Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 11:23:39 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t13561070418a4bepgs3av0o12.htm/, Retrieved Thu, 28 Mar 2024 16:07:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203901, Retrieved Thu, 28 Mar 2024 16:07:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Paper deel 5 MR T40] [2012-12-16 16:05:22] [5ba6a65192e86ec9d9b86886359e5312]
- RM        [Multiple Regression] [] [2012-12-21 16:23:39] [2d2e1459719df4dac57978867f50cb50] [Current]
Feedback Forum

Post a new message
Dataseries X:
1	1	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
1	0	0	0	1	1
0	0	0	0	0	0
0	1	0	0	0	0
0	0	0	0	0	1
1	0	0	0	0	0
1	1	0	0	0	0
0	0	0	0	0	0
0	0	1	0	1	0
1	1	0	0	0	0
0	0	1	0	1	1
0	1	1	0	1	1
1	1	1	1	1	0
1	1	0	0	0	0
0	0	0	0	0	1
0	1	1	1	1	1
1	0	0	0	1	0
1	0	1	0	1	1
0	0	0	0	1	1
1	0	0	0	1	1
0	1	1	0	0	1
0	0	1	0	1	0
1	0	0	0	0	1
0	0	1	0	0	0
0	0	0	0	0	1
0	0	0	0	1	0
0	0	0	0	0	0
1	0	0	0	0	0
1	0	0	0	1	0
0	1	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
1	1	1	0	1	0
0	0	1	0	0	1
0	0	0	0	1	1
0	1	0	0	1	0
0	0	1	1	1	1
0	0	1	0	0	1
1	0	0	0	1	1
1	1	0	0	0	0
0	0	0	0	1	0
0	0	0	0	1	1
0	0	0	0	0	0
0	0	0	0	0	1
0	0	0	0	1	1
0	0	0	0	0	0
0	1	1	0	0	0
1	1	1	1	1	0
0	0	0	0	0	1
0	0	1	1	0	0
0	0	0	0	0	0
0	1	1	0	0	1
0	0	1	0	1	1
0	0	0	0	0	1
0	0	0	0	0	1
1	1	1	1	1	1
1	1	0	0	0	1
0	0	1	0	1	0
0	0	0	0	0	0
1	1	0	0	0	1
0	0	0	0	0	0
0	0	0	0	0	0
0	1	1	1	1	0
1	0	0	0	0	0
0	0	0	0	0	1
0	0	1	0	0	0
0	0	0	0	0	0
0	0	0	0	0	1
0	0	1	0	0	1
1	0	1	0	0	0
0	0	0	0	0	1
0	1	0	0	1	1
0	0	0	0	0	1
0	0	1	0	1	1
0	1	1	1	0	1
0	1	0	0	1	0
0	0	0	0	0	0
1	0	1	0	0	1
0	0	0	0	0	0
0	0	1	1	0	0
0	0	0	0	1	1
1	0	0	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=203901&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=203901&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203901&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'George Udny Yule' @ yule.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0255013504663802 -0.00748440764286597UseLimit[t] + 0.150191020750643T40[t] + 0.28028525650025Used[t] + 0.0639879419186647Useful[t] -0.0462319248323565`Outcome\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  -0.0255013504663802 -0.00748440764286597UseLimit[t] +  0.150191020750643T40[t] +  0.28028525650025Used[t] +  0.0639879419186647Useful[t] -0.0462319248323565`Outcome\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203901&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  -0.0255013504663802 -0.00748440764286597UseLimit[t] +  0.150191020750643T40[t] +  0.28028525650025Used[t] +  0.0639879419186647Useful[t] -0.0462319248323565`Outcome\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203901&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203901&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
CorrectAnalysis[t] = -0.0255013504663802 -0.00748440764286597UseLimit[t] + 0.150191020750643T40[t] + 0.28028525650025Used[t] + 0.0639879419186647Useful[t] -0.0462319248323565`Outcome\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.02550135046638020.04918-0.51850.6055160.302758
UseLimit-0.007484407642865970.067009-0.11170.9113460.455673
T400.1501910207506430.0687582.18430.0318660.015933
Used0.280285256500250.0650264.31034.6e-052.3e-05
Useful0.06398794191866470.0633241.01050.3153110.157655
`Outcome\r`-0.04623192483235650.058251-0.79370.4297370.214869

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.0255013504663802 & 0.04918 & -0.5185 & 0.605516 & 0.302758 \tabularnewline
UseLimit & -0.00748440764286597 & 0.067009 & -0.1117 & 0.911346 & 0.455673 \tabularnewline
T40 & 0.150191020750643 & 0.068758 & 2.1843 & 0.031866 & 0.015933 \tabularnewline
Used & 0.28028525650025 & 0.065026 & 4.3103 & 4.6e-05 & 2.3e-05 \tabularnewline
Useful & 0.0639879419186647 & 0.063324 & 1.0105 & 0.315311 & 0.157655 \tabularnewline
`Outcome\r` & -0.0462319248323565 & 0.058251 & -0.7937 & 0.429737 & 0.214869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203901&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.0255013504663802[/C][C]0.04918[/C][C]-0.5185[/C][C]0.605516[/C][C]0.302758[/C][/ROW]
[ROW][C]UseLimit[/C][C]-0.00748440764286597[/C][C]0.067009[/C][C]-0.1117[/C][C]0.911346[/C][C]0.455673[/C][/ROW]
[ROW][C]T40[/C][C]0.150191020750643[/C][C]0.068758[/C][C]2.1843[/C][C]0.031866[/C][C]0.015933[/C][/ROW]
[ROW][C]Used[/C][C]0.28028525650025[/C][C]0.065026[/C][C]4.3103[/C][C]4.6e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]Useful[/C][C]0.0639879419186647[/C][C]0.063324[/C][C]1.0105[/C][C]0.315311[/C][C]0.157655[/C][/ROW]
[ROW][C]`Outcome\r`[/C][C]-0.0462319248323565[/C][C]0.058251[/C][C]-0.7937[/C][C]0.429737[/C][C]0.214869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203901&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203901&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.02550135046638020.04918-0.51850.6055160.302758
UseLimit-0.007484407642865970.067009-0.11170.9113460.455673
T400.1501910207506430.0687582.18430.0318660.015933
Used0.280285256500250.0650264.31034.6e-052.3e-05
Useful0.06398794191866470.0633241.01050.3153110.157655
`Outcome\r`-0.04623192483235650.058251-0.79370.4297370.214869







Multiple Linear Regression - Regression Statistics
Multiple R0.549017657024751
R-squared0.301420387724947
Adjusted R-squared0.257759161957756
F-TEST (value)6.90361716668635
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value2.10442434196434e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.265265244423797
Sum Squared Residuals5.62925199193735

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.549017657024751 \tabularnewline
R-squared & 0.301420387724947 \tabularnewline
Adjusted R-squared & 0.257759161957756 \tabularnewline
F-TEST (value) & 6.90361716668635 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 80 \tabularnewline
p-value & 2.10442434196434e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.265265244423797 \tabularnewline
Sum Squared Residuals & 5.62925199193735 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203901&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.549017657024751[/C][/ROW]
[ROW][C]R-squared[/C][C]0.301420387724947[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.257759161957756[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.90361716668635[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]80[/C][/ROW]
[ROW][C]p-value[/C][C]2.10442434196434e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.265265244423797[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5.62925199193735[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203901&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203901&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 R0.549017657024751
R-squared0.301420387724947
Adjusted R-squared0.257759161957756
F-TEST (value)6.90361716668635
F-TEST (DF numerator)5
F-TEST (DF denominator)80
p-value2.10442434196434e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.265265244423797
Sum Squared Residuals5.62925199193735







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0709733378090402-0.0709733378090402
20-0.02550135046638020.0255013504663802
30-0.02550135046638020.0255013504663802
40-0.02550135046638040.0255013504663804
50-0.02550135046638010.0255013504663801
60-0.01522974102293810.0152297410229381
70-0.02550135046638020.0255013504663802
800.124689670284263-0.124689670284263
90-0.07173327529873670.0717332752987367
100-0.03298575810924620.0329857581092462
1100.117205262641397-0.117205262641397
120-0.02550135046638020.0255013504663802
1300.318771847952535-0.318771847952535
1400.117205262641397-0.117205262641397
1500.272539923120178-0.272539923120178
1600.422730943870821-0.422730943870821
1710.4614784610603120.538521538939688
1800.117205262641397-0.117205262641397
190-0.07173327529873670.0717332752987367
2010.4227309438708210.577269056129179
2100.0310021838094185-0.0310021838094185
2200.265055515477312-0.265055515477312
230-0.007745333380072090.00774533338007209
240-0.01522974102293810.0152297410229381
2500.358743001952157-0.358743001952157
2600.318771847952535-0.318771847952535
270-0.07921768294160270.0792176829416027
2800.25478390603387-0.25478390603387
290-0.07173327529873670.0717332752987367
3000.0384865914522844-0.0384865914522844
310-0.02550135046638020.0255013504663802
320-0.03298575810924620.0329857581092462
3300.0310021838094185-0.0310021838094185
3400.0784577454519064-0.0784577454519064
350-0.02550135046638020.0255013504663802
360-0.02550135046638020.0255013504663802
3700.461478461060312-0.461478461060312
3800.208551981201514-0.208551981201514
390-0.007745333380072090.00774533338007209
4000.188677612202928-0.188677612202928
4110.2725399231201780.727460076879822
4200.208551981201514-0.208551981201514
430-0.01522974102293810.0152297410229381
4400.117205262641397-0.117205262641397
4500.0384865914522844-0.0384865914522844
460-0.007745333380072090.00774533338007209
470-0.02550135046638020.0255013504663802
480-0.07173327529873670.0717332752987367
490-0.007745333380072090.00774533338007209
500-0.02550135046638020.0255013504663802
5100.404974926784513-0.404974926784513
5210.4614784610603120.538521538939688
530-0.07173327529873670.0717332752987367
5410.254783906033870.74521609396613
550-0.02550135046638020.0255013504663802
5600.358743001952157-0.358743001952157
5700.272539923120178-0.272539923120178
580-0.07173327529873670.0717332752987367
590-0.07173327529873670.0717332752987367
6010.4152465362279560.584753463772044
6100.0709733378090404-0.0709733378090404
6200.318771847952535-0.318771847952535
630-0.02550135046638020.0255013504663802
6400.0709733378090404-0.0709733378090404
650-0.02550135046638020.0255013504663802
660-0.02550135046638020.0255013504663802
6710.4689628687031780.531037131296822
680-0.03298575810924620.0329857581092462
690-0.07173327529873670.0717332752987367
7000.25478390603387-0.25478390603387
710-0.02550135046638020.0255013504663802
720-0.07173327529873670.0717332752987367
7300.208551981201514-0.208551981201514
7400.247299498391004-0.247299498391004
750-0.07173327529873670.0717332752987367
7600.142445687370571-0.142445687370571
770-0.07173327529873670.0717332752987367
7800.272539923120178-0.272539923120178
7910.3587430019521570.641256998047843
8000.188677612202928-0.188677612202928
810-0.02550135046638020.0255013504663802
8200.201067573558648-0.201067573558648
830-0.02550135046638020.0255013504663802
8410.254783906033870.74521609396613
850-0.007745333380072090.00774533338007209
860-0.03298575810924620.0329857581092462

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.0709733378090402 & -0.0709733378090402 \tabularnewline
2 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
3 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
4 & 0 & -0.0255013504663804 & 0.0255013504663804 \tabularnewline
5 & 0 & -0.0255013504663801 & 0.0255013504663801 \tabularnewline
6 & 0 & -0.0152297410229381 & 0.0152297410229381 \tabularnewline
7 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
8 & 0 & 0.124689670284263 & -0.124689670284263 \tabularnewline
9 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
10 & 0 & -0.0329857581092462 & 0.0329857581092462 \tabularnewline
11 & 0 & 0.117205262641397 & -0.117205262641397 \tabularnewline
12 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
13 & 0 & 0.318771847952535 & -0.318771847952535 \tabularnewline
14 & 0 & 0.117205262641397 & -0.117205262641397 \tabularnewline
15 & 0 & 0.272539923120178 & -0.272539923120178 \tabularnewline
16 & 0 & 0.422730943870821 & -0.422730943870821 \tabularnewline
17 & 1 & 0.461478461060312 & 0.538521538939688 \tabularnewline
18 & 0 & 0.117205262641397 & -0.117205262641397 \tabularnewline
19 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
20 & 1 & 0.422730943870821 & 0.577269056129179 \tabularnewline
21 & 0 & 0.0310021838094185 & -0.0310021838094185 \tabularnewline
22 & 0 & 0.265055515477312 & -0.265055515477312 \tabularnewline
23 & 0 & -0.00774533338007209 & 0.00774533338007209 \tabularnewline
24 & 0 & -0.0152297410229381 & 0.0152297410229381 \tabularnewline
25 & 0 & 0.358743001952157 & -0.358743001952157 \tabularnewline
26 & 0 & 0.318771847952535 & -0.318771847952535 \tabularnewline
27 & 0 & -0.0792176829416027 & 0.0792176829416027 \tabularnewline
28 & 0 & 0.25478390603387 & -0.25478390603387 \tabularnewline
29 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
30 & 0 & 0.0384865914522844 & -0.0384865914522844 \tabularnewline
31 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
32 & 0 & -0.0329857581092462 & 0.0329857581092462 \tabularnewline
33 & 0 & 0.0310021838094185 & -0.0310021838094185 \tabularnewline
34 & 0 & 0.0784577454519064 & -0.0784577454519064 \tabularnewline
35 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
36 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
37 & 0 & 0.461478461060312 & -0.461478461060312 \tabularnewline
38 & 0 & 0.208551981201514 & -0.208551981201514 \tabularnewline
39 & 0 & -0.00774533338007209 & 0.00774533338007209 \tabularnewline
40 & 0 & 0.188677612202928 & -0.188677612202928 \tabularnewline
41 & 1 & 0.272539923120178 & 0.727460076879822 \tabularnewline
42 & 0 & 0.208551981201514 & -0.208551981201514 \tabularnewline
43 & 0 & -0.0152297410229381 & 0.0152297410229381 \tabularnewline
44 & 0 & 0.117205262641397 & -0.117205262641397 \tabularnewline
45 & 0 & 0.0384865914522844 & -0.0384865914522844 \tabularnewline
46 & 0 & -0.00774533338007209 & 0.00774533338007209 \tabularnewline
47 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
48 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
49 & 0 & -0.00774533338007209 & 0.00774533338007209 \tabularnewline
50 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
51 & 0 & 0.404974926784513 & -0.404974926784513 \tabularnewline
52 & 1 & 0.461478461060312 & 0.538521538939688 \tabularnewline
53 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
54 & 1 & 0.25478390603387 & 0.74521609396613 \tabularnewline
55 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
56 & 0 & 0.358743001952157 & -0.358743001952157 \tabularnewline
57 & 0 & 0.272539923120178 & -0.272539923120178 \tabularnewline
58 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
59 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
60 & 1 & 0.415246536227956 & 0.584753463772044 \tabularnewline
61 & 0 & 0.0709733378090404 & -0.0709733378090404 \tabularnewline
62 & 0 & 0.318771847952535 & -0.318771847952535 \tabularnewline
63 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
64 & 0 & 0.0709733378090404 & -0.0709733378090404 \tabularnewline
65 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
66 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
67 & 1 & 0.468962868703178 & 0.531037131296822 \tabularnewline
68 & 0 & -0.0329857581092462 & 0.0329857581092462 \tabularnewline
69 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
70 & 0 & 0.25478390603387 & -0.25478390603387 \tabularnewline
71 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
72 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
73 & 0 & 0.208551981201514 & -0.208551981201514 \tabularnewline
74 & 0 & 0.247299498391004 & -0.247299498391004 \tabularnewline
75 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
76 & 0 & 0.142445687370571 & -0.142445687370571 \tabularnewline
77 & 0 & -0.0717332752987367 & 0.0717332752987367 \tabularnewline
78 & 0 & 0.272539923120178 & -0.272539923120178 \tabularnewline
79 & 1 & 0.358743001952157 & 0.641256998047843 \tabularnewline
80 & 0 & 0.188677612202928 & -0.188677612202928 \tabularnewline
81 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
82 & 0 & 0.201067573558648 & -0.201067573558648 \tabularnewline
83 & 0 & -0.0255013504663802 & 0.0255013504663802 \tabularnewline
84 & 1 & 0.25478390603387 & 0.74521609396613 \tabularnewline
85 & 0 & -0.00774533338007209 & 0.00774533338007209 \tabularnewline
86 & 0 & -0.0329857581092462 & 0.0329857581092462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203901&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]0[/C][C]0.0709733378090402[/C][C]-0.0709733378090402[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0255013504663804[/C][C]0.0255013504663804[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0255013504663801[/C][C]0.0255013504663801[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.0152297410229381[/C][C]0.0152297410229381[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.124689670284263[/C][C]-0.124689670284263[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0329857581092462[/C][C]0.0329857581092462[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.117205262641397[/C][C]-0.117205262641397[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.318771847952535[/C][C]-0.318771847952535[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.117205262641397[/C][C]-0.117205262641397[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.272539923120178[/C][C]-0.272539923120178[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.422730943870821[/C][C]-0.422730943870821[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.461478461060312[/C][C]0.538521538939688[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.117205262641397[/C][C]-0.117205262641397[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.422730943870821[/C][C]0.577269056129179[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0310021838094185[/C][C]-0.0310021838094185[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.265055515477312[/C][C]-0.265055515477312[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]-0.00774533338007209[/C][C]0.00774533338007209[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]-0.0152297410229381[/C][C]0.0152297410229381[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.358743001952157[/C][C]-0.358743001952157[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.318771847952535[/C][C]-0.318771847952535[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.0792176829416027[/C][C]0.0792176829416027[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.25478390603387[/C][C]-0.25478390603387[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0384865914522844[/C][C]-0.0384865914522844[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]-0.0329857581092462[/C][C]0.0329857581092462[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0310021838094185[/C][C]-0.0310021838094185[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.0784577454519064[/C][C]-0.0784577454519064[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.461478461060312[/C][C]-0.461478461060312[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.208551981201514[/C][C]-0.208551981201514[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]-0.00774533338007209[/C][C]0.00774533338007209[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.188677612202928[/C][C]-0.188677612202928[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.272539923120178[/C][C]0.727460076879822[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.208551981201514[/C][C]-0.208551981201514[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]-0.0152297410229381[/C][C]0.0152297410229381[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.117205262641397[/C][C]-0.117205262641397[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0384865914522844[/C][C]-0.0384865914522844[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]-0.00774533338007209[/C][C]0.00774533338007209[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]-0.00774533338007209[/C][C]0.00774533338007209[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.404974926784513[/C][C]-0.404974926784513[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.461478461060312[/C][C]0.538521538939688[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.25478390603387[/C][C]0.74521609396613[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.358743001952157[/C][C]-0.358743001952157[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.272539923120178[/C][C]-0.272539923120178[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.415246536227956[/C][C]0.584753463772044[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.0709733378090404[/C][C]-0.0709733378090404[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.318771847952535[/C][C]-0.318771847952535[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.0709733378090404[/C][C]-0.0709733378090404[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.468962868703178[/C][C]0.531037131296822[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]-0.0329857581092462[/C][C]0.0329857581092462[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.25478390603387[/C][C]-0.25478390603387[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.208551981201514[/C][C]-0.208551981201514[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.247299498391004[/C][C]-0.247299498391004[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.142445687370571[/C][C]-0.142445687370571[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-0.0717332752987367[/C][C]0.0717332752987367[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.272539923120178[/C][C]-0.272539923120178[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.358743001952157[/C][C]0.641256998047843[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.188677612202928[/C][C]-0.188677612202928[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.201067573558648[/C][C]-0.201067573558648[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.0255013504663802[/C][C]0.0255013504663802[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.25478390603387[/C][C]0.74521609396613[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]-0.00774533338007209[/C][C]0.00774533338007209[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]-0.0329857581092462[/C][C]0.0329857581092462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203901&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203901&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
100.0709733378090402-0.0709733378090402
20-0.02550135046638020.0255013504663802
30-0.02550135046638020.0255013504663802
40-0.02550135046638040.0255013504663804
50-0.02550135046638010.0255013504663801
60-0.01522974102293810.0152297410229381
70-0.02550135046638020.0255013504663802
800.124689670284263-0.124689670284263
90-0.07173327529873670.0717332752987367
100-0.03298575810924620.0329857581092462
1100.117205262641397-0.117205262641397
120-0.02550135046638020.0255013504663802
1300.318771847952535-0.318771847952535
1400.117205262641397-0.117205262641397
1500.272539923120178-0.272539923120178
1600.422730943870821-0.422730943870821
1710.4614784610603120.538521538939688
1800.117205262641397-0.117205262641397
190-0.07173327529873670.0717332752987367
2010.4227309438708210.577269056129179
2100.0310021838094185-0.0310021838094185
2200.265055515477312-0.265055515477312
230-0.007745333380072090.00774533338007209
240-0.01522974102293810.0152297410229381
2500.358743001952157-0.358743001952157
2600.318771847952535-0.318771847952535
270-0.07921768294160270.0792176829416027
2800.25478390603387-0.25478390603387
290-0.07173327529873670.0717332752987367
3000.0384865914522844-0.0384865914522844
310-0.02550135046638020.0255013504663802
320-0.03298575810924620.0329857581092462
3300.0310021838094185-0.0310021838094185
3400.0784577454519064-0.0784577454519064
350-0.02550135046638020.0255013504663802
360-0.02550135046638020.0255013504663802
3700.461478461060312-0.461478461060312
3800.208551981201514-0.208551981201514
390-0.007745333380072090.00774533338007209
4000.188677612202928-0.188677612202928
4110.2725399231201780.727460076879822
4200.208551981201514-0.208551981201514
430-0.01522974102293810.0152297410229381
4400.117205262641397-0.117205262641397
4500.0384865914522844-0.0384865914522844
460-0.007745333380072090.00774533338007209
470-0.02550135046638020.0255013504663802
480-0.07173327529873670.0717332752987367
490-0.007745333380072090.00774533338007209
500-0.02550135046638020.0255013504663802
5100.404974926784513-0.404974926784513
5210.4614784610603120.538521538939688
530-0.07173327529873670.0717332752987367
5410.254783906033870.74521609396613
550-0.02550135046638020.0255013504663802
5600.358743001952157-0.358743001952157
5700.272539923120178-0.272539923120178
580-0.07173327529873670.0717332752987367
590-0.07173327529873670.0717332752987367
6010.4152465362279560.584753463772044
6100.0709733378090404-0.0709733378090404
6200.318771847952535-0.318771847952535
630-0.02550135046638020.0255013504663802
6400.0709733378090404-0.0709733378090404
650-0.02550135046638020.0255013504663802
660-0.02550135046638020.0255013504663802
6710.4689628687031780.531037131296822
680-0.03298575810924620.0329857581092462
690-0.07173327529873670.0717332752987367
7000.25478390603387-0.25478390603387
710-0.02550135046638020.0255013504663802
720-0.07173327529873670.0717332752987367
7300.208551981201514-0.208551981201514
7400.247299498391004-0.247299498391004
750-0.07173327529873670.0717332752987367
7600.142445687370571-0.142445687370571
770-0.07173327529873670.0717332752987367
7800.272539923120178-0.272539923120178
7910.3587430019521570.641256998047843
8000.188677612202928-0.188677612202928
810-0.02550135046638020.0255013504663802
8200.201067573558648-0.201067573558648
830-0.02550135046638020.0255013504663802
8410.254783906033870.74521609396613
850-0.007745333380072090.00774533338007209
860-0.03298575810924620.0329857581092462







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9001
10001
11001
12001
13001
14001
15001
16001
170.1665107653698420.3330215307396840.833489234630158
180.1326239174515330.2652478349030650.867376082548467
190.1097360929388850.2194721858777690.890263907061115
200.5102283471946370.9795433056107250.489771652805363
210.4270862785262040.8541725570524080.572913721473796
220.4047909920349060.8095819840698120.595209007965094
230.3280955308917070.6561910617834140.671904469108293
240.2599628485203030.5199256970406060.740037151479697
250.2594436733180160.5188873466360320.740556326681984
260.2617303387990440.5234606775980890.738269661200956
270.2204326853483760.4408653706967530.779567314651624
280.1849895925408330.3699791850816670.815010407459167
290.1465652425977520.2931304851955040.853434757402248
300.1117887956073580.2235775912147170.888211204392642
310.08223681805920490.164473636118410.917763181940795
320.05934000254545470.1186800050909090.940659997454545
330.04186241453671320.08372482907342630.958137585463287
340.02981089968005240.05962179936010470.970189100319948
350.02005171772827070.04010343545654130.979948282271729
360.0131460923287570.02629218465751390.986853907671243
370.02602167291483370.05204334582966750.973978327085166
380.01974425927678990.03948851855357980.98025574072321
390.012947586928690.025895173857380.98705241307131
400.01150242307472820.02300484614945630.988497576925272
410.1677488706427330.3354977412854670.832251129357267
420.1455643013978490.2911286027956990.854435698602151
430.11203572512660.22407145025320.8879642748734
440.09104755433388490.182095108667770.908952445666115
450.06757170905445250.1351434181089050.932428290945547
460.0491716668374790.0983433336749580.950828333162521
470.03478303919079430.06956607838158870.965216960809206
480.02449389643645390.04898779287290780.975506103563546
490.0167353609870260.0334707219740520.983264639012974
500.01102497178484490.02204994356968980.988975028215155
510.02868873802239160.05737747604478310.971311261977608
520.0883623935707580.1767247871415160.911637606429242
530.06672858831625210.1334571766325040.933271411683748
540.3127384928810630.6254769857621260.687261507118937
550.254386872862470.5087737457249390.74561312713753
560.4673566197401260.9347132394802520.532643380259874
570.4384966822475850.8769933644951710.561503317752415
580.3749777228035660.7499554456071310.625022277196434
590.3148669166059320.6297338332118640.685133083394068
600.6422022569194870.7155954861610260.357797743080513
610.5848688033116610.8302623933766780.415131196688339
620.5685529781200380.8628940437599230.431447021879962
630.4939055380568340.9878110761136680.506094461943166
640.4386226579359570.8772453158719140.561377342064043
650.3633633636321790.7267267272643580.636636636367821
660.2927093647837070.5854187295674130.707290635216293
670.4377331283226820.8754662566453630.562266871677318
680.3758104929921640.7516209859843270.624189507007836
690.2944044581611920.5888089163223840.705595541838808
700.4062888672793160.8125777345586320.593711132720684
710.3315886447558360.6631772895116730.668411355244164
720.2447071416781410.4894142833562810.755292858321859
730.4085474083863160.8170948167726320.591452591613684
740.3986509709502060.7973019419004110.601349029049794
750.2835022349966140.5670044699932270.716497765003386
760.182262982256030.3645259645120590.81773701774397
770.101901869335470.203803738670940.89809813066453

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0 & 0 & 1 \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.166510765369842 & 0.333021530739684 & 0.833489234630158 \tabularnewline
18 & 0.132623917451533 & 0.265247834903065 & 0.867376082548467 \tabularnewline
19 & 0.109736092938885 & 0.219472185877769 & 0.890263907061115 \tabularnewline
20 & 0.510228347194637 & 0.979543305610725 & 0.489771652805363 \tabularnewline
21 & 0.427086278526204 & 0.854172557052408 & 0.572913721473796 \tabularnewline
22 & 0.404790992034906 & 0.809581984069812 & 0.595209007965094 \tabularnewline
23 & 0.328095530891707 & 0.656191061783414 & 0.671904469108293 \tabularnewline
24 & 0.259962848520303 & 0.519925697040606 & 0.740037151479697 \tabularnewline
25 & 0.259443673318016 & 0.518887346636032 & 0.740556326681984 \tabularnewline
26 & 0.261730338799044 & 0.523460677598089 & 0.738269661200956 \tabularnewline
27 & 0.220432685348376 & 0.440865370696753 & 0.779567314651624 \tabularnewline
28 & 0.184989592540833 & 0.369979185081667 & 0.815010407459167 \tabularnewline
29 & 0.146565242597752 & 0.293130485195504 & 0.853434757402248 \tabularnewline
30 & 0.111788795607358 & 0.223577591214717 & 0.888211204392642 \tabularnewline
31 & 0.0822368180592049 & 0.16447363611841 & 0.917763181940795 \tabularnewline
32 & 0.0593400025454547 & 0.118680005090909 & 0.940659997454545 \tabularnewline
33 & 0.0418624145367132 & 0.0837248290734263 & 0.958137585463287 \tabularnewline
34 & 0.0298108996800524 & 0.0596217993601047 & 0.970189100319948 \tabularnewline
35 & 0.0200517177282707 & 0.0401034354565413 & 0.979948282271729 \tabularnewline
36 & 0.013146092328757 & 0.0262921846575139 & 0.986853907671243 \tabularnewline
37 & 0.0260216729148337 & 0.0520433458296675 & 0.973978327085166 \tabularnewline
38 & 0.0197442592767899 & 0.0394885185535798 & 0.98025574072321 \tabularnewline
39 & 0.01294758692869 & 0.02589517385738 & 0.98705241307131 \tabularnewline
40 & 0.0115024230747282 & 0.0230048461494563 & 0.988497576925272 \tabularnewline
41 & 0.167748870642733 & 0.335497741285467 & 0.832251129357267 \tabularnewline
42 & 0.145564301397849 & 0.291128602795699 & 0.854435698602151 \tabularnewline
43 & 0.1120357251266 & 0.2240714502532 & 0.8879642748734 \tabularnewline
44 & 0.0910475543338849 & 0.18209510866777 & 0.908952445666115 \tabularnewline
45 & 0.0675717090544525 & 0.135143418108905 & 0.932428290945547 \tabularnewline
46 & 0.049171666837479 & 0.098343333674958 & 0.950828333162521 \tabularnewline
47 & 0.0347830391907943 & 0.0695660783815887 & 0.965216960809206 \tabularnewline
48 & 0.0244938964364539 & 0.0489877928729078 & 0.975506103563546 \tabularnewline
49 & 0.016735360987026 & 0.033470721974052 & 0.983264639012974 \tabularnewline
50 & 0.0110249717848449 & 0.0220499435696898 & 0.988975028215155 \tabularnewline
51 & 0.0286887380223916 & 0.0573774760447831 & 0.971311261977608 \tabularnewline
52 & 0.088362393570758 & 0.176724787141516 & 0.911637606429242 \tabularnewline
53 & 0.0667285883162521 & 0.133457176632504 & 0.933271411683748 \tabularnewline
54 & 0.312738492881063 & 0.625476985762126 & 0.687261507118937 \tabularnewline
55 & 0.25438687286247 & 0.508773745724939 & 0.74561312713753 \tabularnewline
56 & 0.467356619740126 & 0.934713239480252 & 0.532643380259874 \tabularnewline
57 & 0.438496682247585 & 0.876993364495171 & 0.561503317752415 \tabularnewline
58 & 0.374977722803566 & 0.749955445607131 & 0.625022277196434 \tabularnewline
59 & 0.314866916605932 & 0.629733833211864 & 0.685133083394068 \tabularnewline
60 & 0.642202256919487 & 0.715595486161026 & 0.357797743080513 \tabularnewline
61 & 0.584868803311661 & 0.830262393376678 & 0.415131196688339 \tabularnewline
62 & 0.568552978120038 & 0.862894043759923 & 0.431447021879962 \tabularnewline
63 & 0.493905538056834 & 0.987811076113668 & 0.506094461943166 \tabularnewline
64 & 0.438622657935957 & 0.877245315871914 & 0.561377342064043 \tabularnewline
65 & 0.363363363632179 & 0.726726727264358 & 0.636636636367821 \tabularnewline
66 & 0.292709364783707 & 0.585418729567413 & 0.707290635216293 \tabularnewline
67 & 0.437733128322682 & 0.875466256645363 & 0.562266871677318 \tabularnewline
68 & 0.375810492992164 & 0.751620985984327 & 0.624189507007836 \tabularnewline
69 & 0.294404458161192 & 0.588808916322384 & 0.705595541838808 \tabularnewline
70 & 0.406288867279316 & 0.812577734558632 & 0.593711132720684 \tabularnewline
71 & 0.331588644755836 & 0.663177289511673 & 0.668411355244164 \tabularnewline
72 & 0.244707141678141 & 0.489414283356281 & 0.755292858321859 \tabularnewline
73 & 0.408547408386316 & 0.817094816772632 & 0.591452591613684 \tabularnewline
74 & 0.398650970950206 & 0.797301941900411 & 0.601349029049794 \tabularnewline
75 & 0.283502234996614 & 0.567004469993227 & 0.716497765003386 \tabularnewline
76 & 0.18226298225603 & 0.364525964512059 & 0.81773701774397 \tabularnewline
77 & 0.10190186933547 & 0.20380373867094 & 0.89809813066453 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203901&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.166510765369842[/C][C]0.333021530739684[/C][C]0.833489234630158[/C][/ROW]
[ROW][C]18[/C][C]0.132623917451533[/C][C]0.265247834903065[/C][C]0.867376082548467[/C][/ROW]
[ROW][C]19[/C][C]0.109736092938885[/C][C]0.219472185877769[/C][C]0.890263907061115[/C][/ROW]
[ROW][C]20[/C][C]0.510228347194637[/C][C]0.979543305610725[/C][C]0.489771652805363[/C][/ROW]
[ROW][C]21[/C][C]0.427086278526204[/C][C]0.854172557052408[/C][C]0.572913721473796[/C][/ROW]
[ROW][C]22[/C][C]0.404790992034906[/C][C]0.809581984069812[/C][C]0.595209007965094[/C][/ROW]
[ROW][C]23[/C][C]0.328095530891707[/C][C]0.656191061783414[/C][C]0.671904469108293[/C][/ROW]
[ROW][C]24[/C][C]0.259962848520303[/C][C]0.519925697040606[/C][C]0.740037151479697[/C][/ROW]
[ROW][C]25[/C][C]0.259443673318016[/C][C]0.518887346636032[/C][C]0.740556326681984[/C][/ROW]
[ROW][C]26[/C][C]0.261730338799044[/C][C]0.523460677598089[/C][C]0.738269661200956[/C][/ROW]
[ROW][C]27[/C][C]0.220432685348376[/C][C]0.440865370696753[/C][C]0.779567314651624[/C][/ROW]
[ROW][C]28[/C][C]0.184989592540833[/C][C]0.369979185081667[/C][C]0.815010407459167[/C][/ROW]
[ROW][C]29[/C][C]0.146565242597752[/C][C]0.293130485195504[/C][C]0.853434757402248[/C][/ROW]
[ROW][C]30[/C][C]0.111788795607358[/C][C]0.223577591214717[/C][C]0.888211204392642[/C][/ROW]
[ROW][C]31[/C][C]0.0822368180592049[/C][C]0.16447363611841[/C][C]0.917763181940795[/C][/ROW]
[ROW][C]32[/C][C]0.0593400025454547[/C][C]0.118680005090909[/C][C]0.940659997454545[/C][/ROW]
[ROW][C]33[/C][C]0.0418624145367132[/C][C]0.0837248290734263[/C][C]0.958137585463287[/C][/ROW]
[ROW][C]34[/C][C]0.0298108996800524[/C][C]0.0596217993601047[/C][C]0.970189100319948[/C][/ROW]
[ROW][C]35[/C][C]0.0200517177282707[/C][C]0.0401034354565413[/C][C]0.979948282271729[/C][/ROW]
[ROW][C]36[/C][C]0.013146092328757[/C][C]0.0262921846575139[/C][C]0.986853907671243[/C][/ROW]
[ROW][C]37[/C][C]0.0260216729148337[/C][C]0.0520433458296675[/C][C]0.973978327085166[/C][/ROW]
[ROW][C]38[/C][C]0.0197442592767899[/C][C]0.0394885185535798[/C][C]0.98025574072321[/C][/ROW]
[ROW][C]39[/C][C]0.01294758692869[/C][C]0.02589517385738[/C][C]0.98705241307131[/C][/ROW]
[ROW][C]40[/C][C]0.0115024230747282[/C][C]0.0230048461494563[/C][C]0.988497576925272[/C][/ROW]
[ROW][C]41[/C][C]0.167748870642733[/C][C]0.335497741285467[/C][C]0.832251129357267[/C][/ROW]
[ROW][C]42[/C][C]0.145564301397849[/C][C]0.291128602795699[/C][C]0.854435698602151[/C][/ROW]
[ROW][C]43[/C][C]0.1120357251266[/C][C]0.2240714502532[/C][C]0.8879642748734[/C][/ROW]
[ROW][C]44[/C][C]0.0910475543338849[/C][C]0.18209510866777[/C][C]0.908952445666115[/C][/ROW]
[ROW][C]45[/C][C]0.0675717090544525[/C][C]0.135143418108905[/C][C]0.932428290945547[/C][/ROW]
[ROW][C]46[/C][C]0.049171666837479[/C][C]0.098343333674958[/C][C]0.950828333162521[/C][/ROW]
[ROW][C]47[/C][C]0.0347830391907943[/C][C]0.0695660783815887[/C][C]0.965216960809206[/C][/ROW]
[ROW][C]48[/C][C]0.0244938964364539[/C][C]0.0489877928729078[/C][C]0.975506103563546[/C][/ROW]
[ROW][C]49[/C][C]0.016735360987026[/C][C]0.033470721974052[/C][C]0.983264639012974[/C][/ROW]
[ROW][C]50[/C][C]0.0110249717848449[/C][C]0.0220499435696898[/C][C]0.988975028215155[/C][/ROW]
[ROW][C]51[/C][C]0.0286887380223916[/C][C]0.0573774760447831[/C][C]0.971311261977608[/C][/ROW]
[ROW][C]52[/C][C]0.088362393570758[/C][C]0.176724787141516[/C][C]0.911637606429242[/C][/ROW]
[ROW][C]53[/C][C]0.0667285883162521[/C][C]0.133457176632504[/C][C]0.933271411683748[/C][/ROW]
[ROW][C]54[/C][C]0.312738492881063[/C][C]0.625476985762126[/C][C]0.687261507118937[/C][/ROW]
[ROW][C]55[/C][C]0.25438687286247[/C][C]0.508773745724939[/C][C]0.74561312713753[/C][/ROW]
[ROW][C]56[/C][C]0.467356619740126[/C][C]0.934713239480252[/C][C]0.532643380259874[/C][/ROW]
[ROW][C]57[/C][C]0.438496682247585[/C][C]0.876993364495171[/C][C]0.561503317752415[/C][/ROW]
[ROW][C]58[/C][C]0.374977722803566[/C][C]0.749955445607131[/C][C]0.625022277196434[/C][/ROW]
[ROW][C]59[/C][C]0.314866916605932[/C][C]0.629733833211864[/C][C]0.685133083394068[/C][/ROW]
[ROW][C]60[/C][C]0.642202256919487[/C][C]0.715595486161026[/C][C]0.357797743080513[/C][/ROW]
[ROW][C]61[/C][C]0.584868803311661[/C][C]0.830262393376678[/C][C]0.415131196688339[/C][/ROW]
[ROW][C]62[/C][C]0.568552978120038[/C][C]0.862894043759923[/C][C]0.431447021879962[/C][/ROW]
[ROW][C]63[/C][C]0.493905538056834[/C][C]0.987811076113668[/C][C]0.506094461943166[/C][/ROW]
[ROW][C]64[/C][C]0.438622657935957[/C][C]0.877245315871914[/C][C]0.561377342064043[/C][/ROW]
[ROW][C]65[/C][C]0.363363363632179[/C][C]0.726726727264358[/C][C]0.636636636367821[/C][/ROW]
[ROW][C]66[/C][C]0.292709364783707[/C][C]0.585418729567413[/C][C]0.707290635216293[/C][/ROW]
[ROW][C]67[/C][C]0.437733128322682[/C][C]0.875466256645363[/C][C]0.562266871677318[/C][/ROW]
[ROW][C]68[/C][C]0.375810492992164[/C][C]0.751620985984327[/C][C]0.624189507007836[/C][/ROW]
[ROW][C]69[/C][C]0.294404458161192[/C][C]0.588808916322384[/C][C]0.705595541838808[/C][/ROW]
[ROW][C]70[/C][C]0.406288867279316[/C][C]0.812577734558632[/C][C]0.593711132720684[/C][/ROW]
[ROW][C]71[/C][C]0.331588644755836[/C][C]0.663177289511673[/C][C]0.668411355244164[/C][/ROW]
[ROW][C]72[/C][C]0.244707141678141[/C][C]0.489414283356281[/C][C]0.755292858321859[/C][/ROW]
[ROW][C]73[/C][C]0.408547408386316[/C][C]0.817094816772632[/C][C]0.591452591613684[/C][/ROW]
[ROW][C]74[/C][C]0.398650970950206[/C][C]0.797301941900411[/C][C]0.601349029049794[/C][/ROW]
[ROW][C]75[/C][C]0.283502234996614[/C][C]0.567004469993227[/C][C]0.716497765003386[/C][/ROW]
[ROW][C]76[/C][C]0.18226298225603[/C][C]0.364525964512059[/C][C]0.81773701774397[/C][/ROW]
[ROW][C]77[/C][C]0.10190186933547[/C][C]0.20380373867094[/C][C]0.89809813066453[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203901&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203901&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
9001
10001
11001
12001
13001
14001
15001
16001
170.1665107653698420.3330215307396840.833489234630158
180.1326239174515330.2652478349030650.867376082548467
190.1097360929388850.2194721858777690.890263907061115
200.5102283471946370.9795433056107250.489771652805363
210.4270862785262040.8541725570524080.572913721473796
220.4047909920349060.8095819840698120.595209007965094
230.3280955308917070.6561910617834140.671904469108293
240.2599628485203030.5199256970406060.740037151479697
250.2594436733180160.5188873466360320.740556326681984
260.2617303387990440.5234606775980890.738269661200956
270.2204326853483760.4408653706967530.779567314651624
280.1849895925408330.3699791850816670.815010407459167
290.1465652425977520.2931304851955040.853434757402248
300.1117887956073580.2235775912147170.888211204392642
310.08223681805920490.164473636118410.917763181940795
320.05934000254545470.1186800050909090.940659997454545
330.04186241453671320.08372482907342630.958137585463287
340.02981089968005240.05962179936010470.970189100319948
350.02005171772827070.04010343545654130.979948282271729
360.0131460923287570.02629218465751390.986853907671243
370.02602167291483370.05204334582966750.973978327085166
380.01974425927678990.03948851855357980.98025574072321
390.012947586928690.025895173857380.98705241307131
400.01150242307472820.02300484614945630.988497576925272
410.1677488706427330.3354977412854670.832251129357267
420.1455643013978490.2911286027956990.854435698602151
430.11203572512660.22407145025320.8879642748734
440.09104755433388490.182095108667770.908952445666115
450.06757170905445250.1351434181089050.932428290945547
460.0491716668374790.0983433336749580.950828333162521
470.03478303919079430.06956607838158870.965216960809206
480.02449389643645390.04898779287290780.975506103563546
490.0167353609870260.0334707219740520.983264639012974
500.01102497178484490.02204994356968980.988975028215155
510.02868873802239160.05737747604478310.971311261977608
520.0883623935707580.1767247871415160.911637606429242
530.06672858831625210.1334571766325040.933271411683748
540.3127384928810630.6254769857621260.687261507118937
550.254386872862470.5087737457249390.74561312713753
560.4673566197401260.9347132394802520.532643380259874
570.4384966822475850.8769933644951710.561503317752415
580.3749777228035660.7499554456071310.625022277196434
590.3148669166059320.6297338332118640.685133083394068
600.6422022569194870.7155954861610260.357797743080513
610.5848688033116610.8302623933766780.415131196688339
620.5685529781200380.8628940437599230.431447021879962
630.4939055380568340.9878110761136680.506094461943166
640.4386226579359570.8772453158719140.561377342064043
650.3633633636321790.7267267272643580.636636636367821
660.2927093647837070.5854187295674130.707290635216293
670.4377331283226820.8754662566453630.562266871677318
680.3758104929921640.7516209859843270.624189507007836
690.2944044581611920.5888089163223840.705595541838808
700.4062888672793160.8125777345586320.593711132720684
710.3315886447558360.6631772895116730.668411355244164
720.2447071416781410.4894142833562810.755292858321859
730.4085474083863160.8170948167726320.591452591613684
740.3986509709502060.7973019419004110.601349029049794
750.2835022349966140.5670044699932270.716497765003386
760.182262982256030.3645259645120590.81773701774397
770.101901869335470.203803738670940.89809813066453







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.115942028985507NOK
5% type I error level160.231884057971014NOK
10% type I error level220.318840579710145NOK

\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 & 8 & 0.115942028985507 & NOK \tabularnewline
5% type I error level & 16 & 0.231884057971014 & NOK \tabularnewline
10% type I error level & 22 & 0.318840579710145 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203901&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]8[/C][C]0.115942028985507[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.231884057971014[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.318840579710145[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203901&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203901&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 level80.115942028985507NOK
5% type I error level160.231884057971014NOK
10% type I error level220.318840579710145NOK



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 ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}