e-130.999999999999948
213.36737965708210e-156.73475931416421e-150.999999999999997
224.21652457672521e-168.43304915345042e-161
232.72649062621617e-175.45298125243233e-171
241.72047124066403e-183.44094248132806e-181
251.06593615638218e-192.13187231276435e-191
264.91715672150619e-199.83431344301239e-191
271.19565503431552e-182.39131006863105e-181
281.04539750310065e-192.0907950062013e-191
292.16104172264669e-204.32208344529337e-201
302.84117319373774e-215.68234638747548e-211
312.83275794674141e-225.66551589348283e-221
322.43219697568112e-234.86439395136224e-231
332.1252352747663e-244.2504705495326e-241
342.17532915864978e-254.35065831729956e-251
351.07727023181189e-252.15454046362379e-251
361.44561490963593e-262.89122981927187e-261
371.65782159459822e-273.31564318919644e-271
385.38229533982967e-281.07645906796593e-271
391.46121926418044e-282.92243852836087e-281
401.41076615984121e-282.82153231968242e-281
412.57845244805132e-055.15690489610264e-050.99997421547552
427.86704036787008e-050.0001573408073574020.999921329596321
430.0007634279309907440.001526855861981490.99923657206901
440.01510417706119240.03020835412238480.984895822938808
450.01180130053857460.02360260107714920.988198699461425
460.0383442050082220.0766884100164440.961655794991778
470.02991140231220010.05982280462440020.9700885976878
480.02191748576499890.04383497152999780.978082514235001
490.02042098171876750.0408419634375350.979579018281233
500.01486706761943470.02973413523886940.985132932380565
510.01099535834980380.02199071669960750.989004641650196
520.008592157857044060.01718431571408810.991407842142956
530.006033916767795790.01206783353559160.993966083232204
540.0054293006764970.0108586013529940.994570699323503
550.004315703009022110.008631406018044230.995684296990978
560.003337968550509730.006675937101019470.99666203144949
570.003072101952641780.006144203905283560.996927898047358
580.002136455830284940.004272911660569880.997863544169715
590.001463406044932980.002926812089865960.998536593955067
600.0009765776272603630.001953155254520730.99902342237274
610.0007339411359574420.001467882271914880.999266058864043
620.001081084551567540.002162169103135080.998918915448432
630.0009593945529363830.001918789105872770.999040605447064
640.0007777549612941040.001555509922588210.999222245038706
650.0005603180375532860.001120636075106570.999439681962447
660.0004368328171292870.0008736656342585730.99956316718287
670.0003142930916454820.0006285861832909650.999685706908354
680.0002463690437819380.0004927380875638750.999753630956218
690.0001718964177342360.0003437928354684710.999828103582266
700.0001351190128747940.0002702380257495890.999864880987125
710.0001102348746151670.0002204697492303330.999889765125385
728.1232198873726e-050.0001624643977474520.999918767801126
735.21638636383846e-050.0001043277272767690.999947836136362
744.23600352405474e-058.47200704810949e-050.99995763996476
753.0934629338428e-056.1869258676856e-050.999969065370662
762.29161306724798e-054.58322613449596e-050.999977083869328
771.56163749688743e-053.12327499377486e-050.99998438362503
781.03137720562639e-052.06275441125278e-050.999989686227944
796.17392934902525e-061.23478586980505e-050.99999382607065
804.29047582573303e-068.58095165146605e-060.999995709524174
813.75454794309432e-067.50909588618865e-060.999996245452057
826.24882552254615e-061.24976510450923e-050.999993751174477
833.7333047478164e-067.4666094956328e-060.999996266695252
843.15407226931965e-066.30814453863931e-060.99999684592773
851.97337188204139e-063.94674376408278e-060.999998026628118
861.17715767373167e-062.35431534746334e-060.999998822842326
871.24601388921746e-062.49202777843491e-060.99999875398611
887.7553230367802e-071.55106460735604e-060.999999224467696
894.60369427306326e-079.20738854612652e-070.999999539630573
902.64645251706388e-075.29290503412776e-070.999999735354748
911.68074764122134e-073.36149528244268e-070.999999831925236
921.00744210046500e-072.01488420093001e-070.99999989925579
937.14001520403878e-081.42800304080776e-070.999999928599848
943.94840740872115e-087.89681481744231e-080.999999960515926
952.5890289048557e-085.1780578097114e-080.99999997410971
961.48533781725720e-082.97067563451441e-080.999999985146622
978.501872510237e-091.7003745020474e-080.999999991498127
985.42071592579504e-091.08414318515901e-080.999999994579284
999.46017673733332e-091.89203534746666e-080.999999990539823
1005.10392474275223e-091.02078494855045e-080.999999994896075
1016.6029229130572e-091.32058458261144e-080.999999993397077
1023.67509364883900e-097.35018729767801e-090.999999996324906
1032.04182769578397e-094.08365539156795e-090.999999997958172
1041.09464825371894e-092.18929650743788e-090.999999998905352
1051.57029037066103e-093.14058074132206e-090.99999999842971
1069.50130844616383e-101.90026168923277e-090.99999999904987
1075.92349080738795e-101.18469816147759e-090.99999999940765
1083.75392862512793e-107.50785725025585e-100.999999999624607
1092.76492087272946e-105.52984174545893e-100.999999999723508
1101.46351239905726e-102.92702479811453e-100.999999999853649
1117.44106160029434e-111.48821232005887e-100.99999999992559
1125.42191175419206e-101.08438235083841e-090.999999999457809
1135.21430280550079e-101.04286056110016e-090.99999999947857
1145.89058775232142e-101.17811755046428e-090.999999999410941
1154.15997005748042e-108.31994011496083e-100.999999999584003
1167.85665950673627e-101.57133190134725e-090.999999999214334
1174.74958089109058e-069.49916178218115e-060.99999525041911
1180.1823389696824380.3646779393648750.817661030317562
1190.3902691120246070.7805382240492150.609730887975393
1200.6285240307244010.7429519385511980.371475969275599
1210.7299667637638320.5400664724723350.270033236236168
1220.7684814804850630.4630370390298730.231518519514936
1230.9141662736775180.1716674526449650.0858337263224823
1240.9254910752164270.1490178495671460.074508924783573
1250.9538710960557830.0922578078884330.0461289039442165
1260.9761604870502960.04767902589940830.0238395129497041
1270.9713769634727770.05724607305444510.0286230365272225
1280.9685974795092520.06280504098149660.0314025204907483
1290.9643373856768710.07132522864625730.0356626143231287
1300.9605408984827220.07891820303455630.0394591015172781
1310.9610901733759530.07781965324809380.0389098266240469
1320.962163625874170.07567274825166060.0378363741258303
1330.9586252738932420.08274945221351650.0413747261067582
1340.970424993313530.05915001337294110.0295750066864705
1350.9676113767367170.0647772465265650.0323886232632825
1360.9783550236548170.04328995269036610.0216449763451830
1370.9751687049828870.04966259003422610.0248312950171131
1380.972881653599160.05423669280167930.0271183464008397
1390.9749498018549080.05010039629018320.0250501981450916
1400.9878973783758870.02420524324822680.0121026216241134
1410.9872792258307320.02544154833853630.0127207741692682
1420.9916373745759150.01672525084816910.00836262542408457
1430.990377328401460.01924534319707800.00962267159853898
1440.9955659973092740.00886800538145220.0044340026907261
1450.9936688901891140.01266221962177160.00633110981088578
1460.9968595725466350.006280854906729940.00314042745336497
1470.9976825598523190.004634880295362730.00231744014768136
1480.9981417126797710.003716574640457690.00185828732022884
1490.9994196482920840.001160703415832500.000580351707916252
1500.999832210190120.0003355796197592330.000167789809879617
1510.999991709217511.65815649813597e-058.29078249067985e-06
15217.01682646413588e-273.50841323206794e-27
15315.845013852657e-262.9225069263285e-26
15414.75523848568593e-252.37761924284297e-25
15511.88939491957205e-249.44697459786026e-25
15611.75321910424696e-248.7660955212348e-25
15714.88613898395969e-242.44306949197985e-24
15813.8289299765845e-231.91446498829225e-23
15913.16670099316466e-221.58335049658233e-22
16012.78942466727984e-211.39471233363992e-21
16112.35204795495301e-201.17602397747651e-20
16211.89291014110312e-199.4645507055156e-20
16311.35354676572101e-186.76773382860506e-19
16414.72603060387511e-182.36301530193756e-18
16512.52444454031183e-181.26222227015591e-18
16611.18848344097491e-175.94241720487456e-18
16711.02581841079045e-165.12909205395224e-17
16818.64342043510031e-164.32171021755016e-16
1690.9999999999999967.66492913589777e-153.83246456794888e-15
1700.999999999999984.08033110047915e-142.04016555023958e-14
1710.9999999999998712.57568304152799e-131.28784152076400e-13
1720.9999999999995878.26717026780455e-134.13358513390227e-13
1730.9999999999986352.73048291618847e-121.36524145809423e-12
1740.9999999999882722.34551276645789e-111.17275638322894e-11
1750.999999999929761.40480781631680e-107.02403908158399e-11
1760.999999999449751.10049959804398e-095.50249799021991e-10
1770.9999999969580136.08397411751767e-093.04198705875884e-09
1780.9999999776741264.46517476354363e-082.23258738177182e-08
1790.999999923933161.52133678610535e-077.60668393052674e-08
1800.9999998278314533.44337093274774e-071.72168546637387e-07
1810.9999989123378742.17532425240695e-061.08766212620348e-06
1820.999997692659524.61468095915202e-062.30734047957601e-06
1830.999982260446853.54791063014154e-051.77395531507077e-05
1840.999844854016020.000310291967961540.00015514598398077
1850.9982921249690720.003415750061856820.00170787503092841

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.000246815876147257 & 0.000493631752294515 & 0.999753184123853 \tabularnewline
11 & 1.75749591610434e-05 & 3.51499183220868e-05 & 0.999982425040839 \tabularnewline
12 & 8.1197712034293e-07 & 1.62395424068586e-06 & 0.99999918802288 \tabularnewline
13 & 1.87550414495736e-07 & 3.75100828991472e-07 & 0.999999812449585 \tabularnewline
14 & 1.20708398580669e-08 & 2.41416797161337e-08 & 0.99999998792916 \tabularnewline
15 & 5.01273743468723e-09 & 1.00254748693745e-08 & 0.999999994987263 \tabularnewline
16 & 3.89466743549885e-10 & 7.7893348709977e-10 & 0.999999999610533 \tabularnewline
17 & 4.22568707590942e-11 & 8.45137415181883e-11 & 0.999999999957743 \tabularnewline
18 & 4.95454520985085e-12 & 9.9090904197017e-12 & 0.999999999995046 \tabularnewline
19 & 7.71489264802238e-13 & 1.54297852960448e-12 & 0.999999999999229 \tabularnewline
20 & 5.22455248481107e-14 & 1.04491049696221e-13 & 0.999999999999948 \tabularnewline
21 & 3.36737965708210e-15 & 6.73475931416421e-15 & 0.999999999999997 \tabularnewline
22 & 4.21652457672521e-16 & 8.43304915345042e-16 & 1 \tabularnewline
23 & 2.72649062621617e-17 & 5.45298125243233e-17 & 1 \tabularnewline
24 & 1.72047124066403e-18 & 3.44094248132806e-18 & 1 \tabularnewline
25 & 1.06593615638218e-19 & 2.13187231276435e-19 & 1 \tabularnewline
26 & 4.91715672150619e-19 & 9.83431344301239e-19 & 1 \tabularnewline
27 & 1.19565503431552e-18 & 2.39131006863105e-18 & 1 \tabularnewline
28 & 1.04539750310065e-19 & 2.0907950062013e-19 & 1 \tabularnewline
29 & 2.16104172264669e-20 & 4.32208344529337e-20 & 1 \tabularnewline
30 & 2.84117319373774e-21 & 5.68234638747548e-21 & 1 \tabularnewline
31 & 2.83275794674141e-22 & 5.66551589348283e-22 & 1 \tabularnewline
32 & 2.43219697568112e-23 & 4.86439395136224e-23 & 1 \tabularnewline
33 & 2.1252352747663e-24 & 4.2504705495326e-24 & 1 \tabularnewline
34 & 2.17532915864978e-25 & 4.35065831729956e-25 & 1 \tabularnewline
35 & 1.07727023181189e-25 & 2.15454046362379e-25 & 1 \tabularnewline
36 & 1.44561490963593e-26 & 2.89122981927187e-26 & 1 \tabularnewline
37 & 1.65782159459822e-27 & 3.31564318919644e-27 & 1 \tabularnewline
38 & 5.38229533982967e-28 & 1.07645906796593e-27 & 1 \tabularnewline
39 & 1.46121926418044e-28 & 2.92243852836087e-28 & 1 \tabularnewline
40 & 1.41076615984121e-28 & 2.82153231968242e-28 & 1 \tabularnewline
41 & 2.57845244805132e-05 & 5.15690489610264e-05 & 0.99997421547552 \tabularnewline
42 & 7.86704036787008e-05 & 0.000157340807357402 & 0.999921329596321 \tabularnewline
43 & 0.000763427930990744 & 0.00152685586198149 & 0.99923657206901 \tabularnewline
44 & 0.0151041770611924 & 0.0302083541223848 & 0.984895822938808 \tabularnewline
45 & 0.0118013005385746 & 0.0236026010771492 & 0.988198699461425 \tabularnewline
46 & 0.038344205008222 & 0.076688410016444 & 0.961655794991778 \tabularnewline
47 & 0.0299114023122001 & 0.0598228046244002 & 0.9700885976878 \tabularnewline
48 & 0.0219174857649989 & 0.0438349715299978 & 0.978082514235001 \tabularnewline
49 & 0.0204209817187675 & 0.040841963437535 & 0.979579018281233 \tabularnewline
50 & 0.0148670676194347 & 0.0297341352388694 & 0.985132932380565 \tabularnewline
51 & 0.0109953583498038 & 0.0219907166996075 & 0.989004641650196 \tabularnewline
52 & 0.00859215785704406 & 0.0171843157140881 & 0.991407842142956 \tabularnewline
53 & 0.00603391676779579 & 0.0120678335355916 & 0.993966083232204 \tabularnewline
54 & 0.005429300676497 & 0.010858601352994 & 0.994570699323503 \tabularnewline
55 & 0.00431570300902211 & 0.00863140601804423 & 0.995684296990978 \tabularnewline
56 & 0.00333796855050973 & 0.00667593710101947 & 0.99666203144949 \tabularnewline
57 & 0.00307210195264178 & 0.00614420390528356 & 0.996927898047358 \tabularnewline
58 & 0.00213645583028494 & 0.00427291166056988 & 0.997863544169715 \tabularnewline
59 & 0.00146340604493298 & 0.00292681208986596 & 0.998536593955067 \tabularnewline
60 & 0.000976577627260363 & 0.00195315525452073 & 0.99902342237274 \tabularnewline
61 & 0.000733941135957442 & 0.00146788227191488 & 0.999266058864043 \tabularnewline
62 & 0.00108108455156754 & 0.00216216910313508 & 0.998918915448432 \tabularnewline
63 & 0.000959394552936383 & 0.00191878910587277 & 0.999040605447064 \tabularnewline
64 & 0.000777754961294104 & 0.00155550992258821 & 0.999222245038706 \tabularnewline
65 & 0.000560318037553286 & 0.00112063607510657 & 0.999439681962447 \tabularnewline
66 & 0.000436832817129287 & 0.000873665634258573 & 0.99956316718287 \tabularnewline
67 & 0.000314293091645482 & 0.000628586183290965 & 0.999685706908354 \tabularnewline
68 & 0.000246369043781938 & 0.000492738087563875 & 0.999753630956218 \tabularnewline
69 & 0.000171896417734236 & 0.000343792835468471 & 0.999828103582266 \tabularnewline
70 & 0.000135119012874794 & 0.000270238025749589 & 0.999864880987125 \tabularnewline
71 & 0.000110234874615167 & 0.000220469749230333 & 0.999889765125385 \tabularnewline
72 & 8.1232198873726e-05 & 0.000162464397747452 & 0.999918767801126 \tabularnewline
73 & 5.21638636383846e-05 & 0.000104327727276769 & 0.999947836136362 \tabularnewline
74 & 4.23600352405474e-05 & 8.47200704810949e-05 & 0.99995763996476 \tabularnewline
75 & 3.0934629338428e-05 & 6.1869258676856e-05 & 0.999969065370662 \tabularnewline
76 & 2.29161306724798e-05 & 4.58322613449596e-05 & 0.999977083869328 \tabularnewline
77 & 1.56163749688743e-05 & 3.12327499377486e-05 & 0.99998438362503 \tabularnewline
78 & 1.03137720562639e-05 & 2.06275441125278e-05 & 0.999989686227944 \tabularnewline
79 & 6.17392934902525e-06 & 1.23478586980505e-05 & 0.99999382607065 \tabularnewline
80 & 4.29047582573303e-06 & 8.58095165146605e-06 & 0.999995709524174 \tabularnewline
81 & 3.75454794309432e-06 & 7.50909588618865e-06 & 0.999996245452057 \tabularnewline
82 & 6.24882552254615e-06 & 1.24976510450923e-05 & 0.999993751174477 \tabularnewline
83 & 3.7333047478164e-06 & 7.4666094956328e-06 & 0.999996266695252 \tabularnewline
84 & 3.15407226931965e-06 & 6.30814453863931e-06 & 0.99999684592773 \tabularnewline
85 & 1.97337188204139e-06 & 3.94674376408278e-06 & 0.999998026628118 \tabularnewline
86 & 1.17715767373167e-06 & 2.35431534746334e-06 & 0.999998822842326 \tabularnewline
87 & 1.24601388921746e-06 & 2.49202777843491e-06 & 0.99999875398611 \tabularnewline
88 & 7.7553230367802e-07 & 1.55106460735604e-06 & 0.999999224467696 \tabularnewline
89 & 4.60369427306326e-07 & 9.20738854612652e-07 & 0.999999539630573 \tabularnewline
90 & 2.64645251706388e-07 & 5.29290503412776e-07 & 0.999999735354748 \tabularnewline
91 & 1.68074764122134e-07 & 3.36149528244268e-07 & 0.999999831925236 \tabularnewline
92 & 1.00744210046500e-07 & 2.01488420093001e-07 & 0.99999989925579 \tabularnewline
93 & 7.14001520403878e-08 & 1.42800304080776e-07 & 0.999999928599848 \tabularnewline
94 & 3.94840740872115e-08 & 7.89681481744231e-08 & 0.999999960515926 \tabularnewline
95 & 2.5890289048557e-08 & 5.1780578097114e-08 & 0.99999997410971 \tabularnewline
96 & 1.48533781725720e-08 & 2.97067563451441e-08 & 0.999999985146622 \tabularnewline
97 & 8.501872510237e-09 & 1.7003745020474e-08 & 0.999999991498127 \tabularnewline
98 & 5.42071592579504e-09 & 1.08414318515901e-08 & 0.999999994579284 \tabularnewline
99 & 9.46017673733332e-09 & 1.89203534746666e-08 & 0.999999990539823 \tabularnewline
100 & 5.10392474275223e-09 & 1.02078494855045e-08 & 0.999999994896075 \tabularnewline
101 & 6.6029229130572e-09 & 1.32058458261144e-08 & 0.999999993397077 \tabularnewline
102 & 3.67509364883900e-09 & 7.35018729767801e-09 & 0.999999996324906 \tabularnewline
103 & 2.04182769578397e-09 & 4.08365539156795e-09 & 0.999999997958172 \tabularnewline
104 & 1.09464825371894e-09 & 2.18929650743788e-09 & 0.999999998905352 \tabularnewline
105 & 1.57029037066103e-09 & 3.14058074132206e-09 & 0.99999999842971 \tabularnewline
106 & 9.50130844616383e-10 & 1.90026168923277e-09 & 0.99999999904987 \tabularnewline
107 & 5.92349080738795e-10 & 1.18469816147759e-09 & 0.99999999940765 \tabularnewline
108 & 3.75392862512793e-10 & 7.50785725025585e-10 & 0.999999999624607 \tabularnewline
109 & 2.76492087272946e-10 & 5.52984174545893e-10 & 0.999999999723508 \tabularnewline
110 & 1.46351239905726e-10 & 2.92702479811453e-10 & 0.999999999853649 \tabularnewline
111 & 7.44106160029434e-11 & 1.48821232005887e-10 & 0.99999999992559 \tabularnewline
112 & 5.42191175419206e-10 & 1.08438235083841e-09 & 0.999999999457809 \tabularnewline
113 & 5.21430280550079e-10 & 1.04286056110016e-09 & 0.99999999947857 \tabularnewline
114 & 5.89058775232142e-10 & 1.17811755046428e-09 & 0.999999999410941 \tabularnewline
115 & 4.15997005748042e-10 & 8.31994011496083e-10 & 0.999999999584003 \tabularnewline
116 & 7.85665950673627e-10 & 1.57133190134725e-09 & 0.999999999214334 \tabularnewline
117 & 4.74958089109058e-06 & 9.49916178218115e-06 & 0.99999525041911 \tabularnewline
118 & 0.182338969682438 & 0.364677939364875 & 0.817661030317562 \tabularnewline
119 & 0.390269112024607 & 0.780538224049215 & 0.609730887975393 \tabularnewline
120 & 0.628524030724401 & 0.742951938551198 & 0.371475969275599 \tabularnewline
121 & 0.729966763763832 & 0.540066472472335 & 0.270033236236168 \tabularnewline
122 & 0.768481480485063 & 0.463037039029873 & 0.231518519514936 \tabularnewline
123 & 0.914166273677518 & 0.171667452644965 & 0.0858337263224823 \tabularnewline
124 & 0.925491075216427 & 0.149017849567146 & 0.074508924783573 \tabularnewline
125 & 0.953871096055783 & 0.092257807888433 & 0.0461289039442165 \tabularnewline
126 & 0.976160487050296 & 0.0476790258994083 & 0.0238395129497041 \tabularnewline
127 & 0.971376963472777 & 0.0572460730544451 & 0.0286230365272225 \tabularnewline
128 & 0.968597479509252 & 0.0628050409814966 & 0.0314025204907483 \tabularnewline
129 & 0.964337385676871 & 0.0713252286462573 & 0.0356626143231287 \tabularnewline
130 & 0.960540898482722 & 0.0789182030345563 & 0.0394591015172781 \tabularnewline
131 & 0.961090173375953 & 0.0778196532480938 & 0.0389098266240469 \tabularnewline
132 & 0.96216362587417 & 0.0756727482516606 & 0.0378363741258303 \tabularnewline
133 & 0.958625273893242 & 0.0827494522135165 & 0.0413747261067582 \tabularnewline
134 & 0.97042499331353 & 0.0591500133729411 & 0.0295750066864705 \tabularnewline
135 & 0.967611376736717 & 0.064777246526565 & 0.0323886232632825 \tabularnewline
136 & 0.978355023654817 & 0.0432899526903661 & 0.0216449763451830 \tabularnewline
137 & 0.975168704982887 & 0.0496625900342261 & 0.0248312950171131 \tabularnewline
138 & 0.97288165359916 & 0.0542366928016793 & 0.0271183464008397 \tabularnewline
139 & 0.974949801854908 & 0.0501003962901832 & 0.0250501981450916 \tabularnewline
140 & 0.987897378375887 & 0.0242052432482268 & 0.0121026216241134 \tabularnewline
141 & 0.987279225830732 & 0.0254415483385363 & 0.0127207741692682 \tabularnewline
142 & 0.991637374575915 & 0.0167252508481691 & 0.00836262542408457 \tabularnewline
143 & 0.99037732840146 & 0.0192453431970780 & 0.00962267159853898 \tabularnewline
144 & 0.995565997309274 & 0.0088680053814522 & 0.0044340026907261 \tabularnewline
145 & 0.993668890189114 & 0.0126622196217716 & 0.00633110981088578 \tabularnewline
146 & 0.996859572546635 & 0.00628085490672994 & 0.00314042745336497 \tabularnewline
147 & 0.997682559852319 & 0.00463488029536273 & 0.00231744014768136 \tabularnewline
148 & 0.998141712679771 & 0.00371657464045769 & 0.00185828732022884 \tabularnewline
149 & 0.999419648292084 & 0.00116070341583250 & 0.000580351707916252 \tabularnewline
150 & 0.99983221019012 & 0.000335579619759233 & 0.000167789809879617 \tabularnewline
151 & 0.99999170921751 & 1.65815649813597e-05 & 8.29078249067985e-06 \tabularnewline
152 & 1 & 7.01682646413588e-27 & 3.50841323206794e-27 \tabularnewline
153 & 1 & 5.845013852657e-26 & 2.9225069263285e-26 \tabularnewline
154 & 1 & 4.75523848568593e-25 & 2.37761924284297e-25 \tabularnewline
155 & 1 & 1.88939491957205e-24 & 9.44697459786026e-25 \tabularnewline
156 & 1 & 1.75321910424696e-24 & 8.7660955212348e-25 \tabularnewline
157 & 1 & 4.88613898395969e-24 & 2.44306949197985e-24 \tabularnewline
158 & 1 & 3.8289299765845e-23 & 1.91446498829225e-23 \tabularnewline
159 & 1 & 3.16670099316466e-22 & 1.58335049658233e-22 \tabularnewline
160 & 1 & 2.78942466727984e-21 & 1.39471233363992e-21 \tabularnewline
161 & 1 & 2.35204795495301e-20 & 1.17602397747651e-20 \tabularnewline
162 & 1 & 1.89291014110312e-19 & 9.4645507055156e-20 \tabularnewline
163 & 1 & 1.35354676572101e-18 & 6.76773382860506e-19 \tabularnewline
164 & 1 & 4.72603060387511e-18 & 2.36301530193756e-18 \tabularnewline
165 & 1 & 2.52444454031183e-18 & 1.26222227015591e-18 \tabularnewline
166 & 1 & 1.18848344097491e-17 & 5.94241720487456e-18 \tabularnewline
167 & 1 & 1.02581841079045e-16 & 5.12909205395224e-17 \tabularnewline
168 & 1 & 8.64342043510031e-16 & 4.32171021755016e-16 \tabularnewline
169 & 0.999999999999996 & 7.66492913589777e-15 & 3.83246456794888e-15 \tabularnewline
170 & 0.99999999999998 & 4.08033110047915e-14 & 2.04016555023958e-14 \tabularnewline
171 & 0.999999999999871 & 2.57568304152799e-13 & 1.28784152076400e-13 \tabularnewline
172 & 0.999999999999587 & 8.26717026780455e-13 & 4.13358513390227e-13 \tabularnewline
173 & 0.999999999998635 & 2.73048291618847e-12 & 1.36524145809423e-12 \tabularnewline
174 & 0.999999999988272 & 2.34551276645789e-11 & 1.17275638322894e-11 \tabularnewline
175 & 0.99999999992976 & 1.40480781631680e-10 & 7.02403908158399e-11 \tabularnewline
176 & 0.99999999944975 & 1.10049959804398e-09 & 5.50249799021991e-10 \tabularnewline
177 & 0.999999996958013 & 6.08397411751767e-09 & 3.04198705875884e-09 \tabularnewline
178 & 0.999999977674126 & 4.46517476354363e-08 & 2.23258738177182e-08 \tabularnewline
179 & 0.99999992393316 & 1.52133678610535e-07 & 7.60668393052674e-08 \tabularnewline
180 & 0.999999827831453 & 3.44337093274774e-07 & 1.72168546637387e-07 \tabularnewline
181 & 0.999998912337874 & 2.17532425240695e-06 & 1.08766212620348e-06 \tabularnewline
182 & 0.99999769265952 & 4.61468095915202e-06 & 2.30734047957601e-06 \tabularnewline
183 & 0.99998226044685 & 3.54791063014154e-05 & 1.77395531507077e-05 \tabularnewline
184 & 0.99984485401602 & 0.00031029196796154 & 0.00015514598398077 \tabularnewline
185 & 0.998292124969072 & 0.00341575006185682 & 0.00170787503092841 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103904&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]10[/C][C]0.000246815876147257[/C][C]0.000493631752294515[/C][C]0.999753184123853[/C][/ROW]
[ROW][C]11[/C][C]1.75749591610434e-05[/C][C]3.51499183220868e-05[/C][C]0.999982425040839[/C][/ROW]
[ROW][C]12[/C][C]8.1197712034293e-07[/C][C]1.62395424068586e-06[/C][C]0.99999918802288[/C][/ROW]
[ROW][C]13[/C][C]1.87550414495736e-07[/C][C]3.75100828991472e-07[/C][C]0.999999812449585[/C][/ROW]
[ROW][C]14[/C][C]1.20708398580669e-08[/C][C]2.41416797161337e-08[/C][C]0.99999998792916[/C][/ROW]
[ROW][C]15[/C][C]5.01273743468723e-09[/C][C]1.00254748693745e-08[/C][C]0.999999994987263[/C][/ROW]
[ROW][C]16[/C][C]3.89466743549885e-10[/C][C]7.7893348709977e-10[/C][C]0.999999999610533[/C][/ROW]
[ROW][C]17[/C][C]4.22568707590942e-11[/C][C]8.45137415181883e-11[/C][C]0.999999999957743[/C][/ROW]
[ROW][C]18[/C][C]4.95454520985085e-12[/C][C]9.9090904197017e-12[/C][C]0.999999999995046[/C][/ROW]
[ROW][C]19[/C][C]7.71489264802238e-13[/C][C]1.54297852960448e-12[/C][C]0.999999999999229[/C][/ROW]
[ROW][C]20[/C][C]5.22455248481107e-14[/C][C]1.04491049696221e-13[/C][C]0.999999999999948[/C][/ROW]
[ROW][C]21[/C][C]3.36737965708210e-15[/C][C]6.73475931416421e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]22[/C][C]4.21652457672521e-16[/C][C]8.43304915345042e-16[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]2.72649062621617e-17[/C][C]5.45298125243233e-17[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]1.72047124066403e-18[/C][C]3.44094248132806e-18[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]1.06593615638218e-19[/C][C]2.13187231276435e-19[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]4.91715672150619e-19[/C][C]9.83431344301239e-19[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]1.19565503431552e-18[/C][C]2.39131006863105e-18[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]1.04539750310065e-19[/C][C]2.0907950062013e-19[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]2.16104172264669e-20[/C][C]4.32208344529337e-20[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]2.84117319373774e-21[/C][C]5.68234638747548e-21[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]2.83275794674141e-22[/C][C]5.66551589348283e-22[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]2.43219697568112e-23[/C][C]4.86439395136224e-23[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]2.1252352747663e-24[/C][C]4.2504705495326e-24[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]2.17532915864978e-25[/C][C]4.35065831729956e-25[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]1.07727023181189e-25[/C][C]2.15454046362379e-25[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]1.44561490963593e-26[/C][C]2.89122981927187e-26[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]1.65782159459822e-27[/C][C]3.31564318919644e-27[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]5.38229533982967e-28[/C][C]1.07645906796593e-27[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]1.46121926418044e-28[/C][C]2.92243852836087e-28[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]1.41076615984121e-28[/C][C]2.82153231968242e-28[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]2.57845244805132e-05[/C][C]5.15690489610264e-05[/C][C]0.99997421547552[/C][/ROW]
[ROW][C]42[/C][C]7.86704036787008e-05[/C][C]0.000157340807357402[/C][C]0.999921329596321[/C][/ROW]
[ROW][C]43[/C][C]0.000763427930990744[/C][C]0.00152685586198149[/C][C]0.99923657206901[/C][/ROW]
[ROW][C]44[/C][C]0.0151041770611924[/C][C]0.0302083541223848[/C][C]0.984895822938808[/C][/ROW]
[ROW][C]45[/C][C]0.0118013005385746[/C][C]0.0236026010771492[/C][C]0.988198699461425[/C][/ROW]
[ROW][C]46[/C][C]0.038344205008222[/C][C]0.076688410016444[/C][C]0.961655794991778[/C][/ROW]
[ROW][C]47[/C][C]0.0299114023122001[/C][C]0.0598228046244002[/C][C]0.9700885976878[/C][/ROW]
[ROW][C]48[/C][C]0.0219174857649989[/C][C]0.0438349715299978[/C][C]0.978082514235001[/C][/ROW]
[ROW][C]49[/C][C]0.0204209817187675[/C][C]0.040841963437535[/C][C]0.979579018281233[/C][/ROW]
[ROW][C]50[/C][C]0.0148670676194347[/C][C]0.0297341352388694[/C][C]0.985132932380565[/C][/ROW]
[ROW][C]51[/C][C]0.0109953583498038[/C][C]0.0219907166996075[/C][C]0.989004641650196[/C][/ROW]
[ROW][C]52[/C][C]0.00859215785704406[/C][C]0.0171843157140881[/C][C]0.991407842142956[/C][/ROW]
[ROW][C]53[/C][C]0.00603391676779579[/C][C]0.0120678335355916[/C][C]0.993966083232204[/C][/ROW]
[ROW][C]54[/C][C]0.005429300676497[/C][C]0.010858601352994[/C][C]0.994570699323503[/C][/ROW]
[ROW][C]55[/C][C]0.00431570300902211[/C][C]0.00863140601804423[/C][C]0.995684296990978[/C][/ROW]
[ROW][C]56[/C][C]0.00333796855050973[/C][C]0.00667593710101947[/C][C]0.99666203144949[/C][/ROW]
[ROW][C]57[/C][C]0.00307210195264178[/C][C]0.00614420390528356[/C][C]0.996927898047358[/C][/ROW]
[ROW][C]58[/C][C]0.00213645583028494[/C][C]0.00427291166056988[/C][C]0.997863544169715[/C][/ROW]
[ROW][C]59[/C][C]0.00146340604493298[/C][C]0.00292681208986596[/C][C]0.998536593955067[/C][/ROW]
[ROW][C]60[/C][C]0.000976577627260363[/C][C]0.00195315525452073[/C][C]0.99902342237274[/C][/ROW]
[ROW][C]61[/C][C]0.000733941135957442[/C][C]0.00146788227191488[/C][C]0.999266058864043[/C][/ROW]
[ROW][C]62[/C][C]0.00108108455156754[/C][C]0.00216216910313508[/C][C]0.998918915448432[/C][/ROW]
[ROW][C]63[/C][C]0.000959394552936383[/C][C]0.00191878910587277[/C][C]0.999040605447064[/C][/ROW]
[ROW][C]64[/C][C]0.000777754961294104[/C][C]0.00155550992258821[/C][C]0.999222245038706[/C][/ROW]
[ROW][C]65[/C][C]0.000560318037553286[/C][C]0.00112063607510657[/C][C]0.999439681962447[/C][/ROW]
[ROW][C]66[/C][C]0.000436832817129287[/C][C]0.000873665634258573[/C][C]0.99956316718287[/C][/ROW]
[ROW][C]67[/C][C]0.000314293091645482[/C][C]0.000628586183290965[/C][C]0.999685706908354[/C][/ROW]
[ROW][C]68[/C][C]0.000246369043781938[/C][C]0.000492738087563875[/C][C]0.999753630956218[/C][/ROW]
[ROW][C]69[/C][C]0.000171896417734236[/C][C]0.000343792835468471[/C][C]0.999828103582266[/C][/ROW]
[ROW][C]70[/C][C]0.000135119012874794[/C][C]0.000270238025749589[/C][C]0.999864880987125[/C][/ROW]
[ROW][C]71[/C][C]0.000110234874615167[/C][C]0.000220469749230333[/C][C]0.999889765125385[/C][/ROW]
[ROW][C]72[/C][C]8.1232198873726e-05[/C][C]0.000162464397747452[/C][C]0.999918767801126[/C][/ROW]
[ROW][C]73[/C][C]5.21638636383846e-05[/C][C]0.000104327727276769[/C][C]0.999947836136362[/C][/ROW]
[ROW][C]74[/C][C]4.23600352405474e-05[/C][C]8.47200704810949e-05[/C][C]0.99995763996476[/C][/ROW]
[ROW][C]75[/C][C]3.0934629338428e-05[/C][C]6.1869258676856e-05[/C][C]0.999969065370662[/C][/ROW]
[ROW][C]76[/C][C]2.29161306724798e-05[/C][C]4.58322613449596e-05[/C][C]0.999977083869328[/C][/ROW]
[ROW][C]77[/C][C]1.56163749688743e-05[/C][C]3.12327499377486e-05[/C][C]0.99998438362503[/C][/ROW]
[ROW][C]78[/C][C]1.03137720562639e-05[/C][C]2.06275441125278e-05[/C][C]0.999989686227944[/C][/ROW]
[ROW][C]79[/C][C]6.17392934902525e-06[/C][C]1.23478586980505e-05[/C][C]0.99999382607065[/C][/ROW]
[ROW][C]80[/C][C]4.29047582573303e-06[/C][C]8.58095165146605e-06[/C][C]0.999995709524174[/C][/ROW]
[ROW][C]81[/C][C]3.75454794309432e-06[/C][C]7.50909588618865e-06[/C][C]0.999996245452057[/C][/ROW]
[ROW][C]82[/C][C]6.24882552254615e-06[/C][C]1.24976510450923e-05[/C][C]0.999993751174477[/C][/ROW]
[ROW][C]83[/C][C]3.7333047478164e-06[/C][C]7.4666094956328e-06[/C][C]0.999996266695252[/C][/ROW]
[ROW][C]84[/C][C]3.15407226931965e-06[/C][C]6.30814453863931e-06[/C][C]0.99999684592773[/C][/ROW]
[ROW][C]85[/C][C]1.97337188204139e-06[/C][C]3.94674376408278e-06[/C][C]0.999998026628118[/C][/ROW]
[ROW][C]86[/C][C]1.17715767373167e-06[/C][C]2.35431534746334e-06[/C][C]0.999998822842326[/C][/ROW]
[ROW][C]87[/C][C]1.24601388921746e-06[/C][C]2.49202777843491e-06[/C][C]0.99999875398611[/C][/ROW]
[ROW][C]88[/C][C]7.7553230367802e-07[/C][C]1.55106460735604e-06[/C][C]0.999999224467696[/C][/ROW]
[ROW][C]89[/C][C]4.60369427306326e-07[/C][C]9.20738854612652e-07[/C][C]0.999999539630573[/C][/ROW]
[ROW][C]90[/C][C]2.64645251706388e-07[/C][C]5.29290503412776e-07[/C][C]0.999999735354748[/C][/ROW]
[ROW][C]91[/C][C]1.68074764122134e-07[/C][C]3.36149528244268e-07[/C][C]0.999999831925236[/C][/ROW]
[ROW][C]92[/C][C]1.00744210046500e-07[/C][C]2.01488420093001e-07[/C][C]0.99999989925579[/C][/ROW]
[ROW][C]93[/C][C]7.14001520403878e-08[/C][C]1.42800304080776e-07[/C][C]0.999999928599848[/C][/ROW]
[ROW][C]94[/C][C]3.94840740872115e-08[/C][C]7.89681481744231e-08[/C][C]0.999999960515926[/C][/ROW]
[ROW][C]95[/C][C]2.5890289048557e-08[/C][C]5.1780578097114e-08[/C][C]0.99999997410971[/C][/ROW]
[ROW][C]96[/C][C]1.48533781725720e-08[/C][C]2.97067563451441e-08[/C][C]0.999999985146622[/C][/ROW]
[ROW][C]97[/C][C]8.501872510237e-09[/C][C]1.7003745020474e-08[/C][C]0.999999991498127[/C][/ROW]
[ROW][C]98[/C][C]5.42071592579504e-09[/C][C]1.08414318515901e-08[/C][C]0.999999994579284[/C][/ROW]
[ROW][C]99[/C][C]9.46017673733332e-09[/C][C]1.89203534746666e-08[/C][C]0.999999990539823[/C][/ROW]
[ROW][C]100[/C][C]5.10392474275223e-09[/C][C]1.02078494855045e-08[/C][C]0.999999994896075[/C][/ROW]
[ROW][C]101[/C][C]6.6029229130572e-09[/C][C]1.32058458261144e-08[/C][C]0.999999993397077[/C][/ROW]
[ROW][C]102[/C][C]3.67509364883900e-09[/C][C]7.35018729767801e-09[/C][C]0.999999996324906[/C][/ROW]
[ROW][C]103[/C][C]2.04182769578397e-09[/C][C]4.08365539156795e-09[/C][C]0.999999997958172[/C][/ROW]
[ROW][C]104[/C][C]1.09464825371894e-09[/C][C]2.18929650743788e-09[/C][C]0.999999998905352[/C][/ROW]
[ROW][C]105[/C][C]1.57029037066103e-09[/C][C]3.14058074132206e-09[/C][C]0.99999999842971[/C][/ROW]
[ROW][C]106[/C][C]9.50130844616383e-10[/C][C]1.90026168923277e-09[/C][C]0.99999999904987[/C][/ROW]
[ROW][C]107[/C][C]5.92349080738795e-10[/C][C]1.18469816147759e-09[/C][C]0.99999999940765[/C][/ROW]
[ROW][C]108[/C][C]3.75392862512793e-10[/C][C]7.50785725025585e-10[/C][C]0.999999999624607[/C][/ROW]
[ROW][C]109[/C][C]2.76492087272946e-10[/C][C]5.52984174545893e-10[/C][C]0.999999999723508[/C][/ROW]
[ROW][C]110[/C][C]1.46351239905726e-10[/C][C]2.92702479811453e-10[/C][C]0.999999999853649[/C][/ROW]
[ROW][C]111[/C][C]7.44106160029434e-11[/C][C]1.48821232005887e-10[/C][C]0.99999999992559[/C][/ROW]
[ROW][C]112[/C][C]5.42191175419206e-10[/C][C]1.08438235083841e-09[/C][C]0.999999999457809[/C][/ROW]
[ROW][C]113[/C][C]5.21430280550079e-10[/C][C]1.04286056110016e-09[/C][C]0.99999999947857[/C][/ROW]
[ROW][C]114[/C][C]5.89058775232142e-10[/C][C]1.17811755046428e-09[/C][C]0.999999999410941[/C][/ROW]
[ROW][C]115[/C][C]4.15997005748042e-10[/C][C]8.31994011496083e-10[/C][C]0.999999999584003[/C][/ROW]
[ROW][C]116[/C][C]7.85665950673627e-10[/C][C]1.57133190134725e-09[/C][C]0.999999999214334[/C][/ROW]
[ROW][C]117[/C][C]4.74958089109058e-06[/C][C]9.49916178218115e-06[/C][C]0.99999525041911[/C][/ROW]
[ROW][C]118[/C][C]0.182338969682438[/C][C]0.364677939364875[/C][C]0.817661030317562[/C][/ROW]
[ROW][C]119[/C][C]0.390269112024607[/C][C]0.780538224049215[/C][C]0.609730887975393[/C][/ROW]
[ROW][C]120[/C][C]0.628524030724401[/C][C]0.742951938551198[/C][C]0.371475969275599[/C][/ROW]
[ROW][C]121[/C][C]0.729966763763832[/C][C]0.540066472472335[/C][C]0.270033236236168[/C][/ROW]
[ROW][C]122[/C][C]0.768481480485063[/C][C]0.463037039029873[/C][C]0.231518519514936[/C][/ROW]
[ROW][C]123[/C][C]0.914166273677518[/C][C]0.171667452644965[/C][C]0.0858337263224823[/C][/ROW]
[ROW][C]124[/C][C]0.925491075216427[/C][C]0.149017849567146[/C][C]0.074508924783573[/C][/ROW]
[ROW][C]125[/C][C]0.953871096055783[/C][C]0.092257807888433[/C][C]0.0461289039442165[/C][/ROW]
[ROW][C]126[/C][C]0.976160487050296[/C][C]0.0476790258994083[/C][C]0.0238395129497041[/C][/ROW]
[ROW][C]127[/C][C]0.971376963472777[/C][C]0.0572460730544451[/C][C]0.0286230365272225[/C][/ROW]
[ROW][C]128[/C][C]0.968597479509252[/C][C]0.0628050409814966[/C][C]0.0314025204907483[/C][/ROW]
[ROW][C]129[/C][C]0.964337385676871[/C][C]0.0713252286462573[/C][C]0.0356626143231287[/C][/ROW]
[ROW][C]130[/C][C]0.960540898482722[/C][C]0.0789182030345563[/C][C]0.0394591015172781[/C][/ROW]
[ROW][C]131[/C][C]0.961090173375953[/C][C]0.0778196532480938[/C][C]0.0389098266240469[/C][/ROW]
[ROW][C]132[/C][C]0.96216362587417[/C][C]0.0756727482516606[/C][C]0.0378363741258303[/C][/ROW]
[ROW][C]133[/C][C]0.958625273893242[/C][C]0.0827494522135165[/C][C]0.0413747261067582[/C][/ROW]
[ROW][C]134[/C][C]0.97042499331353[/C][C]0.0591500133729411[/C][C]0.0295750066864705[/C][/ROW]
[ROW][C]135[/C][C]0.967611376736717[/C][C]0.064777246526565[/C][C]0.0323886232632825[/C][/ROW]
[ROW][C]136[/C][C]0.978355023654817[/C][C]0.0432899526903661[/C][C]0.0216449763451830[/C][/ROW]
[ROW][C]137[/C][C]0.975168704982887[/C][C]0.0496625900342261[/C][C]0.0248312950171131[/C][/ROW]
[ROW][C]138[/C][C]0.97288165359916[/C][C]0.0542366928016793[/C][C]0.0271183464008397[/C][/ROW]
[ROW][C]139[/C][C]0.974949801854908[/C][C]0.0501003962901832[/C][C]0.0250501981450916[/C][/ROW]
[ROW][C]140[/C][C]0.987897378375887[/C][C]0.0242052432482268[/C][C]0.0121026216241134[/C][/ROW]
[ROW][C]141[/C][C]0.987279225830732[/C][C]0.0254415483385363[/C][C]0.0127207741692682[/C][/ROW]
[ROW][C]142[/C][C]0.991637374575915[/C][C]0.0167252508481691[/C][C]0.00836262542408457[/C][/ROW]
[ROW][C]143[/C][C]0.99037732840146[/C][C]0.0192453431970780[/C][C]0.00962267159853898[/C][/ROW]
[ROW][C]144[/C][C]0.995565997309274[/C][C]0.0088680053814522[/C][C]0.0044340026907261[/C][/ROW]
[ROW][C]145[/C][C]0.993668890189114[/C][C]0.0126622196217716[/C][C]0.00633110981088578[/C][/ROW]
[ROW][C]146[/C][C]0.996859572546635[/C][C]0.00628085490672994[/C][C]0.00314042745336497[/C][/ROW]
[ROW][C]147[/C][C]0.997682559852319[/C][C]0.00463488029536273[/C][C]0.00231744014768136[/C][/ROW]
[ROW][C]148[/C][C]0.998141712679771[/C][C]0.00371657464045769[/C][C]0.00185828732022884[/C][/ROW]
[ROW][C]149[/C][C]0.999419648292084[/C][C]0.00116070341583250[/C][C]0.000580351707916252[/C][/ROW]
[ROW][C]150[/C][C]0.99983221019012[/C][C]0.000335579619759233[/C][C]0.000167789809879617[/C][/ROW]
[ROW][C]151[/C][C]0.99999170921751[/C][C]1.65815649813597e-05[/C][C]8.29078249067985e-06[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]7.01682646413588e-27[/C][C]3.50841323206794e-27[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]5.845013852657e-26[/C][C]2.9225069263285e-26[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]4.75523848568593e-25[/C][C]2.37761924284297e-25[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]1.88939491957205e-24[/C][C]9.44697459786026e-25[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]1.75321910424696e-24[/C][C]8.7660955212348e-25[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]4.88613898395969e-24[/C][C]2.44306949197985e-24[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]3.8289299765845e-23[/C][C]1.91446498829225e-23[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]3.16670099316466e-22[/C][C]1.58335049658233e-22[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]2.78942466727984e-21[/C][C]1.39471233363992e-21[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]2.35204795495301e-20[/C][C]1.17602397747651e-20[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]1.89291014110312e-19[/C][C]9.4645507055156e-20[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]1.35354676572101e-18[/C][C]6.76773382860506e-19[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]4.72603060387511e-18[/C][C]2.36301530193756e-18[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]2.52444454031183e-18[/C][C]1.26222227015591e-18[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]1.18848344097491e-17[/C][C]5.94241720487456e-18[/C][/ROW]
[ROW][C]167[/C][C]1[/C][C]1.02581841079045e-16[/C][C]5.12909205395224e-17[/C][/ROW]
[ROW][C]168[/C][C]1[/C][C]8.64342043510031e-16[/C][C]4.32171021755016e-16[/C][/ROW]
[ROW][C]169[/C][C]0.999999999999996[/C][C]7.66492913589777e-15[/C][C]3.83246456794888e-15[/C][/ROW]
[ROW][C]170[/C][C]0.99999999999998[/C][C]4.08033110047915e-14[/C][C]2.04016555023958e-14[/C][/ROW]
[ROW][C]171[/C][C]0.999999999999871[/C][C]2.57568304152799e-13[/C][C]1.28784152076400e-13[/C][/ROW]
[ROW][C]172[/C][C]0.999999999999587[/C][C]8.26717026780455e-13[/C][C]4.13358513390227e-13[/C][/ROW]
[ROW][C]173[/C][C]0.999999999998635[/C][C]2.73048291618847e-12[/C][C]1.36524145809423e-12[/C][/ROW]
[ROW][C]174[/C][C]0.999999999988272[/C][C]2.34551276645789e-11[/C][C]1.17275638322894e-11[/C][/ROW]
[ROW][C]175[/C][C]0.99999999992976[/C][C]1.40480781631680e-10[/C][C]7.02403908158399e-11[/C][/ROW]
[ROW][C]176[/C][C]0.99999999944975[/C][C]1.10049959804398e-09[/C][C]5.50249799021991e-10[/C][/ROW]
[ROW][C]177[/C][C]0.999999996958013[/C][C]6.08397411751767e-09[/C][C]3.04198705875884e-09[/C][/ROW]
[ROW][C]178[/C][C]0.999999977674126[/C][C]4.46517476354363e-08[/C][C]2.23258738177182e-08[/C][/ROW]
[ROW][C]179[/C][C]0.99999992393316[/C][C]1.52133678610535e-07[/C][C]7.60668393052674e-08[/C][/ROW]
[ROW][C]180[/C][C]0.999999827831453[/C][C]3.44337093274774e-07[/C][C]1.72168546637387e-07[/C][/ROW]
[ROW][C]181[/C][C]0.999998912337874[/C][C]2.17532425240695e-06[/C][C]1.08766212620348e-06[/C][/ROW]
[ROW][C]182[/C][C]0.99999769265952[/C][C]4.61468095915202e-06[/C][C]2.30734047957601e-06[/C][/ROW]
[ROW][C]183[/C][C]0.99998226044685[/C][C]3.54791063014154e-05[/C][C]1.77395531507077e-05[/C][/ROW]
[ROW][C]184[/C][C]0.99984485401602[/C][C]0.00031029196796154[/C][C]0.00015514598398077[/C][/ROW]
[ROW][C]185[/C][C]0.998292124969072[/C][C]0.00341575006185682[/C][C]0.00170787503092841[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103904&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103904&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
100.0002468158761472570.0004936317522945150.999753184123853
111.75749591610434e-053.51499183220868e-050.999982425040839
128.1197712034293e-071.62395424068586e-060.99999918802288
131.87550414495736e-073.75100828991472e-070.999999812449585
141.20708398580669e-082.41416797161337e-080.99999998792916
155.01273743468723e-091.00254748693745e-080.999999994987263
163.89466743549885e-107.7893348709977e-100.999999999610533
174.22568707590942e-118.45137415181883e-110.999999999957743
184.95454520985085e-129.9090904197017e-120.999999999995046
197.71489264802238e-131.54297852960448e-120.999999999999229
205.22455248481107e-141.04491049696221e-130.999999999999948
213.36737965708210e-156.73475931416421e-150.999999999999997
224.21652457672521e-168.43304915345042e-161
232.72649062621617e-175.45298125243233e-171
241.72047124066403e-183.44094248132806e-181
251.06593615638218e-192.13187231276435e-191
264.91715672150619e-199.83431344301239e-191
271.19565503431552e-182.39131006863105e-181
281.04539750310065e-192.0907950062013e-191
292.16104172264669e-204.32208344529337e-201
302.84117319373774e-215.68234638747548e-211
312.83275794674141e-225.66551589348283e-221
322.43219697568112e-234.86439395136224e-231
332.1252352747663e-244.2504705495326e-241
342.17532915864978e-254.35065831729956e-251
351.07727023181189e-252.15454046362379e-251
361.44561490963593e-262.89122981927187e-261
371.65782159459822e-273.31564318919644e-271
385.38229533982967e-281.07645906796593e-271
391.46121926418044e-282.92243852836087e-281
401.41076615984121e-282.82153231968242e-281
412.57845244805132e-055.15690489610264e-050.99997421547552
427.86704036787008e-050.0001573408073574020.999921329596321
430.0007634279309907440.001526855861981490.99923657206901
440.01510417706119240.03020835412238480.984895822938808
450.01180130053857460.02360260107714920.988198699461425
460.0383442050082220.0766884100164440.961655794991778
470.02991140231220010.05982280462440020.9700885976878
480.02191748576499890.04383497152999780.978082514235001
490.02042098171876750.0408419634375350.979579018281233
500.01486706761943470.02973413523886940.985132932380565
510.01099535834980380.02199071669960750.989004641650196
520.008592157857044060.01718431571408810.991407842142956
530.006033916767795790.01206783353559160.993966083232204
540.0054293006764970.0108586013529940.994570699323503
550.004315703009022110.008631406018044230.995684296990978
560.003337968550509730.006675937101019470.99666203144949
570.003072101952641780.006144203905283560.996927898047358
580.002136455830284940.004272911660569880.997863544169715
590.001463406044932980.002926812089865960.998536593955067
600.0009765776272603630.001953155254520730.99902342237274
610.0007339411359574420.001467882271914880.999266058864043
620.001081084551567540.002162169103135080.998918915448432
630.0009593945529363830.001918789105872770.999040605447064
640.0007777549612941040.001555509922588210.999222245038706
650.0005603180375532860.001120636075106570.999439681962447
660.0004368328171292870.0008736656342585730.99956316718287
670.0003142930916454820.0006285861832909650.999685706908354
680.0002463690437819380.0004927380875638750.999753630956218
690.0001718964177342360.0003437928354684710.999828103582266
700.0001351190128747940.0002702380257495890.999864880987125
710.0001102348746151670.0002204697492303330.999889765125385
728.1232198873726e-050.0001624643977474520.999918767801126
735.21638636383846e-050.0001043277272767690.999947836136362
744.23600352405474e-058.47200704810949e-050.99995763996476
753.0934629338428e-056.1869258676856e-050.999969065370662
762.29161306724798e-054.58322613449596e-050.999977083869328
771.56163749688743e-053.12327499377486e-050.99998438362503
781.03137720562639e-052.06275441125278e-050.999989686227944
796.17392934902525e-061.23478586980505e-050.99999382607065
804.29047582573303e-068.58095165146605e-060.999995709524174
813.75454794309432e-067.50909588618865e-060.999996245452057
826.24882552254615e-061.24976510450923e-050.999993751174477
833.7333047478164e-067.4666094956328e-060.999996266695252
843.15407226931965e-066.30814453863931e-060.99999684592773
851.97337188204139e-063.94674376408278e-060.999998026628118
861.17715767373167e-062.35431534746334e-060.999998822842326
871.24601388921746e-062.49202777843491e-060.99999875398611
887.7553230367802e-071.55106460735604e-060.999999224467696
894.60369427306326e-079.20738854612652e-070.999999539630573
902.64645251706388e-075.29290503412776e-070.999999735354748
911.68074764122134e-073.36149528244268e-070.999999831925236
921.00744210046500e-072.01488420093001e-070.99999989925579
937.14001520403878e-081.42800304080776e-070.999999928599848
943.94840740872115e-087.89681481744231e-080.999999960515926
952.5890289048557e-085.1780578097114e-080.99999997410971
961.48533781725720e-082.97067563451441e-080.999999985146622
978.501872510237e-091.7003745020474e-080.999999991498127
985.42071592579504e-091.08414318515901e-080.999999994579284
999.46017673733332e-091.89203534746666e-080.999999990539823
1005.10392474275223e-091.02078494855045e-080.999999994896075
1016.6029229130572e-091.32058458261144e-080.999999993397077
1023.67509364883900e-097.35018729767801e-090.999999996324906
1032.04182769578397e-094.08365539156795e-090.999999997958172
1041.09464825371894e-092.18929650743788e-090.999999998905352
1051.57029037066103e-093.14058074132206e-090.99999999842971
1069.50130844616383e-101.90026168923277e-090.99999999904987
1075.92349080738795e-101.18469816147759e-090.99999999940765
1083.75392862512793e-107.50785725025585e-100.999999999624607
1092.76492087272946e-105.52984174545893e-100.999999999723508
1101.46351239905726e-102.92702479811453e-100.999999999853649
1117.44106160029434e-111.48821232005887e-100.99999999992559
1125.42191175419206e-101.08438235083841e-090.999999999457809
1135.21430280550079e-101.04286056110016e-090.99999999947857
1145.89058775232142e-101.17811755046428e-090.999999999410941
1154.15997005748042e-108.31994011496083e-100.999999999584003
1167.85665950673627e-101.57133190134725e-090.999999999214334
1174.74958089109058e-069.49916178218115e-060.99999525041911
1180.1823389696824380.3646779393648750.817661030317562
1190.3902691120246070.7805382240492150.609730887975393
1200.6285240307244010.7429519385511980.371475969275599
1210.7299667637638320.5400664724723350.270033236236168
1220.7684814804850630.4630370390298730.231518519514936
1230.9141662736775180.1716674526449650.0858337263224823
1240.9254910752164270.1490178495671460.074508924783573
1250.9538710960557830.0922578078884330.0461289039442165
1260.9761604870502960.04767902589940830.0238395129497041
1270.9713769634727770.05724607305444510.0286230365272225
1280.9685974795092520.06280504098149660.0314025204907483
1290.9643373856768710.07132522864625730.0356626143231287
1300.9605408984827220.07891820303455630.0394591015172781
1310.9610901733759530.07781965324809380.0389098266240469
1320.962163625874170.07567274825166060.0378363741258303
1330.9586252738932420.08274945221351650.0413747261067582
1340.970424993313530.05915001337294110.0295750066864705
1350.9676113767367170.0647772465265650.0323886232632825
1360.9783550236548170.04328995269036610.0216449763451830
1370.9751687049828870.04966259003422610.0248312950171131
1380.972881653599160.05423669280167930.0271183464008397
1390.9749498018549080.05010039629018320.0250501981450916
1400.9878973783758870.02420524324822680.0121026216241134
1410.9872792258307320.02544154833853630.0127207741692682
1420.9916373745759150.01672525084816910.00836262542408457
1430.990377328401460.01924534319707800.00962267159853898
1440.9955659973092740.00886800538145220.0044340026907261
1450.9936688901891140.01266221962177160.00633110981088578
1460.9968595725466350.006280854906729940.00314042745336497
1470.9976825598523190.004634880295362730.00231744014768136
1480.9981417126797710.003716574640457690.00185828732022884
1490.9994196482920840.001160703415832500.000580351707916252
1500.999832210190120.0003355796197592330.000167789809879617
1510.999991709217511.65815649813597e-058.29078249067985e-06
15217.01682646413588e-273.50841323206794e-27
15315.845013852657e-262.9225069263285e-26
15414.75523848568593e-252.37761924284297e-25
15511.88939491957205e-249.44697459786026e-25
15611.75321910424696e-248.7660955212348e-25
15714.88613898395969e-242.44306949197985e-24
15813.8289299765845e-231.91446498829225e-23
15913.16670099316466e-221.58335049658233e-22
16012.78942466727984e-211.39471233363992e-21
16112.35204795495301e-201.17602397747651e-20
16211.89291014110312e-199.4645507055156e-20
16311.35354676572101e-186.76773382860506e-19
16414.72603060387511e-182.36301530193756e-18
16512.52444454031183e-181.26222227015591e-18
16611.18848344097491e-175.94241720487456e-18
16711.02581841079045e-165.12909205395224e-17
16818.64342043510031e-164.32171021755016e-16
1690.9999999999999967.66492913589777e-153.83246456794888e-15
1700.999999999999984.08033110047915e-142.04016555023958e-14
1710.9999999999998712.57568304152799e-131.28784152076400e-13
1720.9999999999995878.26717026780455e-134.13358513390227e-13
1730.9999999999986352.73048291618847e-121.36524145809423e-12
1740.9999999999882722.34551276645789e-111.17275638322894e-11
1750.999999999929761.40480781631680e-107.02403908158399e-11
1760.999999999449751.10049959804398e-095.50249799021991e-10
1770.9999999969580136.08397411751767e-093.04198705875884e-09
1780.9999999776741264.46517476354363e-082.23258738177182e-08
1790.999999923933161.52133678610535e-077.60668393052674e-08
1800.9999998278314533.44337093274774e-071.72168546637387e-07
1810.9999989123378742.17532425240695e-061.08766212620348e-06
1820.999997692659524.61468095915202e-062.30734047957601e-06
1830.999982260446853.54791063014154e-051.77395531507077e-05
1840.999844854016020.000310291967961540.00015514598398077
1850.9982921249690720.003415750061856820.00170787503092841






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1380.784090909090909NOK
5% type I error level1550.880681818181818NOK
10% type I error level1690.960227272727273NOK

\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 & 138 & 0.784090909090909 & NOK \tabularnewline
5% type I error level & 155 & 0.880681818181818 & NOK \tabularnewline
10% type I error level & 169 & 0.960227272727273 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103904&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]138[/C][C]0.784090909090909[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]155[/C][C]0.880681818181818[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]169[/C][C]0.960227272727273[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103904&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103904&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 level1380.784090909090909NOK
5% type I error level1550.880681818181818NOK
10% type I error level1690.960227272727273NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
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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')
}