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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 09:52:34 -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/t1353336767nc3miexpr1x2mwf.htm/, Retrieved Sun, 28 Apr 2024 13:29:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190555, Retrieved Sun, 28 Apr 2024 13:29:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74

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]
- RMP       [Multiple Regression] [WS 7] [2012-11-19 14:52:34] [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 time9 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.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=190555&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.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=190555&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190555&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 time9 seconds
R Server'Gertrude Mary Cox' @ cox.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] = + 9.93802773724693 -0.529824420571296Month[t] + 0.366629203404525I2[t] + 0.262155562871582I3[t] + 0.264693373911638E1[t] -0.121939422027158E2[t] + 0.0234095617671534E3[t] -0.211295007159597A[t] + 0.053843407628487Happiness[t] + 0.125825104703589`Depression\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
I1[t] =  +  9.93802773724693 -0.529824420571296Month[t] +  0.366629203404525I2[t] +  0.262155562871582I3[t] +  0.264693373911638E1[t] -0.121939422027158E2[t] +  0.0234095617671534E3[t] -0.211295007159597A[t] +  0.053843407628487Happiness[t] +  0.125825104703589`Depression\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190555&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]I1[t] =  +  9.93802773724693 -0.529824420571296Month[t] +  0.366629203404525I2[t] +  0.262155562871582I3[t] +  0.264693373911638E1[t] -0.121939422027158E2[t] +  0.0234095617671534E3[t] -0.211295007159597A[t] +  0.053843407628487Happiness[t] +  0.125825104703589`Depression\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190555&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190555&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] = + 9.93802773724693 -0.529824420571296Month[t] + 0.366629203404525I2[t] + 0.262155562871582I3[t] + 0.264693373911638E1[t] -0.121939422027158E2[t] + 0.0234095617671534E3[t] -0.211295007159597A[t] + 0.053843407628487Happiness[t] + 0.125825104703589`Depression\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.938027737246934.6537642.13550.0343240.017162
Month-0.5298244205712960.401714-1.31890.1891830.094592
I20.3666292034045250.063325.790100
I30.2621555628715820.0508225.15831e-060
E10.2646933739116380.0748723.53530.000540.00027
E2-0.1219394220271580.058854-2.07190.0399650.019982
E30.02340956176715340.0615160.38050.7040730.352036
A-0.2112950071595970.08377-2.52230.0126880.006344
Happiness0.0538434076284870.1014470.53080.5963650.298182
`Depression\r`0.1258251047035890.0759561.65660.0996710.049835

\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) & 9.93802773724693 & 4.653764 & 2.1355 & 0.034324 & 0.017162 \tabularnewline
Month & -0.529824420571296 & 0.401714 & -1.3189 & 0.189183 & 0.094592 \tabularnewline
I2 & 0.366629203404525 & 0.06332 & 5.7901 & 0 & 0 \tabularnewline
I3 & 0.262155562871582 & 0.050822 & 5.1583 & 1e-06 & 0 \tabularnewline
E1 & 0.264693373911638 & 0.074872 & 3.5353 & 0.00054 & 0.00027 \tabularnewline
E2 & -0.121939422027158 & 0.058854 & -2.0719 & 0.039965 & 0.019982 \tabularnewline
E3 & 0.0234095617671534 & 0.061516 & 0.3805 & 0.704073 & 0.352036 \tabularnewline
A & -0.211295007159597 & 0.08377 & -2.5223 & 0.012688 & 0.006344 \tabularnewline
Happiness & 0.053843407628487 & 0.101447 & 0.5308 & 0.596365 & 0.298182 \tabularnewline
`Depression\r` & 0.125825104703589 & 0.075956 & 1.6566 & 0.099671 & 0.049835 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190555&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]9.93802773724693[/C][C]4.653764[/C][C]2.1355[/C][C]0.034324[/C][C]0.017162[/C][/ROW]
[ROW][C]Month[/C][C]-0.529824420571296[/C][C]0.401714[/C][C]-1.3189[/C][C]0.189183[/C][C]0.094592[/C][/ROW]
[ROW][C]I2[/C][C]0.366629203404525[/C][C]0.06332[/C][C]5.7901[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]I3[/C][C]0.262155562871582[/C][C]0.050822[/C][C]5.1583[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]E1[/C][C]0.264693373911638[/C][C]0.074872[/C][C]3.5353[/C][C]0.00054[/C][C]0.00027[/C][/ROW]
[ROW][C]E2[/C][C]-0.121939422027158[/C][C]0.058854[/C][C]-2.0719[/C][C]0.039965[/C][C]0.019982[/C][/ROW]
[ROW][C]E3[/C][C]0.0234095617671534[/C][C]0.061516[/C][C]0.3805[/C][C]0.704073[/C][C]0.352036[/C][/ROW]
[ROW][C]A[/C][C]-0.211295007159597[/C][C]0.08377[/C][C]-2.5223[/C][C]0.012688[/C][C]0.006344[/C][/ROW]
[ROW][C]Happiness[/C][C]0.053843407628487[/C][C]0.101447[/C][C]0.5308[/C][C]0.596365[/C][C]0.298182[/C][/ROW]
[ROW][C]`Depression\r`[/C][C]0.125825104703589[/C][C]0.075956[/C][C]1.6566[/C][C]0.099671[/C][C]0.049835[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190555&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190555&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)9.938027737246934.6537642.13550.0343240.017162
Month-0.5298244205712960.401714-1.31890.1891830.094592
I20.3666292034045250.063325.790100
I30.2621555628715820.0508225.15831e-060
E10.2646933739116380.0748723.53530.000540.00027
E2-0.1219394220271580.058854-2.07190.0399650.019982
E30.02340956176715340.0615160.38050.7040730.352036
A-0.2112950071595970.08377-2.52230.0126880.006344
Happiness0.0538434076284870.1014470.53080.5963650.298182
`Depression\r`0.1258251047035890.0759561.65660.0996710.049835






Multiple Linear Regression - Regression Statistics
Multiple R0.749538505124895
R-squared0.561807970664863
Adjusted R-squared0.535862389980545
F-TEST (value)21.6533203669804
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49147259475513
Sum Squared Residuals943.530224943209

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.749538505124895 \tabularnewline
R-squared & 0.561807970664863 \tabularnewline
Adjusted R-squared & 0.535862389980545 \tabularnewline
F-TEST (value) & 21.6533203669804 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.49147259475513 \tabularnewline
Sum Squared Residuals & 943.530224943209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190555&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.749538505124895[/C][/ROW]
[ROW][C]R-squared[/C][C]0.561807970664863[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.535862389980545[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.6533203669804[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/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]2.49147259475513[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]943.530224943209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190555&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190555&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.749538505124895
R-squared0.561807970664863
Adjusted R-squared0.535862389980545
F-TEST (value)21.6533203669804
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49147259475513
Sum Squared Residuals943.530224943209






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.34601432465761.65398567534243
22021.223948144826-1.22394814482597
31922.036957720196-3.03695772019602
41918.34304730845490.656952691545061
52019.42206338885260.577936611147438
62524.4643655278180.535634472181951
72520.76069201476234.23930798523769
82220.19203849801261.80796150198743
92622.32632821402543.6736717859746
102220.73550845783881.26449154216123
111720.0217409845024-3.02174098450237
122222.8004689803236-0.800468980323612
131921.141175640878-2.14117564087796
142423.00921279207490.990787207925102
152626.9200224908439-0.920022490843918
162119.27518423524381.72481576475621
171316.4576622079585-3.45766220795853
182627.4655205133304-1.46552051333044
192022.3976751809342-2.39767518093422
202219.07294855070872.92705144929133
211417.7818745908826-3.78187459088256
222120.60632817208750.393671827912539
23714.0725326479237-7.07253264792366
242321.90646363013781.09353636986219
251719.1385462197729-2.13854621977291
262522.68632532103712.31367467896289
272523.31487938384371.6851206161563
281916.52646311554682.47353688445319
292016.91613649804833.08386350195173
302321.49035514065151.50964485934848
312224.0245821977744-2.02458219777439
322222.3531887612015-0.353188761201513
332118.13505808310812.86494191689187
341517.8266573353714-2.82665733537142
352016.12163104841473.87836895158529
362221.47774031683830.522259683161737
371817.25508984591950.744910154080547
382021.0163197416082-1.01631974160824
392827.68453692491030.315463075089674
402223.9731381718153-1.97313817181531
411821.4822662933528-3.48226629335283
422321.03748195152261.9625180484774
432021.4070516429703-1.40705164297026
442524.93353918029760.0664608197024198
452622.10883646947033.89116353052967
461514.43662955346590.563370446534117
471717.6169233150715-0.616923315071495
482315.27650821989727.72349178010276
492122.7154235311579-1.71542353115786
501316.1291468940192-3.12914689401916
511820.1961739728088-2.19617397280878
521919.584041248027-0.58404124802703
532222.5997124634862-0.599712463486213
541617.7575896122293-1.75758961222926
552423.12465408993540.875345910064603
561820.1389521445799-2.13895214457993
572021.8632348820038-1.86323488200384
582420.59076898864173.40923101135833
591418.0485401689031-4.04854016890311
602220.77313173170671.22686826829334
612418.87664096582585.12335903417422
621818.6398867913768-0.639886791376825
632124.6608441182255-3.66084411822555
642322.18130398835250.818696011647487
651718.9913507269665-1.99135072696648
662221.98065362651410.0193463734858707
672425.1820764707166-1.18207647071655
682122.5579450078501-1.55794500785006
692221.93406986425040.0659301357496262
701616.9971048332882-0.997104833288247
712124.9809689207908-3.98096892079085
722324.115311715287-1.11531171528704
732219.28309986085582.71690013914425
742422.21240695028031.78759304971966
752424.2894301925601-0.289430192560057
761618.0256520474626-2.02565204746257
771617.5191039275261-1.51910392752615
782123.2233516929418-2.22335169294185
792625.93970747075750.0602925292425448
801516.6068976697516-1.60689766975163
812522.9562538611432.04374613885704
821817.61148459354960.388515406450355
832320.2379529178552.76204708214502
842021.0941275260148-1.0941275260148
851720.5664527985766-3.56645279857661
862524.82386614868540.176133851314586
872422.3851776501641.61482234983596
881714.33402004963862.66597995036144
891918.6811413965280.31885860347198
902020.8081968154812-0.808196815481243
911516.5407145588319-1.54071455883189
922724.76451399985482.23548600014519
932219.4172185359972.58278146400297
942322.95090921435940.0490907856405849
951620.1121579145949-4.11215791459494
961920.6668016480055-1.66680164800546
972520.58203041230744.41796958769265
981919.2446141761924-0.2446141761924
991919.1247721277583-0.124772127758322
1002624.90095943815051.09904056184953
1012118.43779591017032.56220408982974
1022019.98138799249810.018612007501882
1032419.83197831430724.16802168569276
1042223.4370022129634-1.43700221296342
1052021.4482766372848-1.44827663728482
1061819.5017202767278-1.50172027672781
1071816.6281814776461.37181852235398
1082421.52386882546572.4761311745343
1092421.25443702860262.7455629713974
1102224.5522857786068-2.55228577860683
1112319.78807492816583.21192507183415
1122219.85967759085442.14032240914559
1132017.8581116466992.14188835330096
1141819.8383284232784-1.83832842327837
1152524.65274721403550.347252785964461
1161818.1260578126583-0.126057812658314
1171617.5862662881215-1.58626628812154
1182019.86026784791410.139732152085922
1191918.044955474090.955044525909962
1201515.8133431228138-0.813343122813767
1211919.1330102202328-0.133010220232775
1221922.0745920367148-3.07459203671476
1231617.2711372394638-1.2711372394638
1241717.4843841140419-0.484384114041904
1252823.19369822717764.80630177282239
1262322.72893475294580.271065247054209
1272523.34896931782641.65103068217358
1282018.56638033079541.43361966920461
1291720.2019827607585-3.20198276075849
1302322.78269452592650.217305474073528
1311619.7804250424748-3.78042504247477
1322324.0381171358171-1.03811713581712
1331115.8140402590915-4.81404025909148
1341820.4598720577147-2.4598720577147
1352423.07920547378250.9207945262175
1362319.0291674910263.97083250897395
1372121.7644268078499-0.764426807849885
1381619.5337288513825-3.5337288513825
1392425.0264285927377-1.02642859273768
1402321.51519731816661.48480268183344
1411815.63671458535642.36328541464358
1422020.6771515890906-0.677151589090622
143918.6678418822081-9.66784188220813
1442420.35227198394683.64772801605322
1452524.78345116762350.216548832376473
1462018.22029156822611.77970843177388
1472118.97546694927982.02453305072025
1482522.75609764407632.24390235592365
1492222.162293686285-0.162293686284951
1502121.4237990986885-0.423799098688501
1512120.18157970686580.818420293134153
1522220.42439235409481.5756076459052
1532723.20963803056283.79036196943719
1542421.9070688570272.09293114297295
1552424.3193091693651-0.319309169365116
1562120.56667975957040.433320240429635
1571820.8251406209708-2.82514062097084
1581616.1570276038139-0.15702760381391
1592219.38094195710382.6190580428962
1602019.64706098004570.352939019954252
1611819.5342980205589-1.53429802055895
1622020.7981241244574-0.798124124457399

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.3460143246576 & 1.65398567534243 \tabularnewline
2 & 20 & 21.223948144826 & -1.22394814482597 \tabularnewline
3 & 19 & 22.036957720196 & -3.03695772019602 \tabularnewline
4 & 19 & 18.3430473084549 & 0.656952691545061 \tabularnewline
5 & 20 & 19.4220633888526 & 0.577936611147438 \tabularnewline
6 & 25 & 24.464365527818 & 0.535634472181951 \tabularnewline
7 & 25 & 20.7606920147623 & 4.23930798523769 \tabularnewline
8 & 22 & 20.1920384980126 & 1.80796150198743 \tabularnewline
9 & 26 & 22.3263282140254 & 3.6736717859746 \tabularnewline
10 & 22 & 20.7355084578388 & 1.26449154216123 \tabularnewline
11 & 17 & 20.0217409845024 & -3.02174098450237 \tabularnewline
12 & 22 & 22.8004689803236 & -0.800468980323612 \tabularnewline
13 & 19 & 21.141175640878 & -2.14117564087796 \tabularnewline
14 & 24 & 23.0092127920749 & 0.990787207925102 \tabularnewline
15 & 26 & 26.9200224908439 & -0.920022490843918 \tabularnewline
16 & 21 & 19.2751842352438 & 1.72481576475621 \tabularnewline
17 & 13 & 16.4576622079585 & -3.45766220795853 \tabularnewline
18 & 26 & 27.4655205133304 & -1.46552051333044 \tabularnewline
19 & 20 & 22.3976751809342 & -2.39767518093422 \tabularnewline
20 & 22 & 19.0729485507087 & 2.92705144929133 \tabularnewline
21 & 14 & 17.7818745908826 & -3.78187459088256 \tabularnewline
22 & 21 & 20.6063281720875 & 0.393671827912539 \tabularnewline
23 & 7 & 14.0725326479237 & -7.07253264792366 \tabularnewline
24 & 23 & 21.9064636301378 & 1.09353636986219 \tabularnewline
25 & 17 & 19.1385462197729 & -2.13854621977291 \tabularnewline
26 & 25 & 22.6863253210371 & 2.31367467896289 \tabularnewline
27 & 25 & 23.3148793838437 & 1.6851206161563 \tabularnewline
28 & 19 & 16.5264631155468 & 2.47353688445319 \tabularnewline
29 & 20 & 16.9161364980483 & 3.08386350195173 \tabularnewline
30 & 23 & 21.4903551406515 & 1.50964485934848 \tabularnewline
31 & 22 & 24.0245821977744 & -2.02458219777439 \tabularnewline
32 & 22 & 22.3531887612015 & -0.353188761201513 \tabularnewline
33 & 21 & 18.1350580831081 & 2.86494191689187 \tabularnewline
34 & 15 & 17.8266573353714 & -2.82665733537142 \tabularnewline
35 & 20 & 16.1216310484147 & 3.87836895158529 \tabularnewline
36 & 22 & 21.4777403168383 & 0.522259683161737 \tabularnewline
37 & 18 & 17.2550898459195 & 0.744910154080547 \tabularnewline
38 & 20 & 21.0163197416082 & -1.01631974160824 \tabularnewline
39 & 28 & 27.6845369249103 & 0.315463075089674 \tabularnewline
40 & 22 & 23.9731381718153 & -1.97313817181531 \tabularnewline
41 & 18 & 21.4822662933528 & -3.48226629335283 \tabularnewline
42 & 23 & 21.0374819515226 & 1.9625180484774 \tabularnewline
43 & 20 & 21.4070516429703 & -1.40705164297026 \tabularnewline
44 & 25 & 24.9335391802976 & 0.0664608197024198 \tabularnewline
45 & 26 & 22.1088364694703 & 3.89116353052967 \tabularnewline
46 & 15 & 14.4366295534659 & 0.563370446534117 \tabularnewline
47 & 17 & 17.6169233150715 & -0.616923315071495 \tabularnewline
48 & 23 & 15.2765082198972 & 7.72349178010276 \tabularnewline
49 & 21 & 22.7154235311579 & -1.71542353115786 \tabularnewline
50 & 13 & 16.1291468940192 & -3.12914689401916 \tabularnewline
51 & 18 & 20.1961739728088 & -2.19617397280878 \tabularnewline
52 & 19 & 19.584041248027 & -0.58404124802703 \tabularnewline
53 & 22 & 22.5997124634862 & -0.599712463486213 \tabularnewline
54 & 16 & 17.7575896122293 & -1.75758961222926 \tabularnewline
55 & 24 & 23.1246540899354 & 0.875345910064603 \tabularnewline
56 & 18 & 20.1389521445799 & -2.13895214457993 \tabularnewline
57 & 20 & 21.8632348820038 & -1.86323488200384 \tabularnewline
58 & 24 & 20.5907689886417 & 3.40923101135833 \tabularnewline
59 & 14 & 18.0485401689031 & -4.04854016890311 \tabularnewline
60 & 22 & 20.7731317317067 & 1.22686826829334 \tabularnewline
61 & 24 & 18.8766409658258 & 5.12335903417422 \tabularnewline
62 & 18 & 18.6398867913768 & -0.639886791376825 \tabularnewline
63 & 21 & 24.6608441182255 & -3.66084411822555 \tabularnewline
64 & 23 & 22.1813039883525 & 0.818696011647487 \tabularnewline
65 & 17 & 18.9913507269665 & -1.99135072696648 \tabularnewline
66 & 22 & 21.9806536265141 & 0.0193463734858707 \tabularnewline
67 & 24 & 25.1820764707166 & -1.18207647071655 \tabularnewline
68 & 21 & 22.5579450078501 & -1.55794500785006 \tabularnewline
69 & 22 & 21.9340698642504 & 0.0659301357496262 \tabularnewline
70 & 16 & 16.9971048332882 & -0.997104833288247 \tabularnewline
71 & 21 & 24.9809689207908 & -3.98096892079085 \tabularnewline
72 & 23 & 24.115311715287 & -1.11531171528704 \tabularnewline
73 & 22 & 19.2830998608558 & 2.71690013914425 \tabularnewline
74 & 24 & 22.2124069502803 & 1.78759304971966 \tabularnewline
75 & 24 & 24.2894301925601 & -0.289430192560057 \tabularnewline
76 & 16 & 18.0256520474626 & -2.02565204746257 \tabularnewline
77 & 16 & 17.5191039275261 & -1.51910392752615 \tabularnewline
78 & 21 & 23.2233516929418 & -2.22335169294185 \tabularnewline
79 & 26 & 25.9397074707575 & 0.0602925292425448 \tabularnewline
80 & 15 & 16.6068976697516 & -1.60689766975163 \tabularnewline
81 & 25 & 22.956253861143 & 2.04374613885704 \tabularnewline
82 & 18 & 17.6114845935496 & 0.388515406450355 \tabularnewline
83 & 23 & 20.237952917855 & 2.76204708214502 \tabularnewline
84 & 20 & 21.0941275260148 & -1.0941275260148 \tabularnewline
85 & 17 & 20.5664527985766 & -3.56645279857661 \tabularnewline
86 & 25 & 24.8238661486854 & 0.176133851314586 \tabularnewline
87 & 24 & 22.385177650164 & 1.61482234983596 \tabularnewline
88 & 17 & 14.3340200496386 & 2.66597995036144 \tabularnewline
89 & 19 & 18.681141396528 & 0.31885860347198 \tabularnewline
90 & 20 & 20.8081968154812 & -0.808196815481243 \tabularnewline
91 & 15 & 16.5407145588319 & -1.54071455883189 \tabularnewline
92 & 27 & 24.7645139998548 & 2.23548600014519 \tabularnewline
93 & 22 & 19.417218535997 & 2.58278146400297 \tabularnewline
94 & 23 & 22.9509092143594 & 0.0490907856405849 \tabularnewline
95 & 16 & 20.1121579145949 & -4.11215791459494 \tabularnewline
96 & 19 & 20.6668016480055 & -1.66680164800546 \tabularnewline
97 & 25 & 20.5820304123074 & 4.41796958769265 \tabularnewline
98 & 19 & 19.2446141761924 & -0.2446141761924 \tabularnewline
99 & 19 & 19.1247721277583 & -0.124772127758322 \tabularnewline
100 & 26 & 24.9009594381505 & 1.09904056184953 \tabularnewline
101 & 21 & 18.4377959101703 & 2.56220408982974 \tabularnewline
102 & 20 & 19.9813879924981 & 0.018612007501882 \tabularnewline
103 & 24 & 19.8319783143072 & 4.16802168569276 \tabularnewline
104 & 22 & 23.4370022129634 & -1.43700221296342 \tabularnewline
105 & 20 & 21.4482766372848 & -1.44827663728482 \tabularnewline
106 & 18 & 19.5017202767278 & -1.50172027672781 \tabularnewline
107 & 18 & 16.628181477646 & 1.37181852235398 \tabularnewline
108 & 24 & 21.5238688254657 & 2.4761311745343 \tabularnewline
109 & 24 & 21.2544370286026 & 2.7455629713974 \tabularnewline
110 & 22 & 24.5522857786068 & -2.55228577860683 \tabularnewline
111 & 23 & 19.7880749281658 & 3.21192507183415 \tabularnewline
112 & 22 & 19.8596775908544 & 2.14032240914559 \tabularnewline
113 & 20 & 17.858111646699 & 2.14188835330096 \tabularnewline
114 & 18 & 19.8383284232784 & -1.83832842327837 \tabularnewline
115 & 25 & 24.6527472140355 & 0.347252785964461 \tabularnewline
116 & 18 & 18.1260578126583 & -0.126057812658314 \tabularnewline
117 & 16 & 17.5862662881215 & -1.58626628812154 \tabularnewline
118 & 20 & 19.8602678479141 & 0.139732152085922 \tabularnewline
119 & 19 & 18.04495547409 & 0.955044525909962 \tabularnewline
120 & 15 & 15.8133431228138 & -0.813343122813767 \tabularnewline
121 & 19 & 19.1330102202328 & -0.133010220232775 \tabularnewline
122 & 19 & 22.0745920367148 & -3.07459203671476 \tabularnewline
123 & 16 & 17.2711372394638 & -1.2711372394638 \tabularnewline
124 & 17 & 17.4843841140419 & -0.484384114041904 \tabularnewline
125 & 28 & 23.1936982271776 & 4.80630177282239 \tabularnewline
126 & 23 & 22.7289347529458 & 0.271065247054209 \tabularnewline
127 & 25 & 23.3489693178264 & 1.65103068217358 \tabularnewline
128 & 20 & 18.5663803307954 & 1.43361966920461 \tabularnewline
129 & 17 & 20.2019827607585 & -3.20198276075849 \tabularnewline
130 & 23 & 22.7826945259265 & 0.217305474073528 \tabularnewline
131 & 16 & 19.7804250424748 & -3.78042504247477 \tabularnewline
132 & 23 & 24.0381171358171 & -1.03811713581712 \tabularnewline
133 & 11 & 15.8140402590915 & -4.81404025909148 \tabularnewline
134 & 18 & 20.4598720577147 & -2.4598720577147 \tabularnewline
135 & 24 & 23.0792054737825 & 0.9207945262175 \tabularnewline
136 & 23 & 19.029167491026 & 3.97083250897395 \tabularnewline
137 & 21 & 21.7644268078499 & -0.764426807849885 \tabularnewline
138 & 16 & 19.5337288513825 & -3.5337288513825 \tabularnewline
139 & 24 & 25.0264285927377 & -1.02642859273768 \tabularnewline
140 & 23 & 21.5151973181666 & 1.48480268183344 \tabularnewline
141 & 18 & 15.6367145853564 & 2.36328541464358 \tabularnewline
142 & 20 & 20.6771515890906 & -0.677151589090622 \tabularnewline
143 & 9 & 18.6678418822081 & -9.66784188220813 \tabularnewline
144 & 24 & 20.3522719839468 & 3.64772801605322 \tabularnewline
145 & 25 & 24.7834511676235 & 0.216548832376473 \tabularnewline
146 & 20 & 18.2202915682261 & 1.77970843177388 \tabularnewline
147 & 21 & 18.9754669492798 & 2.02453305072025 \tabularnewline
148 & 25 & 22.7560976440763 & 2.24390235592365 \tabularnewline
149 & 22 & 22.162293686285 & -0.162293686284951 \tabularnewline
150 & 21 & 21.4237990986885 & -0.423799098688501 \tabularnewline
151 & 21 & 20.1815797068658 & 0.818420293134153 \tabularnewline
152 & 22 & 20.4243923540948 & 1.5756076459052 \tabularnewline
153 & 27 & 23.2096380305628 & 3.79036196943719 \tabularnewline
154 & 24 & 21.907068857027 & 2.09293114297295 \tabularnewline
155 & 24 & 24.3193091693651 & -0.319309169365116 \tabularnewline
156 & 21 & 20.5666797595704 & 0.433320240429635 \tabularnewline
157 & 18 & 20.8251406209708 & -2.82514062097084 \tabularnewline
158 & 16 & 16.1570276038139 & -0.15702760381391 \tabularnewline
159 & 22 & 19.3809419571038 & 2.6190580428962 \tabularnewline
160 & 20 & 19.6470609800457 & 0.352939019954252 \tabularnewline
161 & 18 & 19.5342980205589 & -1.53429802055895 \tabularnewline
162 & 20 & 20.7981241244574 & -0.798124124457399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190555&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]24.3460143246576[/C][C]1.65398567534243[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]21.223948144826[/C][C]-1.22394814482597[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]22.036957720196[/C][C]-3.03695772019602[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]18.3430473084549[/C][C]0.656952691545061[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]19.4220633888526[/C][C]0.577936611147438[/C][/ROW]
[ROW][C]6[/C][C]25[/C][C]24.464365527818[/C][C]0.535634472181951[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]20.7606920147623[/C][C]4.23930798523769[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.1920384980126[/C][C]1.80796150198743[/C][/ROW]
[ROW][C]9[/C][C]26[/C][C]22.3263282140254[/C][C]3.6736717859746[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]20.7355084578388[/C][C]1.26449154216123[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]20.0217409845024[/C][C]-3.02174098450237[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]22.8004689803236[/C][C]-0.800468980323612[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]21.141175640878[/C][C]-2.14117564087796[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]23.0092127920749[/C][C]0.990787207925102[/C][/ROW]
[ROW][C]15[/C][C]26[/C][C]26.9200224908439[/C][C]-0.920022490843918[/C][/ROW]
[ROW][C]16[/C][C]21[/C][C]19.2751842352438[/C][C]1.72481576475621[/C][/ROW]
[ROW][C]17[/C][C]13[/C][C]16.4576622079585[/C][C]-3.45766220795853[/C][/ROW]
[ROW][C]18[/C][C]26[/C][C]27.4655205133304[/C][C]-1.46552051333044[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]22.3976751809342[/C][C]-2.39767518093422[/C][/ROW]
[ROW][C]20[/C][C]22[/C][C]19.0729485507087[/C][C]2.92705144929133[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]17.7818745908826[/C][C]-3.78187459088256[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]20.6063281720875[/C][C]0.393671827912539[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]14.0725326479237[/C][C]-7.07253264792366[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.9064636301378[/C][C]1.09353636986219[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]19.1385462197729[/C][C]-2.13854621977291[/C][/ROW]
[ROW][C]26[/C][C]25[/C][C]22.6863253210371[/C][C]2.31367467896289[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.3148793838437[/C][C]1.6851206161563[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]16.5264631155468[/C][C]2.47353688445319[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]16.9161364980483[/C][C]3.08386350195173[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]21.4903551406515[/C][C]1.50964485934848[/C][/ROW]
[ROW][C]31[/C][C]22[/C][C]24.0245821977744[/C][C]-2.02458219777439[/C][/ROW]
[ROW][C]32[/C][C]22[/C][C]22.3531887612015[/C][C]-0.353188761201513[/C][/ROW]
[ROW][C]33[/C][C]21[/C][C]18.1350580831081[/C][C]2.86494191689187[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]17.8266573353714[/C][C]-2.82665733537142[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]16.1216310484147[/C][C]3.87836895158529[/C][/ROW]
[ROW][C]36[/C][C]22[/C][C]21.4777403168383[/C][C]0.522259683161737[/C][/ROW]
[ROW][C]37[/C][C]18[/C][C]17.2550898459195[/C][C]0.744910154080547[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]21.0163197416082[/C][C]-1.01631974160824[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]27.6845369249103[/C][C]0.315463075089674[/C][/ROW]
[ROW][C]40[/C][C]22[/C][C]23.9731381718153[/C][C]-1.97313817181531[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]21.4822662933528[/C][C]-3.48226629335283[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]21.0374819515226[/C][C]1.9625180484774[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]21.4070516429703[/C][C]-1.40705164297026[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]24.9335391802976[/C][C]0.0664608197024198[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]22.1088364694703[/C][C]3.89116353052967[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]14.4366295534659[/C][C]0.563370446534117[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]17.6169233150715[/C][C]-0.616923315071495[/C][/ROW]
[ROW][C]48[/C][C]23[/C][C]15.2765082198972[/C][C]7.72349178010276[/C][/ROW]
[ROW][C]49[/C][C]21[/C][C]22.7154235311579[/C][C]-1.71542353115786[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]16.1291468940192[/C][C]-3.12914689401916[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]20.1961739728088[/C][C]-2.19617397280878[/C][/ROW]
[ROW][C]52[/C][C]19[/C][C]19.584041248027[/C][C]-0.58404124802703[/C][/ROW]
[ROW][C]53[/C][C]22[/C][C]22.5997124634862[/C][C]-0.599712463486213[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]17.7575896122293[/C][C]-1.75758961222926[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]23.1246540899354[/C][C]0.875345910064603[/C][/ROW]
[ROW][C]56[/C][C]18[/C][C]20.1389521445799[/C][C]-2.13895214457993[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]21.8632348820038[/C][C]-1.86323488200384[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]20.5907689886417[/C][C]3.40923101135833[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]18.0485401689031[/C][C]-4.04854016890311[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]20.7731317317067[/C][C]1.22686826829334[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]18.8766409658258[/C][C]5.12335903417422[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]18.6398867913768[/C][C]-0.639886791376825[/C][/ROW]
[ROW][C]63[/C][C]21[/C][C]24.6608441182255[/C][C]-3.66084411822555[/C][/ROW]
[ROW][C]64[/C][C]23[/C][C]22.1813039883525[/C][C]0.818696011647487[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]18.9913507269665[/C][C]-1.99135072696648[/C][/ROW]
[ROW][C]66[/C][C]22[/C][C]21.9806536265141[/C][C]0.0193463734858707[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]25.1820764707166[/C][C]-1.18207647071655[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]22.5579450078501[/C][C]-1.55794500785006[/C][/ROW]
[ROW][C]69[/C][C]22[/C][C]21.9340698642504[/C][C]0.0659301357496262[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]16.9971048332882[/C][C]-0.997104833288247[/C][/ROW]
[ROW][C]71[/C][C]21[/C][C]24.9809689207908[/C][C]-3.98096892079085[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]24.115311715287[/C][C]-1.11531171528704[/C][/ROW]
[ROW][C]73[/C][C]22[/C][C]19.2830998608558[/C][C]2.71690013914425[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]22.2124069502803[/C][C]1.78759304971966[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]24.2894301925601[/C][C]-0.289430192560057[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]18.0256520474626[/C][C]-2.02565204746257[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]17.5191039275261[/C][C]-1.51910392752615[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]23.2233516929418[/C][C]-2.22335169294185[/C][/ROW]
[ROW][C]79[/C][C]26[/C][C]25.9397074707575[/C][C]0.0602925292425448[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]16.6068976697516[/C][C]-1.60689766975163[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]22.956253861143[/C][C]2.04374613885704[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]17.6114845935496[/C][C]0.388515406450355[/C][/ROW]
[ROW][C]83[/C][C]23[/C][C]20.237952917855[/C][C]2.76204708214502[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.0941275260148[/C][C]-1.0941275260148[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]20.5664527985766[/C][C]-3.56645279857661[/C][/ROW]
[ROW][C]86[/C][C]25[/C][C]24.8238661486854[/C][C]0.176133851314586[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]22.385177650164[/C][C]1.61482234983596[/C][/ROW]
[ROW][C]88[/C][C]17[/C][C]14.3340200496386[/C][C]2.66597995036144[/C][/ROW]
[ROW][C]89[/C][C]19[/C][C]18.681141396528[/C][C]0.31885860347198[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]20.8081968154812[/C][C]-0.808196815481243[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]16.5407145588319[/C][C]-1.54071455883189[/C][/ROW]
[ROW][C]92[/C][C]27[/C][C]24.7645139998548[/C][C]2.23548600014519[/C][/ROW]
[ROW][C]93[/C][C]22[/C][C]19.417218535997[/C][C]2.58278146400297[/C][/ROW]
[ROW][C]94[/C][C]23[/C][C]22.9509092143594[/C][C]0.0490907856405849[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]20.1121579145949[/C][C]-4.11215791459494[/C][/ROW]
[ROW][C]96[/C][C]19[/C][C]20.6668016480055[/C][C]-1.66680164800546[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]20.5820304123074[/C][C]4.41796958769265[/C][/ROW]
[ROW][C]98[/C][C]19[/C][C]19.2446141761924[/C][C]-0.2446141761924[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.1247721277583[/C][C]-0.124772127758322[/C][/ROW]
[ROW][C]100[/C][C]26[/C][C]24.9009594381505[/C][C]1.09904056184953[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]18.4377959101703[/C][C]2.56220408982974[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]19.9813879924981[/C][C]0.018612007501882[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]19.8319783143072[/C][C]4.16802168569276[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]23.4370022129634[/C][C]-1.43700221296342[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]21.4482766372848[/C][C]-1.44827663728482[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]19.5017202767278[/C][C]-1.50172027672781[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]16.628181477646[/C][C]1.37181852235398[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]21.5238688254657[/C][C]2.4761311745343[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]21.2544370286026[/C][C]2.7455629713974[/C][/ROW]
[ROW][C]110[/C][C]22[/C][C]24.5522857786068[/C][C]-2.55228577860683[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]19.7880749281658[/C][C]3.21192507183415[/C][/ROW]
[ROW][C]112[/C][C]22[/C][C]19.8596775908544[/C][C]2.14032240914559[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]17.858111646699[/C][C]2.14188835330096[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]19.8383284232784[/C][C]-1.83832842327837[/C][/ROW]
[ROW][C]115[/C][C]25[/C][C]24.6527472140355[/C][C]0.347252785964461[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]18.1260578126583[/C][C]-0.126057812658314[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]17.5862662881215[/C][C]-1.58626628812154[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]19.8602678479141[/C][C]0.139732152085922[/C][/ROW]
[ROW][C]119[/C][C]19[/C][C]18.04495547409[/C][C]0.955044525909962[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.8133431228138[/C][C]-0.813343122813767[/C][/ROW]
[ROW][C]121[/C][C]19[/C][C]19.1330102202328[/C][C]-0.133010220232775[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]22.0745920367148[/C][C]-3.07459203671476[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.2711372394638[/C][C]-1.2711372394638[/C][/ROW]
[ROW][C]124[/C][C]17[/C][C]17.4843841140419[/C][C]-0.484384114041904[/C][/ROW]
[ROW][C]125[/C][C]28[/C][C]23.1936982271776[/C][C]4.80630177282239[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]22.7289347529458[/C][C]0.271065247054209[/C][/ROW]
[ROW][C]127[/C][C]25[/C][C]23.3489693178264[/C][C]1.65103068217358[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]18.5663803307954[/C][C]1.43361966920461[/C][/ROW]
[ROW][C]129[/C][C]17[/C][C]20.2019827607585[/C][C]-3.20198276075849[/C][/ROW]
[ROW][C]130[/C][C]23[/C][C]22.7826945259265[/C][C]0.217305474073528[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]19.7804250424748[/C][C]-3.78042504247477[/C][/ROW]
[ROW][C]132[/C][C]23[/C][C]24.0381171358171[/C][C]-1.03811713581712[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]15.8140402590915[/C][C]-4.81404025909148[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.4598720577147[/C][C]-2.4598720577147[/C][/ROW]
[ROW][C]135[/C][C]24[/C][C]23.0792054737825[/C][C]0.9207945262175[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]19.029167491026[/C][C]3.97083250897395[/C][/ROW]
[ROW][C]137[/C][C]21[/C][C]21.7644268078499[/C][C]-0.764426807849885[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]19.5337288513825[/C][C]-3.5337288513825[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]25.0264285927377[/C][C]-1.02642859273768[/C][/ROW]
[ROW][C]140[/C][C]23[/C][C]21.5151973181666[/C][C]1.48480268183344[/C][/ROW]
[ROW][C]141[/C][C]18[/C][C]15.6367145853564[/C][C]2.36328541464358[/C][/ROW]
[ROW][C]142[/C][C]20[/C][C]20.6771515890906[/C][C]-0.677151589090622[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]18.6678418822081[/C][C]-9.66784188220813[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]20.3522719839468[/C][C]3.64772801605322[/C][/ROW]
[ROW][C]145[/C][C]25[/C][C]24.7834511676235[/C][C]0.216548832376473[/C][/ROW]
[ROW][C]146[/C][C]20[/C][C]18.2202915682261[/C][C]1.77970843177388[/C][/ROW]
[ROW][C]147[/C][C]21[/C][C]18.9754669492798[/C][C]2.02453305072025[/C][/ROW]
[ROW][C]148[/C][C]25[/C][C]22.7560976440763[/C][C]2.24390235592365[/C][/ROW]
[ROW][C]149[/C][C]22[/C][C]22.162293686285[/C][C]-0.162293686284951[/C][/ROW]
[ROW][C]150[/C][C]21[/C][C]21.4237990986885[/C][C]-0.423799098688501[/C][/ROW]
[ROW][C]151[/C][C]21[/C][C]20.1815797068658[/C][C]0.818420293134153[/C][/ROW]
[ROW][C]152[/C][C]22[/C][C]20.4243923540948[/C][C]1.5756076459052[/C][/ROW]
[ROW][C]153[/C][C]27[/C][C]23.2096380305628[/C][C]3.79036196943719[/C][/ROW]
[ROW][C]154[/C][C]24[/C][C]21.907068857027[/C][C]2.09293114297295[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]24.3193091693651[/C][C]-0.319309169365116[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]20.5666797595704[/C][C]0.433320240429635[/C][/ROW]
[ROW][C]157[/C][C]18[/C][C]20.8251406209708[/C][C]-2.82514062097084[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]16.1570276038139[/C][C]-0.15702760381391[/C][/ROW]
[ROW][C]159[/C][C]22[/C][C]19.3809419571038[/C][C]2.6190580428962[/C][/ROW]
[ROW][C]160[/C][C]20[/C][C]19.6470609800457[/C][C]0.352939019954252[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]19.5342980205589[/C][C]-1.53429802055895[/C][/ROW]
[ROW][C]162[/C][C]20[/C][C]20.7981241244574[/C][C]-0.798124124457399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190555&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190555&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
12624.34601432465761.65398567534243
22021.223948144826-1.22394814482597
31922.036957720196-3.03695772019602
41918.34304730845490.656952691545061
52019.42206338885260.577936611147438
62524.4643655278180.535634472181951
72520.76069201476234.23930798523769
82220.19203849801261.80796150198743
92622.32632821402543.6736717859746
102220.73550845783881.26449154216123
111720.0217409845024-3.02174098450237
122222.8004689803236-0.800468980323612
131921.141175640878-2.14117564087796
142423.00921279207490.990787207925102
152626.9200224908439-0.920022490843918
162119.27518423524381.72481576475621
171316.4576622079585-3.45766220795853
182627.4655205133304-1.46552051333044
192022.3976751809342-2.39767518093422
202219.07294855070872.92705144929133
211417.7818745908826-3.78187459088256
222120.60632817208750.393671827912539
23714.0725326479237-7.07253264792366
242321.90646363013781.09353636986219
251719.1385462197729-2.13854621977291
262522.68632532103712.31367467896289
272523.31487938384371.6851206161563
281916.52646311554682.47353688445319
292016.91613649804833.08386350195173
302321.49035514065151.50964485934848
312224.0245821977744-2.02458219777439
322222.3531887612015-0.353188761201513
332118.13505808310812.86494191689187
341517.8266573353714-2.82665733537142
352016.12163104841473.87836895158529
362221.47774031683830.522259683161737
371817.25508984591950.744910154080547
382021.0163197416082-1.01631974160824
392827.68453692491030.315463075089674
402223.9731381718153-1.97313817181531
411821.4822662933528-3.48226629335283
422321.03748195152261.9625180484774
432021.4070516429703-1.40705164297026
442524.93353918029760.0664608197024198
452622.10883646947033.89116353052967
461514.43662955346590.563370446534117
471717.6169233150715-0.616923315071495
482315.27650821989727.72349178010276
492122.7154235311579-1.71542353115786
501316.1291468940192-3.12914689401916
511820.1961739728088-2.19617397280878
521919.584041248027-0.58404124802703
532222.5997124634862-0.599712463486213
541617.7575896122293-1.75758961222926
552423.12465408993540.875345910064603
561820.1389521445799-2.13895214457993
572021.8632348820038-1.86323488200384
582420.59076898864173.40923101135833
591418.0485401689031-4.04854016890311
602220.77313173170671.22686826829334
612418.87664096582585.12335903417422
621818.6398867913768-0.639886791376825
632124.6608441182255-3.66084411822555
642322.18130398835250.818696011647487
651718.9913507269665-1.99135072696648
662221.98065362651410.0193463734858707
672425.1820764707166-1.18207647071655
682122.5579450078501-1.55794500785006
692221.93406986425040.0659301357496262
701616.9971048332882-0.997104833288247
712124.9809689207908-3.98096892079085
722324.115311715287-1.11531171528704
732219.28309986085582.71690013914425
742422.21240695028031.78759304971966
752424.2894301925601-0.289430192560057
761618.0256520474626-2.02565204746257
771617.5191039275261-1.51910392752615
782123.2233516929418-2.22335169294185
792625.93970747075750.0602925292425448
801516.6068976697516-1.60689766975163
812522.9562538611432.04374613885704
821817.61148459354960.388515406450355
832320.2379529178552.76204708214502
842021.0941275260148-1.0941275260148
851720.5664527985766-3.56645279857661
862524.82386614868540.176133851314586
872422.3851776501641.61482234983596
881714.33402004963862.66597995036144
891918.6811413965280.31885860347198
902020.8081968154812-0.808196815481243
911516.5407145588319-1.54071455883189
922724.76451399985482.23548600014519
932219.4172185359972.58278146400297
942322.95090921435940.0490907856405849
951620.1121579145949-4.11215791459494
961920.6668016480055-1.66680164800546
972520.58203041230744.41796958769265
981919.2446141761924-0.2446141761924
991919.1247721277583-0.124772127758322
1002624.90095943815051.09904056184953
1012118.43779591017032.56220408982974
1022019.98138799249810.018612007501882
1032419.83197831430724.16802168569276
1042223.4370022129634-1.43700221296342
1052021.4482766372848-1.44827663728482
1061819.5017202767278-1.50172027672781
1071816.6281814776461.37181852235398
1082421.52386882546572.4761311745343
1092421.25443702860262.7455629713974
1102224.5522857786068-2.55228577860683
1112319.78807492816583.21192507183415
1122219.85967759085442.14032240914559
1132017.8581116466992.14188835330096
1141819.8383284232784-1.83832842327837
1152524.65274721403550.347252785964461
1161818.1260578126583-0.126057812658314
1171617.5862662881215-1.58626628812154
1182019.86026784791410.139732152085922
1191918.044955474090.955044525909962
1201515.8133431228138-0.813343122813767
1211919.1330102202328-0.133010220232775
1221922.0745920367148-3.07459203671476
1231617.2711372394638-1.2711372394638
1241717.4843841140419-0.484384114041904
1252823.19369822717764.80630177282239
1262322.72893475294580.271065247054209
1272523.34896931782641.65103068217358
1282018.56638033079541.43361966920461
1291720.2019827607585-3.20198276075849
1302322.78269452592650.217305474073528
1311619.7804250424748-3.78042504247477
1322324.0381171358171-1.03811713581712
1331115.8140402590915-4.81404025909148
1341820.4598720577147-2.4598720577147
1352423.07920547378250.9207945262175
1362319.0291674910263.97083250897395
1372121.7644268078499-0.764426807849885
1381619.5337288513825-3.5337288513825
1392425.0264285927377-1.02642859273768
1402321.51519731816661.48480268183344
1411815.63671458535642.36328541464358
1422020.6771515890906-0.677151589090622
143918.6678418822081-9.66784188220813
1442420.35227198394683.64772801605322
1452524.78345116762350.216548832376473
1462018.22029156822611.77970843177388
1472118.97546694927982.02453305072025
1482522.75609764407632.24390235592365
1492222.162293686285-0.162293686284951
1502121.4237990986885-0.423799098688501
1512120.18157970686580.818420293134153
1522220.42439235409481.5756076459052
1532723.20963803056283.79036196943719
1542421.9070688570272.09293114297295
1552424.3193091693651-0.319309169365116
1562120.56667975957040.433320240429635
1571820.8251406209708-2.82514062097084
1581616.1570276038139-0.15702760381391
1592219.38094195710382.6190580428962
1602019.64706098004570.352939019954252
1611819.5342980205589-1.53429802055895
1622020.7981241244574-0.798124124457399






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.952322436613630.095355126772740.04767756338637
140.9105091432606290.1789817134787420.089490856739371
150.8618014181418370.2763971637163270.138198581858163
160.7943470117771430.4113059764457140.205652988222857
170.7294076988391240.5411846023217520.270592301160876
180.6585745172296870.6828509655406260.341425482770313
190.5888915427782660.8222169144434690.411108457221734
200.5646330462072590.8707339075854810.435366953792741
210.5339396728422850.932120654315430.466060327157715
220.4470715547560780.8941431095121570.552928445243921
230.5838703573369790.8322592853260420.416129642663021
240.5268556048350590.9462887903298830.473144395164941
250.4620980899889860.9241961799779720.537901910011014
260.5023305801162540.9953388397674930.497669419883746
270.4388492847650560.8776985695301110.561150715234944
280.6363186813316920.7273626373366170.363681318668308
290.6788626335618730.6422747328762540.321137366438127
300.6363344379342190.7273311241315620.363665562065781
310.6008243567850530.7983512864298930.399175643214947
320.5576061070345980.8847877859308040.442393892965402
330.5198210943839710.9603578112320570.480178905616029
340.5795682078825270.8408635842349470.420431792117473
350.727893385465120.5442132290697610.27210661453488
360.6822729981577040.6354540036845930.317727001842296
370.630589315187150.7388213696257010.36941068481285
380.5753102484571920.8493795030856160.424689751542808
390.5166396484799350.966720703040130.483360351520065
400.4888015735262250.9776031470524510.511198426473775
410.4962043374714690.9924086749429380.503795662528531
420.4635543986910290.9271087973820590.536445601308971
430.4163261611497120.8326523222994240.583673838850288
440.3841369806985270.7682739613970540.615863019301473
450.4408020022799710.8816040045599430.559197997720029
460.3878444131406140.7756888262812280.612155586859386
470.3465719404758540.6931438809517080.653428059524146
480.7592098992546320.4815802014907360.240790100745368
490.7467045281319240.5065909437361510.253295471868075
500.7774551118503790.4450897762992420.222544888149621
510.762976054129870.4740478917402590.23702394587013
520.7249316349387120.5501367301225760.275068365061288
530.7077879853392760.5844240293214490.292212014660724
540.6869548775324790.6260902449350420.313045122467521
550.6453166665412530.7093666669174950.354683333458747
560.6434224413084640.7131551173830720.356577558691536
570.6317196211664850.736560757667030.368280378833515
580.7156987243161840.5686025513676310.284301275683816
590.7790818067807790.4418363864384410.220918193219221
600.7488521244586510.5022957510826980.251147875541349
610.827093345326650.3458133093467010.17290665467335
620.7959278523299430.4081442953401150.204072147670057
630.8507640614510340.2984718770979330.149235938548966
640.821239431645650.3575211367086990.17876056835435
650.8517085231897990.2965829536204020.148291476810201
660.8226921082800180.3546157834399630.177307891719982
670.808324733380030.3833505332399410.19167526661997
680.7862106406945160.4275787186109670.213789359305484
690.7524723057369350.495055388526130.247527694263065
700.7170839253348480.5658321493303040.282916074665152
710.7715952434454010.4568095131091970.228404756554599
720.7408325252099980.5183349495800040.259167474790002
730.76480128744990.47039742510020.2351987125501
740.7524070450290270.4951859099419460.247592954970973
750.7151411156786950.569717768642610.284858884321305
760.7172105197633570.5655789604732850.282789480236643
770.6848465141158820.6303069717682360.315153485884118
780.673950192978350.65209961404330.32604980702165
790.636196831280120.7276063374397610.36380316871988
800.6103798053369960.7792403893260080.389620194663004
810.5963720658024610.8072558683950780.403627934197539
820.5679439222707680.8641121554584640.432056077729232
830.5909072257348290.8181855485303410.409092774265171
840.5562010719030330.8875978561939330.443798928096966
850.6009344339393440.7981311321213110.399065566060656
860.5550450622826020.8899098754347960.444954937717398
870.5313089711274190.9373820577451620.468691028872581
880.5585153536413250.8829692927173510.441484646358675
890.515912273185960.9681754536280810.48408772681404
900.4903928344601650.9807856689203310.509607165539835
910.4569205033421660.9138410066843320.543079496657834
920.4379708234060710.8759416468121430.562029176593928
930.4384460060341360.8768920120682730.561553993965864
940.3962464445167040.7924928890334070.603753555483296
950.4799001012215780.9598002024431550.520099898778422
960.4568706052666140.9137412105332280.543129394733386
970.571286174878520.857427650242960.42871382512148
980.5249431669529460.9501136660941090.475056833047054
990.4802311469593830.9604622939187670.519768853040617
1000.4380307697632370.8760615395264740.561969230236763
1010.435656164586930.8713123291738610.56434383541307
1020.3887111657755570.7774223315511130.611288834224443
1030.4868199537065760.9736399074131510.513180046293424
1040.4695616609698450.939123321939690.530438339030155
1050.4355190150065370.8710380300130730.564480984993463
1060.4019731431633540.8039462863267070.598026856836646
1070.3702393200791940.7404786401583880.629760679920806
1080.3760727835017190.7521455670034390.623927216498281
1090.3621553692549910.7243107385099820.637844630745009
1100.3487102681897520.6974205363795040.651289731810248
1110.3764483484064590.7528966968129190.623551651593541
1120.3728555852945240.7457111705890480.627144414705476
1130.3678268564046240.7356537128092480.632173143595376
1140.3363840567328470.6727681134656930.663615943267153
1150.2928101273550310.5856202547100620.707189872644969
1160.2526053846015890.5052107692031790.747394615398411
1170.2208013578028860.4416027156057720.779198642197114
1180.1824404502065640.3648809004131290.817559549793436
1190.156960938191140.313921876382280.84303906180886
1200.1342776615884010.2685553231768030.865722338411599
1210.1064710925877180.2129421851754370.893528907412282
1220.1137737489994380.2275474979988760.886226251000562
1230.09042724313490230.1808544862698050.909572756865098
1240.07201067944519450.1440213588903890.927989320554806
1250.177948593614660.355897187229320.82205140638534
1260.1449637052226380.2899274104452760.855036294777362
1270.119929391796640.2398587835932790.88007060820336
1280.09566668721918220.1913333744383640.904333312780818
1290.1380256001646780.2760512003293560.861974399835322
1300.1062027126542510.2124054253085030.893797287345748
1310.1534008110845820.3068016221691640.846599188915418
1320.1270671817462920.2541343634925840.872932818253708
1330.2757145700948190.5514291401896380.724285429905181
1340.2933371871970710.5866743743941420.706662812802929
1350.2605880082763420.5211760165526830.739411991723658
1360.2433776859860160.4867553719720320.756622314013984
1370.2095600105557040.4191200211114080.790439989444296
1380.2351764367086690.4703528734173370.764823563291331
1390.1795273050088130.3590546100176250.820472694991187
1400.1349540075019080.2699080150038160.865045992498092
1410.1026412608546230.2052825217092460.897358739145377
1420.08135104029595140.1627020805919030.918648959704049
1430.8675164636933080.2649670726133830.132483536306691
1440.9892909356989570.02141812860208560.0107090643010428
1450.9796334234578170.04073315308436640.0203665765421832
1460.954848316549380.09030336690124060.0451516834506203
1470.9107397357641050.178520528471790.0892602642358951
1480.8385513675926960.3228972648146070.161448632407304
1490.7148864254205510.5702271491588990.285113574579449

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.95232243661363 & 0.09535512677274 & 0.04767756338637 \tabularnewline
14 & 0.910509143260629 & 0.178981713478742 & 0.089490856739371 \tabularnewline
15 & 0.861801418141837 & 0.276397163716327 & 0.138198581858163 \tabularnewline
16 & 0.794347011777143 & 0.411305976445714 & 0.205652988222857 \tabularnewline
17 & 0.729407698839124 & 0.541184602321752 & 0.270592301160876 \tabularnewline
18 & 0.658574517229687 & 0.682850965540626 & 0.341425482770313 \tabularnewline
19 & 0.588891542778266 & 0.822216914443469 & 0.411108457221734 \tabularnewline
20 & 0.564633046207259 & 0.870733907585481 & 0.435366953792741 \tabularnewline
21 & 0.533939672842285 & 0.93212065431543 & 0.466060327157715 \tabularnewline
22 & 0.447071554756078 & 0.894143109512157 & 0.552928445243921 \tabularnewline
23 & 0.583870357336979 & 0.832259285326042 & 0.416129642663021 \tabularnewline
24 & 0.526855604835059 & 0.946288790329883 & 0.473144395164941 \tabularnewline
25 & 0.462098089988986 & 0.924196179977972 & 0.537901910011014 \tabularnewline
26 & 0.502330580116254 & 0.995338839767493 & 0.497669419883746 \tabularnewline
27 & 0.438849284765056 & 0.877698569530111 & 0.561150715234944 \tabularnewline
28 & 0.636318681331692 & 0.727362637336617 & 0.363681318668308 \tabularnewline
29 & 0.678862633561873 & 0.642274732876254 & 0.321137366438127 \tabularnewline
30 & 0.636334437934219 & 0.727331124131562 & 0.363665562065781 \tabularnewline
31 & 0.600824356785053 & 0.798351286429893 & 0.399175643214947 \tabularnewline
32 & 0.557606107034598 & 0.884787785930804 & 0.442393892965402 \tabularnewline
33 & 0.519821094383971 & 0.960357811232057 & 0.480178905616029 \tabularnewline
34 & 0.579568207882527 & 0.840863584234947 & 0.420431792117473 \tabularnewline
35 & 0.72789338546512 & 0.544213229069761 & 0.27210661453488 \tabularnewline
36 & 0.682272998157704 & 0.635454003684593 & 0.317727001842296 \tabularnewline
37 & 0.63058931518715 & 0.738821369625701 & 0.36941068481285 \tabularnewline
38 & 0.575310248457192 & 0.849379503085616 & 0.424689751542808 \tabularnewline
39 & 0.516639648479935 & 0.96672070304013 & 0.483360351520065 \tabularnewline
40 & 0.488801573526225 & 0.977603147052451 & 0.511198426473775 \tabularnewline
41 & 0.496204337471469 & 0.992408674942938 & 0.503795662528531 \tabularnewline
42 & 0.463554398691029 & 0.927108797382059 & 0.536445601308971 \tabularnewline
43 & 0.416326161149712 & 0.832652322299424 & 0.583673838850288 \tabularnewline
44 & 0.384136980698527 & 0.768273961397054 & 0.615863019301473 \tabularnewline
45 & 0.440802002279971 & 0.881604004559943 & 0.559197997720029 \tabularnewline
46 & 0.387844413140614 & 0.775688826281228 & 0.612155586859386 \tabularnewline
47 & 0.346571940475854 & 0.693143880951708 & 0.653428059524146 \tabularnewline
48 & 0.759209899254632 & 0.481580201490736 & 0.240790100745368 \tabularnewline
49 & 0.746704528131924 & 0.506590943736151 & 0.253295471868075 \tabularnewline
50 & 0.777455111850379 & 0.445089776299242 & 0.222544888149621 \tabularnewline
51 & 0.76297605412987 & 0.474047891740259 & 0.23702394587013 \tabularnewline
52 & 0.724931634938712 & 0.550136730122576 & 0.275068365061288 \tabularnewline
53 & 0.707787985339276 & 0.584424029321449 & 0.292212014660724 \tabularnewline
54 & 0.686954877532479 & 0.626090244935042 & 0.313045122467521 \tabularnewline
55 & 0.645316666541253 & 0.709366666917495 & 0.354683333458747 \tabularnewline
56 & 0.643422441308464 & 0.713155117383072 & 0.356577558691536 \tabularnewline
57 & 0.631719621166485 & 0.73656075766703 & 0.368280378833515 \tabularnewline
58 & 0.715698724316184 & 0.568602551367631 & 0.284301275683816 \tabularnewline
59 & 0.779081806780779 & 0.441836386438441 & 0.220918193219221 \tabularnewline
60 & 0.748852124458651 & 0.502295751082698 & 0.251147875541349 \tabularnewline
61 & 0.82709334532665 & 0.345813309346701 & 0.17290665467335 \tabularnewline
62 & 0.795927852329943 & 0.408144295340115 & 0.204072147670057 \tabularnewline
63 & 0.850764061451034 & 0.298471877097933 & 0.149235938548966 \tabularnewline
64 & 0.82123943164565 & 0.357521136708699 & 0.17876056835435 \tabularnewline
65 & 0.851708523189799 & 0.296582953620402 & 0.148291476810201 \tabularnewline
66 & 0.822692108280018 & 0.354615783439963 & 0.177307891719982 \tabularnewline
67 & 0.80832473338003 & 0.383350533239941 & 0.19167526661997 \tabularnewline
68 & 0.786210640694516 & 0.427578718610967 & 0.213789359305484 \tabularnewline
69 & 0.752472305736935 & 0.49505538852613 & 0.247527694263065 \tabularnewline
70 & 0.717083925334848 & 0.565832149330304 & 0.282916074665152 \tabularnewline
71 & 0.771595243445401 & 0.456809513109197 & 0.228404756554599 \tabularnewline
72 & 0.740832525209998 & 0.518334949580004 & 0.259167474790002 \tabularnewline
73 & 0.7648012874499 & 0.4703974251002 & 0.2351987125501 \tabularnewline
74 & 0.752407045029027 & 0.495185909941946 & 0.247592954970973 \tabularnewline
75 & 0.715141115678695 & 0.56971776864261 & 0.284858884321305 \tabularnewline
76 & 0.717210519763357 & 0.565578960473285 & 0.282789480236643 \tabularnewline
77 & 0.684846514115882 & 0.630306971768236 & 0.315153485884118 \tabularnewline
78 & 0.67395019297835 & 0.6520996140433 & 0.32604980702165 \tabularnewline
79 & 0.63619683128012 & 0.727606337439761 & 0.36380316871988 \tabularnewline
80 & 0.610379805336996 & 0.779240389326008 & 0.389620194663004 \tabularnewline
81 & 0.596372065802461 & 0.807255868395078 & 0.403627934197539 \tabularnewline
82 & 0.567943922270768 & 0.864112155458464 & 0.432056077729232 \tabularnewline
83 & 0.590907225734829 & 0.818185548530341 & 0.409092774265171 \tabularnewline
84 & 0.556201071903033 & 0.887597856193933 & 0.443798928096966 \tabularnewline
85 & 0.600934433939344 & 0.798131132121311 & 0.399065566060656 \tabularnewline
86 & 0.555045062282602 & 0.889909875434796 & 0.444954937717398 \tabularnewline
87 & 0.531308971127419 & 0.937382057745162 & 0.468691028872581 \tabularnewline
88 & 0.558515353641325 & 0.882969292717351 & 0.441484646358675 \tabularnewline
89 & 0.51591227318596 & 0.968175453628081 & 0.48408772681404 \tabularnewline
90 & 0.490392834460165 & 0.980785668920331 & 0.509607165539835 \tabularnewline
91 & 0.456920503342166 & 0.913841006684332 & 0.543079496657834 \tabularnewline
92 & 0.437970823406071 & 0.875941646812143 & 0.562029176593928 \tabularnewline
93 & 0.438446006034136 & 0.876892012068273 & 0.561553993965864 \tabularnewline
94 & 0.396246444516704 & 0.792492889033407 & 0.603753555483296 \tabularnewline
95 & 0.479900101221578 & 0.959800202443155 & 0.520099898778422 \tabularnewline
96 & 0.456870605266614 & 0.913741210533228 & 0.543129394733386 \tabularnewline
97 & 0.57128617487852 & 0.85742765024296 & 0.42871382512148 \tabularnewline
98 & 0.524943166952946 & 0.950113666094109 & 0.475056833047054 \tabularnewline
99 & 0.480231146959383 & 0.960462293918767 & 0.519768853040617 \tabularnewline
100 & 0.438030769763237 & 0.876061539526474 & 0.561969230236763 \tabularnewline
101 & 0.43565616458693 & 0.871312329173861 & 0.56434383541307 \tabularnewline
102 & 0.388711165775557 & 0.777422331551113 & 0.611288834224443 \tabularnewline
103 & 0.486819953706576 & 0.973639907413151 & 0.513180046293424 \tabularnewline
104 & 0.469561660969845 & 0.93912332193969 & 0.530438339030155 \tabularnewline
105 & 0.435519015006537 & 0.871038030013073 & 0.564480984993463 \tabularnewline
106 & 0.401973143163354 & 0.803946286326707 & 0.598026856836646 \tabularnewline
107 & 0.370239320079194 & 0.740478640158388 & 0.629760679920806 \tabularnewline
108 & 0.376072783501719 & 0.752145567003439 & 0.623927216498281 \tabularnewline
109 & 0.362155369254991 & 0.724310738509982 & 0.637844630745009 \tabularnewline
110 & 0.348710268189752 & 0.697420536379504 & 0.651289731810248 \tabularnewline
111 & 0.376448348406459 & 0.752896696812919 & 0.623551651593541 \tabularnewline
112 & 0.372855585294524 & 0.745711170589048 & 0.627144414705476 \tabularnewline
113 & 0.367826856404624 & 0.735653712809248 & 0.632173143595376 \tabularnewline
114 & 0.336384056732847 & 0.672768113465693 & 0.663615943267153 \tabularnewline
115 & 0.292810127355031 & 0.585620254710062 & 0.707189872644969 \tabularnewline
116 & 0.252605384601589 & 0.505210769203179 & 0.747394615398411 \tabularnewline
117 & 0.220801357802886 & 0.441602715605772 & 0.779198642197114 \tabularnewline
118 & 0.182440450206564 & 0.364880900413129 & 0.817559549793436 \tabularnewline
119 & 0.15696093819114 & 0.31392187638228 & 0.84303906180886 \tabularnewline
120 & 0.134277661588401 & 0.268555323176803 & 0.865722338411599 \tabularnewline
121 & 0.106471092587718 & 0.212942185175437 & 0.893528907412282 \tabularnewline
122 & 0.113773748999438 & 0.227547497998876 & 0.886226251000562 \tabularnewline
123 & 0.0904272431349023 & 0.180854486269805 & 0.909572756865098 \tabularnewline
124 & 0.0720106794451945 & 0.144021358890389 & 0.927989320554806 \tabularnewline
125 & 0.17794859361466 & 0.35589718722932 & 0.82205140638534 \tabularnewline
126 & 0.144963705222638 & 0.289927410445276 & 0.855036294777362 \tabularnewline
127 & 0.11992939179664 & 0.239858783593279 & 0.88007060820336 \tabularnewline
128 & 0.0956666872191822 & 0.191333374438364 & 0.904333312780818 \tabularnewline
129 & 0.138025600164678 & 0.276051200329356 & 0.861974399835322 \tabularnewline
130 & 0.106202712654251 & 0.212405425308503 & 0.893797287345748 \tabularnewline
131 & 0.153400811084582 & 0.306801622169164 & 0.846599188915418 \tabularnewline
132 & 0.127067181746292 & 0.254134363492584 & 0.872932818253708 \tabularnewline
133 & 0.275714570094819 & 0.551429140189638 & 0.724285429905181 \tabularnewline
134 & 0.293337187197071 & 0.586674374394142 & 0.706662812802929 \tabularnewline
135 & 0.260588008276342 & 0.521176016552683 & 0.739411991723658 \tabularnewline
136 & 0.243377685986016 & 0.486755371972032 & 0.756622314013984 \tabularnewline
137 & 0.209560010555704 & 0.419120021111408 & 0.790439989444296 \tabularnewline
138 & 0.235176436708669 & 0.470352873417337 & 0.764823563291331 \tabularnewline
139 & 0.179527305008813 & 0.359054610017625 & 0.820472694991187 \tabularnewline
140 & 0.134954007501908 & 0.269908015003816 & 0.865045992498092 \tabularnewline
141 & 0.102641260854623 & 0.205282521709246 & 0.897358739145377 \tabularnewline
142 & 0.0813510402959514 & 0.162702080591903 & 0.918648959704049 \tabularnewline
143 & 0.867516463693308 & 0.264967072613383 & 0.132483536306691 \tabularnewline
144 & 0.989290935698957 & 0.0214181286020856 & 0.0107090643010428 \tabularnewline
145 & 0.979633423457817 & 0.0407331530843664 & 0.0203665765421832 \tabularnewline
146 & 0.95484831654938 & 0.0903033669012406 & 0.0451516834506203 \tabularnewline
147 & 0.910739735764105 & 0.17852052847179 & 0.0892602642358951 \tabularnewline
148 & 0.838551367592696 & 0.322897264814607 & 0.161448632407304 \tabularnewline
149 & 0.714886425420551 & 0.570227149158899 & 0.285113574579449 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190555&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]13[/C][C]0.95232243661363[/C][C]0.09535512677274[/C][C]0.04767756338637[/C][/ROW]
[ROW][C]14[/C][C]0.910509143260629[/C][C]0.178981713478742[/C][C]0.089490856739371[/C][/ROW]
[ROW][C]15[/C][C]0.861801418141837[/C][C]0.276397163716327[/C][C]0.138198581858163[/C][/ROW]
[ROW][C]16[/C][C]0.794347011777143[/C][C]0.411305976445714[/C][C]0.205652988222857[/C][/ROW]
[ROW][C]17[/C][C]0.729407698839124[/C][C]0.541184602321752[/C][C]0.270592301160876[/C][/ROW]
[ROW][C]18[/C][C]0.658574517229687[/C][C]0.682850965540626[/C][C]0.341425482770313[/C][/ROW]
[ROW][C]19[/C][C]0.588891542778266[/C][C]0.822216914443469[/C][C]0.411108457221734[/C][/ROW]
[ROW][C]20[/C][C]0.564633046207259[/C][C]0.870733907585481[/C][C]0.435366953792741[/C][/ROW]
[ROW][C]21[/C][C]0.533939672842285[/C][C]0.93212065431543[/C][C]0.466060327157715[/C][/ROW]
[ROW][C]22[/C][C]0.447071554756078[/C][C]0.894143109512157[/C][C]0.552928445243921[/C][/ROW]
[ROW][C]23[/C][C]0.583870357336979[/C][C]0.832259285326042[/C][C]0.416129642663021[/C][/ROW]
[ROW][C]24[/C][C]0.526855604835059[/C][C]0.946288790329883[/C][C]0.473144395164941[/C][/ROW]
[ROW][C]25[/C][C]0.462098089988986[/C][C]0.924196179977972[/C][C]0.537901910011014[/C][/ROW]
[ROW][C]26[/C][C]0.502330580116254[/C][C]0.995338839767493[/C][C]0.497669419883746[/C][/ROW]
[ROW][C]27[/C][C]0.438849284765056[/C][C]0.877698569530111[/C][C]0.561150715234944[/C][/ROW]
[ROW][C]28[/C][C]0.636318681331692[/C][C]0.727362637336617[/C][C]0.363681318668308[/C][/ROW]
[ROW][C]29[/C][C]0.678862633561873[/C][C]0.642274732876254[/C][C]0.321137366438127[/C][/ROW]
[ROW][C]30[/C][C]0.636334437934219[/C][C]0.727331124131562[/C][C]0.363665562065781[/C][/ROW]
[ROW][C]31[/C][C]0.600824356785053[/C][C]0.798351286429893[/C][C]0.399175643214947[/C][/ROW]
[ROW][C]32[/C][C]0.557606107034598[/C][C]0.884787785930804[/C][C]0.442393892965402[/C][/ROW]
[ROW][C]33[/C][C]0.519821094383971[/C][C]0.960357811232057[/C][C]0.480178905616029[/C][/ROW]
[ROW][C]34[/C][C]0.579568207882527[/C][C]0.840863584234947[/C][C]0.420431792117473[/C][/ROW]
[ROW][C]35[/C][C]0.72789338546512[/C][C]0.544213229069761[/C][C]0.27210661453488[/C][/ROW]
[ROW][C]36[/C][C]0.682272998157704[/C][C]0.635454003684593[/C][C]0.317727001842296[/C][/ROW]
[ROW][C]37[/C][C]0.63058931518715[/C][C]0.738821369625701[/C][C]0.36941068481285[/C][/ROW]
[ROW][C]38[/C][C]0.575310248457192[/C][C]0.849379503085616[/C][C]0.424689751542808[/C][/ROW]
[ROW][C]39[/C][C]0.516639648479935[/C][C]0.96672070304013[/C][C]0.483360351520065[/C][/ROW]
[ROW][C]40[/C][C]0.488801573526225[/C][C]0.977603147052451[/C][C]0.511198426473775[/C][/ROW]
[ROW][C]41[/C][C]0.496204337471469[/C][C]0.992408674942938[/C][C]0.503795662528531[/C][/ROW]
[ROW][C]42[/C][C]0.463554398691029[/C][C]0.927108797382059[/C][C]0.536445601308971[/C][/ROW]
[ROW][C]43[/C][C]0.416326161149712[/C][C]0.832652322299424[/C][C]0.583673838850288[/C][/ROW]
[ROW][C]44[/C][C]0.384136980698527[/C][C]0.768273961397054[/C][C]0.615863019301473[/C][/ROW]
[ROW][C]45[/C][C]0.440802002279971[/C][C]0.881604004559943[/C][C]0.559197997720029[/C][/ROW]
[ROW][C]46[/C][C]0.387844413140614[/C][C]0.775688826281228[/C][C]0.612155586859386[/C][/ROW]
[ROW][C]47[/C][C]0.346571940475854[/C][C]0.693143880951708[/C][C]0.653428059524146[/C][/ROW]
[ROW][C]48[/C][C]0.759209899254632[/C][C]0.481580201490736[/C][C]0.240790100745368[/C][/ROW]
[ROW][C]49[/C][C]0.746704528131924[/C][C]0.506590943736151[/C][C]0.253295471868075[/C][/ROW]
[ROW][C]50[/C][C]0.777455111850379[/C][C]0.445089776299242[/C][C]0.222544888149621[/C][/ROW]
[ROW][C]51[/C][C]0.76297605412987[/C][C]0.474047891740259[/C][C]0.23702394587013[/C][/ROW]
[ROW][C]52[/C][C]0.724931634938712[/C][C]0.550136730122576[/C][C]0.275068365061288[/C][/ROW]
[ROW][C]53[/C][C]0.707787985339276[/C][C]0.584424029321449[/C][C]0.292212014660724[/C][/ROW]
[ROW][C]54[/C][C]0.686954877532479[/C][C]0.626090244935042[/C][C]0.313045122467521[/C][/ROW]
[ROW][C]55[/C][C]0.645316666541253[/C][C]0.709366666917495[/C][C]0.354683333458747[/C][/ROW]
[ROW][C]56[/C][C]0.643422441308464[/C][C]0.713155117383072[/C][C]0.356577558691536[/C][/ROW]
[ROW][C]57[/C][C]0.631719621166485[/C][C]0.73656075766703[/C][C]0.368280378833515[/C][/ROW]
[ROW][C]58[/C][C]0.715698724316184[/C][C]0.568602551367631[/C][C]0.284301275683816[/C][/ROW]
[ROW][C]59[/C][C]0.779081806780779[/C][C]0.441836386438441[/C][C]0.220918193219221[/C][/ROW]
[ROW][C]60[/C][C]0.748852124458651[/C][C]0.502295751082698[/C][C]0.251147875541349[/C][/ROW]
[ROW][C]61[/C][C]0.82709334532665[/C][C]0.345813309346701[/C][C]0.17290665467335[/C][/ROW]
[ROW][C]62[/C][C]0.795927852329943[/C][C]0.408144295340115[/C][C]0.204072147670057[/C][/ROW]
[ROW][C]63[/C][C]0.850764061451034[/C][C]0.298471877097933[/C][C]0.149235938548966[/C][/ROW]
[ROW][C]64[/C][C]0.82123943164565[/C][C]0.357521136708699[/C][C]0.17876056835435[/C][/ROW]
[ROW][C]65[/C][C]0.851708523189799[/C][C]0.296582953620402[/C][C]0.148291476810201[/C][/ROW]
[ROW][C]66[/C][C]0.822692108280018[/C][C]0.354615783439963[/C][C]0.177307891719982[/C][/ROW]
[ROW][C]67[/C][C]0.80832473338003[/C][C]0.383350533239941[/C][C]0.19167526661997[/C][/ROW]
[ROW][C]68[/C][C]0.786210640694516[/C][C]0.427578718610967[/C][C]0.213789359305484[/C][/ROW]
[ROW][C]69[/C][C]0.752472305736935[/C][C]0.49505538852613[/C][C]0.247527694263065[/C][/ROW]
[ROW][C]70[/C][C]0.717083925334848[/C][C]0.565832149330304[/C][C]0.282916074665152[/C][/ROW]
[ROW][C]71[/C][C]0.771595243445401[/C][C]0.456809513109197[/C][C]0.228404756554599[/C][/ROW]
[ROW][C]72[/C][C]0.740832525209998[/C][C]0.518334949580004[/C][C]0.259167474790002[/C][/ROW]
[ROW][C]73[/C][C]0.7648012874499[/C][C]0.4703974251002[/C][C]0.2351987125501[/C][/ROW]
[ROW][C]74[/C][C]0.752407045029027[/C][C]0.495185909941946[/C][C]0.247592954970973[/C][/ROW]
[ROW][C]75[/C][C]0.715141115678695[/C][C]0.56971776864261[/C][C]0.284858884321305[/C][/ROW]
[ROW][C]76[/C][C]0.717210519763357[/C][C]0.565578960473285[/C][C]0.282789480236643[/C][/ROW]
[ROW][C]77[/C][C]0.684846514115882[/C][C]0.630306971768236[/C][C]0.315153485884118[/C][/ROW]
[ROW][C]78[/C][C]0.67395019297835[/C][C]0.6520996140433[/C][C]0.32604980702165[/C][/ROW]
[ROW][C]79[/C][C]0.63619683128012[/C][C]0.727606337439761[/C][C]0.36380316871988[/C][/ROW]
[ROW][C]80[/C][C]0.610379805336996[/C][C]0.779240389326008[/C][C]0.389620194663004[/C][/ROW]
[ROW][C]81[/C][C]0.596372065802461[/C][C]0.807255868395078[/C][C]0.403627934197539[/C][/ROW]
[ROW][C]82[/C][C]0.567943922270768[/C][C]0.864112155458464[/C][C]0.432056077729232[/C][/ROW]
[ROW][C]83[/C][C]0.590907225734829[/C][C]0.818185548530341[/C][C]0.409092774265171[/C][/ROW]
[ROW][C]84[/C][C]0.556201071903033[/C][C]0.887597856193933[/C][C]0.443798928096966[/C][/ROW]
[ROW][C]85[/C][C]0.600934433939344[/C][C]0.798131132121311[/C][C]0.399065566060656[/C][/ROW]
[ROW][C]86[/C][C]0.555045062282602[/C][C]0.889909875434796[/C][C]0.444954937717398[/C][/ROW]
[ROW][C]87[/C][C]0.531308971127419[/C][C]0.937382057745162[/C][C]0.468691028872581[/C][/ROW]
[ROW][C]88[/C][C]0.558515353641325[/C][C]0.882969292717351[/C][C]0.441484646358675[/C][/ROW]
[ROW][C]89[/C][C]0.51591227318596[/C][C]0.968175453628081[/C][C]0.48408772681404[/C][/ROW]
[ROW][C]90[/C][C]0.490392834460165[/C][C]0.980785668920331[/C][C]0.509607165539835[/C][/ROW]
[ROW][C]91[/C][C]0.456920503342166[/C][C]0.913841006684332[/C][C]0.543079496657834[/C][/ROW]
[ROW][C]92[/C][C]0.437970823406071[/C][C]0.875941646812143[/C][C]0.562029176593928[/C][/ROW]
[ROW][C]93[/C][C]0.438446006034136[/C][C]0.876892012068273[/C][C]0.561553993965864[/C][/ROW]
[ROW][C]94[/C][C]0.396246444516704[/C][C]0.792492889033407[/C][C]0.603753555483296[/C][/ROW]
[ROW][C]95[/C][C]0.479900101221578[/C][C]0.959800202443155[/C][C]0.520099898778422[/C][/ROW]
[ROW][C]96[/C][C]0.456870605266614[/C][C]0.913741210533228[/C][C]0.543129394733386[/C][/ROW]
[ROW][C]97[/C][C]0.57128617487852[/C][C]0.85742765024296[/C][C]0.42871382512148[/C][/ROW]
[ROW][C]98[/C][C]0.524943166952946[/C][C]0.950113666094109[/C][C]0.475056833047054[/C][/ROW]
[ROW][C]99[/C][C]0.480231146959383[/C][C]0.960462293918767[/C][C]0.519768853040617[/C][/ROW]
[ROW][C]100[/C][C]0.438030769763237[/C][C]0.876061539526474[/C][C]0.561969230236763[/C][/ROW]
[ROW][C]101[/C][C]0.43565616458693[/C][C]0.871312329173861[/C][C]0.56434383541307[/C][/ROW]
[ROW][C]102[/C][C]0.388711165775557[/C][C]0.777422331551113[/C][C]0.611288834224443[/C][/ROW]
[ROW][C]103[/C][C]0.486819953706576[/C][C]0.973639907413151[/C][C]0.513180046293424[/C][/ROW]
[ROW][C]104[/C][C]0.469561660969845[/C][C]0.93912332193969[/C][C]0.530438339030155[/C][/ROW]
[ROW][C]105[/C][C]0.435519015006537[/C][C]0.871038030013073[/C][C]0.564480984993463[/C][/ROW]
[ROW][C]106[/C][C]0.401973143163354[/C][C]0.803946286326707[/C][C]0.598026856836646[/C][/ROW]
[ROW][C]107[/C][C]0.370239320079194[/C][C]0.740478640158388[/C][C]0.629760679920806[/C][/ROW]
[ROW][C]108[/C][C]0.376072783501719[/C][C]0.752145567003439[/C][C]0.623927216498281[/C][/ROW]
[ROW][C]109[/C][C]0.362155369254991[/C][C]0.724310738509982[/C][C]0.637844630745009[/C][/ROW]
[ROW][C]110[/C][C]0.348710268189752[/C][C]0.697420536379504[/C][C]0.651289731810248[/C][/ROW]
[ROW][C]111[/C][C]0.376448348406459[/C][C]0.752896696812919[/C][C]0.623551651593541[/C][/ROW]
[ROW][C]112[/C][C]0.372855585294524[/C][C]0.745711170589048[/C][C]0.627144414705476[/C][/ROW]
[ROW][C]113[/C][C]0.367826856404624[/C][C]0.735653712809248[/C][C]0.632173143595376[/C][/ROW]
[ROW][C]114[/C][C]0.336384056732847[/C][C]0.672768113465693[/C][C]0.663615943267153[/C][/ROW]
[ROW][C]115[/C][C]0.292810127355031[/C][C]0.585620254710062[/C][C]0.707189872644969[/C][/ROW]
[ROW][C]116[/C][C]0.252605384601589[/C][C]0.505210769203179[/C][C]0.747394615398411[/C][/ROW]
[ROW][C]117[/C][C]0.220801357802886[/C][C]0.441602715605772[/C][C]0.779198642197114[/C][/ROW]
[ROW][C]118[/C][C]0.182440450206564[/C][C]0.364880900413129[/C][C]0.817559549793436[/C][/ROW]
[ROW][C]119[/C][C]0.15696093819114[/C][C]0.31392187638228[/C][C]0.84303906180886[/C][/ROW]
[ROW][C]120[/C][C]0.134277661588401[/C][C]0.268555323176803[/C][C]0.865722338411599[/C][/ROW]
[ROW][C]121[/C][C]0.106471092587718[/C][C]0.212942185175437[/C][C]0.893528907412282[/C][/ROW]
[ROW][C]122[/C][C]0.113773748999438[/C][C]0.227547497998876[/C][C]0.886226251000562[/C][/ROW]
[ROW][C]123[/C][C]0.0904272431349023[/C][C]0.180854486269805[/C][C]0.909572756865098[/C][/ROW]
[ROW][C]124[/C][C]0.0720106794451945[/C][C]0.144021358890389[/C][C]0.927989320554806[/C][/ROW]
[ROW][C]125[/C][C]0.17794859361466[/C][C]0.35589718722932[/C][C]0.82205140638534[/C][/ROW]
[ROW][C]126[/C][C]0.144963705222638[/C][C]0.289927410445276[/C][C]0.855036294777362[/C][/ROW]
[ROW][C]127[/C][C]0.11992939179664[/C][C]0.239858783593279[/C][C]0.88007060820336[/C][/ROW]
[ROW][C]128[/C][C]0.0956666872191822[/C][C]0.191333374438364[/C][C]0.904333312780818[/C][/ROW]
[ROW][C]129[/C][C]0.138025600164678[/C][C]0.276051200329356[/C][C]0.861974399835322[/C][/ROW]
[ROW][C]130[/C][C]0.106202712654251[/C][C]0.212405425308503[/C][C]0.893797287345748[/C][/ROW]
[ROW][C]131[/C][C]0.153400811084582[/C][C]0.306801622169164[/C][C]0.846599188915418[/C][/ROW]
[ROW][C]132[/C][C]0.127067181746292[/C][C]0.254134363492584[/C][C]0.872932818253708[/C][/ROW]
[ROW][C]133[/C][C]0.275714570094819[/C][C]0.551429140189638[/C][C]0.724285429905181[/C][/ROW]
[ROW][C]134[/C][C]0.293337187197071[/C][C]0.586674374394142[/C][C]0.706662812802929[/C][/ROW]
[ROW][C]135[/C][C]0.260588008276342[/C][C]0.521176016552683[/C][C]0.739411991723658[/C][/ROW]
[ROW][C]136[/C][C]0.243377685986016[/C][C]0.486755371972032[/C][C]0.756622314013984[/C][/ROW]
[ROW][C]137[/C][C]0.209560010555704[/C][C]0.419120021111408[/C][C]0.790439989444296[/C][/ROW]
[ROW][C]138[/C][C]0.235176436708669[/C][C]0.470352873417337[/C][C]0.764823563291331[/C][/ROW]
[ROW][C]139[/C][C]0.179527305008813[/C][C]0.359054610017625[/C][C]0.820472694991187[/C][/ROW]
[ROW][C]140[/C][C]0.134954007501908[/C][C]0.269908015003816[/C][C]0.865045992498092[/C][/ROW]
[ROW][C]141[/C][C]0.102641260854623[/C][C]0.205282521709246[/C][C]0.897358739145377[/C][/ROW]
[ROW][C]142[/C][C]0.0813510402959514[/C][C]0.162702080591903[/C][C]0.918648959704049[/C][/ROW]
[ROW][C]143[/C][C]0.867516463693308[/C][C]0.264967072613383[/C][C]0.132483536306691[/C][/ROW]
[ROW][C]144[/C][C]0.989290935698957[/C][C]0.0214181286020856[/C][C]0.0107090643010428[/C][/ROW]
[ROW][C]145[/C][C]0.979633423457817[/C][C]0.0407331530843664[/C][C]0.0203665765421832[/C][/ROW]
[ROW][C]146[/C][C]0.95484831654938[/C][C]0.0903033669012406[/C][C]0.0451516834506203[/C][/ROW]
[ROW][C]147[/C][C]0.910739735764105[/C][C]0.17852052847179[/C][C]0.0892602642358951[/C][/ROW]
[ROW][C]148[/C][C]0.838551367592696[/C][C]0.322897264814607[/C][C]0.161448632407304[/C][/ROW]
[ROW][C]149[/C][C]0.714886425420551[/C][C]0.570227149158899[/C][C]0.285113574579449[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190555&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190555&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
130.952322436613630.095355126772740.04767756338637
140.9105091432606290.1789817134787420.089490856739371
150.8618014181418370.2763971637163270.138198581858163
160.7943470117771430.4113059764457140.205652988222857
170.7294076988391240.5411846023217520.270592301160876
180.6585745172296870.6828509655406260.341425482770313
190.5888915427782660.8222169144434690.411108457221734
200.5646330462072590.8707339075854810.435366953792741
210.5339396728422850.932120654315430.466060327157715
220.4470715547560780.8941431095121570.552928445243921
230.5838703573369790.8322592853260420.416129642663021
240.5268556048350590.9462887903298830.473144395164941
250.4620980899889860.9241961799779720.537901910011014
260.5023305801162540.9953388397674930.497669419883746
270.4388492847650560.8776985695301110.561150715234944
280.6363186813316920.7273626373366170.363681318668308
290.6788626335618730.6422747328762540.321137366438127
300.6363344379342190.7273311241315620.363665562065781
310.6008243567850530.7983512864298930.399175643214947
320.5576061070345980.8847877859308040.442393892965402
330.5198210943839710.9603578112320570.480178905616029
340.5795682078825270.8408635842349470.420431792117473
350.727893385465120.5442132290697610.27210661453488
360.6822729981577040.6354540036845930.317727001842296
370.630589315187150.7388213696257010.36941068481285
380.5753102484571920.8493795030856160.424689751542808
390.5166396484799350.966720703040130.483360351520065
400.4888015735262250.9776031470524510.511198426473775
410.4962043374714690.9924086749429380.503795662528531
420.4635543986910290.9271087973820590.536445601308971
430.4163261611497120.8326523222994240.583673838850288
440.3841369806985270.7682739613970540.615863019301473
450.4408020022799710.8816040045599430.559197997720029
460.3878444131406140.7756888262812280.612155586859386
470.3465719404758540.6931438809517080.653428059524146
480.7592098992546320.4815802014907360.240790100745368
490.7467045281319240.5065909437361510.253295471868075
500.7774551118503790.4450897762992420.222544888149621
510.762976054129870.4740478917402590.23702394587013
520.7249316349387120.5501367301225760.275068365061288
530.7077879853392760.5844240293214490.292212014660724
540.6869548775324790.6260902449350420.313045122467521
550.6453166665412530.7093666669174950.354683333458747
560.6434224413084640.7131551173830720.356577558691536
570.6317196211664850.736560757667030.368280378833515
580.7156987243161840.5686025513676310.284301275683816
590.7790818067807790.4418363864384410.220918193219221
600.7488521244586510.5022957510826980.251147875541349
610.827093345326650.3458133093467010.17290665467335
620.7959278523299430.4081442953401150.204072147670057
630.8507640614510340.2984718770979330.149235938548966
640.821239431645650.3575211367086990.17876056835435
650.8517085231897990.2965829536204020.148291476810201
660.8226921082800180.3546157834399630.177307891719982
670.808324733380030.3833505332399410.19167526661997
680.7862106406945160.4275787186109670.213789359305484
690.7524723057369350.495055388526130.247527694263065
700.7170839253348480.5658321493303040.282916074665152
710.7715952434454010.4568095131091970.228404756554599
720.7408325252099980.5183349495800040.259167474790002
730.76480128744990.47039742510020.2351987125501
740.7524070450290270.4951859099419460.247592954970973
750.7151411156786950.569717768642610.284858884321305
760.7172105197633570.5655789604732850.282789480236643
770.6848465141158820.6303069717682360.315153485884118
780.673950192978350.65209961404330.32604980702165
790.636196831280120.7276063374397610.36380316871988
800.6103798053369960.7792403893260080.389620194663004
810.5963720658024610.8072558683950780.403627934197539
820.5679439222707680.8641121554584640.432056077729232
830.5909072257348290.8181855485303410.409092774265171
840.5562010719030330.8875978561939330.443798928096966
850.6009344339393440.7981311321213110.399065566060656
860.5550450622826020.8899098754347960.444954937717398
870.5313089711274190.9373820577451620.468691028872581
880.5585153536413250.8829692927173510.441484646358675
890.515912273185960.9681754536280810.48408772681404
900.4903928344601650.9807856689203310.509607165539835
910.4569205033421660.9138410066843320.543079496657834
920.4379708234060710.8759416468121430.562029176593928
930.4384460060341360.8768920120682730.561553993965864
940.3962464445167040.7924928890334070.603753555483296
950.4799001012215780.9598002024431550.520099898778422
960.4568706052666140.9137412105332280.543129394733386
970.571286174878520.857427650242960.42871382512148
980.5249431669529460.9501136660941090.475056833047054
990.4802311469593830.9604622939187670.519768853040617
1000.4380307697632370.8760615395264740.561969230236763
1010.435656164586930.8713123291738610.56434383541307
1020.3887111657755570.7774223315511130.611288834224443
1030.4868199537065760.9736399074131510.513180046293424
1040.4695616609698450.939123321939690.530438339030155
1050.4355190150065370.8710380300130730.564480984993463
1060.4019731431633540.8039462863267070.598026856836646
1070.3702393200791940.7404786401583880.629760679920806
1080.3760727835017190.7521455670034390.623927216498281
1090.3621553692549910.7243107385099820.637844630745009
1100.3487102681897520.6974205363795040.651289731810248
1110.3764483484064590.7528966968129190.623551651593541
1120.3728555852945240.7457111705890480.627144414705476
1130.3678268564046240.7356537128092480.632173143595376
1140.3363840567328470.6727681134656930.663615943267153
1150.2928101273550310.5856202547100620.707189872644969
1160.2526053846015890.5052107692031790.747394615398411
1170.2208013578028860.4416027156057720.779198642197114
1180.1824404502065640.3648809004131290.817559549793436
1190.156960938191140.313921876382280.84303906180886
1200.1342776615884010.2685553231768030.865722338411599
1210.1064710925877180.2129421851754370.893528907412282
1220.1137737489994380.2275474979988760.886226251000562
1230.09042724313490230.1808544862698050.909572756865098
1240.07201067944519450.1440213588903890.927989320554806
1250.177948593614660.355897187229320.82205140638534
1260.1449637052226380.2899274104452760.855036294777362
1270.119929391796640.2398587835932790.88007060820336
1280.09566668721918220.1913333744383640.904333312780818
1290.1380256001646780.2760512003293560.861974399835322
1300.1062027126542510.2124054253085030.893797287345748
1310.1534008110845820.3068016221691640.846599188915418
1320.1270671817462920.2541343634925840.872932818253708
1330.2757145700948190.5514291401896380.724285429905181
1340.2933371871970710.5866743743941420.706662812802929
1350.2605880082763420.5211760165526830.739411991723658
1360.2433776859860160.4867553719720320.756622314013984
1370.2095600105557040.4191200211114080.790439989444296
1380.2351764367086690.4703528734173370.764823563291331
1390.1795273050088130.3590546100176250.820472694991187
1400.1349540075019080.2699080150038160.865045992498092
1410.1026412608546230.2052825217092460.897358739145377
1420.08135104029595140.1627020805919030.918648959704049
1430.8675164636933080.2649670726133830.132483536306691
1440.9892909356989570.02141812860208560.0107090643010428
1450.9796334234578170.04073315308436640.0203665765421832
1460.954848316549380.09030336690124060.0451516834506203
1470.9107397357641050.178520528471790.0892602642358951
1480.8385513675926960.3228972648146070.161448632407304
1490.7148864254205510.5702271491588990.285113574579449






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0145985401459854OK
10% type I error level40.0291970802919708OK

\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 & 2 & 0.0145985401459854 & OK \tabularnewline
10% type I error level & 4 & 0.0291970802919708 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190555&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]2[/C][C]0.0145985401459854[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0291970802919708[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190555&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190555&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 level20.0145985401459854OK
10% type I error level40.0291970802919708OK


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 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}