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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 12 Dec 2011 17:02:04 -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/2011/Dec/12/t1323727360vmrgg3in6izxgo4.htm/, Retrieved Fri, 03 May 2024 04:50:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154253, Retrieved Fri, 03 May 2024 04:50:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Workshop 10 - Mul...] [2011-12-12 22:02:04] [5646b3622df538fb37dafdccb2216e7a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1	26	21	21	23	17	23	4
1	1	20	16	15	24	17	20	4
1	1	19	19	18	22	18	20	6
1	2	19	18	11	20	21	21	8
1	1	20	16	8	24	20	24	8
1	1	25	23	19	27	28	22	4
1	2	25	17	4	28	19	23	4
1	1	22	12	20	27	22	20	8
1	1	26	19	16	24	16	25	5
1	1	22	16	14	23	18	23	4
1	2	17	19	10	24	25	27	4
1	2	22	20	13	27	17	27	4
1	1	19	13	14	27	14	22	4
1	1	24	20	8	28	11	24	4
1	1	26	27	23	27	27	25	4
1	2	21	17	11	23	20	22	8
1	1	13	8	9	24	22	28	4
1	2	26	25	24	28	22	28	4
1	2	20	26	5	27	21	27	4
1	1	22	13	15	25	23	25	8
1	2	14	19	5	19	17	16	4
1	1	21	15	19	24	24	28	7
1	1	7	5	6	20	14	21	4
1	2	23	16	13	28	17	24	4
1	1	17	14	11	26	23	27	5
1	1	25	24	17	23	24	14	4
1	1	25	24	17	23	24	14	4
1	1	19	9	5	20	8	27	4
1	2	20	19	9	11	22	20	4
1	1	23	19	15	24	23	21	4
1	2	22	25	17	25	25	22	4
1	1	22	19	17	23	21	21	4
1	1	21	18	20	18	24	12	15
1	2	15	15	12	20	15	20	10
1	2	20	12	7	20	22	24	4
1	2	22	21	16	24	21	19	8
1	1	18	12	7	23	25	28	4
1	2	20	15	14	25	16	23	4
1	2	28	28	24	28	28	27	4
1	1	22	25	15	26	23	22	4
1	1	18	19	15	26	21	27	7
1	1	23	20	10	23	21	26	4
1	1	20	24	14	22	26	22	6
1	2	25	26	18	24	22	21	5
1	2	26	25	12	21	21	19	4
1	1	15	12	9	20	18	24	16
1	2	17	12	9	22	12	19	5
1	2	23	15	8	20	25	26	12
1	1	21	17	18	25	17	22	6
1	2	13	14	10	20	24	28	9
1	1	18	16	17	22	15	21	9
1	1	19	11	14	23	13	23	4
1	1	22	20	16	25	26	28	5
1	1	16	11	10	23	16	10	4
1	2	24	22	19	23	24	24	4
1	1	18	20	10	22	21	21	5
1	1	20	19	14	24	20	21	4
1	1	24	17	10	25	14	24	4
1	2	14	21	4	21	25	24	4
1	2	22	23	19	12	25	25	5
1	1	24	18	9	17	20	25	4
1	1	18	17	12	20	22	23	6
1	1	21	27	16	23	20	21	4
1	2	23	25	11	23	26	16	4
1	1	17	19	18	20	18	17	18
1	2	22	22	11	28	22	25	4
1	2	24	24	24	24	24	24	6
1	2	21	20	17	24	17	23	4
1	1	22	19	18	24	24	25	4
1	1	16	11	9	24	20	23	5
1	1	21	22	19	28	19	28	4
1	2	23	22	18	25	20	26	4
1	2	22	16	12	21	15	22	5
1	1	24	20	23	25	23	19	10
1	1	24	24	22	25	26	26	5
1	1	16	16	14	18	22	18	8
1	1	16	16	14	17	20	18	8
1	2	21	22	16	26	24	25	5
1	2	26	24	23	28	26	27	4
1	2	15	16	7	21	21	12	4
1	2	25	27	10	27	25	15	4
1	1	18	11	12	22	13	21	5
1	0	23	21	12	21	20	23	4
1	1	20	20	12	25	22	22	4
1	2	17	20	17	22	23	21	8
1	2	25	27	21	23	28	24	4
1	1	24	20	16	26	22	27	5
1	1	17	12	11	19	20	22	14
1	1	19	8	14	25	6	28	8
1	1	20	21	13	21	21	26	8
1	1	15	18	9	13	20	10	4
1	2	27	24	19	24	18	19	4
1	1	22	16	13	25	23	22	6
1	1	23	18	19	26	20	21	4
1	1	16	20	13	25	24	24	7
1	1	19	20	13	25	22	25	7
1	2	25	19	13	22	21	21	4
1	1	19	17	14	21	18	20	6
1	2	19	16	12	23	21	21	4
0	2	26	26	22	25	23	24	7
0	1	21	15	11	24	23	23	4
0	2	20	22	5	21	15	18	4
0	1	24	17	18	21	21	24	8
0	1	22	23	19	25	24	24	4
0	2	20	21	14	22	23	19	4
0	1	18	19	15	20	21	20	10
0	2	18	14	12	20	21	18	8
0	1	24	17	19	23	20	20	6
0	1	24	12	15	28	11	27	4
0	1	22	24	17	23	22	23	4
0	1	23	18	8	28	27	26	4
0	1	22	20	10	24	25	23	5
0	1	20	16	12	18	18	17	4
0	1	18	20	12	20	20	21	6
0	1	25	22	20	28	24	25	4
0	2	18	12	12	21	10	23	5
0	1	16	16	12	21	27	27	7
0	1	20	17	14	25	21	24	8
0	2	19	22	6	19	21	20	5
0	1	15	12	10	18	18	27	8
0	1	19	14	18	21	15	21	10
0	1	19	23	18	22	24	24	8
0	1	16	15	7	24	22	21	5
0	1	17	17	18	15	14	15	12
0	1	28	28	9	28	28	25	4
0	2	23	20	17	26	18	25	5
0	1	25	23	22	23	26	22	4
0	1	20	13	11	26	17	24	6
0	2	17	18	15	20	19	21	4
0	2	23	23	17	22	22	22	4
0	1	16	19	15	20	18	23	7
0	2	23	23	22	23	24	22	7
0	2	11	12	9	22	15	20	10
0	2	18	16	13	24	18	23	4
0	2	24	23	20	23	26	25	5
0	1	23	13	14	22	11	23	8
0	1	21	22	14	26	26	22	11
0	2	16	18	12	23	21	25	7
0	2	24	23	20	27	23	26	4
0	1	23	20	20	23	23	22	8
0	1	18	10	8	21	15	24	6
0	1	20	17	17	26	22	24	7
0	1	9	18	9	23	26	25	5
0	2	24	15	18	21	16	20	4
0	1	25	23	22	27	20	26	8
0	1	20	17	10	19	18	21	4
0	2	21	17	13	23	22	26	8
0	2	25	22	15	25	16	21	6
0	2	22	20	18	23	19	22	4
0	2	21	20	18	22	20	16	9
0	1	21	19	12	22	19	26	5
0	1	22	18	12	25	23	28	6
0	1	27	22	20	25	24	18	4
0	2	24	20	12	28	25	25	4
0	2	24	22	16	28	21	23	4
0	2	21	18	16	20	21	21	5
0	1	18	16	18	25	23	20	6
0	1	16	16	16	19	27	25	16
0	1	22	16	13	25	23	22	6
0	1	20	16	17	22	18	21	6
0	2	18	17	13	18	16	16	4
0	1	20	18	17	20	16	18	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'AstonUniversity' @ aston.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 & 7 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.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=154253&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.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=154253&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154253&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 time7 seconds
R Server'AstonUniversity' @ aston.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
A [t] = + 12.5651057544999 -0.248883991744311Pop[t] -0.396143029545024G[t] -0.18560111432307I1[t] -0.139964027631478I2[t] + 0.193936763223233I3[t] -0.178478932982656E1[t] + 0.0842044544763045E2[t] -0.000734755385133922E3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A
[t] =  +  12.5651057544999 -0.248883991744311Pop[t] -0.396143029545024G[t] -0.18560111432307I1[t] -0.139964027631478I2[t] +  0.193936763223233I3[t] -0.178478932982656E1[t] +  0.0842044544763045E2[t] -0.000734755385133922E3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154253&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A
[t] =  +  12.5651057544999 -0.248883991744311Pop[t] -0.396143029545024G[t] -0.18560111432307I1[t] -0.139964027631478I2[t] +  0.193936763223233I3[t] -0.178478932982656E1[t] +  0.0842044544763045E2[t] -0.000734755385133922E3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154253&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154253&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
A [t] = + 12.5651057544999 -0.248883991744311Pop[t] -0.396143029545024G[t] -0.18560111432307I1[t] -0.139964027631478I2[t] + 0.193936763223233I3[t] -0.178478932982656E1[t] + 0.0842044544763045E2[t] -0.000734755385133922E3[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.56510575449991.7665327.112900
Pop-0.2488839917443110.380771-0.65360.514330.257165
G-0.3961430295450240.389647-1.01670.3109150.155458
I1-0.185601114323070.073903-2.51140.0130630.006531
I2-0.1399640276314780.06617-2.11520.0360310.018015
I30.1939367632232330.0489133.96490.0001125.6e-05
E1-0.1784789329826560.071758-2.48720.0139470.006973
E20.08420445447630450.055711.51150.1327310.066366
E3-0.0007347553851339220.057518-0.01280.9898240.494912

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 12.5651057544999 & 1.766532 & 7.1129 & 0 & 0 \tabularnewline
Pop & -0.248883991744311 & 0.380771 & -0.6536 & 0.51433 & 0.257165 \tabularnewline
G & -0.396143029545024 & 0.389647 & -1.0167 & 0.310915 & 0.155458 \tabularnewline
I1 & -0.18560111432307 & 0.073903 & -2.5114 & 0.013063 & 0.006531 \tabularnewline
I2 & -0.139964027631478 & 0.06617 & -2.1152 & 0.036031 & 0.018015 \tabularnewline
I3 & 0.193936763223233 & 0.048913 & 3.9649 & 0.000112 & 5.6e-05 \tabularnewline
E1 & -0.178478932982656 & 0.071758 & -2.4872 & 0.013947 & 0.006973 \tabularnewline
E2 & 0.0842044544763045 & 0.05571 & 1.5115 & 0.132731 & 0.066366 \tabularnewline
E3 & -0.000734755385133922 & 0.057518 & -0.0128 & 0.989824 & 0.494912 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154253&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]12.5651057544999[/C][C]1.766532[/C][C]7.1129[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Pop[/C][C]-0.248883991744311[/C][C]0.380771[/C][C]-0.6536[/C][C]0.51433[/C][C]0.257165[/C][/ROW]
[ROW][C]G[/C][C]-0.396143029545024[/C][C]0.389647[/C][C]-1.0167[/C][C]0.310915[/C][C]0.155458[/C][/ROW]
[ROW][C]I1[/C][C]-0.18560111432307[/C][C]0.073903[/C][C]-2.5114[/C][C]0.013063[/C][C]0.006531[/C][/ROW]
[ROW][C]I2[/C][C]-0.139964027631478[/C][C]0.06617[/C][C]-2.1152[/C][C]0.036031[/C][C]0.018015[/C][/ROW]
[ROW][C]I3[/C][C]0.193936763223233[/C][C]0.048913[/C][C]3.9649[/C][C]0.000112[/C][C]5.6e-05[/C][/ROW]
[ROW][C]E1[/C][C]-0.178478932982656[/C][C]0.071758[/C][C]-2.4872[/C][C]0.013947[/C][C]0.006973[/C][/ROW]
[ROW][C]E2[/C][C]0.0842044544763045[/C][C]0.05571[/C][C]1.5115[/C][C]0.132731[/C][C]0.066366[/C][/ROW]
[ROW][C]E3[/C][C]-0.000734755385133922[/C][C]0.057518[/C][C]-0.0128[/C][C]0.989824[/C][C]0.494912[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154253&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154253&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.56510575449991.7665327.112900
Pop-0.2488839917443110.380771-0.65360.514330.257165
G-0.3961430295450240.389647-1.01670.3109150.155458
I1-0.185601114323070.073903-2.51140.0130630.006531
I2-0.1399640276314780.06617-2.11520.0360310.018015
I30.1939367632232330.0489133.96490.0001125.6e-05
E1-0.1784789329826560.071758-2.48720.0139470.006973
E20.08420445447630450.055711.51150.1327310.066366
E3-0.0007347553851339220.057518-0.01280.9898240.494912






Multiple Linear Regression - Regression Statistics
Multiple R0.490904480368422
R-squared0.24098720884579
Adjusted R-squared0.201300265517465
F-TEST (value)6.07220381907812
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value8.81670427332892e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.3469003825622
Sum Squared Residuals842.715035067605

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.490904480368422 \tabularnewline
R-squared & 0.24098720884579 \tabularnewline
Adjusted R-squared & 0.201300265517465 \tabularnewline
F-TEST (value) & 6.07220381907812 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 8.81670427332892e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.3469003825622 \tabularnewline
Sum Squared Residuals & 842.715035067605 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154253&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.490904480368422[/C][/ROW]
[ROW][C]R-squared[/C][C]0.24098720884579[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.201300265517465[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.07220381907812[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]8.81670427332892e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.3469003825622[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]842.715035067605[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154253&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154253&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.490904480368422
R-squared0.24098720884579
Adjusted R-squared0.201300265517465
F-TEST (value)6.07220381907812
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value8.81670427332892e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.3469003825622
Sum Squared Residuals842.715035067605






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
145.53743810187562-1.53743810187562
246.01096967980473-2.01096967980473
366.79965132134469-0.799651321344687
485.79475145087762.2052485491224
584.903086679130483.09691332086952
645.26830565718309-1.26830565718309
741.86584156642392.1341584335761
887.054892851234230.945107148765771
954.583529442793140.41647055720686
1045.70630980923892-1.70630980923892
1145.45054644233282-1.45054644233282
1243.755314697997290.244685302002714
1345.63300644065185-1.63300644065185
1442.129070289094921.87092971090508
1545.21218676459533-1.21218676459533
1684.943137241053533.05686275894647
1747.6814133510791-3.6814133510791
1844.68820308201692-0.688203082016919
1942.072056472973911.92794352702609
2086.382733551002531.61726644899747
2145.26450730752517-1.26450730752517
2277.32463278425914-0.324632784259136
2348.67852521405837-4.67852521405837
2443.953295027372870.0467049726271261
2556.21507959834054-1.21507959834054
2645.12344406021162-1.12344406021162
2745.12344406021162-1.12344406021162
2845.18788370926376-1.18788370926376
2946.77256238918192-2.77256238918192
3045.53876622541378-1.53876622541378
3144.86550889143425-0.865508891434249
3246.12231089021337-2.12231089021337
33158.181307148646016.81869285135399
34106.646492782814853.35350721718515
3545.75518763777136-1.75518763777136
3685.075293619969772.92470638003023
3746.23677043890294-2.23677043890294
3845.29596626105359-1.29596626105359
3944.40307025271931-0.403070252719307
4044.52689055259754-0.526890552597541
4175.936996489800411.06300351019959
4244.43551462877052-0.435514628770515
4365.810649141011460.189350858988538
4454.289278608995670.710721391004329
4543.532722798206610.467277201793392
46167.130391947472988.86960805252702
4755.50453587338435-0.504535873384352
48125.22357282778966.7764271722104
4966.08726638376696-0.0872663837669628
5097.521747559851671.47825244014833
5196.957859636524912.04214036347509
5246.541911017984-2.54191101798399
5355.84733121807893-0.84733121807893
5446.58513249149593-2.58513249149593
5545.57335617284775-1.57335617284775
5655.54567291029419-0.54567291029419
5745.64901944173085-1.64901944173085
5843.72488606081270.275113939187296
5945.10144221580304-1.10144221580304
6057.8513323357628-2.85133233576279
6145.32330870529193-1.32330870529193
6266.79313132930644-0.793131329306439
6344.91005856578508-0.910058565785076
6443.96185805052420.0381419494758029
65187.5300156821115410.4699843178885
6643.3315259664570.668474033543002
6766.0846330007183-0.0846330007183043
6845.25503868570179-1.25503868570179
6946.38744306234232-2.38744306234232
7056.53998279318851-1.53998279318851
7145.20994658652664-1.20994658652664
7244.86977532930679-0.869775329306788
7355.02737251116896-0.0273725111689596
74106.587685767032713.41231423296729
7556.08136296901655-1.08136296901655
7688.0528027549215-0.0528027549215048
7788.06287277895155-0.0628727789515514
7855.01217767181416-0.0121776718141566
7944.97178291971552-0.971782919715515
8045.86947077602345-1.86947077602345
8142.319405572370071.68059442762993
8256.51958704961352-1.51958704961352
8345.35452483477494-1.35452483477494
8445.11037710823772-1.11037710823772
8586.861097246587481.13890275341252
8645.41262626471898-1.41262626471897
8754.961566993930050.03843300606995
88147.495420597978856.50457940202115
8986.011740276653321.98825972334668
9085.791122099743262.20887790025674
9147.71865579690719-3.71865579690719
9244.05659289170077-0.0565928917007699
9365.577172207817030.42282779218297
9445.84490607654397-1.84490607654397
9576.213657726935580.786342273064425
9675.487710719628621.51228928037138
9744.57209639778885-0.572096397788855
9866.48231125669736-0.482311256697364
9945.73317947041582-1.73317947041582
10075.031829794648091.96817020535191
10145.94149199266694-1.94149199266694
10243.453056244748440.546943755251556
10386.828611071607641.17138892839236
10446.09266352918643-2.09266352918643
10545.83287308883167-1.83287308883167
106107.261897367136592.73810263286341
10786.985233696849521.01476630315048
10866.58432453592979-0.58432453592979
10944.85301957829829-0.85301957829829
11045.75410968750633-1.75410968750633
11144.18928521127586-0.189285211275864
11255.03054288591586-0.0305428859158606
11346.83532570040698-2.83532570040698
11466.45518383997398-0.455183839973975
11545.33358942273883-1.33358942273883
11656.1567600429648-1.1567600429648
11777.7927858951867-0.792785895186699
11886.081352744076361.91864725592364
11954.693309204347620.306690795652382
12087.927966302250440.0720336977495603
121107.6734862654152.32651373458501
12286.990966907880381.00903309211962
12356.01101556768332-1.01101556768332
124128.615874086897133.38412591310287
12542.140515336430411.85948466356959
12654.858497526540690.14150247345931
12746.64450676157511-2.64450676157511
12865.54410181404470.455898185955303
12947.02217581520837-3.02217581520837
13045.4915432596375-1.4915432596375
13177.37828196619842-0.378281966198416
13276.451157051723620.54884294827638
133107.118905159110862.88109484088914
13445.9290395325246-1.9290395325246
13556.04388705375129-1.04388705375129
13685.778532521203122.22146747879688
137115.439944341765035.56005565823497
13876.255999728325850.744000271674152
13945.07662320300662-1.07662320300662
14086.795114183240311.20488581675969
14166.47737159187624-0.47737159187624
14276.568888555239710.431111444760291
14357.79056254084418-2.79056254084418
14446.29431284648458-2.29431284648458
14585.422425281246132.57757471875387
14646.12607019180585-2.12607019180585
14785.745364646656382.25463535334362
14863.832502761755712.16749723824429
14945.85988092366667-1.85988092366667
15096.31257395775952.6874260422405
15155.59350842726896-0.593508427268958
15265.347782848764350.652217151235651
15345.5029672807366-1.5029672807366
15443.935685911470270.0643140885297273
15544.0961566019653-0.096156601965301
15656.64211703009194-1.64211703009194
15767.53961398374006-1.53961398374006
158168.926960324815217.07303967518479
15965.826056199561340.173943800438659
16067.088154763052-1.08815476305199
16146.69668348153238-2.69668348153238
16246.99897993095714-2.99897993095714

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 5.53743810187562 & -1.53743810187562 \tabularnewline
2 & 4 & 6.01096967980473 & -2.01096967980473 \tabularnewline
3 & 6 & 6.79965132134469 & -0.799651321344687 \tabularnewline
4 & 8 & 5.7947514508776 & 2.2052485491224 \tabularnewline
5 & 8 & 4.90308667913048 & 3.09691332086952 \tabularnewline
6 & 4 & 5.26830565718309 & -1.26830565718309 \tabularnewline
7 & 4 & 1.8658415664239 & 2.1341584335761 \tabularnewline
8 & 8 & 7.05489285123423 & 0.945107148765771 \tabularnewline
9 & 5 & 4.58352944279314 & 0.41647055720686 \tabularnewline
10 & 4 & 5.70630980923892 & -1.70630980923892 \tabularnewline
11 & 4 & 5.45054644233282 & -1.45054644233282 \tabularnewline
12 & 4 & 3.75531469799729 & 0.244685302002714 \tabularnewline
13 & 4 & 5.63300644065185 & -1.63300644065185 \tabularnewline
14 & 4 & 2.12907028909492 & 1.87092971090508 \tabularnewline
15 & 4 & 5.21218676459533 & -1.21218676459533 \tabularnewline
16 & 8 & 4.94313724105353 & 3.05686275894647 \tabularnewline
17 & 4 & 7.6814133510791 & -3.6814133510791 \tabularnewline
18 & 4 & 4.68820308201692 & -0.688203082016919 \tabularnewline
19 & 4 & 2.07205647297391 & 1.92794352702609 \tabularnewline
20 & 8 & 6.38273355100253 & 1.61726644899747 \tabularnewline
21 & 4 & 5.26450730752517 & -1.26450730752517 \tabularnewline
22 & 7 & 7.32463278425914 & -0.324632784259136 \tabularnewline
23 & 4 & 8.67852521405837 & -4.67852521405837 \tabularnewline
24 & 4 & 3.95329502737287 & 0.0467049726271261 \tabularnewline
25 & 5 & 6.21507959834054 & -1.21507959834054 \tabularnewline
26 & 4 & 5.12344406021162 & -1.12344406021162 \tabularnewline
27 & 4 & 5.12344406021162 & -1.12344406021162 \tabularnewline
28 & 4 & 5.18788370926376 & -1.18788370926376 \tabularnewline
29 & 4 & 6.77256238918192 & -2.77256238918192 \tabularnewline
30 & 4 & 5.53876622541378 & -1.53876622541378 \tabularnewline
31 & 4 & 4.86550889143425 & -0.865508891434249 \tabularnewline
32 & 4 & 6.12231089021337 & -2.12231089021337 \tabularnewline
33 & 15 & 8.18130714864601 & 6.81869285135399 \tabularnewline
34 & 10 & 6.64649278281485 & 3.35350721718515 \tabularnewline
35 & 4 & 5.75518763777136 & -1.75518763777136 \tabularnewline
36 & 8 & 5.07529361996977 & 2.92470638003023 \tabularnewline
37 & 4 & 6.23677043890294 & -2.23677043890294 \tabularnewline
38 & 4 & 5.29596626105359 & -1.29596626105359 \tabularnewline
39 & 4 & 4.40307025271931 & -0.403070252719307 \tabularnewline
40 & 4 & 4.52689055259754 & -0.526890552597541 \tabularnewline
41 & 7 & 5.93699648980041 & 1.06300351019959 \tabularnewline
42 & 4 & 4.43551462877052 & -0.435514628770515 \tabularnewline
43 & 6 & 5.81064914101146 & 0.189350858988538 \tabularnewline
44 & 5 & 4.28927860899567 & 0.710721391004329 \tabularnewline
45 & 4 & 3.53272279820661 & 0.467277201793392 \tabularnewline
46 & 16 & 7.13039194747298 & 8.86960805252702 \tabularnewline
47 & 5 & 5.50453587338435 & -0.504535873384352 \tabularnewline
48 & 12 & 5.2235728277896 & 6.7764271722104 \tabularnewline
49 & 6 & 6.08726638376696 & -0.0872663837669628 \tabularnewline
50 & 9 & 7.52174755985167 & 1.47825244014833 \tabularnewline
51 & 9 & 6.95785963652491 & 2.04214036347509 \tabularnewline
52 & 4 & 6.541911017984 & -2.54191101798399 \tabularnewline
53 & 5 & 5.84733121807893 & -0.84733121807893 \tabularnewline
54 & 4 & 6.58513249149593 & -2.58513249149593 \tabularnewline
55 & 4 & 5.57335617284775 & -1.57335617284775 \tabularnewline
56 & 5 & 5.54567291029419 & -0.54567291029419 \tabularnewline
57 & 4 & 5.64901944173085 & -1.64901944173085 \tabularnewline
58 & 4 & 3.7248860608127 & 0.275113939187296 \tabularnewline
59 & 4 & 5.10144221580304 & -1.10144221580304 \tabularnewline
60 & 5 & 7.8513323357628 & -2.85133233576279 \tabularnewline
61 & 4 & 5.32330870529193 & -1.32330870529193 \tabularnewline
62 & 6 & 6.79313132930644 & -0.793131329306439 \tabularnewline
63 & 4 & 4.91005856578508 & -0.910058565785076 \tabularnewline
64 & 4 & 3.9618580505242 & 0.0381419494758029 \tabularnewline
65 & 18 & 7.53001568211154 & 10.4699843178885 \tabularnewline
66 & 4 & 3.331525966457 & 0.668474033543002 \tabularnewline
67 & 6 & 6.0846330007183 & -0.0846330007183043 \tabularnewline
68 & 4 & 5.25503868570179 & -1.25503868570179 \tabularnewline
69 & 4 & 6.38744306234232 & -2.38744306234232 \tabularnewline
70 & 5 & 6.53998279318851 & -1.53998279318851 \tabularnewline
71 & 4 & 5.20994658652664 & -1.20994658652664 \tabularnewline
72 & 4 & 4.86977532930679 & -0.869775329306788 \tabularnewline
73 & 5 & 5.02737251116896 & -0.0273725111689596 \tabularnewline
74 & 10 & 6.58768576703271 & 3.41231423296729 \tabularnewline
75 & 5 & 6.08136296901655 & -1.08136296901655 \tabularnewline
76 & 8 & 8.0528027549215 & -0.0528027549215048 \tabularnewline
77 & 8 & 8.06287277895155 & -0.0628727789515514 \tabularnewline
78 & 5 & 5.01217767181416 & -0.0121776718141566 \tabularnewline
79 & 4 & 4.97178291971552 & -0.971782919715515 \tabularnewline
80 & 4 & 5.86947077602345 & -1.86947077602345 \tabularnewline
81 & 4 & 2.31940557237007 & 1.68059442762993 \tabularnewline
82 & 5 & 6.51958704961352 & -1.51958704961352 \tabularnewline
83 & 4 & 5.35452483477494 & -1.35452483477494 \tabularnewline
84 & 4 & 5.11037710823772 & -1.11037710823772 \tabularnewline
85 & 8 & 6.86109724658748 & 1.13890275341252 \tabularnewline
86 & 4 & 5.41262626471898 & -1.41262626471897 \tabularnewline
87 & 5 & 4.96156699393005 & 0.03843300606995 \tabularnewline
88 & 14 & 7.49542059797885 & 6.50457940202115 \tabularnewline
89 & 8 & 6.01174027665332 & 1.98825972334668 \tabularnewline
90 & 8 & 5.79112209974326 & 2.20887790025674 \tabularnewline
91 & 4 & 7.71865579690719 & -3.71865579690719 \tabularnewline
92 & 4 & 4.05659289170077 & -0.0565928917007699 \tabularnewline
93 & 6 & 5.57717220781703 & 0.42282779218297 \tabularnewline
94 & 4 & 5.84490607654397 & -1.84490607654397 \tabularnewline
95 & 7 & 6.21365772693558 & 0.786342273064425 \tabularnewline
96 & 7 & 5.48771071962862 & 1.51228928037138 \tabularnewline
97 & 4 & 4.57209639778885 & -0.572096397788855 \tabularnewline
98 & 6 & 6.48231125669736 & -0.482311256697364 \tabularnewline
99 & 4 & 5.73317947041582 & -1.73317947041582 \tabularnewline
100 & 7 & 5.03182979464809 & 1.96817020535191 \tabularnewline
101 & 4 & 5.94149199266694 & -1.94149199266694 \tabularnewline
102 & 4 & 3.45305624474844 & 0.546943755251556 \tabularnewline
103 & 8 & 6.82861107160764 & 1.17138892839236 \tabularnewline
104 & 4 & 6.09266352918643 & -2.09266352918643 \tabularnewline
105 & 4 & 5.83287308883167 & -1.83287308883167 \tabularnewline
106 & 10 & 7.26189736713659 & 2.73810263286341 \tabularnewline
107 & 8 & 6.98523369684952 & 1.01476630315048 \tabularnewline
108 & 6 & 6.58432453592979 & -0.58432453592979 \tabularnewline
109 & 4 & 4.85301957829829 & -0.85301957829829 \tabularnewline
110 & 4 & 5.75410968750633 & -1.75410968750633 \tabularnewline
111 & 4 & 4.18928521127586 & -0.189285211275864 \tabularnewline
112 & 5 & 5.03054288591586 & -0.0305428859158606 \tabularnewline
113 & 4 & 6.83532570040698 & -2.83532570040698 \tabularnewline
114 & 6 & 6.45518383997398 & -0.455183839973975 \tabularnewline
115 & 4 & 5.33358942273883 & -1.33358942273883 \tabularnewline
116 & 5 & 6.1567600429648 & -1.1567600429648 \tabularnewline
117 & 7 & 7.7927858951867 & -0.792785895186699 \tabularnewline
118 & 8 & 6.08135274407636 & 1.91864725592364 \tabularnewline
119 & 5 & 4.69330920434762 & 0.306690795652382 \tabularnewline
120 & 8 & 7.92796630225044 & 0.0720336977495603 \tabularnewline
121 & 10 & 7.673486265415 & 2.32651373458501 \tabularnewline
122 & 8 & 6.99096690788038 & 1.00903309211962 \tabularnewline
123 & 5 & 6.01101556768332 & -1.01101556768332 \tabularnewline
124 & 12 & 8.61587408689713 & 3.38412591310287 \tabularnewline
125 & 4 & 2.14051533643041 & 1.85948466356959 \tabularnewline
126 & 5 & 4.85849752654069 & 0.14150247345931 \tabularnewline
127 & 4 & 6.64450676157511 & -2.64450676157511 \tabularnewline
128 & 6 & 5.5441018140447 & 0.455898185955303 \tabularnewline
129 & 4 & 7.02217581520837 & -3.02217581520837 \tabularnewline
130 & 4 & 5.4915432596375 & -1.4915432596375 \tabularnewline
131 & 7 & 7.37828196619842 & -0.378281966198416 \tabularnewline
132 & 7 & 6.45115705172362 & 0.54884294827638 \tabularnewline
133 & 10 & 7.11890515911086 & 2.88109484088914 \tabularnewline
134 & 4 & 5.9290395325246 & -1.9290395325246 \tabularnewline
135 & 5 & 6.04388705375129 & -1.04388705375129 \tabularnewline
136 & 8 & 5.77853252120312 & 2.22146747879688 \tabularnewline
137 & 11 & 5.43994434176503 & 5.56005565823497 \tabularnewline
138 & 7 & 6.25599972832585 & 0.744000271674152 \tabularnewline
139 & 4 & 5.07662320300662 & -1.07662320300662 \tabularnewline
140 & 8 & 6.79511418324031 & 1.20488581675969 \tabularnewline
141 & 6 & 6.47737159187624 & -0.47737159187624 \tabularnewline
142 & 7 & 6.56888855523971 & 0.431111444760291 \tabularnewline
143 & 5 & 7.79056254084418 & -2.79056254084418 \tabularnewline
144 & 4 & 6.29431284648458 & -2.29431284648458 \tabularnewline
145 & 8 & 5.42242528124613 & 2.57757471875387 \tabularnewline
146 & 4 & 6.12607019180585 & -2.12607019180585 \tabularnewline
147 & 8 & 5.74536464665638 & 2.25463535334362 \tabularnewline
148 & 6 & 3.83250276175571 & 2.16749723824429 \tabularnewline
149 & 4 & 5.85988092366667 & -1.85988092366667 \tabularnewline
150 & 9 & 6.3125739577595 & 2.6874260422405 \tabularnewline
151 & 5 & 5.59350842726896 & -0.593508427268958 \tabularnewline
152 & 6 & 5.34778284876435 & 0.652217151235651 \tabularnewline
153 & 4 & 5.5029672807366 & -1.5029672807366 \tabularnewline
154 & 4 & 3.93568591147027 & 0.0643140885297273 \tabularnewline
155 & 4 & 4.0961566019653 & -0.096156601965301 \tabularnewline
156 & 5 & 6.64211703009194 & -1.64211703009194 \tabularnewline
157 & 6 & 7.53961398374006 & -1.53961398374006 \tabularnewline
158 & 16 & 8.92696032481521 & 7.07303967518479 \tabularnewline
159 & 6 & 5.82605619956134 & 0.173943800438659 \tabularnewline
160 & 6 & 7.088154763052 & -1.08815476305199 \tabularnewline
161 & 4 & 6.69668348153238 & -2.69668348153238 \tabularnewline
162 & 4 & 6.99897993095714 & -2.99897993095714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154253&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]4[/C][C]5.53743810187562[/C][C]-1.53743810187562[/C][/ROW]
[ROW][C]2[/C][C]4[/C][C]6.01096967980473[/C][C]-2.01096967980473[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]6.79965132134469[/C][C]-0.799651321344687[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]5.7947514508776[/C][C]2.2052485491224[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]4.90308667913048[/C][C]3.09691332086952[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]5.26830565718309[/C][C]-1.26830565718309[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]1.8658415664239[/C][C]2.1341584335761[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]7.05489285123423[/C][C]0.945107148765771[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]4.58352944279314[/C][C]0.41647055720686[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]5.70630980923892[/C][C]-1.70630980923892[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]5.45054644233282[/C][C]-1.45054644233282[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.75531469799729[/C][C]0.244685302002714[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]5.63300644065185[/C][C]-1.63300644065185[/C][/ROW]
[ROW][C]14[/C][C]4[/C][C]2.12907028909492[/C][C]1.87092971090508[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]5.21218676459533[/C][C]-1.21218676459533[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]4.94313724105353[/C][C]3.05686275894647[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]7.6814133510791[/C][C]-3.6814133510791[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]4.68820308201692[/C][C]-0.688203082016919[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]2.07205647297391[/C][C]1.92794352702609[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]6.38273355100253[/C][C]1.61726644899747[/C][/ROW]
[ROW][C]21[/C][C]4[/C][C]5.26450730752517[/C][C]-1.26450730752517[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]7.32463278425914[/C][C]-0.324632784259136[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]8.67852521405837[/C][C]-4.67852521405837[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]3.95329502737287[/C][C]0.0467049726271261[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]6.21507959834054[/C][C]-1.21507959834054[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]5.12344406021162[/C][C]-1.12344406021162[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]5.12344406021162[/C][C]-1.12344406021162[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]5.18788370926376[/C][C]-1.18788370926376[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]6.77256238918192[/C][C]-2.77256238918192[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]5.53876622541378[/C][C]-1.53876622541378[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]4.86550889143425[/C][C]-0.865508891434249[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]6.12231089021337[/C][C]-2.12231089021337[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]8.18130714864601[/C][C]6.81869285135399[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]6.64649278281485[/C][C]3.35350721718515[/C][/ROW]
[ROW][C]35[/C][C]4[/C][C]5.75518763777136[/C][C]-1.75518763777136[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]5.07529361996977[/C][C]2.92470638003023[/C][/ROW]
[ROW][C]37[/C][C]4[/C][C]6.23677043890294[/C][C]-2.23677043890294[/C][/ROW]
[ROW][C]38[/C][C]4[/C][C]5.29596626105359[/C][C]-1.29596626105359[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]4.40307025271931[/C][C]-0.403070252719307[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.52689055259754[/C][C]-0.526890552597541[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]5.93699648980041[/C][C]1.06300351019959[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]4.43551462877052[/C][C]-0.435514628770515[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]5.81064914101146[/C][C]0.189350858988538[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]4.28927860899567[/C][C]0.710721391004329[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]3.53272279820661[/C][C]0.467277201793392[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]7.13039194747298[/C][C]8.86960805252702[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]5.50453587338435[/C][C]-0.504535873384352[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]5.2235728277896[/C][C]6.7764271722104[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]6.08726638376696[/C][C]-0.0872663837669628[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]7.52174755985167[/C][C]1.47825244014833[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]6.95785963652491[/C][C]2.04214036347509[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]6.541911017984[/C][C]-2.54191101798399[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]5.84733121807893[/C][C]-0.84733121807893[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]6.58513249149593[/C][C]-2.58513249149593[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]5.57335617284775[/C][C]-1.57335617284775[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]5.54567291029419[/C][C]-0.54567291029419[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]5.64901944173085[/C][C]-1.64901944173085[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]3.7248860608127[/C][C]0.275113939187296[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]5.10144221580304[/C][C]-1.10144221580304[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]7.8513323357628[/C][C]-2.85133233576279[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]5.32330870529193[/C][C]-1.32330870529193[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.79313132930644[/C][C]-0.793131329306439[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]4.91005856578508[/C][C]-0.910058565785076[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]3.9618580505242[/C][C]0.0381419494758029[/C][/ROW]
[ROW][C]65[/C][C]18[/C][C]7.53001568211154[/C][C]10.4699843178885[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]3.331525966457[/C][C]0.668474033543002[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]6.0846330007183[/C][C]-0.0846330007183043[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]5.25503868570179[/C][C]-1.25503868570179[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]6.38744306234232[/C][C]-2.38744306234232[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]6.53998279318851[/C][C]-1.53998279318851[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]5.20994658652664[/C][C]-1.20994658652664[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]4.86977532930679[/C][C]-0.869775329306788[/C][/ROW]
[ROW][C]73[/C][C]5[/C][C]5.02737251116896[/C][C]-0.0273725111689596[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]6.58768576703271[/C][C]3.41231423296729[/C][/ROW]
[ROW][C]75[/C][C]5[/C][C]6.08136296901655[/C][C]-1.08136296901655[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]8.0528027549215[/C][C]-0.0528027549215048[/C][/ROW]
[ROW][C]77[/C][C]8[/C][C]8.06287277895155[/C][C]-0.0628727789515514[/C][/ROW]
[ROW][C]78[/C][C]5[/C][C]5.01217767181416[/C][C]-0.0121776718141566[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]4.97178291971552[/C][C]-0.971782919715515[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]5.86947077602345[/C][C]-1.86947077602345[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]2.31940557237007[/C][C]1.68059442762993[/C][/ROW]
[ROW][C]82[/C][C]5[/C][C]6.51958704961352[/C][C]-1.51958704961352[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]5.35452483477494[/C][C]-1.35452483477494[/C][/ROW]
[ROW][C]84[/C][C]4[/C][C]5.11037710823772[/C][C]-1.11037710823772[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]6.86109724658748[/C][C]1.13890275341252[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]5.41262626471898[/C][C]-1.41262626471897[/C][/ROW]
[ROW][C]87[/C][C]5[/C][C]4.96156699393005[/C][C]0.03843300606995[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]7.49542059797885[/C][C]6.50457940202115[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]6.01174027665332[/C][C]1.98825972334668[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]5.79112209974326[/C][C]2.20887790025674[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]7.71865579690719[/C][C]-3.71865579690719[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]4.05659289170077[/C][C]-0.0565928917007699[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]5.57717220781703[/C][C]0.42282779218297[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]5.84490607654397[/C][C]-1.84490607654397[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]6.21365772693558[/C][C]0.786342273064425[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]5.48771071962862[/C][C]1.51228928037138[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]4.57209639778885[/C][C]-0.572096397788855[/C][/ROW]
[ROW][C]98[/C][C]6[/C][C]6.48231125669736[/C][C]-0.482311256697364[/C][/ROW]
[ROW][C]99[/C][C]4[/C][C]5.73317947041582[/C][C]-1.73317947041582[/C][/ROW]
[ROW][C]100[/C][C]7[/C][C]5.03182979464809[/C][C]1.96817020535191[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]5.94149199266694[/C][C]-1.94149199266694[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]3.45305624474844[/C][C]0.546943755251556[/C][/ROW]
[ROW][C]103[/C][C]8[/C][C]6.82861107160764[/C][C]1.17138892839236[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]6.09266352918643[/C][C]-2.09266352918643[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]5.83287308883167[/C][C]-1.83287308883167[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]7.26189736713659[/C][C]2.73810263286341[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]6.98523369684952[/C][C]1.01476630315048[/C][/ROW]
[ROW][C]108[/C][C]6[/C][C]6.58432453592979[/C][C]-0.58432453592979[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]4.85301957829829[/C][C]-0.85301957829829[/C][/ROW]
[ROW][C]110[/C][C]4[/C][C]5.75410968750633[/C][C]-1.75410968750633[/C][/ROW]
[ROW][C]111[/C][C]4[/C][C]4.18928521127586[/C][C]-0.189285211275864[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]5.03054288591586[/C][C]-0.0305428859158606[/C][/ROW]
[ROW][C]113[/C][C]4[/C][C]6.83532570040698[/C][C]-2.83532570040698[/C][/ROW]
[ROW][C]114[/C][C]6[/C][C]6.45518383997398[/C][C]-0.455183839973975[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]5.33358942273883[/C][C]-1.33358942273883[/C][/ROW]
[ROW][C]116[/C][C]5[/C][C]6.1567600429648[/C][C]-1.1567600429648[/C][/ROW]
[ROW][C]117[/C][C]7[/C][C]7.7927858951867[/C][C]-0.792785895186699[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]6.08135274407636[/C][C]1.91864725592364[/C][/ROW]
[ROW][C]119[/C][C]5[/C][C]4.69330920434762[/C][C]0.306690795652382[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]7.92796630225044[/C][C]0.0720336977495603[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]7.673486265415[/C][C]2.32651373458501[/C][/ROW]
[ROW][C]122[/C][C]8[/C][C]6.99096690788038[/C][C]1.00903309211962[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]6.01101556768332[/C][C]-1.01101556768332[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]8.61587408689713[/C][C]3.38412591310287[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]2.14051533643041[/C][C]1.85948466356959[/C][/ROW]
[ROW][C]126[/C][C]5[/C][C]4.85849752654069[/C][C]0.14150247345931[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]6.64450676157511[/C][C]-2.64450676157511[/C][/ROW]
[ROW][C]128[/C][C]6[/C][C]5.5441018140447[/C][C]0.455898185955303[/C][/ROW]
[ROW][C]129[/C][C]4[/C][C]7.02217581520837[/C][C]-3.02217581520837[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.4915432596375[/C][C]-1.4915432596375[/C][/ROW]
[ROW][C]131[/C][C]7[/C][C]7.37828196619842[/C][C]-0.378281966198416[/C][/ROW]
[ROW][C]132[/C][C]7[/C][C]6.45115705172362[/C][C]0.54884294827638[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]7.11890515911086[/C][C]2.88109484088914[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]5.9290395325246[/C][C]-1.9290395325246[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]6.04388705375129[/C][C]-1.04388705375129[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]5.77853252120312[/C][C]2.22146747879688[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]5.43994434176503[/C][C]5.56005565823497[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]6.25599972832585[/C][C]0.744000271674152[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]5.07662320300662[/C][C]-1.07662320300662[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]6.79511418324031[/C][C]1.20488581675969[/C][/ROW]
[ROW][C]141[/C][C]6[/C][C]6.47737159187624[/C][C]-0.47737159187624[/C][/ROW]
[ROW][C]142[/C][C]7[/C][C]6.56888855523971[/C][C]0.431111444760291[/C][/ROW]
[ROW][C]143[/C][C]5[/C][C]7.79056254084418[/C][C]-2.79056254084418[/C][/ROW]
[ROW][C]144[/C][C]4[/C][C]6.29431284648458[/C][C]-2.29431284648458[/C][/ROW]
[ROW][C]145[/C][C]8[/C][C]5.42242528124613[/C][C]2.57757471875387[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]6.12607019180585[/C][C]-2.12607019180585[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]5.74536464665638[/C][C]2.25463535334362[/C][/ROW]
[ROW][C]148[/C][C]6[/C][C]3.83250276175571[/C][C]2.16749723824429[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]5.85988092366667[/C][C]-1.85988092366667[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]6.3125739577595[/C][C]2.6874260422405[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]5.59350842726896[/C][C]-0.593508427268958[/C][/ROW]
[ROW][C]152[/C][C]6[/C][C]5.34778284876435[/C][C]0.652217151235651[/C][/ROW]
[ROW][C]153[/C][C]4[/C][C]5.5029672807366[/C][C]-1.5029672807366[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]3.93568591147027[/C][C]0.0643140885297273[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]4.0961566019653[/C][C]-0.096156601965301[/C][/ROW]
[ROW][C]156[/C][C]5[/C][C]6.64211703009194[/C][C]-1.64211703009194[/C][/ROW]
[ROW][C]157[/C][C]6[/C][C]7.53961398374006[/C][C]-1.53961398374006[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]8.92696032481521[/C][C]7.07303967518479[/C][/ROW]
[ROW][C]159[/C][C]6[/C][C]5.82605619956134[/C][C]0.173943800438659[/C][/ROW]
[ROW][C]160[/C][C]6[/C][C]7.088154763052[/C][C]-1.08815476305199[/C][/ROW]
[ROW][C]161[/C][C]4[/C][C]6.69668348153238[/C][C]-2.69668348153238[/C][/ROW]
[ROW][C]162[/C][C]4[/C][C]6.99897993095714[/C][C]-2.99897993095714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154253&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154253&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
145.53743810187562-1.53743810187562
246.01096967980473-2.01096967980473
366.79965132134469-0.799651321344687
485.79475145087762.2052485491224
584.903086679130483.09691332086952
645.26830565718309-1.26830565718309
741.86584156642392.1341584335761
887.054892851234230.945107148765771
954.583529442793140.41647055720686
1045.70630980923892-1.70630980923892
1145.45054644233282-1.45054644233282
1243.755314697997290.244685302002714
1345.63300644065185-1.63300644065185
1442.129070289094921.87092971090508
1545.21218676459533-1.21218676459533
1684.943137241053533.05686275894647
1747.6814133510791-3.6814133510791
1844.68820308201692-0.688203082016919
1942.072056472973911.92794352702609
2086.382733551002531.61726644899747
2145.26450730752517-1.26450730752517
2277.32463278425914-0.324632784259136
2348.67852521405837-4.67852521405837
2443.953295027372870.0467049726271261
2556.21507959834054-1.21507959834054
2645.12344406021162-1.12344406021162
2745.12344406021162-1.12344406021162
2845.18788370926376-1.18788370926376
2946.77256238918192-2.77256238918192
3045.53876622541378-1.53876622541378
3144.86550889143425-0.865508891434249
3246.12231089021337-2.12231089021337
33158.181307148646016.81869285135399
34106.646492782814853.35350721718515
3545.75518763777136-1.75518763777136
3685.075293619969772.92470638003023
3746.23677043890294-2.23677043890294
3845.29596626105359-1.29596626105359
3944.40307025271931-0.403070252719307
4044.52689055259754-0.526890552597541
4175.936996489800411.06300351019959
4244.43551462877052-0.435514628770515
4365.810649141011460.189350858988538
4454.289278608995670.710721391004329
4543.532722798206610.467277201793392
46167.130391947472988.86960805252702
4755.50453587338435-0.504535873384352
48125.22357282778966.7764271722104
4966.08726638376696-0.0872663837669628
5097.521747559851671.47825244014833
5196.957859636524912.04214036347509
5246.541911017984-2.54191101798399
5355.84733121807893-0.84733121807893
5446.58513249149593-2.58513249149593
5545.57335617284775-1.57335617284775
5655.54567291029419-0.54567291029419
5745.64901944173085-1.64901944173085
5843.72488606081270.275113939187296
5945.10144221580304-1.10144221580304
6057.8513323357628-2.85133233576279
6145.32330870529193-1.32330870529193
6266.79313132930644-0.793131329306439
6344.91005856578508-0.910058565785076
6443.96185805052420.0381419494758029
65187.5300156821115410.4699843178885
6643.3315259664570.668474033543002
6766.0846330007183-0.0846330007183043
6845.25503868570179-1.25503868570179
6946.38744306234232-2.38744306234232
7056.53998279318851-1.53998279318851
7145.20994658652664-1.20994658652664
7244.86977532930679-0.869775329306788
7355.02737251116896-0.0273725111689596
74106.587685767032713.41231423296729
7556.08136296901655-1.08136296901655
7688.0528027549215-0.0528027549215048
7788.06287277895155-0.0628727789515514
7855.01217767181416-0.0121776718141566
7944.97178291971552-0.971782919715515
8045.86947077602345-1.86947077602345
8142.319405572370071.68059442762993
8256.51958704961352-1.51958704961352
8345.35452483477494-1.35452483477494
8445.11037710823772-1.11037710823772
8586.861097246587481.13890275341252
8645.41262626471898-1.41262626471897
8754.961566993930050.03843300606995
88147.495420597978856.50457940202115
8986.011740276653321.98825972334668
9085.791122099743262.20887790025674
9147.71865579690719-3.71865579690719
9244.05659289170077-0.0565928917007699
9365.577172207817030.42282779218297
9445.84490607654397-1.84490607654397
9576.213657726935580.786342273064425
9675.487710719628621.51228928037138
9744.57209639778885-0.572096397788855
9866.48231125669736-0.482311256697364
9945.73317947041582-1.73317947041582
10075.031829794648091.96817020535191
10145.94149199266694-1.94149199266694
10243.453056244748440.546943755251556
10386.828611071607641.17138892839236
10446.09266352918643-2.09266352918643
10545.83287308883167-1.83287308883167
106107.261897367136592.73810263286341
10786.985233696849521.01476630315048
10866.58432453592979-0.58432453592979
10944.85301957829829-0.85301957829829
11045.75410968750633-1.75410968750633
11144.18928521127586-0.189285211275864
11255.03054288591586-0.0305428859158606
11346.83532570040698-2.83532570040698
11466.45518383997398-0.455183839973975
11545.33358942273883-1.33358942273883
11656.1567600429648-1.1567600429648
11777.7927858951867-0.792785895186699
11886.081352744076361.91864725592364
11954.693309204347620.306690795652382
12087.927966302250440.0720336977495603
121107.6734862654152.32651373458501
12286.990966907880381.00903309211962
12356.01101556768332-1.01101556768332
124128.615874086897133.38412591310287
12542.140515336430411.85948466356959
12654.858497526540690.14150247345931
12746.64450676157511-2.64450676157511
12865.54410181404470.455898185955303
12947.02217581520837-3.02217581520837
13045.4915432596375-1.4915432596375
13177.37828196619842-0.378281966198416
13276.451157051723620.54884294827638
133107.118905159110862.88109484088914
13445.9290395325246-1.9290395325246
13556.04388705375129-1.04388705375129
13685.778532521203122.22146747879688
137115.439944341765035.56005565823497
13876.255999728325850.744000271674152
13945.07662320300662-1.07662320300662
14086.795114183240311.20488581675969
14166.47737159187624-0.47737159187624
14276.568888555239710.431111444760291
14357.79056254084418-2.79056254084418
14446.29431284648458-2.29431284648458
14585.422425281246132.57757471875387
14646.12607019180585-2.12607019180585
14785.745364646656382.25463535334362
14863.832502761755712.16749723824429
14945.85988092366667-1.85988092366667
15096.31257395775952.6874260422405
15155.59350842726896-0.593508427268958
15265.347782848764350.652217151235651
15345.5029672807366-1.5029672807366
15443.935685911470270.0643140885297273
15544.0961566019653-0.096156601965301
15656.64211703009194-1.64211703009194
15767.53961398374006-1.53961398374006
158168.926960324815217.07303967518479
15965.826056199561340.173943800438659
16067.088154763052-1.08815476305199
16146.69668348153238-2.69668348153238
16246.99897993095714-2.99897993095714






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.615955456720650.7680890865586990.38404454327935
130.4658129350880150.931625870176030.534187064911985
140.3922821460834870.7845642921669730.607717853916513
150.2869581524708430.5739163049416860.713041847529157
160.2567593339901050.513518667980210.743240666009895
170.2091851062358480.4183702124716960.790814893764152
180.1478524859915340.2957049719830680.852147514008466
190.1007582654899070.2015165309798130.899241734510093
200.1113436218088290.2226872436176590.888656378191171
210.1088379326188450.217675865237690.891162067381155
220.07780793260645110.1556158652129020.922192067393549
230.06392486825016410.1278497365003280.936075131749836
240.04531801608562170.09063603217124330.954681983914378
250.02896794590938510.05793589181877030.971032054090615
260.02524953936323230.05049907872646460.974750460636768
270.01774273367987910.03548546735975830.98225726632012
280.01712222081107210.03424444162214430.982877779188928
290.02336340860782760.04672681721565530.976636591392172
300.01781082779384690.03562165558769380.982189172206153
310.01158400187333820.02316800374667640.988415998126662
320.008200289512030780.01640057902406160.991799710487969
330.3755234559432170.7510469118864350.624476544056783
340.4807581490314810.9615162980629620.519241850968519
350.4874519468192320.9749038936384630.512548053180768
360.468375230620460.9367504612409210.53162476937954
370.4265968160846470.8531936321692930.573403183915353
380.4131140945819610.8262281891639230.586885905418039
390.3603597171734820.7207194343469630.639640282826518
400.3074789256717070.6149578513434130.692521074328293
410.3258370330840480.6516740661680950.674162966915952
420.2782810086214350.5565620172428710.721718991378565
430.2419765395595750.483953079119150.758023460440425
440.2022181256191380.4044362512382760.797781874380862
450.1707511876817630.3415023753635260.829248812318237
460.8676310316446760.2647379367106470.132368968355324
470.8431431813578710.3137136372842570.156856818642129
480.9613201230625630.07735975387487450.0386798769374372
490.9496108568564240.1007782862871510.0503891431435757
500.9421079562921990.1157840874156020.0578920437078011
510.9408643150690480.1182713698619040.0591356849309521
520.9403280449530730.1193439100938550.0596719550469274
530.9249979838439380.1500040323121250.0750020161560624
540.9286448810885160.1427102378229690.0713551189114845
550.921652200699830.156695598600340.0783477993001702
560.902496242904090.1950075141918190.0975037570959096
570.8895164952108610.2209670095782780.110483504789139
580.8647641466348410.2704717067303180.135235853365159
590.8421976214371380.3156047571257240.157802378562862
600.8526216050218150.294756789956370.147378394978185
610.8305769379178280.3388461241643450.169423062082172
620.8004012569342330.3991974861315330.199598743065767
630.7700559032517920.4598881934964170.229944096748209
640.7360178859131190.5279642281737620.263982114086881
650.997504649871920.00499070025615930.00249535012807965
660.9965194956815320.006961008636937030.00348050431846851
670.9950683359112730.009863328177454750.00493166408872737
680.993727057607280.01254588478543940.00627294239271968
690.9936619217401550.01267615651969020.00633807825984512
700.9924214509987540.01515709800249280.0075785490012464
710.9905916816125930.01881663677481350.00940831838740674
720.9878192706153260.02436145876934890.0121807293846745
730.9836272397917340.03274552041653130.0163727602082657
740.9884642660690960.02307146786180790.011535733930904
750.9854755956757450.02904880864851040.0145244043242552
760.9805312409938440.03893751801231180.0194687590061559
770.9742238299870.0515523400259990.0257761700129995
780.966316716096650.06736656780670220.0336832839033511
790.9592933470307150.08141330593857040.0407066529692852
800.955974680795230.08805063840953860.0440253192047693
810.9560176218186250.08796475636274980.0439823781813749
820.949780569919720.100438860160560.0502194300802801
830.9418767693408260.1162464613183470.0581232306591735
840.9306197907037430.1387604185925140.069380209296257
850.9168537921993230.1662924156013530.0831462078006767
860.9072053275130350.1855893449739290.0927946724869646
870.8891956507603680.2216086984792640.110804349239632
880.9771158588394060.04576828232118890.0228841411605944
890.9734193147092120.05316137058157530.0265806852907877
900.9719897021298290.05602059574034260.0280102978701713
910.9777046253736420.04459074925271590.022295374626358
920.970979913820840.05804017235831960.0290200861791598
930.9630069063408580.07398618731828310.0369930936591416
940.9578909412940380.08421811741192440.0421090587059622
950.9472099843850510.1055800312298970.0527900156149487
960.9398599556669170.1202800886661650.0601400443330827
970.9255590051034180.1488819897931640.0744409948965818
980.9079301146687820.1841397706624350.0920698853312176
990.890614276587560.2187714468248820.109385723412441
1000.8791465420938150.241706915812370.120853457906185
1010.8765037754618330.2469924490763350.123496224538167
1020.8552441039152210.2895117921695570.144755896084779
1030.8305186346693480.3389627306613030.169481365330652
1040.82911912506250.3417617498750.1708808749375
1050.8104304659721150.379139068055770.189569534027885
1060.8223778574850740.3552442850298520.177622142514926
1070.7992058111599280.4015883776801440.200794188840072
1080.7632267424213780.4735465151572440.236773257578622
1090.7328171722304420.5343656555391160.267182827769558
1100.7188759932360480.5622480135279040.281124006763952
1110.6753096136824170.6493807726351650.324690386317583
1120.6270015918823270.7459968162353450.372998408117673
1130.6348599804499370.7302800391001270.365140019550063
1140.5875133684476520.8249732631046960.412486631552348
1150.5665844957412290.8668310085175420.433415504258771
1160.5198903724074010.9602192551851980.480109627592599
1170.4803104032874720.9606208065749450.519689596712528
1180.451176044399390.902352088798780.54882395560061
1190.4052552996998240.8105105993996480.594744700300176
1200.3549075066237020.7098150132474050.645092493376298
1210.342109887012860.684219774025720.65789011298714
1220.2955559872599490.5911119745198970.704444012740052
1230.2610833824499260.5221667648998530.738916617550074
1240.3759138429417380.7518276858834750.624086157058262
1250.3396489252456280.6792978504912570.660351074754371
1260.2867294282970550.5734588565941110.713270571702945
1270.3049052596543810.6098105193087630.695094740345619
1280.2548234071941540.5096468143883070.745176592805846
1290.2556688311722260.5113376623444520.744331168827774
1300.2177955616184920.4355911232369840.782204438381508
1310.1740993196480530.3481986392961070.825900680351947
1320.1368640915596660.2737281831193330.863135908440334
1330.2112142061538610.4224284123077230.788785793846139
1340.1748485283562760.3496970567125520.825151471643724
1350.1793229953092490.3586459906184990.82067700469075
1360.2434706011540190.4869412023080390.75652939884598
1370.4696069842609510.9392139685219030.530393015739049
1380.4048760874728060.8097521749456130.595123912527194
1390.4398938891626540.8797877783253090.560106110837346
1400.3632270407123610.7264540814247220.636772959287639
1410.3640468047334020.7280936094668040.635953195266598
1420.2927861658392270.5855723316784550.707213834160773
1430.4099458852731870.8198917705463730.590054114726813
1440.3448256306128270.6896512612256550.655174369387173
1450.2773296450337960.5546592900675920.722670354966204
1460.2090038764254120.4180077528508240.790996123574588
1470.167255507590370.334511015180740.83274449240963
1480.4459226119914540.8918452239829070.554077388008546
1490.3235135114717470.6470270229434940.676486488528253
1500.5231252576828980.9537494846342030.476874742317102

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.61595545672065 & 0.768089086558699 & 0.38404454327935 \tabularnewline
13 & 0.465812935088015 & 0.93162587017603 & 0.534187064911985 \tabularnewline
14 & 0.392282146083487 & 0.784564292166973 & 0.607717853916513 \tabularnewline
15 & 0.286958152470843 & 0.573916304941686 & 0.713041847529157 \tabularnewline
16 & 0.256759333990105 & 0.51351866798021 & 0.743240666009895 \tabularnewline
17 & 0.209185106235848 & 0.418370212471696 & 0.790814893764152 \tabularnewline
18 & 0.147852485991534 & 0.295704971983068 & 0.852147514008466 \tabularnewline
19 & 0.100758265489907 & 0.201516530979813 & 0.899241734510093 \tabularnewline
20 & 0.111343621808829 & 0.222687243617659 & 0.888656378191171 \tabularnewline
21 & 0.108837932618845 & 0.21767586523769 & 0.891162067381155 \tabularnewline
22 & 0.0778079326064511 & 0.155615865212902 & 0.922192067393549 \tabularnewline
23 & 0.0639248682501641 & 0.127849736500328 & 0.936075131749836 \tabularnewline
24 & 0.0453180160856217 & 0.0906360321712433 & 0.954681983914378 \tabularnewline
25 & 0.0289679459093851 & 0.0579358918187703 & 0.971032054090615 \tabularnewline
26 & 0.0252495393632323 & 0.0504990787264646 & 0.974750460636768 \tabularnewline
27 & 0.0177427336798791 & 0.0354854673597583 & 0.98225726632012 \tabularnewline
28 & 0.0171222208110721 & 0.0342444416221443 & 0.982877779188928 \tabularnewline
29 & 0.0233634086078276 & 0.0467268172156553 & 0.976636591392172 \tabularnewline
30 & 0.0178108277938469 & 0.0356216555876938 & 0.982189172206153 \tabularnewline
31 & 0.0115840018733382 & 0.0231680037466764 & 0.988415998126662 \tabularnewline
32 & 0.00820028951203078 & 0.0164005790240616 & 0.991799710487969 \tabularnewline
33 & 0.375523455943217 & 0.751046911886435 & 0.624476544056783 \tabularnewline
34 & 0.480758149031481 & 0.961516298062962 & 0.519241850968519 \tabularnewline
35 & 0.487451946819232 & 0.974903893638463 & 0.512548053180768 \tabularnewline
36 & 0.46837523062046 & 0.936750461240921 & 0.53162476937954 \tabularnewline
37 & 0.426596816084647 & 0.853193632169293 & 0.573403183915353 \tabularnewline
38 & 0.413114094581961 & 0.826228189163923 & 0.586885905418039 \tabularnewline
39 & 0.360359717173482 & 0.720719434346963 & 0.639640282826518 \tabularnewline
40 & 0.307478925671707 & 0.614957851343413 & 0.692521074328293 \tabularnewline
41 & 0.325837033084048 & 0.651674066168095 & 0.674162966915952 \tabularnewline
42 & 0.278281008621435 & 0.556562017242871 & 0.721718991378565 \tabularnewline
43 & 0.241976539559575 & 0.48395307911915 & 0.758023460440425 \tabularnewline
44 & 0.202218125619138 & 0.404436251238276 & 0.797781874380862 \tabularnewline
45 & 0.170751187681763 & 0.341502375363526 & 0.829248812318237 \tabularnewline
46 & 0.867631031644676 & 0.264737936710647 & 0.132368968355324 \tabularnewline
47 & 0.843143181357871 & 0.313713637284257 & 0.156856818642129 \tabularnewline
48 & 0.961320123062563 & 0.0773597538748745 & 0.0386798769374372 \tabularnewline
49 & 0.949610856856424 & 0.100778286287151 & 0.0503891431435757 \tabularnewline
50 & 0.942107956292199 & 0.115784087415602 & 0.0578920437078011 \tabularnewline
51 & 0.940864315069048 & 0.118271369861904 & 0.0591356849309521 \tabularnewline
52 & 0.940328044953073 & 0.119343910093855 & 0.0596719550469274 \tabularnewline
53 & 0.924997983843938 & 0.150004032312125 & 0.0750020161560624 \tabularnewline
54 & 0.928644881088516 & 0.142710237822969 & 0.0713551189114845 \tabularnewline
55 & 0.92165220069983 & 0.15669559860034 & 0.0783477993001702 \tabularnewline
56 & 0.90249624290409 & 0.195007514191819 & 0.0975037570959096 \tabularnewline
57 & 0.889516495210861 & 0.220967009578278 & 0.110483504789139 \tabularnewline
58 & 0.864764146634841 & 0.270471706730318 & 0.135235853365159 \tabularnewline
59 & 0.842197621437138 & 0.315604757125724 & 0.157802378562862 \tabularnewline
60 & 0.852621605021815 & 0.29475678995637 & 0.147378394978185 \tabularnewline
61 & 0.830576937917828 & 0.338846124164345 & 0.169423062082172 \tabularnewline
62 & 0.800401256934233 & 0.399197486131533 & 0.199598743065767 \tabularnewline
63 & 0.770055903251792 & 0.459888193496417 & 0.229944096748209 \tabularnewline
64 & 0.736017885913119 & 0.527964228173762 & 0.263982114086881 \tabularnewline
65 & 0.99750464987192 & 0.0049907002561593 & 0.00249535012807965 \tabularnewline
66 & 0.996519495681532 & 0.00696100863693703 & 0.00348050431846851 \tabularnewline
67 & 0.995068335911273 & 0.00986332817745475 & 0.00493166408872737 \tabularnewline
68 & 0.99372705760728 & 0.0125458847854394 & 0.00627294239271968 \tabularnewline
69 & 0.993661921740155 & 0.0126761565196902 & 0.00633807825984512 \tabularnewline
70 & 0.992421450998754 & 0.0151570980024928 & 0.0075785490012464 \tabularnewline
71 & 0.990591681612593 & 0.0188166367748135 & 0.00940831838740674 \tabularnewline
72 & 0.987819270615326 & 0.0243614587693489 & 0.0121807293846745 \tabularnewline
73 & 0.983627239791734 & 0.0327455204165313 & 0.0163727602082657 \tabularnewline
74 & 0.988464266069096 & 0.0230714678618079 & 0.011535733930904 \tabularnewline
75 & 0.985475595675745 & 0.0290488086485104 & 0.0145244043242552 \tabularnewline
76 & 0.980531240993844 & 0.0389375180123118 & 0.0194687590061559 \tabularnewline
77 & 0.974223829987 & 0.051552340025999 & 0.0257761700129995 \tabularnewline
78 & 0.96631671609665 & 0.0673665678067022 & 0.0336832839033511 \tabularnewline
79 & 0.959293347030715 & 0.0814133059385704 & 0.0407066529692852 \tabularnewline
80 & 0.95597468079523 & 0.0880506384095386 & 0.0440253192047693 \tabularnewline
81 & 0.956017621818625 & 0.0879647563627498 & 0.0439823781813749 \tabularnewline
82 & 0.94978056991972 & 0.10043886016056 & 0.0502194300802801 \tabularnewline
83 & 0.941876769340826 & 0.116246461318347 & 0.0581232306591735 \tabularnewline
84 & 0.930619790703743 & 0.138760418592514 & 0.069380209296257 \tabularnewline
85 & 0.916853792199323 & 0.166292415601353 & 0.0831462078006767 \tabularnewline
86 & 0.907205327513035 & 0.185589344973929 & 0.0927946724869646 \tabularnewline
87 & 0.889195650760368 & 0.221608698479264 & 0.110804349239632 \tabularnewline
88 & 0.977115858839406 & 0.0457682823211889 & 0.0228841411605944 \tabularnewline
89 & 0.973419314709212 & 0.0531613705815753 & 0.0265806852907877 \tabularnewline
90 & 0.971989702129829 & 0.0560205957403426 & 0.0280102978701713 \tabularnewline
91 & 0.977704625373642 & 0.0445907492527159 & 0.022295374626358 \tabularnewline
92 & 0.97097991382084 & 0.0580401723583196 & 0.0290200861791598 \tabularnewline
93 & 0.963006906340858 & 0.0739861873182831 & 0.0369930936591416 \tabularnewline
94 & 0.957890941294038 & 0.0842181174119244 & 0.0421090587059622 \tabularnewline
95 & 0.947209984385051 & 0.105580031229897 & 0.0527900156149487 \tabularnewline
96 & 0.939859955666917 & 0.120280088666165 & 0.0601400443330827 \tabularnewline
97 & 0.925559005103418 & 0.148881989793164 & 0.0744409948965818 \tabularnewline
98 & 0.907930114668782 & 0.184139770662435 & 0.0920698853312176 \tabularnewline
99 & 0.89061427658756 & 0.218771446824882 & 0.109385723412441 \tabularnewline
100 & 0.879146542093815 & 0.24170691581237 & 0.120853457906185 \tabularnewline
101 & 0.876503775461833 & 0.246992449076335 & 0.123496224538167 \tabularnewline
102 & 0.855244103915221 & 0.289511792169557 & 0.144755896084779 \tabularnewline
103 & 0.830518634669348 & 0.338962730661303 & 0.169481365330652 \tabularnewline
104 & 0.8291191250625 & 0.341761749875 & 0.1708808749375 \tabularnewline
105 & 0.810430465972115 & 0.37913906805577 & 0.189569534027885 \tabularnewline
106 & 0.822377857485074 & 0.355244285029852 & 0.177622142514926 \tabularnewline
107 & 0.799205811159928 & 0.401588377680144 & 0.200794188840072 \tabularnewline
108 & 0.763226742421378 & 0.473546515157244 & 0.236773257578622 \tabularnewline
109 & 0.732817172230442 & 0.534365655539116 & 0.267182827769558 \tabularnewline
110 & 0.718875993236048 & 0.562248013527904 & 0.281124006763952 \tabularnewline
111 & 0.675309613682417 & 0.649380772635165 & 0.324690386317583 \tabularnewline
112 & 0.627001591882327 & 0.745996816235345 & 0.372998408117673 \tabularnewline
113 & 0.634859980449937 & 0.730280039100127 & 0.365140019550063 \tabularnewline
114 & 0.587513368447652 & 0.824973263104696 & 0.412486631552348 \tabularnewline
115 & 0.566584495741229 & 0.866831008517542 & 0.433415504258771 \tabularnewline
116 & 0.519890372407401 & 0.960219255185198 & 0.480109627592599 \tabularnewline
117 & 0.480310403287472 & 0.960620806574945 & 0.519689596712528 \tabularnewline
118 & 0.45117604439939 & 0.90235208879878 & 0.54882395560061 \tabularnewline
119 & 0.405255299699824 & 0.810510599399648 & 0.594744700300176 \tabularnewline
120 & 0.354907506623702 & 0.709815013247405 & 0.645092493376298 \tabularnewline
121 & 0.34210988701286 & 0.68421977402572 & 0.65789011298714 \tabularnewline
122 & 0.295555987259949 & 0.591111974519897 & 0.704444012740052 \tabularnewline
123 & 0.261083382449926 & 0.522166764899853 & 0.738916617550074 \tabularnewline
124 & 0.375913842941738 & 0.751827685883475 & 0.624086157058262 \tabularnewline
125 & 0.339648925245628 & 0.679297850491257 & 0.660351074754371 \tabularnewline
126 & 0.286729428297055 & 0.573458856594111 & 0.713270571702945 \tabularnewline
127 & 0.304905259654381 & 0.609810519308763 & 0.695094740345619 \tabularnewline
128 & 0.254823407194154 & 0.509646814388307 & 0.745176592805846 \tabularnewline
129 & 0.255668831172226 & 0.511337662344452 & 0.744331168827774 \tabularnewline
130 & 0.217795561618492 & 0.435591123236984 & 0.782204438381508 \tabularnewline
131 & 0.174099319648053 & 0.348198639296107 & 0.825900680351947 \tabularnewline
132 & 0.136864091559666 & 0.273728183119333 & 0.863135908440334 \tabularnewline
133 & 0.211214206153861 & 0.422428412307723 & 0.788785793846139 \tabularnewline
134 & 0.174848528356276 & 0.349697056712552 & 0.825151471643724 \tabularnewline
135 & 0.179322995309249 & 0.358645990618499 & 0.82067700469075 \tabularnewline
136 & 0.243470601154019 & 0.486941202308039 & 0.75652939884598 \tabularnewline
137 & 0.469606984260951 & 0.939213968521903 & 0.530393015739049 \tabularnewline
138 & 0.404876087472806 & 0.809752174945613 & 0.595123912527194 \tabularnewline
139 & 0.439893889162654 & 0.879787778325309 & 0.560106110837346 \tabularnewline
140 & 0.363227040712361 & 0.726454081424722 & 0.636772959287639 \tabularnewline
141 & 0.364046804733402 & 0.728093609466804 & 0.635953195266598 \tabularnewline
142 & 0.292786165839227 & 0.585572331678455 & 0.707213834160773 \tabularnewline
143 & 0.409945885273187 & 0.819891770546373 & 0.590054114726813 \tabularnewline
144 & 0.344825630612827 & 0.689651261225655 & 0.655174369387173 \tabularnewline
145 & 0.277329645033796 & 0.554659290067592 & 0.722670354966204 \tabularnewline
146 & 0.209003876425412 & 0.418007752850824 & 0.790996123574588 \tabularnewline
147 & 0.16725550759037 & 0.33451101518074 & 0.83274449240963 \tabularnewline
148 & 0.445922611991454 & 0.891845223982907 & 0.554077388008546 \tabularnewline
149 & 0.323513511471747 & 0.647027022943494 & 0.676486488528253 \tabularnewline
150 & 0.523125257682898 & 0.953749484634203 & 0.476874742317102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154253&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]12[/C][C]0.61595545672065[/C][C]0.768089086558699[/C][C]0.38404454327935[/C][/ROW]
[ROW][C]13[/C][C]0.465812935088015[/C][C]0.93162587017603[/C][C]0.534187064911985[/C][/ROW]
[ROW][C]14[/C][C]0.392282146083487[/C][C]0.784564292166973[/C][C]0.607717853916513[/C][/ROW]
[ROW][C]15[/C][C]0.286958152470843[/C][C]0.573916304941686[/C][C]0.713041847529157[/C][/ROW]
[ROW][C]16[/C][C]0.256759333990105[/C][C]0.51351866798021[/C][C]0.743240666009895[/C][/ROW]
[ROW][C]17[/C][C]0.209185106235848[/C][C]0.418370212471696[/C][C]0.790814893764152[/C][/ROW]
[ROW][C]18[/C][C]0.147852485991534[/C][C]0.295704971983068[/C][C]0.852147514008466[/C][/ROW]
[ROW][C]19[/C][C]0.100758265489907[/C][C]0.201516530979813[/C][C]0.899241734510093[/C][/ROW]
[ROW][C]20[/C][C]0.111343621808829[/C][C]0.222687243617659[/C][C]0.888656378191171[/C][/ROW]
[ROW][C]21[/C][C]0.108837932618845[/C][C]0.21767586523769[/C][C]0.891162067381155[/C][/ROW]
[ROW][C]22[/C][C]0.0778079326064511[/C][C]0.155615865212902[/C][C]0.922192067393549[/C][/ROW]
[ROW][C]23[/C][C]0.0639248682501641[/C][C]0.127849736500328[/C][C]0.936075131749836[/C][/ROW]
[ROW][C]24[/C][C]0.0453180160856217[/C][C]0.0906360321712433[/C][C]0.954681983914378[/C][/ROW]
[ROW][C]25[/C][C]0.0289679459093851[/C][C]0.0579358918187703[/C][C]0.971032054090615[/C][/ROW]
[ROW][C]26[/C][C]0.0252495393632323[/C][C]0.0504990787264646[/C][C]0.974750460636768[/C][/ROW]
[ROW][C]27[/C][C]0.0177427336798791[/C][C]0.0354854673597583[/C][C]0.98225726632012[/C][/ROW]
[ROW][C]28[/C][C]0.0171222208110721[/C][C]0.0342444416221443[/C][C]0.982877779188928[/C][/ROW]
[ROW][C]29[/C][C]0.0233634086078276[/C][C]0.0467268172156553[/C][C]0.976636591392172[/C][/ROW]
[ROW][C]30[/C][C]0.0178108277938469[/C][C]0.0356216555876938[/C][C]0.982189172206153[/C][/ROW]
[ROW][C]31[/C][C]0.0115840018733382[/C][C]0.0231680037466764[/C][C]0.988415998126662[/C][/ROW]
[ROW][C]32[/C][C]0.00820028951203078[/C][C]0.0164005790240616[/C][C]0.991799710487969[/C][/ROW]
[ROW][C]33[/C][C]0.375523455943217[/C][C]0.751046911886435[/C][C]0.624476544056783[/C][/ROW]
[ROW][C]34[/C][C]0.480758149031481[/C][C]0.961516298062962[/C][C]0.519241850968519[/C][/ROW]
[ROW][C]35[/C][C]0.487451946819232[/C][C]0.974903893638463[/C][C]0.512548053180768[/C][/ROW]
[ROW][C]36[/C][C]0.46837523062046[/C][C]0.936750461240921[/C][C]0.53162476937954[/C][/ROW]
[ROW][C]37[/C][C]0.426596816084647[/C][C]0.853193632169293[/C][C]0.573403183915353[/C][/ROW]
[ROW][C]38[/C][C]0.413114094581961[/C][C]0.826228189163923[/C][C]0.586885905418039[/C][/ROW]
[ROW][C]39[/C][C]0.360359717173482[/C][C]0.720719434346963[/C][C]0.639640282826518[/C][/ROW]
[ROW][C]40[/C][C]0.307478925671707[/C][C]0.614957851343413[/C][C]0.692521074328293[/C][/ROW]
[ROW][C]41[/C][C]0.325837033084048[/C][C]0.651674066168095[/C][C]0.674162966915952[/C][/ROW]
[ROW][C]42[/C][C]0.278281008621435[/C][C]0.556562017242871[/C][C]0.721718991378565[/C][/ROW]
[ROW][C]43[/C][C]0.241976539559575[/C][C]0.48395307911915[/C][C]0.758023460440425[/C][/ROW]
[ROW][C]44[/C][C]0.202218125619138[/C][C]0.404436251238276[/C][C]0.797781874380862[/C][/ROW]
[ROW][C]45[/C][C]0.170751187681763[/C][C]0.341502375363526[/C][C]0.829248812318237[/C][/ROW]
[ROW][C]46[/C][C]0.867631031644676[/C][C]0.264737936710647[/C][C]0.132368968355324[/C][/ROW]
[ROW][C]47[/C][C]0.843143181357871[/C][C]0.313713637284257[/C][C]0.156856818642129[/C][/ROW]
[ROW][C]48[/C][C]0.961320123062563[/C][C]0.0773597538748745[/C][C]0.0386798769374372[/C][/ROW]
[ROW][C]49[/C][C]0.949610856856424[/C][C]0.100778286287151[/C][C]0.0503891431435757[/C][/ROW]
[ROW][C]50[/C][C]0.942107956292199[/C][C]0.115784087415602[/C][C]0.0578920437078011[/C][/ROW]
[ROW][C]51[/C][C]0.940864315069048[/C][C]0.118271369861904[/C][C]0.0591356849309521[/C][/ROW]
[ROW][C]52[/C][C]0.940328044953073[/C][C]0.119343910093855[/C][C]0.0596719550469274[/C][/ROW]
[ROW][C]53[/C][C]0.924997983843938[/C][C]0.150004032312125[/C][C]0.0750020161560624[/C][/ROW]
[ROW][C]54[/C][C]0.928644881088516[/C][C]0.142710237822969[/C][C]0.0713551189114845[/C][/ROW]
[ROW][C]55[/C][C]0.92165220069983[/C][C]0.15669559860034[/C][C]0.0783477993001702[/C][/ROW]
[ROW][C]56[/C][C]0.90249624290409[/C][C]0.195007514191819[/C][C]0.0975037570959096[/C][/ROW]
[ROW][C]57[/C][C]0.889516495210861[/C][C]0.220967009578278[/C][C]0.110483504789139[/C][/ROW]
[ROW][C]58[/C][C]0.864764146634841[/C][C]0.270471706730318[/C][C]0.135235853365159[/C][/ROW]
[ROW][C]59[/C][C]0.842197621437138[/C][C]0.315604757125724[/C][C]0.157802378562862[/C][/ROW]
[ROW][C]60[/C][C]0.852621605021815[/C][C]0.29475678995637[/C][C]0.147378394978185[/C][/ROW]
[ROW][C]61[/C][C]0.830576937917828[/C][C]0.338846124164345[/C][C]0.169423062082172[/C][/ROW]
[ROW][C]62[/C][C]0.800401256934233[/C][C]0.399197486131533[/C][C]0.199598743065767[/C][/ROW]
[ROW][C]63[/C][C]0.770055903251792[/C][C]0.459888193496417[/C][C]0.229944096748209[/C][/ROW]
[ROW][C]64[/C][C]0.736017885913119[/C][C]0.527964228173762[/C][C]0.263982114086881[/C][/ROW]
[ROW][C]65[/C][C]0.99750464987192[/C][C]0.0049907002561593[/C][C]0.00249535012807965[/C][/ROW]
[ROW][C]66[/C][C]0.996519495681532[/C][C]0.00696100863693703[/C][C]0.00348050431846851[/C][/ROW]
[ROW][C]67[/C][C]0.995068335911273[/C][C]0.00986332817745475[/C][C]0.00493166408872737[/C][/ROW]
[ROW][C]68[/C][C]0.99372705760728[/C][C]0.0125458847854394[/C][C]0.00627294239271968[/C][/ROW]
[ROW][C]69[/C][C]0.993661921740155[/C][C]0.0126761565196902[/C][C]0.00633807825984512[/C][/ROW]
[ROW][C]70[/C][C]0.992421450998754[/C][C]0.0151570980024928[/C][C]0.0075785490012464[/C][/ROW]
[ROW][C]71[/C][C]0.990591681612593[/C][C]0.0188166367748135[/C][C]0.00940831838740674[/C][/ROW]
[ROW][C]72[/C][C]0.987819270615326[/C][C]0.0243614587693489[/C][C]0.0121807293846745[/C][/ROW]
[ROW][C]73[/C][C]0.983627239791734[/C][C]0.0327455204165313[/C][C]0.0163727602082657[/C][/ROW]
[ROW][C]74[/C][C]0.988464266069096[/C][C]0.0230714678618079[/C][C]0.011535733930904[/C][/ROW]
[ROW][C]75[/C][C]0.985475595675745[/C][C]0.0290488086485104[/C][C]0.0145244043242552[/C][/ROW]
[ROW][C]76[/C][C]0.980531240993844[/C][C]0.0389375180123118[/C][C]0.0194687590061559[/C][/ROW]
[ROW][C]77[/C][C]0.974223829987[/C][C]0.051552340025999[/C][C]0.0257761700129995[/C][/ROW]
[ROW][C]78[/C][C]0.96631671609665[/C][C]0.0673665678067022[/C][C]0.0336832839033511[/C][/ROW]
[ROW][C]79[/C][C]0.959293347030715[/C][C]0.0814133059385704[/C][C]0.0407066529692852[/C][/ROW]
[ROW][C]80[/C][C]0.95597468079523[/C][C]0.0880506384095386[/C][C]0.0440253192047693[/C][/ROW]
[ROW][C]81[/C][C]0.956017621818625[/C][C]0.0879647563627498[/C][C]0.0439823781813749[/C][/ROW]
[ROW][C]82[/C][C]0.94978056991972[/C][C]0.10043886016056[/C][C]0.0502194300802801[/C][/ROW]
[ROW][C]83[/C][C]0.941876769340826[/C][C]0.116246461318347[/C][C]0.0581232306591735[/C][/ROW]
[ROW][C]84[/C][C]0.930619790703743[/C][C]0.138760418592514[/C][C]0.069380209296257[/C][/ROW]
[ROW][C]85[/C][C]0.916853792199323[/C][C]0.166292415601353[/C][C]0.0831462078006767[/C][/ROW]
[ROW][C]86[/C][C]0.907205327513035[/C][C]0.185589344973929[/C][C]0.0927946724869646[/C][/ROW]
[ROW][C]87[/C][C]0.889195650760368[/C][C]0.221608698479264[/C][C]0.110804349239632[/C][/ROW]
[ROW][C]88[/C][C]0.977115858839406[/C][C]0.0457682823211889[/C][C]0.0228841411605944[/C][/ROW]
[ROW][C]89[/C][C]0.973419314709212[/C][C]0.0531613705815753[/C][C]0.0265806852907877[/C][/ROW]
[ROW][C]90[/C][C]0.971989702129829[/C][C]0.0560205957403426[/C][C]0.0280102978701713[/C][/ROW]
[ROW][C]91[/C][C]0.977704625373642[/C][C]0.0445907492527159[/C][C]0.022295374626358[/C][/ROW]
[ROW][C]92[/C][C]0.97097991382084[/C][C]0.0580401723583196[/C][C]0.0290200861791598[/C][/ROW]
[ROW][C]93[/C][C]0.963006906340858[/C][C]0.0739861873182831[/C][C]0.0369930936591416[/C][/ROW]
[ROW][C]94[/C][C]0.957890941294038[/C][C]0.0842181174119244[/C][C]0.0421090587059622[/C][/ROW]
[ROW][C]95[/C][C]0.947209984385051[/C][C]0.105580031229897[/C][C]0.0527900156149487[/C][/ROW]
[ROW][C]96[/C][C]0.939859955666917[/C][C]0.120280088666165[/C][C]0.0601400443330827[/C][/ROW]
[ROW][C]97[/C][C]0.925559005103418[/C][C]0.148881989793164[/C][C]0.0744409948965818[/C][/ROW]
[ROW][C]98[/C][C]0.907930114668782[/C][C]0.184139770662435[/C][C]0.0920698853312176[/C][/ROW]
[ROW][C]99[/C][C]0.89061427658756[/C][C]0.218771446824882[/C][C]0.109385723412441[/C][/ROW]
[ROW][C]100[/C][C]0.879146542093815[/C][C]0.24170691581237[/C][C]0.120853457906185[/C][/ROW]
[ROW][C]101[/C][C]0.876503775461833[/C][C]0.246992449076335[/C][C]0.123496224538167[/C][/ROW]
[ROW][C]102[/C][C]0.855244103915221[/C][C]0.289511792169557[/C][C]0.144755896084779[/C][/ROW]
[ROW][C]103[/C][C]0.830518634669348[/C][C]0.338962730661303[/C][C]0.169481365330652[/C][/ROW]
[ROW][C]104[/C][C]0.8291191250625[/C][C]0.341761749875[/C][C]0.1708808749375[/C][/ROW]
[ROW][C]105[/C][C]0.810430465972115[/C][C]0.37913906805577[/C][C]0.189569534027885[/C][/ROW]
[ROW][C]106[/C][C]0.822377857485074[/C][C]0.355244285029852[/C][C]0.177622142514926[/C][/ROW]
[ROW][C]107[/C][C]0.799205811159928[/C][C]0.401588377680144[/C][C]0.200794188840072[/C][/ROW]
[ROW][C]108[/C][C]0.763226742421378[/C][C]0.473546515157244[/C][C]0.236773257578622[/C][/ROW]
[ROW][C]109[/C][C]0.732817172230442[/C][C]0.534365655539116[/C][C]0.267182827769558[/C][/ROW]
[ROW][C]110[/C][C]0.718875993236048[/C][C]0.562248013527904[/C][C]0.281124006763952[/C][/ROW]
[ROW][C]111[/C][C]0.675309613682417[/C][C]0.649380772635165[/C][C]0.324690386317583[/C][/ROW]
[ROW][C]112[/C][C]0.627001591882327[/C][C]0.745996816235345[/C][C]0.372998408117673[/C][/ROW]
[ROW][C]113[/C][C]0.634859980449937[/C][C]0.730280039100127[/C][C]0.365140019550063[/C][/ROW]
[ROW][C]114[/C][C]0.587513368447652[/C][C]0.824973263104696[/C][C]0.412486631552348[/C][/ROW]
[ROW][C]115[/C][C]0.566584495741229[/C][C]0.866831008517542[/C][C]0.433415504258771[/C][/ROW]
[ROW][C]116[/C][C]0.519890372407401[/C][C]0.960219255185198[/C][C]0.480109627592599[/C][/ROW]
[ROW][C]117[/C][C]0.480310403287472[/C][C]0.960620806574945[/C][C]0.519689596712528[/C][/ROW]
[ROW][C]118[/C][C]0.45117604439939[/C][C]0.90235208879878[/C][C]0.54882395560061[/C][/ROW]
[ROW][C]119[/C][C]0.405255299699824[/C][C]0.810510599399648[/C][C]0.594744700300176[/C][/ROW]
[ROW][C]120[/C][C]0.354907506623702[/C][C]0.709815013247405[/C][C]0.645092493376298[/C][/ROW]
[ROW][C]121[/C][C]0.34210988701286[/C][C]0.68421977402572[/C][C]0.65789011298714[/C][/ROW]
[ROW][C]122[/C][C]0.295555987259949[/C][C]0.591111974519897[/C][C]0.704444012740052[/C][/ROW]
[ROW][C]123[/C][C]0.261083382449926[/C][C]0.522166764899853[/C][C]0.738916617550074[/C][/ROW]
[ROW][C]124[/C][C]0.375913842941738[/C][C]0.751827685883475[/C][C]0.624086157058262[/C][/ROW]
[ROW][C]125[/C][C]0.339648925245628[/C][C]0.679297850491257[/C][C]0.660351074754371[/C][/ROW]
[ROW][C]126[/C][C]0.286729428297055[/C][C]0.573458856594111[/C][C]0.713270571702945[/C][/ROW]
[ROW][C]127[/C][C]0.304905259654381[/C][C]0.609810519308763[/C][C]0.695094740345619[/C][/ROW]
[ROW][C]128[/C][C]0.254823407194154[/C][C]0.509646814388307[/C][C]0.745176592805846[/C][/ROW]
[ROW][C]129[/C][C]0.255668831172226[/C][C]0.511337662344452[/C][C]0.744331168827774[/C][/ROW]
[ROW][C]130[/C][C]0.217795561618492[/C][C]0.435591123236984[/C][C]0.782204438381508[/C][/ROW]
[ROW][C]131[/C][C]0.174099319648053[/C][C]0.348198639296107[/C][C]0.825900680351947[/C][/ROW]
[ROW][C]132[/C][C]0.136864091559666[/C][C]0.273728183119333[/C][C]0.863135908440334[/C][/ROW]
[ROW][C]133[/C][C]0.211214206153861[/C][C]0.422428412307723[/C][C]0.788785793846139[/C][/ROW]
[ROW][C]134[/C][C]0.174848528356276[/C][C]0.349697056712552[/C][C]0.825151471643724[/C][/ROW]
[ROW][C]135[/C][C]0.179322995309249[/C][C]0.358645990618499[/C][C]0.82067700469075[/C][/ROW]
[ROW][C]136[/C][C]0.243470601154019[/C][C]0.486941202308039[/C][C]0.75652939884598[/C][/ROW]
[ROW][C]137[/C][C]0.469606984260951[/C][C]0.939213968521903[/C][C]0.530393015739049[/C][/ROW]
[ROW][C]138[/C][C]0.404876087472806[/C][C]0.809752174945613[/C][C]0.595123912527194[/C][/ROW]
[ROW][C]139[/C][C]0.439893889162654[/C][C]0.879787778325309[/C][C]0.560106110837346[/C][/ROW]
[ROW][C]140[/C][C]0.363227040712361[/C][C]0.726454081424722[/C][C]0.636772959287639[/C][/ROW]
[ROW][C]141[/C][C]0.364046804733402[/C][C]0.728093609466804[/C][C]0.635953195266598[/C][/ROW]
[ROW][C]142[/C][C]0.292786165839227[/C][C]0.585572331678455[/C][C]0.707213834160773[/C][/ROW]
[ROW][C]143[/C][C]0.409945885273187[/C][C]0.819891770546373[/C][C]0.590054114726813[/C][/ROW]
[ROW][C]144[/C][C]0.344825630612827[/C][C]0.689651261225655[/C][C]0.655174369387173[/C][/ROW]
[ROW][C]145[/C][C]0.277329645033796[/C][C]0.554659290067592[/C][C]0.722670354966204[/C][/ROW]
[ROW][C]146[/C][C]0.209003876425412[/C][C]0.418007752850824[/C][C]0.790996123574588[/C][/ROW]
[ROW][C]147[/C][C]0.16725550759037[/C][C]0.33451101518074[/C][C]0.83274449240963[/C][/ROW]
[ROW][C]148[/C][C]0.445922611991454[/C][C]0.891845223982907[/C][C]0.554077388008546[/C][/ROW]
[ROW][C]149[/C][C]0.323513511471747[/C][C]0.647027022943494[/C][C]0.676486488528253[/C][/ROW]
[ROW][C]150[/C][C]0.523125257682898[/C][C]0.953749484634203[/C][C]0.476874742317102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154253&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154253&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
120.615955456720650.7680890865586990.38404454327935
130.4658129350880150.931625870176030.534187064911985
140.3922821460834870.7845642921669730.607717853916513
150.2869581524708430.5739163049416860.713041847529157
160.2567593339901050.513518667980210.743240666009895
170.2091851062358480.4183702124716960.790814893764152
180.1478524859915340.2957049719830680.852147514008466
190.1007582654899070.2015165309798130.899241734510093
200.1113436218088290.2226872436176590.888656378191171
210.1088379326188450.217675865237690.891162067381155
220.07780793260645110.1556158652129020.922192067393549
230.06392486825016410.1278497365003280.936075131749836
240.04531801608562170.09063603217124330.954681983914378
250.02896794590938510.05793589181877030.971032054090615
260.02524953936323230.05049907872646460.974750460636768
270.01774273367987910.03548546735975830.98225726632012
280.01712222081107210.03424444162214430.982877779188928
290.02336340860782760.04672681721565530.976636591392172
300.01781082779384690.03562165558769380.982189172206153
310.01158400187333820.02316800374667640.988415998126662
320.008200289512030780.01640057902406160.991799710487969
330.3755234559432170.7510469118864350.624476544056783
340.4807581490314810.9615162980629620.519241850968519
350.4874519468192320.9749038936384630.512548053180768
360.468375230620460.9367504612409210.53162476937954
370.4265968160846470.8531936321692930.573403183915353
380.4131140945819610.8262281891639230.586885905418039
390.3603597171734820.7207194343469630.639640282826518
400.3074789256717070.6149578513434130.692521074328293
410.3258370330840480.6516740661680950.674162966915952
420.2782810086214350.5565620172428710.721718991378565
430.2419765395595750.483953079119150.758023460440425
440.2022181256191380.4044362512382760.797781874380862
450.1707511876817630.3415023753635260.829248812318237
460.8676310316446760.2647379367106470.132368968355324
470.8431431813578710.3137136372842570.156856818642129
480.9613201230625630.07735975387487450.0386798769374372
490.9496108568564240.1007782862871510.0503891431435757
500.9421079562921990.1157840874156020.0578920437078011
510.9408643150690480.1182713698619040.0591356849309521
520.9403280449530730.1193439100938550.0596719550469274
530.9249979838439380.1500040323121250.0750020161560624
540.9286448810885160.1427102378229690.0713551189114845
550.921652200699830.156695598600340.0783477993001702
560.902496242904090.1950075141918190.0975037570959096
570.8895164952108610.2209670095782780.110483504789139
580.8647641466348410.2704717067303180.135235853365159
590.8421976214371380.3156047571257240.157802378562862
600.8526216050218150.294756789956370.147378394978185
610.8305769379178280.3388461241643450.169423062082172
620.8004012569342330.3991974861315330.199598743065767
630.7700559032517920.4598881934964170.229944096748209
640.7360178859131190.5279642281737620.263982114086881
650.997504649871920.00499070025615930.00249535012807965
660.9965194956815320.006961008636937030.00348050431846851
670.9950683359112730.009863328177454750.00493166408872737
680.993727057607280.01254588478543940.00627294239271968
690.9936619217401550.01267615651969020.00633807825984512
700.9924214509987540.01515709800249280.0075785490012464
710.9905916816125930.01881663677481350.00940831838740674
720.9878192706153260.02436145876934890.0121807293846745
730.9836272397917340.03274552041653130.0163727602082657
740.9884642660690960.02307146786180790.011535733930904
750.9854755956757450.02904880864851040.0145244043242552
760.9805312409938440.03893751801231180.0194687590061559
770.9742238299870.0515523400259990.0257761700129995
780.966316716096650.06736656780670220.0336832839033511
790.9592933470307150.08141330593857040.0407066529692852
800.955974680795230.08805063840953860.0440253192047693
810.9560176218186250.08796475636274980.0439823781813749
820.949780569919720.100438860160560.0502194300802801
830.9418767693408260.1162464613183470.0581232306591735
840.9306197907037430.1387604185925140.069380209296257
850.9168537921993230.1662924156013530.0831462078006767
860.9072053275130350.1855893449739290.0927946724869646
870.8891956507603680.2216086984792640.110804349239632
880.9771158588394060.04576828232118890.0228841411605944
890.9734193147092120.05316137058157530.0265806852907877
900.9719897021298290.05602059574034260.0280102978701713
910.9777046253736420.04459074925271590.022295374626358
920.970979913820840.05804017235831960.0290200861791598
930.9630069063408580.07398618731828310.0369930936591416
940.9578909412940380.08421811741192440.0421090587059622
950.9472099843850510.1055800312298970.0527900156149487
960.9398599556669170.1202800886661650.0601400443330827
970.9255590051034180.1488819897931640.0744409948965818
980.9079301146687820.1841397706624350.0920698853312176
990.890614276587560.2187714468248820.109385723412441
1000.8791465420938150.241706915812370.120853457906185
1010.8765037754618330.2469924490763350.123496224538167
1020.8552441039152210.2895117921695570.144755896084779
1030.8305186346693480.3389627306613030.169481365330652
1040.82911912506250.3417617498750.1708808749375
1050.8104304659721150.379139068055770.189569534027885
1060.8223778574850740.3552442850298520.177622142514926
1070.7992058111599280.4015883776801440.200794188840072
1080.7632267424213780.4735465151572440.236773257578622
1090.7328171722304420.5343656555391160.267182827769558
1100.7188759932360480.5622480135279040.281124006763952
1110.6753096136824170.6493807726351650.324690386317583
1120.6270015918823270.7459968162353450.372998408117673
1130.6348599804499370.7302800391001270.365140019550063
1140.5875133684476520.8249732631046960.412486631552348
1150.5665844957412290.8668310085175420.433415504258771
1160.5198903724074010.9602192551851980.480109627592599
1170.4803104032874720.9606208065749450.519689596712528
1180.451176044399390.902352088798780.54882395560061
1190.4052552996998240.8105105993996480.594744700300176
1200.3549075066237020.7098150132474050.645092493376298
1210.342109887012860.684219774025720.65789011298714
1220.2955559872599490.5911119745198970.704444012740052
1230.2610833824499260.5221667648998530.738916617550074
1240.3759138429417380.7518276858834750.624086157058262
1250.3396489252456280.6792978504912570.660351074754371
1260.2867294282970550.5734588565941110.713270571702945
1270.3049052596543810.6098105193087630.695094740345619
1280.2548234071941540.5096468143883070.745176592805846
1290.2556688311722260.5113376623444520.744331168827774
1300.2177955616184920.4355911232369840.782204438381508
1310.1740993196480530.3481986392961070.825900680351947
1320.1368640915596660.2737281831193330.863135908440334
1330.2112142061538610.4224284123077230.788785793846139
1340.1748485283562760.3496970567125520.825151471643724
1350.1793229953092490.3586459906184990.82067700469075
1360.2434706011540190.4869412023080390.75652939884598
1370.4696069842609510.9392139685219030.530393015739049
1380.4048760874728060.8097521749456130.595123912527194
1390.4398938891626540.8797877783253090.560106110837346
1400.3632270407123610.7264540814247220.636772959287639
1410.3640468047334020.7280936094668040.635953195266598
1420.2927861658392270.5855723316784550.707213834160773
1430.4099458852731870.8198917705463730.590054114726813
1440.3448256306128270.6896512612256550.655174369387173
1450.2773296450337960.5546592900675920.722670354966204
1460.2090038764254120.4180077528508240.790996123574588
1470.167255507590370.334511015180740.83274449240963
1480.4459226119914540.8918452239829070.554077388008546
1490.3235135114717470.6470270229434940.676486488528253
1500.5231252576828980.9537494846342030.476874742317102






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0215827338129496NOK
5% type I error level200.143884892086331NOK
10% type I error level340.244604316546763NOK

\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 & 3 & 0.0215827338129496 & NOK \tabularnewline
5% type I error level & 20 & 0.143884892086331 & NOK \tabularnewline
10% type I error level & 34 & 0.244604316546763 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154253&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]3[/C][C]0.0215827338129496[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.143884892086331[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.244604316546763[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154253&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154253&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 level30.0215827338129496NOK
5% type I error level200.143884892086331NOK
10% type I error level340.244604316546763NOK


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 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}