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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 computationSat, 25 Dec 2010 21:03:17 +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/25/t1293310872itkhyxgyklxpniy.htm/, Retrieved Sun, 28 Apr 2024 20:36:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115455, Retrieved Sun, 28 Apr 2024 20:36:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- RMPD  [Multiple Regression] [multiple regression] [2010-12-24 14:36:05] [d6e648f00513dd750579ba7880c5fbf5]
-    D      [Multiple Regression] [] [2010-12-25 21:03:17] [15be6a7406d91c9912c2afdb984c54ee] [Current]
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Dataset

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

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'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115455&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115455&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115455&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'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
Teamwork33[t] = + 55.6998054685839 -0.189599030063311geslacht[t] -0.36257731331915leeftijd[t] -0.312586350508122opleiding[t] -0.437634633702588Neuroticisme[t] -0.193414361348253Extraversie[t] -0.451755541671208Openheid[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Teamwork33[t] =  +  55.6998054685839 -0.189599030063311geslacht[t] -0.36257731331915leeftijd[t] -0.312586350508122opleiding[t] -0.437634633702588Neuroticisme[t] -0.193414361348253Extraversie[t] -0.451755541671208Openheid[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115455&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Teamwork33[t] =  +  55.6998054685839 -0.189599030063311geslacht[t] -0.36257731331915leeftijd[t] -0.312586350508122opleiding[t] -0.437634633702588Neuroticisme[t] -0.193414361348253Extraversie[t] -0.451755541671208Openheid[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115455&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115455&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Teamwork33[t] = + 55.6998054685839 -0.189599030063311geslacht[t] -0.36257731331915leeftijd[t] -0.312586350508122opleiding[t] -0.437634633702588Neuroticisme[t] -0.193414361348253Extraversie[t] -0.451755541671208Openheid[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)55.69980546858395.40410910.306900
geslacht-0.1895990300633110.056822-3.33670.0010220.000511
leeftijd-0.362577313319150.059629-6.080500
opleiding-0.3125863505081220.076485-4.08696.5e-053.2e-05
Neuroticisme-0.4376346337025880.057654-7.590700
Extraversie-0.1934143613482530.065404-2.95720.0035030.001751
Openheid-0.4517555416712080.057789-7.817400

\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) & 55.6998054685839 & 5.404109 & 10.3069 & 0 & 0 \tabularnewline
geslacht & -0.189599030063311 & 0.056822 & -3.3367 & 0.001022 & 0.000511 \tabularnewline
leeftijd & -0.36257731331915 & 0.059629 & -6.0805 & 0 & 0 \tabularnewline
opleiding & -0.312586350508122 & 0.076485 & -4.0869 & 6.5e-05 & 3.2e-05 \tabularnewline
Neuroticisme & -0.437634633702588 & 0.057654 & -7.5907 & 0 & 0 \tabularnewline
Extraversie & -0.193414361348253 & 0.065404 & -2.9572 & 0.003503 & 0.001751 \tabularnewline
Openheid & -0.451755541671208 & 0.057789 & -7.8174 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115455&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]55.6998054685839[/C][C]5.404109[/C][C]10.3069[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]geslacht[/C][C]-0.189599030063311[/C][C]0.056822[/C][C]-3.3367[/C][C]0.001022[/C][C]0.000511[/C][/ROW]
[ROW][C]leeftijd[/C][C]-0.36257731331915[/C][C]0.059629[/C][C]-6.0805[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]opleiding[/C][C]-0.312586350508122[/C][C]0.076485[/C][C]-4.0869[/C][C]6.5e-05[/C][C]3.2e-05[/C][/ROW]
[ROW][C]Neuroticisme[/C][C]-0.437634633702588[/C][C]0.057654[/C][C]-7.5907[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Extraversie[/C][C]-0.193414361348253[/C][C]0.065404[/C][C]-2.9572[/C][C]0.003503[/C][C]0.001751[/C][/ROW]
[ROW][C]Openheid[/C][C]-0.451755541671208[/C][C]0.057789[/C][C]-7.8174[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115455&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115455&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)55.69980546858395.40410910.306900
geslacht-0.1895990300633110.056822-3.33670.0010220.000511
leeftijd-0.362577313319150.059629-6.080500
opleiding-0.3125863505081220.076485-4.08696.5e-053.2e-05
Neuroticisme-0.4376346337025880.057654-7.590700
Extraversie-0.1934143613482530.065404-2.95720.0035030.001751
Openheid-0.4517555416712080.057789-7.817400






Multiple Linear Regression - Regression Statistics
Multiple R0.726089862984427
R-squared0.527206489128744
Adjusted R-squared0.51211733452647
F-TEST (value)34.9394318651416
F-TEST (DF numerator)6
F-TEST (DF denominator)188
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.51089716761969
Sum Squared Residuals13617.8496723858

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.726089862984427 \tabularnewline
R-squared & 0.527206489128744 \tabularnewline
Adjusted R-squared & 0.51211733452647 \tabularnewline
F-TEST (value) & 34.9394318651416 \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 & 8.51089716761969 \tabularnewline
Sum Squared Residuals & 13617.8496723858 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115455&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.726089862984427[/C][/ROW]
[ROW][C]R-squared[/C][C]0.527206489128744[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.51211733452647[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]34.9394318651416[/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]8.51089716761969[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13617.8496723858[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115455&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115455&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 R0.726089862984427
R-squared0.527206489128744
Adjusted R-squared0.51211733452647
F-TEST (value)34.9394318651416
F-TEST (DF numerator)6
F-TEST (DF denominator)188
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.51089716761969
Sum Squared Residuals13617.8496723858






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
147.49043908553882-3.49043908553882
246.2028772369415-2.20287723694151
3521.438441876034-16.438441876034
425.81601751335397-3.81601751335397
5310.3891311690326-7.38913116903258
657.1577885804154-2.15778858041540
74-0.3936443199512184.39364431995122
84-0.7472582996803514.74725829968035
945.47530936369507-1.47530936369507
1043.545841553487260.454158446512739
1154.550291262980580.449708737019420
1244.30267725524188-0.302677255241885
1334.43567060495026-1.43567060495026
1443.201773298115010.79822670188499
1536.68257747079457-3.68257747079457
1638.60823237338215-5.60823237338215
17418.1420458360980-14.1420458360980
1830.1533074964404452.84669250355956
194-2.108135300979516.10813530097951
2025.22727821541455-3.22727821541455
2143.526979921325770.473020078674229
2239.0157532294905-6.0157532294905
2340.3540097711938733.64599022880613
2447.64971419845399-3.64971419845399
2537.42339441874735-4.42339441874735
2630.7047931212779572.29520687872204
2728.29219486957127-6.29219486957127
2849.72445772923752-5.72445772923752
29512.0051185913063-7.00511859130632
30421.1695795231163-17.1695795231163
3119.73524341423399-8.73524341423399
3216.48561452294026-5.48561452294026
3316.41353518685714-5.41353518685714
3418.3706636596714-7.37066365967139
35114.9229789148632-13.9229789148632
36211.8026163579263-9.80261635792626
3716.38087003596339-5.38087003596339
3829.86435009272884-7.86435009272884
3929.5505726852-7.5505726852
4018.9724934369157-7.9724934369157
414820.574347734373727.4256522656263
424812.755498331215235.2445016687848
432619.73200980688296.26799019311715
444615.325884324967930.6741156750321
4531.283148579551441.71685142044856
46312.0330128244449-9.03301282444492
4742.798201905132541.20179809486746
4820.8547169210410331.14528307895897
4923.05685881270109-1.05685881270109
5031.243359395104341.75664060489566
5126.13673461768359-4.13673461768359
524-1.315767982536605.3157679825366
5357.19052874315968-2.19052874315968
54310.0906202084505-7.09062020845054
5548.5589228315201-4.55892283152010
5654.524788577985660.475211422014343
575-3.590288685521728.59028868552172
5833.55667583791473-0.55667583791473
5947.8463824282773-3.8463824282773
6031.945508626919881.05449137308012
613-1.180919580395124.18091958039512
62212.2205870112505-10.2205870112505
6338.17655727478-5.17655727477999
6449.92320152995496-5.92320152995495
654-0.3173910069354644.31739100693546
664-2.031044677698956.03104467769895
6740.5124350693020133.48756493069799
683-0.2366507179530863.23665071795309
6936.81370066366179-3.81370066366179
703-1.381882234810984.38188223481098
7129.14829708889404-7.14829708889404
72313.0848406879182-10.0848406879182
7337.10599088934759-4.10599088934759
7437.58704453420239-4.58704453420239
75310.5228571031859-7.52285710318591
7650.2748225693119124.72517743068809
77313.0671145430685-10.0671145430685
785-0.2001727224124825.20017272241248
7940.9215993957380233.07840060426198
8047.45902975122535-3.45902975122536
81412.4558171993673-8.45581719936729
82519.8807693942138-14.8807693942138
8343.273419149926010.726580850073986
845-0.5295962347105195.52959623471052
8532.598970494246810.401029505753186
863-1.460444069536574.46044406953657
87214.5647980920523-12.5647980920523
8832.238546746573960.76145325342604
8941.984743242834362.01525675716564
9057.22862136896056-2.22862136896056
9159.7920390734676-4.79203907346761
9232.788908341102510.211091658897495
932-0.2845123759444782.28451237594448
943-0.0847929395324013.0847929395324
95411.3852339257169-7.38523392571692
9616.91731042626236-5.91731042626236
9749.52457273048141-5.52457273048141
9830.8978399056181542.10216009438185
9939.6197855591749-6.6197855591749
10044.55595875620675-0.555958756206747
101313.2430831143542-10.2430831143542
10246.44344747070793-2.44344747070793
10326.66629642871685-4.66629642871685
10431.834330342490571.16566965750943
105312.1496154968744-9.14961549687444
10630.7583735906996632.24162640930034
10727.40157032588241-5.40157032588241
1085-0.3287149523098235.32871495230982
109511.0764859960405-6.07648599604053
1104-0.3345793522207444.33457935222074
11121.566618921430150.433381078569853
112315.0818025005225-12.0818025005225
113310.1449546796304-7.14495467963036
11435.85813409527247-2.85813409527247
1154-0.3005009212270964.3005009212271
11651.215044168903293.78495583109671
117410.5089164907078-6.50891649070777
1182225.2902108490328-3.29021084903285
1191625.703353397549-9.70335339754897
1203624.658420633921911.3415793660781
1213530.12522031878584.87477968121417
1222526.9689653449368-1.96896534493676
1232733.8978088897112-6.89780888971123
1243225.59336823921426.40663176078581
1253622.268864185064613.7311358149354
1265127.621335944529723.3786640554703
1273024.28542433680595.71457566319407
1282025.7606513910220-5.76065139102197
1292923.66975007400805.33024992599203
1302626.3567442029008-0.35674420290077
1312024.7596110009139-4.75961100091395
1324024.139682530741815.8603174692582
1332923.65681593609685.34318406390317
1343222.24180913918489.75819086081517
1353330.27767321181342.7223267881866
1363229.27773076334752.72226923665249
1373426.33246869854627.66753130145384
1382422.45308504167961.54691495832038
1392526.5822879859135-1.58228798591351
1404128.032673521380812.9673264786192
1413923.735665462760115.2643345372399
1422121.4490311081455-0.449031108145488
1433823.345761658866614.6542383411334
1442826.64741155147571.35258844852431
1453724.089549079524912.9104509204751
1464612.463381362949133.5366186370509
1473912.305878749480326.6941212505197
1482111.57118270374359.42881729625647
1493124.48489149093066.51510850906942
1502510.894697334097414.1053026659026
1512913.735649231037715.2643507689623
1523122.83578773665978.16421226334031
15335.2871865835644-2.2871865835644
1544-2.596575845688186.59657584568818
155110.045590208559-9.045590208559
15619.52600784199112-8.52600784199112
15752.887301060132172.11269893986783
15844.38806400479118-0.388064004791184
159311.5278536673446-8.52785366734465
16032.085325274116180.914674725883816
16143.005833176371930.99416682362807
16230.5749156308463712.42508436915363
163212.3705193858617-10.3705193858617
16411.45944363063282-0.459443630632818
16518.13608420833946-7.13608420833946
16654.145638572322110.854361427677887
1674-1.775496689979645.77549668997964
168312.9822080310787-9.98220803107873
16949.10209637283875-5.10209637283875
170513.507974096692-8.50797409669199
1714-0.3720172215578854.37201722155788
172410.0298591080536-6.02985910805359
1732-1.528719059253483.52871905925348
17433.70069691844701-0.700696918447011
1754-0.4698137122851094.46981371228511
17633.42071674560706-0.420716745607056
17741.276557271616632.72344272838337
17833.28638587754914-0.286385877549136
17946.5252853613354-2.5252853613354
18010.4683764055988050.531623594401195
18124.29927877414268-2.29927877414268
1823-8.485416286624811.4854162866248
18331.414492342348791.58550765765121
1845-9.1332635274227614.1332635274228
18540.3821078673381013.6178921326619
18634.60459360858908-1.60459360858908
18731.363873110385241.63612688961476
18835.76147521941344-2.76147521941344
189311.9830168702179-8.98301687021793
19046.60651852317355-2.60651852317355
191312.1621882751171-9.1621882751171
192210.0663235120214-8.0663235120214
1934-4.534112952068.53411295206
19425.16872141993684-3.16872141993684
19547.49043908553895-3.49043908553895

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 7.49043908553882 & -3.49043908553882 \tabularnewline
2 & 4 & 6.2028772369415 & -2.20287723694151 \tabularnewline
3 & 5 & 21.438441876034 & -16.438441876034 \tabularnewline
4 & 2 & 5.81601751335397 & -3.81601751335397 \tabularnewline
5 & 3 & 10.3891311690326 & -7.38913116903258 \tabularnewline
6 & 5 & 7.1577885804154 & -2.15778858041540 \tabularnewline
7 & 4 & -0.393644319951218 & 4.39364431995122 \tabularnewline
8 & 4 & -0.747258299680351 & 4.74725829968035 \tabularnewline
9 & 4 & 5.47530936369507 & -1.47530936369507 \tabularnewline
10 & 4 & 3.54584155348726 & 0.454158446512739 \tabularnewline
11 & 5 & 4.55029126298058 & 0.449708737019420 \tabularnewline
12 & 4 & 4.30267725524188 & -0.302677255241885 \tabularnewline
13 & 3 & 4.43567060495026 & -1.43567060495026 \tabularnewline
14 & 4 & 3.20177329811501 & 0.79822670188499 \tabularnewline
15 & 3 & 6.68257747079457 & -3.68257747079457 \tabularnewline
16 & 3 & 8.60823237338215 & -5.60823237338215 \tabularnewline
17 & 4 & 18.1420458360980 & -14.1420458360980 \tabularnewline
18 & 3 & 0.153307496440445 & 2.84669250355956 \tabularnewline
19 & 4 & -2.10813530097951 & 6.10813530097951 \tabularnewline
20 & 2 & 5.22727821541455 & -3.22727821541455 \tabularnewline
21 & 4 & 3.52697992132577 & 0.473020078674229 \tabularnewline
22 & 3 & 9.0157532294905 & -6.0157532294905 \tabularnewline
23 & 4 & 0.354009771193873 & 3.64599022880613 \tabularnewline
24 & 4 & 7.64971419845399 & -3.64971419845399 \tabularnewline
25 & 3 & 7.42339441874735 & -4.42339441874735 \tabularnewline
26 & 3 & 0.704793121277957 & 2.29520687872204 \tabularnewline
27 & 2 & 8.29219486957127 & -6.29219486957127 \tabularnewline
28 & 4 & 9.72445772923752 & -5.72445772923752 \tabularnewline
29 & 5 & 12.0051185913063 & -7.00511859130632 \tabularnewline
30 & 4 & 21.1695795231163 & -17.1695795231163 \tabularnewline
31 & 1 & 9.73524341423399 & -8.73524341423399 \tabularnewline
32 & 1 & 6.48561452294026 & -5.48561452294026 \tabularnewline
33 & 1 & 6.41353518685714 & -5.41353518685714 \tabularnewline
34 & 1 & 8.3706636596714 & -7.37066365967139 \tabularnewline
35 & 1 & 14.9229789148632 & -13.9229789148632 \tabularnewline
36 & 2 & 11.8026163579263 & -9.80261635792626 \tabularnewline
37 & 1 & 6.38087003596339 & -5.38087003596339 \tabularnewline
38 & 2 & 9.86435009272884 & -7.86435009272884 \tabularnewline
39 & 2 & 9.5505726852 & -7.5505726852 \tabularnewline
40 & 1 & 8.9724934369157 & -7.9724934369157 \tabularnewline
41 & 48 & 20.5743477343737 & 27.4256522656263 \tabularnewline
42 & 48 & 12.7554983312152 & 35.2445016687848 \tabularnewline
43 & 26 & 19.7320098068829 & 6.26799019311715 \tabularnewline
44 & 46 & 15.3258843249679 & 30.6741156750321 \tabularnewline
45 & 3 & 1.28314857955144 & 1.71685142044856 \tabularnewline
46 & 3 & 12.0330128244449 & -9.03301282444492 \tabularnewline
47 & 4 & 2.79820190513254 & 1.20179809486746 \tabularnewline
48 & 2 & 0.854716921041033 & 1.14528307895897 \tabularnewline
49 & 2 & 3.05685881270109 & -1.05685881270109 \tabularnewline
50 & 3 & 1.24335939510434 & 1.75664060489566 \tabularnewline
51 & 2 & 6.13673461768359 & -4.13673461768359 \tabularnewline
52 & 4 & -1.31576798253660 & 5.3157679825366 \tabularnewline
53 & 5 & 7.19052874315968 & -2.19052874315968 \tabularnewline
54 & 3 & 10.0906202084505 & -7.09062020845054 \tabularnewline
55 & 4 & 8.5589228315201 & -4.55892283152010 \tabularnewline
56 & 5 & 4.52478857798566 & 0.475211422014343 \tabularnewline
57 & 5 & -3.59028868552172 & 8.59028868552172 \tabularnewline
58 & 3 & 3.55667583791473 & -0.55667583791473 \tabularnewline
59 & 4 & 7.8463824282773 & -3.8463824282773 \tabularnewline
60 & 3 & 1.94550862691988 & 1.05449137308012 \tabularnewline
61 & 3 & -1.18091958039512 & 4.18091958039512 \tabularnewline
62 & 2 & 12.2205870112505 & -10.2205870112505 \tabularnewline
63 & 3 & 8.17655727478 & -5.17655727477999 \tabularnewline
64 & 4 & 9.92320152995496 & -5.92320152995495 \tabularnewline
65 & 4 & -0.317391006935464 & 4.31739100693546 \tabularnewline
66 & 4 & -2.03104467769895 & 6.03104467769895 \tabularnewline
67 & 4 & 0.512435069302013 & 3.48756493069799 \tabularnewline
68 & 3 & -0.236650717953086 & 3.23665071795309 \tabularnewline
69 & 3 & 6.81370066366179 & -3.81370066366179 \tabularnewline
70 & 3 & -1.38188223481098 & 4.38188223481098 \tabularnewline
71 & 2 & 9.14829708889404 & -7.14829708889404 \tabularnewline
72 & 3 & 13.0848406879182 & -10.0848406879182 \tabularnewline
73 & 3 & 7.10599088934759 & -4.10599088934759 \tabularnewline
74 & 3 & 7.58704453420239 & -4.58704453420239 \tabularnewline
75 & 3 & 10.5228571031859 & -7.52285710318591 \tabularnewline
76 & 5 & 0.274822569311912 & 4.72517743068809 \tabularnewline
77 & 3 & 13.0671145430685 & -10.0671145430685 \tabularnewline
78 & 5 & -0.200172722412482 & 5.20017272241248 \tabularnewline
79 & 4 & 0.921599395738023 & 3.07840060426198 \tabularnewline
80 & 4 & 7.45902975122535 & -3.45902975122536 \tabularnewline
81 & 4 & 12.4558171993673 & -8.45581719936729 \tabularnewline
82 & 5 & 19.8807693942138 & -14.8807693942138 \tabularnewline
83 & 4 & 3.27341914992601 & 0.726580850073986 \tabularnewline
84 & 5 & -0.529596234710519 & 5.52959623471052 \tabularnewline
85 & 3 & 2.59897049424681 & 0.401029505753186 \tabularnewline
86 & 3 & -1.46044406953657 & 4.46044406953657 \tabularnewline
87 & 2 & 14.5647980920523 & -12.5647980920523 \tabularnewline
88 & 3 & 2.23854674657396 & 0.76145325342604 \tabularnewline
89 & 4 & 1.98474324283436 & 2.01525675716564 \tabularnewline
90 & 5 & 7.22862136896056 & -2.22862136896056 \tabularnewline
91 & 5 & 9.7920390734676 & -4.79203907346761 \tabularnewline
92 & 3 & 2.78890834110251 & 0.211091658897495 \tabularnewline
93 & 2 & -0.284512375944478 & 2.28451237594448 \tabularnewline
94 & 3 & -0.084792939532401 & 3.0847929395324 \tabularnewline
95 & 4 & 11.3852339257169 & -7.38523392571692 \tabularnewline
96 & 1 & 6.91731042626236 & -5.91731042626236 \tabularnewline
97 & 4 & 9.52457273048141 & -5.52457273048141 \tabularnewline
98 & 3 & 0.897839905618154 & 2.10216009438185 \tabularnewline
99 & 3 & 9.6197855591749 & -6.6197855591749 \tabularnewline
100 & 4 & 4.55595875620675 & -0.555958756206747 \tabularnewline
101 & 3 & 13.2430831143542 & -10.2430831143542 \tabularnewline
102 & 4 & 6.44344747070793 & -2.44344747070793 \tabularnewline
103 & 2 & 6.66629642871685 & -4.66629642871685 \tabularnewline
104 & 3 & 1.83433034249057 & 1.16566965750943 \tabularnewline
105 & 3 & 12.1496154968744 & -9.14961549687444 \tabularnewline
106 & 3 & 0.758373590699663 & 2.24162640930034 \tabularnewline
107 & 2 & 7.40157032588241 & -5.40157032588241 \tabularnewline
108 & 5 & -0.328714952309823 & 5.32871495230982 \tabularnewline
109 & 5 & 11.0764859960405 & -6.07648599604053 \tabularnewline
110 & 4 & -0.334579352220744 & 4.33457935222074 \tabularnewline
111 & 2 & 1.56661892143015 & 0.433381078569853 \tabularnewline
112 & 3 & 15.0818025005225 & -12.0818025005225 \tabularnewline
113 & 3 & 10.1449546796304 & -7.14495467963036 \tabularnewline
114 & 3 & 5.85813409527247 & -2.85813409527247 \tabularnewline
115 & 4 & -0.300500921227096 & 4.3005009212271 \tabularnewline
116 & 5 & 1.21504416890329 & 3.78495583109671 \tabularnewline
117 & 4 & 10.5089164907078 & -6.50891649070777 \tabularnewline
118 & 22 & 25.2902108490328 & -3.29021084903285 \tabularnewline
119 & 16 & 25.703353397549 & -9.70335339754897 \tabularnewline
120 & 36 & 24.6584206339219 & 11.3415793660781 \tabularnewline
121 & 35 & 30.1252203187858 & 4.87477968121417 \tabularnewline
122 & 25 & 26.9689653449368 & -1.96896534493676 \tabularnewline
123 & 27 & 33.8978088897112 & -6.89780888971123 \tabularnewline
124 & 32 & 25.5933682392142 & 6.40663176078581 \tabularnewline
125 & 36 & 22.2688641850646 & 13.7311358149354 \tabularnewline
126 & 51 & 27.6213359445297 & 23.3786640554703 \tabularnewline
127 & 30 & 24.2854243368059 & 5.71457566319407 \tabularnewline
128 & 20 & 25.7606513910220 & -5.76065139102197 \tabularnewline
129 & 29 & 23.6697500740080 & 5.33024992599203 \tabularnewline
130 & 26 & 26.3567442029008 & -0.35674420290077 \tabularnewline
131 & 20 & 24.7596110009139 & -4.75961100091395 \tabularnewline
132 & 40 & 24.1396825307418 & 15.8603174692582 \tabularnewline
133 & 29 & 23.6568159360968 & 5.34318406390317 \tabularnewline
134 & 32 & 22.2418091391848 & 9.75819086081517 \tabularnewline
135 & 33 & 30.2776732118134 & 2.7223267881866 \tabularnewline
136 & 32 & 29.2777307633475 & 2.72226923665249 \tabularnewline
137 & 34 & 26.3324686985462 & 7.66753130145384 \tabularnewline
138 & 24 & 22.4530850416796 & 1.54691495832038 \tabularnewline
139 & 25 & 26.5822879859135 & -1.58228798591351 \tabularnewline
140 & 41 & 28.0326735213808 & 12.9673264786192 \tabularnewline
141 & 39 & 23.7356654627601 & 15.2643345372399 \tabularnewline
142 & 21 & 21.4490311081455 & -0.449031108145488 \tabularnewline
143 & 38 & 23.3457616588666 & 14.6542383411334 \tabularnewline
144 & 28 & 26.6474115514757 & 1.35258844852431 \tabularnewline
145 & 37 & 24.0895490795249 & 12.9104509204751 \tabularnewline
146 & 46 & 12.4633813629491 & 33.5366186370509 \tabularnewline
147 & 39 & 12.3058787494803 & 26.6941212505197 \tabularnewline
148 & 21 & 11.5711827037435 & 9.42881729625647 \tabularnewline
149 & 31 & 24.4848914909306 & 6.51510850906942 \tabularnewline
150 & 25 & 10.8946973340974 & 14.1053026659026 \tabularnewline
151 & 29 & 13.7356492310377 & 15.2643507689623 \tabularnewline
152 & 31 & 22.8357877366597 & 8.16421226334031 \tabularnewline
153 & 3 & 5.2871865835644 & -2.2871865835644 \tabularnewline
154 & 4 & -2.59657584568818 & 6.59657584568818 \tabularnewline
155 & 1 & 10.045590208559 & -9.045590208559 \tabularnewline
156 & 1 & 9.52600784199112 & -8.52600784199112 \tabularnewline
157 & 5 & 2.88730106013217 & 2.11269893986783 \tabularnewline
158 & 4 & 4.38806400479118 & -0.388064004791184 \tabularnewline
159 & 3 & 11.5278536673446 & -8.52785366734465 \tabularnewline
160 & 3 & 2.08532527411618 & 0.914674725883816 \tabularnewline
161 & 4 & 3.00583317637193 & 0.99416682362807 \tabularnewline
162 & 3 & 0.574915630846371 & 2.42508436915363 \tabularnewline
163 & 2 & 12.3705193858617 & -10.3705193858617 \tabularnewline
164 & 1 & 1.45944363063282 & -0.459443630632818 \tabularnewline
165 & 1 & 8.13608420833946 & -7.13608420833946 \tabularnewline
166 & 5 & 4.14563857232211 & 0.854361427677887 \tabularnewline
167 & 4 & -1.77549668997964 & 5.77549668997964 \tabularnewline
168 & 3 & 12.9822080310787 & -9.98220803107873 \tabularnewline
169 & 4 & 9.10209637283875 & -5.10209637283875 \tabularnewline
170 & 5 & 13.507974096692 & -8.50797409669199 \tabularnewline
171 & 4 & -0.372017221557885 & 4.37201722155788 \tabularnewline
172 & 4 & 10.0298591080536 & -6.02985910805359 \tabularnewline
173 & 2 & -1.52871905925348 & 3.52871905925348 \tabularnewline
174 & 3 & 3.70069691844701 & -0.700696918447011 \tabularnewline
175 & 4 & -0.469813712285109 & 4.46981371228511 \tabularnewline
176 & 3 & 3.42071674560706 & -0.420716745607056 \tabularnewline
177 & 4 & 1.27655727161663 & 2.72344272838337 \tabularnewline
178 & 3 & 3.28638587754914 & -0.286385877549136 \tabularnewline
179 & 4 & 6.5252853613354 & -2.5252853613354 \tabularnewline
180 & 1 & 0.468376405598805 & 0.531623594401195 \tabularnewline
181 & 2 & 4.29927877414268 & -2.29927877414268 \tabularnewline
182 & 3 & -8.4854162866248 & 11.4854162866248 \tabularnewline
183 & 3 & 1.41449234234879 & 1.58550765765121 \tabularnewline
184 & 5 & -9.13326352742276 & 14.1332635274228 \tabularnewline
185 & 4 & 0.382107867338101 & 3.6178921326619 \tabularnewline
186 & 3 & 4.60459360858908 & -1.60459360858908 \tabularnewline
187 & 3 & 1.36387311038524 & 1.63612688961476 \tabularnewline
188 & 3 & 5.76147521941344 & -2.76147521941344 \tabularnewline
189 & 3 & 11.9830168702179 & -8.98301687021793 \tabularnewline
190 & 4 & 6.60651852317355 & -2.60651852317355 \tabularnewline
191 & 3 & 12.1621882751171 & -9.1621882751171 \tabularnewline
192 & 2 & 10.0663235120214 & -8.0663235120214 \tabularnewline
193 & 4 & -4.53411295206 & 8.53411295206 \tabularnewline
194 & 2 & 5.16872141993684 & -3.16872141993684 \tabularnewline
195 & 4 & 7.49043908553895 & -3.49043908553895 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115455&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]4[/C][C]7.49043908553882[/C][C]-3.49043908553882[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]6.2028772369415[/C][C]-2.20287723694151[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]21.438441876034[/C][C]-16.438441876034[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]5.81601751335397[/C][C]-3.81601751335397[/C][/ROW]
[ROW][C]5[/C][C]3[/C][C]10.3891311690326[/C][C]-7.38913116903258[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]7.1577885804154[/C][C]-2.15778858041540[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]-0.393644319951218[/C][C]4.39364431995122[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]-0.747258299680351[/C][C]4.74725829968035[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.47530936369507[/C][C]-1.47530936369507[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.54584155348726[/C][C]0.454158446512739[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]4.55029126298058[/C][C]0.449708737019420[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]4.30267725524188[/C][C]-0.302677255241885[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]4.43567060495026[/C][C]-1.43567060495026[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]3.20177329811501[/C][C]0.79822670188499[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]6.68257747079457[/C][C]-3.68257747079457[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]8.60823237338215[/C][C]-5.60823237338215[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]18.1420458360980[/C][C]-14.1420458360980[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]0.153307496440445[/C][C]2.84669250355956[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]-2.10813530097951[/C][C]6.10813530097951[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]5.22727821541455[/C][C]-3.22727821541455[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]3.52697992132577[/C][C]0.473020078674229[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]9.0157532294905[/C][C]-6.0157532294905[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]0.354009771193873[/C][C]3.64599022880613[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]7.64971419845399[/C][C]-3.64971419845399[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]7.42339441874735[/C][C]-4.42339441874735[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]0.704793121277957[/C][C]2.29520687872204[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]8.29219486957127[/C][C]-6.29219486957127[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]9.72445772923752[/C][C]-5.72445772923752[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]12.0051185913063[/C][C]-7.00511859130632[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]21.1695795231163[/C][C]-17.1695795231163[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]9.73524341423399[/C][C]-8.73524341423399[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]6.48561452294026[/C][C]-5.48561452294026[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]6.41353518685714[/C][C]-5.41353518685714[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]8.3706636596714[/C][C]-7.37066365967139[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]14.9229789148632[/C][C]-13.9229789148632[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]11.8026163579263[/C][C]-9.80261635792626[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]6.38087003596339[/C][C]-5.38087003596339[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]9.86435009272884[/C][C]-7.86435009272884[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]9.5505726852[/C][C]-7.5505726852[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]8.9724934369157[/C][C]-7.9724934369157[/C][/ROW]
[ROW][C]41[/C][C]48[/C][C]20.5743477343737[/C][C]27.4256522656263[/C][/ROW]
[ROW][C]42[/C][C]48[/C][C]12.7554983312152[/C][C]35.2445016687848[/C][/ROW]
[ROW][C]43[/C][C]26[/C][C]19.7320098068829[/C][C]6.26799019311715[/C][/ROW]
[ROW][C]44[/C][C]46[/C][C]15.3258843249679[/C][C]30.6741156750321[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]1.28314857955144[/C][C]1.71685142044856[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]12.0330128244449[/C][C]-9.03301282444492[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]2.79820190513254[/C][C]1.20179809486746[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]0.854716921041033[/C][C]1.14528307895897[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]3.05685881270109[/C][C]-1.05685881270109[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]1.24335939510434[/C][C]1.75664060489566[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]6.13673461768359[/C][C]-4.13673461768359[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]-1.31576798253660[/C][C]5.3157679825366[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]7.19052874315968[/C][C]-2.19052874315968[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]10.0906202084505[/C][C]-7.09062020845054[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]8.5589228315201[/C][C]-4.55892283152010[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]4.52478857798566[/C][C]0.475211422014343[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]-3.59028868552172[/C][C]8.59028868552172[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]3.55667583791473[/C][C]-0.55667583791473[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]7.8463824282773[/C][C]-3.8463824282773[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]1.94550862691988[/C][C]1.05449137308012[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]-1.18091958039512[/C][C]4.18091958039512[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]12.2205870112505[/C][C]-10.2205870112505[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]8.17655727478[/C][C]-5.17655727477999[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]9.92320152995496[/C][C]-5.92320152995495[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]-0.317391006935464[/C][C]4.31739100693546[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]-2.03104467769895[/C][C]6.03104467769895[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]0.512435069302013[/C][C]3.48756493069799[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]-0.236650717953086[/C][C]3.23665071795309[/C][/ROW]
[ROW][C]69[/C][C]3[/C][C]6.81370066366179[/C][C]-3.81370066366179[/C][/ROW]
[ROW][C]70[/C][C]3[/C][C]-1.38188223481098[/C][C]4.38188223481098[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]9.14829708889404[/C][C]-7.14829708889404[/C][/ROW]
[ROW][C]72[/C][C]3[/C][C]13.0848406879182[/C][C]-10.0848406879182[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]7.10599088934759[/C][C]-4.10599088934759[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]7.58704453420239[/C][C]-4.58704453420239[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]10.5228571031859[/C][C]-7.52285710318591[/C][/ROW]
[ROW][C]76[/C][C]5[/C][C]0.274822569311912[/C][C]4.72517743068809[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]13.0671145430685[/C][C]-10.0671145430685[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]-0.200172722412482[/C][C]5.20017272241248[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]0.921599395738023[/C][C]3.07840060426198[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]7.45902975122535[/C][C]-3.45902975122536[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]12.4558171993673[/C][C]-8.45581719936729[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]19.8807693942138[/C][C]-14.8807693942138[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]3.27341914992601[/C][C]0.726580850073986[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]-0.529596234710519[/C][C]5.52959623471052[/C][/ROW]
[ROW][C]85[/C][C]3[/C][C]2.59897049424681[/C][C]0.401029505753186[/C][/ROW]
[ROW][C]86[/C][C]3[/C][C]-1.46044406953657[/C][C]4.46044406953657[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]14.5647980920523[/C][C]-12.5647980920523[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]2.23854674657396[/C][C]0.76145325342604[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]1.98474324283436[/C][C]2.01525675716564[/C][/ROW]
[ROW][C]90[/C][C]5[/C][C]7.22862136896056[/C][C]-2.22862136896056[/C][/ROW]
[ROW][C]91[/C][C]5[/C][C]9.7920390734676[/C][C]-4.79203907346761[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]2.78890834110251[/C][C]0.211091658897495[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]-0.284512375944478[/C][C]2.28451237594448[/C][/ROW]
[ROW][C]94[/C][C]3[/C][C]-0.084792939532401[/C][C]3.0847929395324[/C][/ROW]
[ROW][C]95[/C][C]4[/C][C]11.3852339257169[/C][C]-7.38523392571692[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]6.91731042626236[/C][C]-5.91731042626236[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]9.52457273048141[/C][C]-5.52457273048141[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]0.897839905618154[/C][C]2.10216009438185[/C][/ROW]
[ROW][C]99[/C][C]3[/C][C]9.6197855591749[/C][C]-6.6197855591749[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]4.55595875620675[/C][C]-0.555958756206747[/C][/ROW]
[ROW][C]101[/C][C]3[/C][C]13.2430831143542[/C][C]-10.2430831143542[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]6.44344747070793[/C][C]-2.44344747070793[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]6.66629642871685[/C][C]-4.66629642871685[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]1.83433034249057[/C][C]1.16566965750943[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]12.1496154968744[/C][C]-9.14961549687444[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]0.758373590699663[/C][C]2.24162640930034[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]7.40157032588241[/C][C]-5.40157032588241[/C][/ROW]
[ROW][C]108[/C][C]5[/C][C]-0.328714952309823[/C][C]5.32871495230982[/C][/ROW]
[ROW][C]109[/C][C]5[/C][C]11.0764859960405[/C][C]-6.07648599604053[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]-0.334579352220744[/C][C]4.33457935222074[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]1.56661892143015[/C][C]0.433381078569853[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]15.0818025005225[/C][C]-12.0818025005225[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]10.1449546796304[/C][C]-7.14495467963036[/C][/ROW]
[ROW][C]114[/C][C]3[/C][C]5.85813409527247[/C][C]-2.85813409527247[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]-0.300500921227096[/C][C]4.3005009212271[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]1.21504416890329[/C][C]3.78495583109671[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]10.5089164907078[/C][C]-6.50891649070777[/C][/ROW]
[ROW][C]118[/C][C]22[/C][C]25.2902108490328[/C][C]-3.29021084903285[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]25.703353397549[/C][C]-9.70335339754897[/C][/ROW]
[ROW][C]120[/C][C]36[/C][C]24.6584206339219[/C][C]11.3415793660781[/C][/ROW]
[ROW][C]121[/C][C]35[/C][C]30.1252203187858[/C][C]4.87477968121417[/C][/ROW]
[ROW][C]122[/C][C]25[/C][C]26.9689653449368[/C][C]-1.96896534493676[/C][/ROW]
[ROW][C]123[/C][C]27[/C][C]33.8978088897112[/C][C]-6.89780888971123[/C][/ROW]
[ROW][C]124[/C][C]32[/C][C]25.5933682392142[/C][C]6.40663176078581[/C][/ROW]
[ROW][C]125[/C][C]36[/C][C]22.2688641850646[/C][C]13.7311358149354[/C][/ROW]
[ROW][C]126[/C][C]51[/C][C]27.6213359445297[/C][C]23.3786640554703[/C][/ROW]
[ROW][C]127[/C][C]30[/C][C]24.2854243368059[/C][C]5.71457566319407[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]25.7606513910220[/C][C]-5.76065139102197[/C][/ROW]
[ROW][C]129[/C][C]29[/C][C]23.6697500740080[/C][C]5.33024992599203[/C][/ROW]
[ROW][C]130[/C][C]26[/C][C]26.3567442029008[/C][C]-0.35674420290077[/C][/ROW]
[ROW][C]131[/C][C]20[/C][C]24.7596110009139[/C][C]-4.75961100091395[/C][/ROW]
[ROW][C]132[/C][C]40[/C][C]24.1396825307418[/C][C]15.8603174692582[/C][/ROW]
[ROW][C]133[/C][C]29[/C][C]23.6568159360968[/C][C]5.34318406390317[/C][/ROW]
[ROW][C]134[/C][C]32[/C][C]22.2418091391848[/C][C]9.75819086081517[/C][/ROW]
[ROW][C]135[/C][C]33[/C][C]30.2776732118134[/C][C]2.7223267881866[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]29.2777307633475[/C][C]2.72226923665249[/C][/ROW]
[ROW][C]137[/C][C]34[/C][C]26.3324686985462[/C][C]7.66753130145384[/C][/ROW]
[ROW][C]138[/C][C]24[/C][C]22.4530850416796[/C][C]1.54691495832038[/C][/ROW]
[ROW][C]139[/C][C]25[/C][C]26.5822879859135[/C][C]-1.58228798591351[/C][/ROW]
[ROW][C]140[/C][C]41[/C][C]28.0326735213808[/C][C]12.9673264786192[/C][/ROW]
[ROW][C]141[/C][C]39[/C][C]23.7356654627601[/C][C]15.2643345372399[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]21.4490311081455[/C][C]-0.449031108145488[/C][/ROW]
[ROW][C]143[/C][C]38[/C][C]23.3457616588666[/C][C]14.6542383411334[/C][/ROW]
[ROW][C]144[/C][C]28[/C][C]26.6474115514757[/C][C]1.35258844852431[/C][/ROW]
[ROW][C]145[/C][C]37[/C][C]24.0895490795249[/C][C]12.9104509204751[/C][/ROW]
[ROW][C]146[/C][C]46[/C][C]12.4633813629491[/C][C]33.5366186370509[/C][/ROW]
[ROW][C]147[/C][C]39[/C][C]12.3058787494803[/C][C]26.6941212505197[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]11.5711827037435[/C][C]9.42881729625647[/C][/ROW]
[ROW][C]149[/C][C]31[/C][C]24.4848914909306[/C][C]6.51510850906942[/C][/ROW]
[ROW][C]150[/C][C]25[/C][C]10.8946973340974[/C][C]14.1053026659026[/C][/ROW]
[ROW][C]151[/C][C]29[/C][C]13.7356492310377[/C][C]15.2643507689623[/C][/ROW]
[ROW][C]152[/C][C]31[/C][C]22.8357877366597[/C][C]8.16421226334031[/C][/ROW]
[ROW][C]153[/C][C]3[/C][C]5.2871865835644[/C][C]-2.2871865835644[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]-2.59657584568818[/C][C]6.59657584568818[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]10.045590208559[/C][C]-9.045590208559[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]9.52600784199112[/C][C]-8.52600784199112[/C][/ROW]
[ROW][C]157[/C][C]5[/C][C]2.88730106013217[/C][C]2.11269893986783[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]4.38806400479118[/C][C]-0.388064004791184[/C][/ROW]
[ROW][C]159[/C][C]3[/C][C]11.5278536673446[/C][C]-8.52785366734465[/C][/ROW]
[ROW][C]160[/C][C]3[/C][C]2.08532527411618[/C][C]0.914674725883816[/C][/ROW]
[ROW][C]161[/C][C]4[/C][C]3.00583317637193[/C][C]0.99416682362807[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]0.574915630846371[/C][C]2.42508436915363[/C][/ROW]
[ROW][C]163[/C][C]2[/C][C]12.3705193858617[/C][C]-10.3705193858617[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]1.45944363063282[/C][C]-0.459443630632818[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]8.13608420833946[/C][C]-7.13608420833946[/C][/ROW]
[ROW][C]166[/C][C]5[/C][C]4.14563857232211[/C][C]0.854361427677887[/C][/ROW]
[ROW][C]167[/C][C]4[/C][C]-1.77549668997964[/C][C]5.77549668997964[/C][/ROW]
[ROW][C]168[/C][C]3[/C][C]12.9822080310787[/C][C]-9.98220803107873[/C][/ROW]
[ROW][C]169[/C][C]4[/C][C]9.10209637283875[/C][C]-5.10209637283875[/C][/ROW]
[ROW][C]170[/C][C]5[/C][C]13.507974096692[/C][C]-8.50797409669199[/C][/ROW]
[ROW][C]171[/C][C]4[/C][C]-0.372017221557885[/C][C]4.37201722155788[/C][/ROW]
[ROW][C]172[/C][C]4[/C][C]10.0298591080536[/C][C]-6.02985910805359[/C][/ROW]
[ROW][C]173[/C][C]2[/C][C]-1.52871905925348[/C][C]3.52871905925348[/C][/ROW]
[ROW][C]174[/C][C]3[/C][C]3.70069691844701[/C][C]-0.700696918447011[/C][/ROW]
[ROW][C]175[/C][C]4[/C][C]-0.469813712285109[/C][C]4.46981371228511[/C][/ROW]
[ROW][C]176[/C][C]3[/C][C]3.42071674560706[/C][C]-0.420716745607056[/C][/ROW]
[ROW][C]177[/C][C]4[/C][C]1.27655727161663[/C][C]2.72344272838337[/C][/ROW]
[ROW][C]178[/C][C]3[/C][C]3.28638587754914[/C][C]-0.286385877549136[/C][/ROW]
[ROW][C]179[/C][C]4[/C][C]6.5252853613354[/C][C]-2.5252853613354[/C][/ROW]
[ROW][C]180[/C][C]1[/C][C]0.468376405598805[/C][C]0.531623594401195[/C][/ROW]
[ROW][C]181[/C][C]2[/C][C]4.29927877414268[/C][C]-2.29927877414268[/C][/ROW]
[ROW][C]182[/C][C]3[/C][C]-8.4854162866248[/C][C]11.4854162866248[/C][/ROW]
[ROW][C]183[/C][C]3[/C][C]1.41449234234879[/C][C]1.58550765765121[/C][/ROW]
[ROW][C]184[/C][C]5[/C][C]-9.13326352742276[/C][C]14.1332635274228[/C][/ROW]
[ROW][C]185[/C][C]4[/C][C]0.382107867338101[/C][C]3.6178921326619[/C][/ROW]
[ROW][C]186[/C][C]3[/C][C]4.60459360858908[/C][C]-1.60459360858908[/C][/ROW]
[ROW][C]187[/C][C]3[/C][C]1.36387311038524[/C][C]1.63612688961476[/C][/ROW]
[ROW][C]188[/C][C]3[/C][C]5.76147521941344[/C][C]-2.76147521941344[/C][/ROW]
[ROW][C]189[/C][C]3[/C][C]11.9830168702179[/C][C]-8.98301687021793[/C][/ROW]
[ROW][C]190[/C][C]4[/C][C]6.60651852317355[/C][C]-2.60651852317355[/C][/ROW]
[ROW][C]191[/C][C]3[/C][C]12.1621882751171[/C][C]-9.1621882751171[/C][/ROW]
[ROW][C]192[/C][C]2[/C][C]10.0663235120214[/C][C]-8.0663235120214[/C][/ROW]
[ROW][C]193[/C][C]4[/C][C]-4.53411295206[/C][C]8.53411295206[/C][/ROW]
[ROW][C]194[/C][C]2[/C][C]5.16872141993684[/C][C]-3.16872141993684[/C][/ROW]
[ROW][C]195[/C][C]4[/C][C]7.49043908553895[/C][C]-3.49043908553895[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115455&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115455&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
147.49043908553882-3.49043908553882
246.2028772369415-2.20287723694151
3521.438441876034-16.438441876034
425.81601751335397-3.81601751335397
5310.3891311690326-7.38913116903258
657.1577885804154-2.15778858041540
74-0.3936443199512184.39364431995122
84-0.7472582996803514.74725829968035
945.47530936369507-1.47530936369507
1043.545841553487260.454158446512739
1154.550291262980580.449708737019420
1244.30267725524188-0.302677255241885
1334.43567060495026-1.43567060495026
1443.201773298115010.79822670188499
1536.68257747079457-3.68257747079457
1638.60823237338215-5.60823237338215
17418.1420458360980-14.1420458360980
1830.1533074964404452.84669250355956
194-2.108135300979516.10813530097951
2025.22727821541455-3.22727821541455
2143.526979921325770.473020078674229
2239.0157532294905-6.0157532294905
2340.3540097711938733.64599022880613
2447.64971419845399-3.64971419845399
2537.42339441874735-4.42339441874735
2630.7047931212779572.29520687872204
2728.29219486957127-6.29219486957127
2849.72445772923752-5.72445772923752
29512.0051185913063-7.00511859130632
30421.1695795231163-17.1695795231163
3119.73524341423399-8.73524341423399
3216.48561452294026-5.48561452294026
3316.41353518685714-5.41353518685714
3418.3706636596714-7.37066365967139
35114.9229789148632-13.9229789148632
36211.8026163579263-9.80261635792626
3716.38087003596339-5.38087003596339
3829.86435009272884-7.86435009272884
3929.5505726852-7.5505726852
4018.9724934369157-7.9724934369157
414820.574347734373727.4256522656263
424812.755498331215235.2445016687848
432619.73200980688296.26799019311715
444615.325884324967930.6741156750321
4531.283148579551441.71685142044856
46312.0330128244449-9.03301282444492
4742.798201905132541.20179809486746
4820.8547169210410331.14528307895897
4923.05685881270109-1.05685881270109
5031.243359395104341.75664060489566
5126.13673461768359-4.13673461768359
524-1.315767982536605.3157679825366
5357.19052874315968-2.19052874315968
54310.0906202084505-7.09062020845054
5548.5589228315201-4.55892283152010
5654.524788577985660.475211422014343
575-3.590288685521728.59028868552172
5833.55667583791473-0.55667583791473
5947.8463824282773-3.8463824282773
6031.945508626919881.05449137308012
613-1.180919580395124.18091958039512
62212.2205870112505-10.2205870112505
6338.17655727478-5.17655727477999
6449.92320152995496-5.92320152995495
654-0.3173910069354644.31739100693546
664-2.031044677698956.03104467769895
6740.5124350693020133.48756493069799
683-0.2366507179530863.23665071795309
6936.81370066366179-3.81370066366179
703-1.381882234810984.38188223481098
7129.14829708889404-7.14829708889404
72313.0848406879182-10.0848406879182
7337.10599088934759-4.10599088934759
7437.58704453420239-4.58704453420239
75310.5228571031859-7.52285710318591
7650.2748225693119124.72517743068809
77313.0671145430685-10.0671145430685
785-0.2001727224124825.20017272241248
7940.9215993957380233.07840060426198
8047.45902975122535-3.45902975122536
81412.4558171993673-8.45581719936729
82519.8807693942138-14.8807693942138
8343.273419149926010.726580850073986
845-0.5295962347105195.52959623471052
8532.598970494246810.401029505753186
863-1.460444069536574.46044406953657
87214.5647980920523-12.5647980920523
8832.238546746573960.76145325342604
8941.984743242834362.01525675716564
9057.22862136896056-2.22862136896056
9159.7920390734676-4.79203907346761
9232.788908341102510.211091658897495
932-0.2845123759444782.28451237594448
943-0.0847929395324013.0847929395324
95411.3852339257169-7.38523392571692
9616.91731042626236-5.91731042626236
9749.52457273048141-5.52457273048141
9830.8978399056181542.10216009438185
9939.6197855591749-6.6197855591749
10044.55595875620675-0.555958756206747
101313.2430831143542-10.2430831143542
10246.44344747070793-2.44344747070793
10326.66629642871685-4.66629642871685
10431.834330342490571.16566965750943
105312.1496154968744-9.14961549687444
10630.7583735906996632.24162640930034
10727.40157032588241-5.40157032588241
1085-0.3287149523098235.32871495230982
109511.0764859960405-6.07648599604053
1104-0.3345793522207444.33457935222074
11121.566618921430150.433381078569853
112315.0818025005225-12.0818025005225
113310.1449546796304-7.14495467963036
11435.85813409527247-2.85813409527247
1154-0.3005009212270964.3005009212271
11651.215044168903293.78495583109671
117410.5089164907078-6.50891649070777
1182225.2902108490328-3.29021084903285
1191625.703353397549-9.70335339754897
1203624.658420633921911.3415793660781
1213530.12522031878584.87477968121417
1222526.9689653449368-1.96896534493676
1232733.8978088897112-6.89780888971123
1243225.59336823921426.40663176078581
1253622.268864185064613.7311358149354
1265127.621335944529723.3786640554703
1273024.28542433680595.71457566319407
1282025.7606513910220-5.76065139102197
1292923.66975007400805.33024992599203
1302626.3567442029008-0.35674420290077
1312024.7596110009139-4.75961100091395
1324024.139682530741815.8603174692582
1332923.65681593609685.34318406390317
1343222.24180913918489.75819086081517
1353330.27767321181342.7223267881866
1363229.27773076334752.72226923665249
1373426.33246869854627.66753130145384
1382422.45308504167961.54691495832038
1392526.5822879859135-1.58228798591351
1404128.032673521380812.9673264786192
1413923.735665462760115.2643345372399
1422121.4490311081455-0.449031108145488
1433823.345761658866614.6542383411334
1442826.64741155147571.35258844852431
1453724.089549079524912.9104509204751
1464612.463381362949133.5366186370509
1473912.305878749480326.6941212505197
1482111.57118270374359.42881729625647
1493124.48489149093066.51510850906942
1502510.894697334097414.1053026659026
1512913.735649231037715.2643507689623
1523122.83578773665978.16421226334031
15335.2871865835644-2.2871865835644
1544-2.596575845688186.59657584568818
155110.045590208559-9.045590208559
15619.52600784199112-8.52600784199112
15752.887301060132172.11269893986783
15844.38806400479118-0.388064004791184
159311.5278536673446-8.52785366734465
16032.085325274116180.914674725883816
16143.005833176371930.99416682362807
16230.5749156308463712.42508436915363
163212.3705193858617-10.3705193858617
16411.45944363063282-0.459443630632818
16518.13608420833946-7.13608420833946
16654.145638572322110.854361427677887
1674-1.775496689979645.77549668997964
168312.9822080310787-9.98220803107873
16949.10209637283875-5.10209637283875
170513.507974096692-8.50797409669199
1714-0.3720172215578854.37201722155788
172410.0298591080536-6.02985910805359
1732-1.528719059253483.52871905925348
17433.70069691844701-0.700696918447011
1754-0.4698137122851094.46981371228511
17633.42071674560706-0.420716745607056
17741.276557271616632.72344272838337
17833.28638587754914-0.286385877549136
17946.5252853613354-2.5252853613354
18010.4683764055988050.531623594401195
18124.29927877414268-2.29927877414268
1823-8.485416286624811.4854162866248
18331.414492342348791.58550765765121
1845-9.1332635274227614.1332635274228
18540.3821078673381013.6178921326619
18634.60459360858908-1.60459360858908
18731.363873110385241.63612688961476
18835.76147521941344-2.76147521941344
189311.9830168702179-8.98301687021793
19046.60651852317355-2.60651852317355
191312.1621882751171-9.1621882751171
192210.0663235120214-8.0663235120214
1934-4.534112952068.53411295206
19425.16872141993684-3.16872141993684
19547.49043908553895-3.49043908553895






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.00021598355666220.00043196711332440.999784016443338
111.46898784608529e-052.93797569217058e-050.99998531012154
126.47391040054466e-071.29478208010893e-060.99999935260896
131.42939907504496e-072.85879815008991e-070.999999857060093
148.76737946807497e-091.75347589361499e-080.99999999123262
153.48740631134593e-096.97481262269186e-090.999999996512594
162.58045211477036e-105.16090422954072e-100.999999999741955
172.70998481543949e-115.41996963087897e-110.9999999999729
183.04604061880162e-126.09208123760324e-120.999999999996954
194.59578877009915e-139.19157754019831e-130.99999999999954
202.94392286527705e-145.8878457305541e-140.99999999999997
211.81292991311512e-153.62585982623024e-150.999999999999998
222.12178326141188e-164.24356652282376e-161
231.31978406470402e-172.63956812940804e-171
247.89269626156615e-191.57853925231323e-181
254.63504125564217e-209.27008251128435e-201
262.03002650192292e-194.06005300384585e-191
274.84475265354455e-199.6895053070891e-191
284.0623244326456e-208.1246488652912e-201
297.97349869816906e-211.59469973963381e-201
301.35975919985693e-212.71951839971386e-211
311.30837291067363e-222.61674582134725e-221
321.54593735070913e-233.09187470141826e-231
331.25881621932171e-242.51763243864341e-241
341.10894328696068e-252.21788657392136e-251
352.36739003898186e-264.73478007796373e-261
367.62434716354625e-271.52486943270925e-261
371.84682593789920e-273.69365187579841e-271
382.86479148741725e-285.72958297483451e-281
395.55277178399254e-291.11055435679851e-281
402.71954942962187e-295.43909885924374e-291
416.36432108100397e-050.0001272864216200790.99993635678919
420.0004105907232427190.0008211814464854370.999589409276757
430.001567633027974460.003135266055948910.998432366972026
440.01563058825674790.03126117651349580.984369411743252
450.054849496726530.109698993453060.94515050327347
460.07839547526319230.1567909505263850.921604524736808
470.08206500176131520.1641300035226300.917934998238685
480.07033835218464010.1406767043692800.92966164781536
490.05511016022509720.1102203204501940.944889839774903
500.05815670432904160.1163134086580830.941843295670958
510.04655360240973280.09310720481946560.953446397590267
520.06229025151988860.1245805030397770.937709748480111
530.04842828169940330.09685656339880650.951571718300597
540.04257441589055140.08514883178110270.957425584109449
550.04862830006037090.09725660012074190.951371699939629
560.03801172715538010.07602345431076030.96198827284462
570.036004254199080.072008508398160.96399574580092
580.03244354718242440.06488709436484890.967556452817576
590.03506153224811790.07012306449623570.964938467751882
600.03197972158050890.06395944316101770.96802027841949
610.02518367997337990.05036735994675980.97481632002662
620.0307667209092550.061533441818510.969233279090745
630.02695019817842720.05390039635685440.973049801821573
640.02200097273011570.04400194546023140.977999027269884
650.01791335638199110.03582671276398210.98208664361801
660.01398846707092530.02797693414185060.986011532929075
670.01077712763893250.02155425527786500.989222872361068
680.01003770032601150.02007540065202300.989962299673989
690.007895194893503910.01579038978700780.992104805106496
700.006921850360738320.01384370072147660.993078149639262
710.005151738465314960.01030347693062990.994848261534685
720.00443158616369550.0088631723273910.995568413836305
730.006512863425902150.01302572685180430.993487136574098
740.006786686356327430.01357337271265490.993213313643672
750.00542181519495730.01084363038991460.994578184805043
760.004724941191992240.009449882383984480.995275058808008
770.005125033016239540.01025006603247910.99487496698376
780.005727813151969610.01145562630393920.99427218684803
790.004553457759442390.009106915518884780.995446542240558
800.003485571371499640.006971142742999280.9965144286285
810.002784784030544830.005569568061089650.997215215969455
820.003058917239159580.006117834478319160.99694108276084
830.002220686415148250.00444137283029650.997779313584852
840.002018723203696270.004037446407392540.997981276796304
850.001486114596497250.00297222919299450.998513885403503
860.002167880351719530.004335760703439060.99783211964828
870.002213869664460290.004427739328920590.99778613033554
880.001832082169519770.003664164339039530.99816791783048
890.001382070000984080.002764140001968170.998617929999016
900.0009792417507692830.001958483501538570.99902075824923
910.0006813992816985940.001362798563397190.999318600718301
920.0005080994977250680.001016198995450140.999491900502275
930.0004782805853092090.0009565611706184180.99952171941469
940.0003509631470023010.0007019262940046020.999649036852998
950.0003316340052130780.0006632680104261570.999668365994787
960.0002451273586961710.0004902547173923420.999754872641304
970.0002188548383332490.0004377096766664990.999781145161667
980.0001781248497474660.0003562496994949320.999821875150253
990.0002597578169325220.0005195156338650450.999740242183067
1000.0001915552534067190.0003831105068134370.999808444746593
1010.0001515960384585390.0003031920769170790.999848403961541
1020.0001003509307892270.0002007018615784530.99989964906921
1030.0001611106566634410.0003222213133268820.999838889343337
1040.0001122434673677350.0002244869347354700.999887756532632
1059.06556921776654e-050.0001813113843553310.999909344307822
1067.70631204286503e-050.0001541262408573010.999922936879571
1076.16398339991299e-050.0001232796679982600.999938360166
1084.54095164131261e-059.08190328262521e-050.999954590483587
1097.25040277361824e-050.0001450080554723650.999927495972264
1105.80819581568907e-050.0001161639163137810.999941918041843
1114.60505596644957e-059.21011193289914e-050.999953949440336
1125.33111158856538e-050.0001066222317713080.999946688884114
1134.39428816598311e-058.78857633196622e-050.99995605711834
1145.76035197264051e-050.0001152070394528100.999942396480274
1154.66613068630819e-059.33226137261638e-050.999953338693137
1165.09551491611034e-050.0001019102983222070.999949044850839
1170.0001632230936550030.0003264461873100060.999836776906345
1180.007129520396518950.01425904079303790.992870479603481
1190.04937991602752220.09875983205504430.950620083972478
1200.2605406043920890.5210812087841770.739459395607911
1210.3949850304963920.7899700609927840.605014969503608
1220.4365478953079220.8730957906158440.563452104692078
1230.3994668734945690.7989337469891370.600533126505431
1240.4425910312425140.8851820624850290.557408968757486
1250.5381545470751170.9236909058497660.461845452924883
1260.9678969355240850.06420612895183050.0321030644759153
1270.9687628535395040.0624742929209920.031237146460496
1280.9916635594392950.016672881121410.008336440560705
1290.992190792074340.01561841585132130.00780920792566063
1300.9920177371838720.01596452563225590.00798226281612796
1310.9946701703290430.01065965934191340.00532982967095671
1320.9981540921683480.003691815663304540.00184590783165227
1330.9978597335185890.004280532962822510.00214026648141126
1340.9973343395765260.005331320846948110.00266566042347406
1350.9967764988791080.006447002241784450.00322350112089222
1360.9977190573466880.0045618853066240.002280942653312
1370.9970084484772820.005983103045435670.00299155152271783
1380.9984271360520920.003145727895815790.00157286394790789
1390.99841800411550.003163991769001930.00158199588450096
1400.9985852330538720.002829533892256170.00141476694612809
1410.9995185584260160.0009628831479669580.000481441573983479
1420.9999494506185050.0001010987629895655.05493814947824e-05
1430.999950730203359.85395932984664e-054.92697966492332e-05
1440.9999196668618990.0001606662762022038.03331381011017e-05
1450.9999550567697198.98864605624566e-054.49432302812283e-05
1460.9999999966567686.68646338306663e-093.34323169153332e-09
1470.9999999974532735.09345350101669e-092.54672675050835e-09
1480.99999999511929.76159905468904e-094.88079952734452e-09
1490.9999999979095854.18082969165697e-092.09041484582849e-09
1500.9999999976900244.61995127703249e-092.30997563851624e-09
1510.9999999980951263.80974830521087e-091.90487415260543e-09
15213.30634857095627e-271.65317428547813e-27
15312.79630346664577e-261.39815173332289e-26
15412.15871539322375e-251.07935769661187e-25
15518.30831465454634e-254.15415732727317e-25
15617.19969358263877e-253.59984679131939e-25
15712.05820906109168e-241.02910453054584e-24
15811.66759614120743e-238.33798070603714e-24
15911.33597651320567e-226.67988256602833e-23
16011.20825050737338e-216.04125253686689e-22
16111.04677953003320e-205.23389765016598e-21
16218.64889831781923e-204.32444915890962e-20
16316.25197171023116e-193.12598585511558e-19
16412.25171591530662e-181.12585795765331e-18
16511.19402217062670e-185.97011085313349e-19
16615.80672969566772e-182.90336484783386e-18
16715.04938992744853e-172.52469496372426e-17
16814.15616664309051e-162.07808332154526e-16
1690.9999999999999983.7896173548211e-151.89480867741055e-15
1700.999999999999992.05355241677018e-141.02677620838509e-14
1710.9999999999999331.32990798283547e-136.64953991417735e-14
1720.9999999999997664.69081929565034e-132.34540964782517e-13
1730.99999999999921.60057042611828e-128.00285213059138e-13
1740.9999999999928741.42513972608697e-117.12569863043483e-12
1750.9999999999563878.72253583271511e-114.36126791635756e-11
1760.9999999996450777.09844999959725e-103.54922499979862e-10
1770.9999999979738534.05229422485849e-092.02614711242925e-09
1780.9999999845665733.08668546203582e-081.54334273101791e-08
1790.9999999452390671.09521866451742e-075.4760933225871e-08
1800.9999998713492172.57301566426044e-071.28650783213022e-07
1810.999999157671191.68465762039943e-068.42328810199713e-07
1820.9999981740121573.65197568562205e-061.82598784281103e-06
1830.999985407657642.91846847209865e-051.45923423604932e-05
1840.9998701799544660.0002596400910674650.000129820045533732
1850.9985037709930020.002992458013995610.00149622900699781

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0002159835566622 & 0.0004319671133244 & 0.999784016443338 \tabularnewline
11 & 1.46898784608529e-05 & 2.93797569217058e-05 & 0.99998531012154 \tabularnewline
12 & 6.47391040054466e-07 & 1.29478208010893e-06 & 0.99999935260896 \tabularnewline
13 & 1.42939907504496e-07 & 2.85879815008991e-07 & 0.999999857060093 \tabularnewline
14 & 8.76737946807497e-09 & 1.75347589361499e-08 & 0.99999999123262 \tabularnewline
15 & 3.48740631134593e-09 & 6.97481262269186e-09 & 0.999999996512594 \tabularnewline
16 & 2.58045211477036e-10 & 5.16090422954072e-10 & 0.999999999741955 \tabularnewline
17 & 2.70998481543949e-11 & 5.41996963087897e-11 & 0.9999999999729 \tabularnewline
18 & 3.04604061880162e-12 & 6.09208123760324e-12 & 0.999999999996954 \tabularnewline
19 & 4.59578877009915e-13 & 9.19157754019831e-13 & 0.99999999999954 \tabularnewline
20 & 2.94392286527705e-14 & 5.8878457305541e-14 & 0.99999999999997 \tabularnewline
21 & 1.81292991311512e-15 & 3.62585982623024e-15 & 0.999999999999998 \tabularnewline
22 & 2.12178326141188e-16 & 4.24356652282376e-16 & 1 \tabularnewline
23 & 1.31978406470402e-17 & 2.63956812940804e-17 & 1 \tabularnewline
24 & 7.89269626156615e-19 & 1.57853925231323e-18 & 1 \tabularnewline
25 & 4.63504125564217e-20 & 9.27008251128435e-20 & 1 \tabularnewline
26 & 2.03002650192292e-19 & 4.06005300384585e-19 & 1 \tabularnewline
27 & 4.84475265354455e-19 & 9.6895053070891e-19 & 1 \tabularnewline
28 & 4.0623244326456e-20 & 8.1246488652912e-20 & 1 \tabularnewline
29 & 7.97349869816906e-21 & 1.59469973963381e-20 & 1 \tabularnewline
30 & 1.35975919985693e-21 & 2.71951839971386e-21 & 1 \tabularnewline
31 & 1.30837291067363e-22 & 2.61674582134725e-22 & 1 \tabularnewline
32 & 1.54593735070913e-23 & 3.09187470141826e-23 & 1 \tabularnewline
33 & 1.25881621932171e-24 & 2.51763243864341e-24 & 1 \tabularnewline
34 & 1.10894328696068e-25 & 2.21788657392136e-25 & 1 \tabularnewline
35 & 2.36739003898186e-26 & 4.73478007796373e-26 & 1 \tabularnewline
36 & 7.62434716354625e-27 & 1.52486943270925e-26 & 1 \tabularnewline
37 & 1.84682593789920e-27 & 3.69365187579841e-27 & 1 \tabularnewline
38 & 2.86479148741725e-28 & 5.72958297483451e-28 & 1 \tabularnewline
39 & 5.55277178399254e-29 & 1.11055435679851e-28 & 1 \tabularnewline
40 & 2.71954942962187e-29 & 5.43909885924374e-29 & 1 \tabularnewline
41 & 6.36432108100397e-05 & 0.000127286421620079 & 0.99993635678919 \tabularnewline
42 & 0.000410590723242719 & 0.000821181446485437 & 0.999589409276757 \tabularnewline
43 & 0.00156763302797446 & 0.00313526605594891 & 0.998432366972026 \tabularnewline
44 & 0.0156305882567479 & 0.0312611765134958 & 0.984369411743252 \tabularnewline
45 & 0.05484949672653 & 0.10969899345306 & 0.94515050327347 \tabularnewline
46 & 0.0783954752631923 & 0.156790950526385 & 0.921604524736808 \tabularnewline
47 & 0.0820650017613152 & 0.164130003522630 & 0.917934998238685 \tabularnewline
48 & 0.0703383521846401 & 0.140676704369280 & 0.92966164781536 \tabularnewline
49 & 0.0551101602250972 & 0.110220320450194 & 0.944889839774903 \tabularnewline
50 & 0.0581567043290416 & 0.116313408658083 & 0.941843295670958 \tabularnewline
51 & 0.0465536024097328 & 0.0931072048194656 & 0.953446397590267 \tabularnewline
52 & 0.0622902515198886 & 0.124580503039777 & 0.937709748480111 \tabularnewline
53 & 0.0484282816994033 & 0.0968565633988065 & 0.951571718300597 \tabularnewline
54 & 0.0425744158905514 & 0.0851488317811027 & 0.957425584109449 \tabularnewline
55 & 0.0486283000603709 & 0.0972566001207419 & 0.951371699939629 \tabularnewline
56 & 0.0380117271553801 & 0.0760234543107603 & 0.96198827284462 \tabularnewline
57 & 0.03600425419908 & 0.07200850839816 & 0.96399574580092 \tabularnewline
58 & 0.0324435471824244 & 0.0648870943648489 & 0.967556452817576 \tabularnewline
59 & 0.0350615322481179 & 0.0701230644962357 & 0.964938467751882 \tabularnewline
60 & 0.0319797215805089 & 0.0639594431610177 & 0.96802027841949 \tabularnewline
61 & 0.0251836799733799 & 0.0503673599467598 & 0.97481632002662 \tabularnewline
62 & 0.030766720909255 & 0.06153344181851 & 0.969233279090745 \tabularnewline
63 & 0.0269501981784272 & 0.0539003963568544 & 0.973049801821573 \tabularnewline
64 & 0.0220009727301157 & 0.0440019454602314 & 0.977999027269884 \tabularnewline
65 & 0.0179133563819911 & 0.0358267127639821 & 0.98208664361801 \tabularnewline
66 & 0.0139884670709253 & 0.0279769341418506 & 0.986011532929075 \tabularnewline
67 & 0.0107771276389325 & 0.0215542552778650 & 0.989222872361068 \tabularnewline
68 & 0.0100377003260115 & 0.0200754006520230 & 0.989962299673989 \tabularnewline
69 & 0.00789519489350391 & 0.0157903897870078 & 0.992104805106496 \tabularnewline
70 & 0.00692185036073832 & 0.0138437007214766 & 0.993078149639262 \tabularnewline
71 & 0.00515173846531496 & 0.0103034769306299 & 0.994848261534685 \tabularnewline
72 & 0.0044315861636955 & 0.008863172327391 & 0.995568413836305 \tabularnewline
73 & 0.00651286342590215 & 0.0130257268518043 & 0.993487136574098 \tabularnewline
74 & 0.00678668635632743 & 0.0135733727126549 & 0.993213313643672 \tabularnewline
75 & 0.0054218151949573 & 0.0108436303899146 & 0.994578184805043 \tabularnewline
76 & 0.00472494119199224 & 0.00944988238398448 & 0.995275058808008 \tabularnewline
77 & 0.00512503301623954 & 0.0102500660324791 & 0.99487496698376 \tabularnewline
78 & 0.00572781315196961 & 0.0114556263039392 & 0.99427218684803 \tabularnewline
79 & 0.00455345775944239 & 0.00910691551888478 & 0.995446542240558 \tabularnewline
80 & 0.00348557137149964 & 0.00697114274299928 & 0.9965144286285 \tabularnewline
81 & 0.00278478403054483 & 0.00556956806108965 & 0.997215215969455 \tabularnewline
82 & 0.00305891723915958 & 0.00611783447831916 & 0.99694108276084 \tabularnewline
83 & 0.00222068641514825 & 0.0044413728302965 & 0.997779313584852 \tabularnewline
84 & 0.00201872320369627 & 0.00403744640739254 & 0.997981276796304 \tabularnewline
85 & 0.00148611459649725 & 0.0029722291929945 & 0.998513885403503 \tabularnewline
86 & 0.00216788035171953 & 0.00433576070343906 & 0.99783211964828 \tabularnewline
87 & 0.00221386966446029 & 0.00442773932892059 & 0.99778613033554 \tabularnewline
88 & 0.00183208216951977 & 0.00366416433903953 & 0.99816791783048 \tabularnewline
89 & 0.00138207000098408 & 0.00276414000196817 & 0.998617929999016 \tabularnewline
90 & 0.000979241750769283 & 0.00195848350153857 & 0.99902075824923 \tabularnewline
91 & 0.000681399281698594 & 0.00136279856339719 & 0.999318600718301 \tabularnewline
92 & 0.000508099497725068 & 0.00101619899545014 & 0.999491900502275 \tabularnewline
93 & 0.000478280585309209 & 0.000956561170618418 & 0.99952171941469 \tabularnewline
94 & 0.000350963147002301 & 0.000701926294004602 & 0.999649036852998 \tabularnewline
95 & 0.000331634005213078 & 0.000663268010426157 & 0.999668365994787 \tabularnewline
96 & 0.000245127358696171 & 0.000490254717392342 & 0.999754872641304 \tabularnewline
97 & 0.000218854838333249 & 0.000437709676666499 & 0.999781145161667 \tabularnewline
98 & 0.000178124849747466 & 0.000356249699494932 & 0.999821875150253 \tabularnewline
99 & 0.000259757816932522 & 0.000519515633865045 & 0.999740242183067 \tabularnewline
100 & 0.000191555253406719 & 0.000383110506813437 & 0.999808444746593 \tabularnewline
101 & 0.000151596038458539 & 0.000303192076917079 & 0.999848403961541 \tabularnewline
102 & 0.000100350930789227 & 0.000200701861578453 & 0.99989964906921 \tabularnewline
103 & 0.000161110656663441 & 0.000322221313326882 & 0.999838889343337 \tabularnewline
104 & 0.000112243467367735 & 0.000224486934735470 & 0.999887756532632 \tabularnewline
105 & 9.06556921776654e-05 & 0.000181311384355331 & 0.999909344307822 \tabularnewline
106 & 7.70631204286503e-05 & 0.000154126240857301 & 0.999922936879571 \tabularnewline
107 & 6.16398339991299e-05 & 0.000123279667998260 & 0.999938360166 \tabularnewline
108 & 4.54095164131261e-05 & 9.08190328262521e-05 & 0.999954590483587 \tabularnewline
109 & 7.25040277361824e-05 & 0.000145008055472365 & 0.999927495972264 \tabularnewline
110 & 5.80819581568907e-05 & 0.000116163916313781 & 0.999941918041843 \tabularnewline
111 & 4.60505596644957e-05 & 9.21011193289914e-05 & 0.999953949440336 \tabularnewline
112 & 5.33111158856538e-05 & 0.000106622231771308 & 0.999946688884114 \tabularnewline
113 & 4.39428816598311e-05 & 8.78857633196622e-05 & 0.99995605711834 \tabularnewline
114 & 5.76035197264051e-05 & 0.000115207039452810 & 0.999942396480274 \tabularnewline
115 & 4.66613068630819e-05 & 9.33226137261638e-05 & 0.999953338693137 \tabularnewline
116 & 5.09551491611034e-05 & 0.000101910298322207 & 0.999949044850839 \tabularnewline
117 & 0.000163223093655003 & 0.000326446187310006 & 0.999836776906345 \tabularnewline
118 & 0.00712952039651895 & 0.0142590407930379 & 0.992870479603481 \tabularnewline
119 & 0.0493799160275222 & 0.0987598320550443 & 0.950620083972478 \tabularnewline
120 & 0.260540604392089 & 0.521081208784177 & 0.739459395607911 \tabularnewline
121 & 0.394985030496392 & 0.789970060992784 & 0.605014969503608 \tabularnewline
122 & 0.436547895307922 & 0.873095790615844 & 0.563452104692078 \tabularnewline
123 & 0.399466873494569 & 0.798933746989137 & 0.600533126505431 \tabularnewline
124 & 0.442591031242514 & 0.885182062485029 & 0.557408968757486 \tabularnewline
125 & 0.538154547075117 & 0.923690905849766 & 0.461845452924883 \tabularnewline
126 & 0.967896935524085 & 0.0642061289518305 & 0.0321030644759153 \tabularnewline
127 & 0.968762853539504 & 0.062474292920992 & 0.031237146460496 \tabularnewline
128 & 0.991663559439295 & 0.01667288112141 & 0.008336440560705 \tabularnewline
129 & 0.99219079207434 & 0.0156184158513213 & 0.00780920792566063 \tabularnewline
130 & 0.992017737183872 & 0.0159645256322559 & 0.00798226281612796 \tabularnewline
131 & 0.994670170329043 & 0.0106596593419134 & 0.00532982967095671 \tabularnewline
132 & 0.998154092168348 & 0.00369181566330454 & 0.00184590783165227 \tabularnewline
133 & 0.997859733518589 & 0.00428053296282251 & 0.00214026648141126 \tabularnewline
134 & 0.997334339576526 & 0.00533132084694811 & 0.00266566042347406 \tabularnewline
135 & 0.996776498879108 & 0.00644700224178445 & 0.00322350112089222 \tabularnewline
136 & 0.997719057346688 & 0.004561885306624 & 0.002280942653312 \tabularnewline
137 & 0.997008448477282 & 0.00598310304543567 & 0.00299155152271783 \tabularnewline
138 & 0.998427136052092 & 0.00314572789581579 & 0.00157286394790789 \tabularnewline
139 & 0.9984180041155 & 0.00316399176900193 & 0.00158199588450096 \tabularnewline
140 & 0.998585233053872 & 0.00282953389225617 & 0.00141476694612809 \tabularnewline
141 & 0.999518558426016 & 0.000962883147966958 & 0.000481441573983479 \tabularnewline
142 & 0.999949450618505 & 0.000101098762989565 & 5.05493814947824e-05 \tabularnewline
143 & 0.99995073020335 & 9.85395932984664e-05 & 4.92697966492332e-05 \tabularnewline
144 & 0.999919666861899 & 0.000160666276202203 & 8.03331381011017e-05 \tabularnewline
145 & 0.999955056769719 & 8.98864605624566e-05 & 4.49432302812283e-05 \tabularnewline
146 & 0.999999996656768 & 6.68646338306663e-09 & 3.34323169153332e-09 \tabularnewline
147 & 0.999999997453273 & 5.09345350101669e-09 & 2.54672675050835e-09 \tabularnewline
148 & 0.9999999951192 & 9.76159905468904e-09 & 4.88079952734452e-09 \tabularnewline
149 & 0.999999997909585 & 4.18082969165697e-09 & 2.09041484582849e-09 \tabularnewline
150 & 0.999999997690024 & 4.61995127703249e-09 & 2.30997563851624e-09 \tabularnewline
151 & 0.999999998095126 & 3.80974830521087e-09 & 1.90487415260543e-09 \tabularnewline
152 & 1 & 3.30634857095627e-27 & 1.65317428547813e-27 \tabularnewline
153 & 1 & 2.79630346664577e-26 & 1.39815173332289e-26 \tabularnewline
154 & 1 & 2.15871539322375e-25 & 1.07935769661187e-25 \tabularnewline
155 & 1 & 8.30831465454634e-25 & 4.15415732727317e-25 \tabularnewline
156 & 1 & 7.19969358263877e-25 & 3.59984679131939e-25 \tabularnewline
157 & 1 & 2.05820906109168e-24 & 1.02910453054584e-24 \tabularnewline
158 & 1 & 1.66759614120743e-23 & 8.33798070603714e-24 \tabularnewline
159 & 1 & 1.33597651320567e-22 & 6.67988256602833e-23 \tabularnewline
160 & 1 & 1.20825050737338e-21 & 6.04125253686689e-22 \tabularnewline
161 & 1 & 1.04677953003320e-20 & 5.23389765016598e-21 \tabularnewline
162 & 1 & 8.64889831781923e-20 & 4.32444915890962e-20 \tabularnewline
163 & 1 & 6.25197171023116e-19 & 3.12598585511558e-19 \tabularnewline
164 & 1 & 2.25171591530662e-18 & 1.12585795765331e-18 \tabularnewline
165 & 1 & 1.19402217062670e-18 & 5.97011085313349e-19 \tabularnewline
166 & 1 & 5.80672969566772e-18 & 2.90336484783386e-18 \tabularnewline
167 & 1 & 5.04938992744853e-17 & 2.52469496372426e-17 \tabularnewline
168 & 1 & 4.15616664309051e-16 & 2.07808332154526e-16 \tabularnewline
169 & 0.999999999999998 & 3.7896173548211e-15 & 1.89480867741055e-15 \tabularnewline
170 & 0.99999999999999 & 2.05355241677018e-14 & 1.02677620838509e-14 \tabularnewline
171 & 0.999999999999933 & 1.32990798283547e-13 & 6.64953991417735e-14 \tabularnewline
172 & 0.999999999999766 & 4.69081929565034e-13 & 2.34540964782517e-13 \tabularnewline
173 & 0.9999999999992 & 1.60057042611828e-12 & 8.00285213059138e-13 \tabularnewline
174 & 0.999999999992874 & 1.42513972608697e-11 & 7.12569863043483e-12 \tabularnewline
175 & 0.999999999956387 & 8.72253583271511e-11 & 4.36126791635756e-11 \tabularnewline
176 & 0.999999999645077 & 7.09844999959725e-10 & 3.54922499979862e-10 \tabularnewline
177 & 0.999999997973853 & 4.05229422485849e-09 & 2.02614711242925e-09 \tabularnewline
178 & 0.999999984566573 & 3.08668546203582e-08 & 1.54334273101791e-08 \tabularnewline
179 & 0.999999945239067 & 1.09521866451742e-07 & 5.4760933225871e-08 \tabularnewline
180 & 0.999999871349217 & 2.57301566426044e-07 & 1.28650783213022e-07 \tabularnewline
181 & 0.99999915767119 & 1.68465762039943e-06 & 8.42328810199713e-07 \tabularnewline
182 & 0.999998174012157 & 3.65197568562205e-06 & 1.82598784281103e-06 \tabularnewline
183 & 0.99998540765764 & 2.91846847209865e-05 & 1.45923423604932e-05 \tabularnewline
184 & 0.999870179954466 & 0.000259640091067465 & 0.000129820045533732 \tabularnewline
185 & 0.998503770993002 & 0.00299245801399561 & 0.00149622900699781 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115455&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]0.0002159835566622[/C][C]0.0004319671133244[/C][C]0.999784016443338[/C][/ROW]
[ROW][C]11[/C][C]1.46898784608529e-05[/C][C]2.93797569217058e-05[/C][C]0.99998531012154[/C][/ROW]
[ROW][C]12[/C][C]6.47391040054466e-07[/C][C]1.29478208010893e-06[/C][C]0.99999935260896[/C][/ROW]
[ROW][C]13[/C][C]1.42939907504496e-07[/C][C]2.85879815008991e-07[/C][C]0.999999857060093[/C][/ROW]
[ROW][C]14[/C][C]8.76737946807497e-09[/C][C]1.75347589361499e-08[/C][C]0.99999999123262[/C][/ROW]
[ROW][C]15[/C][C]3.48740631134593e-09[/C][C]6.97481262269186e-09[/C][C]0.999999996512594[/C][/ROW]
[ROW][C]16[/C][C]2.58045211477036e-10[/C][C]5.16090422954072e-10[/C][C]0.999999999741955[/C][/ROW]
[ROW][C]17[/C][C]2.70998481543949e-11[/C][C]5.41996963087897e-11[/C][C]0.9999999999729[/C][/ROW]
[ROW][C]18[/C][C]3.04604061880162e-12[/C][C]6.09208123760324e-12[/C][C]0.999999999996954[/C][/ROW]
[ROW][C]19[/C][C]4.59578877009915e-13[/C][C]9.19157754019831e-13[/C][C]0.99999999999954[/C][/ROW]
[ROW][C]20[/C][C]2.94392286527705e-14[/C][C]5.8878457305541e-14[/C][C]0.99999999999997[/C][/ROW]
[ROW][C]21[/C][C]1.81292991311512e-15[/C][C]3.62585982623024e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]22[/C][C]2.12178326141188e-16[/C][C]4.24356652282376e-16[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]1.31978406470402e-17[/C][C]2.63956812940804e-17[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]7.89269626156615e-19[/C][C]1.57853925231323e-18[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]4.63504125564217e-20[/C][C]9.27008251128435e-20[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]2.03002650192292e-19[/C][C]4.06005300384585e-19[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]4.84475265354455e-19[/C][C]9.6895053070891e-19[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]4.0623244326456e-20[/C][C]8.1246488652912e-20[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]7.97349869816906e-21[/C][C]1.59469973963381e-20[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.35975919985693e-21[/C][C]2.71951839971386e-21[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.30837291067363e-22[/C][C]2.61674582134725e-22[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]1.54593735070913e-23[/C][C]3.09187470141826e-23[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]1.25881621932171e-24[/C][C]2.51763243864341e-24[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]1.10894328696068e-25[/C][C]2.21788657392136e-25[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]2.36739003898186e-26[/C][C]4.73478007796373e-26[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]7.62434716354625e-27[/C][C]1.52486943270925e-26[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]1.84682593789920e-27[/C][C]3.69365187579841e-27[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]2.86479148741725e-28[/C][C]5.72958297483451e-28[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]5.55277178399254e-29[/C][C]1.11055435679851e-28[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]2.71954942962187e-29[/C][C]5.43909885924374e-29[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]6.36432108100397e-05[/C][C]0.000127286421620079[/C][C]0.99993635678919[/C][/ROW]
[ROW][C]42[/C][C]0.000410590723242719[/C][C]0.000821181446485437[/C][C]0.999589409276757[/C][/ROW]
[ROW][C]43[/C][C]0.00156763302797446[/C][C]0.00313526605594891[/C][C]0.998432366972026[/C][/ROW]
[ROW][C]44[/C][C]0.0156305882567479[/C][C]0.0312611765134958[/C][C]0.984369411743252[/C][/ROW]
[ROW][C]45[/C][C]0.05484949672653[/C][C]0.10969899345306[/C][C]0.94515050327347[/C][/ROW]
[ROW][C]46[/C][C]0.0783954752631923[/C][C]0.156790950526385[/C][C]0.921604524736808[/C][/ROW]
[ROW][C]47[/C][C]0.0820650017613152[/C][C]0.164130003522630[/C][C]0.917934998238685[/C][/ROW]
[ROW][C]48[/C][C]0.0703383521846401[/C][C]0.140676704369280[/C][C]0.92966164781536[/C][/ROW]
[ROW][C]49[/C][C]0.0551101602250972[/C][C]0.110220320450194[/C][C]0.944889839774903[/C][/ROW]
[ROW][C]50[/C][C]0.0581567043290416[/C][C]0.116313408658083[/C][C]0.941843295670958[/C][/ROW]
[ROW][C]51[/C][C]0.0465536024097328[/C][C]0.0931072048194656[/C][C]0.953446397590267[/C][/ROW]
[ROW][C]52[/C][C]0.0622902515198886[/C][C]0.124580503039777[/C][C]0.937709748480111[/C][/ROW]
[ROW][C]53[/C][C]0.0484282816994033[/C][C]0.0968565633988065[/C][C]0.951571718300597[/C][/ROW]
[ROW][C]54[/C][C]0.0425744158905514[/C][C]0.0851488317811027[/C][C]0.957425584109449[/C][/ROW]
[ROW][C]55[/C][C]0.0486283000603709[/C][C]0.0972566001207419[/C][C]0.951371699939629[/C][/ROW]
[ROW][C]56[/C][C]0.0380117271553801[/C][C]0.0760234543107603[/C][C]0.96198827284462[/C][/ROW]
[ROW][C]57[/C][C]0.03600425419908[/C][C]0.07200850839816[/C][C]0.96399574580092[/C][/ROW]
[ROW][C]58[/C][C]0.0324435471824244[/C][C]0.0648870943648489[/C][C]0.967556452817576[/C][/ROW]
[ROW][C]59[/C][C]0.0350615322481179[/C][C]0.0701230644962357[/C][C]0.964938467751882[/C][/ROW]
[ROW][C]60[/C][C]0.0319797215805089[/C][C]0.0639594431610177[/C][C]0.96802027841949[/C][/ROW]
[ROW][C]61[/C][C]0.0251836799733799[/C][C]0.0503673599467598[/C][C]0.97481632002662[/C][/ROW]
[ROW][C]62[/C][C]0.030766720909255[/C][C]0.06153344181851[/C][C]0.969233279090745[/C][/ROW]
[ROW][C]63[/C][C]0.0269501981784272[/C][C]0.0539003963568544[/C][C]0.973049801821573[/C][/ROW]
[ROW][C]64[/C][C]0.0220009727301157[/C][C]0.0440019454602314[/C][C]0.977999027269884[/C][/ROW]
[ROW][C]65[/C][C]0.0179133563819911[/C][C]0.0358267127639821[/C][C]0.98208664361801[/C][/ROW]
[ROW][C]66[/C][C]0.0139884670709253[/C][C]0.0279769341418506[/C][C]0.986011532929075[/C][/ROW]
[ROW][C]67[/C][C]0.0107771276389325[/C][C]0.0215542552778650[/C][C]0.989222872361068[/C][/ROW]
[ROW][C]68[/C][C]0.0100377003260115[/C][C]0.0200754006520230[/C][C]0.989962299673989[/C][/ROW]
[ROW][C]69[/C][C]0.00789519489350391[/C][C]0.0157903897870078[/C][C]0.992104805106496[/C][/ROW]
[ROW][C]70[/C][C]0.00692185036073832[/C][C]0.0138437007214766[/C][C]0.993078149639262[/C][/ROW]
[ROW][C]71[/C][C]0.00515173846531496[/C][C]0.0103034769306299[/C][C]0.994848261534685[/C][/ROW]
[ROW][C]72[/C][C]0.0044315861636955[/C][C]0.008863172327391[/C][C]0.995568413836305[/C][/ROW]
[ROW][C]73[/C][C]0.00651286342590215[/C][C]0.0130257268518043[/C][C]0.993487136574098[/C][/ROW]
[ROW][C]74[/C][C]0.00678668635632743[/C][C]0.0135733727126549[/C][C]0.993213313643672[/C][/ROW]
[ROW][C]75[/C][C]0.0054218151949573[/C][C]0.0108436303899146[/C][C]0.994578184805043[/C][/ROW]
[ROW][C]76[/C][C]0.00472494119199224[/C][C]0.00944988238398448[/C][C]0.995275058808008[/C][/ROW]
[ROW][C]77[/C][C]0.00512503301623954[/C][C]0.0102500660324791[/C][C]0.99487496698376[/C][/ROW]
[ROW][C]78[/C][C]0.00572781315196961[/C][C]0.0114556263039392[/C][C]0.99427218684803[/C][/ROW]
[ROW][C]79[/C][C]0.00455345775944239[/C][C]0.00910691551888478[/C][C]0.995446542240558[/C][/ROW]
[ROW][C]80[/C][C]0.00348557137149964[/C][C]0.00697114274299928[/C][C]0.9965144286285[/C][/ROW]
[ROW][C]81[/C][C]0.00278478403054483[/C][C]0.00556956806108965[/C][C]0.997215215969455[/C][/ROW]
[ROW][C]82[/C][C]0.00305891723915958[/C][C]0.00611783447831916[/C][C]0.99694108276084[/C][/ROW]
[ROW][C]83[/C][C]0.00222068641514825[/C][C]0.0044413728302965[/C][C]0.997779313584852[/C][/ROW]
[ROW][C]84[/C][C]0.00201872320369627[/C][C]0.00403744640739254[/C][C]0.997981276796304[/C][/ROW]
[ROW][C]85[/C][C]0.00148611459649725[/C][C]0.0029722291929945[/C][C]0.998513885403503[/C][/ROW]
[ROW][C]86[/C][C]0.00216788035171953[/C][C]0.00433576070343906[/C][C]0.99783211964828[/C][/ROW]
[ROW][C]87[/C][C]0.00221386966446029[/C][C]0.00442773932892059[/C][C]0.99778613033554[/C][/ROW]
[ROW][C]88[/C][C]0.00183208216951977[/C][C]0.00366416433903953[/C][C]0.99816791783048[/C][/ROW]
[ROW][C]89[/C][C]0.00138207000098408[/C][C]0.00276414000196817[/C][C]0.998617929999016[/C][/ROW]
[ROW][C]90[/C][C]0.000979241750769283[/C][C]0.00195848350153857[/C][C]0.99902075824923[/C][/ROW]
[ROW][C]91[/C][C]0.000681399281698594[/C][C]0.00136279856339719[/C][C]0.999318600718301[/C][/ROW]
[ROW][C]92[/C][C]0.000508099497725068[/C][C]0.00101619899545014[/C][C]0.999491900502275[/C][/ROW]
[ROW][C]93[/C][C]0.000478280585309209[/C][C]0.000956561170618418[/C][C]0.99952171941469[/C][/ROW]
[ROW][C]94[/C][C]0.000350963147002301[/C][C]0.000701926294004602[/C][C]0.999649036852998[/C][/ROW]
[ROW][C]95[/C][C]0.000331634005213078[/C][C]0.000663268010426157[/C][C]0.999668365994787[/C][/ROW]
[ROW][C]96[/C][C]0.000245127358696171[/C][C]0.000490254717392342[/C][C]0.999754872641304[/C][/ROW]
[ROW][C]97[/C][C]0.000218854838333249[/C][C]0.000437709676666499[/C][C]0.999781145161667[/C][/ROW]
[ROW][C]98[/C][C]0.000178124849747466[/C][C]0.000356249699494932[/C][C]0.999821875150253[/C][/ROW]
[ROW][C]99[/C][C]0.000259757816932522[/C][C]0.000519515633865045[/C][C]0.999740242183067[/C][/ROW]
[ROW][C]100[/C][C]0.000191555253406719[/C][C]0.000383110506813437[/C][C]0.999808444746593[/C][/ROW]
[ROW][C]101[/C][C]0.000151596038458539[/C][C]0.000303192076917079[/C][C]0.999848403961541[/C][/ROW]
[ROW][C]102[/C][C]0.000100350930789227[/C][C]0.000200701861578453[/C][C]0.99989964906921[/C][/ROW]
[ROW][C]103[/C][C]0.000161110656663441[/C][C]0.000322221313326882[/C][C]0.999838889343337[/C][/ROW]
[ROW][C]104[/C][C]0.000112243467367735[/C][C]0.000224486934735470[/C][C]0.999887756532632[/C][/ROW]
[ROW][C]105[/C][C]9.06556921776654e-05[/C][C]0.000181311384355331[/C][C]0.999909344307822[/C][/ROW]
[ROW][C]106[/C][C]7.70631204286503e-05[/C][C]0.000154126240857301[/C][C]0.999922936879571[/C][/ROW]
[ROW][C]107[/C][C]6.16398339991299e-05[/C][C]0.000123279667998260[/C][C]0.999938360166[/C][/ROW]
[ROW][C]108[/C][C]4.54095164131261e-05[/C][C]9.08190328262521e-05[/C][C]0.999954590483587[/C][/ROW]
[ROW][C]109[/C][C]7.25040277361824e-05[/C][C]0.000145008055472365[/C][C]0.999927495972264[/C][/ROW]
[ROW][C]110[/C][C]5.80819581568907e-05[/C][C]0.000116163916313781[/C][C]0.999941918041843[/C][/ROW]
[ROW][C]111[/C][C]4.60505596644957e-05[/C][C]9.21011193289914e-05[/C][C]0.999953949440336[/C][/ROW]
[ROW][C]112[/C][C]5.33111158856538e-05[/C][C]0.000106622231771308[/C][C]0.999946688884114[/C][/ROW]
[ROW][C]113[/C][C]4.39428816598311e-05[/C][C]8.78857633196622e-05[/C][C]0.99995605711834[/C][/ROW]
[ROW][C]114[/C][C]5.76035197264051e-05[/C][C]0.000115207039452810[/C][C]0.999942396480274[/C][/ROW]
[ROW][C]115[/C][C]4.66613068630819e-05[/C][C]9.33226137261638e-05[/C][C]0.999953338693137[/C][/ROW]
[ROW][C]116[/C][C]5.09551491611034e-05[/C][C]0.000101910298322207[/C][C]0.999949044850839[/C][/ROW]
[ROW][C]117[/C][C]0.000163223093655003[/C][C]0.000326446187310006[/C][C]0.999836776906345[/C][/ROW]
[ROW][C]118[/C][C]0.00712952039651895[/C][C]0.0142590407930379[/C][C]0.992870479603481[/C][/ROW]
[ROW][C]119[/C][C]0.0493799160275222[/C][C]0.0987598320550443[/C][C]0.950620083972478[/C][/ROW]
[ROW][C]120[/C][C]0.260540604392089[/C][C]0.521081208784177[/C][C]0.739459395607911[/C][/ROW]
[ROW][C]121[/C][C]0.394985030496392[/C][C]0.789970060992784[/C][C]0.605014969503608[/C][/ROW]
[ROW][C]122[/C][C]0.436547895307922[/C][C]0.873095790615844[/C][C]0.563452104692078[/C][/ROW]
[ROW][C]123[/C][C]0.399466873494569[/C][C]0.798933746989137[/C][C]0.600533126505431[/C][/ROW]
[ROW][C]124[/C][C]0.442591031242514[/C][C]0.885182062485029[/C][C]0.557408968757486[/C][/ROW]
[ROW][C]125[/C][C]0.538154547075117[/C][C]0.923690905849766[/C][C]0.461845452924883[/C][/ROW]
[ROW][C]126[/C][C]0.967896935524085[/C][C]0.0642061289518305[/C][C]0.0321030644759153[/C][/ROW]
[ROW][C]127[/C][C]0.968762853539504[/C][C]0.062474292920992[/C][C]0.031237146460496[/C][/ROW]
[ROW][C]128[/C][C]0.991663559439295[/C][C]0.01667288112141[/C][C]0.008336440560705[/C][/ROW]
[ROW][C]129[/C][C]0.99219079207434[/C][C]0.0156184158513213[/C][C]0.00780920792566063[/C][/ROW]
[ROW][C]130[/C][C]0.992017737183872[/C][C]0.0159645256322559[/C][C]0.00798226281612796[/C][/ROW]
[ROW][C]131[/C][C]0.994670170329043[/C][C]0.0106596593419134[/C][C]0.00532982967095671[/C][/ROW]
[ROW][C]132[/C][C]0.998154092168348[/C][C]0.00369181566330454[/C][C]0.00184590783165227[/C][/ROW]
[ROW][C]133[/C][C]0.997859733518589[/C][C]0.00428053296282251[/C][C]0.00214026648141126[/C][/ROW]
[ROW][C]134[/C][C]0.997334339576526[/C][C]0.00533132084694811[/C][C]0.00266566042347406[/C][/ROW]
[ROW][C]135[/C][C]0.996776498879108[/C][C]0.00644700224178445[/C][C]0.00322350112089222[/C][/ROW]
[ROW][C]136[/C][C]0.997719057346688[/C][C]0.004561885306624[/C][C]0.002280942653312[/C][/ROW]
[ROW][C]137[/C][C]0.997008448477282[/C][C]0.00598310304543567[/C][C]0.00299155152271783[/C][/ROW]
[ROW][C]138[/C][C]0.998427136052092[/C][C]0.00314572789581579[/C][C]0.00157286394790789[/C][/ROW]
[ROW][C]139[/C][C]0.9984180041155[/C][C]0.00316399176900193[/C][C]0.00158199588450096[/C][/ROW]
[ROW][C]140[/C][C]0.998585233053872[/C][C]0.00282953389225617[/C][C]0.00141476694612809[/C][/ROW]
[ROW][C]141[/C][C]0.999518558426016[/C][C]0.000962883147966958[/C][C]0.000481441573983479[/C][/ROW]
[ROW][C]142[/C][C]0.999949450618505[/C][C]0.000101098762989565[/C][C]5.05493814947824e-05[/C][/ROW]
[ROW][C]143[/C][C]0.99995073020335[/C][C]9.85395932984664e-05[/C][C]4.92697966492332e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999919666861899[/C][C]0.000160666276202203[/C][C]8.03331381011017e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999955056769719[/C][C]8.98864605624566e-05[/C][C]4.49432302812283e-05[/C][/ROW]
[ROW][C]146[/C][C]0.999999996656768[/C][C]6.68646338306663e-09[/C][C]3.34323169153332e-09[/C][/ROW]
[ROW][C]147[/C][C]0.999999997453273[/C][C]5.09345350101669e-09[/C][C]2.54672675050835e-09[/C][/ROW]
[ROW][C]148[/C][C]0.9999999951192[/C][C]9.76159905468904e-09[/C][C]4.88079952734452e-09[/C][/ROW]
[ROW][C]149[/C][C]0.999999997909585[/C][C]4.18082969165697e-09[/C][C]2.09041484582849e-09[/C][/ROW]
[ROW][C]150[/C][C]0.999999997690024[/C][C]4.61995127703249e-09[/C][C]2.30997563851624e-09[/C][/ROW]
[ROW][C]151[/C][C]0.999999998095126[/C][C]3.80974830521087e-09[/C][C]1.90487415260543e-09[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]3.30634857095627e-27[/C][C]1.65317428547813e-27[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]2.79630346664577e-26[/C][C]1.39815173332289e-26[/C][/ROW]
[ROW][C]154[/C][C]1[/C][C]2.15871539322375e-25[/C][C]1.07935769661187e-25[/C][/ROW]
[ROW][C]155[/C][C]1[/C][C]8.30831465454634e-25[/C][C]4.15415732727317e-25[/C][/ROW]
[ROW][C]156[/C][C]1[/C][C]7.19969358263877e-25[/C][C]3.59984679131939e-25[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]2.05820906109168e-24[/C][C]1.02910453054584e-24[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]1.66759614120743e-23[/C][C]8.33798070603714e-24[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]1.33597651320567e-22[/C][C]6.67988256602833e-23[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]1.20825050737338e-21[/C][C]6.04125253686689e-22[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]1.04677953003320e-20[/C][C]5.23389765016598e-21[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]8.64889831781923e-20[/C][C]4.32444915890962e-20[/C][/ROW]
[ROW][C]163[/C][C]1[/C][C]6.25197171023116e-19[/C][C]3.12598585511558e-19[/C][/ROW]
[ROW][C]164[/C][C]1[/C][C]2.25171591530662e-18[/C][C]1.12585795765331e-18[/C][/ROW]
[ROW][C]165[/C][C]1[/C][C]1.19402217062670e-18[/C][C]5.97011085313349e-19[/C][/ROW]
[ROW][C]166[/C][C]1[/C][C]5.80672969566772e-18[/C][C]2.90336484783386e-18[/C][/ROW]
[ROW][C]167[/C][C]1[/C][C]5.04938992744853e-17[/C][C]2.52469496372426e-17[/C][/ROW]
[ROW][C]168[/C][C]1[/C][C]4.15616664309051e-16[/C][C]2.07808332154526e-16[/C][/ROW]
[ROW][C]169[/C][C]0.999999999999998[/C][C]3.7896173548211e-15[/C][C]1.89480867741055e-15[/C][/ROW]
[ROW][C]170[/C][C]0.99999999999999[/C][C]2.05355241677018e-14[/C][C]1.02677620838509e-14[/C][/ROW]
[ROW][C]171[/C][C]0.999999999999933[/C][C]1.32990798283547e-13[/C][C]6.64953991417735e-14[/C][/ROW]
[ROW][C]172[/C][C]0.999999999999766[/C][C]4.69081929565034e-13[/C][C]2.34540964782517e-13[/C][/ROW]
[ROW][C]173[/C][C]0.9999999999992[/C][C]1.60057042611828e-12[/C][C]8.00285213059138e-13[/C][/ROW]
[ROW][C]174[/C][C]0.999999999992874[/C][C]1.42513972608697e-11[/C][C]7.12569863043483e-12[/C][/ROW]
[ROW][C]175[/C][C]0.999999999956387[/C][C]8.72253583271511e-11[/C][C]4.36126791635756e-11[/C][/ROW]
[ROW][C]176[/C][C]0.999999999645077[/C][C]7.09844999959725e-10[/C][C]3.54922499979862e-10[/C][/ROW]
[ROW][C]177[/C][C]0.999999997973853[/C][C]4.05229422485849e-09[/C][C]2.02614711242925e-09[/C][/ROW]
[ROW][C]178[/C][C]0.999999984566573[/C][C]3.08668546203582e-08[/C][C]1.54334273101791e-08[/C][/ROW]
[ROW][C]179[/C][C]0.999999945239067[/C][C]1.09521866451742e-07[/C][C]5.4760933225871e-08[/C][/ROW]
[ROW][C]180[/C][C]0.999999871349217[/C][C]2.57301566426044e-07[/C][C]1.28650783213022e-07[/C][/ROW]
[ROW][C]181[/C][C]0.99999915767119[/C][C]1.68465762039943e-06[/C][C]8.42328810199713e-07[/C][/ROW]
[ROW][C]182[/C][C]0.999998174012157[/C][C]3.65197568562205e-06[/C][C]1.82598784281103e-06[/C][/ROW]
[ROW][C]183[/C][C]0.99998540765764[/C][C]2.91846847209865e-05[/C][C]1.45923423604932e-05[/C][/ROW]
[ROW][C]184[/C][C]0.999870179954466[/C][C]0.000259640091067465[/C][C]0.000129820045533732[/C][/ROW]
[ROW][C]185[/C][C]0.998503770993002[/C][C]0.00299245801399561[/C][C]0.00149622900699781[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115455&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115455&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
100.00021598355666220.00043196711332440.999784016443338
111.46898784608529e-052.93797569217058e-050.99998531012154
126.47391040054466e-071.29478208010893e-060.99999935260896
131.42939907504496e-072.85879815008991e-070.999999857060093
148.76737946807497e-091.75347589361499e-080.99999999123262
153.48740631134593e-096.97481262269186e-090.999999996512594
162.58045211477036e-105.16090422954072e-100.999999999741955
172.70998481543949e-115.41996963087897e-110.9999999999729
183.04604061880162e-126.09208123760324e-120.999999999996954
194.59578877009915e-139.19157754019831e-130.99999999999954
202.94392286527705e-145.8878457305541e-140.99999999999997
211.81292991311512e-153.62585982623024e-150.999999999999998
222.12178326141188e-164.24356652282376e-161
231.31978406470402e-172.63956812940804e-171
247.89269626156615e-191.57853925231323e-181
254.63504125564217e-209.27008251128435e-201
262.03002650192292e-194.06005300384585e-191
274.84475265354455e-199.6895053070891e-191
284.0623244326456e-208.1246488652912e-201
297.97349869816906e-211.59469973963381e-201
301.35975919985693e-212.71951839971386e-211
311.30837291067363e-222.61674582134725e-221
321.54593735070913e-233.09187470141826e-231
331.25881621932171e-242.51763243864341e-241
341.10894328696068e-252.21788657392136e-251
352.36739003898186e-264.73478007796373e-261
367.62434716354625e-271.52486943270925e-261
371.84682593789920e-273.69365187579841e-271
382.86479148741725e-285.72958297483451e-281
395.55277178399254e-291.11055435679851e-281
402.71954942962187e-295.43909885924374e-291
416.36432108100397e-050.0001272864216200790.99993635678919
420.0004105907232427190.0008211814464854370.999589409276757
430.001567633027974460.003135266055948910.998432366972026
440.01563058825674790.03126117651349580.984369411743252
450.054849496726530.109698993453060.94515050327347
460.07839547526319230.1567909505263850.921604524736808
470.08206500176131520.1641300035226300.917934998238685
480.07033835218464010.1406767043692800.92966164781536
490.05511016022509720.1102203204501940.944889839774903
500.05815670432904160.1163134086580830.941843295670958
510.04655360240973280.09310720481946560.953446397590267
520.06229025151988860.1245805030397770.937709748480111
530.04842828169940330.09685656339880650.951571718300597
540.04257441589055140.08514883178110270.957425584109449
550.04862830006037090.09725660012074190.951371699939629
560.03801172715538010.07602345431076030.96198827284462
570.036004254199080.072008508398160.96399574580092
580.03244354718242440.06488709436484890.967556452817576
590.03506153224811790.07012306449623570.964938467751882
600.03197972158050890.06395944316101770.96802027841949
610.02518367997337990.05036735994675980.97481632002662
620.0307667209092550.061533441818510.969233279090745
630.02695019817842720.05390039635685440.973049801821573
640.02200097273011570.04400194546023140.977999027269884
650.01791335638199110.03582671276398210.98208664361801
660.01398846707092530.02797693414185060.986011532929075
670.01077712763893250.02155425527786500.989222872361068
680.01003770032601150.02007540065202300.989962299673989
690.007895194893503910.01579038978700780.992104805106496
700.006921850360738320.01384370072147660.993078149639262
710.005151738465314960.01030347693062990.994848261534685
720.00443158616369550.0088631723273910.995568413836305
730.006512863425902150.01302572685180430.993487136574098
740.006786686356327430.01357337271265490.993213313643672
750.00542181519495730.01084363038991460.994578184805043
760.004724941191992240.009449882383984480.995275058808008
770.005125033016239540.01025006603247910.99487496698376
780.005727813151969610.01145562630393920.99427218684803
790.004553457759442390.009106915518884780.995446542240558
800.003485571371499640.006971142742999280.9965144286285
810.002784784030544830.005569568061089650.997215215969455
820.003058917239159580.006117834478319160.99694108276084
830.002220686415148250.00444137283029650.997779313584852
840.002018723203696270.004037446407392540.997981276796304
850.001486114596497250.00297222919299450.998513885403503
860.002167880351719530.004335760703439060.99783211964828
870.002213869664460290.004427739328920590.99778613033554
880.001832082169519770.003664164339039530.99816791783048
890.001382070000984080.002764140001968170.998617929999016
900.0009792417507692830.001958483501538570.99902075824923
910.0006813992816985940.001362798563397190.999318600718301
920.0005080994977250680.001016198995450140.999491900502275
930.0004782805853092090.0009565611706184180.99952171941469
940.0003509631470023010.0007019262940046020.999649036852998
950.0003316340052130780.0006632680104261570.999668365994787
960.0002451273586961710.0004902547173923420.999754872641304
970.0002188548383332490.0004377096766664990.999781145161667
980.0001781248497474660.0003562496994949320.999821875150253
990.0002597578169325220.0005195156338650450.999740242183067
1000.0001915552534067190.0003831105068134370.999808444746593
1010.0001515960384585390.0003031920769170790.999848403961541
1020.0001003509307892270.0002007018615784530.99989964906921
1030.0001611106566634410.0003222213133268820.999838889343337
1040.0001122434673677350.0002244869347354700.999887756532632
1059.06556921776654e-050.0001813113843553310.999909344307822
1067.70631204286503e-050.0001541262408573010.999922936879571
1076.16398339991299e-050.0001232796679982600.999938360166
1084.54095164131261e-059.08190328262521e-050.999954590483587
1097.25040277361824e-050.0001450080554723650.999927495972264
1105.80819581568907e-050.0001161639163137810.999941918041843
1114.60505596644957e-059.21011193289914e-050.999953949440336
1125.33111158856538e-050.0001066222317713080.999946688884114
1134.39428816598311e-058.78857633196622e-050.99995605711834
1145.76035197264051e-050.0001152070394528100.999942396480274
1154.66613068630819e-059.33226137261638e-050.999953338693137
1165.09551491611034e-050.0001019102983222070.999949044850839
1170.0001632230936550030.0003264461873100060.999836776906345
1180.007129520396518950.01425904079303790.992870479603481
1190.04937991602752220.09875983205504430.950620083972478
1200.2605406043920890.5210812087841770.739459395607911
1210.3949850304963920.7899700609927840.605014969503608
1220.4365478953079220.8730957906158440.563452104692078
1230.3994668734945690.7989337469891370.600533126505431
1240.4425910312425140.8851820624850290.557408968757486
1250.5381545470751170.9236909058497660.461845452924883
1260.9678969355240850.06420612895183050.0321030644759153
1270.9687628535395040.0624742929209920.031237146460496
1280.9916635594392950.016672881121410.008336440560705
1290.992190792074340.01561841585132130.00780920792566063
1300.9920177371838720.01596452563225590.00798226281612796
1310.9946701703290430.01065965934191340.00532982967095671
1320.9981540921683480.003691815663304540.00184590783165227
1330.9978597335185890.004280532962822510.00214026648141126
1340.9973343395765260.005331320846948110.00266566042347406
1350.9967764988791080.006447002241784450.00322350112089222
1360.9977190573466880.0045618853066240.002280942653312
1370.9970084484772820.005983103045435670.00299155152271783
1380.9984271360520920.003145727895815790.00157286394790789
1390.99841800411550.003163991769001930.00158199588450096
1400.9985852330538720.002829533892256170.00141476694612809
1410.9995185584260160.0009628831479669580.000481441573983479
1420.9999494506185050.0001010987629895655.05493814947824e-05
1430.999950730203359.85395932984664e-054.92697966492332e-05
1440.9999196668618990.0001606662762022038.03331381011017e-05
1450.9999550567697198.98864605624566e-054.49432302812283e-05
1460.9999999966567686.68646338306663e-093.34323169153332e-09
1470.9999999974532735.09345350101669e-092.54672675050835e-09
1480.99999999511929.76159905468904e-094.88079952734452e-09
1490.9999999979095854.18082969165697e-092.09041484582849e-09
1500.9999999976900244.61995127703249e-092.30997563851624e-09
1510.9999999980951263.80974830521087e-091.90487415260543e-09
15213.30634857095627e-271.65317428547813e-27
15312.79630346664577e-261.39815173332289e-26
15412.15871539322375e-251.07935769661187e-25
15518.30831465454634e-254.15415732727317e-25
15617.19969358263877e-253.59984679131939e-25
15712.05820906109168e-241.02910453054584e-24
15811.66759614120743e-238.33798070603714e-24
15911.33597651320567e-226.67988256602833e-23
16011.20825050737338e-216.04125253686689e-22
16111.04677953003320e-205.23389765016598e-21
16218.64889831781923e-204.32444915890962e-20
16316.25197171023116e-193.12598585511558e-19
16412.25171591530662e-181.12585795765331e-18
16511.19402217062670e-185.97011085313349e-19
16615.80672969566772e-182.90336484783386e-18
16715.04938992744853e-172.52469496372426e-17
16814.15616664309051e-162.07808332154526e-16
1690.9999999999999983.7896173548211e-151.89480867741055e-15
1700.999999999999992.05355241677018e-141.02677620838509e-14
1710.9999999999999331.32990798283547e-136.64953991417735e-14
1720.9999999999997664.69081929565034e-132.34540964782517e-13
1730.99999999999921.60057042611828e-128.00285213059138e-13
1740.9999999999928741.42513972608697e-117.12569863043483e-12
1750.9999999999563878.72253583271511e-114.36126791635756e-11
1760.9999999996450777.09844999959725e-103.54922499979862e-10
1770.9999999979738534.05229422485849e-092.02614711242925e-09
1780.9999999845665733.08668546203582e-081.54334273101791e-08
1790.9999999452390671.09521866451742e-075.4760933225871e-08
1800.9999998713492172.57301566426044e-071.28650783213022e-07
1810.999999157671191.68465762039943e-068.42328810199713e-07
1820.9999981740121573.65197568562205e-061.82598784281103e-06
1830.999985407657642.91846847209865e-051.45923423604932e-05
1840.9998701799544660.0002596400910674650.000129820045533732
1850.9985037709930020.002992458013995610.00149622900699781






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1290.732954545454545NOK
5% type I error level1480.840909090909091NOK
10% type I error level1630.926136363636364NOK

\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 & 129 & 0.732954545454545 & NOK \tabularnewline
5% type I error level & 148 & 0.840909090909091 & NOK \tabularnewline
10% type I error level & 163 & 0.926136363636364 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115455&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]129[/C][C]0.732954545454545[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]148[/C][C]0.840909090909091[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]163[/C][C]0.926136363636364[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115455&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115455&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 level1290.732954545454545NOK
5% type I error level1480.840909090909091NOK
10% type I error level1630.926136363636364NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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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')
}