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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, 18 Dec 2010 15:41:02 +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/18/t1292687576sa90jvhqyqyj4sn.htm/, Retrieved Fri, 29 Mar 2024 01:33:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112064, Retrieved Fri, 29 Mar 2024 01:33:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS7 verschillende...] [2010-11-22 22:30:04] [49c7a512c56172bc46ae7e93e5b58c1c]
-   PD    [Multiple Regression] [WS7 verschillende...] [2010-11-23 13:14:37] [49c7a512c56172bc46ae7e93e5b58c1c]
-    D        [Multiple Regression] [Paper Multiple re...] [2010-12-18 15:41:02] [628a2d48b4bd249e4129ba023c5511b0] [Current]
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Dataseries X:
1	41	25	25	15	15	9	9	3	3
1	38	25	25	15	15	9	9	4	4
1	37	19	19	14	14	9	9	4	4
1	42	18	18	10	10	8	8	4	4
1	40	23	23	18	18	15	15	3	3
1	43	25	25	14	14	9	9	4	4
1	40	23	23	11	11	11	11	4	4
1	45	30	30	17	17	6	6	5	5
1	45	32	32	21	21	10	10	4	4
1	44	25	25	7	7	11	11	4	4
1	42	26	26	18	18	16	16	4	4
1	32	25	25	13	13	11	11	5	5
1	32	25	25	13	13	11	11	5	5
1	41	35	35	18	18	7	7	4	4
1	38	20	20	12	12	10	10	4	4
1	38	21	21	9	9	9	9	4	4
1	24	23	23	11	11	15	15	3	3
1	46	17	17	11	11	6	6	5	5
1	42	27	27	16	16	12	12	4	4
1	46	25	25	12	12	10	10	4	4
1	43	18	18	14	14	14	14	5	5
1	38	22	22	13	13	9	9	4	4
1	39	23	23	17	17	14	14	4	4
1	40	25	25	13	13	14	14	3	3
1	37	19	19	13	13	9	9	2	2
1	41	20	20	12	12	8	8	4	4
1	46	26	26	12	12	10	10	4	4
1	26	16	16	12	12	9	9	3	3
1	37	22	22	9	9	9	9	3	3
1	39	25	25	17	17	9	9	4	4
1	44	29	29	18	18	11	11	5	5
1	38	22	22	12	12	10	10	2	2
1	38	32	32	12	12	8	8	0	0
1	38	23	23	9	9	14	14	4	4
1	33	18	18	13	13	10	10	3	3
1	43	26	26	11	11	14	14	4	4
1	41	14	14	13	13	15	15	2	2
1	49	20	20	6	6	8	8	4	4
1	45	25	25	11	11	10	10	5	5
1	31	21	21	18	18	13	13	3	3
1	30	21	21	18	18	13	13	3	3
1	38	23	23	15	15	10	10	4	4
1	39	24	24	11	11	11	11	4	4
1	40	21	21	14	14	10	10	4	4
1	36	17	17	12	12	16	16	2	2
1	49	29	29	8	8	6	6	5	5
1	41	25	25	11	11	11	11	4	4
1	42	25	25	17	17	14	14	3	3
1	41	25	25	16	16	9	9	5	5
1	43	21	21	13	13	11	11	4	4
1	46	23	23	15	15	8	8	3	3
1	41	25	25	16	16	8	8	5	5
1	39	25	25	7	7	11	11	4	4
1	42	24	24	16	16	16	16	4	4
1	35	21	21	13	13	12	12	5	5
1	36	22	22	15	15	14	14	3	3
1	48	14	14	12	12	8	8	4	4
1	41	20	20	12	12	10	10	4	4
1	47	21	21	24	24	14	14	3	3
1	41	22	22	15	15	10	10	3	3
1	31	19	19	8	8	5	5	5	5
1	36	28	28	18	18	12	12	4	4
1	46	25	25	17	17	9	9	4	4
1	44	21	21	15	15	8	8	4	4
1	43	27	27	11	11	16	16	2	2
1	40	19	19	12	12	13	13	5	5
1	40	20	20	14	14	8	8	3	3
1	46	17	17	11	11	14	14	3	3
1	39	22	22	10	10	8	8	4	4
1	44	26	26	11	11	7	7	4	4
1	38	17	17	12	12	11	11	2	2
1	39	15	15	6	6	6	6	4	4
1	41	27	27	15	15	9	9	5	5
1	39	25	25	14	14	14	14	3	3
1	40	19	19	16	16	12	12	4	4
1	44	18	18	16	16	8	8	4	4
1	42	15	15	11	11	8	8	4	4
1	46	29	29	15	15	12	12	5	5
1	44	24	24	12	12	13	13	4	4
1	37	24	24	13	13	11	11	4	4
1	39	22	22	14	14	12	12	2	2
1	40	22	22	12	12	13	13	3	3
1	42	25	25	17	17	14	14	3	3
1	37	21	21	11	11	9	9	3	3
1	33	21	21	13	13	8	8	2	2
1	35	18	18	9	9	8	8	4	4
1	42	10	10	12	12	9	9	2	2
0	36	18	36	10	20	14	28	2	4
0	44	23	46	9	18	14	28	4	8
0	45	24	48	11	22	14	28	4	8
0	47	32	64	9	18	14	28	4	8
0	40	24	48	16	32	9	18	4	8
0	49	17	34	14	28	14	28	4	8
0	48	30	60	24	48	8	16	5	10
0	29	25	50	9	18	10	20	4	8
0	45	23	46	11	22	11	22	5	10
0	29	19	38	14	28	13	26	2	4
0	41	21	42	12	24	9	18	4	8
0	34	24	48	8	16	13	26	2	4
0	38	23	46	5	10	16	32	2	4
0	37	19	38	10	20	12	24	3	6
0	48	27	54	15	30	4	8	5	10
0	39	26	52	10	20	10	20	4	8
0	34	26	52	18	36	14	28	4	8
0	35	16	32	12	24	10	20	2	4
0	41	27	54	13	26	9	18	3	6
0	43	14	28	11	22	8	16	4	8
0	41	18	36	12	24	9	18	3	6
0	39	21	42	7	14	15	30	2	4
0	36	22	44	17	34	8	16	4	8
0	32	31	62	9	18	11	22	4	8
0	46	23	46	10	20	12	24	4	8
0	42	24	48	12	24	9	18	4	8
0	42	19	38	10	20	13	26	2	4
0	45	22	44	7	14	7	14	3	6
0	39	24	48	13	26	10	20	4	8
0	45	28	56	9	18	11	22	4	8
0	48	24	48	9	18	8	16	5	10
0	28	15	30	12	24	14	28	4	8
0	35	21	42	11	22	9	18	2	4
0	38	21	42	14	28	16	32	4	8
0	42	13	26	8	16	11	22	4	8
0	36	20	40	11	22	12	24	3	6
0	37	22	44	11	22	8	16	4	8
0	38	19	38	12	24	7	14	3	6
0	43	26	52	20	40	13	26	4	8
0	35	19	38	8	16	20	40	2	4
0	36	20	40	11	22	11	22	4	8
0	33	14	28	15	30	10	20	2	4
0	39	17	34	12	24	16	32	4	8
0	32	29	58	12	24	12	24	4	8
0	45	21	42	12	24	8	16	3	6
0	35	19	38	11	22	10	20	4	8
0	38	17	34	9	18	11	22	3	6
0	36	19	38	8	16	14	28	3	6
0	42	17	34	12	24	10	20	3	6
0	41	19	38	13	26	12	24	4	8
0	47	21	42	17	34	11	22	3	6
0	35	20	40	16	32	11	22	3	6
0	43	20	40	11	22	14	28	3	6
0	40	29	58	9	18	16	32	4	8
0	46	23	46	11	22	9	18	4	8
0	44	23	46	11	22	11	22	5	10
0	35	19	38	13	26	9	18	3	6
0	29	22	44	15	30	14	28	4	8




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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112064&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112064&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112064&T=0

As an alternative you can also use a 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







Multiple Linear Regression - Estimated Regression Equation
Career[t] = + 35.6197069170407 -1.39109054250955G[t] + 0.20798572444025PersonalStandards[t] -0.0619444415950565PeG[t] + 0.304770109016755ParentalExpectations[t] -0.222891855918395PaG[t] -0.0280035539064598Doubts[t] -0.143741846502613DoG[t] -0.286076268589505LeadershipPreference[t] + 1.16963899061508LeaderG[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Career[t] =  +  35.6197069170407 -1.39109054250955G[t] +  0.20798572444025PersonalStandards[t] -0.0619444415950565PeG[t] +  0.304770109016755ParentalExpectations[t] -0.222891855918395PaG[t] -0.0280035539064598Doubts[t] -0.143741846502613DoG[t] -0.286076268589505LeadershipPreference[t] +  1.16963899061508LeaderG[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112064&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Career[t] =  +  35.6197069170407 -1.39109054250955G[t] +  0.20798572444025PersonalStandards[t] -0.0619444415950565PeG[t] +  0.304770109016755ParentalExpectations[t] -0.222891855918395PaG[t] -0.0280035539064598Doubts[t] -0.143741846502613DoG[t] -0.286076268589505LeadershipPreference[t] +  1.16963899061508LeaderG[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112064&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112064&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Career[t] = + 35.6197069170407 -1.39109054250955G[t] + 0.20798572444025PersonalStandards[t] -0.0619444415950565PeG[t] + 0.304770109016755ParentalExpectations[t] -0.222891855918395PaG[t] -0.0280035539064598Doubts[t] -0.143741846502613DoG[t] -0.286076268589505LeadershipPreference[t] + 1.16963899061508LeaderG[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)35.61970691704075.2536186.7800
G-1.391090542509556.622594-0.21010.8339440.416972
PersonalStandards0.207985724440250.3086070.67390.5014960.250748
PeG-0.06194444159505650.209717-0.29540.7681640.384082
ParentalExpectations0.3047701090167550.4050790.75240.4531370.226569
PaG-0.2228918559183950.268232-0.8310.407460.20373
Doubts-0.02800355390645980.475306-0.05890.9531050.476553
DoG-0.1437418465026130.311976-0.46070.6457220.322861
LeadershipPreference-0.2860762685895051.418512-0.20170.8404760.420238
LeaderG1.169638990615081.016081.15110.2517140.125857

\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) & 35.6197069170407 & 5.253618 & 6.78 & 0 & 0 \tabularnewline
G & -1.39109054250955 & 6.622594 & -0.2101 & 0.833944 & 0.416972 \tabularnewline
PersonalStandards & 0.20798572444025 & 0.308607 & 0.6739 & 0.501496 & 0.250748 \tabularnewline
PeG & -0.0619444415950565 & 0.209717 & -0.2954 & 0.768164 & 0.384082 \tabularnewline
ParentalExpectations & 0.304770109016755 & 0.405079 & 0.7524 & 0.453137 & 0.226569 \tabularnewline
PaG & -0.222891855918395 & 0.268232 & -0.831 & 0.40746 & 0.20373 \tabularnewline
Doubts & -0.0280035539064598 & 0.475306 & -0.0589 & 0.953105 & 0.476553 \tabularnewline
DoG & -0.143741846502613 & 0.311976 & -0.4607 & 0.645722 & 0.322861 \tabularnewline
LeadershipPreference & -0.286076268589505 & 1.418512 & -0.2017 & 0.840476 & 0.420238 \tabularnewline
LeaderG & 1.16963899061508 & 1.01608 & 1.1511 & 0.251714 & 0.125857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112064&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]35.6197069170407[/C][C]5.253618[/C][C]6.78[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]G[/C][C]-1.39109054250955[/C][C]6.622594[/C][C]-0.2101[/C][C]0.833944[/C][C]0.416972[/C][/ROW]
[ROW][C]PersonalStandards[/C][C]0.20798572444025[/C][C]0.308607[/C][C]0.6739[/C][C]0.501496[/C][C]0.250748[/C][/ROW]
[ROW][C]PeG[/C][C]-0.0619444415950565[/C][C]0.209717[/C][C]-0.2954[/C][C]0.768164[/C][C]0.384082[/C][/ROW]
[ROW][C]ParentalExpectations[/C][C]0.304770109016755[/C][C]0.405079[/C][C]0.7524[/C][C]0.453137[/C][C]0.226569[/C][/ROW]
[ROW][C]PaG[/C][C]-0.222891855918395[/C][C]0.268232[/C][C]-0.831[/C][C]0.40746[/C][C]0.20373[/C][/ROW]
[ROW][C]Doubts[/C][C]-0.0280035539064598[/C][C]0.475306[/C][C]-0.0589[/C][C]0.953105[/C][C]0.476553[/C][/ROW]
[ROW][C]DoG[/C][C]-0.143741846502613[/C][C]0.311976[/C][C]-0.4607[/C][C]0.645722[/C][C]0.322861[/C][/ROW]
[ROW][C]LeadershipPreference[/C][C]-0.286076268589505[/C][C]1.418512[/C][C]-0.2017[/C][C]0.840476[/C][C]0.420238[/C][/ROW]
[ROW][C]LeaderG[/C][C]1.16963899061508[/C][C]1.01608[/C][C]1.1511[/C][C]0.251714[/C][C]0.125857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112064&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112064&T=2

As an alternative you can also use a 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)35.61970691704075.2536186.7800
G-1.391090542509556.622594-0.21010.8339440.416972
PersonalStandards0.207985724440250.3086070.67390.5014960.250748
PeG-0.06194444159505650.209717-0.29540.7681640.384082
ParentalExpectations0.3047701090167550.4050790.75240.4531370.226569
PaG-0.2228918559183950.268232-0.8310.407460.20373
Doubts-0.02800355390645980.475306-0.05890.9531050.476553
DoG-0.1437418465026130.311976-0.46070.6457220.322861
LeadershipPreference-0.2860762685895051.418512-0.20170.8404760.420238
LeaderG1.169638990615081.016081.15110.2517140.125857







Multiple Linear Regression - Regression Statistics
Multiple R0.360462109240757
R-squared0.129932932198296
Adjusted R-squared0.0719284610115152
F-TEST (value)2.24005028474268
F-TEST (DF numerator)9
F-TEST (DF denominator)135
p-value0.0230062379670575
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.80099053007766
Sum Squared Residuals3111.68385943587

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.360462109240757 \tabularnewline
R-squared & 0.129932932198296 \tabularnewline
Adjusted R-squared & 0.0719284610115152 \tabularnewline
F-TEST (value) & 2.24005028474268 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 135 \tabularnewline
p-value & 0.0230062379670575 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.80099053007766 \tabularnewline
Sum Squared Residuals & 3111.68385943587 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112064&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.360462109240757[/C][/ROW]
[ROW][C]R-squared[/C][C]0.129932932198296[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0719284610115152[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.24005028474268[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]135[/C][/ROW]
[ROW][C]p-value[/C][C]0.0230062379670575[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.80099053007766[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3111.68385943587[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112064&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112064&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.360462109240757
R-squared0.129932932198296
Adjusted R-squared0.0719284610115152
F-TEST (value)2.24005028474268
F-TEST (DF numerator)9
F-TEST (DF denominator)135
p-value0.0230062379670575
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.80099053007766
Sum Squared Residuals3111.68385943587







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14140.21280180453130.787198195468745
23841.096364526557-3.09636452655697
33740.1382385763875-3.13823857638747
44239.83642968155792.16357031844209
54039.13588159568170.864118404318318
64341.01448627345861.98551372654136
74040.133278147655-0.133278147655021
84543.38912637023251.61087362976753
94542.43817762465442.56182237534557
104440.0978477009523.90215229904804
114240.28582276583381.71417723416624
123241.4726799415677-9.4726799415677
133241.4726799415677-9.4726799415677
144143.1459029151222-2.14590291512216
153839.9487779526269-1.94877795262687
163840.0209298765861-2.02092987658606
172438.5627338239932-14.5627338239932
184640.99932017465485.00067982534521
194240.95508914411851.04491085588148
204640.67898436685285.32101563314716
214340.01703301352252.98296698647752
223840.4944841718247-2.49448417182469
233940.109311465018-1.10931146501796
244039.19031829628930.80968170371066
253738.289234879238-1.28923487923797
264140.2922687534450.707731246554982
274640.8250256496985.17497435030197
282638.6527954996296-12.6527954996296
293739.2834084374057-2.28340843740568
303941.2601210327537-2.26012103275372
314442.46623633844031.53376366155973
323838.4737350742661-0.473735074266121
333838.5105132594851-0.510513259485067
343839.4542854402311-1.45428544023108
353338.8550109180093-5.85501091800928
364340.05616579496342.94383420503662
374136.52855606255764.47144393744243
384939.80099923485499.19900076514515
394541.48066883578013.51933116421995
403139.1872898308094-8.18728983080944
413039.1872898308094-9.18728983080944
423840.6325365604575-2.63253656045754
433940.2793194305002-1.27931943050021
444040.2585757416688-0.258575741668787
453636.7130562575857-0.713056257585715
464942.5061808095026.49381919049797
474140.42536071334540.574639286654593
484239.51783130868282.48216869131722
494142.0618055016809-1.06180550168093
504340.00495208816142.99504791183865
514640.09246463925015.90753536074989
524142.23355090209-1.23355090209000
533940.097847700952-1.09784770095196
544239.82998369394672.17001630605335
553540.7167694097779-5.71676940977785
563638.9159509539505-2.91595095395048
574839.41602105637398.58397894362615
584139.94877795262691.05122204737313
594739.50681394899057.49318605100947
604139.60293255558681.39706744441323
613141.2175133814592-10.2175133814592
623641.2648869331604-5.26488693316044
634641.26012103275374.73987896724628
644440.68394479558533.31605520441471
654338.09159083293934.90840916706071
664040.17106319058-0.171063190580029
674039.57246253761620.42753746238383
684637.85823152733118.14176847266893
693940.4205948129387-1.42059481293868
704441.25838359782692.74161640217311
713837.57178325963110.42821674036892
723939.414283621447-0.414283621447033
734142.2720098142729-1.27200981427295
743939.2721965493877-0.272196549387701
754039.7867588813570.213241118643025
764440.32769920014813.67230079985193
774239.48018408612072.51981591387931
784642.04885617873613.95114382126388
794440.01770688278043.98229311721957
803740.4430759366969-3.44307593669693
813938.29400077964470.705999220355303
824038.84206159506451.15793840493553
834239.51783130868282.48216869131722
843739.3011236607572-2.30112366075721
853338.7530628453374-5.75306284533743
863539.7545514284595-4.75455142845955
874236.89298508053295.10701491946713
883635.41289599986050.587104000139514
894440.08079723421253.9192027657875
904539.88286686982265.11713313017744
914740.83766880546376.16233119453627
924040.7552350902808-0.755235090280824
934938.871148172611510.1288518273885
944842.50039627477375.4996037252263
952941.5109399043595-12.5109399043595
964542.79843348194812.20156651805187
972935.2484256767422-6.24842567674217
984141.0669989778105-0.0669989778105502
993436.5149914999131-2.51499149991306
1003835.9074737263882.09252627361203
1013738.1811690475746-1.18116904757464
1024844.77917716405033.22082283594965
1033941.4540231427896-2.45402314278962
1043439.0639653325826-5.0639653325826
1053536.2246240993669-1.22462409936689
1064139.37736470985071.62263529014931
1074340.93482193879132.06517806120869
1084138.76150674141952.23849325858051
1093935.77274008515933.22725991484069
1103640.7615150518722-4.7615150518722
1113241.7000337049487-9.70003370494865
1124640.57075812521585.42924187478417
1134241.3192895015610.680710498439039
1144235.81248008802236.18751991197769
1154540.43393661434364.56606338565642
1163940.8627886518292-1.86278865182924
1174541.44774318119823.55225681880176
1184844.11101926957343.88898073042661
1192838.9849816957513-10.9849816957513
1203537.1016091553493-2.10160915534929
1213838.5765610437887-0.576561043788678
1224240.32730416526621.67269583473378
1233638.1242522860047-2.12425228600474
1243741.6075966687924-4.60759666879241
1253839.476578076493-1.47657807649300
1264339.09742537385423.90257462614578
1273533.88609656528061.11390343471943
1283640.4929412455571-4.49294124555707
1293335.6333896084065-2.63338960840651
1303938.52220088442820.477799115571802
1313240.7933119670766-8.79331196707659
1324539.32928451208165.67071548791841
1333540.7243316512186-5.72433165121862
1343838.4694762148061-0.469476214806086
1353637.8322217593913-1.83222175939134
1364238.36192265325773.63807734674233
1374139.81132995175521.18867004824482
1384737.67775475724649.32224524275364
1393537.7346715188163-2.73467151881626
1404337.49327779218145.50672220781863
1414039.95440378788990.0455962121100531
1424641.37620626313094.62379373686914
1434442.79843348194811.20156651805187
1443538.7045899798496-3.70458997984960
1452939.1506187760422-10.1506187760422

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 40.2128018045313 & 0.787198195468745 \tabularnewline
2 & 38 & 41.096364526557 & -3.09636452655697 \tabularnewline
3 & 37 & 40.1382385763875 & -3.13823857638747 \tabularnewline
4 & 42 & 39.8364296815579 & 2.16357031844209 \tabularnewline
5 & 40 & 39.1358815956817 & 0.864118404318318 \tabularnewline
6 & 43 & 41.0144862734586 & 1.98551372654136 \tabularnewline
7 & 40 & 40.133278147655 & -0.133278147655021 \tabularnewline
8 & 45 & 43.3891263702325 & 1.61087362976753 \tabularnewline
9 & 45 & 42.4381776246544 & 2.56182237534557 \tabularnewline
10 & 44 & 40.097847700952 & 3.90215229904804 \tabularnewline
11 & 42 & 40.2858227658338 & 1.71417723416624 \tabularnewline
12 & 32 & 41.4726799415677 & -9.4726799415677 \tabularnewline
13 & 32 & 41.4726799415677 & -9.4726799415677 \tabularnewline
14 & 41 & 43.1459029151222 & -2.14590291512216 \tabularnewline
15 & 38 & 39.9487779526269 & -1.94877795262687 \tabularnewline
16 & 38 & 40.0209298765861 & -2.02092987658606 \tabularnewline
17 & 24 & 38.5627338239932 & -14.5627338239932 \tabularnewline
18 & 46 & 40.9993201746548 & 5.00067982534521 \tabularnewline
19 & 42 & 40.9550891441185 & 1.04491085588148 \tabularnewline
20 & 46 & 40.6789843668528 & 5.32101563314716 \tabularnewline
21 & 43 & 40.0170330135225 & 2.98296698647752 \tabularnewline
22 & 38 & 40.4944841718247 & -2.49448417182469 \tabularnewline
23 & 39 & 40.109311465018 & -1.10931146501796 \tabularnewline
24 & 40 & 39.1903182962893 & 0.80968170371066 \tabularnewline
25 & 37 & 38.289234879238 & -1.28923487923797 \tabularnewline
26 & 41 & 40.292268753445 & 0.707731246554982 \tabularnewline
27 & 46 & 40.825025649698 & 5.17497435030197 \tabularnewline
28 & 26 & 38.6527954996296 & -12.6527954996296 \tabularnewline
29 & 37 & 39.2834084374057 & -2.28340843740568 \tabularnewline
30 & 39 & 41.2601210327537 & -2.26012103275372 \tabularnewline
31 & 44 & 42.4662363384403 & 1.53376366155973 \tabularnewline
32 & 38 & 38.4737350742661 & -0.473735074266121 \tabularnewline
33 & 38 & 38.5105132594851 & -0.510513259485067 \tabularnewline
34 & 38 & 39.4542854402311 & -1.45428544023108 \tabularnewline
35 & 33 & 38.8550109180093 & -5.85501091800928 \tabularnewline
36 & 43 & 40.0561657949634 & 2.94383420503662 \tabularnewline
37 & 41 & 36.5285560625576 & 4.47144393744243 \tabularnewline
38 & 49 & 39.8009992348549 & 9.19900076514515 \tabularnewline
39 & 45 & 41.4806688357801 & 3.51933116421995 \tabularnewline
40 & 31 & 39.1872898308094 & -8.18728983080944 \tabularnewline
41 & 30 & 39.1872898308094 & -9.18728983080944 \tabularnewline
42 & 38 & 40.6325365604575 & -2.63253656045754 \tabularnewline
43 & 39 & 40.2793194305002 & -1.27931943050021 \tabularnewline
44 & 40 & 40.2585757416688 & -0.258575741668787 \tabularnewline
45 & 36 & 36.7130562575857 & -0.713056257585715 \tabularnewline
46 & 49 & 42.506180809502 & 6.49381919049797 \tabularnewline
47 & 41 & 40.4253607133454 & 0.574639286654593 \tabularnewline
48 & 42 & 39.5178313086828 & 2.48216869131722 \tabularnewline
49 & 41 & 42.0618055016809 & -1.06180550168093 \tabularnewline
50 & 43 & 40.0049520881614 & 2.99504791183865 \tabularnewline
51 & 46 & 40.0924646392501 & 5.90753536074989 \tabularnewline
52 & 41 & 42.23355090209 & -1.23355090209000 \tabularnewline
53 & 39 & 40.097847700952 & -1.09784770095196 \tabularnewline
54 & 42 & 39.8299836939467 & 2.17001630605335 \tabularnewline
55 & 35 & 40.7167694097779 & -5.71676940977785 \tabularnewline
56 & 36 & 38.9159509539505 & -2.91595095395048 \tabularnewline
57 & 48 & 39.4160210563739 & 8.58397894362615 \tabularnewline
58 & 41 & 39.9487779526269 & 1.05122204737313 \tabularnewline
59 & 47 & 39.5068139489905 & 7.49318605100947 \tabularnewline
60 & 41 & 39.6029325555868 & 1.39706744441323 \tabularnewline
61 & 31 & 41.2175133814592 & -10.2175133814592 \tabularnewline
62 & 36 & 41.2648869331604 & -5.26488693316044 \tabularnewline
63 & 46 & 41.2601210327537 & 4.73987896724628 \tabularnewline
64 & 44 & 40.6839447955853 & 3.31605520441471 \tabularnewline
65 & 43 & 38.0915908329393 & 4.90840916706071 \tabularnewline
66 & 40 & 40.17106319058 & -0.171063190580029 \tabularnewline
67 & 40 & 39.5724625376162 & 0.42753746238383 \tabularnewline
68 & 46 & 37.8582315273311 & 8.14176847266893 \tabularnewline
69 & 39 & 40.4205948129387 & -1.42059481293868 \tabularnewline
70 & 44 & 41.2583835978269 & 2.74161640217311 \tabularnewline
71 & 38 & 37.5717832596311 & 0.42821674036892 \tabularnewline
72 & 39 & 39.414283621447 & -0.414283621447033 \tabularnewline
73 & 41 & 42.2720098142729 & -1.27200981427295 \tabularnewline
74 & 39 & 39.2721965493877 & -0.272196549387701 \tabularnewline
75 & 40 & 39.786758881357 & 0.213241118643025 \tabularnewline
76 & 44 & 40.3276992001481 & 3.67230079985193 \tabularnewline
77 & 42 & 39.4801840861207 & 2.51981591387931 \tabularnewline
78 & 46 & 42.0488561787361 & 3.95114382126388 \tabularnewline
79 & 44 & 40.0177068827804 & 3.98229311721957 \tabularnewline
80 & 37 & 40.4430759366969 & -3.44307593669693 \tabularnewline
81 & 39 & 38.2940007796447 & 0.705999220355303 \tabularnewline
82 & 40 & 38.8420615950645 & 1.15793840493553 \tabularnewline
83 & 42 & 39.5178313086828 & 2.48216869131722 \tabularnewline
84 & 37 & 39.3011236607572 & -2.30112366075721 \tabularnewline
85 & 33 & 38.7530628453374 & -5.75306284533743 \tabularnewline
86 & 35 & 39.7545514284595 & -4.75455142845955 \tabularnewline
87 & 42 & 36.8929850805329 & 5.10701491946713 \tabularnewline
88 & 36 & 35.4128959998605 & 0.587104000139514 \tabularnewline
89 & 44 & 40.0807972342125 & 3.9192027657875 \tabularnewline
90 & 45 & 39.8828668698226 & 5.11713313017744 \tabularnewline
91 & 47 & 40.8376688054637 & 6.16233119453627 \tabularnewline
92 & 40 & 40.7552350902808 & -0.755235090280824 \tabularnewline
93 & 49 & 38.8711481726115 & 10.1288518273885 \tabularnewline
94 & 48 & 42.5003962747737 & 5.4996037252263 \tabularnewline
95 & 29 & 41.5109399043595 & -12.5109399043595 \tabularnewline
96 & 45 & 42.7984334819481 & 2.20156651805187 \tabularnewline
97 & 29 & 35.2484256767422 & -6.24842567674217 \tabularnewline
98 & 41 & 41.0669989778105 & -0.0669989778105502 \tabularnewline
99 & 34 & 36.5149914999131 & -2.51499149991306 \tabularnewline
100 & 38 & 35.907473726388 & 2.09252627361203 \tabularnewline
101 & 37 & 38.1811690475746 & -1.18116904757464 \tabularnewline
102 & 48 & 44.7791771640503 & 3.22082283594965 \tabularnewline
103 & 39 & 41.4540231427896 & -2.45402314278962 \tabularnewline
104 & 34 & 39.0639653325826 & -5.0639653325826 \tabularnewline
105 & 35 & 36.2246240993669 & -1.22462409936689 \tabularnewline
106 & 41 & 39.3773647098507 & 1.62263529014931 \tabularnewline
107 & 43 & 40.9348219387913 & 2.06517806120869 \tabularnewline
108 & 41 & 38.7615067414195 & 2.23849325858051 \tabularnewline
109 & 39 & 35.7727400851593 & 3.22725991484069 \tabularnewline
110 & 36 & 40.7615150518722 & -4.7615150518722 \tabularnewline
111 & 32 & 41.7000337049487 & -9.70003370494865 \tabularnewline
112 & 46 & 40.5707581252158 & 5.42924187478417 \tabularnewline
113 & 42 & 41.319289501561 & 0.680710498439039 \tabularnewline
114 & 42 & 35.8124800880223 & 6.18751991197769 \tabularnewline
115 & 45 & 40.4339366143436 & 4.56606338565642 \tabularnewline
116 & 39 & 40.8627886518292 & -1.86278865182924 \tabularnewline
117 & 45 & 41.4477431811982 & 3.55225681880176 \tabularnewline
118 & 48 & 44.1110192695734 & 3.88898073042661 \tabularnewline
119 & 28 & 38.9849816957513 & -10.9849816957513 \tabularnewline
120 & 35 & 37.1016091553493 & -2.10160915534929 \tabularnewline
121 & 38 & 38.5765610437887 & -0.576561043788678 \tabularnewline
122 & 42 & 40.3273041652662 & 1.67269583473378 \tabularnewline
123 & 36 & 38.1242522860047 & -2.12425228600474 \tabularnewline
124 & 37 & 41.6075966687924 & -4.60759666879241 \tabularnewline
125 & 38 & 39.476578076493 & -1.47657807649300 \tabularnewline
126 & 43 & 39.0974253738542 & 3.90257462614578 \tabularnewline
127 & 35 & 33.8860965652806 & 1.11390343471943 \tabularnewline
128 & 36 & 40.4929412455571 & -4.49294124555707 \tabularnewline
129 & 33 & 35.6333896084065 & -2.63338960840651 \tabularnewline
130 & 39 & 38.5222008844282 & 0.477799115571802 \tabularnewline
131 & 32 & 40.7933119670766 & -8.79331196707659 \tabularnewline
132 & 45 & 39.3292845120816 & 5.67071548791841 \tabularnewline
133 & 35 & 40.7243316512186 & -5.72433165121862 \tabularnewline
134 & 38 & 38.4694762148061 & -0.469476214806086 \tabularnewline
135 & 36 & 37.8322217593913 & -1.83222175939134 \tabularnewline
136 & 42 & 38.3619226532577 & 3.63807734674233 \tabularnewline
137 & 41 & 39.8113299517552 & 1.18867004824482 \tabularnewline
138 & 47 & 37.6777547572464 & 9.32224524275364 \tabularnewline
139 & 35 & 37.7346715188163 & -2.73467151881626 \tabularnewline
140 & 43 & 37.4932777921814 & 5.50672220781863 \tabularnewline
141 & 40 & 39.9544037878899 & 0.0455962121100531 \tabularnewline
142 & 46 & 41.3762062631309 & 4.62379373686914 \tabularnewline
143 & 44 & 42.7984334819481 & 1.20156651805187 \tabularnewline
144 & 35 & 38.7045899798496 & -3.70458997984960 \tabularnewline
145 & 29 & 39.1506187760422 & -10.1506187760422 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112064&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]41[/C][C]40.2128018045313[/C][C]0.787198195468745[/C][/ROW]
[ROW][C]2[/C][C]38[/C][C]41.096364526557[/C][C]-3.09636452655697[/C][/ROW]
[ROW][C]3[/C][C]37[/C][C]40.1382385763875[/C][C]-3.13823857638747[/C][/ROW]
[ROW][C]4[/C][C]42[/C][C]39.8364296815579[/C][C]2.16357031844209[/C][/ROW]
[ROW][C]5[/C][C]40[/C][C]39.1358815956817[/C][C]0.864118404318318[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]41.0144862734586[/C][C]1.98551372654136[/C][/ROW]
[ROW][C]7[/C][C]40[/C][C]40.133278147655[/C][C]-0.133278147655021[/C][/ROW]
[ROW][C]8[/C][C]45[/C][C]43.3891263702325[/C][C]1.61087362976753[/C][/ROW]
[ROW][C]9[/C][C]45[/C][C]42.4381776246544[/C][C]2.56182237534557[/C][/ROW]
[ROW][C]10[/C][C]44[/C][C]40.097847700952[/C][C]3.90215229904804[/C][/ROW]
[ROW][C]11[/C][C]42[/C][C]40.2858227658338[/C][C]1.71417723416624[/C][/ROW]
[ROW][C]12[/C][C]32[/C][C]41.4726799415677[/C][C]-9.4726799415677[/C][/ROW]
[ROW][C]13[/C][C]32[/C][C]41.4726799415677[/C][C]-9.4726799415677[/C][/ROW]
[ROW][C]14[/C][C]41[/C][C]43.1459029151222[/C][C]-2.14590291512216[/C][/ROW]
[ROW][C]15[/C][C]38[/C][C]39.9487779526269[/C][C]-1.94877795262687[/C][/ROW]
[ROW][C]16[/C][C]38[/C][C]40.0209298765861[/C][C]-2.02092987658606[/C][/ROW]
[ROW][C]17[/C][C]24[/C][C]38.5627338239932[/C][C]-14.5627338239932[/C][/ROW]
[ROW][C]18[/C][C]46[/C][C]40.9993201746548[/C][C]5.00067982534521[/C][/ROW]
[ROW][C]19[/C][C]42[/C][C]40.9550891441185[/C][C]1.04491085588148[/C][/ROW]
[ROW][C]20[/C][C]46[/C][C]40.6789843668528[/C][C]5.32101563314716[/C][/ROW]
[ROW][C]21[/C][C]43[/C][C]40.0170330135225[/C][C]2.98296698647752[/C][/ROW]
[ROW][C]22[/C][C]38[/C][C]40.4944841718247[/C][C]-2.49448417182469[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]40.109311465018[/C][C]-1.10931146501796[/C][/ROW]
[ROW][C]24[/C][C]40[/C][C]39.1903182962893[/C][C]0.80968170371066[/C][/ROW]
[ROW][C]25[/C][C]37[/C][C]38.289234879238[/C][C]-1.28923487923797[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]40.292268753445[/C][C]0.707731246554982[/C][/ROW]
[ROW][C]27[/C][C]46[/C][C]40.825025649698[/C][C]5.17497435030197[/C][/ROW]
[ROW][C]28[/C][C]26[/C][C]38.6527954996296[/C][C]-12.6527954996296[/C][/ROW]
[ROW][C]29[/C][C]37[/C][C]39.2834084374057[/C][C]-2.28340843740568[/C][/ROW]
[ROW][C]30[/C][C]39[/C][C]41.2601210327537[/C][C]-2.26012103275372[/C][/ROW]
[ROW][C]31[/C][C]44[/C][C]42.4662363384403[/C][C]1.53376366155973[/C][/ROW]
[ROW][C]32[/C][C]38[/C][C]38.4737350742661[/C][C]-0.473735074266121[/C][/ROW]
[ROW][C]33[/C][C]38[/C][C]38.5105132594851[/C][C]-0.510513259485067[/C][/ROW]
[ROW][C]34[/C][C]38[/C][C]39.4542854402311[/C][C]-1.45428544023108[/C][/ROW]
[ROW][C]35[/C][C]33[/C][C]38.8550109180093[/C][C]-5.85501091800928[/C][/ROW]
[ROW][C]36[/C][C]43[/C][C]40.0561657949634[/C][C]2.94383420503662[/C][/ROW]
[ROW][C]37[/C][C]41[/C][C]36.5285560625576[/C][C]4.47144393744243[/C][/ROW]
[ROW][C]38[/C][C]49[/C][C]39.8009992348549[/C][C]9.19900076514515[/C][/ROW]
[ROW][C]39[/C][C]45[/C][C]41.4806688357801[/C][C]3.51933116421995[/C][/ROW]
[ROW][C]40[/C][C]31[/C][C]39.1872898308094[/C][C]-8.18728983080944[/C][/ROW]
[ROW][C]41[/C][C]30[/C][C]39.1872898308094[/C][C]-9.18728983080944[/C][/ROW]
[ROW][C]42[/C][C]38[/C][C]40.6325365604575[/C][C]-2.63253656045754[/C][/ROW]
[ROW][C]43[/C][C]39[/C][C]40.2793194305002[/C][C]-1.27931943050021[/C][/ROW]
[ROW][C]44[/C][C]40[/C][C]40.2585757416688[/C][C]-0.258575741668787[/C][/ROW]
[ROW][C]45[/C][C]36[/C][C]36.7130562575857[/C][C]-0.713056257585715[/C][/ROW]
[ROW][C]46[/C][C]49[/C][C]42.506180809502[/C][C]6.49381919049797[/C][/ROW]
[ROW][C]47[/C][C]41[/C][C]40.4253607133454[/C][C]0.574639286654593[/C][/ROW]
[ROW][C]48[/C][C]42[/C][C]39.5178313086828[/C][C]2.48216869131722[/C][/ROW]
[ROW][C]49[/C][C]41[/C][C]42.0618055016809[/C][C]-1.06180550168093[/C][/ROW]
[ROW][C]50[/C][C]43[/C][C]40.0049520881614[/C][C]2.99504791183865[/C][/ROW]
[ROW][C]51[/C][C]46[/C][C]40.0924646392501[/C][C]5.90753536074989[/C][/ROW]
[ROW][C]52[/C][C]41[/C][C]42.23355090209[/C][C]-1.23355090209000[/C][/ROW]
[ROW][C]53[/C][C]39[/C][C]40.097847700952[/C][C]-1.09784770095196[/C][/ROW]
[ROW][C]54[/C][C]42[/C][C]39.8299836939467[/C][C]2.17001630605335[/C][/ROW]
[ROW][C]55[/C][C]35[/C][C]40.7167694097779[/C][C]-5.71676940977785[/C][/ROW]
[ROW][C]56[/C][C]36[/C][C]38.9159509539505[/C][C]-2.91595095395048[/C][/ROW]
[ROW][C]57[/C][C]48[/C][C]39.4160210563739[/C][C]8.58397894362615[/C][/ROW]
[ROW][C]58[/C][C]41[/C][C]39.9487779526269[/C][C]1.05122204737313[/C][/ROW]
[ROW][C]59[/C][C]47[/C][C]39.5068139489905[/C][C]7.49318605100947[/C][/ROW]
[ROW][C]60[/C][C]41[/C][C]39.6029325555868[/C][C]1.39706744441323[/C][/ROW]
[ROW][C]61[/C][C]31[/C][C]41.2175133814592[/C][C]-10.2175133814592[/C][/ROW]
[ROW][C]62[/C][C]36[/C][C]41.2648869331604[/C][C]-5.26488693316044[/C][/ROW]
[ROW][C]63[/C][C]46[/C][C]41.2601210327537[/C][C]4.73987896724628[/C][/ROW]
[ROW][C]64[/C][C]44[/C][C]40.6839447955853[/C][C]3.31605520441471[/C][/ROW]
[ROW][C]65[/C][C]43[/C][C]38.0915908329393[/C][C]4.90840916706071[/C][/ROW]
[ROW][C]66[/C][C]40[/C][C]40.17106319058[/C][C]-0.171063190580029[/C][/ROW]
[ROW][C]67[/C][C]40[/C][C]39.5724625376162[/C][C]0.42753746238383[/C][/ROW]
[ROW][C]68[/C][C]46[/C][C]37.8582315273311[/C][C]8.14176847266893[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]40.4205948129387[/C][C]-1.42059481293868[/C][/ROW]
[ROW][C]70[/C][C]44[/C][C]41.2583835978269[/C][C]2.74161640217311[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]37.5717832596311[/C][C]0.42821674036892[/C][/ROW]
[ROW][C]72[/C][C]39[/C][C]39.414283621447[/C][C]-0.414283621447033[/C][/ROW]
[ROW][C]73[/C][C]41[/C][C]42.2720098142729[/C][C]-1.27200981427295[/C][/ROW]
[ROW][C]74[/C][C]39[/C][C]39.2721965493877[/C][C]-0.272196549387701[/C][/ROW]
[ROW][C]75[/C][C]40[/C][C]39.786758881357[/C][C]0.213241118643025[/C][/ROW]
[ROW][C]76[/C][C]44[/C][C]40.3276992001481[/C][C]3.67230079985193[/C][/ROW]
[ROW][C]77[/C][C]42[/C][C]39.4801840861207[/C][C]2.51981591387931[/C][/ROW]
[ROW][C]78[/C][C]46[/C][C]42.0488561787361[/C][C]3.95114382126388[/C][/ROW]
[ROW][C]79[/C][C]44[/C][C]40.0177068827804[/C][C]3.98229311721957[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]40.4430759366969[/C][C]-3.44307593669693[/C][/ROW]
[ROW][C]81[/C][C]39[/C][C]38.2940007796447[/C][C]0.705999220355303[/C][/ROW]
[ROW][C]82[/C][C]40[/C][C]38.8420615950645[/C][C]1.15793840493553[/C][/ROW]
[ROW][C]83[/C][C]42[/C][C]39.5178313086828[/C][C]2.48216869131722[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]39.3011236607572[/C][C]-2.30112366075721[/C][/ROW]
[ROW][C]85[/C][C]33[/C][C]38.7530628453374[/C][C]-5.75306284533743[/C][/ROW]
[ROW][C]86[/C][C]35[/C][C]39.7545514284595[/C][C]-4.75455142845955[/C][/ROW]
[ROW][C]87[/C][C]42[/C][C]36.8929850805329[/C][C]5.10701491946713[/C][/ROW]
[ROW][C]88[/C][C]36[/C][C]35.4128959998605[/C][C]0.587104000139514[/C][/ROW]
[ROW][C]89[/C][C]44[/C][C]40.0807972342125[/C][C]3.9192027657875[/C][/ROW]
[ROW][C]90[/C][C]45[/C][C]39.8828668698226[/C][C]5.11713313017744[/C][/ROW]
[ROW][C]91[/C][C]47[/C][C]40.8376688054637[/C][C]6.16233119453627[/C][/ROW]
[ROW][C]92[/C][C]40[/C][C]40.7552350902808[/C][C]-0.755235090280824[/C][/ROW]
[ROW][C]93[/C][C]49[/C][C]38.8711481726115[/C][C]10.1288518273885[/C][/ROW]
[ROW][C]94[/C][C]48[/C][C]42.5003962747737[/C][C]5.4996037252263[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]41.5109399043595[/C][C]-12.5109399043595[/C][/ROW]
[ROW][C]96[/C][C]45[/C][C]42.7984334819481[/C][C]2.20156651805187[/C][/ROW]
[ROW][C]97[/C][C]29[/C][C]35.2484256767422[/C][C]-6.24842567674217[/C][/ROW]
[ROW][C]98[/C][C]41[/C][C]41.0669989778105[/C][C]-0.0669989778105502[/C][/ROW]
[ROW][C]99[/C][C]34[/C][C]36.5149914999131[/C][C]-2.51499149991306[/C][/ROW]
[ROW][C]100[/C][C]38[/C][C]35.907473726388[/C][C]2.09252627361203[/C][/ROW]
[ROW][C]101[/C][C]37[/C][C]38.1811690475746[/C][C]-1.18116904757464[/C][/ROW]
[ROW][C]102[/C][C]48[/C][C]44.7791771640503[/C][C]3.22082283594965[/C][/ROW]
[ROW][C]103[/C][C]39[/C][C]41.4540231427896[/C][C]-2.45402314278962[/C][/ROW]
[ROW][C]104[/C][C]34[/C][C]39.0639653325826[/C][C]-5.0639653325826[/C][/ROW]
[ROW][C]105[/C][C]35[/C][C]36.2246240993669[/C][C]-1.22462409936689[/C][/ROW]
[ROW][C]106[/C][C]41[/C][C]39.3773647098507[/C][C]1.62263529014931[/C][/ROW]
[ROW][C]107[/C][C]43[/C][C]40.9348219387913[/C][C]2.06517806120869[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]38.7615067414195[/C][C]2.23849325858051[/C][/ROW]
[ROW][C]109[/C][C]39[/C][C]35.7727400851593[/C][C]3.22725991484069[/C][/ROW]
[ROW][C]110[/C][C]36[/C][C]40.7615150518722[/C][C]-4.7615150518722[/C][/ROW]
[ROW][C]111[/C][C]32[/C][C]41.7000337049487[/C][C]-9.70003370494865[/C][/ROW]
[ROW][C]112[/C][C]46[/C][C]40.5707581252158[/C][C]5.42924187478417[/C][/ROW]
[ROW][C]113[/C][C]42[/C][C]41.319289501561[/C][C]0.680710498439039[/C][/ROW]
[ROW][C]114[/C][C]42[/C][C]35.8124800880223[/C][C]6.18751991197769[/C][/ROW]
[ROW][C]115[/C][C]45[/C][C]40.4339366143436[/C][C]4.56606338565642[/C][/ROW]
[ROW][C]116[/C][C]39[/C][C]40.8627886518292[/C][C]-1.86278865182924[/C][/ROW]
[ROW][C]117[/C][C]45[/C][C]41.4477431811982[/C][C]3.55225681880176[/C][/ROW]
[ROW][C]118[/C][C]48[/C][C]44.1110192695734[/C][C]3.88898073042661[/C][/ROW]
[ROW][C]119[/C][C]28[/C][C]38.9849816957513[/C][C]-10.9849816957513[/C][/ROW]
[ROW][C]120[/C][C]35[/C][C]37.1016091553493[/C][C]-2.10160915534929[/C][/ROW]
[ROW][C]121[/C][C]38[/C][C]38.5765610437887[/C][C]-0.576561043788678[/C][/ROW]
[ROW][C]122[/C][C]42[/C][C]40.3273041652662[/C][C]1.67269583473378[/C][/ROW]
[ROW][C]123[/C][C]36[/C][C]38.1242522860047[/C][C]-2.12425228600474[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]41.6075966687924[/C][C]-4.60759666879241[/C][/ROW]
[ROW][C]125[/C][C]38[/C][C]39.476578076493[/C][C]-1.47657807649300[/C][/ROW]
[ROW][C]126[/C][C]43[/C][C]39.0974253738542[/C][C]3.90257462614578[/C][/ROW]
[ROW][C]127[/C][C]35[/C][C]33.8860965652806[/C][C]1.11390343471943[/C][/ROW]
[ROW][C]128[/C][C]36[/C][C]40.4929412455571[/C][C]-4.49294124555707[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.6333896084065[/C][C]-2.63338960840651[/C][/ROW]
[ROW][C]130[/C][C]39[/C][C]38.5222008844282[/C][C]0.477799115571802[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]40.7933119670766[/C][C]-8.79331196707659[/C][/ROW]
[ROW][C]132[/C][C]45[/C][C]39.3292845120816[/C][C]5.67071548791841[/C][/ROW]
[ROW][C]133[/C][C]35[/C][C]40.7243316512186[/C][C]-5.72433165121862[/C][/ROW]
[ROW][C]134[/C][C]38[/C][C]38.4694762148061[/C][C]-0.469476214806086[/C][/ROW]
[ROW][C]135[/C][C]36[/C][C]37.8322217593913[/C][C]-1.83222175939134[/C][/ROW]
[ROW][C]136[/C][C]42[/C][C]38.3619226532577[/C][C]3.63807734674233[/C][/ROW]
[ROW][C]137[/C][C]41[/C][C]39.8113299517552[/C][C]1.18867004824482[/C][/ROW]
[ROW][C]138[/C][C]47[/C][C]37.6777547572464[/C][C]9.32224524275364[/C][/ROW]
[ROW][C]139[/C][C]35[/C][C]37.7346715188163[/C][C]-2.73467151881626[/C][/ROW]
[ROW][C]140[/C][C]43[/C][C]37.4932777921814[/C][C]5.50672220781863[/C][/ROW]
[ROW][C]141[/C][C]40[/C][C]39.9544037878899[/C][C]0.0455962121100531[/C][/ROW]
[ROW][C]142[/C][C]46[/C][C]41.3762062631309[/C][C]4.62379373686914[/C][/ROW]
[ROW][C]143[/C][C]44[/C][C]42.7984334819481[/C][C]1.20156651805187[/C][/ROW]
[ROW][C]144[/C][C]35[/C][C]38.7045899798496[/C][C]-3.70458997984960[/C][/ROW]
[ROW][C]145[/C][C]29[/C][C]39.1506187760422[/C][C]-10.1506187760422[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112064&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112064&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14140.21280180453130.787198195468745
23841.096364526557-3.09636452655697
33740.1382385763875-3.13823857638747
44239.83642968155792.16357031844209
54039.13588159568170.864118404318318
64341.01448627345861.98551372654136
74040.133278147655-0.133278147655021
84543.38912637023251.61087362976753
94542.43817762465442.56182237534557
104440.0978477009523.90215229904804
114240.28582276583381.71417723416624
123241.4726799415677-9.4726799415677
133241.4726799415677-9.4726799415677
144143.1459029151222-2.14590291512216
153839.9487779526269-1.94877795262687
163840.0209298765861-2.02092987658606
172438.5627338239932-14.5627338239932
184640.99932017465485.00067982534521
194240.95508914411851.04491085588148
204640.67898436685285.32101563314716
214340.01703301352252.98296698647752
223840.4944841718247-2.49448417182469
233940.109311465018-1.10931146501796
244039.19031829628930.80968170371066
253738.289234879238-1.28923487923797
264140.2922687534450.707731246554982
274640.8250256496985.17497435030197
282638.6527954996296-12.6527954996296
293739.2834084374057-2.28340843740568
303941.2601210327537-2.26012103275372
314442.46623633844031.53376366155973
323838.4737350742661-0.473735074266121
333838.5105132594851-0.510513259485067
343839.4542854402311-1.45428544023108
353338.8550109180093-5.85501091800928
364340.05616579496342.94383420503662
374136.52855606255764.47144393744243
384939.80099923485499.19900076514515
394541.48066883578013.51933116421995
403139.1872898308094-8.18728983080944
413039.1872898308094-9.18728983080944
423840.6325365604575-2.63253656045754
433940.2793194305002-1.27931943050021
444040.2585757416688-0.258575741668787
453636.7130562575857-0.713056257585715
464942.5061808095026.49381919049797
474140.42536071334540.574639286654593
484239.51783130868282.48216869131722
494142.0618055016809-1.06180550168093
504340.00495208816142.99504791183865
514640.09246463925015.90753536074989
524142.23355090209-1.23355090209000
533940.097847700952-1.09784770095196
544239.82998369394672.17001630605335
553540.7167694097779-5.71676940977785
563638.9159509539505-2.91595095395048
574839.41602105637398.58397894362615
584139.94877795262691.05122204737313
594739.50681394899057.49318605100947
604139.60293255558681.39706744441323
613141.2175133814592-10.2175133814592
623641.2648869331604-5.26488693316044
634641.26012103275374.73987896724628
644440.68394479558533.31605520441471
654338.09159083293934.90840916706071
664040.17106319058-0.171063190580029
674039.57246253761620.42753746238383
684637.85823152733118.14176847266893
693940.4205948129387-1.42059481293868
704441.25838359782692.74161640217311
713837.57178325963110.42821674036892
723939.414283621447-0.414283621447033
734142.2720098142729-1.27200981427295
743939.2721965493877-0.272196549387701
754039.7867588813570.213241118643025
764440.32769920014813.67230079985193
774239.48018408612072.51981591387931
784642.04885617873613.95114382126388
794440.01770688278043.98229311721957
803740.4430759366969-3.44307593669693
813938.29400077964470.705999220355303
824038.84206159506451.15793840493553
834239.51783130868282.48216869131722
843739.3011236607572-2.30112366075721
853338.7530628453374-5.75306284533743
863539.7545514284595-4.75455142845955
874236.89298508053295.10701491946713
883635.41289599986050.587104000139514
894440.08079723421253.9192027657875
904539.88286686982265.11713313017744
914740.83766880546376.16233119453627
924040.7552350902808-0.755235090280824
934938.871148172611510.1288518273885
944842.50039627477375.4996037252263
952941.5109399043595-12.5109399043595
964542.79843348194812.20156651805187
972935.2484256767422-6.24842567674217
984141.0669989778105-0.0669989778105502
993436.5149914999131-2.51499149991306
1003835.9074737263882.09252627361203
1013738.1811690475746-1.18116904757464
1024844.77917716405033.22082283594965
1033941.4540231427896-2.45402314278962
1043439.0639653325826-5.0639653325826
1053536.2246240993669-1.22462409936689
1064139.37736470985071.62263529014931
1074340.93482193879132.06517806120869
1084138.76150674141952.23849325858051
1093935.77274008515933.22725991484069
1103640.7615150518722-4.7615150518722
1113241.7000337049487-9.70003370494865
1124640.57075812521585.42924187478417
1134241.3192895015610.680710498439039
1144235.81248008802236.18751991197769
1154540.43393661434364.56606338565642
1163940.8627886518292-1.86278865182924
1174541.44774318119823.55225681880176
1184844.11101926957343.88898073042661
1192838.9849816957513-10.9849816957513
1203537.1016091553493-2.10160915534929
1213838.5765610437887-0.576561043788678
1224240.32730416526621.67269583473378
1233638.1242522860047-2.12425228600474
1243741.6075966687924-4.60759666879241
1253839.476578076493-1.47657807649300
1264339.09742537385423.90257462614578
1273533.88609656528061.11390343471943
1283640.4929412455571-4.49294124555707
1293335.6333896084065-2.63338960840651
1303938.52220088442820.477799115571802
1313240.7933119670766-8.79331196707659
1324539.32928451208165.67071548791841
1333540.7243316512186-5.72433165121862
1343838.4694762148061-0.469476214806086
1353637.8322217593913-1.83222175939134
1364238.36192265325773.63807734674233
1374139.81132995175521.18867004824482
1384737.67775475724649.32224524275364
1393537.7346715188163-2.73467151881626
1404337.49327779218145.50672220781863
1414039.95440378788990.0455962121100531
1424641.37620626313094.62379373686914
1434442.79843348194811.20156651805187
1443538.7045899798496-3.70458997984960
1452939.1506187760422-10.1506187760422







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.8390641465699730.3218717068600540.160935853430027
140.8193166560742140.3613666878515720.180683343925786
150.7180670149272310.5638659701455380.281932985072769
160.613031359273930.7739372814521390.386968640726069
170.9589073488042970.0821853023914070.0410926511957035
180.9511080175727250.09778396485454980.0488919824272749
190.9330899201124930.1338201597750140.0669100798875071
200.9454303566583540.1091392866832920.0545696433416459
210.941539424465690.1169211510686200.0584605755343101
220.9206505352492320.1586989295015350.0793494647507677
230.8869435249260920.2261129501478160.113056475073908
240.8625991003211350.2748017993577310.137400899678865
250.8240051022613820.3519897954772360.175994897738618
260.7713293256986510.4573413486026970.228670674301349
270.794116381253170.411767237493660.20588361874683
280.9375381458552390.1249237082895230.0624618541447614
290.9152647348334490.1694705303331020.084735265166551
300.8912341879200860.2175316241598280.108765812079914
310.8583456970913940.2833086058172120.141654302908606
320.8250064709033060.3499870581933880.174993529096694
330.7820557321689240.4358885356621530.217944267831076
340.7360940257909220.5278119484181550.263905974209078
350.7234670789138390.5530658421723210.276532921086161
360.6973654667596930.6052690664806130.302634533240307
370.7495994730114790.5008010539770430.250400526988521
380.8474033456087990.3051933087824030.152596654391201
390.8240557943834790.3518884112330430.175944205616522
400.853131225657370.2937375486852610.146868774342631
410.8949921641811450.2100156716377110.105007835818855
420.8730341481284990.2539317037430020.126965851871501
430.8438425892251450.3123148215497090.156157410774855
440.809525579821690.3809488403566190.190474420178310
450.7835499683918080.4329000632163830.216450031608192
460.8088927387779140.3822145224441720.191107261222086
470.7705590870615270.4588818258769470.229440912938473
480.7579196336400220.4841607327199550.242080366359978
490.7141839693173650.5716320613652710.285816030682635
500.6904138265130260.6191723469739480.309586173486974
510.7314619720749550.5370760558500910.268538027925045
520.6873981136986810.6252037726026380.312601886301319
530.6522772982136320.6954454035727360.347722701786368
540.6227838693331760.7544322613336480.377216130666824
550.6494015642582130.7011968714835740.350598435741787
560.6329867609644340.7340264780711320.367013239035566
570.734241182432150.53151763513570.26575881756785
580.6915391481249540.6169217037500930.308460851875046
590.7621346071968040.4757307856063920.237865392803196
600.723716260913670.5525674781726610.276283739086331
610.830483416759420.3390331664811610.169516583240581
620.8427350853695160.3145298292609680.157264914630484
630.8374154495015620.3251691009968760.162584550498438
640.8198236331874480.3603527336251040.180176366812552
650.8160226088143320.3679547823713360.183977391185668
660.7957805634124810.4084388731750380.204219436587519
670.7595170732472970.4809658535054050.240482926752703
680.7968271030684540.4063457938630910.203172896931546
690.7608991793958320.4782016412083360.239100820604168
700.7606344098394430.4787311803211130.239365590160557
710.7196200539247410.5607598921505190.280379946075259
720.6797794700078670.6404410599842660.320220529992133
730.6343270481965160.7313459036069680.365672951803484
740.5881486641629470.8237026716741070.411851335837053
750.5827027543460240.8345944913079530.417297245653976
760.5468660053653410.9062679892693180.453133994634659
770.5025697737133420.9948604525733160.497430226286658
780.4952736045251620.9905472090503230.504726395474838
790.4715950643388430.9431901286776870.528404935661157
800.4327708193057750.865541638611550.567229180694225
810.3834739954670740.7669479909341480.616526004532926
820.3415979270815210.6831958541630410.658402072918479
830.3010041443161940.6020082886323890.698995855683806
840.2624012182995690.5248024365991370.737598781700431
850.2449416774081460.4898833548162920.755058322591854
860.2201633460075110.4403266920150220.779836653992489
870.2022121304076270.4044242608152540.797787869592373
880.1678087694837310.3356175389674610.83219123051627
890.1476989934652680.2953979869305360.852301006534732
900.1377937453408050.275587490681610.862206254659195
910.1448131848877040.2896263697754070.855186815112296
920.1171141476102970.2342282952205930.882885852389703
930.1738097695135360.3476195390270710.826190230486464
940.1853867183592740.3707734367185490.814613281640726
950.3335135153568460.6670270307136930.666486484643154
960.3093530624655720.6187061249311440.690646937534428
970.3360117518491410.6720235036982820.663988248150859
980.3207421735997380.6414843471994760.679257826400262
990.3056691045013330.6113382090026650.694330895498667
1000.2628370479497160.5256740958994320.737162952050284
1010.2226783036780280.4453566073560560.777321696321972
1020.2609121271000320.5218242542000630.739087872899968
1030.2229517220823580.4459034441647160.777048277917642
1040.2489347347991440.4978694695982880.751065265200856
1050.2186792822992130.4373585645984270.781320717700787
1060.1876337574516990.3752675149033980.812366242548301
1070.1573810342573940.3147620685147890.842618965742606
1080.1302061665460450.2604123330920900.869793833453955
1090.1067648088754650.2135296177509310.893235191124535
1100.09705816994128780.1941163398825760.902941830058712
1110.2000146292236120.4000292584472230.799985370776388
1120.2036531117418780.4073062234837570.796346888258122
1130.1616465265310790.3232930530621580.838353473468921
1140.1672288402169520.3344576804339050.832771159783048
1150.1539843867287120.3079687734574230.846015613271288
1160.12165313896840.24330627793680.8783468610316
1170.1019130364069280.2038260728138560.898086963593072
1180.1011690607129490.2023381214258980.898830939287051
1190.238924149409370.477848298818740.76107585059063
1200.1923921940713570.3847843881427140.807607805928643
1210.1444733505527840.2889467011055670.855526649447216
1220.1162403211158420.2324806422316840.883759678884158
1230.08548748947992120.1709749789598420.914512510520079
1240.06799908197328510.1359981639465700.932000918026715
1250.04643181766323140.09286363532646290.953568182336769
1260.04000159372829220.08000318745658440.959998406271708
1270.02389821303836530.04779642607673060.976101786961635
1280.01739206475580200.03478412951160390.982607935244198
1290.01427222022099040.02854444044198070.98572777977901
1300.008086736612854690.01617347322570940.991913263387145
1310.02223733873523030.04447467747046060.97776266126477
1320.01018998215575420.02037996431150830.989810017844246

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.839064146569973 & 0.321871706860054 & 0.160935853430027 \tabularnewline
14 & 0.819316656074214 & 0.361366687851572 & 0.180683343925786 \tabularnewline
15 & 0.718067014927231 & 0.563865970145538 & 0.281932985072769 \tabularnewline
16 & 0.61303135927393 & 0.773937281452139 & 0.386968640726069 \tabularnewline
17 & 0.958907348804297 & 0.082185302391407 & 0.0410926511957035 \tabularnewline
18 & 0.951108017572725 & 0.0977839648545498 & 0.0488919824272749 \tabularnewline
19 & 0.933089920112493 & 0.133820159775014 & 0.0669100798875071 \tabularnewline
20 & 0.945430356658354 & 0.109139286683292 & 0.0545696433416459 \tabularnewline
21 & 0.94153942446569 & 0.116921151068620 & 0.0584605755343101 \tabularnewline
22 & 0.920650535249232 & 0.158698929501535 & 0.0793494647507677 \tabularnewline
23 & 0.886943524926092 & 0.226112950147816 & 0.113056475073908 \tabularnewline
24 & 0.862599100321135 & 0.274801799357731 & 0.137400899678865 \tabularnewline
25 & 0.824005102261382 & 0.351989795477236 & 0.175994897738618 \tabularnewline
26 & 0.771329325698651 & 0.457341348602697 & 0.228670674301349 \tabularnewline
27 & 0.79411638125317 & 0.41176723749366 & 0.20588361874683 \tabularnewline
28 & 0.937538145855239 & 0.124923708289523 & 0.0624618541447614 \tabularnewline
29 & 0.915264734833449 & 0.169470530333102 & 0.084735265166551 \tabularnewline
30 & 0.891234187920086 & 0.217531624159828 & 0.108765812079914 \tabularnewline
31 & 0.858345697091394 & 0.283308605817212 & 0.141654302908606 \tabularnewline
32 & 0.825006470903306 & 0.349987058193388 & 0.174993529096694 \tabularnewline
33 & 0.782055732168924 & 0.435888535662153 & 0.217944267831076 \tabularnewline
34 & 0.736094025790922 & 0.527811948418155 & 0.263905974209078 \tabularnewline
35 & 0.723467078913839 & 0.553065842172321 & 0.276532921086161 \tabularnewline
36 & 0.697365466759693 & 0.605269066480613 & 0.302634533240307 \tabularnewline
37 & 0.749599473011479 & 0.500801053977043 & 0.250400526988521 \tabularnewline
38 & 0.847403345608799 & 0.305193308782403 & 0.152596654391201 \tabularnewline
39 & 0.824055794383479 & 0.351888411233043 & 0.175944205616522 \tabularnewline
40 & 0.85313122565737 & 0.293737548685261 & 0.146868774342631 \tabularnewline
41 & 0.894992164181145 & 0.210015671637711 & 0.105007835818855 \tabularnewline
42 & 0.873034148128499 & 0.253931703743002 & 0.126965851871501 \tabularnewline
43 & 0.843842589225145 & 0.312314821549709 & 0.156157410774855 \tabularnewline
44 & 0.80952557982169 & 0.380948840356619 & 0.190474420178310 \tabularnewline
45 & 0.783549968391808 & 0.432900063216383 & 0.216450031608192 \tabularnewline
46 & 0.808892738777914 & 0.382214522444172 & 0.191107261222086 \tabularnewline
47 & 0.770559087061527 & 0.458881825876947 & 0.229440912938473 \tabularnewline
48 & 0.757919633640022 & 0.484160732719955 & 0.242080366359978 \tabularnewline
49 & 0.714183969317365 & 0.571632061365271 & 0.285816030682635 \tabularnewline
50 & 0.690413826513026 & 0.619172346973948 & 0.309586173486974 \tabularnewline
51 & 0.731461972074955 & 0.537076055850091 & 0.268538027925045 \tabularnewline
52 & 0.687398113698681 & 0.625203772602638 & 0.312601886301319 \tabularnewline
53 & 0.652277298213632 & 0.695445403572736 & 0.347722701786368 \tabularnewline
54 & 0.622783869333176 & 0.754432261333648 & 0.377216130666824 \tabularnewline
55 & 0.649401564258213 & 0.701196871483574 & 0.350598435741787 \tabularnewline
56 & 0.632986760964434 & 0.734026478071132 & 0.367013239035566 \tabularnewline
57 & 0.73424118243215 & 0.5315176351357 & 0.26575881756785 \tabularnewline
58 & 0.691539148124954 & 0.616921703750093 & 0.308460851875046 \tabularnewline
59 & 0.762134607196804 & 0.475730785606392 & 0.237865392803196 \tabularnewline
60 & 0.72371626091367 & 0.552567478172661 & 0.276283739086331 \tabularnewline
61 & 0.83048341675942 & 0.339033166481161 & 0.169516583240581 \tabularnewline
62 & 0.842735085369516 & 0.314529829260968 & 0.157264914630484 \tabularnewline
63 & 0.837415449501562 & 0.325169100996876 & 0.162584550498438 \tabularnewline
64 & 0.819823633187448 & 0.360352733625104 & 0.180176366812552 \tabularnewline
65 & 0.816022608814332 & 0.367954782371336 & 0.183977391185668 \tabularnewline
66 & 0.795780563412481 & 0.408438873175038 & 0.204219436587519 \tabularnewline
67 & 0.759517073247297 & 0.480965853505405 & 0.240482926752703 \tabularnewline
68 & 0.796827103068454 & 0.406345793863091 & 0.203172896931546 \tabularnewline
69 & 0.760899179395832 & 0.478201641208336 & 0.239100820604168 \tabularnewline
70 & 0.760634409839443 & 0.478731180321113 & 0.239365590160557 \tabularnewline
71 & 0.719620053924741 & 0.560759892150519 & 0.280379946075259 \tabularnewline
72 & 0.679779470007867 & 0.640441059984266 & 0.320220529992133 \tabularnewline
73 & 0.634327048196516 & 0.731345903606968 & 0.365672951803484 \tabularnewline
74 & 0.588148664162947 & 0.823702671674107 & 0.411851335837053 \tabularnewline
75 & 0.582702754346024 & 0.834594491307953 & 0.417297245653976 \tabularnewline
76 & 0.546866005365341 & 0.906267989269318 & 0.453133994634659 \tabularnewline
77 & 0.502569773713342 & 0.994860452573316 & 0.497430226286658 \tabularnewline
78 & 0.495273604525162 & 0.990547209050323 & 0.504726395474838 \tabularnewline
79 & 0.471595064338843 & 0.943190128677687 & 0.528404935661157 \tabularnewline
80 & 0.432770819305775 & 0.86554163861155 & 0.567229180694225 \tabularnewline
81 & 0.383473995467074 & 0.766947990934148 & 0.616526004532926 \tabularnewline
82 & 0.341597927081521 & 0.683195854163041 & 0.658402072918479 \tabularnewline
83 & 0.301004144316194 & 0.602008288632389 & 0.698995855683806 \tabularnewline
84 & 0.262401218299569 & 0.524802436599137 & 0.737598781700431 \tabularnewline
85 & 0.244941677408146 & 0.489883354816292 & 0.755058322591854 \tabularnewline
86 & 0.220163346007511 & 0.440326692015022 & 0.779836653992489 \tabularnewline
87 & 0.202212130407627 & 0.404424260815254 & 0.797787869592373 \tabularnewline
88 & 0.167808769483731 & 0.335617538967461 & 0.83219123051627 \tabularnewline
89 & 0.147698993465268 & 0.295397986930536 & 0.852301006534732 \tabularnewline
90 & 0.137793745340805 & 0.27558749068161 & 0.862206254659195 \tabularnewline
91 & 0.144813184887704 & 0.289626369775407 & 0.855186815112296 \tabularnewline
92 & 0.117114147610297 & 0.234228295220593 & 0.882885852389703 \tabularnewline
93 & 0.173809769513536 & 0.347619539027071 & 0.826190230486464 \tabularnewline
94 & 0.185386718359274 & 0.370773436718549 & 0.814613281640726 \tabularnewline
95 & 0.333513515356846 & 0.667027030713693 & 0.666486484643154 \tabularnewline
96 & 0.309353062465572 & 0.618706124931144 & 0.690646937534428 \tabularnewline
97 & 0.336011751849141 & 0.672023503698282 & 0.663988248150859 \tabularnewline
98 & 0.320742173599738 & 0.641484347199476 & 0.679257826400262 \tabularnewline
99 & 0.305669104501333 & 0.611338209002665 & 0.694330895498667 \tabularnewline
100 & 0.262837047949716 & 0.525674095899432 & 0.737162952050284 \tabularnewline
101 & 0.222678303678028 & 0.445356607356056 & 0.777321696321972 \tabularnewline
102 & 0.260912127100032 & 0.521824254200063 & 0.739087872899968 \tabularnewline
103 & 0.222951722082358 & 0.445903444164716 & 0.777048277917642 \tabularnewline
104 & 0.248934734799144 & 0.497869469598288 & 0.751065265200856 \tabularnewline
105 & 0.218679282299213 & 0.437358564598427 & 0.781320717700787 \tabularnewline
106 & 0.187633757451699 & 0.375267514903398 & 0.812366242548301 \tabularnewline
107 & 0.157381034257394 & 0.314762068514789 & 0.842618965742606 \tabularnewline
108 & 0.130206166546045 & 0.260412333092090 & 0.869793833453955 \tabularnewline
109 & 0.106764808875465 & 0.213529617750931 & 0.893235191124535 \tabularnewline
110 & 0.0970581699412878 & 0.194116339882576 & 0.902941830058712 \tabularnewline
111 & 0.200014629223612 & 0.400029258447223 & 0.799985370776388 \tabularnewline
112 & 0.203653111741878 & 0.407306223483757 & 0.796346888258122 \tabularnewline
113 & 0.161646526531079 & 0.323293053062158 & 0.838353473468921 \tabularnewline
114 & 0.167228840216952 & 0.334457680433905 & 0.832771159783048 \tabularnewline
115 & 0.153984386728712 & 0.307968773457423 & 0.846015613271288 \tabularnewline
116 & 0.1216531389684 & 0.2433062779368 & 0.8783468610316 \tabularnewline
117 & 0.101913036406928 & 0.203826072813856 & 0.898086963593072 \tabularnewline
118 & 0.101169060712949 & 0.202338121425898 & 0.898830939287051 \tabularnewline
119 & 0.23892414940937 & 0.47784829881874 & 0.76107585059063 \tabularnewline
120 & 0.192392194071357 & 0.384784388142714 & 0.807607805928643 \tabularnewline
121 & 0.144473350552784 & 0.288946701105567 & 0.855526649447216 \tabularnewline
122 & 0.116240321115842 & 0.232480642231684 & 0.883759678884158 \tabularnewline
123 & 0.0854874894799212 & 0.170974978959842 & 0.914512510520079 \tabularnewline
124 & 0.0679990819732851 & 0.135998163946570 & 0.932000918026715 \tabularnewline
125 & 0.0464318176632314 & 0.0928636353264629 & 0.953568182336769 \tabularnewline
126 & 0.0400015937282922 & 0.0800031874565844 & 0.959998406271708 \tabularnewline
127 & 0.0238982130383653 & 0.0477964260767306 & 0.976101786961635 \tabularnewline
128 & 0.0173920647558020 & 0.0347841295116039 & 0.982607935244198 \tabularnewline
129 & 0.0142722202209904 & 0.0285444404419807 & 0.98572777977901 \tabularnewline
130 & 0.00808673661285469 & 0.0161734732257094 & 0.991913263387145 \tabularnewline
131 & 0.0222373387352303 & 0.0444746774704606 & 0.97776266126477 \tabularnewline
132 & 0.0101899821557542 & 0.0203799643115083 & 0.989810017844246 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112064&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]13[/C][C]0.839064146569973[/C][C]0.321871706860054[/C][C]0.160935853430027[/C][/ROW]
[ROW][C]14[/C][C]0.819316656074214[/C][C]0.361366687851572[/C][C]0.180683343925786[/C][/ROW]
[ROW][C]15[/C][C]0.718067014927231[/C][C]0.563865970145538[/C][C]0.281932985072769[/C][/ROW]
[ROW][C]16[/C][C]0.61303135927393[/C][C]0.773937281452139[/C][C]0.386968640726069[/C][/ROW]
[ROW][C]17[/C][C]0.958907348804297[/C][C]0.082185302391407[/C][C]0.0410926511957035[/C][/ROW]
[ROW][C]18[/C][C]0.951108017572725[/C][C]0.0977839648545498[/C][C]0.0488919824272749[/C][/ROW]
[ROW][C]19[/C][C]0.933089920112493[/C][C]0.133820159775014[/C][C]0.0669100798875071[/C][/ROW]
[ROW][C]20[/C][C]0.945430356658354[/C][C]0.109139286683292[/C][C]0.0545696433416459[/C][/ROW]
[ROW][C]21[/C][C]0.94153942446569[/C][C]0.116921151068620[/C][C]0.0584605755343101[/C][/ROW]
[ROW][C]22[/C][C]0.920650535249232[/C][C]0.158698929501535[/C][C]0.0793494647507677[/C][/ROW]
[ROW][C]23[/C][C]0.886943524926092[/C][C]0.226112950147816[/C][C]0.113056475073908[/C][/ROW]
[ROW][C]24[/C][C]0.862599100321135[/C][C]0.274801799357731[/C][C]0.137400899678865[/C][/ROW]
[ROW][C]25[/C][C]0.824005102261382[/C][C]0.351989795477236[/C][C]0.175994897738618[/C][/ROW]
[ROW][C]26[/C][C]0.771329325698651[/C][C]0.457341348602697[/C][C]0.228670674301349[/C][/ROW]
[ROW][C]27[/C][C]0.79411638125317[/C][C]0.41176723749366[/C][C]0.20588361874683[/C][/ROW]
[ROW][C]28[/C][C]0.937538145855239[/C][C]0.124923708289523[/C][C]0.0624618541447614[/C][/ROW]
[ROW][C]29[/C][C]0.915264734833449[/C][C]0.169470530333102[/C][C]0.084735265166551[/C][/ROW]
[ROW][C]30[/C][C]0.891234187920086[/C][C]0.217531624159828[/C][C]0.108765812079914[/C][/ROW]
[ROW][C]31[/C][C]0.858345697091394[/C][C]0.283308605817212[/C][C]0.141654302908606[/C][/ROW]
[ROW][C]32[/C][C]0.825006470903306[/C][C]0.349987058193388[/C][C]0.174993529096694[/C][/ROW]
[ROW][C]33[/C][C]0.782055732168924[/C][C]0.435888535662153[/C][C]0.217944267831076[/C][/ROW]
[ROW][C]34[/C][C]0.736094025790922[/C][C]0.527811948418155[/C][C]0.263905974209078[/C][/ROW]
[ROW][C]35[/C][C]0.723467078913839[/C][C]0.553065842172321[/C][C]0.276532921086161[/C][/ROW]
[ROW][C]36[/C][C]0.697365466759693[/C][C]0.605269066480613[/C][C]0.302634533240307[/C][/ROW]
[ROW][C]37[/C][C]0.749599473011479[/C][C]0.500801053977043[/C][C]0.250400526988521[/C][/ROW]
[ROW][C]38[/C][C]0.847403345608799[/C][C]0.305193308782403[/C][C]0.152596654391201[/C][/ROW]
[ROW][C]39[/C][C]0.824055794383479[/C][C]0.351888411233043[/C][C]0.175944205616522[/C][/ROW]
[ROW][C]40[/C][C]0.85313122565737[/C][C]0.293737548685261[/C][C]0.146868774342631[/C][/ROW]
[ROW][C]41[/C][C]0.894992164181145[/C][C]0.210015671637711[/C][C]0.105007835818855[/C][/ROW]
[ROW][C]42[/C][C]0.873034148128499[/C][C]0.253931703743002[/C][C]0.126965851871501[/C][/ROW]
[ROW][C]43[/C][C]0.843842589225145[/C][C]0.312314821549709[/C][C]0.156157410774855[/C][/ROW]
[ROW][C]44[/C][C]0.80952557982169[/C][C]0.380948840356619[/C][C]0.190474420178310[/C][/ROW]
[ROW][C]45[/C][C]0.783549968391808[/C][C]0.432900063216383[/C][C]0.216450031608192[/C][/ROW]
[ROW][C]46[/C][C]0.808892738777914[/C][C]0.382214522444172[/C][C]0.191107261222086[/C][/ROW]
[ROW][C]47[/C][C]0.770559087061527[/C][C]0.458881825876947[/C][C]0.229440912938473[/C][/ROW]
[ROW][C]48[/C][C]0.757919633640022[/C][C]0.484160732719955[/C][C]0.242080366359978[/C][/ROW]
[ROW][C]49[/C][C]0.714183969317365[/C][C]0.571632061365271[/C][C]0.285816030682635[/C][/ROW]
[ROW][C]50[/C][C]0.690413826513026[/C][C]0.619172346973948[/C][C]0.309586173486974[/C][/ROW]
[ROW][C]51[/C][C]0.731461972074955[/C][C]0.537076055850091[/C][C]0.268538027925045[/C][/ROW]
[ROW][C]52[/C][C]0.687398113698681[/C][C]0.625203772602638[/C][C]0.312601886301319[/C][/ROW]
[ROW][C]53[/C][C]0.652277298213632[/C][C]0.695445403572736[/C][C]0.347722701786368[/C][/ROW]
[ROW][C]54[/C][C]0.622783869333176[/C][C]0.754432261333648[/C][C]0.377216130666824[/C][/ROW]
[ROW][C]55[/C][C]0.649401564258213[/C][C]0.701196871483574[/C][C]0.350598435741787[/C][/ROW]
[ROW][C]56[/C][C]0.632986760964434[/C][C]0.734026478071132[/C][C]0.367013239035566[/C][/ROW]
[ROW][C]57[/C][C]0.73424118243215[/C][C]0.5315176351357[/C][C]0.26575881756785[/C][/ROW]
[ROW][C]58[/C][C]0.691539148124954[/C][C]0.616921703750093[/C][C]0.308460851875046[/C][/ROW]
[ROW][C]59[/C][C]0.762134607196804[/C][C]0.475730785606392[/C][C]0.237865392803196[/C][/ROW]
[ROW][C]60[/C][C]0.72371626091367[/C][C]0.552567478172661[/C][C]0.276283739086331[/C][/ROW]
[ROW][C]61[/C][C]0.83048341675942[/C][C]0.339033166481161[/C][C]0.169516583240581[/C][/ROW]
[ROW][C]62[/C][C]0.842735085369516[/C][C]0.314529829260968[/C][C]0.157264914630484[/C][/ROW]
[ROW][C]63[/C][C]0.837415449501562[/C][C]0.325169100996876[/C][C]0.162584550498438[/C][/ROW]
[ROW][C]64[/C][C]0.819823633187448[/C][C]0.360352733625104[/C][C]0.180176366812552[/C][/ROW]
[ROW][C]65[/C][C]0.816022608814332[/C][C]0.367954782371336[/C][C]0.183977391185668[/C][/ROW]
[ROW][C]66[/C][C]0.795780563412481[/C][C]0.408438873175038[/C][C]0.204219436587519[/C][/ROW]
[ROW][C]67[/C][C]0.759517073247297[/C][C]0.480965853505405[/C][C]0.240482926752703[/C][/ROW]
[ROW][C]68[/C][C]0.796827103068454[/C][C]0.406345793863091[/C][C]0.203172896931546[/C][/ROW]
[ROW][C]69[/C][C]0.760899179395832[/C][C]0.478201641208336[/C][C]0.239100820604168[/C][/ROW]
[ROW][C]70[/C][C]0.760634409839443[/C][C]0.478731180321113[/C][C]0.239365590160557[/C][/ROW]
[ROW][C]71[/C][C]0.719620053924741[/C][C]0.560759892150519[/C][C]0.280379946075259[/C][/ROW]
[ROW][C]72[/C][C]0.679779470007867[/C][C]0.640441059984266[/C][C]0.320220529992133[/C][/ROW]
[ROW][C]73[/C][C]0.634327048196516[/C][C]0.731345903606968[/C][C]0.365672951803484[/C][/ROW]
[ROW][C]74[/C][C]0.588148664162947[/C][C]0.823702671674107[/C][C]0.411851335837053[/C][/ROW]
[ROW][C]75[/C][C]0.582702754346024[/C][C]0.834594491307953[/C][C]0.417297245653976[/C][/ROW]
[ROW][C]76[/C][C]0.546866005365341[/C][C]0.906267989269318[/C][C]0.453133994634659[/C][/ROW]
[ROW][C]77[/C][C]0.502569773713342[/C][C]0.994860452573316[/C][C]0.497430226286658[/C][/ROW]
[ROW][C]78[/C][C]0.495273604525162[/C][C]0.990547209050323[/C][C]0.504726395474838[/C][/ROW]
[ROW][C]79[/C][C]0.471595064338843[/C][C]0.943190128677687[/C][C]0.528404935661157[/C][/ROW]
[ROW][C]80[/C][C]0.432770819305775[/C][C]0.86554163861155[/C][C]0.567229180694225[/C][/ROW]
[ROW][C]81[/C][C]0.383473995467074[/C][C]0.766947990934148[/C][C]0.616526004532926[/C][/ROW]
[ROW][C]82[/C][C]0.341597927081521[/C][C]0.683195854163041[/C][C]0.658402072918479[/C][/ROW]
[ROW][C]83[/C][C]0.301004144316194[/C][C]0.602008288632389[/C][C]0.698995855683806[/C][/ROW]
[ROW][C]84[/C][C]0.262401218299569[/C][C]0.524802436599137[/C][C]0.737598781700431[/C][/ROW]
[ROW][C]85[/C][C]0.244941677408146[/C][C]0.489883354816292[/C][C]0.755058322591854[/C][/ROW]
[ROW][C]86[/C][C]0.220163346007511[/C][C]0.440326692015022[/C][C]0.779836653992489[/C][/ROW]
[ROW][C]87[/C][C]0.202212130407627[/C][C]0.404424260815254[/C][C]0.797787869592373[/C][/ROW]
[ROW][C]88[/C][C]0.167808769483731[/C][C]0.335617538967461[/C][C]0.83219123051627[/C][/ROW]
[ROW][C]89[/C][C]0.147698993465268[/C][C]0.295397986930536[/C][C]0.852301006534732[/C][/ROW]
[ROW][C]90[/C][C]0.137793745340805[/C][C]0.27558749068161[/C][C]0.862206254659195[/C][/ROW]
[ROW][C]91[/C][C]0.144813184887704[/C][C]0.289626369775407[/C][C]0.855186815112296[/C][/ROW]
[ROW][C]92[/C][C]0.117114147610297[/C][C]0.234228295220593[/C][C]0.882885852389703[/C][/ROW]
[ROW][C]93[/C][C]0.173809769513536[/C][C]0.347619539027071[/C][C]0.826190230486464[/C][/ROW]
[ROW][C]94[/C][C]0.185386718359274[/C][C]0.370773436718549[/C][C]0.814613281640726[/C][/ROW]
[ROW][C]95[/C][C]0.333513515356846[/C][C]0.667027030713693[/C][C]0.666486484643154[/C][/ROW]
[ROW][C]96[/C][C]0.309353062465572[/C][C]0.618706124931144[/C][C]0.690646937534428[/C][/ROW]
[ROW][C]97[/C][C]0.336011751849141[/C][C]0.672023503698282[/C][C]0.663988248150859[/C][/ROW]
[ROW][C]98[/C][C]0.320742173599738[/C][C]0.641484347199476[/C][C]0.679257826400262[/C][/ROW]
[ROW][C]99[/C][C]0.305669104501333[/C][C]0.611338209002665[/C][C]0.694330895498667[/C][/ROW]
[ROW][C]100[/C][C]0.262837047949716[/C][C]0.525674095899432[/C][C]0.737162952050284[/C][/ROW]
[ROW][C]101[/C][C]0.222678303678028[/C][C]0.445356607356056[/C][C]0.777321696321972[/C][/ROW]
[ROW][C]102[/C][C]0.260912127100032[/C][C]0.521824254200063[/C][C]0.739087872899968[/C][/ROW]
[ROW][C]103[/C][C]0.222951722082358[/C][C]0.445903444164716[/C][C]0.777048277917642[/C][/ROW]
[ROW][C]104[/C][C]0.248934734799144[/C][C]0.497869469598288[/C][C]0.751065265200856[/C][/ROW]
[ROW][C]105[/C][C]0.218679282299213[/C][C]0.437358564598427[/C][C]0.781320717700787[/C][/ROW]
[ROW][C]106[/C][C]0.187633757451699[/C][C]0.375267514903398[/C][C]0.812366242548301[/C][/ROW]
[ROW][C]107[/C][C]0.157381034257394[/C][C]0.314762068514789[/C][C]0.842618965742606[/C][/ROW]
[ROW][C]108[/C][C]0.130206166546045[/C][C]0.260412333092090[/C][C]0.869793833453955[/C][/ROW]
[ROW][C]109[/C][C]0.106764808875465[/C][C]0.213529617750931[/C][C]0.893235191124535[/C][/ROW]
[ROW][C]110[/C][C]0.0970581699412878[/C][C]0.194116339882576[/C][C]0.902941830058712[/C][/ROW]
[ROW][C]111[/C][C]0.200014629223612[/C][C]0.400029258447223[/C][C]0.799985370776388[/C][/ROW]
[ROW][C]112[/C][C]0.203653111741878[/C][C]0.407306223483757[/C][C]0.796346888258122[/C][/ROW]
[ROW][C]113[/C][C]0.161646526531079[/C][C]0.323293053062158[/C][C]0.838353473468921[/C][/ROW]
[ROW][C]114[/C][C]0.167228840216952[/C][C]0.334457680433905[/C][C]0.832771159783048[/C][/ROW]
[ROW][C]115[/C][C]0.153984386728712[/C][C]0.307968773457423[/C][C]0.846015613271288[/C][/ROW]
[ROW][C]116[/C][C]0.1216531389684[/C][C]0.2433062779368[/C][C]0.8783468610316[/C][/ROW]
[ROW][C]117[/C][C]0.101913036406928[/C][C]0.203826072813856[/C][C]0.898086963593072[/C][/ROW]
[ROW][C]118[/C][C]0.101169060712949[/C][C]0.202338121425898[/C][C]0.898830939287051[/C][/ROW]
[ROW][C]119[/C][C]0.23892414940937[/C][C]0.47784829881874[/C][C]0.76107585059063[/C][/ROW]
[ROW][C]120[/C][C]0.192392194071357[/C][C]0.384784388142714[/C][C]0.807607805928643[/C][/ROW]
[ROW][C]121[/C][C]0.144473350552784[/C][C]0.288946701105567[/C][C]0.855526649447216[/C][/ROW]
[ROW][C]122[/C][C]0.116240321115842[/C][C]0.232480642231684[/C][C]0.883759678884158[/C][/ROW]
[ROW][C]123[/C][C]0.0854874894799212[/C][C]0.170974978959842[/C][C]0.914512510520079[/C][/ROW]
[ROW][C]124[/C][C]0.0679990819732851[/C][C]0.135998163946570[/C][C]0.932000918026715[/C][/ROW]
[ROW][C]125[/C][C]0.0464318176632314[/C][C]0.0928636353264629[/C][C]0.953568182336769[/C][/ROW]
[ROW][C]126[/C][C]0.0400015937282922[/C][C]0.0800031874565844[/C][C]0.959998406271708[/C][/ROW]
[ROW][C]127[/C][C]0.0238982130383653[/C][C]0.0477964260767306[/C][C]0.976101786961635[/C][/ROW]
[ROW][C]128[/C][C]0.0173920647558020[/C][C]0.0347841295116039[/C][C]0.982607935244198[/C][/ROW]
[ROW][C]129[/C][C]0.0142722202209904[/C][C]0.0285444404419807[/C][C]0.98572777977901[/C][/ROW]
[ROW][C]130[/C][C]0.00808673661285469[/C][C]0.0161734732257094[/C][C]0.991913263387145[/C][/ROW]
[ROW][C]131[/C][C]0.0222373387352303[/C][C]0.0444746774704606[/C][C]0.97776266126477[/C][/ROW]
[ROW][C]132[/C][C]0.0101899821557542[/C][C]0.0203799643115083[/C][C]0.989810017844246[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112064&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112064&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.8390641465699730.3218717068600540.160935853430027
140.8193166560742140.3613666878515720.180683343925786
150.7180670149272310.5638659701455380.281932985072769
160.613031359273930.7739372814521390.386968640726069
170.9589073488042970.0821853023914070.0410926511957035
180.9511080175727250.09778396485454980.0488919824272749
190.9330899201124930.1338201597750140.0669100798875071
200.9454303566583540.1091392866832920.0545696433416459
210.941539424465690.1169211510686200.0584605755343101
220.9206505352492320.1586989295015350.0793494647507677
230.8869435249260920.2261129501478160.113056475073908
240.8625991003211350.2748017993577310.137400899678865
250.8240051022613820.3519897954772360.175994897738618
260.7713293256986510.4573413486026970.228670674301349
270.794116381253170.411767237493660.20588361874683
280.9375381458552390.1249237082895230.0624618541447614
290.9152647348334490.1694705303331020.084735265166551
300.8912341879200860.2175316241598280.108765812079914
310.8583456970913940.2833086058172120.141654302908606
320.8250064709033060.3499870581933880.174993529096694
330.7820557321689240.4358885356621530.217944267831076
340.7360940257909220.5278119484181550.263905974209078
350.7234670789138390.5530658421723210.276532921086161
360.6973654667596930.6052690664806130.302634533240307
370.7495994730114790.5008010539770430.250400526988521
380.8474033456087990.3051933087824030.152596654391201
390.8240557943834790.3518884112330430.175944205616522
400.853131225657370.2937375486852610.146868774342631
410.8949921641811450.2100156716377110.105007835818855
420.8730341481284990.2539317037430020.126965851871501
430.8438425892251450.3123148215497090.156157410774855
440.809525579821690.3809488403566190.190474420178310
450.7835499683918080.4329000632163830.216450031608192
460.8088927387779140.3822145224441720.191107261222086
470.7705590870615270.4588818258769470.229440912938473
480.7579196336400220.4841607327199550.242080366359978
490.7141839693173650.5716320613652710.285816030682635
500.6904138265130260.6191723469739480.309586173486974
510.7314619720749550.5370760558500910.268538027925045
520.6873981136986810.6252037726026380.312601886301319
530.6522772982136320.6954454035727360.347722701786368
540.6227838693331760.7544322613336480.377216130666824
550.6494015642582130.7011968714835740.350598435741787
560.6329867609644340.7340264780711320.367013239035566
570.734241182432150.53151763513570.26575881756785
580.6915391481249540.6169217037500930.308460851875046
590.7621346071968040.4757307856063920.237865392803196
600.723716260913670.5525674781726610.276283739086331
610.830483416759420.3390331664811610.169516583240581
620.8427350853695160.3145298292609680.157264914630484
630.8374154495015620.3251691009968760.162584550498438
640.8198236331874480.3603527336251040.180176366812552
650.8160226088143320.3679547823713360.183977391185668
660.7957805634124810.4084388731750380.204219436587519
670.7595170732472970.4809658535054050.240482926752703
680.7968271030684540.4063457938630910.203172896931546
690.7608991793958320.4782016412083360.239100820604168
700.7606344098394430.4787311803211130.239365590160557
710.7196200539247410.5607598921505190.280379946075259
720.6797794700078670.6404410599842660.320220529992133
730.6343270481965160.7313459036069680.365672951803484
740.5881486641629470.8237026716741070.411851335837053
750.5827027543460240.8345944913079530.417297245653976
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770.5025697737133420.9948604525733160.497430226286658
780.4952736045251620.9905472090503230.504726395474838
790.4715950643388430.9431901286776870.528404935661157
800.4327708193057750.865541638611550.567229180694225
810.3834739954670740.7669479909341480.616526004532926
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830.3010041443161940.6020082886323890.698995855683806
840.2624012182995690.5248024365991370.737598781700431
850.2449416774081460.4898833548162920.755058322591854
860.2201633460075110.4403266920150220.779836653992489
870.2022121304076270.4044242608152540.797787869592373
880.1678087694837310.3356175389674610.83219123051627
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900.1377937453408050.275587490681610.862206254659195
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940.1853867183592740.3707734367185490.814613281640726
950.3335135153568460.6670270307136930.666486484643154
960.3093530624655720.6187061249311440.690646937534428
970.3360117518491410.6720235036982820.663988248150859
980.3207421735997380.6414843471994760.679257826400262
990.3056691045013330.6113382090026650.694330895498667
1000.2628370479497160.5256740958994320.737162952050284
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1100.09705816994128780.1941163398825760.902941830058712
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1310.02223733873523030.04447467747046060.97776266126477
1320.01018998215575420.02037996431150830.989810017844246







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level60.05NOK
10% type I error level100.0833333333333333OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 6 & 0.05 & NOK \tabularnewline
10% type I error level & 10 & 0.0833333333333333 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112064&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.05[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.0833333333333333[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112064&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112064&T=6

As an alternative you can also use a 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 level00OK
5% type I error level60.05NOK
10% type I error level100.0833333333333333OK



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