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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 22:51:32 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290552638grcyesj6i1pc9h5.htm/, Retrieved Thu, 28 Mar 2024 13:39:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99688, Retrieved Thu, 28 Mar 2024 13:39:06 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Multiple Regressi...] [2010-11-23 22:51:32] [278a0539dc236556c5f30b5bc56ff9eb] [Current]
-   P       [Multiple Regression] [Multiple Regressi...] [2010-11-23 23:09:01] [b8e188bcc949964bed729335b3416734]
-    D        [Multiple Regression] [ws7] [2011-11-23 16:14:14] [27e29806e0b1d1351a97bc4ee4116294]
-    D        [Multiple Regression] [ws 7] [2011-11-23 16:40:30] [27e29806e0b1d1351a97bc4ee4116294]
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Dataseries X:
7	7	1	7	7	1	7	7	1
5	6	1	5	5	1	5	5	1
6	6	2	5	6	1	4	5	1
4	5	2	5	6	2	5	6	2
5	6	2	5	6	2	5	6	1
6	7	1	7	5	1	6	7	1
7	7	1	7	7	1	7	6	2
6	7	1	5	6	1	5	7	1
6	7	1	3	7	2	7	7	1
6	6	1	6	6	1	5	6	1
5	4	1	7	7	1	4	7	2
5	6	1	6	7	1	6	7	2
4	6	1	5	6	1	4	5	1
6	7	1	3	6	1	6	6	1
6	6	1	7	7	1	7	7	1
5	6	2	5	6	3	6	6	2
3	4	1	7	7	1	4	7	1
7	7	1	7	7	1	6	7	2
3	7	1	7	7	1	6	7	2
5	6	2	6	7	2	6	6	1
3	3	1	5	5	1	4	4	2
5	7	1	7	7	NA	5	7	1
2	5	1	4	5	1	2	6	1
6	7	1	7	6	1	6	7	2
3	6	1	7	7	2	5	7	1
6	5	1	7	6	1	6	5	1
6	5	1	7	6	1	6	5	1
5	6	1	3	6	1	5	7	1
5	5	1	7	6	1	5	6	2
7	6	1	5	6	1	5	6	1
6	6	1	7	6	1	6	6	2
5	5	1	5	5	1	6	6	1
5	4	4	5	3	6	5	1	1
4	5	3	4	3	3	4	5	2
4	4	1	5	5	1	6	7	2
6	6	2	6	6	2	5	5	2
5	6	1	7	7	1	5	7	1
5	7	1	5	7	1	5	5	2
7	7	1	7	7	1	7	7	2
5	7	1	7	6	1	5	6	1
5	7	1	6	7	1	5	7	1
6	5	1	6	7	1	7	6	1
5	6	2	7	6	2	5	6	1
6	6	1	7	6	2	7	5	2
7	3	1	6	5	1	6	6	2
5	6	4	6	6	4	3	6	1
5	5	1	4	6	2	4	5	2
5	4	3	7	7	3	6	7	2
6	6	2	5	6	2	5	6	1
2	6	3	6	7	2	4	7	2
4	6	2	5	6	2	4	5	1
4	5	1	3	5	1	6	5	1
6	6	2	7	7	1	5	7	1
3	5	1	6	4	1	4	3	1
6	7	1	6	7	1	6	6	2
6	6	1	5	5	2	5	6	1
5	6	1	5	6	1	5	5	1
6	7	1	7	7	1	6	6	1
1	4	1	7	7	1	6	6	2
5	3	2	7	7	1	6	7	2
7	4	1	6	7	1	5	7	1
4	4	3	6	6	1	5	6	1
5	5	1	7	6	1	5	5	1
6	4	1	7	6	1	5	4	2
4	6	4	5	4	4	4	5	1
6	7	1	7	6	1	5	6	2
6	6	1	6	6	2	6	6	2
5	6	1	5	7	1	6	7	2
5	6	1	6	7	1	5	6	1
3	6	1	5	7	2	5	7	1
5	7	1	5	7	1	5	7	1
6	6	1	6	7	1	6	7	2
5	6	1	6	6	2	6	6	2
6	6	1	6	5	3	6	5	1
6	7	1	7	7	2	6	7	1
4	5	2	6	5	2	4	5	1
4	4	2	5	5	2	4	5	1
6	7	1	7	7	2	5	6	2
7	7	1	7	7	1	6	7	2
4	6	1	6	2	1	3	3	2
5	7	1	7	6	1	7	4	2
6	6	1	6	6	1	5	5	1
6	5	1	6	6	1	6	6	1
5	7	1	7	6	1	6	6	1
3	6	2	6	5	2	5	6	2
7	5	1	7	6	1	6	6	2
6	6	1	7	7	2	6	7	1
4	5	4	5	5	3	4	7	1
4	7	3	3	7	2	6	7	1
5	6	2	6	6	2	5	7	1
3	2	1	6	5	1	4	2	1
7	5	1	5	6	1	7	5	2
6	7	1	6	7	3	6	6	1
6	7	1	6	7	1	6	6	1
4	7	2	6	6	1	4	6	1
5	7	1	7	7	1	5	7	1
6	6	1	6	6	1	6	5	2
5	5	2	6	5	1	5	5	1
6	6	1	6	5	1	4	6	2
6	6	3	7	6	2	7	6	2
4	5	1	6	6	1	6	7	1
5	7	1	5	6	1	5	4	2
6	5	2	5	6	2	6	6	1
5	6	1	6	6	1	6	6	1
5	5	1	6	5	1	5	5	2
4	5	2	6	5	3	5	5	1
4	5	2	5	5	2	5	5	2
6	5	1	6	7	2	5	6	1
5	7	1	4	7	1	7	7	1
6	6	1	6	6	1	6	6	1
5	7	1	7	7	1	7	7	1
6	6	1	7	7	2	6	7	1
5	5	1	5	4	1	5	5	1
4	5	2	5	5	2	4	6	1
6	7	1	7	7	1	6	7	1
4	6	1	3	7	2	4	7	2
5	5	2	7	7	2	3	7	1
5	7	2	5	6	4	5	7	1
6	4	1	7	5	2	5	5	2
3	3	2	5	7	1	5	7	1
5	7	2	3	NA	NA	5	7	1
4	5	2	6	6	2	5	6	1
5	6	2	5	6	1	5	5	1
5	4	4	4	3	3	3	5	1
7	7	1	7	7	1	7	7	1
5	7	2	6	6	1	6	7	2
7	5	1	7	7	1	6	6	1
5	7	1	2	6	2	4	6	1
4	3	1	5	5	1	4	6	2
6	6	1	6	6	1	6	6	2
4	5	3	6	6	2	4	6	1
4	5	2	6	7	2	6	6	2
4	6	1	2	6	7	2	5	2
4	5	1	6	7	1	5	6	2
6	6	1	7	6	2	5	7	2
6	6	2	4	6	3	6	6	1
5	7	5	7	7	3	5	7	1
3	5	1	7	7	4	4	7	2
6	7	1	6	7	1	6	6	2
5	6	2	6	7	2	6	6	1
4	6	1	2	6	2	5	7	1
5	7	2	7	7	2	5	5	1
2	7	1	7	7	2	2	5	1
5	5	1	5	6	1	6	6	2
7	7	1	5	7	5	6	7	1
4	5	1	6	6	1	5	5	1
4	6	2	5	7	3	6	7	2
7	7	1	6	6	2	7	5	2
6	6	1	6	5	1	5	6	2
5	5	2	6	4	3	5	5	2
5	6	1	5	7	2	5	7	1
5	7	1	6	7	2	6	7	1
7	6	1	7	5	1	7	5	1
6	7	1	7	7	1	7	7	2
6	7	1	6	6	1	6	6	2
5	6	2	6	5	1	5	6	2
2	6	2	6	6	2	6	6	1
4	4	4	7	7	4	4	7	1
6	7	1	6	7	3	6	6	1
5	6	1	5	6	1	6	5	1
5	4	1	5	5	1	4	5	2
5	5	1	5	6	1	5	5	1
4	6	1	4	5	1	5	7	1
4	5	5	4	6	4	5	7	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99688&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99688&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99688&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
Q1_7[t] = + 3.06528780326958 + 0.164175161185608Q1_2[t] -0.0765245287154905Q1_3[t] + 0.243401719833989Q1_5[t] + 0.419839287888058Q1_8[t] -0.303558928060235Q1_12[t] + 0.085748375843493Q1_16[t] -0.161019595095292Q1_22[t] + 0.306324782495529GENDER[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Q1_7[t] =  +  3.06528780326958 +  0.164175161185608Q1_2[t] -0.0765245287154905Q1_3[t] +  0.243401719833989Q1_5[t] +  0.419839287888058Q1_8[t] -0.303558928060235Q1_12[t] +  0.085748375843493Q1_16[t] -0.161019595095292Q1_22[t] +  0.306324782495529GENDER[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99688&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Q1_7[t] =  +  3.06528780326958 +  0.164175161185608Q1_2[t] -0.0765245287154905Q1_3[t] +  0.243401719833989Q1_5[t] +  0.419839287888058Q1_8[t] -0.303558928060235Q1_12[t] +  0.085748375843493Q1_16[t] -0.161019595095292Q1_22[t] +  0.306324782495529GENDER[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99688&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99688&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Q1_7[t] = + 3.06528780326958 + 0.164175161185608Q1_2[t] -0.0765245287154905Q1_3[t] + 0.243401719833989Q1_5[t] + 0.419839287888058Q1_8[t] -0.303558928060235Q1_12[t] + 0.085748375843493Q1_16[t] -0.161019595095292Q1_22[t] + 0.306324782495529GENDER[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.065287803269580.8160553.75620.0002450.000122
Q1_20.1641751611856080.0858251.91290.0576290.028814
Q1_3-0.07652452871549050.088532-0.86440.3887350.194367
Q1_50.2434017198339890.1271811.91380.057510.028755
Q1_80.4198392878880580.1198763.50230.0006050.000302
Q1_12-0.3035589280602350.103292-2.93880.0038050.001903
Q1_160.0857483758434930.1040480.82410.4111530.205577
Q1_22-0.1610195950952920.106772-1.50810.1336010.0668
GENDER0.3063247824955290.1761771.73870.0840930.042046

\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) & 3.06528780326958 & 0.816055 & 3.7562 & 0.000245 & 0.000122 \tabularnewline
Q1_2 & 0.164175161185608 & 0.085825 & 1.9129 & 0.057629 & 0.028814 \tabularnewline
Q1_3 & -0.0765245287154905 & 0.088532 & -0.8644 & 0.388735 & 0.194367 \tabularnewline
Q1_5 & 0.243401719833989 & 0.127181 & 1.9138 & 0.05751 & 0.028755 \tabularnewline
Q1_8 & 0.419839287888058 & 0.119876 & 3.5023 & 0.000605 & 0.000302 \tabularnewline
Q1_12 & -0.303558928060235 & 0.103292 & -2.9388 & 0.003805 & 0.001903 \tabularnewline
Q1_16 & 0.085748375843493 & 0.104048 & 0.8241 & 0.411153 & 0.205577 \tabularnewline
Q1_22 & -0.161019595095292 & 0.106772 & -1.5081 & 0.133601 & 0.0668 \tabularnewline
GENDER & 0.306324782495529 & 0.176177 & 1.7387 & 0.084093 & 0.042046 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99688&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]3.06528780326958[/C][C]0.816055[/C][C]3.7562[/C][C]0.000245[/C][C]0.000122[/C][/ROW]
[ROW][C]Q1_2[/C][C]0.164175161185608[/C][C]0.085825[/C][C]1.9129[/C][C]0.057629[/C][C]0.028814[/C][/ROW]
[ROW][C]Q1_3[/C][C]-0.0765245287154905[/C][C]0.088532[/C][C]-0.8644[/C][C]0.388735[/C][C]0.194367[/C][/ROW]
[ROW][C]Q1_5[/C][C]0.243401719833989[/C][C]0.127181[/C][C]1.9138[/C][C]0.05751[/C][C]0.028755[/C][/ROW]
[ROW][C]Q1_8[/C][C]0.419839287888058[/C][C]0.119876[/C][C]3.5023[/C][C]0.000605[/C][C]0.000302[/C][/ROW]
[ROW][C]Q1_12[/C][C]-0.303558928060235[/C][C]0.103292[/C][C]-2.9388[/C][C]0.003805[/C][C]0.001903[/C][/ROW]
[ROW][C]Q1_16[/C][C]0.085748375843493[/C][C]0.104048[/C][C]0.8241[/C][C]0.411153[/C][C]0.205577[/C][/ROW]
[ROW][C]Q1_22[/C][C]-0.161019595095292[/C][C]0.106772[/C][C]-1.5081[/C][C]0.133601[/C][C]0.0668[/C][/ROW]
[ROW][C]GENDER[/C][C]0.306324782495529[/C][C]0.176177[/C][C]1.7387[/C][C]0.084093[/C][C]0.042046[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99688&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99688&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.065287803269580.8160553.75620.0002450.000122
Q1_20.1641751611856080.0858251.91290.0576290.028814
Q1_3-0.07652452871549050.088532-0.86440.3887350.194367
Q1_50.2434017198339890.1271811.91380.057510.028755
Q1_80.4198392878880580.1198763.50230.0006050.000302
Q1_12-0.3035589280602350.103292-2.93880.0038050.001903
Q1_160.0857483758434930.1040480.82410.4111530.205577
Q1_22-0.1610195950952920.106772-1.50810.1336010.0668
GENDER0.3063247824955290.1761771.73870.0840930.042046







Multiple Linear Regression - Regression Statistics
Multiple R0.450959020796799
R-squared0.203364038438007
Adjusted R-squared0.161709870513197
F-TEST (value)4.8822014355226
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value2.21235956312515e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.06402597940044
Sum Squared Residuals173.219146580378

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.450959020796799 \tabularnewline
R-squared & 0.203364038438007 \tabularnewline
Adjusted R-squared & 0.161709870513197 \tabularnewline
F-TEST (value) & 4.8822014355226 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 2.21235956312515e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.06402597940044 \tabularnewline
Sum Squared Residuals & 173.219146580378 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99688&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.450959020796799[/C][/ROW]
[ROW][C]R-squared[/C][C]0.203364038438007[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.161709870513197[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.8822014355226[/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]2.21235956312515e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.06402597940044[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]173.219146580378[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99688&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99688&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.450959020796799
R-squared0.203364038438007
Adjusted R-squared0.161709870513197
F-TEST (value)4.8822014355226
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value2.21235956312515e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.06402597940044
Sum Squared Residuals173.219146580378







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
176.33698628528350.663013714716503
255.39602435435525-0.396024354355251
356.13769214741941-1.13769214741941
455.81336098894718-0.813360988947182
555.59468683892177-0.59468683892177
675.247384172478281.75261582752172
776.804330662874320.195669337125685
855.58147508452284-0.581475084522841
935.86925219603765-2.86925219603765
1065.819019208333620.180980791666376
1176.28728920402380.7127107959762
1266.3057368982798-0.305736898279805
1355.56594010521421-0.565940105214208
1435.82824305546163-2.82824305546163
1576.249335652813380.750664347186624
1655.68320106920056-0.683201069200558
1775.652614099157051.34738590084295
1876.557562691935530.44243730806447
1975.90086204719311.0991379528069
2066.10027450265332-0.100274502653322
2155.67884361987783-0.678843619877834
2277.56175867688814-0.561758676888145
2342.973548242861861.02645175713814
2475.281754489509331.71824551049067
2576.14231170798790.8576882920121
2676.14231170798790.8576882920121
2779.49382445205272-2.49382445205272
2832.037693358359040.962306641640964
2977.98319436951923-0.983194369519232
3054.211092366672650.788907633327353
3177.39727766381894-0.397277663818941
3254.565883735592080.434116264407923
3355.56895713630856-0.568957136308561
3444.45493221874906-0.454932218749061
3555.2262063776982-0.2262063776982
3664.913663739940781.08633626005922
3778.4655031839114-1.46550318391141
3854.643311067779020.356688932220977
3975.578319518432531.42168048156747
4076.83713921122530.162860788774708
4166.48687977662416-0.486879776624159
4264.594686838921771.40531316107823
4376.15430140955120.845698590448803
4477.18500182611667-0.185001826116668
4565.302875670782290.697124329217706
4667.8094056495506-1.8094056495506
4743.33847153925830.661528460741705
4877.75886200010738-0.758862000107379
4954.824959174643740.175040825356262
5066.50578289698796-0.505782896987962
5157.39412209772863-2.39412209772863
5232.321240620960380.67875937903962
5375.960650087158561.03934991284144
5466.55440712584521-0.554407125845215
5566.09562099238533-0.0956209923853308
5655.81586364224331-0.815863642243308
5754.248082343349680.751917656650315
5875.963104906063651.03689509393635
5976.778712204260270.221287795739735
6077.39506311974298-0.39506311974298
6166.13052138306137-0.130521383061368
6264.89238817095881.1076118290412
6376.600432238450720.399567761549281
6476.545789904759350.454210095240646
6554.048819462113660.951180537886337
6676.907533438612410.0924665613875884
6767.3057368982798-1.3057368982798
6855.07468333503607-0.0746833350360746
6966.28175448950933-0.281754489509331
7055.83713921122529-0.837139211225291
7155.46991205946541-0.469912059465413
7265.74335827742680.256641722573196
7365.038830035263880.961169964736119
7464.783503820194161.21649617980584
7576.16246813781540.837531862184606
7666.23899266653088-0.238992666530884
7754.165099821941490.834900178058513
7876.557562691935530.44243730806447
7975.429198550504591.57080144949541
8065.378180242805620.621819757194375
8176.980038803428920.0199611965710835
8265.981292112892610.0187078871073924
8364.664067894276021.33593210572398
8476.152822011158030.847177988841974
8565.451792056573740.548207943426255
8675.860028348909651.13997165109035
8777.02367345923255-0.023673459232553
8857.94195693749092-2.94195693749092
8932.433667243826480.566332756173522
9065.771082556288380.22891744371162
9167.69856002751253-1.69856002751253
9254.640964487229220.359035512770785
9366.24808234334968-0.248082343349685
9465.571797701237410.428202298762586
9564.837139211225291.16286078877471
9677.37211196176794-0.372111961767939
9765.715950602904730.28404939709527
9865.61975632709760.380243672902398
9965.480085254123880.519914745876117
10076.49192219542610.508077804573901
10167.20668349111864-1.20668349111864
10255.92113490466636-0.921134904666362
10354.740592422991510.259407577008491
10465.778873665566270.22112633443373
10564.944657585598651.05534241440135
10666.55454129615442-0.554541296154416
10755.01182409687694-0.0118240968769383
10868.00863596291228-2.00863596291228
10943.904767584177120.095232415822883
11065.008635962912280.991364037087722
11175.860028348909651.13997165109035
11277.05270959518268-0.052709595182682
11355.0014485427201-0.00144854272010161
11454.087062748254390.912937251745607
11579.66650605734698-2.66650605734698
11631.758534308743041.24146569125696
11776.750024858990520.249975141009482
11853.716014427407131.28398557259287
11978.05828872355003-1.05828872355003
12054.507036206451650.492963793548347
12134.0592653620773-1.0592653620773
12266.66098538770463-0.660985387704627
12354.336986285283490.663013714716506
12444.05277480151025-0.0527748015102448
12576.565306561966270.434693438033726
12669.1890122145288-3.1890122145288
12777.52097959087286-0.520979590872858
12822.21109236667265-0.211092366672646
12954.664689550442150.335310449557851
13066.31894865267873-0.318948652678734
13167.87941456766134-1.87941456766134
13266.29335748506149-0.293357485061486
13320.6607654676736261.33923453232637
13467.54105144789064-1.54105144789064
13576.203628234440780.79637176555922
13641.971737568756392.02826243124361
13777.55440712584521-0.554407125845215
13877.10027450265332-0.100274502653322
13969.02609036280688-3.02609036280688
14065.099021193189630.900978806810369
14120.1058488622683391.89415113773166
14278.12344173420253-1.12344173420253
14377.03700219719906-0.0370021971990613
14454.728213009773190.271786990226809
14555.77784560080772-0.777845600807716
14666.24195204202131-0.241952042021315
14754.70550470294110.294495297058905
14864.995318241391731.00468175860827
14966.61010481188055-0.610104811880548
15065.619328659008550.38067134099145
15153.895871428413451.10412857158655
15265.479135906593410.520864093406585
15377.13456783795716-0.134567837957156
15476.784731261589480.215268738410524
15565.187909731208440.81209026879156
15664.636317635663931.36368236433607
15765.640964487229220.359035512770785
15877.9016120180868-0.901612018086801
15966.76964981843827-0.769649818438267
16055.8923881709588-0.892388170958799
16155.90981000297906-0.909810002979057
16256.46910391473786-1.46910391473786
1634NANA
1644NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7 & 6.3369862852835 & 0.663013714716503 \tabularnewline
2 & 5 & 5.39602435435525 & -0.396024354355251 \tabularnewline
3 & 5 & 6.13769214741941 & -1.13769214741941 \tabularnewline
4 & 5 & 5.81336098894718 & -0.813360988947182 \tabularnewline
5 & 5 & 5.59468683892177 & -0.59468683892177 \tabularnewline
6 & 7 & 5.24738417247828 & 1.75261582752172 \tabularnewline
7 & 7 & 6.80433066287432 & 0.195669337125685 \tabularnewline
8 & 5 & 5.58147508452284 & -0.581475084522841 \tabularnewline
9 & 3 & 5.86925219603765 & -2.86925219603765 \tabularnewline
10 & 6 & 5.81901920833362 & 0.180980791666376 \tabularnewline
11 & 7 & 6.2872892040238 & 0.7127107959762 \tabularnewline
12 & 6 & 6.3057368982798 & -0.305736898279805 \tabularnewline
13 & 5 & 5.56594010521421 & -0.565940105214208 \tabularnewline
14 & 3 & 5.82824305546163 & -2.82824305546163 \tabularnewline
15 & 7 & 6.24933565281338 & 0.750664347186624 \tabularnewline
16 & 5 & 5.68320106920056 & -0.683201069200558 \tabularnewline
17 & 7 & 5.65261409915705 & 1.34738590084295 \tabularnewline
18 & 7 & 6.55756269193553 & 0.44243730806447 \tabularnewline
19 & 7 & 5.9008620471931 & 1.0991379528069 \tabularnewline
20 & 6 & 6.10027450265332 & -0.100274502653322 \tabularnewline
21 & 5 & 5.67884361987783 & -0.678843619877834 \tabularnewline
22 & 7 & 7.56175867688814 & -0.561758676888145 \tabularnewline
23 & 4 & 2.97354824286186 & 1.02645175713814 \tabularnewline
24 & 7 & 5.28175448950933 & 1.71824551049067 \tabularnewline
25 & 7 & 6.1423117079879 & 0.8576882920121 \tabularnewline
26 & 7 & 6.1423117079879 & 0.8576882920121 \tabularnewline
27 & 7 & 9.49382445205272 & -2.49382445205272 \tabularnewline
28 & 3 & 2.03769335835904 & 0.962306641640964 \tabularnewline
29 & 7 & 7.98319436951923 & -0.983194369519232 \tabularnewline
30 & 5 & 4.21109236667265 & 0.788907633327353 \tabularnewline
31 & 7 & 7.39727766381894 & -0.397277663818941 \tabularnewline
32 & 5 & 4.56588373559208 & 0.434116264407923 \tabularnewline
33 & 5 & 5.56895713630856 & -0.568957136308561 \tabularnewline
34 & 4 & 4.45493221874906 & -0.454932218749061 \tabularnewline
35 & 5 & 5.2262063776982 & -0.2262063776982 \tabularnewline
36 & 6 & 4.91366373994078 & 1.08633626005922 \tabularnewline
37 & 7 & 8.4655031839114 & -1.46550318391141 \tabularnewline
38 & 5 & 4.64331106777902 & 0.356688932220977 \tabularnewline
39 & 7 & 5.57831951843253 & 1.42168048156747 \tabularnewline
40 & 7 & 6.8371392112253 & 0.162860788774708 \tabularnewline
41 & 6 & 6.48687977662416 & -0.486879776624159 \tabularnewline
42 & 6 & 4.59468683892177 & 1.40531316107823 \tabularnewline
43 & 7 & 6.1543014095512 & 0.845698590448803 \tabularnewline
44 & 7 & 7.18500182611667 & -0.185001826116668 \tabularnewline
45 & 6 & 5.30287567078229 & 0.697124329217706 \tabularnewline
46 & 6 & 7.8094056495506 & -1.8094056495506 \tabularnewline
47 & 4 & 3.3384715392583 & 0.661528460741705 \tabularnewline
48 & 7 & 7.75886200010738 & -0.758862000107379 \tabularnewline
49 & 5 & 4.82495917464374 & 0.175040825356262 \tabularnewline
50 & 6 & 6.50578289698796 & -0.505782896987962 \tabularnewline
51 & 5 & 7.39412209772863 & -2.39412209772863 \tabularnewline
52 & 3 & 2.32124062096038 & 0.67875937903962 \tabularnewline
53 & 7 & 5.96065008715856 & 1.03934991284144 \tabularnewline
54 & 6 & 6.55440712584521 & -0.554407125845215 \tabularnewline
55 & 6 & 6.09562099238533 & -0.0956209923853308 \tabularnewline
56 & 5 & 5.81586364224331 & -0.815863642243308 \tabularnewline
57 & 5 & 4.24808234334968 & 0.751917656650315 \tabularnewline
58 & 7 & 5.96310490606365 & 1.03689509393635 \tabularnewline
59 & 7 & 6.77871220426027 & 0.221287795739735 \tabularnewline
60 & 7 & 7.39506311974298 & -0.39506311974298 \tabularnewline
61 & 6 & 6.13052138306137 & -0.130521383061368 \tabularnewline
62 & 6 & 4.8923881709588 & 1.1076118290412 \tabularnewline
63 & 7 & 6.60043223845072 & 0.399567761549281 \tabularnewline
64 & 7 & 6.54578990475935 & 0.454210095240646 \tabularnewline
65 & 5 & 4.04881946211366 & 0.951180537886337 \tabularnewline
66 & 7 & 6.90753343861241 & 0.0924665613875884 \tabularnewline
67 & 6 & 7.3057368982798 & -1.3057368982798 \tabularnewline
68 & 5 & 5.07468333503607 & -0.0746833350360746 \tabularnewline
69 & 6 & 6.28175448950933 & -0.281754489509331 \tabularnewline
70 & 5 & 5.83713921122529 & -0.837139211225291 \tabularnewline
71 & 5 & 5.46991205946541 & -0.469912059465413 \tabularnewline
72 & 6 & 5.7433582774268 & 0.256641722573196 \tabularnewline
73 & 6 & 5.03883003526388 & 0.961169964736119 \tabularnewline
74 & 6 & 4.78350382019416 & 1.21649617980584 \tabularnewline
75 & 7 & 6.1624681378154 & 0.837531862184606 \tabularnewline
76 & 6 & 6.23899266653088 & -0.238992666530884 \tabularnewline
77 & 5 & 4.16509982194149 & 0.834900178058513 \tabularnewline
78 & 7 & 6.55756269193553 & 0.44243730806447 \tabularnewline
79 & 7 & 5.42919855050459 & 1.57080144949541 \tabularnewline
80 & 6 & 5.37818024280562 & 0.621819757194375 \tabularnewline
81 & 7 & 6.98003880342892 & 0.0199611965710835 \tabularnewline
82 & 6 & 5.98129211289261 & 0.0187078871073924 \tabularnewline
83 & 6 & 4.66406789427602 & 1.33593210572398 \tabularnewline
84 & 7 & 6.15282201115803 & 0.847177988841974 \tabularnewline
85 & 6 & 5.45179205657374 & 0.548207943426255 \tabularnewline
86 & 7 & 5.86002834890965 & 1.13997165109035 \tabularnewline
87 & 7 & 7.02367345923255 & -0.023673459232553 \tabularnewline
88 & 5 & 7.94195693749092 & -2.94195693749092 \tabularnewline
89 & 3 & 2.43366724382648 & 0.566332756173522 \tabularnewline
90 & 6 & 5.77108255628838 & 0.22891744371162 \tabularnewline
91 & 6 & 7.69856002751253 & -1.69856002751253 \tabularnewline
92 & 5 & 4.64096448722922 & 0.359035512770785 \tabularnewline
93 & 6 & 6.24808234334968 & -0.248082343349685 \tabularnewline
94 & 6 & 5.57179770123741 & 0.428202298762586 \tabularnewline
95 & 6 & 4.83713921122529 & 1.16286078877471 \tabularnewline
96 & 7 & 7.37211196176794 & -0.372111961767939 \tabularnewline
97 & 6 & 5.71595060290473 & 0.28404939709527 \tabularnewline
98 & 6 & 5.6197563270976 & 0.380243672902398 \tabularnewline
99 & 6 & 5.48008525412388 & 0.519914745876117 \tabularnewline
100 & 7 & 6.4919221954261 & 0.508077804573901 \tabularnewline
101 & 6 & 7.20668349111864 & -1.20668349111864 \tabularnewline
102 & 5 & 5.92113490466636 & -0.921134904666362 \tabularnewline
103 & 5 & 4.74059242299151 & 0.259407577008491 \tabularnewline
104 & 6 & 5.77887366556627 & 0.22112633443373 \tabularnewline
105 & 6 & 4.94465758559865 & 1.05534241440135 \tabularnewline
106 & 6 & 6.55454129615442 & -0.554541296154416 \tabularnewline
107 & 5 & 5.01182409687694 & -0.0118240968769383 \tabularnewline
108 & 6 & 8.00863596291228 & -2.00863596291228 \tabularnewline
109 & 4 & 3.90476758417712 & 0.095232415822883 \tabularnewline
110 & 6 & 5.00863596291228 & 0.991364037087722 \tabularnewline
111 & 7 & 5.86002834890965 & 1.13997165109035 \tabularnewline
112 & 7 & 7.05270959518268 & -0.052709595182682 \tabularnewline
113 & 5 & 5.0014485427201 & -0.00144854272010161 \tabularnewline
114 & 5 & 4.08706274825439 & 0.912937251745607 \tabularnewline
115 & 7 & 9.66650605734698 & -2.66650605734698 \tabularnewline
116 & 3 & 1.75853430874304 & 1.24146569125696 \tabularnewline
117 & 7 & 6.75002485899052 & 0.249975141009482 \tabularnewline
118 & 5 & 3.71601442740713 & 1.28398557259287 \tabularnewline
119 & 7 & 8.05828872355003 & -1.05828872355003 \tabularnewline
120 & 5 & 4.50703620645165 & 0.492963793548347 \tabularnewline
121 & 3 & 4.0592653620773 & -1.0592653620773 \tabularnewline
122 & 6 & 6.66098538770463 & -0.660985387704627 \tabularnewline
123 & 5 & 4.33698628528349 & 0.663013714716506 \tabularnewline
124 & 4 & 4.05277480151025 & -0.0527748015102448 \tabularnewline
125 & 7 & 6.56530656196627 & 0.434693438033726 \tabularnewline
126 & 6 & 9.1890122145288 & -3.1890122145288 \tabularnewline
127 & 7 & 7.52097959087286 & -0.520979590872858 \tabularnewline
128 & 2 & 2.21109236667265 & -0.211092366672646 \tabularnewline
129 & 5 & 4.66468955044215 & 0.335310449557851 \tabularnewline
130 & 6 & 6.31894865267873 & -0.318948652678734 \tabularnewline
131 & 6 & 7.87941456766134 & -1.87941456766134 \tabularnewline
132 & 6 & 6.29335748506149 & -0.293357485061486 \tabularnewline
133 & 2 & 0.660765467673626 & 1.33923453232637 \tabularnewline
134 & 6 & 7.54105144789064 & -1.54105144789064 \tabularnewline
135 & 7 & 6.20362823444078 & 0.79637176555922 \tabularnewline
136 & 4 & 1.97173756875639 & 2.02826243124361 \tabularnewline
137 & 7 & 7.55440712584521 & -0.554407125845215 \tabularnewline
138 & 7 & 7.10027450265332 & -0.100274502653322 \tabularnewline
139 & 6 & 9.02609036280688 & -3.02609036280688 \tabularnewline
140 & 6 & 5.09902119318963 & 0.900978806810369 \tabularnewline
141 & 2 & 0.105848862268339 & 1.89415113773166 \tabularnewline
142 & 7 & 8.12344173420253 & -1.12344173420253 \tabularnewline
143 & 7 & 7.03700219719906 & -0.0370021971990613 \tabularnewline
144 & 5 & 4.72821300977319 & 0.271786990226809 \tabularnewline
145 & 5 & 5.77784560080772 & -0.777845600807716 \tabularnewline
146 & 6 & 6.24195204202131 & -0.241952042021315 \tabularnewline
147 & 5 & 4.7055047029411 & 0.294495297058905 \tabularnewline
148 & 6 & 4.99531824139173 & 1.00468175860827 \tabularnewline
149 & 6 & 6.61010481188055 & -0.610104811880548 \tabularnewline
150 & 6 & 5.61932865900855 & 0.38067134099145 \tabularnewline
151 & 5 & 3.89587142841345 & 1.10412857158655 \tabularnewline
152 & 6 & 5.47913590659341 & 0.520864093406585 \tabularnewline
153 & 7 & 7.13456783795716 & -0.134567837957156 \tabularnewline
154 & 7 & 6.78473126158948 & 0.215268738410524 \tabularnewline
155 & 6 & 5.18790973120844 & 0.81209026879156 \tabularnewline
156 & 6 & 4.63631763566393 & 1.36368236433607 \tabularnewline
157 & 6 & 5.64096448722922 & 0.359035512770785 \tabularnewline
158 & 7 & 7.9016120180868 & -0.901612018086801 \tabularnewline
159 & 6 & 6.76964981843827 & -0.769649818438267 \tabularnewline
160 & 5 & 5.8923881709588 & -0.892388170958799 \tabularnewline
161 & 5 & 5.90981000297906 & -0.909810002979057 \tabularnewline
162 & 5 & 6.46910391473786 & -1.46910391473786 \tabularnewline
163 & 4 & NA & NA \tabularnewline
164 & 4 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99688&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]7[/C][C]6.3369862852835[/C][C]0.663013714716503[/C][/ROW]
[ROW][C]2[/C][C]5[/C][C]5.39602435435525[/C][C]-0.396024354355251[/C][/ROW]
[ROW][C]3[/C][C]5[/C][C]6.13769214741941[/C][C]-1.13769214741941[/C][/ROW]
[ROW][C]4[/C][C]5[/C][C]5.81336098894718[/C][C]-0.813360988947182[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]5.59468683892177[/C][C]-0.59468683892177[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]5.24738417247828[/C][C]1.75261582752172[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]6.80433066287432[/C][C]0.195669337125685[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]5.58147508452284[/C][C]-0.581475084522841[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]5.86925219603765[/C][C]-2.86925219603765[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]5.81901920833362[/C][C]0.180980791666376[/C][/ROW]
[ROW][C]11[/C][C]7[/C][C]6.2872892040238[/C][C]0.7127107959762[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]6.3057368982798[/C][C]-0.305736898279805[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]5.56594010521421[/C][C]-0.565940105214208[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]5.82824305546163[/C][C]-2.82824305546163[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]6.24933565281338[/C][C]0.750664347186624[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]5.68320106920056[/C][C]-0.683201069200558[/C][/ROW]
[ROW][C]17[/C][C]7[/C][C]5.65261409915705[/C][C]1.34738590084295[/C][/ROW]
[ROW][C]18[/C][C]7[/C][C]6.55756269193553[/C][C]0.44243730806447[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]5.9008620471931[/C][C]1.0991379528069[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]6.10027450265332[/C][C]-0.100274502653322[/C][/ROW]
[ROW][C]21[/C][C]5[/C][C]5.67884361987783[/C][C]-0.678843619877834[/C][/ROW]
[ROW][C]22[/C][C]7[/C][C]7.56175867688814[/C][C]-0.561758676888145[/C][/ROW]
[ROW][C]23[/C][C]4[/C][C]2.97354824286186[/C][C]1.02645175713814[/C][/ROW]
[ROW][C]24[/C][C]7[/C][C]5.28175448950933[/C][C]1.71824551049067[/C][/ROW]
[ROW][C]25[/C][C]7[/C][C]6.1423117079879[/C][C]0.8576882920121[/C][/ROW]
[ROW][C]26[/C][C]7[/C][C]6.1423117079879[/C][C]0.8576882920121[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]9.49382445205272[/C][C]-2.49382445205272[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]2.03769335835904[/C][C]0.962306641640964[/C][/ROW]
[ROW][C]29[/C][C]7[/C][C]7.98319436951923[/C][C]-0.983194369519232[/C][/ROW]
[ROW][C]30[/C][C]5[/C][C]4.21109236667265[/C][C]0.788907633327353[/C][/ROW]
[ROW][C]31[/C][C]7[/C][C]7.39727766381894[/C][C]-0.397277663818941[/C][/ROW]
[ROW][C]32[/C][C]5[/C][C]4.56588373559208[/C][C]0.434116264407923[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]5.56895713630856[/C][C]-0.568957136308561[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]4.45493221874906[/C][C]-0.454932218749061[/C][/ROW]
[ROW][C]35[/C][C]5[/C][C]5.2262063776982[/C][C]-0.2262063776982[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]4.91366373994078[/C][C]1.08633626005922[/C][/ROW]
[ROW][C]37[/C][C]7[/C][C]8.4655031839114[/C][C]-1.46550318391141[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]4.64331106777902[/C][C]0.356688932220977[/C][/ROW]
[ROW][C]39[/C][C]7[/C][C]5.57831951843253[/C][C]1.42168048156747[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]6.8371392112253[/C][C]0.162860788774708[/C][/ROW]
[ROW][C]41[/C][C]6[/C][C]6.48687977662416[/C][C]-0.486879776624159[/C][/ROW]
[ROW][C]42[/C][C]6[/C][C]4.59468683892177[/C][C]1.40531316107823[/C][/ROW]
[ROW][C]43[/C][C]7[/C][C]6.1543014095512[/C][C]0.845698590448803[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]7.18500182611667[/C][C]-0.185001826116668[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]5.30287567078229[/C][C]0.697124329217706[/C][/ROW]
[ROW][C]46[/C][C]6[/C][C]7.8094056495506[/C][C]-1.8094056495506[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]3.3384715392583[/C][C]0.661528460741705[/C][/ROW]
[ROW][C]48[/C][C]7[/C][C]7.75886200010738[/C][C]-0.758862000107379[/C][/ROW]
[ROW][C]49[/C][C]5[/C][C]4.82495917464374[/C][C]0.175040825356262[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]6.50578289698796[/C][C]-0.505782896987962[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]7.39412209772863[/C][C]-2.39412209772863[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]2.32124062096038[/C][C]0.67875937903962[/C][/ROW]
[ROW][C]53[/C][C]7[/C][C]5.96065008715856[/C][C]1.03934991284144[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]6.55440712584521[/C][C]-0.554407125845215[/C][/ROW]
[ROW][C]55[/C][C]6[/C][C]6.09562099238533[/C][C]-0.0956209923853308[/C][/ROW]
[ROW][C]56[/C][C]5[/C][C]5.81586364224331[/C][C]-0.815863642243308[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]4.24808234334968[/C][C]0.751917656650315[/C][/ROW]
[ROW][C]58[/C][C]7[/C][C]5.96310490606365[/C][C]1.03689509393635[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]6.77871220426027[/C][C]0.221287795739735[/C][/ROW]
[ROW][C]60[/C][C]7[/C][C]7.39506311974298[/C][C]-0.39506311974298[/C][/ROW]
[ROW][C]61[/C][C]6[/C][C]6.13052138306137[/C][C]-0.130521383061368[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]4.8923881709588[/C][C]1.1076118290412[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]6.60043223845072[/C][C]0.399567761549281[/C][/ROW]
[ROW][C]64[/C][C]7[/C][C]6.54578990475935[/C][C]0.454210095240646[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]4.04881946211366[/C][C]0.951180537886337[/C][/ROW]
[ROW][C]66[/C][C]7[/C][C]6.90753343861241[/C][C]0.0924665613875884[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]7.3057368982798[/C][C]-1.3057368982798[/C][/ROW]
[ROW][C]68[/C][C]5[/C][C]5.07468333503607[/C][C]-0.0746833350360746[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]6.28175448950933[/C][C]-0.281754489509331[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.83713921122529[/C][C]-0.837139211225291[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]5.46991205946541[/C][C]-0.469912059465413[/C][/ROW]
[ROW][C]72[/C][C]6[/C][C]5.7433582774268[/C][C]0.256641722573196[/C][/ROW]
[ROW][C]73[/C][C]6[/C][C]5.03883003526388[/C][C]0.961169964736119[/C][/ROW]
[ROW][C]74[/C][C]6[/C][C]4.78350382019416[/C][C]1.21649617980584[/C][/ROW]
[ROW][C]75[/C][C]7[/C][C]6.1624681378154[/C][C]0.837531862184606[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]6.23899266653088[/C][C]-0.238992666530884[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]4.16509982194149[/C][C]0.834900178058513[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]6.55756269193553[/C][C]0.44243730806447[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]5.42919855050459[/C][C]1.57080144949541[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]5.37818024280562[/C][C]0.621819757194375[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]6.98003880342892[/C][C]0.0199611965710835[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]5.98129211289261[/C][C]0.0187078871073924[/C][/ROW]
[ROW][C]83[/C][C]6[/C][C]4.66406789427602[/C][C]1.33593210572398[/C][/ROW]
[ROW][C]84[/C][C]7[/C][C]6.15282201115803[/C][C]0.847177988841974[/C][/ROW]
[ROW][C]85[/C][C]6[/C][C]5.45179205657374[/C][C]0.548207943426255[/C][/ROW]
[ROW][C]86[/C][C]7[/C][C]5.86002834890965[/C][C]1.13997165109035[/C][/ROW]
[ROW][C]87[/C][C]7[/C][C]7.02367345923255[/C][C]-0.023673459232553[/C][/ROW]
[ROW][C]88[/C][C]5[/C][C]7.94195693749092[/C][C]-2.94195693749092[/C][/ROW]
[ROW][C]89[/C][C]3[/C][C]2.43366724382648[/C][C]0.566332756173522[/C][/ROW]
[ROW][C]90[/C][C]6[/C][C]5.77108255628838[/C][C]0.22891744371162[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]7.69856002751253[/C][C]-1.69856002751253[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]4.64096448722922[/C][C]0.359035512770785[/C][/ROW]
[ROW][C]93[/C][C]6[/C][C]6.24808234334968[/C][C]-0.248082343349685[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]5.57179770123741[/C][C]0.428202298762586[/C][/ROW]
[ROW][C]95[/C][C]6[/C][C]4.83713921122529[/C][C]1.16286078877471[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]7.37211196176794[/C][C]-0.372111961767939[/C][/ROW]
[ROW][C]97[/C][C]6[/C][C]5.71595060290473[/C][C]0.28404939709527[/C][/ROW]
[ROW][C]98[/C][C]6[/C][C]5.6197563270976[/C][C]0.380243672902398[/C][/ROW]
[ROW][C]99[/C][C]6[/C][C]5.48008525412388[/C][C]0.519914745876117[/C][/ROW]
[ROW][C]100[/C][C]7[/C][C]6.4919221954261[/C][C]0.508077804573901[/C][/ROW]
[ROW][C]101[/C][C]6[/C][C]7.20668349111864[/C][C]-1.20668349111864[/C][/ROW]
[ROW][C]102[/C][C]5[/C][C]5.92113490466636[/C][C]-0.921134904666362[/C][/ROW]
[ROW][C]103[/C][C]5[/C][C]4.74059242299151[/C][C]0.259407577008491[/C][/ROW]
[ROW][C]104[/C][C]6[/C][C]5.77887366556627[/C][C]0.22112633443373[/C][/ROW]
[ROW][C]105[/C][C]6[/C][C]4.94465758559865[/C][C]1.05534241440135[/C][/ROW]
[ROW][C]106[/C][C]6[/C][C]6.55454129615442[/C][C]-0.554541296154416[/C][/ROW]
[ROW][C]107[/C][C]5[/C][C]5.01182409687694[/C][C]-0.0118240968769383[/C][/ROW]
[ROW][C]108[/C][C]6[/C][C]8.00863596291228[/C][C]-2.00863596291228[/C][/ROW]
[ROW][C]109[/C][C]4[/C][C]3.90476758417712[/C][C]0.095232415822883[/C][/ROW]
[ROW][C]110[/C][C]6[/C][C]5.00863596291228[/C][C]0.991364037087722[/C][/ROW]
[ROW][C]111[/C][C]7[/C][C]5.86002834890965[/C][C]1.13997165109035[/C][/ROW]
[ROW][C]112[/C][C]7[/C][C]7.05270959518268[/C][C]-0.052709595182682[/C][/ROW]
[ROW][C]113[/C][C]5[/C][C]5.0014485427201[/C][C]-0.00144854272010161[/C][/ROW]
[ROW][C]114[/C][C]5[/C][C]4.08706274825439[/C][C]0.912937251745607[/C][/ROW]
[ROW][C]115[/C][C]7[/C][C]9.66650605734698[/C][C]-2.66650605734698[/C][/ROW]
[ROW][C]116[/C][C]3[/C][C]1.75853430874304[/C][C]1.24146569125696[/C][/ROW]
[ROW][C]117[/C][C]7[/C][C]6.75002485899052[/C][C]0.249975141009482[/C][/ROW]
[ROW][C]118[/C][C]5[/C][C]3.71601442740713[/C][C]1.28398557259287[/C][/ROW]
[ROW][C]119[/C][C]7[/C][C]8.05828872355003[/C][C]-1.05828872355003[/C][/ROW]
[ROW][C]120[/C][C]5[/C][C]4.50703620645165[/C][C]0.492963793548347[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]4.0592653620773[/C][C]-1.0592653620773[/C][/ROW]
[ROW][C]122[/C][C]6[/C][C]6.66098538770463[/C][C]-0.660985387704627[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]4.33698628528349[/C][C]0.663013714716506[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]4.05277480151025[/C][C]-0.0527748015102448[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]6.56530656196627[/C][C]0.434693438033726[/C][/ROW]
[ROW][C]126[/C][C]6[/C][C]9.1890122145288[/C][C]-3.1890122145288[/C][/ROW]
[ROW][C]127[/C][C]7[/C][C]7.52097959087286[/C][C]-0.520979590872858[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]2.21109236667265[/C][C]-0.211092366672646[/C][/ROW]
[ROW][C]129[/C][C]5[/C][C]4.66468955044215[/C][C]0.335310449557851[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]6.31894865267873[/C][C]-0.318948652678734[/C][/ROW]
[ROW][C]131[/C][C]6[/C][C]7.87941456766134[/C][C]-1.87941456766134[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]6.29335748506149[/C][C]-0.293357485061486[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]0.660765467673626[/C][C]1.33923453232637[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]7.54105144789064[/C][C]-1.54105144789064[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]6.20362823444078[/C][C]0.79637176555922[/C][/ROW]
[ROW][C]136[/C][C]4[/C][C]1.97173756875639[/C][C]2.02826243124361[/C][/ROW]
[ROW][C]137[/C][C]7[/C][C]7.55440712584521[/C][C]-0.554407125845215[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]7.10027450265332[/C][C]-0.100274502653322[/C][/ROW]
[ROW][C]139[/C][C]6[/C][C]9.02609036280688[/C][C]-3.02609036280688[/C][/ROW]
[ROW][C]140[/C][C]6[/C][C]5.09902119318963[/C][C]0.900978806810369[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]0.105848862268339[/C][C]1.89415113773166[/C][/ROW]
[ROW][C]142[/C][C]7[/C][C]8.12344173420253[/C][C]-1.12344173420253[/C][/ROW]
[ROW][C]143[/C][C]7[/C][C]7.03700219719906[/C][C]-0.0370021971990613[/C][/ROW]
[ROW][C]144[/C][C]5[/C][C]4.72821300977319[/C][C]0.271786990226809[/C][/ROW]
[ROW][C]145[/C][C]5[/C][C]5.77784560080772[/C][C]-0.777845600807716[/C][/ROW]
[ROW][C]146[/C][C]6[/C][C]6.24195204202131[/C][C]-0.241952042021315[/C][/ROW]
[ROW][C]147[/C][C]5[/C][C]4.7055047029411[/C][C]0.294495297058905[/C][/ROW]
[ROW][C]148[/C][C]6[/C][C]4.99531824139173[/C][C]1.00468175860827[/C][/ROW]
[ROW][C]149[/C][C]6[/C][C]6.61010481188055[/C][C]-0.610104811880548[/C][/ROW]
[ROW][C]150[/C][C]6[/C][C]5.61932865900855[/C][C]0.38067134099145[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]3.89587142841345[/C][C]1.10412857158655[/C][/ROW]
[ROW][C]152[/C][C]6[/C][C]5.47913590659341[/C][C]0.520864093406585[/C][/ROW]
[ROW][C]153[/C][C]7[/C][C]7.13456783795716[/C][C]-0.134567837957156[/C][/ROW]
[ROW][C]154[/C][C]7[/C][C]6.78473126158948[/C][C]0.215268738410524[/C][/ROW]
[ROW][C]155[/C][C]6[/C][C]5.18790973120844[/C][C]0.81209026879156[/C][/ROW]
[ROW][C]156[/C][C]6[/C][C]4.63631763566393[/C][C]1.36368236433607[/C][/ROW]
[ROW][C]157[/C][C]6[/C][C]5.64096448722922[/C][C]0.359035512770785[/C][/ROW]
[ROW][C]158[/C][C]7[/C][C]7.9016120180868[/C][C]-0.901612018086801[/C][/ROW]
[ROW][C]159[/C][C]6[/C][C]6.76964981843827[/C][C]-0.769649818438267[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]5.8923881709588[/C][C]-0.892388170958799[/C][/ROW]
[ROW][C]161[/C][C]5[/C][C]5.90981000297906[/C][C]-0.909810002979057[/C][/ROW]
[ROW][C]162[/C][C]5[/C][C]6.46910391473786[/C][C]-1.46910391473786[/C][/ROW]
[ROW][C]163[/C][C]4[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]164[/C][C]4[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99688&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99688&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
176.33698628528350.663013714716503
255.39602435435525-0.396024354355251
356.13769214741941-1.13769214741941
455.81336098894718-0.813360988947182
555.59468683892177-0.59468683892177
675.247384172478281.75261582752172
776.804330662874320.195669337125685
855.58147508452284-0.581475084522841
935.86925219603765-2.86925219603765
1065.819019208333620.180980791666376
1176.28728920402380.7127107959762
1266.3057368982798-0.305736898279805
1355.56594010521421-0.565940105214208
1435.82824305546163-2.82824305546163
1576.249335652813380.750664347186624
1655.68320106920056-0.683201069200558
1775.652614099157051.34738590084295
1876.557562691935530.44243730806447
1975.90086204719311.0991379528069
2066.10027450265332-0.100274502653322
2155.67884361987783-0.678843619877834
2277.56175867688814-0.561758676888145
2342.973548242861861.02645175713814
2475.281754489509331.71824551049067
2576.14231170798790.8576882920121
2676.14231170798790.8576882920121
2779.49382445205272-2.49382445205272
2832.037693358359040.962306641640964
2977.98319436951923-0.983194369519232
3054.211092366672650.788907633327353
3177.39727766381894-0.397277663818941
3254.565883735592080.434116264407923
3355.56895713630856-0.568957136308561
3444.45493221874906-0.454932218749061
3555.2262063776982-0.2262063776982
3664.913663739940781.08633626005922
3778.4655031839114-1.46550318391141
3854.643311067779020.356688932220977
3975.578319518432531.42168048156747
4076.83713921122530.162860788774708
4166.48687977662416-0.486879776624159
4264.594686838921771.40531316107823
4376.15430140955120.845698590448803
4477.18500182611667-0.185001826116668
4565.302875670782290.697124329217706
4667.8094056495506-1.8094056495506
4743.33847153925830.661528460741705
4877.75886200010738-0.758862000107379
4954.824959174643740.175040825356262
5066.50578289698796-0.505782896987962
5157.39412209772863-2.39412209772863
5232.321240620960380.67875937903962
5375.960650087158561.03934991284144
5466.55440712584521-0.554407125845215
5566.09562099238533-0.0956209923853308
5655.81586364224331-0.815863642243308
5754.248082343349680.751917656650315
5875.963104906063651.03689509393635
5976.778712204260270.221287795739735
6077.39506311974298-0.39506311974298
6166.13052138306137-0.130521383061368
6264.89238817095881.1076118290412
6376.600432238450720.399567761549281
6476.545789904759350.454210095240646
6554.048819462113660.951180537886337
6676.907533438612410.0924665613875884
6767.3057368982798-1.3057368982798
6855.07468333503607-0.0746833350360746
6966.28175448950933-0.281754489509331
7055.83713921122529-0.837139211225291
7155.46991205946541-0.469912059465413
7265.74335827742680.256641722573196
7365.038830035263880.961169964736119
7464.783503820194161.21649617980584
7576.16246813781540.837531862184606
7666.23899266653088-0.238992666530884
7754.165099821941490.834900178058513
7876.557562691935530.44243730806447
7975.429198550504591.57080144949541
8065.378180242805620.621819757194375
8176.980038803428920.0199611965710835
8265.981292112892610.0187078871073924
8364.664067894276021.33593210572398
8476.152822011158030.847177988841974
8565.451792056573740.548207943426255
8675.860028348909651.13997165109035
8777.02367345923255-0.023673459232553
8857.94195693749092-2.94195693749092
8932.433667243826480.566332756173522
9065.771082556288380.22891744371162
9167.69856002751253-1.69856002751253
9254.640964487229220.359035512770785
9366.24808234334968-0.248082343349685
9465.571797701237410.428202298762586
9564.837139211225291.16286078877471
9677.37211196176794-0.372111961767939
9765.715950602904730.28404939709527
9865.61975632709760.380243672902398
9965.480085254123880.519914745876117
10076.49192219542610.508077804573901
10167.20668349111864-1.20668349111864
10255.92113490466636-0.921134904666362
10354.740592422991510.259407577008491
10465.778873665566270.22112633443373
10564.944657585598651.05534241440135
10666.55454129615442-0.554541296154416
10755.01182409687694-0.0118240968769383
10868.00863596291228-2.00863596291228
10943.904767584177120.095232415822883
11065.008635962912280.991364037087722
11175.860028348909651.13997165109035
11277.05270959518268-0.052709595182682
11355.0014485427201-0.00144854272010161
11454.087062748254390.912937251745607
11579.66650605734698-2.66650605734698
11631.758534308743041.24146569125696
11776.750024858990520.249975141009482
11853.716014427407131.28398557259287
11978.05828872355003-1.05828872355003
12054.507036206451650.492963793548347
12134.0592653620773-1.0592653620773
12266.66098538770463-0.660985387704627
12354.336986285283490.663013714716506
12444.05277480151025-0.0527748015102448
12576.565306561966270.434693438033726
12669.1890122145288-3.1890122145288
12777.52097959087286-0.520979590872858
12822.21109236667265-0.211092366672646
12954.664689550442150.335310449557851
13066.31894865267873-0.318948652678734
13167.87941456766134-1.87941456766134
13266.29335748506149-0.293357485061486
13320.6607654676736261.33923453232637
13467.54105144789064-1.54105144789064
13576.203628234440780.79637176555922
13641.971737568756392.02826243124361
13777.55440712584521-0.554407125845215
13877.10027450265332-0.100274502653322
13969.02609036280688-3.02609036280688
14065.099021193189630.900978806810369
14120.1058488622683391.89415113773166
14278.12344173420253-1.12344173420253
14377.03700219719906-0.0370021971990613
14454.728213009773190.271786990226809
14555.77784560080772-0.777845600807716
14666.24195204202131-0.241952042021315
14754.70550470294110.294495297058905
14864.995318241391731.00468175860827
14966.61010481188055-0.610104811880548
15065.619328659008550.38067134099145
15153.895871428413451.10412857158655
15265.479135906593410.520864093406585
15377.13456783795716-0.134567837957156
15476.784731261589480.215268738410524
15565.187909731208440.81209026879156
15664.636317635663931.36368236433607
15765.640964487229220.359035512770785
15877.9016120180868-0.901612018086801
15966.76964981843827-0.769649818438267
16055.8923881709588-0.892388170958799
16155.90981000297906-0.909810002979057
16256.46910391473786-1.46910391473786
1634NANA
1644NANA







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3279178602955750.6558357205911490.672082139704425
130.5488238567965580.9023522864068840.451176143203442
140.9038009192669210.1923981614661570.0961990807330785
150.8454069005847030.3091861988305930.154593099415297
160.8411979586940390.3176040826119230.158802041305961
170.825435208905990.3491295821880190.174564791094009
180.7887325309314460.4225349381371090.211267469068554
190.7684313409690810.4631373180618380.231568659030919
200.7469955914155530.5060088171688940.253004408584447
210.7026972964020320.5946054071959360.297302703597968
220.6388490167655910.7223019664688180.361150983234409
230.5753956019656840.8492087960686320.424604398034316
240.789879703343630.4202405933127410.210120296656371
250.8147986659535890.3704026680928210.185201334046411
260.804663813828360.3906723723432790.195336186171639
270.9560223807197330.08795523856053490.0439776192802674
280.9459247252716930.1081505494566140.0540752747283068
290.9308191248535820.1383617502928360.0691808751464181
300.9147278429405650.170544314118870.0852721570594351
310.8999701028788290.2000597942423430.100029897121171
320.9389976166223660.1220047667552670.0610023833776335
330.9215288283846670.1569423432306670.0784711716153334
340.9163758546831920.1672482906336170.0836241453168083
350.8912523391360980.2174953217278050.108747660863902
360.8910387946967470.2179224106065050.108961205303253
370.8970774468920250.2058451062159490.102922553107975
380.8711697001050410.2576605997899180.128830299894959
390.9082665557507080.1834668884985840.0917334442492921
400.8843423379218460.2313153241563080.115657662078154
410.8664676369241730.2670647261516530.133532363075827
420.8860453976921880.2279092046156240.113954602307812
430.8725786206000110.2548427587999770.127421379399988
440.84441252730720.3111749453856010.1555874726928
450.8210755653313980.3578488693372030.178924434668602
460.856264563499090.2874708730018190.14373543650091
470.8312105328160030.3375789343679950.168789467183997
480.812589111373020.3748217772539590.187410888626979
490.7812495307087950.437500938582410.218750469291205
500.7476388005827060.5047223988345890.252361199417294
510.8611883264476620.2776233471046770.138811673552338
520.8390950317679360.3218099364641280.160904968232064
530.865572249590250.2688555008194990.134427750409749
540.8419191872471110.3161616255057770.158080812752889
550.8110721965874840.3778556068250320.188927803412516
560.7915934383783070.4168131232433860.208406561621693
570.7740224842146710.4519550315706570.225977515785329
580.7647600919580770.4704798160838460.235239908041923
590.7324932389872030.5350135220255950.267506761012797
600.6960792366342810.6078415267314380.303920763365719
610.6561507074473240.6876985851053510.343849292552676
620.6628902871840110.6742194256319770.337109712815989
630.6282419644919470.7435160710161060.371758035508053
640.5890698152664670.8218603694670650.410930184733533
650.5856940109269980.8286119781460040.414305989073002
660.5385459846532980.9229080306934040.461454015346702
670.5610197997300690.8779604005398630.438980200269931
680.5134528113396160.9730943773207670.486547188660384
690.4704071648478440.9408143296956890.529592835152156
700.451270177723850.90254035544770.54872982227615
710.4122738823315570.8245477646631150.587726117668443
720.3702094052191710.7404188104383420.629790594780829
730.3624393093327270.7248786186654540.637560690667273
740.3667455082191270.7334910164382540.633254491780873
750.3485689748396140.6971379496792290.651431025160386
760.3078472969523480.6156945939046970.692152703047652
770.2886676312474140.5773352624948290.711332368752586
780.2560302132351850.512060426470370.743969786764815
790.3075520092478480.6151040184956960.692447990752152
800.283436274718660.5668725494373190.71656372528134
810.2449462532916480.4898925065832960.755053746708352
820.2094823079478440.4189646158956890.790517692052156
830.227347977181180.454695954362360.77265202281882
840.221205319679380.4424106393587610.77879468032062
850.1948881153642920.3897762307285840.805111884635708
860.193770929161450.38754185832290.80622907083855
870.1630638225195410.3261276450390820.836936177480459
880.3882373801049120.7764747602098250.611762619895088
890.3549188574293680.7098377148587350.645081142570632
900.31628663050940.6325732610188010.6837133694906
910.3729857315517510.7459714631035010.62701426844825
920.3314473539260320.6628947078520650.668552646073968
930.2918356332574990.5836712665149980.708164366742501
940.2610342906526460.5220685813052930.738965709347354
950.2664928126624370.5329856253248750.733507187337563
960.2322463443285310.4644926886570620.767753655671469
970.2017097860700450.4034195721400890.798290213929955
980.1758629466932820.3517258933865650.824137053306718
990.1550697391902050.310139478380410.844930260809795
1000.1357504850001290.2715009700002590.86424951499987
1010.1402130338045720.2804260676091430.859786966195428
1020.1342839364256280.2685678728512570.865716063574372
1030.111738650460150.2234773009203010.88826134953985
1040.09160055257050220.1832011051410040.908399447429498
1050.09237028979363780.1847405795872760.907629710206362
1060.07701650774031650.1540330154806330.922983492259684
1070.06103165951242070.1220633190248410.93896834048758
1080.09877946698016990.197558933960340.90122053301983
1090.07880204313517470.1576040862703490.921197956864825
1100.07679166459593810.1535833291918760.923208335404062
1110.0759300593350530.1518601186701060.924069940664947
1120.06107965923859860.1221593184771970.938920340761401
1130.04854378582317910.09708757164635810.951456214176821
1140.045861021921560.091722043843120.95413897807844
1150.1508150290610950.301630058122190.849184970938905
1160.1554549157829180.3109098315658360.844545084217082
1170.1349581697577620.2699163395155250.865041830242238
1180.151974612375010.303949224750020.84802538762499
1190.1464322820409810.2928645640819610.85356771795902
1200.1265434437962960.2530868875925930.873456556203704
1210.1217670890284970.2435341780569940.878232910971503
1220.09903849753510560.1980769950702110.900961502464894
1230.08760762892856260.1752152578571250.912392371071437
1240.06715300239287540.1343060047857510.932846997607125
1250.05440486739425430.1088097347885090.945595132605746
1260.2569878933883330.5139757867766650.743012106611667
1270.2146342471168030.4292684942336050.785365752883197
1280.1739204372376410.3478408744752810.82607956276236
1290.139634607699450.2792692153988990.86036539230055
1300.1116142751641140.2232285503282280.888385724835886
1310.3433895527495150.686779105499030.656610447250485
1320.2906130684685120.5812261369370240.709386931531488
1330.3422044814142390.6844089628284780.657795518585761
1340.3865233525248240.7730467050496480.613476647475176
1350.3442564710159820.6885129420319630.655743528984018
1360.4666029657936520.9332059315873030.533397034206348
1370.4224813737494910.8449627474989810.577518626250509
1380.3500089245539490.7000178491078970.649991075446051
1390.7239471073903420.5521057852193170.276052892609658
1400.6548077074947530.6903845850104950.345192292505247
1410.6324472419423760.7351055161152480.367552758057624
1420.6288576595857330.7422846808285340.371142340414267
1430.557648962367930.8847020752641390.442351037632069
1440.4881641543590830.9763283087181650.511835845640917
1450.5716679304502840.8566641390994320.428332069549716
1460.5680687062540140.8638625874919720.431931293745986
1470.4841300171259870.9682600342519750.515869982874013
1480.3651655244387640.7303310488775270.634834475561236
1490.2586791941694340.5173583883388680.741320805830566
1500.1540132559047540.3080265118095090.845986744095246
1510.7527716702676740.4944566594646510.247228329732326
1520.5661134440986010.8677731118027980.433886555901399

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.327917860295575 & 0.655835720591149 & 0.672082139704425 \tabularnewline
13 & 0.548823856796558 & 0.902352286406884 & 0.451176143203442 \tabularnewline
14 & 0.903800919266921 & 0.192398161466157 & 0.0961990807330785 \tabularnewline
15 & 0.845406900584703 & 0.309186198830593 & 0.154593099415297 \tabularnewline
16 & 0.841197958694039 & 0.317604082611923 & 0.158802041305961 \tabularnewline
17 & 0.82543520890599 & 0.349129582188019 & 0.174564791094009 \tabularnewline
18 & 0.788732530931446 & 0.422534938137109 & 0.211267469068554 \tabularnewline
19 & 0.768431340969081 & 0.463137318061838 & 0.231568659030919 \tabularnewline
20 & 0.746995591415553 & 0.506008817168894 & 0.253004408584447 \tabularnewline
21 & 0.702697296402032 & 0.594605407195936 & 0.297302703597968 \tabularnewline
22 & 0.638849016765591 & 0.722301966468818 & 0.361150983234409 \tabularnewline
23 & 0.575395601965684 & 0.849208796068632 & 0.424604398034316 \tabularnewline
24 & 0.78987970334363 & 0.420240593312741 & 0.210120296656371 \tabularnewline
25 & 0.814798665953589 & 0.370402668092821 & 0.185201334046411 \tabularnewline
26 & 0.80466381382836 & 0.390672372343279 & 0.195336186171639 \tabularnewline
27 & 0.956022380719733 & 0.0879552385605349 & 0.0439776192802674 \tabularnewline
28 & 0.945924725271693 & 0.108150549456614 & 0.0540752747283068 \tabularnewline
29 & 0.930819124853582 & 0.138361750292836 & 0.0691808751464181 \tabularnewline
30 & 0.914727842940565 & 0.17054431411887 & 0.0852721570594351 \tabularnewline
31 & 0.899970102878829 & 0.200059794242343 & 0.100029897121171 \tabularnewline
32 & 0.938997616622366 & 0.122004766755267 & 0.0610023833776335 \tabularnewline
33 & 0.921528828384667 & 0.156942343230667 & 0.0784711716153334 \tabularnewline
34 & 0.916375854683192 & 0.167248290633617 & 0.0836241453168083 \tabularnewline
35 & 0.891252339136098 & 0.217495321727805 & 0.108747660863902 \tabularnewline
36 & 0.891038794696747 & 0.217922410606505 & 0.108961205303253 \tabularnewline
37 & 0.897077446892025 & 0.205845106215949 & 0.102922553107975 \tabularnewline
38 & 0.871169700105041 & 0.257660599789918 & 0.128830299894959 \tabularnewline
39 & 0.908266555750708 & 0.183466888498584 & 0.0917334442492921 \tabularnewline
40 & 0.884342337921846 & 0.231315324156308 & 0.115657662078154 \tabularnewline
41 & 0.866467636924173 & 0.267064726151653 & 0.133532363075827 \tabularnewline
42 & 0.886045397692188 & 0.227909204615624 & 0.113954602307812 \tabularnewline
43 & 0.872578620600011 & 0.254842758799977 & 0.127421379399988 \tabularnewline
44 & 0.8444125273072 & 0.311174945385601 & 0.1555874726928 \tabularnewline
45 & 0.821075565331398 & 0.357848869337203 & 0.178924434668602 \tabularnewline
46 & 0.85626456349909 & 0.287470873001819 & 0.14373543650091 \tabularnewline
47 & 0.831210532816003 & 0.337578934367995 & 0.168789467183997 \tabularnewline
48 & 0.81258911137302 & 0.374821777253959 & 0.187410888626979 \tabularnewline
49 & 0.781249530708795 & 0.43750093858241 & 0.218750469291205 \tabularnewline
50 & 0.747638800582706 & 0.504722398834589 & 0.252361199417294 \tabularnewline
51 & 0.861188326447662 & 0.277623347104677 & 0.138811673552338 \tabularnewline
52 & 0.839095031767936 & 0.321809936464128 & 0.160904968232064 \tabularnewline
53 & 0.86557224959025 & 0.268855500819499 & 0.134427750409749 \tabularnewline
54 & 0.841919187247111 & 0.316161625505777 & 0.158080812752889 \tabularnewline
55 & 0.811072196587484 & 0.377855606825032 & 0.188927803412516 \tabularnewline
56 & 0.791593438378307 & 0.416813123243386 & 0.208406561621693 \tabularnewline
57 & 0.774022484214671 & 0.451955031570657 & 0.225977515785329 \tabularnewline
58 & 0.764760091958077 & 0.470479816083846 & 0.235239908041923 \tabularnewline
59 & 0.732493238987203 & 0.535013522025595 & 0.267506761012797 \tabularnewline
60 & 0.696079236634281 & 0.607841526731438 & 0.303920763365719 \tabularnewline
61 & 0.656150707447324 & 0.687698585105351 & 0.343849292552676 \tabularnewline
62 & 0.662890287184011 & 0.674219425631977 & 0.337109712815989 \tabularnewline
63 & 0.628241964491947 & 0.743516071016106 & 0.371758035508053 \tabularnewline
64 & 0.589069815266467 & 0.821860369467065 & 0.410930184733533 \tabularnewline
65 & 0.585694010926998 & 0.828611978146004 & 0.414305989073002 \tabularnewline
66 & 0.538545984653298 & 0.922908030693404 & 0.461454015346702 \tabularnewline
67 & 0.561019799730069 & 0.877960400539863 & 0.438980200269931 \tabularnewline
68 & 0.513452811339616 & 0.973094377320767 & 0.486547188660384 \tabularnewline
69 & 0.470407164847844 & 0.940814329695689 & 0.529592835152156 \tabularnewline
70 & 0.45127017772385 & 0.9025403554477 & 0.54872982227615 \tabularnewline
71 & 0.412273882331557 & 0.824547764663115 & 0.587726117668443 \tabularnewline
72 & 0.370209405219171 & 0.740418810438342 & 0.629790594780829 \tabularnewline
73 & 0.362439309332727 & 0.724878618665454 & 0.637560690667273 \tabularnewline
74 & 0.366745508219127 & 0.733491016438254 & 0.633254491780873 \tabularnewline
75 & 0.348568974839614 & 0.697137949679229 & 0.651431025160386 \tabularnewline
76 & 0.307847296952348 & 0.615694593904697 & 0.692152703047652 \tabularnewline
77 & 0.288667631247414 & 0.577335262494829 & 0.711332368752586 \tabularnewline
78 & 0.256030213235185 & 0.51206042647037 & 0.743969786764815 \tabularnewline
79 & 0.307552009247848 & 0.615104018495696 & 0.692447990752152 \tabularnewline
80 & 0.28343627471866 & 0.566872549437319 & 0.71656372528134 \tabularnewline
81 & 0.244946253291648 & 0.489892506583296 & 0.755053746708352 \tabularnewline
82 & 0.209482307947844 & 0.418964615895689 & 0.790517692052156 \tabularnewline
83 & 0.22734797718118 & 0.45469595436236 & 0.77265202281882 \tabularnewline
84 & 0.22120531967938 & 0.442410639358761 & 0.77879468032062 \tabularnewline
85 & 0.194888115364292 & 0.389776230728584 & 0.805111884635708 \tabularnewline
86 & 0.19377092916145 & 0.3875418583229 & 0.80622907083855 \tabularnewline
87 & 0.163063822519541 & 0.326127645039082 & 0.836936177480459 \tabularnewline
88 & 0.388237380104912 & 0.776474760209825 & 0.611762619895088 \tabularnewline
89 & 0.354918857429368 & 0.709837714858735 & 0.645081142570632 \tabularnewline
90 & 0.3162866305094 & 0.632573261018801 & 0.6837133694906 \tabularnewline
91 & 0.372985731551751 & 0.745971463103501 & 0.62701426844825 \tabularnewline
92 & 0.331447353926032 & 0.662894707852065 & 0.668552646073968 \tabularnewline
93 & 0.291835633257499 & 0.583671266514998 & 0.708164366742501 \tabularnewline
94 & 0.261034290652646 & 0.522068581305293 & 0.738965709347354 \tabularnewline
95 & 0.266492812662437 & 0.532985625324875 & 0.733507187337563 \tabularnewline
96 & 0.232246344328531 & 0.464492688657062 & 0.767753655671469 \tabularnewline
97 & 0.201709786070045 & 0.403419572140089 & 0.798290213929955 \tabularnewline
98 & 0.175862946693282 & 0.351725893386565 & 0.824137053306718 \tabularnewline
99 & 0.155069739190205 & 0.31013947838041 & 0.844930260809795 \tabularnewline
100 & 0.135750485000129 & 0.271500970000259 & 0.86424951499987 \tabularnewline
101 & 0.140213033804572 & 0.280426067609143 & 0.859786966195428 \tabularnewline
102 & 0.134283936425628 & 0.268567872851257 & 0.865716063574372 \tabularnewline
103 & 0.11173865046015 & 0.223477300920301 & 0.88826134953985 \tabularnewline
104 & 0.0916005525705022 & 0.183201105141004 & 0.908399447429498 \tabularnewline
105 & 0.0923702897936378 & 0.184740579587276 & 0.907629710206362 \tabularnewline
106 & 0.0770165077403165 & 0.154033015480633 & 0.922983492259684 \tabularnewline
107 & 0.0610316595124207 & 0.122063319024841 & 0.93896834048758 \tabularnewline
108 & 0.0987794669801699 & 0.19755893396034 & 0.90122053301983 \tabularnewline
109 & 0.0788020431351747 & 0.157604086270349 & 0.921197956864825 \tabularnewline
110 & 0.0767916645959381 & 0.153583329191876 & 0.923208335404062 \tabularnewline
111 & 0.075930059335053 & 0.151860118670106 & 0.924069940664947 \tabularnewline
112 & 0.0610796592385986 & 0.122159318477197 & 0.938920340761401 \tabularnewline
113 & 0.0485437858231791 & 0.0970875716463581 & 0.951456214176821 \tabularnewline
114 & 0.04586102192156 & 0.09172204384312 & 0.95413897807844 \tabularnewline
115 & 0.150815029061095 & 0.30163005812219 & 0.849184970938905 \tabularnewline
116 & 0.155454915782918 & 0.310909831565836 & 0.844545084217082 \tabularnewline
117 & 0.134958169757762 & 0.269916339515525 & 0.865041830242238 \tabularnewline
118 & 0.15197461237501 & 0.30394922475002 & 0.84802538762499 \tabularnewline
119 & 0.146432282040981 & 0.292864564081961 & 0.85356771795902 \tabularnewline
120 & 0.126543443796296 & 0.253086887592593 & 0.873456556203704 \tabularnewline
121 & 0.121767089028497 & 0.243534178056994 & 0.878232910971503 \tabularnewline
122 & 0.0990384975351056 & 0.198076995070211 & 0.900961502464894 \tabularnewline
123 & 0.0876076289285626 & 0.175215257857125 & 0.912392371071437 \tabularnewline
124 & 0.0671530023928754 & 0.134306004785751 & 0.932846997607125 \tabularnewline
125 & 0.0544048673942543 & 0.108809734788509 & 0.945595132605746 \tabularnewline
126 & 0.256987893388333 & 0.513975786776665 & 0.743012106611667 \tabularnewline
127 & 0.214634247116803 & 0.429268494233605 & 0.785365752883197 \tabularnewline
128 & 0.173920437237641 & 0.347840874475281 & 0.82607956276236 \tabularnewline
129 & 0.13963460769945 & 0.279269215398899 & 0.86036539230055 \tabularnewline
130 & 0.111614275164114 & 0.223228550328228 & 0.888385724835886 \tabularnewline
131 & 0.343389552749515 & 0.68677910549903 & 0.656610447250485 \tabularnewline
132 & 0.290613068468512 & 0.581226136937024 & 0.709386931531488 \tabularnewline
133 & 0.342204481414239 & 0.684408962828478 & 0.657795518585761 \tabularnewline
134 & 0.386523352524824 & 0.773046705049648 & 0.613476647475176 \tabularnewline
135 & 0.344256471015982 & 0.688512942031963 & 0.655743528984018 \tabularnewline
136 & 0.466602965793652 & 0.933205931587303 & 0.533397034206348 \tabularnewline
137 & 0.422481373749491 & 0.844962747498981 & 0.577518626250509 \tabularnewline
138 & 0.350008924553949 & 0.700017849107897 & 0.649991075446051 \tabularnewline
139 & 0.723947107390342 & 0.552105785219317 & 0.276052892609658 \tabularnewline
140 & 0.654807707494753 & 0.690384585010495 & 0.345192292505247 \tabularnewline
141 & 0.632447241942376 & 0.735105516115248 & 0.367552758057624 \tabularnewline
142 & 0.628857659585733 & 0.742284680828534 & 0.371142340414267 \tabularnewline
143 & 0.55764896236793 & 0.884702075264139 & 0.442351037632069 \tabularnewline
144 & 0.488164154359083 & 0.976328308718165 & 0.511835845640917 \tabularnewline
145 & 0.571667930450284 & 0.856664139099432 & 0.428332069549716 \tabularnewline
146 & 0.568068706254014 & 0.863862587491972 & 0.431931293745986 \tabularnewline
147 & 0.484130017125987 & 0.968260034251975 & 0.515869982874013 \tabularnewline
148 & 0.365165524438764 & 0.730331048877527 & 0.634834475561236 \tabularnewline
149 & 0.258679194169434 & 0.517358388338868 & 0.741320805830566 \tabularnewline
150 & 0.154013255904754 & 0.308026511809509 & 0.845986744095246 \tabularnewline
151 & 0.752771670267674 & 0.494456659464651 & 0.247228329732326 \tabularnewline
152 & 0.566113444098601 & 0.867773111802798 & 0.433886555901399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99688&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]0.327917860295575[/C][C]0.655835720591149[/C][C]0.672082139704425[/C][/ROW]
[ROW][C]13[/C][C]0.548823856796558[/C][C]0.902352286406884[/C][C]0.451176143203442[/C][/ROW]
[ROW][C]14[/C][C]0.903800919266921[/C][C]0.192398161466157[/C][C]0.0961990807330785[/C][/ROW]
[ROW][C]15[/C][C]0.845406900584703[/C][C]0.309186198830593[/C][C]0.154593099415297[/C][/ROW]
[ROW][C]16[/C][C]0.841197958694039[/C][C]0.317604082611923[/C][C]0.158802041305961[/C][/ROW]
[ROW][C]17[/C][C]0.82543520890599[/C][C]0.349129582188019[/C][C]0.174564791094009[/C][/ROW]
[ROW][C]18[/C][C]0.788732530931446[/C][C]0.422534938137109[/C][C]0.211267469068554[/C][/ROW]
[ROW][C]19[/C][C]0.768431340969081[/C][C]0.463137318061838[/C][C]0.231568659030919[/C][/ROW]
[ROW][C]20[/C][C]0.746995591415553[/C][C]0.506008817168894[/C][C]0.253004408584447[/C][/ROW]
[ROW][C]21[/C][C]0.702697296402032[/C][C]0.594605407195936[/C][C]0.297302703597968[/C][/ROW]
[ROW][C]22[/C][C]0.638849016765591[/C][C]0.722301966468818[/C][C]0.361150983234409[/C][/ROW]
[ROW][C]23[/C][C]0.575395601965684[/C][C]0.849208796068632[/C][C]0.424604398034316[/C][/ROW]
[ROW][C]24[/C][C]0.78987970334363[/C][C]0.420240593312741[/C][C]0.210120296656371[/C][/ROW]
[ROW][C]25[/C][C]0.814798665953589[/C][C]0.370402668092821[/C][C]0.185201334046411[/C][/ROW]
[ROW][C]26[/C][C]0.80466381382836[/C][C]0.390672372343279[/C][C]0.195336186171639[/C][/ROW]
[ROW][C]27[/C][C]0.956022380719733[/C][C]0.0879552385605349[/C][C]0.0439776192802674[/C][/ROW]
[ROW][C]28[/C][C]0.945924725271693[/C][C]0.108150549456614[/C][C]0.0540752747283068[/C][/ROW]
[ROW][C]29[/C][C]0.930819124853582[/C][C]0.138361750292836[/C][C]0.0691808751464181[/C][/ROW]
[ROW][C]30[/C][C]0.914727842940565[/C][C]0.17054431411887[/C][C]0.0852721570594351[/C][/ROW]
[ROW][C]31[/C][C]0.899970102878829[/C][C]0.200059794242343[/C][C]0.100029897121171[/C][/ROW]
[ROW][C]32[/C][C]0.938997616622366[/C][C]0.122004766755267[/C][C]0.0610023833776335[/C][/ROW]
[ROW][C]33[/C][C]0.921528828384667[/C][C]0.156942343230667[/C][C]0.0784711716153334[/C][/ROW]
[ROW][C]34[/C][C]0.916375854683192[/C][C]0.167248290633617[/C][C]0.0836241453168083[/C][/ROW]
[ROW][C]35[/C][C]0.891252339136098[/C][C]0.217495321727805[/C][C]0.108747660863902[/C][/ROW]
[ROW][C]36[/C][C]0.891038794696747[/C][C]0.217922410606505[/C][C]0.108961205303253[/C][/ROW]
[ROW][C]37[/C][C]0.897077446892025[/C][C]0.205845106215949[/C][C]0.102922553107975[/C][/ROW]
[ROW][C]38[/C][C]0.871169700105041[/C][C]0.257660599789918[/C][C]0.128830299894959[/C][/ROW]
[ROW][C]39[/C][C]0.908266555750708[/C][C]0.183466888498584[/C][C]0.0917334442492921[/C][/ROW]
[ROW][C]40[/C][C]0.884342337921846[/C][C]0.231315324156308[/C][C]0.115657662078154[/C][/ROW]
[ROW][C]41[/C][C]0.866467636924173[/C][C]0.267064726151653[/C][C]0.133532363075827[/C][/ROW]
[ROW][C]42[/C][C]0.886045397692188[/C][C]0.227909204615624[/C][C]0.113954602307812[/C][/ROW]
[ROW][C]43[/C][C]0.872578620600011[/C][C]0.254842758799977[/C][C]0.127421379399988[/C][/ROW]
[ROW][C]44[/C][C]0.8444125273072[/C][C]0.311174945385601[/C][C]0.1555874726928[/C][/ROW]
[ROW][C]45[/C][C]0.821075565331398[/C][C]0.357848869337203[/C][C]0.178924434668602[/C][/ROW]
[ROW][C]46[/C][C]0.85626456349909[/C][C]0.287470873001819[/C][C]0.14373543650091[/C][/ROW]
[ROW][C]47[/C][C]0.831210532816003[/C][C]0.337578934367995[/C][C]0.168789467183997[/C][/ROW]
[ROW][C]48[/C][C]0.81258911137302[/C][C]0.374821777253959[/C][C]0.187410888626979[/C][/ROW]
[ROW][C]49[/C][C]0.781249530708795[/C][C]0.43750093858241[/C][C]0.218750469291205[/C][/ROW]
[ROW][C]50[/C][C]0.747638800582706[/C][C]0.504722398834589[/C][C]0.252361199417294[/C][/ROW]
[ROW][C]51[/C][C]0.861188326447662[/C][C]0.277623347104677[/C][C]0.138811673552338[/C][/ROW]
[ROW][C]52[/C][C]0.839095031767936[/C][C]0.321809936464128[/C][C]0.160904968232064[/C][/ROW]
[ROW][C]53[/C][C]0.86557224959025[/C][C]0.268855500819499[/C][C]0.134427750409749[/C][/ROW]
[ROW][C]54[/C][C]0.841919187247111[/C][C]0.316161625505777[/C][C]0.158080812752889[/C][/ROW]
[ROW][C]55[/C][C]0.811072196587484[/C][C]0.377855606825032[/C][C]0.188927803412516[/C][/ROW]
[ROW][C]56[/C][C]0.791593438378307[/C][C]0.416813123243386[/C][C]0.208406561621693[/C][/ROW]
[ROW][C]57[/C][C]0.774022484214671[/C][C]0.451955031570657[/C][C]0.225977515785329[/C][/ROW]
[ROW][C]58[/C][C]0.764760091958077[/C][C]0.470479816083846[/C][C]0.235239908041923[/C][/ROW]
[ROW][C]59[/C][C]0.732493238987203[/C][C]0.535013522025595[/C][C]0.267506761012797[/C][/ROW]
[ROW][C]60[/C][C]0.696079236634281[/C][C]0.607841526731438[/C][C]0.303920763365719[/C][/ROW]
[ROW][C]61[/C][C]0.656150707447324[/C][C]0.687698585105351[/C][C]0.343849292552676[/C][/ROW]
[ROW][C]62[/C][C]0.662890287184011[/C][C]0.674219425631977[/C][C]0.337109712815989[/C][/ROW]
[ROW][C]63[/C][C]0.628241964491947[/C][C]0.743516071016106[/C][C]0.371758035508053[/C][/ROW]
[ROW][C]64[/C][C]0.589069815266467[/C][C]0.821860369467065[/C][C]0.410930184733533[/C][/ROW]
[ROW][C]65[/C][C]0.585694010926998[/C][C]0.828611978146004[/C][C]0.414305989073002[/C][/ROW]
[ROW][C]66[/C][C]0.538545984653298[/C][C]0.922908030693404[/C][C]0.461454015346702[/C][/ROW]
[ROW][C]67[/C][C]0.561019799730069[/C][C]0.877960400539863[/C][C]0.438980200269931[/C][/ROW]
[ROW][C]68[/C][C]0.513452811339616[/C][C]0.973094377320767[/C][C]0.486547188660384[/C][/ROW]
[ROW][C]69[/C][C]0.470407164847844[/C][C]0.940814329695689[/C][C]0.529592835152156[/C][/ROW]
[ROW][C]70[/C][C]0.45127017772385[/C][C]0.9025403554477[/C][C]0.54872982227615[/C][/ROW]
[ROW][C]71[/C][C]0.412273882331557[/C][C]0.824547764663115[/C][C]0.587726117668443[/C][/ROW]
[ROW][C]72[/C][C]0.370209405219171[/C][C]0.740418810438342[/C][C]0.629790594780829[/C][/ROW]
[ROW][C]73[/C][C]0.362439309332727[/C][C]0.724878618665454[/C][C]0.637560690667273[/C][/ROW]
[ROW][C]74[/C][C]0.366745508219127[/C][C]0.733491016438254[/C][C]0.633254491780873[/C][/ROW]
[ROW][C]75[/C][C]0.348568974839614[/C][C]0.697137949679229[/C][C]0.651431025160386[/C][/ROW]
[ROW][C]76[/C][C]0.307847296952348[/C][C]0.615694593904697[/C][C]0.692152703047652[/C][/ROW]
[ROW][C]77[/C][C]0.288667631247414[/C][C]0.577335262494829[/C][C]0.711332368752586[/C][/ROW]
[ROW][C]78[/C][C]0.256030213235185[/C][C]0.51206042647037[/C][C]0.743969786764815[/C][/ROW]
[ROW][C]79[/C][C]0.307552009247848[/C][C]0.615104018495696[/C][C]0.692447990752152[/C][/ROW]
[ROW][C]80[/C][C]0.28343627471866[/C][C]0.566872549437319[/C][C]0.71656372528134[/C][/ROW]
[ROW][C]81[/C][C]0.244946253291648[/C][C]0.489892506583296[/C][C]0.755053746708352[/C][/ROW]
[ROW][C]82[/C][C]0.209482307947844[/C][C]0.418964615895689[/C][C]0.790517692052156[/C][/ROW]
[ROW][C]83[/C][C]0.22734797718118[/C][C]0.45469595436236[/C][C]0.77265202281882[/C][/ROW]
[ROW][C]84[/C][C]0.22120531967938[/C][C]0.442410639358761[/C][C]0.77879468032062[/C][/ROW]
[ROW][C]85[/C][C]0.194888115364292[/C][C]0.389776230728584[/C][C]0.805111884635708[/C][/ROW]
[ROW][C]86[/C][C]0.19377092916145[/C][C]0.3875418583229[/C][C]0.80622907083855[/C][/ROW]
[ROW][C]87[/C][C]0.163063822519541[/C][C]0.326127645039082[/C][C]0.836936177480459[/C][/ROW]
[ROW][C]88[/C][C]0.388237380104912[/C][C]0.776474760209825[/C][C]0.611762619895088[/C][/ROW]
[ROW][C]89[/C][C]0.354918857429368[/C][C]0.709837714858735[/C][C]0.645081142570632[/C][/ROW]
[ROW][C]90[/C][C]0.3162866305094[/C][C]0.632573261018801[/C][C]0.6837133694906[/C][/ROW]
[ROW][C]91[/C][C]0.372985731551751[/C][C]0.745971463103501[/C][C]0.62701426844825[/C][/ROW]
[ROW][C]92[/C][C]0.331447353926032[/C][C]0.662894707852065[/C][C]0.668552646073968[/C][/ROW]
[ROW][C]93[/C][C]0.291835633257499[/C][C]0.583671266514998[/C][C]0.708164366742501[/C][/ROW]
[ROW][C]94[/C][C]0.261034290652646[/C][C]0.522068581305293[/C][C]0.738965709347354[/C][/ROW]
[ROW][C]95[/C][C]0.266492812662437[/C][C]0.532985625324875[/C][C]0.733507187337563[/C][/ROW]
[ROW][C]96[/C][C]0.232246344328531[/C][C]0.464492688657062[/C][C]0.767753655671469[/C][/ROW]
[ROW][C]97[/C][C]0.201709786070045[/C][C]0.403419572140089[/C][C]0.798290213929955[/C][/ROW]
[ROW][C]98[/C][C]0.175862946693282[/C][C]0.351725893386565[/C][C]0.824137053306718[/C][/ROW]
[ROW][C]99[/C][C]0.155069739190205[/C][C]0.31013947838041[/C][C]0.844930260809795[/C][/ROW]
[ROW][C]100[/C][C]0.135750485000129[/C][C]0.271500970000259[/C][C]0.86424951499987[/C][/ROW]
[ROW][C]101[/C][C]0.140213033804572[/C][C]0.280426067609143[/C][C]0.859786966195428[/C][/ROW]
[ROW][C]102[/C][C]0.134283936425628[/C][C]0.268567872851257[/C][C]0.865716063574372[/C][/ROW]
[ROW][C]103[/C][C]0.11173865046015[/C][C]0.223477300920301[/C][C]0.88826134953985[/C][/ROW]
[ROW][C]104[/C][C]0.0916005525705022[/C][C]0.183201105141004[/C][C]0.908399447429498[/C][/ROW]
[ROW][C]105[/C][C]0.0923702897936378[/C][C]0.184740579587276[/C][C]0.907629710206362[/C][/ROW]
[ROW][C]106[/C][C]0.0770165077403165[/C][C]0.154033015480633[/C][C]0.922983492259684[/C][/ROW]
[ROW][C]107[/C][C]0.0610316595124207[/C][C]0.122063319024841[/C][C]0.93896834048758[/C][/ROW]
[ROW][C]108[/C][C]0.0987794669801699[/C][C]0.19755893396034[/C][C]0.90122053301983[/C][/ROW]
[ROW][C]109[/C][C]0.0788020431351747[/C][C]0.157604086270349[/C][C]0.921197956864825[/C][/ROW]
[ROW][C]110[/C][C]0.0767916645959381[/C][C]0.153583329191876[/C][C]0.923208335404062[/C][/ROW]
[ROW][C]111[/C][C]0.075930059335053[/C][C]0.151860118670106[/C][C]0.924069940664947[/C][/ROW]
[ROW][C]112[/C][C]0.0610796592385986[/C][C]0.122159318477197[/C][C]0.938920340761401[/C][/ROW]
[ROW][C]113[/C][C]0.0485437858231791[/C][C]0.0970875716463581[/C][C]0.951456214176821[/C][/ROW]
[ROW][C]114[/C][C]0.04586102192156[/C][C]0.09172204384312[/C][C]0.95413897807844[/C][/ROW]
[ROW][C]115[/C][C]0.150815029061095[/C][C]0.30163005812219[/C][C]0.849184970938905[/C][/ROW]
[ROW][C]116[/C][C]0.155454915782918[/C][C]0.310909831565836[/C][C]0.844545084217082[/C][/ROW]
[ROW][C]117[/C][C]0.134958169757762[/C][C]0.269916339515525[/C][C]0.865041830242238[/C][/ROW]
[ROW][C]118[/C][C]0.15197461237501[/C][C]0.30394922475002[/C][C]0.84802538762499[/C][/ROW]
[ROW][C]119[/C][C]0.146432282040981[/C][C]0.292864564081961[/C][C]0.85356771795902[/C][/ROW]
[ROW][C]120[/C][C]0.126543443796296[/C][C]0.253086887592593[/C][C]0.873456556203704[/C][/ROW]
[ROW][C]121[/C][C]0.121767089028497[/C][C]0.243534178056994[/C][C]0.878232910971503[/C][/ROW]
[ROW][C]122[/C][C]0.0990384975351056[/C][C]0.198076995070211[/C][C]0.900961502464894[/C][/ROW]
[ROW][C]123[/C][C]0.0876076289285626[/C][C]0.175215257857125[/C][C]0.912392371071437[/C][/ROW]
[ROW][C]124[/C][C]0.0671530023928754[/C][C]0.134306004785751[/C][C]0.932846997607125[/C][/ROW]
[ROW][C]125[/C][C]0.0544048673942543[/C][C]0.108809734788509[/C][C]0.945595132605746[/C][/ROW]
[ROW][C]126[/C][C]0.256987893388333[/C][C]0.513975786776665[/C][C]0.743012106611667[/C][/ROW]
[ROW][C]127[/C][C]0.214634247116803[/C][C]0.429268494233605[/C][C]0.785365752883197[/C][/ROW]
[ROW][C]128[/C][C]0.173920437237641[/C][C]0.347840874475281[/C][C]0.82607956276236[/C][/ROW]
[ROW][C]129[/C][C]0.13963460769945[/C][C]0.279269215398899[/C][C]0.86036539230055[/C][/ROW]
[ROW][C]130[/C][C]0.111614275164114[/C][C]0.223228550328228[/C][C]0.888385724835886[/C][/ROW]
[ROW][C]131[/C][C]0.343389552749515[/C][C]0.68677910549903[/C][C]0.656610447250485[/C][/ROW]
[ROW][C]132[/C][C]0.290613068468512[/C][C]0.581226136937024[/C][C]0.709386931531488[/C][/ROW]
[ROW][C]133[/C][C]0.342204481414239[/C][C]0.684408962828478[/C][C]0.657795518585761[/C][/ROW]
[ROW][C]134[/C][C]0.386523352524824[/C][C]0.773046705049648[/C][C]0.613476647475176[/C][/ROW]
[ROW][C]135[/C][C]0.344256471015982[/C][C]0.688512942031963[/C][C]0.655743528984018[/C][/ROW]
[ROW][C]136[/C][C]0.466602965793652[/C][C]0.933205931587303[/C][C]0.533397034206348[/C][/ROW]
[ROW][C]137[/C][C]0.422481373749491[/C][C]0.844962747498981[/C][C]0.577518626250509[/C][/ROW]
[ROW][C]138[/C][C]0.350008924553949[/C][C]0.700017849107897[/C][C]0.649991075446051[/C][/ROW]
[ROW][C]139[/C][C]0.723947107390342[/C][C]0.552105785219317[/C][C]0.276052892609658[/C][/ROW]
[ROW][C]140[/C][C]0.654807707494753[/C][C]0.690384585010495[/C][C]0.345192292505247[/C][/ROW]
[ROW][C]141[/C][C]0.632447241942376[/C][C]0.735105516115248[/C][C]0.367552758057624[/C][/ROW]
[ROW][C]142[/C][C]0.628857659585733[/C][C]0.742284680828534[/C][C]0.371142340414267[/C][/ROW]
[ROW][C]143[/C][C]0.55764896236793[/C][C]0.884702075264139[/C][C]0.442351037632069[/C][/ROW]
[ROW][C]144[/C][C]0.488164154359083[/C][C]0.976328308718165[/C][C]0.511835845640917[/C][/ROW]
[ROW][C]145[/C][C]0.571667930450284[/C][C]0.856664139099432[/C][C]0.428332069549716[/C][/ROW]
[ROW][C]146[/C][C]0.568068706254014[/C][C]0.863862587491972[/C][C]0.431931293745986[/C][/ROW]
[ROW][C]147[/C][C]0.484130017125987[/C][C]0.968260034251975[/C][C]0.515869982874013[/C][/ROW]
[ROW][C]148[/C][C]0.365165524438764[/C][C]0.730331048877527[/C][C]0.634834475561236[/C][/ROW]
[ROW][C]149[/C][C]0.258679194169434[/C][C]0.517358388338868[/C][C]0.741320805830566[/C][/ROW]
[ROW][C]150[/C][C]0.154013255904754[/C][C]0.308026511809509[/C][C]0.845986744095246[/C][/ROW]
[ROW][C]151[/C][C]0.752771670267674[/C][C]0.494456659464651[/C][C]0.247228329732326[/C][/ROW]
[ROW][C]152[/C][C]0.566113444098601[/C][C]0.867773111802798[/C][C]0.433886555901399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99688&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99688&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3279178602955750.6558357205911490.672082139704425
130.5488238567965580.9023522864068840.451176143203442
140.9038009192669210.1923981614661570.0961990807330785
150.8454069005847030.3091861988305930.154593099415297
160.8411979586940390.3176040826119230.158802041305961
170.825435208905990.3491295821880190.174564791094009
180.7887325309314460.4225349381371090.211267469068554
190.7684313409690810.4631373180618380.231568659030919
200.7469955914155530.5060088171688940.253004408584447
210.7026972964020320.5946054071959360.297302703597968
220.6388490167655910.7223019664688180.361150983234409
230.5753956019656840.8492087960686320.424604398034316
240.789879703343630.4202405933127410.210120296656371
250.8147986659535890.3704026680928210.185201334046411
260.804663813828360.3906723723432790.195336186171639
270.9560223807197330.08795523856053490.0439776192802674
280.9459247252716930.1081505494566140.0540752747283068
290.9308191248535820.1383617502928360.0691808751464181
300.9147278429405650.170544314118870.0852721570594351
310.8999701028788290.2000597942423430.100029897121171
320.9389976166223660.1220047667552670.0610023833776335
330.9215288283846670.1569423432306670.0784711716153334
340.9163758546831920.1672482906336170.0836241453168083
350.8912523391360980.2174953217278050.108747660863902
360.8910387946967470.2179224106065050.108961205303253
370.8970774468920250.2058451062159490.102922553107975
380.8711697001050410.2576605997899180.128830299894959
390.9082665557507080.1834668884985840.0917334442492921
400.8843423379218460.2313153241563080.115657662078154
410.8664676369241730.2670647261516530.133532363075827
420.8860453976921880.2279092046156240.113954602307812
430.8725786206000110.2548427587999770.127421379399988
440.84441252730720.3111749453856010.1555874726928
450.8210755653313980.3578488693372030.178924434668602
460.856264563499090.2874708730018190.14373543650091
470.8312105328160030.3375789343679950.168789467183997
480.812589111373020.3748217772539590.187410888626979
490.7812495307087950.437500938582410.218750469291205
500.7476388005827060.5047223988345890.252361199417294
510.8611883264476620.2776233471046770.138811673552338
520.8390950317679360.3218099364641280.160904968232064
530.865572249590250.2688555008194990.134427750409749
540.8419191872471110.3161616255057770.158080812752889
550.8110721965874840.3778556068250320.188927803412516
560.7915934383783070.4168131232433860.208406561621693
570.7740224842146710.4519550315706570.225977515785329
580.7647600919580770.4704798160838460.235239908041923
590.7324932389872030.5350135220255950.267506761012797
600.6960792366342810.6078415267314380.303920763365719
610.6561507074473240.6876985851053510.343849292552676
620.6628902871840110.6742194256319770.337109712815989
630.6282419644919470.7435160710161060.371758035508053
640.5890698152664670.8218603694670650.410930184733533
650.5856940109269980.8286119781460040.414305989073002
660.5385459846532980.9229080306934040.461454015346702
670.5610197997300690.8779604005398630.438980200269931
680.5134528113396160.9730943773207670.486547188660384
690.4704071648478440.9408143296956890.529592835152156
700.451270177723850.90254035544770.54872982227615
710.4122738823315570.8245477646631150.587726117668443
720.3702094052191710.7404188104383420.629790594780829
730.3624393093327270.7248786186654540.637560690667273
740.3667455082191270.7334910164382540.633254491780873
750.3485689748396140.6971379496792290.651431025160386
760.3078472969523480.6156945939046970.692152703047652
770.2886676312474140.5773352624948290.711332368752586
780.2560302132351850.512060426470370.743969786764815
790.3075520092478480.6151040184956960.692447990752152
800.283436274718660.5668725494373190.71656372528134
810.2449462532916480.4898925065832960.755053746708352
820.2094823079478440.4189646158956890.790517692052156
830.227347977181180.454695954362360.77265202281882
840.221205319679380.4424106393587610.77879468032062
850.1948881153642920.3897762307285840.805111884635708
860.193770929161450.38754185832290.80622907083855
870.1630638225195410.3261276450390820.836936177480459
880.3882373801049120.7764747602098250.611762619895088
890.3549188574293680.7098377148587350.645081142570632
900.31628663050940.6325732610188010.6837133694906
910.3729857315517510.7459714631035010.62701426844825
920.3314473539260320.6628947078520650.668552646073968
930.2918356332574990.5836712665149980.708164366742501
940.2610342906526460.5220685813052930.738965709347354
950.2664928126624370.5329856253248750.733507187337563
960.2322463443285310.4644926886570620.767753655671469
970.2017097860700450.4034195721400890.798290213929955
980.1758629466932820.3517258933865650.824137053306718
990.1550697391902050.310139478380410.844930260809795
1000.1357504850001290.2715009700002590.86424951499987
1010.1402130338045720.2804260676091430.859786966195428
1020.1342839364256280.2685678728512570.865716063574372
1030.111738650460150.2234773009203010.88826134953985
1040.09160055257050220.1832011051410040.908399447429498
1050.09237028979363780.1847405795872760.907629710206362
1060.07701650774031650.1540330154806330.922983492259684
1070.06103165951242070.1220633190248410.93896834048758
1080.09877946698016990.197558933960340.90122053301983
1090.07880204313517470.1576040862703490.921197956864825
1100.07679166459593810.1535833291918760.923208335404062
1110.0759300593350530.1518601186701060.924069940664947
1120.06107965923859860.1221593184771970.938920340761401
1130.04854378582317910.09708757164635810.951456214176821
1140.045861021921560.091722043843120.95413897807844
1150.1508150290610950.301630058122190.849184970938905
1160.1554549157829180.3109098315658360.844545084217082
1170.1349581697577620.2699163395155250.865041830242238
1180.151974612375010.303949224750020.84802538762499
1190.1464322820409810.2928645640819610.85356771795902
1200.1265434437962960.2530868875925930.873456556203704
1210.1217670890284970.2435341780569940.878232910971503
1220.09903849753510560.1980769950702110.900961502464894
1230.08760762892856260.1752152578571250.912392371071437
1240.06715300239287540.1343060047857510.932846997607125
1250.05440486739425430.1088097347885090.945595132605746
1260.2569878933883330.5139757867766650.743012106611667
1270.2146342471168030.4292684942336050.785365752883197
1280.1739204372376410.3478408744752810.82607956276236
1290.139634607699450.2792692153988990.86036539230055
1300.1116142751641140.2232285503282280.888385724835886
1310.3433895527495150.686779105499030.656610447250485
1320.2906130684685120.5812261369370240.709386931531488
1330.3422044814142390.6844089628284780.657795518585761
1340.3865233525248240.7730467050496480.613476647475176
1350.3442564710159820.6885129420319630.655743528984018
1360.4666029657936520.9332059315873030.533397034206348
1370.4224813737494910.8449627474989810.577518626250509
1380.3500089245539490.7000178491078970.649991075446051
1390.7239471073903420.5521057852193170.276052892609658
1400.6548077074947530.6903845850104950.345192292505247
1410.6324472419423760.7351055161152480.367552758057624
1420.6288576595857330.7422846808285340.371142340414267
1430.557648962367930.8847020752641390.442351037632069
1440.4881641543590830.9763283087181650.511835845640917
1450.5716679304502840.8566641390994320.428332069549716
1460.5680687062540140.8638625874919720.431931293745986
1470.4841300171259870.9682600342519750.515869982874013
1480.3651655244387640.7303310488775270.634834475561236
1490.2586791941694340.5173583883388680.741320805830566
1500.1540132559047540.3080265118095090.845986744095246
1510.7527716702676740.4944566594646510.247228329732326
1520.5661134440986010.8677731118027980.433886555901399







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.0212765957446809OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.0212765957446809 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99688&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0212765957446809[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99688&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99688&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.0212765957446809OK



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