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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 11:30:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356107444fk3xebz8xff0hjr.htm/, Retrieved Fri, 29 Mar 2024 14:02:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203915, Retrieved Fri, 29 Mar 2024 14:02:37 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [MR T20 ] [2012-12-19 21:52:40] [a87a0df67d47b041e4d219678c935cd7]
- R     [Multiple Regression] [multiple regressi...] [2012-12-21 14:05:31] [596b77fae3c3637cb35f2a3968c5e5d1]
-           [Multiple Regression] [Paper deel 5 T40 ...] [2012-12-21 16:30:18] [fc11b595ce1e8c0e30ce0084e7aafbef] [Current]
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Dataseries X:
1	0	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
1	0	0	0	1	1
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	0
1	0	0	0	0	0
0	0	0	0	0	0
0	0	1	0	1	0
1	0	0	0	0	0
0	0	1	0	1	1
0	0	1	0	1	1
1	0	1	1	1	0
1	0	0	0	0	0
0	0	0	0	0	1
0	0	1	1	1	1
1	0	0	0	1	0
1	0	1	0	1	1
0	0	0	0	1	1
1	0	0	0	1	1
0	0	1	0	0	1
0	0	1	0	1	0
1	0	0	0	0	1
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0	0	0	0	0	1
0	0	0	0	1	0
0	0	0	0	0	0
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1	0	0	0	1	0
0	0	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
1	0	1	0	1	0
0	0	1	0	0	1
0	0	0	0	1	1
0	0	0	0	1	0
0	0	1	1	1	1
0	0	1	0	0	1
1	0	0	0	1	1
1	0	0	0	0	0
0	0	0	0	1	0
0	0	0	0	1	1
0	0	0	0	0	0
0	0	0	0	0	1
0	0	0	0	1	1
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1	0	1	1	1	0
0	0	0	0	0	1
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1	1	0	0	0	0
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1	0	1	1	1	0
1	0	1	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 15 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203915&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203915&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203915&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 time15 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
ca[t] = + 0.0129459729750044 + 0.0115146695582435UseLim[t] -0.162614333886684T20[t] + 0.278287880707321used[t] + 0.0447956642870392Useful[t] -0.0354843689768406Outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ca[t] =  +  0.0129459729750044 +  0.0115146695582435UseLim[t] -0.162614333886684T20[t] +  0.278287880707321used[t] +  0.0447956642870392Useful[t] -0.0354843689768406Outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203915&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ca[t] =  +  0.0129459729750044 +  0.0115146695582435UseLim[t] -0.162614333886684T20[t] +  0.278287880707321used[t] +  0.0447956642870392Useful[t] -0.0354843689768406Outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203915&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203915&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
ca[t] = + 0.0129459729750044 + 0.0115146695582435UseLim[t] -0.162614333886684T20[t] + 0.278287880707321used[t] + 0.0447956642870392Useful[t] -0.0354843689768406Outcome[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.01294597297500440.031290.41370.6796590.339829
UseLim0.01151466955824350.0413710.27830.7811480.390574
T20-0.1626143338866840.063615-2.55620.0115890.005794
used0.2782878807073210.0448556.204200
Useful0.04479566428703920.045920.97550.3308990.165449
Outcome-0.03548436897684060.040089-0.88510.3775150.188757

\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) & 0.0129459729750044 & 0.03129 & 0.4137 & 0.679659 & 0.339829 \tabularnewline
UseLim & 0.0115146695582435 & 0.041371 & 0.2783 & 0.781148 & 0.390574 \tabularnewline
T20 & -0.162614333886684 & 0.063615 & -2.5562 & 0.011589 & 0.005794 \tabularnewline
used & 0.278287880707321 & 0.044855 & 6.2042 & 0 & 0 \tabularnewline
Useful & 0.0447956642870392 & 0.04592 & 0.9755 & 0.330899 & 0.165449 \tabularnewline
Outcome & -0.0354843689768406 & 0.040089 & -0.8851 & 0.377515 & 0.188757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203915&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]0.0129459729750044[/C][C]0.03129[/C][C]0.4137[/C][C]0.679659[/C][C]0.339829[/C][/ROW]
[ROW][C]UseLim[/C][C]0.0115146695582435[/C][C]0.041371[/C][C]0.2783[/C][C]0.781148[/C][C]0.390574[/C][/ROW]
[ROW][C]T20[/C][C]-0.162614333886684[/C][C]0.063615[/C][C]-2.5562[/C][C]0.011589[/C][C]0.005794[/C][/ROW]
[ROW][C]used[/C][C]0.278287880707321[/C][C]0.044855[/C][C]6.2042[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Useful[/C][C]0.0447956642870392[/C][C]0.04592[/C][C]0.9755[/C][C]0.330899[/C][C]0.165449[/C][/ROW]
[ROW][C]Outcome[/C][C]-0.0354843689768406[/C][C]0.040089[/C][C]-0.8851[/C][C]0.377515[/C][C]0.188757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203915&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203915&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)0.01294597297500440.031290.41370.6796590.339829
UseLim0.01151466955824350.0413710.27830.7811480.390574
T20-0.1626143338866840.063615-2.55620.0115890.005794
used0.2782878807073210.0448556.204200
Useful0.04479566428703920.045920.97550.3308990.165449
Outcome-0.03548436897684060.040089-0.88510.3775150.188757







Multiple Linear Regression - Regression Statistics
Multiple R0.498844450594095
R-squared0.248845785888525
Adjusted R-squared0.223468954330704
F-TEST (value)9.80602268338895
F-TEST (DF numerator)5
F-TEST (DF denominator)148
p-value4.12008822614496e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.236978055246082
Sum Squared Residuals8.31147260289581

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.498844450594095 \tabularnewline
R-squared & 0.248845785888525 \tabularnewline
Adjusted R-squared & 0.223468954330704 \tabularnewline
F-TEST (value) & 9.80602268338895 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 148 \tabularnewline
p-value & 4.12008822614496e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.236978055246082 \tabularnewline
Sum Squared Residuals & 8.31147260289581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203915&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.498844450594095[/C][/ROW]
[ROW][C]R-squared[/C][C]0.248845785888525[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.223468954330704[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.80602268338895[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]148[/C][/ROW]
[ROW][C]p-value[/C][C]4.12008822614496e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.236978055246082[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8.31147260289581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203915&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203915&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 R0.498844450594095
R-squared0.248845785888525
Adjusted R-squared0.223468954330704
F-TEST (value)9.80602268338895
F-TEST (DF numerator)5
F-TEST (DF denominator)148
p-value4.12008822614496e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.236978055246082
Sum Squared Residuals8.31147260289581







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10-0.01102372644359260.0110237264435926
200.0129459729750045-0.0129459729750045
300.0129459729750043-0.0129459729750043
400.0129459729750042-0.0129459729750042
500.0129459729750044-0.0129459729750044
600.0337719378434466-0.0337719378434466
700.0129459729750044-0.0129459729750044
800.0129459729750044-0.0129459729750044
90-0.02253839600183620.0225383960018362
1000.0244606425332479-0.0244606425332479
1100.0244606425332479-0.0244606425332479
1200.0129459729750044-0.0129459729750044
1300.336029517969365-0.336029517969365
1400.0244606425332479-0.0244606425332479
1500.300545148992524-0.300545148992524
1600.300545148992524-0.300545148992524
1710.3475441875276080.652455812472392
1800.0244606425332479-0.0244606425332479
190-0.02253839600183620.0225383960018362
2010.3005451489925240.699454851007476
2100.0692563068202871-0.0692563068202871
2200.312059818550767-0.312059818550767
2300.0222572682852031-0.0222572682852031
2400.0337719378434466-0.0337719378434466
2500.255749484705485-0.255749484705485
2600.336029517969365-0.336029517969365
270-0.01102372644359270.0110237264435927
2800.291233853682325-0.291233853682325
290-0.02253839600183620.0225383960018362
3000.0577416372620436-0.0577416372620436
3100.0129459729750044-0.0129459729750044
3200.0244606425332479-0.0244606425332479
3300.0692563068202871-0.0692563068202871
340-0.02253839600183620.0225383960018362
3500.0129459729750044-0.0129459729750044
3600.0129459729750044-0.0129459729750044
3700.347544187527608-0.347544187527608
3800.255749484705485-0.255749484705485
3900.0222572682852031-0.0222572682852031
4000.0577416372620436-0.0577416372620436
4110.3005451489925240.699454851007476
4200.255749484705485-0.255749484705485
4300.0337719378434466-0.0337719378434466
4400.0244606425332479-0.0244606425332479
4500.0577416372620436-0.0577416372620436
4600.0222572682852031-0.0222572682852031
4700.0129459729750044-0.0129459729750044
480-0.02253839600183620.0225383960018362
4900.0222572682852031-0.0222572682852031
5000.0129459729750044-0.0129459729750044
5100.291233853682325-0.291233853682325
5210.3475441875276080.652455812472392
530-0.02253839600183620.0225383960018362
5410.2912338536823250.708766146317675
5500.0129459729750044-0.0129459729750044
5600.255749484705485-0.255749484705485
5700.300545148992524-0.300545148992524
580-0.02253839600183620.0225383960018362
590-0.02253839600183620.0225383960018362
6010.3120598185507680.687940181449232
610-0.01102372644359270.0110237264435927
6200.336029517969365-0.336029517969365
6300.0129459729750044-0.0129459729750044
640-0.01102372644359270.0110237264435927
6500.0129459729750044-0.0129459729750044
6600.0129459729750044-0.0129459729750044
6710.3360295179693650.663970482030635
6800.0244606425332479-0.0244606425332479
690-0.02253839600183620.0225383960018362
7000.291233853682325-0.291233853682325
7100.0129459729750044-0.0129459729750044
720-0.02253839600183620.0225383960018362
7300.255749484705485-0.255749484705485
7400.302748523240569-0.302748523240569
750-0.02253839600183620.0225383960018362
7600.0222572682852031-0.0222572682852031
770-0.02253839600183620.0225383960018362
7800.300545148992524-0.300545148992524
7910.2557494847054850.744250515294515
8000.0577416372620436-0.0577416372620436
8100.0129459729750044-0.0129459729750044
8200.267264154263728-0.267264154263728
8300.0129459729750044-0.0129459729750044
8410.2912338536823250.708766146317675
8500.0222572682852031-0.0222572682852031
8600.0244606425332479-0.0244606425332479
870-0.01102372644359270.0110237264435927
8800.104649820377044-0.104649820377044
8900.0129459729750044-0.0129459729750044
900-0.02253839600183620.0225383960018362
9100.0577416372620436-0.0577416372620436
920-0.1381536913534360.138153691353436
9300.0692563068202871-0.0692563068202871
9400.0129459729750044-0.0129459729750044
950-0.149668360911680.14966836091168
960-0.02253839600183620.0225383960018362
970-0.1381536913534360.138153691353436
9800.0129459729750044-0.0129459729750044
9900.0244606425332479-0.0244606425332479
1000-0.02253839600183620.0225383960018362
1010-0.01102372644359270.0110237264435927
10200.0129459729750044-0.0129459729750044
10300.0129459729750044-0.0129459729750044
10400.0129459729750044-0.0129459729750044
10500.128619519795641-0.128619519795641
10600.0129459729750044-0.0129459729750044
10700.0129459729750044-0.0129459729750044
10800.140134189353885-0.140134189353885
10900.0129459729750044-0.0129459729750044
11000.0244606425332479-0.0244606425332479
11100.184929853640924-0.184929853640924
1120-0.149668360911680.14966836091168
11300.291233853682325-0.291233853682325
11400.140134189353885-0.140134189353885
11500.0244606425332479-0.0244606425332479
11600.0129459729750044-0.0129459729750044
1170-0.01102372644359270.0110237264435927
11800.0244606425332479-0.0244606425332479
11900.0129459729750044-0.0129459729750044
1200-0.02253839600183620.0225383960018362
12100.0244606425332479-0.0244606425332479
12200.0129459729750044-0.0129459729750044
12300.140134189353885-0.140134189353885
12400.300545148992524-0.300545148992524
1250-0.02253839600183620.0225383960018362
1260-0.149668360911680.14966836091168
12700.0577416372620436-0.0577416372620436
1280-0.02253839600183620.0225383960018362
12900.0129459729750044-0.0129459729750044
1300-0.02253839600183620.0225383960018362
13100.0244606425332479-0.0244606425332479
1320-0.01102372644359270.0110237264435927
13300.302748523240569-0.302748523240569
13400.0129459729750044-0.0129459729750044
13500.0129459729750044-0.0129459729750044
13600.0129459729750044-0.0129459729750044
13700.312059818550767-0.312059818550767
13800.149445484664084-0.149445484664084
1390-0.149668360911680.14966836091168
14000.0129459729750044-0.0129459729750044
14110.2557494847054850.744250515294515
14200.0931351508188008-0.0931351508188008
14300.0244606425332479-0.0244606425332479
14400.0222572682852031-0.0222572682852031
14500.0577416372620436-0.0577416372620436
1460-0.185152729888520.18515272988852
14700.128619519795641-0.128619519795641
1480-0.149668360911680.14966836091168
14900.0244606425332479-0.0244606425332479
15000.0222572682852031-0.0222572682852031
1510-0.02253839600183620.0225383960018362
15210.3027485232405690.697251476759431
15310.3475441875276080.652455812472392
15400.302748523240569-0.302748523240569

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & -0.0110237264435926 & 0.0110237264435926 \tabularnewline
2 & 0 & 0.0129459729750045 & -0.0129459729750045 \tabularnewline
3 & 0 & 0.0129459729750043 & -0.0129459729750043 \tabularnewline
4 & 0 & 0.0129459729750042 & -0.0129459729750042 \tabularnewline
5 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
6 & 0 & 0.0337719378434466 & -0.0337719378434466 \tabularnewline
7 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
8 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
9 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
10 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
11 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
12 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
13 & 0 & 0.336029517969365 & -0.336029517969365 \tabularnewline
14 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
15 & 0 & 0.300545148992524 & -0.300545148992524 \tabularnewline
16 & 0 & 0.300545148992524 & -0.300545148992524 \tabularnewline
17 & 1 & 0.347544187527608 & 0.652455812472392 \tabularnewline
18 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
19 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
20 & 1 & 0.300545148992524 & 0.699454851007476 \tabularnewline
21 & 0 & 0.0692563068202871 & -0.0692563068202871 \tabularnewline
22 & 0 & 0.312059818550767 & -0.312059818550767 \tabularnewline
23 & 0 & 0.0222572682852031 & -0.0222572682852031 \tabularnewline
24 & 0 & 0.0337719378434466 & -0.0337719378434466 \tabularnewline
25 & 0 & 0.255749484705485 & -0.255749484705485 \tabularnewline
26 & 0 & 0.336029517969365 & -0.336029517969365 \tabularnewline
27 & 0 & -0.0110237264435927 & 0.0110237264435927 \tabularnewline
28 & 0 & 0.291233853682325 & -0.291233853682325 \tabularnewline
29 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
30 & 0 & 0.0577416372620436 & -0.0577416372620436 \tabularnewline
31 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
32 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
33 & 0 & 0.0692563068202871 & -0.0692563068202871 \tabularnewline
34 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
35 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
36 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
37 & 0 & 0.347544187527608 & -0.347544187527608 \tabularnewline
38 & 0 & 0.255749484705485 & -0.255749484705485 \tabularnewline
39 & 0 & 0.0222572682852031 & -0.0222572682852031 \tabularnewline
40 & 0 & 0.0577416372620436 & -0.0577416372620436 \tabularnewline
41 & 1 & 0.300545148992524 & 0.699454851007476 \tabularnewline
42 & 0 & 0.255749484705485 & -0.255749484705485 \tabularnewline
43 & 0 & 0.0337719378434466 & -0.0337719378434466 \tabularnewline
44 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
45 & 0 & 0.0577416372620436 & -0.0577416372620436 \tabularnewline
46 & 0 & 0.0222572682852031 & -0.0222572682852031 \tabularnewline
47 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
48 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
49 & 0 & 0.0222572682852031 & -0.0222572682852031 \tabularnewline
50 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
51 & 0 & 0.291233853682325 & -0.291233853682325 \tabularnewline
52 & 1 & 0.347544187527608 & 0.652455812472392 \tabularnewline
53 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
54 & 1 & 0.291233853682325 & 0.708766146317675 \tabularnewline
55 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
56 & 0 & 0.255749484705485 & -0.255749484705485 \tabularnewline
57 & 0 & 0.300545148992524 & -0.300545148992524 \tabularnewline
58 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
59 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
60 & 1 & 0.312059818550768 & 0.687940181449232 \tabularnewline
61 & 0 & -0.0110237264435927 & 0.0110237264435927 \tabularnewline
62 & 0 & 0.336029517969365 & -0.336029517969365 \tabularnewline
63 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
64 & 0 & -0.0110237264435927 & 0.0110237264435927 \tabularnewline
65 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
66 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
67 & 1 & 0.336029517969365 & 0.663970482030635 \tabularnewline
68 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
69 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
70 & 0 & 0.291233853682325 & -0.291233853682325 \tabularnewline
71 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
72 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
73 & 0 & 0.255749484705485 & -0.255749484705485 \tabularnewline
74 & 0 & 0.302748523240569 & -0.302748523240569 \tabularnewline
75 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
76 & 0 & 0.0222572682852031 & -0.0222572682852031 \tabularnewline
77 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
78 & 0 & 0.300545148992524 & -0.300545148992524 \tabularnewline
79 & 1 & 0.255749484705485 & 0.744250515294515 \tabularnewline
80 & 0 & 0.0577416372620436 & -0.0577416372620436 \tabularnewline
81 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
82 & 0 & 0.267264154263728 & -0.267264154263728 \tabularnewline
83 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
84 & 1 & 0.291233853682325 & 0.708766146317675 \tabularnewline
85 & 0 & 0.0222572682852031 & -0.0222572682852031 \tabularnewline
86 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
87 & 0 & -0.0110237264435927 & 0.0110237264435927 \tabularnewline
88 & 0 & 0.104649820377044 & -0.104649820377044 \tabularnewline
89 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
90 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
91 & 0 & 0.0577416372620436 & -0.0577416372620436 \tabularnewline
92 & 0 & -0.138153691353436 & 0.138153691353436 \tabularnewline
93 & 0 & 0.0692563068202871 & -0.0692563068202871 \tabularnewline
94 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
95 & 0 & -0.14966836091168 & 0.14966836091168 \tabularnewline
96 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
97 & 0 & -0.138153691353436 & 0.138153691353436 \tabularnewline
98 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
99 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
100 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
101 & 0 & -0.0110237264435927 & 0.0110237264435927 \tabularnewline
102 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
103 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
104 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
105 & 0 & 0.128619519795641 & -0.128619519795641 \tabularnewline
106 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
107 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
108 & 0 & 0.140134189353885 & -0.140134189353885 \tabularnewline
109 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
110 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
111 & 0 & 0.184929853640924 & -0.184929853640924 \tabularnewline
112 & 0 & -0.14966836091168 & 0.14966836091168 \tabularnewline
113 & 0 & 0.291233853682325 & -0.291233853682325 \tabularnewline
114 & 0 & 0.140134189353885 & -0.140134189353885 \tabularnewline
115 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
116 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
117 & 0 & -0.0110237264435927 & 0.0110237264435927 \tabularnewline
118 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
119 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
120 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
121 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
122 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
123 & 0 & 0.140134189353885 & -0.140134189353885 \tabularnewline
124 & 0 & 0.300545148992524 & -0.300545148992524 \tabularnewline
125 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
126 & 0 & -0.14966836091168 & 0.14966836091168 \tabularnewline
127 & 0 & 0.0577416372620436 & -0.0577416372620436 \tabularnewline
128 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
129 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
130 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
131 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
132 & 0 & -0.0110237264435927 & 0.0110237264435927 \tabularnewline
133 & 0 & 0.302748523240569 & -0.302748523240569 \tabularnewline
134 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
135 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
136 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
137 & 0 & 0.312059818550767 & -0.312059818550767 \tabularnewline
138 & 0 & 0.149445484664084 & -0.149445484664084 \tabularnewline
139 & 0 & -0.14966836091168 & 0.14966836091168 \tabularnewline
140 & 0 & 0.0129459729750044 & -0.0129459729750044 \tabularnewline
141 & 1 & 0.255749484705485 & 0.744250515294515 \tabularnewline
142 & 0 & 0.0931351508188008 & -0.0931351508188008 \tabularnewline
143 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
144 & 0 & 0.0222572682852031 & -0.0222572682852031 \tabularnewline
145 & 0 & 0.0577416372620436 & -0.0577416372620436 \tabularnewline
146 & 0 & -0.18515272988852 & 0.18515272988852 \tabularnewline
147 & 0 & 0.128619519795641 & -0.128619519795641 \tabularnewline
148 & 0 & -0.14966836091168 & 0.14966836091168 \tabularnewline
149 & 0 & 0.0244606425332479 & -0.0244606425332479 \tabularnewline
150 & 0 & 0.0222572682852031 & -0.0222572682852031 \tabularnewline
151 & 0 & -0.0225383960018362 & 0.0225383960018362 \tabularnewline
152 & 1 & 0.302748523240569 & 0.697251476759431 \tabularnewline
153 & 1 & 0.347544187527608 & 0.652455812472392 \tabularnewline
154 & 0 & 0.302748523240569 & -0.302748523240569 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203915&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]0[/C][C]-0.0110237264435926[/C][C]0.0110237264435926[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.0129459729750045[/C][C]-0.0129459729750045[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.0129459729750043[/C][C]-0.0129459729750043[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.0129459729750042[/C][C]-0.0129459729750042[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.0337719378434466[/C][C]-0.0337719378434466[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.336029517969365[/C][C]-0.336029517969365[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.300545148992524[/C][C]-0.300545148992524[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.300545148992524[/C][C]-0.300545148992524[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.347544187527608[/C][C]0.652455812472392[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.300545148992524[/C][C]0.699454851007476[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0692563068202871[/C][C]-0.0692563068202871[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.312059818550767[/C][C]-0.312059818550767[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.0222572682852031[/C][C]-0.0222572682852031[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.0337719378434466[/C][C]-0.0337719378434466[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.255749484705485[/C][C]-0.255749484705485[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.336029517969365[/C][C]-0.336029517969365[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.0110237264435927[/C][C]0.0110237264435927[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.291233853682325[/C][C]-0.291233853682325[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0577416372620436[/C][C]-0.0577416372620436[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0692563068202871[/C][C]-0.0692563068202871[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.347544187527608[/C][C]-0.347544187527608[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.255749484705485[/C][C]-0.255749484705485[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0222572682852031[/C][C]-0.0222572682852031[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.0577416372620436[/C][C]-0.0577416372620436[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.300545148992524[/C][C]0.699454851007476[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.255749484705485[/C][C]-0.255749484705485[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0337719378434466[/C][C]-0.0337719378434466[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0577416372620436[/C][C]-0.0577416372620436[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.0222572682852031[/C][C]-0.0222572682852031[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0222572682852031[/C][C]-0.0222572682852031[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.291233853682325[/C][C]-0.291233853682325[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.347544187527608[/C][C]0.652455812472392[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.291233853682325[/C][C]0.708766146317675[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.255749484705485[/C][C]-0.255749484705485[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.300545148992524[/C][C]-0.300545148992524[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.312059818550768[/C][C]0.687940181449232[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]-0.0110237264435927[/C][C]0.0110237264435927[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.336029517969365[/C][C]-0.336029517969365[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]-0.0110237264435927[/C][C]0.0110237264435927[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.336029517969365[/C][C]0.663970482030635[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.291233853682325[/C][C]-0.291233853682325[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.255749484705485[/C][C]-0.255749484705485[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.302748523240569[/C][C]-0.302748523240569[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.0222572682852031[/C][C]-0.0222572682852031[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.300545148992524[/C][C]-0.300545148992524[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.255749484705485[/C][C]0.744250515294515[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.0577416372620436[/C][C]-0.0577416372620436[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.267264154263728[/C][C]-0.267264154263728[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.291233853682325[/C][C]0.708766146317675[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.0222572682852031[/C][C]-0.0222572682852031[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]-0.0110237264435927[/C][C]0.0110237264435927[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.104649820377044[/C][C]-0.104649820377044[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.0577416372620436[/C][C]-0.0577416372620436[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]-0.138153691353436[/C][C]0.138153691353436[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0692563068202871[/C][C]-0.0692563068202871[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]-0.14966836091168[/C][C]0.14966836091168[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]-0.138153691353436[/C][C]0.138153691353436[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]-0.0110237264435927[/C][C]0.0110237264435927[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.128619519795641[/C][C]-0.128619519795641[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.140134189353885[/C][C]-0.140134189353885[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.184929853640924[/C][C]-0.184929853640924[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]-0.14966836091168[/C][C]0.14966836091168[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.291233853682325[/C][C]-0.291233853682325[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.140134189353885[/C][C]-0.140134189353885[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]-0.0110237264435927[/C][C]0.0110237264435927[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.140134189353885[/C][C]-0.140134189353885[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.300545148992524[/C][C]-0.300545148992524[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]-0.14966836091168[/C][C]0.14966836091168[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.0577416372620436[/C][C]-0.0577416372620436[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]-0.0110237264435927[/C][C]0.0110237264435927[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.302748523240569[/C][C]-0.302748523240569[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.312059818550767[/C][C]-0.312059818550767[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.149445484664084[/C][C]-0.149445484664084[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]-0.14966836091168[/C][C]0.14966836091168[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.0129459729750044[/C][C]-0.0129459729750044[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.255749484705485[/C][C]0.744250515294515[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.0931351508188008[/C][C]-0.0931351508188008[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.0222572682852031[/C][C]-0.0222572682852031[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.0577416372620436[/C][C]-0.0577416372620436[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]-0.18515272988852[/C][C]0.18515272988852[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.128619519795641[/C][C]-0.128619519795641[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]-0.14966836091168[/C][C]0.14966836091168[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.0244606425332479[/C][C]-0.0244606425332479[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.0222572682852031[/C][C]-0.0222572682852031[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]-0.0225383960018362[/C][C]0.0225383960018362[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.302748523240569[/C][C]0.697251476759431[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.347544187527608[/C][C]0.652455812472392[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.302748523240569[/C][C]-0.302748523240569[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203915&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203915&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
10-0.01102372644359260.0110237264435926
200.0129459729750045-0.0129459729750045
300.0129459729750043-0.0129459729750043
400.0129459729750042-0.0129459729750042
500.0129459729750044-0.0129459729750044
600.0337719378434466-0.0337719378434466
700.0129459729750044-0.0129459729750044
800.0129459729750044-0.0129459729750044
90-0.02253839600183620.0225383960018362
1000.0244606425332479-0.0244606425332479
1100.0244606425332479-0.0244606425332479
1200.0129459729750044-0.0129459729750044
1300.336029517969365-0.336029517969365
1400.0244606425332479-0.0244606425332479
1500.300545148992524-0.300545148992524
1600.300545148992524-0.300545148992524
1710.3475441875276080.652455812472392
1800.0244606425332479-0.0244606425332479
190-0.02253839600183620.0225383960018362
2010.3005451489925240.699454851007476
2100.0692563068202871-0.0692563068202871
2200.312059818550767-0.312059818550767
2300.0222572682852031-0.0222572682852031
2400.0337719378434466-0.0337719378434466
2500.255749484705485-0.255749484705485
2600.336029517969365-0.336029517969365
270-0.01102372644359270.0110237264435927
2800.291233853682325-0.291233853682325
290-0.02253839600183620.0225383960018362
3000.0577416372620436-0.0577416372620436
3100.0129459729750044-0.0129459729750044
3200.0244606425332479-0.0244606425332479
3300.0692563068202871-0.0692563068202871
340-0.02253839600183620.0225383960018362
3500.0129459729750044-0.0129459729750044
3600.0129459729750044-0.0129459729750044
3700.347544187527608-0.347544187527608
3800.255749484705485-0.255749484705485
3900.0222572682852031-0.0222572682852031
4000.0577416372620436-0.0577416372620436
4110.3005451489925240.699454851007476
4200.255749484705485-0.255749484705485
4300.0337719378434466-0.0337719378434466
4400.0244606425332479-0.0244606425332479
4500.0577416372620436-0.0577416372620436
4600.0222572682852031-0.0222572682852031
4700.0129459729750044-0.0129459729750044
480-0.02253839600183620.0225383960018362
4900.0222572682852031-0.0222572682852031
5000.0129459729750044-0.0129459729750044
5100.291233853682325-0.291233853682325
5210.3475441875276080.652455812472392
530-0.02253839600183620.0225383960018362
5410.2912338536823250.708766146317675
5500.0129459729750044-0.0129459729750044
5600.255749484705485-0.255749484705485
5700.300545148992524-0.300545148992524
580-0.02253839600183620.0225383960018362
590-0.02253839600183620.0225383960018362
6010.3120598185507680.687940181449232
610-0.01102372644359270.0110237264435927
6200.336029517969365-0.336029517969365
6300.0129459729750044-0.0129459729750044
640-0.01102372644359270.0110237264435927
6500.0129459729750044-0.0129459729750044
6600.0129459729750044-0.0129459729750044
6710.3360295179693650.663970482030635
6800.0244606425332479-0.0244606425332479
690-0.02253839600183620.0225383960018362
7000.291233853682325-0.291233853682325
7100.0129459729750044-0.0129459729750044
720-0.02253839600183620.0225383960018362
7300.255749484705485-0.255749484705485
7400.302748523240569-0.302748523240569
750-0.02253839600183620.0225383960018362
7600.0222572682852031-0.0222572682852031
770-0.02253839600183620.0225383960018362
7800.300545148992524-0.300545148992524
7910.2557494847054850.744250515294515
8000.0577416372620436-0.0577416372620436
8100.0129459729750044-0.0129459729750044
8200.267264154263728-0.267264154263728
8300.0129459729750044-0.0129459729750044
8410.2912338536823250.708766146317675
8500.0222572682852031-0.0222572682852031
8600.0244606425332479-0.0244606425332479
870-0.01102372644359270.0110237264435927
8800.104649820377044-0.104649820377044
8900.0129459729750044-0.0129459729750044
900-0.02253839600183620.0225383960018362
9100.0577416372620436-0.0577416372620436
920-0.1381536913534360.138153691353436
9300.0692563068202871-0.0692563068202871
9400.0129459729750044-0.0129459729750044
950-0.149668360911680.14966836091168
960-0.02253839600183620.0225383960018362
970-0.1381536913534360.138153691353436
9800.0129459729750044-0.0129459729750044
9900.0244606425332479-0.0244606425332479
1000-0.02253839600183620.0225383960018362
1010-0.01102372644359270.0110237264435927
10200.0129459729750044-0.0129459729750044
10300.0129459729750044-0.0129459729750044
10400.0129459729750044-0.0129459729750044
10500.128619519795641-0.128619519795641
10600.0129459729750044-0.0129459729750044
10700.0129459729750044-0.0129459729750044
10800.140134189353885-0.140134189353885
10900.0129459729750044-0.0129459729750044
11000.0244606425332479-0.0244606425332479
11100.184929853640924-0.184929853640924
1120-0.149668360911680.14966836091168
11300.291233853682325-0.291233853682325
11400.140134189353885-0.140134189353885
11500.0244606425332479-0.0244606425332479
11600.0129459729750044-0.0129459729750044
1170-0.01102372644359270.0110237264435927
11800.0244606425332479-0.0244606425332479
11900.0129459729750044-0.0129459729750044
1200-0.02253839600183620.0225383960018362
12100.0244606425332479-0.0244606425332479
12200.0129459729750044-0.0129459729750044
12300.140134189353885-0.140134189353885
12400.300545148992524-0.300545148992524
1250-0.02253839600183620.0225383960018362
1260-0.149668360911680.14966836091168
12700.0577416372620436-0.0577416372620436
1280-0.02253839600183620.0225383960018362
12900.0129459729750044-0.0129459729750044
1300-0.02253839600183620.0225383960018362
13100.0244606425332479-0.0244606425332479
1320-0.01102372644359270.0110237264435927
13300.302748523240569-0.302748523240569
13400.0129459729750044-0.0129459729750044
13500.0129459729750044-0.0129459729750044
13600.0129459729750044-0.0129459729750044
13700.312059818550767-0.312059818550767
13800.149445484664084-0.149445484664084
1390-0.149668360911680.14966836091168
14000.0129459729750044-0.0129459729750044
14110.2557494847054850.744250515294515
14200.0931351508188008-0.0931351508188008
14300.0244606425332479-0.0244606425332479
14400.0222572682852031-0.0222572682852031
14500.0577416372620436-0.0577416372620436
1460-0.185152729888520.18515272988852
14700.128619519795641-0.128619519795641
1480-0.149668360911680.14966836091168
14900.0244606425332479-0.0244606425332479
15000.0222572682852031-0.0222572682852031
1510-0.02253839600183620.0225383960018362
15210.3027485232405690.697251476759431
15310.3475441875276080.652455812472392
15400.302748523240569-0.302748523240569







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9001
10001
11001
12001
13001
14001
15001
16001
170.3547882474310710.7095764948621420.645211752568929
180.3063625176416210.6127250352832430.693637482358379
190.2754327721545530.5508655443091060.724567227845447
200.8513403997558570.2973192004882860.148659600244143
210.8030963954864230.3938072090271540.196903604513577
220.8703109921830570.2593780156338870.129689007816944
230.8318652288735070.3362695422529860.168134771126493
240.7837360554760720.4325278890478570.216263944523928
250.7723041359775160.4553917280449690.227695864022484
260.8009507032872490.3980985934255010.199049296712751
270.7513008261236520.4973983477526960.248699173876348
280.7385122510113330.5229754979773340.261487748988667
290.6875164285639080.6249671428721840.312483571436092
300.6314015830363570.7371968339272860.368598416963643
310.5716019660972520.8567960678054950.428398033902747
320.5107608074153560.9784783851692880.489239192584644
330.4581372495142650.916274499028530.541862750485735
340.4009182807103990.8018365614207980.599081719289601
350.3445798389709030.6891596779418060.655420161029097
360.2918728453844660.5837456907689310.708127154615535
370.3183949483755560.6367898967511120.681605051624444
380.2952030443996160.5904060887992330.704796955600384
390.2480063484834870.4960126969669730.751993651516513
400.2067952221703440.4135904443406870.793204777829656
410.6621131777601280.6757736444797450.337886822239872
420.6485192258945240.7029615482109510.351480774105476
430.6014156348889290.7971687302221420.398584365111071
440.5491371507758650.9017256984482690.450862849224135
450.4992190794346880.9984381588693770.500780920565312
460.4484048696630320.8968097393260640.551595130336968
470.3974910260851880.7949820521703760.602508973914812
480.3481439021177560.6962878042355110.651856097882244
490.3026282639170580.6052565278341170.697371736082942
500.2593616349917370.5187232699834750.740638365008263
510.2570682084424960.5141364168849920.742931791557504
520.5981663395477890.8036673209044210.401833660452211
530.5511228124682410.8977543750635180.448877187531759
540.8717047497423890.2565905005152220.128295250257611
550.8440446478858080.3119107042283850.155955352114192
560.8445695829502130.3108608340995740.155430417049787
570.8579760450124930.2840479099750140.142023954987507
580.8303844680403190.3392310639193630.169615531959681
590.7994128220136520.4011743559726950.200587177986348
600.9523157698397040.09536846032059170.0476842301602959
610.9391628323439280.1216743353121450.0608371676560723
620.9505932405606610.09881351887867710.0494067594393385
630.9374918672427170.1250162655145650.0625081327572827
640.9217805269084890.1564389461830230.0782194730915113
650.9033598083769940.1932803832460130.0966401916230064
660.881971422752610.236057154494780.11802857724739
670.9776818351251650.044636329749670.022318164874835
680.9706646306770920.05867073864581690.0293353693229084
690.9622105133418960.07557897331620720.0377894866581036
700.9674331906656560.06513361866868830.0325668093343442
710.9580975509210070.08380489815798670.0419024490789933
720.9468151373401440.1063697253197110.0531848626598557
730.9513014463254560.09739710734908860.0486985536745443
740.9593741220316930.08125175593661390.0406258779683069
750.9483613154369990.1032773691260010.0516386845630005
760.9349323119089480.1301353761821040.0650676880910522
770.919098224966250.16180355006750.0809017750337502
780.9301178974024590.1397642051950830.0698821025975413
790.993369828920190.01326034215961960.00663017107980978
800.990842170233170.01831565953365920.00915782976682961
810.9875565106313620.02488697873727680.0124434893686384
820.9890104126018670.02197917479626540.0109895873981327
830.9851457914748010.02970841705039750.0148542085251988
840.9992785798023560.001442840395288580.000721420197644289
850.9989043777264760.002191244547048840.00109562227352442
860.9983677140278240.003264571944352450.00163228597217622
870.9975881571868660.004823685626268530.00241184281313427
880.9966808810215590.006638237956880950.00331911897844047
890.9952235279046840.009552944190632810.0047764720953164
900.9932311038185920.01353779236281620.00676889618140809
910.9905363104923320.01892737901533620.00946368950766808
920.9883731995485870.0232536009028250.0116268004514125
930.9842019817915560.03159603641688770.0157980182084438
940.978571053022050.04285789395589910.0214289469779495
950.973779480838740.05244103832251990.02622051916126
960.9652172620802250.06956547583954960.0347827379197748
970.9582669703833770.08346605923324640.0417330296166232
980.9457198294007740.1085603411984520.0542801705992259
990.9302188166507860.1395623666984280.069781183349214
1000.9116444904105820.1767110191788360.0883555095894178
1010.8891984147307820.2216031705384370.110801585269218
1020.8630789173017450.2738421653965090.136921082698255
1030.8329431884325960.3341136231348080.167056811567404
1040.7987297424268110.4025405151463790.201270257573189
1050.7742423139890040.4515153720219920.225757686010996
1060.733492538081760.533014923836480.26650746191824
1070.6892070827865780.6215858344268440.310792917213422
1080.6560562384629940.6878875230740130.343943761537006
1090.6066308674616060.7867382650767870.393369132538394
1100.5543640228606170.8912719542787660.445635977139383
1110.518384616014140.9632307679717210.48161538398586
1120.4828851855809650.9657703711619310.517114814419035
1130.5374229769590640.9251540460818730.462577023040936
1140.5016638530731370.9966722938537270.498336146926863
1150.4459407030326320.8918814060652650.554059296967368
1160.3933515163724730.7867030327449470.606648483627527
1170.3401697435233840.6803394870467680.659830256476616
1180.2893389527075880.5786779054151750.710661047292412
1190.2445105231709380.4890210463418760.755489476829062
1200.2011930471505480.4023860943010960.798806952849452
1210.1625379388292420.3250758776584850.837462061170758
1220.1308073135864780.2616146271729560.869192686413522
1230.1137861069602150.227572213920430.886213893039785
1240.143105432787470.286210865574940.85689456721253
1250.1112228836292310.2224457672584610.888777116370769
1260.09361851209725420.1872370241945080.906381487902746
1270.07106255697201720.1421251139440340.928937443027983
1280.0518753840637390.1037507681274780.948124615936261
1290.03785456397317210.07570912794634410.962145436026828
1300.02633001263742250.05266002527484490.973669987362578
1310.01757011001356570.03514022002713140.982429889986434
1320.01157493528749750.0231498705749950.988425064712502
1330.0219721783562650.043944356712530.978027821643735
1340.01520017289366940.03040034578733890.984799827106331
1350.01052140548276210.02104281096552420.989478594517238
1360.007469503879040030.01493900775808010.99253049612096
1370.01350522420680860.02701044841361720.986494775793191
1380.01175574048421570.02351148096843140.988244259515784
1390.01005593748226090.02011187496452170.989944062517739
1400.005439065184234390.01087813036846880.994560934815766
1410.05250084560071810.1050016912014360.947499154399282
1420.04511135693433970.09022271386867930.95488864306566
1430.02750467337292990.05500934674585990.97249532662707
1440.01511930618276550.03023861236553110.984880693817234
1450.006243364495131530.01248672899026310.993756635504868

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0 & 0 & 1 \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.354788247431071 & 0.709576494862142 & 0.645211752568929 \tabularnewline
18 & 0.306362517641621 & 0.612725035283243 & 0.693637482358379 \tabularnewline
19 & 0.275432772154553 & 0.550865544309106 & 0.724567227845447 \tabularnewline
20 & 0.851340399755857 & 0.297319200488286 & 0.148659600244143 \tabularnewline
21 & 0.803096395486423 & 0.393807209027154 & 0.196903604513577 \tabularnewline
22 & 0.870310992183057 & 0.259378015633887 & 0.129689007816944 \tabularnewline
23 & 0.831865228873507 & 0.336269542252986 & 0.168134771126493 \tabularnewline
24 & 0.783736055476072 & 0.432527889047857 & 0.216263944523928 \tabularnewline
25 & 0.772304135977516 & 0.455391728044969 & 0.227695864022484 \tabularnewline
26 & 0.800950703287249 & 0.398098593425501 & 0.199049296712751 \tabularnewline
27 & 0.751300826123652 & 0.497398347752696 & 0.248699173876348 \tabularnewline
28 & 0.738512251011333 & 0.522975497977334 & 0.261487748988667 \tabularnewline
29 & 0.687516428563908 & 0.624967142872184 & 0.312483571436092 \tabularnewline
30 & 0.631401583036357 & 0.737196833927286 & 0.368598416963643 \tabularnewline
31 & 0.571601966097252 & 0.856796067805495 & 0.428398033902747 \tabularnewline
32 & 0.510760807415356 & 0.978478385169288 & 0.489239192584644 \tabularnewline
33 & 0.458137249514265 & 0.91627449902853 & 0.541862750485735 \tabularnewline
34 & 0.400918280710399 & 0.801836561420798 & 0.599081719289601 \tabularnewline
35 & 0.344579838970903 & 0.689159677941806 & 0.655420161029097 \tabularnewline
36 & 0.291872845384466 & 0.583745690768931 & 0.708127154615535 \tabularnewline
37 & 0.318394948375556 & 0.636789896751112 & 0.681605051624444 \tabularnewline
38 & 0.295203044399616 & 0.590406088799233 & 0.704796955600384 \tabularnewline
39 & 0.248006348483487 & 0.496012696966973 & 0.751993651516513 \tabularnewline
40 & 0.206795222170344 & 0.413590444340687 & 0.793204777829656 \tabularnewline
41 & 0.662113177760128 & 0.675773644479745 & 0.337886822239872 \tabularnewline
42 & 0.648519225894524 & 0.702961548210951 & 0.351480774105476 \tabularnewline
43 & 0.601415634888929 & 0.797168730222142 & 0.398584365111071 \tabularnewline
44 & 0.549137150775865 & 0.901725698448269 & 0.450862849224135 \tabularnewline
45 & 0.499219079434688 & 0.998438158869377 & 0.500780920565312 \tabularnewline
46 & 0.448404869663032 & 0.896809739326064 & 0.551595130336968 \tabularnewline
47 & 0.397491026085188 & 0.794982052170376 & 0.602508973914812 \tabularnewline
48 & 0.348143902117756 & 0.696287804235511 & 0.651856097882244 \tabularnewline
49 & 0.302628263917058 & 0.605256527834117 & 0.697371736082942 \tabularnewline
50 & 0.259361634991737 & 0.518723269983475 & 0.740638365008263 \tabularnewline
51 & 0.257068208442496 & 0.514136416884992 & 0.742931791557504 \tabularnewline
52 & 0.598166339547789 & 0.803667320904421 & 0.401833660452211 \tabularnewline
53 & 0.551122812468241 & 0.897754375063518 & 0.448877187531759 \tabularnewline
54 & 0.871704749742389 & 0.256590500515222 & 0.128295250257611 \tabularnewline
55 & 0.844044647885808 & 0.311910704228385 & 0.155955352114192 \tabularnewline
56 & 0.844569582950213 & 0.310860834099574 & 0.155430417049787 \tabularnewline
57 & 0.857976045012493 & 0.284047909975014 & 0.142023954987507 \tabularnewline
58 & 0.830384468040319 & 0.339231063919363 & 0.169615531959681 \tabularnewline
59 & 0.799412822013652 & 0.401174355972695 & 0.200587177986348 \tabularnewline
60 & 0.952315769839704 & 0.0953684603205917 & 0.0476842301602959 \tabularnewline
61 & 0.939162832343928 & 0.121674335312145 & 0.0608371676560723 \tabularnewline
62 & 0.950593240560661 & 0.0988135188786771 & 0.0494067594393385 \tabularnewline
63 & 0.937491867242717 & 0.125016265514565 & 0.0625081327572827 \tabularnewline
64 & 0.921780526908489 & 0.156438946183023 & 0.0782194730915113 \tabularnewline
65 & 0.903359808376994 & 0.193280383246013 & 0.0966401916230064 \tabularnewline
66 & 0.88197142275261 & 0.23605715449478 & 0.11802857724739 \tabularnewline
67 & 0.977681835125165 & 0.04463632974967 & 0.022318164874835 \tabularnewline
68 & 0.970664630677092 & 0.0586707386458169 & 0.0293353693229084 \tabularnewline
69 & 0.962210513341896 & 0.0755789733162072 & 0.0377894866581036 \tabularnewline
70 & 0.967433190665656 & 0.0651336186686883 & 0.0325668093343442 \tabularnewline
71 & 0.958097550921007 & 0.0838048981579867 & 0.0419024490789933 \tabularnewline
72 & 0.946815137340144 & 0.106369725319711 & 0.0531848626598557 \tabularnewline
73 & 0.951301446325456 & 0.0973971073490886 & 0.0486985536745443 \tabularnewline
74 & 0.959374122031693 & 0.0812517559366139 & 0.0406258779683069 \tabularnewline
75 & 0.948361315436999 & 0.103277369126001 & 0.0516386845630005 \tabularnewline
76 & 0.934932311908948 & 0.130135376182104 & 0.0650676880910522 \tabularnewline
77 & 0.91909822496625 & 0.1618035500675 & 0.0809017750337502 \tabularnewline
78 & 0.930117897402459 & 0.139764205195083 & 0.0698821025975413 \tabularnewline
79 & 0.99336982892019 & 0.0132603421596196 & 0.00663017107980978 \tabularnewline
80 & 0.99084217023317 & 0.0183156595336592 & 0.00915782976682961 \tabularnewline
81 & 0.987556510631362 & 0.0248869787372768 & 0.0124434893686384 \tabularnewline
82 & 0.989010412601867 & 0.0219791747962654 & 0.0109895873981327 \tabularnewline
83 & 0.985145791474801 & 0.0297084170503975 & 0.0148542085251988 \tabularnewline
84 & 0.999278579802356 & 0.00144284039528858 & 0.000721420197644289 \tabularnewline
85 & 0.998904377726476 & 0.00219124454704884 & 0.00109562227352442 \tabularnewline
86 & 0.998367714027824 & 0.00326457194435245 & 0.00163228597217622 \tabularnewline
87 & 0.997588157186866 & 0.00482368562626853 & 0.00241184281313427 \tabularnewline
88 & 0.996680881021559 & 0.00663823795688095 & 0.00331911897844047 \tabularnewline
89 & 0.995223527904684 & 0.00955294419063281 & 0.0047764720953164 \tabularnewline
90 & 0.993231103818592 & 0.0135377923628162 & 0.00676889618140809 \tabularnewline
91 & 0.990536310492332 & 0.0189273790153362 & 0.00946368950766808 \tabularnewline
92 & 0.988373199548587 & 0.023253600902825 & 0.0116268004514125 \tabularnewline
93 & 0.984201981791556 & 0.0315960364168877 & 0.0157980182084438 \tabularnewline
94 & 0.97857105302205 & 0.0428578939558991 & 0.0214289469779495 \tabularnewline
95 & 0.97377948083874 & 0.0524410383225199 & 0.02622051916126 \tabularnewline
96 & 0.965217262080225 & 0.0695654758395496 & 0.0347827379197748 \tabularnewline
97 & 0.958266970383377 & 0.0834660592332464 & 0.0417330296166232 \tabularnewline
98 & 0.945719829400774 & 0.108560341198452 & 0.0542801705992259 \tabularnewline
99 & 0.930218816650786 & 0.139562366698428 & 0.069781183349214 \tabularnewline
100 & 0.911644490410582 & 0.176711019178836 & 0.0883555095894178 \tabularnewline
101 & 0.889198414730782 & 0.221603170538437 & 0.110801585269218 \tabularnewline
102 & 0.863078917301745 & 0.273842165396509 & 0.136921082698255 \tabularnewline
103 & 0.832943188432596 & 0.334113623134808 & 0.167056811567404 \tabularnewline
104 & 0.798729742426811 & 0.402540515146379 & 0.201270257573189 \tabularnewline
105 & 0.774242313989004 & 0.451515372021992 & 0.225757686010996 \tabularnewline
106 & 0.73349253808176 & 0.53301492383648 & 0.26650746191824 \tabularnewline
107 & 0.689207082786578 & 0.621585834426844 & 0.310792917213422 \tabularnewline
108 & 0.656056238462994 & 0.687887523074013 & 0.343943761537006 \tabularnewline
109 & 0.606630867461606 & 0.786738265076787 & 0.393369132538394 \tabularnewline
110 & 0.554364022860617 & 0.891271954278766 & 0.445635977139383 \tabularnewline
111 & 0.51838461601414 & 0.963230767971721 & 0.48161538398586 \tabularnewline
112 & 0.482885185580965 & 0.965770371161931 & 0.517114814419035 \tabularnewline
113 & 0.537422976959064 & 0.925154046081873 & 0.462577023040936 \tabularnewline
114 & 0.501663853073137 & 0.996672293853727 & 0.498336146926863 \tabularnewline
115 & 0.445940703032632 & 0.891881406065265 & 0.554059296967368 \tabularnewline
116 & 0.393351516372473 & 0.786703032744947 & 0.606648483627527 \tabularnewline
117 & 0.340169743523384 & 0.680339487046768 & 0.659830256476616 \tabularnewline
118 & 0.289338952707588 & 0.578677905415175 & 0.710661047292412 \tabularnewline
119 & 0.244510523170938 & 0.489021046341876 & 0.755489476829062 \tabularnewline
120 & 0.201193047150548 & 0.402386094301096 & 0.798806952849452 \tabularnewline
121 & 0.162537938829242 & 0.325075877658485 & 0.837462061170758 \tabularnewline
122 & 0.130807313586478 & 0.261614627172956 & 0.869192686413522 \tabularnewline
123 & 0.113786106960215 & 0.22757221392043 & 0.886213893039785 \tabularnewline
124 & 0.14310543278747 & 0.28621086557494 & 0.85689456721253 \tabularnewline
125 & 0.111222883629231 & 0.222445767258461 & 0.888777116370769 \tabularnewline
126 & 0.0936185120972542 & 0.187237024194508 & 0.906381487902746 \tabularnewline
127 & 0.0710625569720172 & 0.142125113944034 & 0.928937443027983 \tabularnewline
128 & 0.051875384063739 & 0.103750768127478 & 0.948124615936261 \tabularnewline
129 & 0.0378545639731721 & 0.0757091279463441 & 0.962145436026828 \tabularnewline
130 & 0.0263300126374225 & 0.0526600252748449 & 0.973669987362578 \tabularnewline
131 & 0.0175701100135657 & 0.0351402200271314 & 0.982429889986434 \tabularnewline
132 & 0.0115749352874975 & 0.023149870574995 & 0.988425064712502 \tabularnewline
133 & 0.021972178356265 & 0.04394435671253 & 0.978027821643735 \tabularnewline
134 & 0.0152001728936694 & 0.0304003457873389 & 0.984799827106331 \tabularnewline
135 & 0.0105214054827621 & 0.0210428109655242 & 0.989478594517238 \tabularnewline
136 & 0.00746950387904003 & 0.0149390077580801 & 0.99253049612096 \tabularnewline
137 & 0.0135052242068086 & 0.0270104484136172 & 0.986494775793191 \tabularnewline
138 & 0.0117557404842157 & 0.0235114809684314 & 0.988244259515784 \tabularnewline
139 & 0.0100559374822609 & 0.0201118749645217 & 0.989944062517739 \tabularnewline
140 & 0.00543906518423439 & 0.0108781303684688 & 0.994560934815766 \tabularnewline
141 & 0.0525008456007181 & 0.105001691201436 & 0.947499154399282 \tabularnewline
142 & 0.0451113569343397 & 0.0902227138686793 & 0.95488864306566 \tabularnewline
143 & 0.0275046733729299 & 0.0550093467458599 & 0.97249532662707 \tabularnewline
144 & 0.0151193061827655 & 0.0302386123655311 & 0.984880693817234 \tabularnewline
145 & 0.00624336449513153 & 0.0124867289902631 & 0.993756635504868 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203915&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]9[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.354788247431071[/C][C]0.709576494862142[/C][C]0.645211752568929[/C][/ROW]
[ROW][C]18[/C][C]0.306362517641621[/C][C]0.612725035283243[/C][C]0.693637482358379[/C][/ROW]
[ROW][C]19[/C][C]0.275432772154553[/C][C]0.550865544309106[/C][C]0.724567227845447[/C][/ROW]
[ROW][C]20[/C][C]0.851340399755857[/C][C]0.297319200488286[/C][C]0.148659600244143[/C][/ROW]
[ROW][C]21[/C][C]0.803096395486423[/C][C]0.393807209027154[/C][C]0.196903604513577[/C][/ROW]
[ROW][C]22[/C][C]0.870310992183057[/C][C]0.259378015633887[/C][C]0.129689007816944[/C][/ROW]
[ROW][C]23[/C][C]0.831865228873507[/C][C]0.336269542252986[/C][C]0.168134771126493[/C][/ROW]
[ROW][C]24[/C][C]0.783736055476072[/C][C]0.432527889047857[/C][C]0.216263944523928[/C][/ROW]
[ROW][C]25[/C][C]0.772304135977516[/C][C]0.455391728044969[/C][C]0.227695864022484[/C][/ROW]
[ROW][C]26[/C][C]0.800950703287249[/C][C]0.398098593425501[/C][C]0.199049296712751[/C][/ROW]
[ROW][C]27[/C][C]0.751300826123652[/C][C]0.497398347752696[/C][C]0.248699173876348[/C][/ROW]
[ROW][C]28[/C][C]0.738512251011333[/C][C]0.522975497977334[/C][C]0.261487748988667[/C][/ROW]
[ROW][C]29[/C][C]0.687516428563908[/C][C]0.624967142872184[/C][C]0.312483571436092[/C][/ROW]
[ROW][C]30[/C][C]0.631401583036357[/C][C]0.737196833927286[/C][C]0.368598416963643[/C][/ROW]
[ROW][C]31[/C][C]0.571601966097252[/C][C]0.856796067805495[/C][C]0.428398033902747[/C][/ROW]
[ROW][C]32[/C][C]0.510760807415356[/C][C]0.978478385169288[/C][C]0.489239192584644[/C][/ROW]
[ROW][C]33[/C][C]0.458137249514265[/C][C]0.91627449902853[/C][C]0.541862750485735[/C][/ROW]
[ROW][C]34[/C][C]0.400918280710399[/C][C]0.801836561420798[/C][C]0.599081719289601[/C][/ROW]
[ROW][C]35[/C][C]0.344579838970903[/C][C]0.689159677941806[/C][C]0.655420161029097[/C][/ROW]
[ROW][C]36[/C][C]0.291872845384466[/C][C]0.583745690768931[/C][C]0.708127154615535[/C][/ROW]
[ROW][C]37[/C][C]0.318394948375556[/C][C]0.636789896751112[/C][C]0.681605051624444[/C][/ROW]
[ROW][C]38[/C][C]0.295203044399616[/C][C]0.590406088799233[/C][C]0.704796955600384[/C][/ROW]
[ROW][C]39[/C][C]0.248006348483487[/C][C]0.496012696966973[/C][C]0.751993651516513[/C][/ROW]
[ROW][C]40[/C][C]0.206795222170344[/C][C]0.413590444340687[/C][C]0.793204777829656[/C][/ROW]
[ROW][C]41[/C][C]0.662113177760128[/C][C]0.675773644479745[/C][C]0.337886822239872[/C][/ROW]
[ROW][C]42[/C][C]0.648519225894524[/C][C]0.702961548210951[/C][C]0.351480774105476[/C][/ROW]
[ROW][C]43[/C][C]0.601415634888929[/C][C]0.797168730222142[/C][C]0.398584365111071[/C][/ROW]
[ROW][C]44[/C][C]0.549137150775865[/C][C]0.901725698448269[/C][C]0.450862849224135[/C][/ROW]
[ROW][C]45[/C][C]0.499219079434688[/C][C]0.998438158869377[/C][C]0.500780920565312[/C][/ROW]
[ROW][C]46[/C][C]0.448404869663032[/C][C]0.896809739326064[/C][C]0.551595130336968[/C][/ROW]
[ROW][C]47[/C][C]0.397491026085188[/C][C]0.794982052170376[/C][C]0.602508973914812[/C][/ROW]
[ROW][C]48[/C][C]0.348143902117756[/C][C]0.696287804235511[/C][C]0.651856097882244[/C][/ROW]
[ROW][C]49[/C][C]0.302628263917058[/C][C]0.605256527834117[/C][C]0.697371736082942[/C][/ROW]
[ROW][C]50[/C][C]0.259361634991737[/C][C]0.518723269983475[/C][C]0.740638365008263[/C][/ROW]
[ROW][C]51[/C][C]0.257068208442496[/C][C]0.514136416884992[/C][C]0.742931791557504[/C][/ROW]
[ROW][C]52[/C][C]0.598166339547789[/C][C]0.803667320904421[/C][C]0.401833660452211[/C][/ROW]
[ROW][C]53[/C][C]0.551122812468241[/C][C]0.897754375063518[/C][C]0.448877187531759[/C][/ROW]
[ROW][C]54[/C][C]0.871704749742389[/C][C]0.256590500515222[/C][C]0.128295250257611[/C][/ROW]
[ROW][C]55[/C][C]0.844044647885808[/C][C]0.311910704228385[/C][C]0.155955352114192[/C][/ROW]
[ROW][C]56[/C][C]0.844569582950213[/C][C]0.310860834099574[/C][C]0.155430417049787[/C][/ROW]
[ROW][C]57[/C][C]0.857976045012493[/C][C]0.284047909975014[/C][C]0.142023954987507[/C][/ROW]
[ROW][C]58[/C][C]0.830384468040319[/C][C]0.339231063919363[/C][C]0.169615531959681[/C][/ROW]
[ROW][C]59[/C][C]0.799412822013652[/C][C]0.401174355972695[/C][C]0.200587177986348[/C][/ROW]
[ROW][C]60[/C][C]0.952315769839704[/C][C]0.0953684603205917[/C][C]0.0476842301602959[/C][/ROW]
[ROW][C]61[/C][C]0.939162832343928[/C][C]0.121674335312145[/C][C]0.0608371676560723[/C][/ROW]
[ROW][C]62[/C][C]0.950593240560661[/C][C]0.0988135188786771[/C][C]0.0494067594393385[/C][/ROW]
[ROW][C]63[/C][C]0.937491867242717[/C][C]0.125016265514565[/C][C]0.0625081327572827[/C][/ROW]
[ROW][C]64[/C][C]0.921780526908489[/C][C]0.156438946183023[/C][C]0.0782194730915113[/C][/ROW]
[ROW][C]65[/C][C]0.903359808376994[/C][C]0.193280383246013[/C][C]0.0966401916230064[/C][/ROW]
[ROW][C]66[/C][C]0.88197142275261[/C][C]0.23605715449478[/C][C]0.11802857724739[/C][/ROW]
[ROW][C]67[/C][C]0.977681835125165[/C][C]0.04463632974967[/C][C]0.022318164874835[/C][/ROW]
[ROW][C]68[/C][C]0.970664630677092[/C][C]0.0586707386458169[/C][C]0.0293353693229084[/C][/ROW]
[ROW][C]69[/C][C]0.962210513341896[/C][C]0.0755789733162072[/C][C]0.0377894866581036[/C][/ROW]
[ROW][C]70[/C][C]0.967433190665656[/C][C]0.0651336186686883[/C][C]0.0325668093343442[/C][/ROW]
[ROW][C]71[/C][C]0.958097550921007[/C][C]0.0838048981579867[/C][C]0.0419024490789933[/C][/ROW]
[ROW][C]72[/C][C]0.946815137340144[/C][C]0.106369725319711[/C][C]0.0531848626598557[/C][/ROW]
[ROW][C]73[/C][C]0.951301446325456[/C][C]0.0973971073490886[/C][C]0.0486985536745443[/C][/ROW]
[ROW][C]74[/C][C]0.959374122031693[/C][C]0.0812517559366139[/C][C]0.0406258779683069[/C][/ROW]
[ROW][C]75[/C][C]0.948361315436999[/C][C]0.103277369126001[/C][C]0.0516386845630005[/C][/ROW]
[ROW][C]76[/C][C]0.934932311908948[/C][C]0.130135376182104[/C][C]0.0650676880910522[/C][/ROW]
[ROW][C]77[/C][C]0.91909822496625[/C][C]0.1618035500675[/C][C]0.0809017750337502[/C][/ROW]
[ROW][C]78[/C][C]0.930117897402459[/C][C]0.139764205195083[/C][C]0.0698821025975413[/C][/ROW]
[ROW][C]79[/C][C]0.99336982892019[/C][C]0.0132603421596196[/C][C]0.00663017107980978[/C][/ROW]
[ROW][C]80[/C][C]0.99084217023317[/C][C]0.0183156595336592[/C][C]0.00915782976682961[/C][/ROW]
[ROW][C]81[/C][C]0.987556510631362[/C][C]0.0248869787372768[/C][C]0.0124434893686384[/C][/ROW]
[ROW][C]82[/C][C]0.989010412601867[/C][C]0.0219791747962654[/C][C]0.0109895873981327[/C][/ROW]
[ROW][C]83[/C][C]0.985145791474801[/C][C]0.0297084170503975[/C][C]0.0148542085251988[/C][/ROW]
[ROW][C]84[/C][C]0.999278579802356[/C][C]0.00144284039528858[/C][C]0.000721420197644289[/C][/ROW]
[ROW][C]85[/C][C]0.998904377726476[/C][C]0.00219124454704884[/C][C]0.00109562227352442[/C][/ROW]
[ROW][C]86[/C][C]0.998367714027824[/C][C]0.00326457194435245[/C][C]0.00163228597217622[/C][/ROW]
[ROW][C]87[/C][C]0.997588157186866[/C][C]0.00482368562626853[/C][C]0.00241184281313427[/C][/ROW]
[ROW][C]88[/C][C]0.996680881021559[/C][C]0.00663823795688095[/C][C]0.00331911897844047[/C][/ROW]
[ROW][C]89[/C][C]0.995223527904684[/C][C]0.00955294419063281[/C][C]0.0047764720953164[/C][/ROW]
[ROW][C]90[/C][C]0.993231103818592[/C][C]0.0135377923628162[/C][C]0.00676889618140809[/C][/ROW]
[ROW][C]91[/C][C]0.990536310492332[/C][C]0.0189273790153362[/C][C]0.00946368950766808[/C][/ROW]
[ROW][C]92[/C][C]0.988373199548587[/C][C]0.023253600902825[/C][C]0.0116268004514125[/C][/ROW]
[ROW][C]93[/C][C]0.984201981791556[/C][C]0.0315960364168877[/C][C]0.0157980182084438[/C][/ROW]
[ROW][C]94[/C][C]0.97857105302205[/C][C]0.0428578939558991[/C][C]0.0214289469779495[/C][/ROW]
[ROW][C]95[/C][C]0.97377948083874[/C][C]0.0524410383225199[/C][C]0.02622051916126[/C][/ROW]
[ROW][C]96[/C][C]0.965217262080225[/C][C]0.0695654758395496[/C][C]0.0347827379197748[/C][/ROW]
[ROW][C]97[/C][C]0.958266970383377[/C][C]0.0834660592332464[/C][C]0.0417330296166232[/C][/ROW]
[ROW][C]98[/C][C]0.945719829400774[/C][C]0.108560341198452[/C][C]0.0542801705992259[/C][/ROW]
[ROW][C]99[/C][C]0.930218816650786[/C][C]0.139562366698428[/C][C]0.069781183349214[/C][/ROW]
[ROW][C]100[/C][C]0.911644490410582[/C][C]0.176711019178836[/C][C]0.0883555095894178[/C][/ROW]
[ROW][C]101[/C][C]0.889198414730782[/C][C]0.221603170538437[/C][C]0.110801585269218[/C][/ROW]
[ROW][C]102[/C][C]0.863078917301745[/C][C]0.273842165396509[/C][C]0.136921082698255[/C][/ROW]
[ROW][C]103[/C][C]0.832943188432596[/C][C]0.334113623134808[/C][C]0.167056811567404[/C][/ROW]
[ROW][C]104[/C][C]0.798729742426811[/C][C]0.402540515146379[/C][C]0.201270257573189[/C][/ROW]
[ROW][C]105[/C][C]0.774242313989004[/C][C]0.451515372021992[/C][C]0.225757686010996[/C][/ROW]
[ROW][C]106[/C][C]0.73349253808176[/C][C]0.53301492383648[/C][C]0.26650746191824[/C][/ROW]
[ROW][C]107[/C][C]0.689207082786578[/C][C]0.621585834426844[/C][C]0.310792917213422[/C][/ROW]
[ROW][C]108[/C][C]0.656056238462994[/C][C]0.687887523074013[/C][C]0.343943761537006[/C][/ROW]
[ROW][C]109[/C][C]0.606630867461606[/C][C]0.786738265076787[/C][C]0.393369132538394[/C][/ROW]
[ROW][C]110[/C][C]0.554364022860617[/C][C]0.891271954278766[/C][C]0.445635977139383[/C][/ROW]
[ROW][C]111[/C][C]0.51838461601414[/C][C]0.963230767971721[/C][C]0.48161538398586[/C][/ROW]
[ROW][C]112[/C][C]0.482885185580965[/C][C]0.965770371161931[/C][C]0.517114814419035[/C][/ROW]
[ROW][C]113[/C][C]0.537422976959064[/C][C]0.925154046081873[/C][C]0.462577023040936[/C][/ROW]
[ROW][C]114[/C][C]0.501663853073137[/C][C]0.996672293853727[/C][C]0.498336146926863[/C][/ROW]
[ROW][C]115[/C][C]0.445940703032632[/C][C]0.891881406065265[/C][C]0.554059296967368[/C][/ROW]
[ROW][C]116[/C][C]0.393351516372473[/C][C]0.786703032744947[/C][C]0.606648483627527[/C][/ROW]
[ROW][C]117[/C][C]0.340169743523384[/C][C]0.680339487046768[/C][C]0.659830256476616[/C][/ROW]
[ROW][C]118[/C][C]0.289338952707588[/C][C]0.578677905415175[/C][C]0.710661047292412[/C][/ROW]
[ROW][C]119[/C][C]0.244510523170938[/C][C]0.489021046341876[/C][C]0.755489476829062[/C][/ROW]
[ROW][C]120[/C][C]0.201193047150548[/C][C]0.402386094301096[/C][C]0.798806952849452[/C][/ROW]
[ROW][C]121[/C][C]0.162537938829242[/C][C]0.325075877658485[/C][C]0.837462061170758[/C][/ROW]
[ROW][C]122[/C][C]0.130807313586478[/C][C]0.261614627172956[/C][C]0.869192686413522[/C][/ROW]
[ROW][C]123[/C][C]0.113786106960215[/C][C]0.22757221392043[/C][C]0.886213893039785[/C][/ROW]
[ROW][C]124[/C][C]0.14310543278747[/C][C]0.28621086557494[/C][C]0.85689456721253[/C][/ROW]
[ROW][C]125[/C][C]0.111222883629231[/C][C]0.222445767258461[/C][C]0.888777116370769[/C][/ROW]
[ROW][C]126[/C][C]0.0936185120972542[/C][C]0.187237024194508[/C][C]0.906381487902746[/C][/ROW]
[ROW][C]127[/C][C]0.0710625569720172[/C][C]0.142125113944034[/C][C]0.928937443027983[/C][/ROW]
[ROW][C]128[/C][C]0.051875384063739[/C][C]0.103750768127478[/C][C]0.948124615936261[/C][/ROW]
[ROW][C]129[/C][C]0.0378545639731721[/C][C]0.0757091279463441[/C][C]0.962145436026828[/C][/ROW]
[ROW][C]130[/C][C]0.0263300126374225[/C][C]0.0526600252748449[/C][C]0.973669987362578[/C][/ROW]
[ROW][C]131[/C][C]0.0175701100135657[/C][C]0.0351402200271314[/C][C]0.982429889986434[/C][/ROW]
[ROW][C]132[/C][C]0.0115749352874975[/C][C]0.023149870574995[/C][C]0.988425064712502[/C][/ROW]
[ROW][C]133[/C][C]0.021972178356265[/C][C]0.04394435671253[/C][C]0.978027821643735[/C][/ROW]
[ROW][C]134[/C][C]0.0152001728936694[/C][C]0.0304003457873389[/C][C]0.984799827106331[/C][/ROW]
[ROW][C]135[/C][C]0.0105214054827621[/C][C]0.0210428109655242[/C][C]0.989478594517238[/C][/ROW]
[ROW][C]136[/C][C]0.00746950387904003[/C][C]0.0149390077580801[/C][C]0.99253049612096[/C][/ROW]
[ROW][C]137[/C][C]0.0135052242068086[/C][C]0.0270104484136172[/C][C]0.986494775793191[/C][/ROW]
[ROW][C]138[/C][C]0.0117557404842157[/C][C]0.0235114809684314[/C][C]0.988244259515784[/C][/ROW]
[ROW][C]139[/C][C]0.0100559374822609[/C][C]0.0201118749645217[/C][C]0.989944062517739[/C][/ROW]
[ROW][C]140[/C][C]0.00543906518423439[/C][C]0.0108781303684688[/C][C]0.994560934815766[/C][/ROW]
[ROW][C]141[/C][C]0.0525008456007181[/C][C]0.105001691201436[/C][C]0.947499154399282[/C][/ROW]
[ROW][C]142[/C][C]0.0451113569343397[/C][C]0.0902227138686793[/C][C]0.95488864306566[/C][/ROW]
[ROW][C]143[/C][C]0.0275046733729299[/C][C]0.0550093467458599[/C][C]0.97249532662707[/C][/ROW]
[ROW][C]144[/C][C]0.0151193061827655[/C][C]0.0302386123655311[/C][C]0.984880693817234[/C][/ROW]
[ROW][C]145[/C][C]0.00624336449513153[/C][C]0.0124867289902631[/C][C]0.993756635504868[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203915&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203915&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
9001
10001
11001
12001
13001
14001
15001
16001
170.3547882474310710.7095764948621420.645211752568929
180.3063625176416210.6127250352832430.693637482358379
190.2754327721545530.5508655443091060.724567227845447
200.8513403997558570.2973192004882860.148659600244143
210.8030963954864230.3938072090271540.196903604513577
220.8703109921830570.2593780156338870.129689007816944
230.8318652288735070.3362695422529860.168134771126493
240.7837360554760720.4325278890478570.216263944523928
250.7723041359775160.4553917280449690.227695864022484
260.8009507032872490.3980985934255010.199049296712751
270.7513008261236520.4973983477526960.248699173876348
280.7385122510113330.5229754979773340.261487748988667
290.6875164285639080.6249671428721840.312483571436092
300.6314015830363570.7371968339272860.368598416963643
310.5716019660972520.8567960678054950.428398033902747
320.5107608074153560.9784783851692880.489239192584644
330.4581372495142650.916274499028530.541862750485735
340.4009182807103990.8018365614207980.599081719289601
350.3445798389709030.6891596779418060.655420161029097
360.2918728453844660.5837456907689310.708127154615535
370.3183949483755560.6367898967511120.681605051624444
380.2952030443996160.5904060887992330.704796955600384
390.2480063484834870.4960126969669730.751993651516513
400.2067952221703440.4135904443406870.793204777829656
410.6621131777601280.6757736444797450.337886822239872
420.6485192258945240.7029615482109510.351480774105476
430.6014156348889290.7971687302221420.398584365111071
440.5491371507758650.9017256984482690.450862849224135
450.4992190794346880.9984381588693770.500780920565312
460.4484048696630320.8968097393260640.551595130336968
470.3974910260851880.7949820521703760.602508973914812
480.3481439021177560.6962878042355110.651856097882244
490.3026282639170580.6052565278341170.697371736082942
500.2593616349917370.5187232699834750.740638365008263
510.2570682084424960.5141364168849920.742931791557504
520.5981663395477890.8036673209044210.401833660452211
530.5511228124682410.8977543750635180.448877187531759
540.8717047497423890.2565905005152220.128295250257611
550.8440446478858080.3119107042283850.155955352114192
560.8445695829502130.3108608340995740.155430417049787
570.8579760450124930.2840479099750140.142023954987507
580.8303844680403190.3392310639193630.169615531959681
590.7994128220136520.4011743559726950.200587177986348
600.9523157698397040.09536846032059170.0476842301602959
610.9391628323439280.1216743353121450.0608371676560723
620.9505932405606610.09881351887867710.0494067594393385
630.9374918672427170.1250162655145650.0625081327572827
640.9217805269084890.1564389461830230.0782194730915113
650.9033598083769940.1932803832460130.0966401916230064
660.881971422752610.236057154494780.11802857724739
670.9776818351251650.044636329749670.022318164874835
680.9706646306770920.05867073864581690.0293353693229084
690.9622105133418960.07557897331620720.0377894866581036
700.9674331906656560.06513361866868830.0325668093343442
710.9580975509210070.08380489815798670.0419024490789933
720.9468151373401440.1063697253197110.0531848626598557
730.9513014463254560.09739710734908860.0486985536745443
740.9593741220316930.08125175593661390.0406258779683069
750.9483613154369990.1032773691260010.0516386845630005
760.9349323119089480.1301353761821040.0650676880910522
770.919098224966250.16180355006750.0809017750337502
780.9301178974024590.1397642051950830.0698821025975413
790.993369828920190.01326034215961960.00663017107980978
800.990842170233170.01831565953365920.00915782976682961
810.9875565106313620.02488697873727680.0124434893686384
820.9890104126018670.02197917479626540.0109895873981327
830.9851457914748010.02970841705039750.0148542085251988
840.9992785798023560.001442840395288580.000721420197644289
850.9989043777264760.002191244547048840.00109562227352442
860.9983677140278240.003264571944352450.00163228597217622
870.9975881571868660.004823685626268530.00241184281313427
880.9966808810215590.006638237956880950.00331911897844047
890.9952235279046840.009552944190632810.0047764720953164
900.9932311038185920.01353779236281620.00676889618140809
910.9905363104923320.01892737901533620.00946368950766808
920.9883731995485870.0232536009028250.0116268004514125
930.9842019817915560.03159603641688770.0157980182084438
940.978571053022050.04285789395589910.0214289469779495
950.973779480838740.05244103832251990.02622051916126
960.9652172620802250.06956547583954960.0347827379197748
970.9582669703833770.08346605923324640.0417330296166232
980.9457198294007740.1085603411984520.0542801705992259
990.9302188166507860.1395623666984280.069781183349214
1000.9116444904105820.1767110191788360.0883555095894178
1010.8891984147307820.2216031705384370.110801585269218
1020.8630789173017450.2738421653965090.136921082698255
1030.8329431884325960.3341136231348080.167056811567404
1040.7987297424268110.4025405151463790.201270257573189
1050.7742423139890040.4515153720219920.225757686010996
1060.733492538081760.533014923836480.26650746191824
1070.6892070827865780.6215858344268440.310792917213422
1080.6560562384629940.6878875230740130.343943761537006
1090.6066308674616060.7867382650767870.393369132538394
1100.5543640228606170.8912719542787660.445635977139383
1110.518384616014140.9632307679717210.48161538398586
1120.4828851855809650.9657703711619310.517114814419035
1130.5374229769590640.9251540460818730.462577023040936
1140.5016638530731370.9966722938537270.498336146926863
1150.4459407030326320.8918814060652650.554059296967368
1160.3933515163724730.7867030327449470.606648483627527
1170.3401697435233840.6803394870467680.659830256476616
1180.2893389527075880.5786779054151750.710661047292412
1190.2445105231709380.4890210463418760.755489476829062
1200.2011930471505480.4023860943010960.798806952849452
1210.1625379388292420.3250758776584850.837462061170758
1220.1308073135864780.2616146271729560.869192686413522
1230.1137861069602150.227572213920430.886213893039785
1240.143105432787470.286210865574940.85689456721253
1250.1112228836292310.2224457672584610.888777116370769
1260.09361851209725420.1872370241945080.906381487902746
1270.07106255697201720.1421251139440340.928937443027983
1280.0518753840637390.1037507681274780.948124615936261
1290.03785456397317210.07570912794634410.962145436026828
1300.02633001263742250.05266002527484490.973669987362578
1310.01757011001356570.03514022002713140.982429889986434
1320.01157493528749750.0231498705749950.988425064712502
1330.0219721783562650.043944356712530.978027821643735
1340.01520017289366940.03040034578733890.984799827106331
1350.01052140548276210.02104281096552420.989478594517238
1360.007469503879040030.01493900775808010.99253049612096
1370.01350522420680860.02701044841361720.986494775793191
1380.01175574048421570.02351148096843140.988244259515784
1390.01005593748226090.02011187496452170.989944062517739
1400.005439065184234390.01087813036846880.994560934815766
1410.05250084560071810.1050016912014360.947499154399282
1420.04511135693433970.09022271386867930.95488864306566
1430.02750467337292990.05500934674585990.97249532662707
1440.01511930618276550.03023861236553110.984880693817234
1450.006243364495131530.01248672899026310.993756635504868







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.102189781021898NOK
5% type I error level370.27007299270073NOK
10% type I error level520.37956204379562NOK

\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 & 14 & 0.102189781021898 & NOK \tabularnewline
5% type I error level & 37 & 0.27007299270073 & NOK \tabularnewline
10% type I error level & 52 & 0.37956204379562 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203915&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]14[/C][C]0.102189781021898[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.27007299270073[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]52[/C][C]0.37956204379562[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203915&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203915&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 level140.102189781021898NOK
5% type I error level370.27007299270073NOK
10% type I error level520.37956204379562NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}