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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationMon, 19 Nov 2012 09:47:34 -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/Nov/19/t1353336474y1fe28j4tmd9vq3.htm/, Retrieved Sat, 27 Apr 2024 19:13:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190547, Retrieved Sat, 27 Apr 2024 19:13:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact79

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS 7 + month] [2012-11-17 15:45:21] [f8ee2fa4f3a14474001c30fec05fcd2b]
- R       [Multiple Regression] [WS 7 Trend] [2012-11-17 16:03:29] [f8ee2fa4f3a14474001c30fec05fcd2b]
-  MP         [Multiple Regression] [WS 7:] [2012-11-19 14:47:34] [0d2ad79739942b80a90a457d326f3d01] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
9	26	21	21	23	17	23	4	14	12
9	20	16	15	24	17	20	4	18	11
9	19	19	18	22	18	20	6	11	14
9	19	18	11	20	21	21	8	12	12
9	20	16	8	24	20	24	8	16	21
9	25	23	19	27	28	22	4	18	12
9	25	17	4	28	19	23	4	14	22
9	22	12	20	27	22	20	8	14	11
9	26	19	16	24	16	25	5	15	10
9	22	16	14	23	18	23	4	15	13
9	17	19	10	24	25	27	4	17	10
9	22	20	13	27	17	27	4	19	8
9	19	13	14	27	14	22	4	10	15
9	24	20	8	28	11	24	4	16	14
9	26	27	23	27	27	25	4	18	10
9	21	17	11	23	20	22	8	14	14
9	13	8	9	24	22	28	4	14	14
9	26	25	24	28	22	28	4	17	11
9	20	26	5	27	21	27	4	14	10
9	22	13	15	25	23	25	8	16	13
9	14	19	5	19	17	16	4	18	7
9	21	15	19	24	24	28	7	11	14
9	7	5	6	20	14	21	4	14	12
9	23	16	13	28	17	24	4	12	14
9	17	14	11	26	23	27	5	17	11
9	25	24	17	23	24	14	4	9	9
9	25	24	17	23	24	14	4	16	11
9	19	9	5	20	8	27	4	14	15
9	20	19	9	11	22	20	4	15	14
9	23	19	15	24	23	21	4	11	13
9	22	25	17	25	25	22	4	16	9
9	22	19	17	23	21	21	4	13	15
9	21	18	20	18	24	12	15	17	10
9	15	15	12	20	15	20	10	15	11
9	20	12	7	20	22	24	4	14	13
9	22	21	16	24	21	19	8	16	8
9	18	12	7	23	25	28	4	9	20
9	20	15	14	25	16	23	4	15	12
9	28	28	24	28	28	27	4	17	10
9	22	25	15	26	23	22	4	13	10
9	18	19	15	26	21	27	7	15	9
9	23	20	10	23	21	26	4	16	14
9	20	24	14	22	26	22	6	16	8
9	25	26	18	24	22	21	5	12	14
9	26	25	12	21	21	19	4	12	11
9	15	12	9	20	18	24	16	11	13
9	17	12	9	22	12	19	5	15	9
9	23	15	8	20	25	26	12	15	11
9	21	17	18	25	17	22	6	17	15
9	13	14	10	20	24	28	9	13	11
9	18	16	17	22	15	21	9	16	10
9	19	11	14	23	13	23	4	14	14
9	22	20	16	25	26	28	5	11	18
9	16	11	10	23	16	10	4	12	14
9	24	22	19	23	24	24	4	12	11
9	18	20	10	22	21	21	5	15	12
9	20	19	14	24	20	21	4	16	13
9	24	17	10	25	14	24	4	15	9
9	14	21	4	21	25	24	4	12	10
9	22	23	19	12	25	25	5	12	15
9	24	18	9	17	20	25	4	8	20
9	18	17	12	20	22	23	6	13	12
9	21	27	16	23	20	21	4	11	12
9	23	25	11	23	26	16	4	14	14
9	17	19	18	20	18	17	18	15	13
10	22	22	11	28	22	25	4	10	11
10	24	24	24	24	24	24	6	11	17
10	21	20	17	24	17	23	4	12	12
10	22	19	18	24	24	25	4	15	13
10	16	11	9	24	20	23	5	15	14
10	21	22	19	28	19	28	4	14	13
10	23	22	18	25	20	26	4	16	15
10	22	16	12	21	15	22	5	15	13
10	24	20	23	25	23	19	10	15	10
10	24	24	22	25	26	26	5	13	11
10	16	16	14	18	22	18	8	12	19
10	16	16	14	17	20	18	8	17	13
10	21	22	16	26	24	25	5	13	17
10	26	24	23	28	26	27	4	15	13
10	15	16	7	21	21	12	4	13	9
10	25	27	10	27	25	15	4	15	11
10	18	11	12	22	13	21	5	16	10
10	23	21	12	21	20	23	4	15	9
10	20	20	12	25	22	22	4	16	12
10	17	20	17	22	23	21	8	15	12
10	25	27	21	23	28	24	4	14	13
10	24	20	16	26	22	27	5	15	13
10	17	12	11	19	20	22	14	14	12
10	19	8	14	25	6	28	8	13	15
10	20	21	13	21	21	26	8	7	22
10	15	18	9	13	20	10	4	17	13
10	27	24	19	24	18	19	4	13	15
10	22	16	13	25	23	22	6	15	13
10	23	18	19	26	20	21	4	14	15
10	16	20	13	25	24	24	7	13	10
10	19	20	13	25	22	25	7	16	11
10	25	19	13	22	21	21	4	12	16
10	19	17	14	21	18	20	6	14	11
10	19	16	12	23	21	21	4	17	11
10	26	26	22	25	23	24	7	15	10
10	21	15	11	24	23	23	4	17	10
10	20	22	5	21	15	18	4	12	16
10	24	17	18	21	21	24	8	16	12
10	22	23	19	25	24	24	4	11	11
10	20	21	14	22	23	19	4	15	16
10	18	19	15	20	21	20	10	9	19
10	18	14	12	20	21	18	8	16	11
10	24	17	19	23	20	20	6	15	16
10	24	12	15	28	11	27	4	10	15
10	22	24	17	23	22	23	4	10	24
10	23	18	8	28	27	26	4	15	14
10	22	20	10	24	25	23	5	11	15
10	20	16	12	18	18	17	4	13	11
10	18	20	12	20	20	21	6	14	15
10	25	22	20	28	24	25	4	18	12
10	18	12	12	21	10	23	5	16	10
10	16	16	12	21	27	27	7	14	14
10	20	17	14	25	21	24	8	14	13
10	19	22	6	19	21	20	5	14	9
10	15	12	10	18	18	27	8	14	15
10	19	14	18	21	15	21	10	12	15
10	19	23	18	22	24	24	8	14	14
10	16	15	7	24	22	21	5	15	11
10	17	17	18	15	14	15	12	15	8
10	28	28	9	28	28	25	4	15	11
10	23	20	17	26	18	25	5	13	11
10	25	23	22	23	26	22	4	17	8
10	20	13	11	26	17	24	6	17	10
10	17	18	15	20	19	21	4	19	11
10	23	23	17	22	22	22	4	15	13
10	16	19	15	20	18	23	7	13	11
10	23	23	22	23	24	22	7	9	20
10	11	12	9	22	15	20	10	15	10
10	18	16	13	24	18	23	4	15	15
10	24	23	20	23	26	25	5	15	12
10	23	13	14	22	11	23	8	16	14
10	21	22	14	26	26	22	11	11	23
10	16	18	12	23	21	25	7	14	14
10	24	23	20	27	23	26	4	11	16
10	23	20	20	23	23	22	8	15	11
10	18	10	8	21	15	24	6	13	12
10	20	17	17	26	22	24	7	15	10
10	9	18	9	23	26	25	5	16	14
10	24	15	18	21	16	20	4	14	12
10	25	23	22	27	20	26	8	15	12
10	20	17	10	19	18	21	4	16	11
10	21	17	13	23	22	26	8	16	12
10	25	22	15	25	16	21	6	11	13
10	22	20	18	23	19	22	4	12	11
10	21	20	18	22	20	16	9	9	19
10	21	19	12	22	19	26	5	16	12
10	22	18	12	25	23	28	6	13	17
10	27	22	20	25	24	18	4	16	9
9	24	20	12	28	25	25	4	12	12
10	24	22	16	28	21	23	4	9	19
10	21	18	16	20	21	21	5	13	18
10	18	16	18	25	23	20	6	13	15
10	16	16	16	19	27	25	16	14	14
10	22	16	13	25	23	22	6	19	11
10	20	16	17	22	18	21	6	13	9
10	18	17	13	18	16	16	4	12	18
11	20	18	17	20	16	18	4	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190547&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190547&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190547&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
I1[t] = + 12.2426139710004 -0.812022350622843Month[t] + 0.366415335982668I2[t] + 0.263119372715402I3[t] + 0.267206841902655E1[t] -0.123305108901185E2[t] + 0.022526495877855E3[t] -0.215225699587043A[t] + 0.0597217620697523Happiness[t] + 0.127970017141604`Depression\r`[t] + 0.00364365193725671t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
I1[t] =  +  12.2426139710004 -0.812022350622843Month[t] +  0.366415335982668I2[t] +  0.263119372715402I3[t] +  0.267206841902655E1[t] -0.123305108901185E2[t] +  0.022526495877855E3[t] -0.215225699587043A[t] +  0.0597217620697523Happiness[t] +  0.127970017141604`Depression\r`[t] +  0.00364365193725671t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190547&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]I1[t] =  +  12.2426139710004 -0.812022350622843Month[t] +  0.366415335982668I2[t] +  0.263119372715402I3[t] +  0.267206841902655E1[t] -0.123305108901185E2[t] +  0.022526495877855E3[t] -0.215225699587043A[t] +  0.0597217620697523Happiness[t] +  0.127970017141604`Depression\r`[t] +  0.00364365193725671t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190547&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190547&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
I1[t] = + 12.2426139710004 -0.812022350622843Month[t] + 0.366415335982668I2[t] + 0.263119372715402I3[t] + 0.267206841902655E1[t] -0.123305108901185E2[t] + 0.022526495877855E3[t] -0.215225699587043A[t] + 0.0597217620697523Happiness[t] + 0.127970017141604`Depression\r`[t] + 0.00364365193725671t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.24261397100046.7370581.81720.0711680.035584
Month-0.8120223506228430.718578-1.130.2602510.130125
I20.3664153359826680.0634835.771800
I30.2631193727154020.0509925.161e-060
E10.2672068419026550.0752513.55090.0005120.000256
E2-0.1233051089011850.059075-2.08730.0385440.019272
E30.0225264958778550.0617020.36510.7155570.357778
A-0.2152256995870430.084393-2.55030.0117590.00588
Happiness0.05972176206975230.102460.58290.5608430.280421
`Depression\r`0.1279700171416040.0762841.67750.0955050.047753
t0.003643651937256710.0076840.47420.6360450.318022

\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) & 12.2426139710004 & 6.737058 & 1.8172 & 0.071168 & 0.035584 \tabularnewline
Month & -0.812022350622843 & 0.718578 & -1.13 & 0.260251 & 0.130125 \tabularnewline
I2 & 0.366415335982668 & 0.063483 & 5.7718 & 0 & 0 \tabularnewline
I3 & 0.263119372715402 & 0.050992 & 5.16 & 1e-06 & 0 \tabularnewline
E1 & 0.267206841902655 & 0.075251 & 3.5509 & 0.000512 & 0.000256 \tabularnewline
E2 & -0.123305108901185 & 0.059075 & -2.0873 & 0.038544 & 0.019272 \tabularnewline
E3 & 0.022526495877855 & 0.061702 & 0.3651 & 0.715557 & 0.357778 \tabularnewline
A & -0.215225699587043 & 0.084393 & -2.5503 & 0.011759 & 0.00588 \tabularnewline
Happiness & 0.0597217620697523 & 0.10246 & 0.5829 & 0.560843 & 0.280421 \tabularnewline
`Depression\r` & 0.127970017141604 & 0.076284 & 1.6775 & 0.095505 & 0.047753 \tabularnewline
t & 0.00364365193725671 & 0.007684 & 0.4742 & 0.636045 & 0.318022 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190547&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]12.2426139710004[/C][C]6.737058[/C][C]1.8172[/C][C]0.071168[/C][C]0.035584[/C][/ROW]
[ROW][C]Month[/C][C]-0.812022350622843[/C][C]0.718578[/C][C]-1.13[/C][C]0.260251[/C][C]0.130125[/C][/ROW]
[ROW][C]I2[/C][C]0.366415335982668[/C][C]0.063483[/C][C]5.7718[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]I3[/C][C]0.263119372715402[/C][C]0.050992[/C][C]5.16[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]E1[/C][C]0.267206841902655[/C][C]0.075251[/C][C]3.5509[/C][C]0.000512[/C][C]0.000256[/C][/ROW]
[ROW][C]E2[/C][C]-0.123305108901185[/C][C]0.059075[/C][C]-2.0873[/C][C]0.038544[/C][C]0.019272[/C][/ROW]
[ROW][C]E3[/C][C]0.022526495877855[/C][C]0.061702[/C][C]0.3651[/C][C]0.715557[/C][C]0.357778[/C][/ROW]
[ROW][C]A[/C][C]-0.215225699587043[/C][C]0.084393[/C][C]-2.5503[/C][C]0.011759[/C][C]0.00588[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0597217620697523[/C][C]0.10246[/C][C]0.5829[/C][C]0.560843[/C][C]0.280421[/C][/ROW]
[ROW][C]`Depression\r`[/C][C]0.127970017141604[/C][C]0.076284[/C][C]1.6775[/C][C]0.095505[/C][C]0.047753[/C][/ROW]
[ROW][C]t[/C][C]0.00364365193725671[/C][C]0.007684[/C][C]0.4742[/C][C]0.636045[/C][C]0.318022[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190547&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190547&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.24261397100046.7370581.81720.0711680.035584
Month-0.8120223506228430.718578-1.130.2602510.130125
I20.3664153359826680.0634835.771800
I30.2631193727154020.0509925.161e-060
E10.2672068419026550.0752513.55090.0005120.000256
E2-0.1233051089011850.059075-2.08730.0385440.019272
E30.0225264958778550.0617020.36510.7155570.357778
A-0.2152256995870430.084393-2.55030.0117590.00588
Happiness0.05972176206975230.102460.58290.5608430.280421
`Depression\r`0.1279700171416040.0762841.67750.0955050.047753
t0.003643651937256710.0076840.47420.6360450.318022






Multiple Linear Regression - Regression Statistics
Multiple R0.749973023374418
R-squared0.562459535789365
Adjusted R-squared0.533483346106542
F-TEST (value)19.411093796186
F-TEST (DF numerator)10
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49784974069936
Sum Squared Residuals942.127252393892

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.749973023374418 \tabularnewline
R-squared & 0.562459535789365 \tabularnewline
Adjusted R-squared & 0.533483346106542 \tabularnewline
F-TEST (value) & 19.411093796186 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.49784974069936 \tabularnewline
Sum Squared Residuals & 942.127252393892 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190547&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.749973023374418[/C][/ROW]
[ROW][C]R-squared[/C][C]0.562459535789365[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.533483346106542[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.411093796186[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.49784974069936[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]942.127252393892[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190547&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190547&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.749973023374418
R-squared0.562459535789365
Adjusted R-squared0.533483346106542
F-TEST (value)19.411093796186
F-TEST (DF numerator)10
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49784974069936
Sum Squared Residuals942.127252393892






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.23680734395081.76319265604925
22021.1402024650887-1.14020246508873
31921.9101377681762-2.91013776817616
41918.19705828910440.802941710895617
52019.32884231762890.671157682371081
62524.39044925227030.609550747729715
72520.68910273860874.31089726139129
82220.09730503093351.90269496906653
92622.24164999466553.75835000533451
102220.66007459277511.33992540722494
111719.9401972976192-2.94019729761918
122222.7511792904591-0.751179290459052
131921.0686120719108-2.06861207191078
142422.97098255507311.02901744492687
152626.8763255167494-0.876325516749414
162119.19720246527851.80279753472149
171316.3935277456565-3.39352774565655
182627.4371053024252-1.43710530242525
192022.3343326765223-2.33433267652235
202219.02214457169172.9778554283083
211417.7394638710995-3.73946387109947
222120.53639448160280.463605518397232
23714.0307735293565-7.03077352935654
242321.87863689240591.12136310759409
251719.1160193367051-2.11601933670508
262522.6262740724862.37372592751402
272523.30391009319471.69608990680529
281916.51043344035712.48956655964285
292016.87364111502543.12635888497463
302321.46205426954561.53794573045439
312224.0202805445702-2.02028054457016
322222.3503672531734-0.350367253173354
332118.09981995527012.90018004472986
341517.8082892394123-2.80828923941231
352016.11163271076343.88836728923661
362221.47927937818910.520720621810886
371817.23791250679620.762087493203799
382021.0487353955017-1.04873539550165
392827.72254083462910.277459165370927
402223.989456457212-1.98945645721204
411821.4996471986817-3.49964719868171
422321.07521124797771.9247887520223
432021.4248838627635-1.42488386276353
442524.96310205521760.036897944782419
452622.12956137447953.8704386255205
461514.4092983818550.590701618145016
471717.6690435667854-0.669043566785448
482315.27847936275357.72152063724652
492122.8011943012496-1.80119430124961
501316.1401817513547-3.14018175135466
511820.2561611461037-2.2561611461037
521919.6658050938043-0.665805093804278
532222.6549943452656-0.654994345265646
541617.838411609562-1.83841160956201
552423.18571833140290.814281668597095
561820.2154955578674-2.21549555786736
572021.9658376361885-1.96583763618845
582420.68718827760593.31281172239407
591418.1013982025901-4.10139820259013
602220.82695242209871.1730475779013
612418.93607415800385.06392584199615
621818.7170151822964-0.71701518229637
632124.7514753117111-3.75147531171112
642322.28933361580210.71066638419789
651719.0622596529401-2.06225965294009
662221.79454856942070.205451430579334
672425.0107011230779-1.01070112307793
682122.397780164043-1.39778016404296
692221.78718038551120.212819614488773
701616.8523387565521-0.852338756552115
712124.8500437077295-3.8500437077295
722323.9959729190092-0.995972919009212
732219.14911301620192.85088698379807
742422.06749957134681.93250042865317
752424.1461103298781-0.146110329878098
761617.8743981637961-1.87439816379607
771617.3882338991322-1.38823389913217
782123.1046050441089-2.10460504410892
792625.83856058993810.161439410061896
801516.4778282108812-1.47782821088118
812522.85438233864152.14561766135855
821817.5169314781960.48306852180399
832320.12697279788042.87302720211961
842021.0075235812599-1.00752358125994
851720.4586874058693-3.45868740586928
862524.72712773899710.272872261002863
872422.30379390471011.69620609528994
881714.19932477480872.80067522519127
891918.60687923913850.393120760861509
902020.6848054408236-0.684805440823637
911516.4683422983568-1.4683422983568
922724.70734862098062.29265137901944
932219.35226622447662.64773377552344
942322.90872212878750.0912778712125335
951620.0283834799849-4.02838347998491
961920.6082991489533-1.60829914895325
972520.52374620077984.47625379922022
981919.1870025816476-0.18700258164755
991919.0946336905343-0.0946336905342887
1002624.85591674304651.14408325695349
1012118.41006588442512.58993411557488
1022019.94129981056170.0587001894382555
1032419.79485113111034.20514886888967
1042223.3933421834239-1.39334218342387
1052021.4363475376098-1.43634753760975
1061819.439228201656-1.43922820165603
1071816.60112766030761.39887233969245
1082421.52641127837782.47358872162216
1092421.25283899199282.7471610080072
1102224.5489391844896-2.54893918448958
1112319.79201325766583.20798674233415
1122219.8387869588332.16121304116702
1132017.85053290305572.14946709694427
1141819.8388977799001-1.8388977799001
1152524.70029576468210.299704235317883
1161818.154992456602-0.154992456602017
1171617.580201729914-1.58020172991405
1182019.87438227992030.125617720079707
1191918.0455976253150.954402374684969
1201515.8201023683216-0.820102368321603
1211919.1480136277782-0.148013627778212
1221922.096360559157-3.09636055915702
1231617.3093016467437-1.30930164674369
1241717.4960194408-0.496019440800049
1252823.24055546112574.75944453887433
1262322.78179958840480.218200411595211
1272523.41484792375731.58515207624271
1282018.64193324868191.35806675131809
1291720.2905652549974-3.29056525499744
1302322.85660217126270.14339782873735
1311619.8286183243319-3.82861832433187
1322324.091865411626-1.09186541162605
1331115.8748279711474-4.87482797114737
1341820.5598925858425-2.55989258584251
1352423.1625487262980.837451273702028
1362319.13062427956743.86937572043262
1372121.8361744387282-0.836174438728189
1381619.6187404380185-3.61874043801848
1392425.1266112441135-1.12661124411348
1402321.61020970120351.38979029879649
1411815.72832559206532.27167440793467
1422020.7861271877941-0.786127187794113
143918.7809699583662-9.78096995836622
1442420.47928902377573.52071097622431
1452524.91073140923580.0892685907641989
1462018.34742811916041.6525718808396
1472119.09573651943241.90426348056755
1482522.87912006050392.12087993949608
1492222.2917217709517-0.29172177095167
1502121.5381608498063-0.538160849806321
1512120.32840800999340.671591990006561
1522220.56454850771871.43545149228131
1532723.37609496588063.6239050341194
1542422.33499920584971.66500079415028
1552424.4767209476221-0.476720947622082
1562120.72768686020210.272313139797922
1571820.992500330426-2.992500330426
1581616.2655709783588-0.265570978358774
1592219.57569426633132.42430573366869
1602019.80992332534860.190076674651446
1611819.7151149845881-1.71511498458812
1622020.7088775160944-0.708877516094371

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.2368073439508 & 1.76319265604925 \tabularnewline
2 & 20 & 21.1402024650887 & -1.14020246508873 \tabularnewline
3 & 19 & 21.9101377681762 & -2.91013776817616 \tabularnewline
4 & 19 & 18.1970582891044 & 0.802941710895617 \tabularnewline
5 & 20 & 19.3288423176289 & 0.671157682371081 \tabularnewline
6 & 25 & 24.3904492522703 & 0.609550747729715 \tabularnewline
7 & 25 & 20.6891027386087 & 4.31089726139129 \tabularnewline
8 & 22 & 20.0973050309335 & 1.90269496906653 \tabularnewline
9 & 26 & 22.2416499946655 & 3.75835000533451 \tabularnewline
10 & 22 & 20.6600745927751 & 1.33992540722494 \tabularnewline
11 & 17 & 19.9401972976192 & -2.94019729761918 \tabularnewline
12 & 22 & 22.7511792904591 & -0.751179290459052 \tabularnewline
13 & 19 & 21.0686120719108 & -2.06861207191078 \tabularnewline
14 & 24 & 22.9709825550731 & 1.02901744492687 \tabularnewline
15 & 26 & 26.8763255167494 & -0.876325516749414 \tabularnewline
16 & 21 & 19.1972024652785 & 1.80279753472149 \tabularnewline
17 & 13 & 16.3935277456565 & -3.39352774565655 \tabularnewline
18 & 26 & 27.4371053024252 & -1.43710530242525 \tabularnewline
19 & 20 & 22.3343326765223 & -2.33433267652235 \tabularnewline
20 & 22 & 19.0221445716917 & 2.9778554283083 \tabularnewline
21 & 14 & 17.7394638710995 & -3.73946387109947 \tabularnewline
22 & 21 & 20.5363944816028 & 0.463605518397232 \tabularnewline
23 & 7 & 14.0307735293565 & -7.03077352935654 \tabularnewline
24 & 23 & 21.8786368924059 & 1.12136310759409 \tabularnewline
25 & 17 & 19.1160193367051 & -2.11601933670508 \tabularnewline
26 & 25 & 22.626274072486 & 2.37372592751402 \tabularnewline
27 & 25 & 23.3039100931947 & 1.69608990680529 \tabularnewline
28 & 19 & 16.5104334403571 & 2.48956655964285 \tabularnewline
29 & 20 & 16.8736411150254 & 3.12635888497463 \tabularnewline
30 & 23 & 21.4620542695456 & 1.53794573045439 \tabularnewline
31 & 22 & 24.0202805445702 & -2.02028054457016 \tabularnewline
32 & 22 & 22.3503672531734 & -0.350367253173354 \tabularnewline
33 & 21 & 18.0998199552701 & 2.90018004472986 \tabularnewline
34 & 15 & 17.8082892394123 & -2.80828923941231 \tabularnewline
35 & 20 & 16.1116327107634 & 3.88836728923661 \tabularnewline
36 & 22 & 21.4792793781891 & 0.520720621810886 \tabularnewline
37 & 18 & 17.2379125067962 & 0.762087493203799 \tabularnewline
38 & 20 & 21.0487353955017 & -1.04873539550165 \tabularnewline
39 & 28 & 27.7225408346291 & 0.277459165370927 \tabularnewline
40 & 22 & 23.989456457212 & -1.98945645721204 \tabularnewline
41 & 18 & 21.4996471986817 & -3.49964719868171 \tabularnewline
42 & 23 & 21.0752112479777 & 1.9247887520223 \tabularnewline
43 & 20 & 21.4248838627635 & -1.42488386276353 \tabularnewline
44 & 25 & 24.9631020552176 & 0.036897944782419 \tabularnewline
45 & 26 & 22.1295613744795 & 3.8704386255205 \tabularnewline
46 & 15 & 14.409298381855 & 0.590701618145016 \tabularnewline
47 & 17 & 17.6690435667854 & -0.669043566785448 \tabularnewline
48 & 23 & 15.2784793627535 & 7.72152063724652 \tabularnewline
49 & 21 & 22.8011943012496 & -1.80119430124961 \tabularnewline
50 & 13 & 16.1401817513547 & -3.14018175135466 \tabularnewline
51 & 18 & 20.2561611461037 & -2.2561611461037 \tabularnewline
52 & 19 & 19.6658050938043 & -0.665805093804278 \tabularnewline
53 & 22 & 22.6549943452656 & -0.654994345265646 \tabularnewline
54 & 16 & 17.838411609562 & -1.83841160956201 \tabularnewline
55 & 24 & 23.1857183314029 & 0.814281668597095 \tabularnewline
56 & 18 & 20.2154955578674 & -2.21549555786736 \tabularnewline
57 & 20 & 21.9658376361885 & -1.96583763618845 \tabularnewline
58 & 24 & 20.6871882776059 & 3.31281172239407 \tabularnewline
59 & 14 & 18.1013982025901 & -4.10139820259013 \tabularnewline
60 & 22 & 20.8269524220987 & 1.1730475779013 \tabularnewline
61 & 24 & 18.9360741580038 & 5.06392584199615 \tabularnewline
62 & 18 & 18.7170151822964 & -0.71701518229637 \tabularnewline
63 & 21 & 24.7514753117111 & -3.75147531171112 \tabularnewline
64 & 23 & 22.2893336158021 & 0.71066638419789 \tabularnewline
65 & 17 & 19.0622596529401 & -2.06225965294009 \tabularnewline
66 & 22 & 21.7945485694207 & 0.205451430579334 \tabularnewline
67 & 24 & 25.0107011230779 & -1.01070112307793 \tabularnewline
68 & 21 & 22.397780164043 & -1.39778016404296 \tabularnewline
69 & 22 & 21.7871803855112 & 0.212819614488773 \tabularnewline
70 & 16 & 16.8523387565521 & -0.852338756552115 \tabularnewline
71 & 21 & 24.8500437077295 & -3.8500437077295 \tabularnewline
72 & 23 & 23.9959729190092 & -0.995972919009212 \tabularnewline
73 & 22 & 19.1491130162019 & 2.85088698379807 \tabularnewline
74 & 24 & 22.0674995713468 & 1.93250042865317 \tabularnewline
75 & 24 & 24.1461103298781 & -0.146110329878098 \tabularnewline
76 & 16 & 17.8743981637961 & -1.87439816379607 \tabularnewline
77 & 16 & 17.3882338991322 & -1.38823389913217 \tabularnewline
78 & 21 & 23.1046050441089 & -2.10460504410892 \tabularnewline
79 & 26 & 25.8385605899381 & 0.161439410061896 \tabularnewline
80 & 15 & 16.4778282108812 & -1.47782821088118 \tabularnewline
81 & 25 & 22.8543823386415 & 2.14561766135855 \tabularnewline
82 & 18 & 17.516931478196 & 0.48306852180399 \tabularnewline
83 & 23 & 20.1269727978804 & 2.87302720211961 \tabularnewline
84 & 20 & 21.0075235812599 & -1.00752358125994 \tabularnewline
85 & 17 & 20.4586874058693 & -3.45868740586928 \tabularnewline
86 & 25 & 24.7271277389971 & 0.272872261002863 \tabularnewline
87 & 24 & 22.3037939047101 & 1.69620609528994 \tabularnewline
88 & 17 & 14.1993247748087 & 2.80067522519127 \tabularnewline
89 & 19 & 18.6068792391385 & 0.393120760861509 \tabularnewline
90 & 20 & 20.6848054408236 & -0.684805440823637 \tabularnewline
91 & 15 & 16.4683422983568 & -1.4683422983568 \tabularnewline
92 & 27 & 24.7073486209806 & 2.29265137901944 \tabularnewline
93 & 22 & 19.3522662244766 & 2.64773377552344 \tabularnewline
94 & 23 & 22.9087221287875 & 0.0912778712125335 \tabularnewline
95 & 16 & 20.0283834799849 & -4.02838347998491 \tabularnewline
96 & 19 & 20.6082991489533 & -1.60829914895325 \tabularnewline
97 & 25 & 20.5237462007798 & 4.47625379922022 \tabularnewline
98 & 19 & 19.1870025816476 & -0.18700258164755 \tabularnewline
99 & 19 & 19.0946336905343 & -0.0946336905342887 \tabularnewline
100 & 26 & 24.8559167430465 & 1.14408325695349 \tabularnewline
101 & 21 & 18.4100658844251 & 2.58993411557488 \tabularnewline
102 & 20 & 19.9412998105617 & 0.0587001894382555 \tabularnewline
103 & 24 & 19.7948511311103 & 4.20514886888967 \tabularnewline
104 & 22 & 23.3933421834239 & -1.39334218342387 \tabularnewline
105 & 20 & 21.4363475376098 & -1.43634753760975 \tabularnewline
106 & 18 & 19.439228201656 & -1.43922820165603 \tabularnewline
107 & 18 & 16.6011276603076 & 1.39887233969245 \tabularnewline
108 & 24 & 21.5264112783778 & 2.47358872162216 \tabularnewline
109 & 24 & 21.2528389919928 & 2.7471610080072 \tabularnewline
110 & 22 & 24.5489391844896 & -2.54893918448958 \tabularnewline
111 & 23 & 19.7920132576658 & 3.20798674233415 \tabularnewline
112 & 22 & 19.838786958833 & 2.16121304116702 \tabularnewline
113 & 20 & 17.8505329030557 & 2.14946709694427 \tabularnewline
114 & 18 & 19.8388977799001 & -1.8388977799001 \tabularnewline
115 & 25 & 24.7002957646821 & 0.299704235317883 \tabularnewline
116 & 18 & 18.154992456602 & -0.154992456602017 \tabularnewline
117 & 16 & 17.580201729914 & -1.58020172991405 \tabularnewline
118 & 20 & 19.8743822799203 & 0.125617720079707 \tabularnewline
119 & 19 & 18.045597625315 & 0.954402374684969 \tabularnewline
120 & 15 & 15.8201023683216 & -0.820102368321603 \tabularnewline
121 & 19 & 19.1480136277782 & -0.148013627778212 \tabularnewline
122 & 19 & 22.096360559157 & -3.09636055915702 \tabularnewline
123 & 16 & 17.3093016467437 & -1.30930164674369 \tabularnewline
124 & 17 & 17.4960194408 & -0.496019440800049 \tabularnewline
125 & 28 & 23.2405554611257 & 4.75944453887433 \tabularnewline
126 & 23 & 22.7817995884048 & 0.218200411595211 \tabularnewline
127 & 25 & 23.4148479237573 & 1.58515207624271 \tabularnewline
128 & 20 & 18.6419332486819 & 1.35806675131809 \tabularnewline
129 & 17 & 20.2905652549974 & -3.29056525499744 \tabularnewline
130 & 23 & 22.8566021712627 & 0.14339782873735 \tabularnewline
131 & 16 & 19.8286183243319 & -3.82861832433187 \tabularnewline
132 & 23 & 24.091865411626 & -1.09186541162605 \tabularnewline
133 & 11 & 15.8748279711474 & -4.87482797114737 \tabularnewline
134 & 18 & 20.5598925858425 & -2.55989258584251 \tabularnewline
135 & 24 & 23.162548726298 & 0.837451273702028 \tabularnewline
136 & 23 & 19.1306242795674 & 3.86937572043262 \tabularnewline
137 & 21 & 21.8361744387282 & -0.836174438728189 \tabularnewline
138 & 16 & 19.6187404380185 & -3.61874043801848 \tabularnewline
139 & 24 & 25.1266112441135 & -1.12661124411348 \tabularnewline
140 & 23 & 21.6102097012035 & 1.38979029879649 \tabularnewline
141 & 18 & 15.7283255920653 & 2.27167440793467 \tabularnewline
142 & 20 & 20.7861271877941 & -0.786127187794113 \tabularnewline
143 & 9 & 18.7809699583662 & -9.78096995836622 \tabularnewline
144 & 24 & 20.4792890237757 & 3.52071097622431 \tabularnewline
145 & 25 & 24.9107314092358 & 0.0892685907641989 \tabularnewline
146 & 20 & 18.3474281191604 & 1.6525718808396 \tabularnewline
147 & 21 & 19.0957365194324 & 1.90426348056755 \tabularnewline
148 & 25 & 22.8791200605039 & 2.12087993949608 \tabularnewline
149 & 22 & 22.2917217709517 & -0.29172177095167 \tabularnewline
150 & 21 & 21.5381608498063 & -0.538160849806321 \tabularnewline
151 & 21 & 20.3284080099934 & 0.671591990006561 \tabularnewline
152 & 22 & 20.5645485077187 & 1.43545149228131 \tabularnewline
153 & 27 & 23.3760949658806 & 3.6239050341194 \tabularnewline
154 & 24 & 22.3349992058497 & 1.66500079415028 \tabularnewline
155 & 24 & 24.4767209476221 & -0.476720947622082 \tabularnewline
156 & 21 & 20.7276868602021 & 0.272313139797922 \tabularnewline
157 & 18 & 20.992500330426 & -2.992500330426 \tabularnewline
158 & 16 & 16.2655709783588 & -0.265570978358774 \tabularnewline
159 & 22 & 19.5756942663313 & 2.42430573366869 \tabularnewline
160 & 20 & 19.8099233253486 & 0.190076674651446 \tabularnewline
161 & 18 & 19.7151149845881 & -1.71511498458812 \tabularnewline
162 & 20 & 20.7088775160944 & -0.708877516094371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190547&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]26[/C][C]24.2368073439508[/C][C]1.76319265604925[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]21.1402024650887[/C][C]-1.14020246508873[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]21.9101377681762[/C][C]-2.91013776817616[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]18.1970582891044[/C][C]0.802941710895617[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]19.3288423176289[/C][C]0.671157682371081[/C][/ROW]
[ROW][C]6[/C][C]25[/C][C]24.3904492522703[/C][C]0.609550747729715[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]20.6891027386087[/C][C]4.31089726139129[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.0973050309335[/C][C]1.90269496906653[/C][/ROW]
[ROW][C]9[/C][C]26[/C][C]22.2416499946655[/C][C]3.75835000533451[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]20.6600745927751[/C][C]1.33992540722494[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]19.9401972976192[/C][C]-2.94019729761918[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]22.7511792904591[/C][C]-0.751179290459052[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]21.0686120719108[/C][C]-2.06861207191078[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]22.9709825550731[/C][C]1.02901744492687[/C][/ROW]
[ROW][C]15[/C][C]26[/C][C]26.8763255167494[/C][C]-0.876325516749414[/C][/ROW]
[ROW][C]16[/C][C]21[/C][C]19.1972024652785[/C][C]1.80279753472149[/C][/ROW]
[ROW][C]17[/C][C]13[/C][C]16.3935277456565[/C][C]-3.39352774565655[/C][/ROW]
[ROW][C]18[/C][C]26[/C][C]27.4371053024252[/C][C]-1.43710530242525[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]22.3343326765223[/C][C]-2.33433267652235[/C][/ROW]
[ROW][C]20[/C][C]22[/C][C]19.0221445716917[/C][C]2.9778554283083[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]17.7394638710995[/C][C]-3.73946387109947[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]20.5363944816028[/C][C]0.463605518397232[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]14.0307735293565[/C][C]-7.03077352935654[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.8786368924059[/C][C]1.12136310759409[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]19.1160193367051[/C][C]-2.11601933670508[/C][/ROW]
[ROW][C]26[/C][C]25[/C][C]22.626274072486[/C][C]2.37372592751402[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.3039100931947[/C][C]1.69608990680529[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]16.5104334403571[/C][C]2.48956655964285[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]16.8736411150254[/C][C]3.12635888497463[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]21.4620542695456[/C][C]1.53794573045439[/C][/ROW]
[ROW][C]31[/C][C]22[/C][C]24.0202805445702[/C][C]-2.02028054457016[/C][/ROW]
[ROW][C]32[/C][C]22[/C][C]22.3503672531734[/C][C]-0.350367253173354[/C][/ROW]
[ROW][C]33[/C][C]21[/C][C]18.0998199552701[/C][C]2.90018004472986[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]17.8082892394123[/C][C]-2.80828923941231[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]16.1116327107634[/C][C]3.88836728923661[/C][/ROW]
[ROW][C]36[/C][C]22[/C][C]21.4792793781891[/C][C]0.520720621810886[/C][/ROW]
[ROW][C]37[/C][C]18[/C][C]17.2379125067962[/C][C]0.762087493203799[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]21.0487353955017[/C][C]-1.04873539550165[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]27.7225408346291[/C][C]0.277459165370927[/C][/ROW]
[ROW][C]40[/C][C]22[/C][C]23.989456457212[/C][C]-1.98945645721204[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]21.4996471986817[/C][C]-3.49964719868171[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]21.0752112479777[/C][C]1.9247887520223[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]21.4248838627635[/C][C]-1.42488386276353[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]24.9631020552176[/C][C]0.036897944782419[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]22.1295613744795[/C][C]3.8704386255205[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]14.409298381855[/C][C]0.590701618145016[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]17.6690435667854[/C][C]-0.669043566785448[/C][/ROW]
[ROW][C]48[/C][C]23[/C][C]15.2784793627535[/C][C]7.72152063724652[/C][/ROW]
[ROW][C]49[/C][C]21[/C][C]22.8011943012496[/C][C]-1.80119430124961[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]16.1401817513547[/C][C]-3.14018175135466[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]20.2561611461037[/C][C]-2.2561611461037[/C][/ROW]
[ROW][C]52[/C][C]19[/C][C]19.6658050938043[/C][C]-0.665805093804278[/C][/ROW]
[ROW][C]53[/C][C]22[/C][C]22.6549943452656[/C][C]-0.654994345265646[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]17.838411609562[/C][C]-1.83841160956201[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]23.1857183314029[/C][C]0.814281668597095[/C][/ROW]
[ROW][C]56[/C][C]18[/C][C]20.2154955578674[/C][C]-2.21549555786736[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]21.9658376361885[/C][C]-1.96583763618845[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]20.6871882776059[/C][C]3.31281172239407[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]18.1013982025901[/C][C]-4.10139820259013[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]20.8269524220987[/C][C]1.1730475779013[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]18.9360741580038[/C][C]5.06392584199615[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]18.7170151822964[/C][C]-0.71701518229637[/C][/ROW]
[ROW][C]63[/C][C]21[/C][C]24.7514753117111[/C][C]-3.75147531171112[/C][/ROW]
[ROW][C]64[/C][C]23[/C][C]22.2893336158021[/C][C]0.71066638419789[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]19.0622596529401[/C][C]-2.06225965294009[/C][/ROW]
[ROW][C]66[/C][C]22[/C][C]21.7945485694207[/C][C]0.205451430579334[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]25.0107011230779[/C][C]-1.01070112307793[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]22.397780164043[/C][C]-1.39778016404296[/C][/ROW]
[ROW][C]69[/C][C]22[/C][C]21.7871803855112[/C][C]0.212819614488773[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]16.8523387565521[/C][C]-0.852338756552115[/C][/ROW]
[ROW][C]71[/C][C]21[/C][C]24.8500437077295[/C][C]-3.8500437077295[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]23.9959729190092[/C][C]-0.995972919009212[/C][/ROW]
[ROW][C]73[/C][C]22[/C][C]19.1491130162019[/C][C]2.85088698379807[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]22.0674995713468[/C][C]1.93250042865317[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]24.1461103298781[/C][C]-0.146110329878098[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]17.8743981637961[/C][C]-1.87439816379607[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]17.3882338991322[/C][C]-1.38823389913217[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]23.1046050441089[/C][C]-2.10460504410892[/C][/ROW]
[ROW][C]79[/C][C]26[/C][C]25.8385605899381[/C][C]0.161439410061896[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]16.4778282108812[/C][C]-1.47782821088118[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]22.8543823386415[/C][C]2.14561766135855[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]17.516931478196[/C][C]0.48306852180399[/C][/ROW]
[ROW][C]83[/C][C]23[/C][C]20.1269727978804[/C][C]2.87302720211961[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.0075235812599[/C][C]-1.00752358125994[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]20.4586874058693[/C][C]-3.45868740586928[/C][/ROW]
[ROW][C]86[/C][C]25[/C][C]24.7271277389971[/C][C]0.272872261002863[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]22.3037939047101[/C][C]1.69620609528994[/C][/ROW]
[ROW][C]88[/C][C]17[/C][C]14.1993247748087[/C][C]2.80067522519127[/C][/ROW]
[ROW][C]89[/C][C]19[/C][C]18.6068792391385[/C][C]0.393120760861509[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]20.6848054408236[/C][C]-0.684805440823637[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]16.4683422983568[/C][C]-1.4683422983568[/C][/ROW]
[ROW][C]92[/C][C]27[/C][C]24.7073486209806[/C][C]2.29265137901944[/C][/ROW]
[ROW][C]93[/C][C]22[/C][C]19.3522662244766[/C][C]2.64773377552344[/C][/ROW]
[ROW][C]94[/C][C]23[/C][C]22.9087221287875[/C][C]0.0912778712125335[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]20.0283834799849[/C][C]-4.02838347998491[/C][/ROW]
[ROW][C]96[/C][C]19[/C][C]20.6082991489533[/C][C]-1.60829914895325[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]20.5237462007798[/C][C]4.47625379922022[/C][/ROW]
[ROW][C]98[/C][C]19[/C][C]19.1870025816476[/C][C]-0.18700258164755[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.0946336905343[/C][C]-0.0946336905342887[/C][/ROW]
[ROW][C]100[/C][C]26[/C][C]24.8559167430465[/C][C]1.14408325695349[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]18.4100658844251[/C][C]2.58993411557488[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]19.9412998105617[/C][C]0.0587001894382555[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]19.7948511311103[/C][C]4.20514886888967[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]23.3933421834239[/C][C]-1.39334218342387[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]21.4363475376098[/C][C]-1.43634753760975[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]19.439228201656[/C][C]-1.43922820165603[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]16.6011276603076[/C][C]1.39887233969245[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]21.5264112783778[/C][C]2.47358872162216[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]21.2528389919928[/C][C]2.7471610080072[/C][/ROW]
[ROW][C]110[/C][C]22[/C][C]24.5489391844896[/C][C]-2.54893918448958[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]19.7920132576658[/C][C]3.20798674233415[/C][/ROW]
[ROW][C]112[/C][C]22[/C][C]19.838786958833[/C][C]2.16121304116702[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]17.8505329030557[/C][C]2.14946709694427[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]19.8388977799001[/C][C]-1.8388977799001[/C][/ROW]
[ROW][C]115[/C][C]25[/C][C]24.7002957646821[/C][C]0.299704235317883[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]18.154992456602[/C][C]-0.154992456602017[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]17.580201729914[/C][C]-1.58020172991405[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]19.8743822799203[/C][C]0.125617720079707[/C][/ROW]
[ROW][C]119[/C][C]19[/C][C]18.045597625315[/C][C]0.954402374684969[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.8201023683216[/C][C]-0.820102368321603[/C][/ROW]
[ROW][C]121[/C][C]19[/C][C]19.1480136277782[/C][C]-0.148013627778212[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]22.096360559157[/C][C]-3.09636055915702[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.3093016467437[/C][C]-1.30930164674369[/C][/ROW]
[ROW][C]124[/C][C]17[/C][C]17.4960194408[/C][C]-0.496019440800049[/C][/ROW]
[ROW][C]125[/C][C]28[/C][C]23.2405554611257[/C][C]4.75944453887433[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]22.7817995884048[/C][C]0.218200411595211[/C][/ROW]
[ROW][C]127[/C][C]25[/C][C]23.4148479237573[/C][C]1.58515207624271[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]18.6419332486819[/C][C]1.35806675131809[/C][/ROW]
[ROW][C]129[/C][C]17[/C][C]20.2905652549974[/C][C]-3.29056525499744[/C][/ROW]
[ROW][C]130[/C][C]23[/C][C]22.8566021712627[/C][C]0.14339782873735[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]19.8286183243319[/C][C]-3.82861832433187[/C][/ROW]
[ROW][C]132[/C][C]23[/C][C]24.091865411626[/C][C]-1.09186541162605[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]15.8748279711474[/C][C]-4.87482797114737[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.5598925858425[/C][C]-2.55989258584251[/C][/ROW]
[ROW][C]135[/C][C]24[/C][C]23.162548726298[/C][C]0.837451273702028[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]19.1306242795674[/C][C]3.86937572043262[/C][/ROW]
[ROW][C]137[/C][C]21[/C][C]21.8361744387282[/C][C]-0.836174438728189[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]19.6187404380185[/C][C]-3.61874043801848[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]25.1266112441135[/C][C]-1.12661124411348[/C][/ROW]
[ROW][C]140[/C][C]23[/C][C]21.6102097012035[/C][C]1.38979029879649[/C][/ROW]
[ROW][C]141[/C][C]18[/C][C]15.7283255920653[/C][C]2.27167440793467[/C][/ROW]
[ROW][C]142[/C][C]20[/C][C]20.7861271877941[/C][C]-0.786127187794113[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]18.7809699583662[/C][C]-9.78096995836622[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]20.4792890237757[/C][C]3.52071097622431[/C][/ROW]
[ROW][C]145[/C][C]25[/C][C]24.9107314092358[/C][C]0.0892685907641989[/C][/ROW]
[ROW][C]146[/C][C]20[/C][C]18.3474281191604[/C][C]1.6525718808396[/C][/ROW]
[ROW][C]147[/C][C]21[/C][C]19.0957365194324[/C][C]1.90426348056755[/C][/ROW]
[ROW][C]148[/C][C]25[/C][C]22.8791200605039[/C][C]2.12087993949608[/C][/ROW]
[ROW][C]149[/C][C]22[/C][C]22.2917217709517[/C][C]-0.29172177095167[/C][/ROW]
[ROW][C]150[/C][C]21[/C][C]21.5381608498063[/C][C]-0.538160849806321[/C][/ROW]
[ROW][C]151[/C][C]21[/C][C]20.3284080099934[/C][C]0.671591990006561[/C][/ROW]
[ROW][C]152[/C][C]22[/C][C]20.5645485077187[/C][C]1.43545149228131[/C][/ROW]
[ROW][C]153[/C][C]27[/C][C]23.3760949658806[/C][C]3.6239050341194[/C][/ROW]
[ROW][C]154[/C][C]24[/C][C]22.3349992058497[/C][C]1.66500079415028[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]24.4767209476221[/C][C]-0.476720947622082[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]20.7276868602021[/C][C]0.272313139797922[/C][/ROW]
[ROW][C]157[/C][C]18[/C][C]20.992500330426[/C][C]-2.992500330426[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]16.2655709783588[/C][C]-0.265570978358774[/C][/ROW]
[ROW][C]159[/C][C]22[/C][C]19.5756942663313[/C][C]2.42430573366869[/C][/ROW]
[ROW][C]160[/C][C]20[/C][C]19.8099233253486[/C][C]0.190076674651446[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]19.7151149845881[/C][C]-1.71511498458812[/C][/ROW]
[ROW][C]162[/C][C]20[/C][C]20.7088775160944[/C][C]-0.708877516094371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190547&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190547&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.23680734395081.76319265604925
22021.1402024650887-1.14020246508873
31921.9101377681762-2.91013776817616
41918.19705828910440.802941710895617
52019.32884231762890.671157682371081
62524.39044925227030.609550747729715
72520.68910273860874.31089726139129
82220.09730503093351.90269496906653
92622.24164999466553.75835000533451
102220.66007459277511.33992540722494
111719.9401972976192-2.94019729761918
122222.7511792904591-0.751179290459052
131921.0686120719108-2.06861207191078
142422.97098255507311.02901744492687
152626.8763255167494-0.876325516749414
162119.19720246527851.80279753472149
171316.3935277456565-3.39352774565655
182627.4371053024252-1.43710530242525
192022.3343326765223-2.33433267652235
202219.02214457169172.9778554283083
211417.7394638710995-3.73946387109947
222120.53639448160280.463605518397232
23714.0307735293565-7.03077352935654
242321.87863689240591.12136310759409
251719.1160193367051-2.11601933670508
262522.6262740724862.37372592751402
272523.30391009319471.69608990680529
281916.51043344035712.48956655964285
292016.87364111502543.12635888497463
302321.46205426954561.53794573045439
312224.0202805445702-2.02028054457016
322222.3503672531734-0.350367253173354
332118.09981995527012.90018004472986
341517.8082892394123-2.80828923941231
352016.11163271076343.88836728923661
362221.47927937818910.520720621810886
371817.23791250679620.762087493203799
382021.0487353955017-1.04873539550165
392827.72254083462910.277459165370927
402223.989456457212-1.98945645721204
411821.4996471986817-3.49964719868171
422321.07521124797771.9247887520223
432021.4248838627635-1.42488386276353
442524.96310205521760.036897944782419
452622.12956137447953.8704386255205
461514.4092983818550.590701618145016
471717.6690435667854-0.669043566785448
482315.27847936275357.72152063724652
492122.8011943012496-1.80119430124961
501316.1401817513547-3.14018175135466
511820.2561611461037-2.2561611461037
521919.6658050938043-0.665805093804278
532222.6549943452656-0.654994345265646
541617.838411609562-1.83841160956201
552423.18571833140290.814281668597095
561820.2154955578674-2.21549555786736
572021.9658376361885-1.96583763618845
582420.68718827760593.31281172239407
591418.1013982025901-4.10139820259013
602220.82695242209871.1730475779013
612418.93607415800385.06392584199615
621818.7170151822964-0.71701518229637
632124.7514753117111-3.75147531171112
642322.28933361580210.71066638419789
651719.0622596529401-2.06225965294009
662221.79454856942070.205451430579334
672425.0107011230779-1.01070112307793
682122.397780164043-1.39778016404296
692221.78718038551120.212819614488773
701616.8523387565521-0.852338756552115
712124.8500437077295-3.8500437077295
722323.9959729190092-0.995972919009212
732219.14911301620192.85088698379807
742422.06749957134681.93250042865317
752424.1461103298781-0.146110329878098
761617.8743981637961-1.87439816379607
771617.3882338991322-1.38823389913217
782123.1046050441089-2.10460504410892
792625.83856058993810.161439410061896
801516.4778282108812-1.47782821088118
812522.85438233864152.14561766135855
821817.5169314781960.48306852180399
832320.12697279788042.87302720211961
842021.0075235812599-1.00752358125994
851720.4586874058693-3.45868740586928
862524.72712773899710.272872261002863
872422.30379390471011.69620609528994
881714.19932477480872.80067522519127
891918.60687923913850.393120760861509
902020.6848054408236-0.684805440823637
911516.4683422983568-1.4683422983568
922724.70734862098062.29265137901944
932219.35226622447662.64773377552344
942322.90872212878750.0912778712125335
951620.0283834799849-4.02838347998491
961920.6082991489533-1.60829914895325
972520.52374620077984.47625379922022
981919.1870025816476-0.18700258164755
991919.0946336905343-0.0946336905342887
1002624.85591674304651.14408325695349
1012118.41006588442512.58993411557488
1022019.94129981056170.0587001894382555
1032419.79485113111034.20514886888967
1042223.3933421834239-1.39334218342387
1052021.4363475376098-1.43634753760975
1061819.439228201656-1.43922820165603
1071816.60112766030761.39887233969245
1082421.52641127837782.47358872162216
1092421.25283899199282.7471610080072
1102224.5489391844896-2.54893918448958
1112319.79201325766583.20798674233415
1122219.8387869588332.16121304116702
1132017.85053290305572.14946709694427
1141819.8388977799001-1.8388977799001
1152524.70029576468210.299704235317883
1161818.154992456602-0.154992456602017
1171617.580201729914-1.58020172991405
1182019.87438227992030.125617720079707
1191918.0455976253150.954402374684969
1201515.8201023683216-0.820102368321603
1211919.1480136277782-0.148013627778212
1221922.096360559157-3.09636055915702
1231617.3093016467437-1.30930164674369
1241717.4960194408-0.496019440800049
1252823.24055546112574.75944453887433
1262322.78179958840480.218200411595211
1272523.41484792375731.58515207624271
1282018.64193324868191.35806675131809
1291720.2905652549974-3.29056525499744
1302322.85660217126270.14339782873735
1311619.8286183243319-3.82861832433187
1322324.091865411626-1.09186541162605
1331115.8748279711474-4.87482797114737
1341820.5598925858425-2.55989258584251
1352423.1625487262980.837451273702028
1362319.13062427956743.86937572043262
1372121.8361744387282-0.836174438728189
1381619.6187404380185-3.61874043801848
1392425.1266112441135-1.12661124411348
1402321.61020970120351.38979029879649
1411815.72832559206532.27167440793467
1422020.7861271877941-0.786127187794113
143918.7809699583662-9.78096995836622
1442420.47928902377573.52071097622431
1452524.91073140923580.0892685907641989
1462018.34742811916041.6525718808396
1472119.09573651943241.90426348056755
1482522.87912006050392.12087993949608
1492222.2917217709517-0.29172177095167
1502121.5381608498063-0.538160849806321
1512120.32840800999340.671591990006561
1522220.56454850771871.43545149228131
1532723.37609496588063.6239050341194
1542422.33499920584971.66500079415028
1552424.4767209476221-0.476720947622082
1562120.72768686020210.272313139797922
1571820.992500330426-2.992500330426
1581616.2655709783588-0.265570978358774
1592219.57569426633132.42430573366869
1602019.80992332534860.190076674651446
1611819.7151149845881-1.71511498458812
1622020.7088775160944-0.708877516094371






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9501376062992520.09972478740149620.0498623937007481
150.9011470453535690.1977059092928610.0988529546464306
160.8620252356383390.2759495287233220.137974764361661
170.7999242167901270.4001515664197470.200075783209873
180.7276847557697310.5446304884605390.272315244230269
190.6595346495699210.6809307008601580.340465350430079
200.6474362290445460.7051275419109080.352563770955454
210.5833777111240490.8332445777519020.416622288875951
220.5048635589950940.9902728820098120.495136441004906
230.5850134005477180.8299731989045640.414986599452282
240.5586591556097780.8826816887804440.441340844390222
250.4863238441296780.9726476882593550.513676155870322
260.5603650822855550.8792698354288910.439634917714445
270.498715240085380.997430480170760.50128475991462
280.646923581686370.706152836627260.35307641831363
290.6568404901483930.6863190197032140.343159509851607
300.5974630415300610.8050739169398770.402536958469938
310.5946178009089070.8107643981821860.405382199091093
320.5765831431147630.8468337137704740.423416856885237
330.5540983108974570.8918033782050860.445901689102543
340.6236911301338780.7526177397322430.376308869866122
350.7536007002011750.492798599597650.246399299798825
360.7053715860275860.5892568279448290.294628413972414
370.659205721741950.6815885565160990.34079427825805
380.6074740265460610.7850519469078770.392525973453939
390.5506093918881360.8987812162237270.449390608111864
400.5267098071012150.946580385797570.473290192898785
410.5338148245391380.9323703509217250.466185175460862
420.4992178795321240.9984357590642480.500782120467876
430.4510886594587670.9021773189175340.548911340541233
440.4183671747298160.8367343494596330.581632825270184
450.4743119484774920.9486238969549850.525688051522508
460.4205507821377090.8411015642754190.579449217862291
470.374642043790340.749284087580680.62535795620966
480.7779292706800160.4441414586399680.222070729319984
490.7739232322521090.4521535354957810.22607676774789
500.8008776857046550.398244628590690.199122314295345
510.7846378043550830.4307243912898350.215362195644917
520.7482364297833220.5035271404333560.251763570216678
530.7272351308552240.5455297382895530.272764869144776
540.7002411727249180.5995176545501640.299758827275082
550.6620854332010830.6758291335978340.337914566798917
560.6502299230601130.6995401538797740.349770076939887
570.6281056106617220.7437887786765560.371894389338278
580.730868723695450.5382625526090990.26913127630455
590.7859834195974240.4280331608051510.214016580402576
600.7551238298378340.4897523403243310.244876170162166
610.837635415351010.3247291692979790.16236458464899
620.8064793410485890.3870413179028230.193520658951411
630.8514322823807420.2971354352385150.148567717619258
640.8225114150006260.3549771699987490.177488584999374
650.8415604816418820.3168790367162370.158439518358118
660.8104743990727290.3790512018545410.189525600927271
670.7944498776738570.4111002446522860.205550122326143
680.7687095335779840.4625809328440320.231290466422016
690.7348987544022550.530202491195490.265101245597745
700.6969229704940140.6061540590119720.303077029505986
710.7480970948768790.5038058102462430.251902905123121
720.715559908461580.568880183076840.28444009153842
730.740967794506330.5180644109873410.259032205493671
740.7293348855369550.541330228926090.270665114463045
750.6900422471296340.6199155057407310.309957752870366
760.6946604327717060.6106791344565880.305339567228294
770.6609344830496420.6781310339007160.339065516950358
780.6479902526930520.7040194946138950.352009747306948
790.6125548969206560.7748902061586890.387445103079344
800.5884572509993470.8230854980013050.411542749000653
810.5756717628723680.8486564742552640.424328237127632
820.552081401360930.8958371972781390.44791859863907
830.5730228971969130.8539542056061740.426977102803087
840.5392962524121240.9214074951757510.460703747587876
850.5907852072945470.8184295854109060.409214792705453
860.5439373334919020.9121253330161970.456062666508098
870.5211811871971580.9576376256056830.478818812802842
880.5408182793696810.9183634412606380.459181720630319
890.5013106076433340.9973787847133320.498689392356666
900.474165296868250.9483305937364990.52583470313175
910.440891300592410.881782601184820.55910869940759
920.4225907773101150.845181554620230.577409222689885
930.4251352534756980.8502705069513950.574864746524302
940.3844408805218980.7688817610437950.615559119478102
950.4723993295866610.9447986591733210.527600670413339
960.4549080451477710.9098160902955410.545091954852229
970.5591430359179590.8817139281640820.440856964082041
980.514157107963020.971685784073960.48584289203698
990.4723070200512320.9446140401024640.527692979948768
1000.430698671792320.861397343584640.56930132820768
1010.4257856836932110.8515713673864220.574214316306789
1020.3782019147604480.7564038295208950.621798085239552
1030.4712346266665090.9424692533330180.528765373333491
1040.4544351682067350.908870336413470.545564831793265
1050.4210976836994280.8421953673988570.578902316300572
1060.3871867839633450.7743735679266910.612813216036655
1070.3534801304714520.7069602609429030.646519869528548
1080.3597545693277690.7195091386555380.640245430672231
1090.3474734296164870.6949468592329750.652526570383513
1100.3322213496300890.6644426992601790.667778650369911
1110.3673783744574780.7347567489149560.632621625542522
1120.3805158706315480.7610317412630970.619484129368452
1130.4043176375230610.8086352750461210.595682362476939
1140.3660374578868330.7320749157736650.633962542113167
1150.3162487594377770.6324975188755540.683751240562223
1160.269777364259640.5395547285192790.73022263574036
1170.2450824326895050.490164865379010.754917567310495
1180.2091574482023910.4183148964047810.790842551797609
1190.1876955456282260.3753910912564530.812304454371774
1200.1784766309104350.356953261820870.821523369089565
1210.1536492561022020.3072985122044040.846350743897798
1220.1466020432935450.293204086587090.853397956706455
1230.1221969487498960.2443938974997930.877803051250104
1240.0953577796361090.1907155592722180.904642220363891
1250.2866898372788820.5733796745577640.713310162721118
1260.2376060675026380.4752121350052760.762393932497362
1270.2327607513883920.4655215027767850.767239248611608
1280.2275630557927540.4551261115855080.772436944207246
1290.2459446950426470.4918893900852940.754055304957353
1300.2095902959499290.4191805918998580.790409704050071
1310.2245079475855880.4490158951711760.775492052414412
1320.179417631129430.3588352622588590.82058236887057
1330.2764795526871750.552959105374350.723520447312825
1340.2661772002386590.5323544004773190.73382279976134
1350.2591870409722030.5183740819444060.740812959027797
1360.233862368199620.4677247363992410.76613763180038
1370.2368928146663530.4737856293327050.763107185333647
1380.2240001058584640.4480002117169280.775999894141536
1390.17338821461570.3467764292313990.8266117853843
1400.1439427851621990.2878855703243990.856057214837801
1410.1226913388385840.2453826776771680.877308661161416
1420.08350829604623410.1670165920924680.916491703953766
1430.8839027925622810.2321944148754380.116097207437719
1440.9760046857000430.04799062859991450.0239953142999573
1450.9554235389126490.08915292217470160.0445764610873508
1460.9080962122147680.1838075755704650.0919037877852323
1470.8261323864989410.3477352270021180.173867613501059
1480.701824605213520.5963507895729590.29817539478648

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.950137606299252 & 0.0997247874014962 & 0.0498623937007481 \tabularnewline
15 & 0.901147045353569 & 0.197705909292861 & 0.0988529546464306 \tabularnewline
16 & 0.862025235638339 & 0.275949528723322 & 0.137974764361661 \tabularnewline
17 & 0.799924216790127 & 0.400151566419747 & 0.200075783209873 \tabularnewline
18 & 0.727684755769731 & 0.544630488460539 & 0.272315244230269 \tabularnewline
19 & 0.659534649569921 & 0.680930700860158 & 0.340465350430079 \tabularnewline
20 & 0.647436229044546 & 0.705127541910908 & 0.352563770955454 \tabularnewline
21 & 0.583377711124049 & 0.833244577751902 & 0.416622288875951 \tabularnewline
22 & 0.504863558995094 & 0.990272882009812 & 0.495136441004906 \tabularnewline
23 & 0.585013400547718 & 0.829973198904564 & 0.414986599452282 \tabularnewline
24 & 0.558659155609778 & 0.882681688780444 & 0.441340844390222 \tabularnewline
25 & 0.486323844129678 & 0.972647688259355 & 0.513676155870322 \tabularnewline
26 & 0.560365082285555 & 0.879269835428891 & 0.439634917714445 \tabularnewline
27 & 0.49871524008538 & 0.99743048017076 & 0.50128475991462 \tabularnewline
28 & 0.64692358168637 & 0.70615283662726 & 0.35307641831363 \tabularnewline
29 & 0.656840490148393 & 0.686319019703214 & 0.343159509851607 \tabularnewline
30 & 0.597463041530061 & 0.805073916939877 & 0.402536958469938 \tabularnewline
31 & 0.594617800908907 & 0.810764398182186 & 0.405382199091093 \tabularnewline
32 & 0.576583143114763 & 0.846833713770474 & 0.423416856885237 \tabularnewline
33 & 0.554098310897457 & 0.891803378205086 & 0.445901689102543 \tabularnewline
34 & 0.623691130133878 & 0.752617739732243 & 0.376308869866122 \tabularnewline
35 & 0.753600700201175 & 0.49279859959765 & 0.246399299798825 \tabularnewline
36 & 0.705371586027586 & 0.589256827944829 & 0.294628413972414 \tabularnewline
37 & 0.65920572174195 & 0.681588556516099 & 0.34079427825805 \tabularnewline
38 & 0.607474026546061 & 0.785051946907877 & 0.392525973453939 \tabularnewline
39 & 0.550609391888136 & 0.898781216223727 & 0.449390608111864 \tabularnewline
40 & 0.526709807101215 & 0.94658038579757 & 0.473290192898785 \tabularnewline
41 & 0.533814824539138 & 0.932370350921725 & 0.466185175460862 \tabularnewline
42 & 0.499217879532124 & 0.998435759064248 & 0.500782120467876 \tabularnewline
43 & 0.451088659458767 & 0.902177318917534 & 0.548911340541233 \tabularnewline
44 & 0.418367174729816 & 0.836734349459633 & 0.581632825270184 \tabularnewline
45 & 0.474311948477492 & 0.948623896954985 & 0.525688051522508 \tabularnewline
46 & 0.420550782137709 & 0.841101564275419 & 0.579449217862291 \tabularnewline
47 & 0.37464204379034 & 0.74928408758068 & 0.62535795620966 \tabularnewline
48 & 0.777929270680016 & 0.444141458639968 & 0.222070729319984 \tabularnewline
49 & 0.773923232252109 & 0.452153535495781 & 0.22607676774789 \tabularnewline
50 & 0.800877685704655 & 0.39824462859069 & 0.199122314295345 \tabularnewline
51 & 0.784637804355083 & 0.430724391289835 & 0.215362195644917 \tabularnewline
52 & 0.748236429783322 & 0.503527140433356 & 0.251763570216678 \tabularnewline
53 & 0.727235130855224 & 0.545529738289553 & 0.272764869144776 \tabularnewline
54 & 0.700241172724918 & 0.599517654550164 & 0.299758827275082 \tabularnewline
55 & 0.662085433201083 & 0.675829133597834 & 0.337914566798917 \tabularnewline
56 & 0.650229923060113 & 0.699540153879774 & 0.349770076939887 \tabularnewline
57 & 0.628105610661722 & 0.743788778676556 & 0.371894389338278 \tabularnewline
58 & 0.73086872369545 & 0.538262552609099 & 0.26913127630455 \tabularnewline
59 & 0.785983419597424 & 0.428033160805151 & 0.214016580402576 \tabularnewline
60 & 0.755123829837834 & 0.489752340324331 & 0.244876170162166 \tabularnewline
61 & 0.83763541535101 & 0.324729169297979 & 0.16236458464899 \tabularnewline
62 & 0.806479341048589 & 0.387041317902823 & 0.193520658951411 \tabularnewline
63 & 0.851432282380742 & 0.297135435238515 & 0.148567717619258 \tabularnewline
64 & 0.822511415000626 & 0.354977169998749 & 0.177488584999374 \tabularnewline
65 & 0.841560481641882 & 0.316879036716237 & 0.158439518358118 \tabularnewline
66 & 0.810474399072729 & 0.379051201854541 & 0.189525600927271 \tabularnewline
67 & 0.794449877673857 & 0.411100244652286 & 0.205550122326143 \tabularnewline
68 & 0.768709533577984 & 0.462580932844032 & 0.231290466422016 \tabularnewline
69 & 0.734898754402255 & 0.53020249119549 & 0.265101245597745 \tabularnewline
70 & 0.696922970494014 & 0.606154059011972 & 0.303077029505986 \tabularnewline
71 & 0.748097094876879 & 0.503805810246243 & 0.251902905123121 \tabularnewline
72 & 0.71555990846158 & 0.56888018307684 & 0.28444009153842 \tabularnewline
73 & 0.74096779450633 & 0.518064410987341 & 0.259032205493671 \tabularnewline
74 & 0.729334885536955 & 0.54133022892609 & 0.270665114463045 \tabularnewline
75 & 0.690042247129634 & 0.619915505740731 & 0.309957752870366 \tabularnewline
76 & 0.694660432771706 & 0.610679134456588 & 0.305339567228294 \tabularnewline
77 & 0.660934483049642 & 0.678131033900716 & 0.339065516950358 \tabularnewline
78 & 0.647990252693052 & 0.704019494613895 & 0.352009747306948 \tabularnewline
79 & 0.612554896920656 & 0.774890206158689 & 0.387445103079344 \tabularnewline
80 & 0.588457250999347 & 0.823085498001305 & 0.411542749000653 \tabularnewline
81 & 0.575671762872368 & 0.848656474255264 & 0.424328237127632 \tabularnewline
82 & 0.55208140136093 & 0.895837197278139 & 0.44791859863907 \tabularnewline
83 & 0.573022897196913 & 0.853954205606174 & 0.426977102803087 \tabularnewline
84 & 0.539296252412124 & 0.921407495175751 & 0.460703747587876 \tabularnewline
85 & 0.590785207294547 & 0.818429585410906 & 0.409214792705453 \tabularnewline
86 & 0.543937333491902 & 0.912125333016197 & 0.456062666508098 \tabularnewline
87 & 0.521181187197158 & 0.957637625605683 & 0.478818812802842 \tabularnewline
88 & 0.540818279369681 & 0.918363441260638 & 0.459181720630319 \tabularnewline
89 & 0.501310607643334 & 0.997378784713332 & 0.498689392356666 \tabularnewline
90 & 0.47416529686825 & 0.948330593736499 & 0.52583470313175 \tabularnewline
91 & 0.44089130059241 & 0.88178260118482 & 0.55910869940759 \tabularnewline
92 & 0.422590777310115 & 0.84518155462023 & 0.577409222689885 \tabularnewline
93 & 0.425135253475698 & 0.850270506951395 & 0.574864746524302 \tabularnewline
94 & 0.384440880521898 & 0.768881761043795 & 0.615559119478102 \tabularnewline
95 & 0.472399329586661 & 0.944798659173321 & 0.527600670413339 \tabularnewline
96 & 0.454908045147771 & 0.909816090295541 & 0.545091954852229 \tabularnewline
97 & 0.559143035917959 & 0.881713928164082 & 0.440856964082041 \tabularnewline
98 & 0.51415710796302 & 0.97168578407396 & 0.48584289203698 \tabularnewline
99 & 0.472307020051232 & 0.944614040102464 & 0.527692979948768 \tabularnewline
100 & 0.43069867179232 & 0.86139734358464 & 0.56930132820768 \tabularnewline
101 & 0.425785683693211 & 0.851571367386422 & 0.574214316306789 \tabularnewline
102 & 0.378201914760448 & 0.756403829520895 & 0.621798085239552 \tabularnewline
103 & 0.471234626666509 & 0.942469253333018 & 0.528765373333491 \tabularnewline
104 & 0.454435168206735 & 0.90887033641347 & 0.545564831793265 \tabularnewline
105 & 0.421097683699428 & 0.842195367398857 & 0.578902316300572 \tabularnewline
106 & 0.387186783963345 & 0.774373567926691 & 0.612813216036655 \tabularnewline
107 & 0.353480130471452 & 0.706960260942903 & 0.646519869528548 \tabularnewline
108 & 0.359754569327769 & 0.719509138655538 & 0.640245430672231 \tabularnewline
109 & 0.347473429616487 & 0.694946859232975 & 0.652526570383513 \tabularnewline
110 & 0.332221349630089 & 0.664442699260179 & 0.667778650369911 \tabularnewline
111 & 0.367378374457478 & 0.734756748914956 & 0.632621625542522 \tabularnewline
112 & 0.380515870631548 & 0.761031741263097 & 0.619484129368452 \tabularnewline
113 & 0.404317637523061 & 0.808635275046121 & 0.595682362476939 \tabularnewline
114 & 0.366037457886833 & 0.732074915773665 & 0.633962542113167 \tabularnewline
115 & 0.316248759437777 & 0.632497518875554 & 0.683751240562223 \tabularnewline
116 & 0.26977736425964 & 0.539554728519279 & 0.73022263574036 \tabularnewline
117 & 0.245082432689505 & 0.49016486537901 & 0.754917567310495 \tabularnewline
118 & 0.209157448202391 & 0.418314896404781 & 0.790842551797609 \tabularnewline
119 & 0.187695545628226 & 0.375391091256453 & 0.812304454371774 \tabularnewline
120 & 0.178476630910435 & 0.35695326182087 & 0.821523369089565 \tabularnewline
121 & 0.153649256102202 & 0.307298512204404 & 0.846350743897798 \tabularnewline
122 & 0.146602043293545 & 0.29320408658709 & 0.853397956706455 \tabularnewline
123 & 0.122196948749896 & 0.244393897499793 & 0.877803051250104 \tabularnewline
124 & 0.095357779636109 & 0.190715559272218 & 0.904642220363891 \tabularnewline
125 & 0.286689837278882 & 0.573379674557764 & 0.713310162721118 \tabularnewline
126 & 0.237606067502638 & 0.475212135005276 & 0.762393932497362 \tabularnewline
127 & 0.232760751388392 & 0.465521502776785 & 0.767239248611608 \tabularnewline
128 & 0.227563055792754 & 0.455126111585508 & 0.772436944207246 \tabularnewline
129 & 0.245944695042647 & 0.491889390085294 & 0.754055304957353 \tabularnewline
130 & 0.209590295949929 & 0.419180591899858 & 0.790409704050071 \tabularnewline
131 & 0.224507947585588 & 0.449015895171176 & 0.775492052414412 \tabularnewline
132 & 0.17941763112943 & 0.358835262258859 & 0.82058236887057 \tabularnewline
133 & 0.276479552687175 & 0.55295910537435 & 0.723520447312825 \tabularnewline
134 & 0.266177200238659 & 0.532354400477319 & 0.73382279976134 \tabularnewline
135 & 0.259187040972203 & 0.518374081944406 & 0.740812959027797 \tabularnewline
136 & 0.23386236819962 & 0.467724736399241 & 0.76613763180038 \tabularnewline
137 & 0.236892814666353 & 0.473785629332705 & 0.763107185333647 \tabularnewline
138 & 0.224000105858464 & 0.448000211716928 & 0.775999894141536 \tabularnewline
139 & 0.1733882146157 & 0.346776429231399 & 0.8266117853843 \tabularnewline
140 & 0.143942785162199 & 0.287885570324399 & 0.856057214837801 \tabularnewline
141 & 0.122691338838584 & 0.245382677677168 & 0.877308661161416 \tabularnewline
142 & 0.0835082960462341 & 0.167016592092468 & 0.916491703953766 \tabularnewline
143 & 0.883902792562281 & 0.232194414875438 & 0.116097207437719 \tabularnewline
144 & 0.976004685700043 & 0.0479906285999145 & 0.0239953142999573 \tabularnewline
145 & 0.955423538912649 & 0.0891529221747016 & 0.0445764610873508 \tabularnewline
146 & 0.908096212214768 & 0.183807575570465 & 0.0919037877852323 \tabularnewline
147 & 0.826132386498941 & 0.347735227002118 & 0.173867613501059 \tabularnewline
148 & 0.70182460521352 & 0.596350789572959 & 0.29817539478648 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190547&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]14[/C][C]0.950137606299252[/C][C]0.0997247874014962[/C][C]0.0498623937007481[/C][/ROW]
[ROW][C]15[/C][C]0.901147045353569[/C][C]0.197705909292861[/C][C]0.0988529546464306[/C][/ROW]
[ROW][C]16[/C][C]0.862025235638339[/C][C]0.275949528723322[/C][C]0.137974764361661[/C][/ROW]
[ROW][C]17[/C][C]0.799924216790127[/C][C]0.400151566419747[/C][C]0.200075783209873[/C][/ROW]
[ROW][C]18[/C][C]0.727684755769731[/C][C]0.544630488460539[/C][C]0.272315244230269[/C][/ROW]
[ROW][C]19[/C][C]0.659534649569921[/C][C]0.680930700860158[/C][C]0.340465350430079[/C][/ROW]
[ROW][C]20[/C][C]0.647436229044546[/C][C]0.705127541910908[/C][C]0.352563770955454[/C][/ROW]
[ROW][C]21[/C][C]0.583377711124049[/C][C]0.833244577751902[/C][C]0.416622288875951[/C][/ROW]
[ROW][C]22[/C][C]0.504863558995094[/C][C]0.990272882009812[/C][C]0.495136441004906[/C][/ROW]
[ROW][C]23[/C][C]0.585013400547718[/C][C]0.829973198904564[/C][C]0.414986599452282[/C][/ROW]
[ROW][C]24[/C][C]0.558659155609778[/C][C]0.882681688780444[/C][C]0.441340844390222[/C][/ROW]
[ROW][C]25[/C][C]0.486323844129678[/C][C]0.972647688259355[/C][C]0.513676155870322[/C][/ROW]
[ROW][C]26[/C][C]0.560365082285555[/C][C]0.879269835428891[/C][C]0.439634917714445[/C][/ROW]
[ROW][C]27[/C][C]0.49871524008538[/C][C]0.99743048017076[/C][C]0.50128475991462[/C][/ROW]
[ROW][C]28[/C][C]0.64692358168637[/C][C]0.70615283662726[/C][C]0.35307641831363[/C][/ROW]
[ROW][C]29[/C][C]0.656840490148393[/C][C]0.686319019703214[/C][C]0.343159509851607[/C][/ROW]
[ROW][C]30[/C][C]0.597463041530061[/C][C]0.805073916939877[/C][C]0.402536958469938[/C][/ROW]
[ROW][C]31[/C][C]0.594617800908907[/C][C]0.810764398182186[/C][C]0.405382199091093[/C][/ROW]
[ROW][C]32[/C][C]0.576583143114763[/C][C]0.846833713770474[/C][C]0.423416856885237[/C][/ROW]
[ROW][C]33[/C][C]0.554098310897457[/C][C]0.891803378205086[/C][C]0.445901689102543[/C][/ROW]
[ROW][C]34[/C][C]0.623691130133878[/C][C]0.752617739732243[/C][C]0.376308869866122[/C][/ROW]
[ROW][C]35[/C][C]0.753600700201175[/C][C]0.49279859959765[/C][C]0.246399299798825[/C][/ROW]
[ROW][C]36[/C][C]0.705371586027586[/C][C]0.589256827944829[/C][C]0.294628413972414[/C][/ROW]
[ROW][C]37[/C][C]0.65920572174195[/C][C]0.681588556516099[/C][C]0.34079427825805[/C][/ROW]
[ROW][C]38[/C][C]0.607474026546061[/C][C]0.785051946907877[/C][C]0.392525973453939[/C][/ROW]
[ROW][C]39[/C][C]0.550609391888136[/C][C]0.898781216223727[/C][C]0.449390608111864[/C][/ROW]
[ROW][C]40[/C][C]0.526709807101215[/C][C]0.94658038579757[/C][C]0.473290192898785[/C][/ROW]
[ROW][C]41[/C][C]0.533814824539138[/C][C]0.932370350921725[/C][C]0.466185175460862[/C][/ROW]
[ROW][C]42[/C][C]0.499217879532124[/C][C]0.998435759064248[/C][C]0.500782120467876[/C][/ROW]
[ROW][C]43[/C][C]0.451088659458767[/C][C]0.902177318917534[/C][C]0.548911340541233[/C][/ROW]
[ROW][C]44[/C][C]0.418367174729816[/C][C]0.836734349459633[/C][C]0.581632825270184[/C][/ROW]
[ROW][C]45[/C][C]0.474311948477492[/C][C]0.948623896954985[/C][C]0.525688051522508[/C][/ROW]
[ROW][C]46[/C][C]0.420550782137709[/C][C]0.841101564275419[/C][C]0.579449217862291[/C][/ROW]
[ROW][C]47[/C][C]0.37464204379034[/C][C]0.74928408758068[/C][C]0.62535795620966[/C][/ROW]
[ROW][C]48[/C][C]0.777929270680016[/C][C]0.444141458639968[/C][C]0.222070729319984[/C][/ROW]
[ROW][C]49[/C][C]0.773923232252109[/C][C]0.452153535495781[/C][C]0.22607676774789[/C][/ROW]
[ROW][C]50[/C][C]0.800877685704655[/C][C]0.39824462859069[/C][C]0.199122314295345[/C][/ROW]
[ROW][C]51[/C][C]0.784637804355083[/C][C]0.430724391289835[/C][C]0.215362195644917[/C][/ROW]
[ROW][C]52[/C][C]0.748236429783322[/C][C]0.503527140433356[/C][C]0.251763570216678[/C][/ROW]
[ROW][C]53[/C][C]0.727235130855224[/C][C]0.545529738289553[/C][C]0.272764869144776[/C][/ROW]
[ROW][C]54[/C][C]0.700241172724918[/C][C]0.599517654550164[/C][C]0.299758827275082[/C][/ROW]
[ROW][C]55[/C][C]0.662085433201083[/C][C]0.675829133597834[/C][C]0.337914566798917[/C][/ROW]
[ROW][C]56[/C][C]0.650229923060113[/C][C]0.699540153879774[/C][C]0.349770076939887[/C][/ROW]
[ROW][C]57[/C][C]0.628105610661722[/C][C]0.743788778676556[/C][C]0.371894389338278[/C][/ROW]
[ROW][C]58[/C][C]0.73086872369545[/C][C]0.538262552609099[/C][C]0.26913127630455[/C][/ROW]
[ROW][C]59[/C][C]0.785983419597424[/C][C]0.428033160805151[/C][C]0.214016580402576[/C][/ROW]
[ROW][C]60[/C][C]0.755123829837834[/C][C]0.489752340324331[/C][C]0.244876170162166[/C][/ROW]
[ROW][C]61[/C][C]0.83763541535101[/C][C]0.324729169297979[/C][C]0.16236458464899[/C][/ROW]
[ROW][C]62[/C][C]0.806479341048589[/C][C]0.387041317902823[/C][C]0.193520658951411[/C][/ROW]
[ROW][C]63[/C][C]0.851432282380742[/C][C]0.297135435238515[/C][C]0.148567717619258[/C][/ROW]
[ROW][C]64[/C][C]0.822511415000626[/C][C]0.354977169998749[/C][C]0.177488584999374[/C][/ROW]
[ROW][C]65[/C][C]0.841560481641882[/C][C]0.316879036716237[/C][C]0.158439518358118[/C][/ROW]
[ROW][C]66[/C][C]0.810474399072729[/C][C]0.379051201854541[/C][C]0.189525600927271[/C][/ROW]
[ROW][C]67[/C][C]0.794449877673857[/C][C]0.411100244652286[/C][C]0.205550122326143[/C][/ROW]
[ROW][C]68[/C][C]0.768709533577984[/C][C]0.462580932844032[/C][C]0.231290466422016[/C][/ROW]
[ROW][C]69[/C][C]0.734898754402255[/C][C]0.53020249119549[/C][C]0.265101245597745[/C][/ROW]
[ROW][C]70[/C][C]0.696922970494014[/C][C]0.606154059011972[/C][C]0.303077029505986[/C][/ROW]
[ROW][C]71[/C][C]0.748097094876879[/C][C]0.503805810246243[/C][C]0.251902905123121[/C][/ROW]
[ROW][C]72[/C][C]0.71555990846158[/C][C]0.56888018307684[/C][C]0.28444009153842[/C][/ROW]
[ROW][C]73[/C][C]0.74096779450633[/C][C]0.518064410987341[/C][C]0.259032205493671[/C][/ROW]
[ROW][C]74[/C][C]0.729334885536955[/C][C]0.54133022892609[/C][C]0.270665114463045[/C][/ROW]
[ROW][C]75[/C][C]0.690042247129634[/C][C]0.619915505740731[/C][C]0.309957752870366[/C][/ROW]
[ROW][C]76[/C][C]0.694660432771706[/C][C]0.610679134456588[/C][C]0.305339567228294[/C][/ROW]
[ROW][C]77[/C][C]0.660934483049642[/C][C]0.678131033900716[/C][C]0.339065516950358[/C][/ROW]
[ROW][C]78[/C][C]0.647990252693052[/C][C]0.704019494613895[/C][C]0.352009747306948[/C][/ROW]
[ROW][C]79[/C][C]0.612554896920656[/C][C]0.774890206158689[/C][C]0.387445103079344[/C][/ROW]
[ROW][C]80[/C][C]0.588457250999347[/C][C]0.823085498001305[/C][C]0.411542749000653[/C][/ROW]
[ROW][C]81[/C][C]0.575671762872368[/C][C]0.848656474255264[/C][C]0.424328237127632[/C][/ROW]
[ROW][C]82[/C][C]0.55208140136093[/C][C]0.895837197278139[/C][C]0.44791859863907[/C][/ROW]
[ROW][C]83[/C][C]0.573022897196913[/C][C]0.853954205606174[/C][C]0.426977102803087[/C][/ROW]
[ROW][C]84[/C][C]0.539296252412124[/C][C]0.921407495175751[/C][C]0.460703747587876[/C][/ROW]
[ROW][C]85[/C][C]0.590785207294547[/C][C]0.818429585410906[/C][C]0.409214792705453[/C][/ROW]
[ROW][C]86[/C][C]0.543937333491902[/C][C]0.912125333016197[/C][C]0.456062666508098[/C][/ROW]
[ROW][C]87[/C][C]0.521181187197158[/C][C]0.957637625605683[/C][C]0.478818812802842[/C][/ROW]
[ROW][C]88[/C][C]0.540818279369681[/C][C]0.918363441260638[/C][C]0.459181720630319[/C][/ROW]
[ROW][C]89[/C][C]0.501310607643334[/C][C]0.997378784713332[/C][C]0.498689392356666[/C][/ROW]
[ROW][C]90[/C][C]0.47416529686825[/C][C]0.948330593736499[/C][C]0.52583470313175[/C][/ROW]
[ROW][C]91[/C][C]0.44089130059241[/C][C]0.88178260118482[/C][C]0.55910869940759[/C][/ROW]
[ROW][C]92[/C][C]0.422590777310115[/C][C]0.84518155462023[/C][C]0.577409222689885[/C][/ROW]
[ROW][C]93[/C][C]0.425135253475698[/C][C]0.850270506951395[/C][C]0.574864746524302[/C][/ROW]
[ROW][C]94[/C][C]0.384440880521898[/C][C]0.768881761043795[/C][C]0.615559119478102[/C][/ROW]
[ROW][C]95[/C][C]0.472399329586661[/C][C]0.944798659173321[/C][C]0.527600670413339[/C][/ROW]
[ROW][C]96[/C][C]0.454908045147771[/C][C]0.909816090295541[/C][C]0.545091954852229[/C][/ROW]
[ROW][C]97[/C][C]0.559143035917959[/C][C]0.881713928164082[/C][C]0.440856964082041[/C][/ROW]
[ROW][C]98[/C][C]0.51415710796302[/C][C]0.97168578407396[/C][C]0.48584289203698[/C][/ROW]
[ROW][C]99[/C][C]0.472307020051232[/C][C]0.944614040102464[/C][C]0.527692979948768[/C][/ROW]
[ROW][C]100[/C][C]0.43069867179232[/C][C]0.86139734358464[/C][C]0.56930132820768[/C][/ROW]
[ROW][C]101[/C][C]0.425785683693211[/C][C]0.851571367386422[/C][C]0.574214316306789[/C][/ROW]
[ROW][C]102[/C][C]0.378201914760448[/C][C]0.756403829520895[/C][C]0.621798085239552[/C][/ROW]
[ROW][C]103[/C][C]0.471234626666509[/C][C]0.942469253333018[/C][C]0.528765373333491[/C][/ROW]
[ROW][C]104[/C][C]0.454435168206735[/C][C]0.90887033641347[/C][C]0.545564831793265[/C][/ROW]
[ROW][C]105[/C][C]0.421097683699428[/C][C]0.842195367398857[/C][C]0.578902316300572[/C][/ROW]
[ROW][C]106[/C][C]0.387186783963345[/C][C]0.774373567926691[/C][C]0.612813216036655[/C][/ROW]
[ROW][C]107[/C][C]0.353480130471452[/C][C]0.706960260942903[/C][C]0.646519869528548[/C][/ROW]
[ROW][C]108[/C][C]0.359754569327769[/C][C]0.719509138655538[/C][C]0.640245430672231[/C][/ROW]
[ROW][C]109[/C][C]0.347473429616487[/C][C]0.694946859232975[/C][C]0.652526570383513[/C][/ROW]
[ROW][C]110[/C][C]0.332221349630089[/C][C]0.664442699260179[/C][C]0.667778650369911[/C][/ROW]
[ROW][C]111[/C][C]0.367378374457478[/C][C]0.734756748914956[/C][C]0.632621625542522[/C][/ROW]
[ROW][C]112[/C][C]0.380515870631548[/C][C]0.761031741263097[/C][C]0.619484129368452[/C][/ROW]
[ROW][C]113[/C][C]0.404317637523061[/C][C]0.808635275046121[/C][C]0.595682362476939[/C][/ROW]
[ROW][C]114[/C][C]0.366037457886833[/C][C]0.732074915773665[/C][C]0.633962542113167[/C][/ROW]
[ROW][C]115[/C][C]0.316248759437777[/C][C]0.632497518875554[/C][C]0.683751240562223[/C][/ROW]
[ROW][C]116[/C][C]0.26977736425964[/C][C]0.539554728519279[/C][C]0.73022263574036[/C][/ROW]
[ROW][C]117[/C][C]0.245082432689505[/C][C]0.49016486537901[/C][C]0.754917567310495[/C][/ROW]
[ROW][C]118[/C][C]0.209157448202391[/C][C]0.418314896404781[/C][C]0.790842551797609[/C][/ROW]
[ROW][C]119[/C][C]0.187695545628226[/C][C]0.375391091256453[/C][C]0.812304454371774[/C][/ROW]
[ROW][C]120[/C][C]0.178476630910435[/C][C]0.35695326182087[/C][C]0.821523369089565[/C][/ROW]
[ROW][C]121[/C][C]0.153649256102202[/C][C]0.307298512204404[/C][C]0.846350743897798[/C][/ROW]
[ROW][C]122[/C][C]0.146602043293545[/C][C]0.29320408658709[/C][C]0.853397956706455[/C][/ROW]
[ROW][C]123[/C][C]0.122196948749896[/C][C]0.244393897499793[/C][C]0.877803051250104[/C][/ROW]
[ROW][C]124[/C][C]0.095357779636109[/C][C]0.190715559272218[/C][C]0.904642220363891[/C][/ROW]
[ROW][C]125[/C][C]0.286689837278882[/C][C]0.573379674557764[/C][C]0.713310162721118[/C][/ROW]
[ROW][C]126[/C][C]0.237606067502638[/C][C]0.475212135005276[/C][C]0.762393932497362[/C][/ROW]
[ROW][C]127[/C][C]0.232760751388392[/C][C]0.465521502776785[/C][C]0.767239248611608[/C][/ROW]
[ROW][C]128[/C][C]0.227563055792754[/C][C]0.455126111585508[/C][C]0.772436944207246[/C][/ROW]
[ROW][C]129[/C][C]0.245944695042647[/C][C]0.491889390085294[/C][C]0.754055304957353[/C][/ROW]
[ROW][C]130[/C][C]0.209590295949929[/C][C]0.419180591899858[/C][C]0.790409704050071[/C][/ROW]
[ROW][C]131[/C][C]0.224507947585588[/C][C]0.449015895171176[/C][C]0.775492052414412[/C][/ROW]
[ROW][C]132[/C][C]0.17941763112943[/C][C]0.358835262258859[/C][C]0.82058236887057[/C][/ROW]
[ROW][C]133[/C][C]0.276479552687175[/C][C]0.55295910537435[/C][C]0.723520447312825[/C][/ROW]
[ROW][C]134[/C][C]0.266177200238659[/C][C]0.532354400477319[/C][C]0.73382279976134[/C][/ROW]
[ROW][C]135[/C][C]0.259187040972203[/C][C]0.518374081944406[/C][C]0.740812959027797[/C][/ROW]
[ROW][C]136[/C][C]0.23386236819962[/C][C]0.467724736399241[/C][C]0.76613763180038[/C][/ROW]
[ROW][C]137[/C][C]0.236892814666353[/C][C]0.473785629332705[/C][C]0.763107185333647[/C][/ROW]
[ROW][C]138[/C][C]0.224000105858464[/C][C]0.448000211716928[/C][C]0.775999894141536[/C][/ROW]
[ROW][C]139[/C][C]0.1733882146157[/C][C]0.346776429231399[/C][C]0.8266117853843[/C][/ROW]
[ROW][C]140[/C][C]0.143942785162199[/C][C]0.287885570324399[/C][C]0.856057214837801[/C][/ROW]
[ROW][C]141[/C][C]0.122691338838584[/C][C]0.245382677677168[/C][C]0.877308661161416[/C][/ROW]
[ROW][C]142[/C][C]0.0835082960462341[/C][C]0.167016592092468[/C][C]0.916491703953766[/C][/ROW]
[ROW][C]143[/C][C]0.883902792562281[/C][C]0.232194414875438[/C][C]0.116097207437719[/C][/ROW]
[ROW][C]144[/C][C]0.976004685700043[/C][C]0.0479906285999145[/C][C]0.0239953142999573[/C][/ROW]
[ROW][C]145[/C][C]0.955423538912649[/C][C]0.0891529221747016[/C][C]0.0445764610873508[/C][/ROW]
[ROW][C]146[/C][C]0.908096212214768[/C][C]0.183807575570465[/C][C]0.0919037877852323[/C][/ROW]
[ROW][C]147[/C][C]0.826132386498941[/C][C]0.347735227002118[/C][C]0.173867613501059[/C][/ROW]
[ROW][C]148[/C][C]0.70182460521352[/C][C]0.596350789572959[/C][C]0.29817539478648[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190547&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190547&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9501376062992520.09972478740149620.0498623937007481
150.9011470453535690.1977059092928610.0988529546464306
160.8620252356383390.2759495287233220.137974764361661
170.7999242167901270.4001515664197470.200075783209873
180.7276847557697310.5446304884605390.272315244230269
190.6595346495699210.6809307008601580.340465350430079
200.6474362290445460.7051275419109080.352563770955454
210.5833777111240490.8332445777519020.416622288875951
220.5048635589950940.9902728820098120.495136441004906
230.5850134005477180.8299731989045640.414986599452282
240.5586591556097780.8826816887804440.441340844390222
250.4863238441296780.9726476882593550.513676155870322
260.5603650822855550.8792698354288910.439634917714445
270.498715240085380.997430480170760.50128475991462
280.646923581686370.706152836627260.35307641831363
290.6568404901483930.6863190197032140.343159509851607
300.5974630415300610.8050739169398770.402536958469938
310.5946178009089070.8107643981821860.405382199091093
320.5765831431147630.8468337137704740.423416856885237
330.5540983108974570.8918033782050860.445901689102543
340.6236911301338780.7526177397322430.376308869866122
350.7536007002011750.492798599597650.246399299798825
360.7053715860275860.5892568279448290.294628413972414
370.659205721741950.6815885565160990.34079427825805
380.6074740265460610.7850519469078770.392525973453939
390.5506093918881360.8987812162237270.449390608111864
400.5267098071012150.946580385797570.473290192898785
410.5338148245391380.9323703509217250.466185175460862
420.4992178795321240.9984357590642480.500782120467876
430.4510886594587670.9021773189175340.548911340541233
440.4183671747298160.8367343494596330.581632825270184
450.4743119484774920.9486238969549850.525688051522508
460.4205507821377090.8411015642754190.579449217862291
470.374642043790340.749284087580680.62535795620966
480.7779292706800160.4441414586399680.222070729319984
490.7739232322521090.4521535354957810.22607676774789
500.8008776857046550.398244628590690.199122314295345
510.7846378043550830.4307243912898350.215362195644917
520.7482364297833220.5035271404333560.251763570216678
530.7272351308552240.5455297382895530.272764869144776
540.7002411727249180.5995176545501640.299758827275082
550.6620854332010830.6758291335978340.337914566798917
560.6502299230601130.6995401538797740.349770076939887
570.6281056106617220.7437887786765560.371894389338278
580.730868723695450.5382625526090990.26913127630455
590.7859834195974240.4280331608051510.214016580402576
600.7551238298378340.4897523403243310.244876170162166
610.837635415351010.3247291692979790.16236458464899
620.8064793410485890.3870413179028230.193520658951411
630.8514322823807420.2971354352385150.148567717619258
640.8225114150006260.3549771699987490.177488584999374
650.8415604816418820.3168790367162370.158439518358118
660.8104743990727290.3790512018545410.189525600927271
670.7944498776738570.4111002446522860.205550122326143
680.7687095335779840.4625809328440320.231290466422016
690.7348987544022550.530202491195490.265101245597745
700.6969229704940140.6061540590119720.303077029505986
710.7480970948768790.5038058102462430.251902905123121
720.715559908461580.568880183076840.28444009153842
730.740967794506330.5180644109873410.259032205493671
740.7293348855369550.541330228926090.270665114463045
750.6900422471296340.6199155057407310.309957752870366
760.6946604327717060.6106791344565880.305339567228294
770.6609344830496420.6781310339007160.339065516950358
780.6479902526930520.7040194946138950.352009747306948
790.6125548969206560.7748902061586890.387445103079344
800.5884572509993470.8230854980013050.411542749000653
810.5756717628723680.8486564742552640.424328237127632
820.552081401360930.8958371972781390.44791859863907
830.5730228971969130.8539542056061740.426977102803087
840.5392962524121240.9214074951757510.460703747587876
850.5907852072945470.8184295854109060.409214792705453
860.5439373334919020.9121253330161970.456062666508098
870.5211811871971580.9576376256056830.478818812802842
880.5408182793696810.9183634412606380.459181720630319
890.5013106076433340.9973787847133320.498689392356666
900.474165296868250.9483305937364990.52583470313175
910.440891300592410.881782601184820.55910869940759
920.4225907773101150.845181554620230.577409222689885
930.4251352534756980.8502705069513950.574864746524302
940.3844408805218980.7688817610437950.615559119478102
950.4723993295866610.9447986591733210.527600670413339
960.4549080451477710.9098160902955410.545091954852229
970.5591430359179590.8817139281640820.440856964082041
980.514157107963020.971685784073960.48584289203698
990.4723070200512320.9446140401024640.527692979948768
1000.430698671792320.861397343584640.56930132820768
1010.4257856836932110.8515713673864220.574214316306789
1020.3782019147604480.7564038295208950.621798085239552
1030.4712346266665090.9424692533330180.528765373333491
1040.4544351682067350.908870336413470.545564831793265
1050.4210976836994280.8421953673988570.578902316300572
1060.3871867839633450.7743735679266910.612813216036655
1070.3534801304714520.7069602609429030.646519869528548
1080.3597545693277690.7195091386555380.640245430672231
1090.3474734296164870.6949468592329750.652526570383513
1100.3322213496300890.6644426992601790.667778650369911
1110.3673783744574780.7347567489149560.632621625542522
1120.3805158706315480.7610317412630970.619484129368452
1130.4043176375230610.8086352750461210.595682362476939
1140.3660374578868330.7320749157736650.633962542113167
1150.3162487594377770.6324975188755540.683751240562223
1160.269777364259640.5395547285192790.73022263574036
1170.2450824326895050.490164865379010.754917567310495
1180.2091574482023910.4183148964047810.790842551797609
1190.1876955456282260.3753910912564530.812304454371774
1200.1784766309104350.356953261820870.821523369089565
1210.1536492561022020.3072985122044040.846350743897798
1220.1466020432935450.293204086587090.853397956706455
1230.1221969487498960.2443938974997930.877803051250104
1240.0953577796361090.1907155592722180.904642220363891
1250.2866898372788820.5733796745577640.713310162721118
1260.2376060675026380.4752121350052760.762393932497362
1270.2327607513883920.4655215027767850.767239248611608
1280.2275630557927540.4551261115855080.772436944207246
1290.2459446950426470.4918893900852940.754055304957353
1300.2095902959499290.4191805918998580.790409704050071
1310.2245079475855880.4490158951711760.775492052414412
1320.179417631129430.3588352622588590.82058236887057
1330.2764795526871750.552959105374350.723520447312825
1340.2661772002386590.5323544004773190.73382279976134
1350.2591870409722030.5183740819444060.740812959027797
1360.233862368199620.4677247363992410.76613763180038
1370.2368928146663530.4737856293327050.763107185333647
1380.2240001058584640.4480002117169280.775999894141536
1390.17338821461570.3467764292313990.8266117853843
1400.1439427851621990.2878855703243990.856057214837801
1410.1226913388385840.2453826776771680.877308661161416
1420.08350829604623410.1670165920924680.916491703953766
1430.8839027925622810.2321944148754380.116097207437719
1440.9760046857000430.04799062859991450.0239953142999573
1450.9554235389126490.08915292217470160.0445764610873508
1460.9080962122147680.1838075755704650.0919037877852323
1470.8261323864989410.3477352270021180.173867613501059
1480.701824605213520.5963507895729590.29817539478648






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00740740740740741OK
10% type I error level30.0222222222222222OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.00740740740740741 & OK \tabularnewline
10% type I error level & 3 & 0.0222222222222222 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190547&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.00740740740740741[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0222222222222222[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190547&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190547&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00740740740740741OK
10% type I error level30.0222222222222222OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}