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R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 01 Dec 2015 11:39:35 +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/2015/Dec/01/t144897002114d8dvify3kx041.htm/, Retrieved Thu, 31 Oct 2024 23:56:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284679, Retrieved Thu, 31 Oct 2024 23:56:44 +0000
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IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multiple regression] [2015-12-01 11:39:35] [60e7016130b28a0c8bd4011f80276a66] [Current]
- R P     [Multiple Regression] [multiple regression] [2015-12-09 09:55:02] [414a104444a80061dc262f896789948b]
- R P     [Multiple Regression] [multiple regression] [2015-12-09 13:16:55] [414a104444a80061dc262f896789948b]
- R P     [Multiple Regression] [multiple regression] [2015-12-09 14:33:38] [414a104444a80061dc262f896789948b]
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Dataseries X:
2.3 6.5
1.9 6.8
0.6 6.8
0.6 6.5
-0.4 6.2
-1.1 6.2
-1.7 6.6
-0.8 6.7
-1.2 6.5
-1 6.4
-0.1 6.5
0.3 6.8
0.6 7.1
0.7 7.2
1.7 7.1
1.8 7
2.3 6.9
2.5 6.9
2.6 7.4
2.3 7.3
2.9 7
3 6.8
2.9 6.5
3.1 6.4
3.2 6.3
3.4 6
3.5 5.9
3.4 5.7
3.4 5.7
3.7 5.7
3.8 6.2
3.6 6.4
3.6 6.2
3.6 6.2
3.9 6.1
3.5 6.1
3.7 6.2
3.7 6.1
3.4 6.1
3.2 6.2
2.8 6.2
2.3 6.2
2.3 6.4
2.9 6.4
2.8 6.4
2.8 6.7
2.3 6.9
2.2 7.1
1.5 7.3
1.2 7.2
1.1 7.1
1 6.9
1.2 6.8
1.6 6.7
1.5 7.2
1 7.2
0.9 7.1
0.6 7.1
0.8 7
1 7.1
1.1 7.3
1 7.2
0.9 7.1
0.6 7
0.4 6.9
0.3 7
0.3 7.5
0 7.6
-0.1 7.5
0.1 7.3
-0.1 7.3
-0.4 7.4
-0.7 7.7
-0.4 7.8
-0.4 7.7
0.3 7.5
0.6 7.3
0.6 7.3
0.5 7.6
0.9 7.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284679&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284679&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284679&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 time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
inflatie[t] = + 2.32696 -0.302509werkloosheid[t] + 1.0171`inflatie(t-1)`[t] -0.0403095`inflatie(t-2)`[t] + 0.0343057`inflatie(t-3)`[t] + 0.15618`inflatie(t-4)`[t] -0.31618`inflatie(t-5)`[t] -0.13707M1[t] -0.0366307M2[t] + 0.116085M3[t] -0.00287516M4[t] + 0.00458259M5[t] + 0.0507854M6[t] -0.0502439M7[t] -0.033727M8[t] + 0.0164569M9[t] + 0.0503192M10[t] -0.0352712M11[t] -6.89296e-05t + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
inflatie[t] =  +  2.32696 -0.302509werkloosheid[t] +  1.0171`inflatie(t-1)`[t] -0.0403095`inflatie(t-2)`[t] +  0.0343057`inflatie(t-3)`[t] +  0.15618`inflatie(t-4)`[t] -0.31618`inflatie(t-5)`[t] -0.13707M1[t] -0.0366307M2[t] +  0.116085M3[t] -0.00287516M4[t] +  0.00458259M5[t] +  0.0507854M6[t] -0.0502439M7[t] -0.033727M8[t] +  0.0164569M9[t] +  0.0503192M10[t] -0.0352712M11[t] -6.89296e-05t  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284679&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]inflatie[t] =  +  2.32696 -0.302509werkloosheid[t] +  1.0171`inflatie(t-1)`[t] -0.0403095`inflatie(t-2)`[t] +  0.0343057`inflatie(t-3)`[t] +  0.15618`inflatie(t-4)`[t] -0.31618`inflatie(t-5)`[t] -0.13707M1[t] -0.0366307M2[t] +  0.116085M3[t] -0.00287516M4[t] +  0.00458259M5[t] +  0.0507854M6[t] -0.0502439M7[t] -0.033727M8[t] +  0.0164569M9[t] +  0.0503192M10[t] -0.0352712M11[t] -6.89296e-05t  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284679&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284679&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
inflatie[t] = + 2.32696 -0.302509werkloosheid[t] + 1.0171`inflatie(t-1)`[t] -0.0403095`inflatie(t-2)`[t] + 0.0343057`inflatie(t-3)`[t] + 0.15618`inflatie(t-4)`[t] -0.31618`inflatie(t-5)`[t] -0.13707M1[t] -0.0366307M2[t] + 0.116085M3[t] -0.00287516M4[t] + 0.00458259M5[t] + 0.0507854M6[t] -0.0502439M7[t] -0.033727M8[t] + 0.0164569M9[t] + 0.0503192M10[t] -0.0352712M11[t] -6.89296e-05t + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+2.327 1.036+2.2450e+00 0.02872 0.01436
werkloosheid-0.3025 0.1565-1.9330e+00 0.05832 0.02916
`inflatie(t-1)`+1.017 0.1213+8.3820e+00 1.828e-11 9.142e-12
`inflatie(t-2)`-0.04031 0.1753-2.3000e-01 0.819 0.4095
`inflatie(t-3)`+0.03431 0.1676+2.0460e-01 0.8386 0.4193
`inflatie(t-4)`+0.1562 0.1634+9.5560e-01 0.3434 0.1717
`inflatie(t-5)`-0.3162 0.1193-2.6490e+00 0.01046 0.00523
M1-0.1371 0.1899-7.2170e-01 0.4735 0.2367
M2-0.03663 0.1996-1.8360e-01 0.855 0.4275
M3+0.1161 0.2001+5.8020e-01 0.5641 0.2821
M4-0.002875 0.2024-1.4210e-02 0.9887 0.4944
M5+0.004583 0.199+2.3030e-02 0.9817 0.4909
M6+0.05078 0.1978+2.5680e-01 0.7983 0.3991
M7-0.05024 0.1995-2.5180e-01 0.8021 0.401
M8-0.03373 0.2041-1.6520e-01 0.8694 0.4347
M9+0.01646 0.2014+8.1730e-02 0.9352 0.4676
M10+0.05032 0.1998+2.5190e-01 0.8021 0.401
M11-0.03527 0.1972-1.7890e-01 0.8587 0.4293
t-6.893e-05 0.00263-2.6210e-02 0.9792 0.4896

\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) & +2.327 &  1.036 & +2.2450e+00 &  0.02872 &  0.01436 \tabularnewline
werkloosheid & -0.3025 &  0.1565 & -1.9330e+00 &  0.05832 &  0.02916 \tabularnewline
`inflatie(t-1)` & +1.017 &  0.1213 & +8.3820e+00 &  1.828e-11 &  9.142e-12 \tabularnewline
`inflatie(t-2)` & -0.04031 &  0.1753 & -2.3000e-01 &  0.819 &  0.4095 \tabularnewline
`inflatie(t-3)` & +0.03431 &  0.1676 & +2.0460e-01 &  0.8386 &  0.4193 \tabularnewline
`inflatie(t-4)` & +0.1562 &  0.1634 & +9.5560e-01 &  0.3434 &  0.1717 \tabularnewline
`inflatie(t-5)` & -0.3162 &  0.1193 & -2.6490e+00 &  0.01046 &  0.00523 \tabularnewline
M1 & -0.1371 &  0.1899 & -7.2170e-01 &  0.4735 &  0.2367 \tabularnewline
M2 & -0.03663 &  0.1996 & -1.8360e-01 &  0.855 &  0.4275 \tabularnewline
M3 & +0.1161 &  0.2001 & +5.8020e-01 &  0.5641 &  0.2821 \tabularnewline
M4 & -0.002875 &  0.2024 & -1.4210e-02 &  0.9887 &  0.4944 \tabularnewline
M5 & +0.004583 &  0.199 & +2.3030e-02 &  0.9817 &  0.4909 \tabularnewline
M6 & +0.05078 &  0.1978 & +2.5680e-01 &  0.7983 &  0.3991 \tabularnewline
M7 & -0.05024 &  0.1995 & -2.5180e-01 &  0.8021 &  0.401 \tabularnewline
M8 & -0.03373 &  0.2041 & -1.6520e-01 &  0.8694 &  0.4347 \tabularnewline
M9 & +0.01646 &  0.2014 & +8.1730e-02 &  0.9352 &  0.4676 \tabularnewline
M10 & +0.05032 &  0.1998 & +2.5190e-01 &  0.8021 &  0.401 \tabularnewline
M11 & -0.03527 &  0.1972 & -1.7890e-01 &  0.8587 &  0.4293 \tabularnewline
t & -6.893e-05 &  0.00263 & -2.6210e-02 &  0.9792 &  0.4896 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284679&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]+2.327[/C][C] 1.036[/C][C]+2.2450e+00[/C][C] 0.02872[/C][C] 0.01436[/C][/ROW]
[ROW][C]werkloosheid[/C][C]-0.3025[/C][C] 0.1565[/C][C]-1.9330e+00[/C][C] 0.05832[/C][C] 0.02916[/C][/ROW]
[ROW][C]`inflatie(t-1)`[/C][C]+1.017[/C][C] 0.1213[/C][C]+8.3820e+00[/C][C] 1.828e-11[/C][C] 9.142e-12[/C][/ROW]
[ROW][C]`inflatie(t-2)`[/C][C]-0.04031[/C][C] 0.1753[/C][C]-2.3000e-01[/C][C] 0.819[/C][C] 0.4095[/C][/ROW]
[ROW][C]`inflatie(t-3)`[/C][C]+0.03431[/C][C] 0.1676[/C][C]+2.0460e-01[/C][C] 0.8386[/C][C] 0.4193[/C][/ROW]
[ROW][C]`inflatie(t-4)`[/C][C]+0.1562[/C][C] 0.1634[/C][C]+9.5560e-01[/C][C] 0.3434[/C][C] 0.1717[/C][/ROW]
[ROW][C]`inflatie(t-5)`[/C][C]-0.3162[/C][C] 0.1193[/C][C]-2.6490e+00[/C][C] 0.01046[/C][C] 0.00523[/C][/ROW]
[ROW][C]M1[/C][C]-0.1371[/C][C] 0.1899[/C][C]-7.2170e-01[/C][C] 0.4735[/C][C] 0.2367[/C][/ROW]
[ROW][C]M2[/C][C]-0.03663[/C][C] 0.1996[/C][C]-1.8360e-01[/C][C] 0.855[/C][C] 0.4275[/C][/ROW]
[ROW][C]M3[/C][C]+0.1161[/C][C] 0.2001[/C][C]+5.8020e-01[/C][C] 0.5641[/C][C] 0.2821[/C][/ROW]
[ROW][C]M4[/C][C]-0.002875[/C][C] 0.2024[/C][C]-1.4210e-02[/C][C] 0.9887[/C][C] 0.4944[/C][/ROW]
[ROW][C]M5[/C][C]+0.004583[/C][C] 0.199[/C][C]+2.3030e-02[/C][C] 0.9817[/C][C] 0.4909[/C][/ROW]
[ROW][C]M6[/C][C]+0.05078[/C][C] 0.1978[/C][C]+2.5680e-01[/C][C] 0.7983[/C][C] 0.3991[/C][/ROW]
[ROW][C]M7[/C][C]-0.05024[/C][C] 0.1995[/C][C]-2.5180e-01[/C][C] 0.8021[/C][C] 0.401[/C][/ROW]
[ROW][C]M8[/C][C]-0.03373[/C][C] 0.2041[/C][C]-1.6520e-01[/C][C] 0.8694[/C][C] 0.4347[/C][/ROW]
[ROW][C]M9[/C][C]+0.01646[/C][C] 0.2014[/C][C]+8.1730e-02[/C][C] 0.9352[/C][C] 0.4676[/C][/ROW]
[ROW][C]M10[/C][C]+0.05032[/C][C] 0.1998[/C][C]+2.5190e-01[/C][C] 0.8021[/C][C] 0.401[/C][/ROW]
[ROW][C]M11[/C][C]-0.03527[/C][C] 0.1972[/C][C]-1.7890e-01[/C][C] 0.8587[/C][C] 0.4293[/C][/ROW]
[ROW][C]t[/C][C]-6.893e-05[/C][C] 0.00263[/C][C]-2.6210e-02[/C][C] 0.9792[/C][C] 0.4896[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284679&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284679&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)+2.327 1.036+2.2450e+00 0.02872 0.01436
werkloosheid-0.3025 0.1565-1.9330e+00 0.05832 0.02916
`inflatie(t-1)`+1.017 0.1213+8.3820e+00 1.828e-11 9.142e-12
`inflatie(t-2)`-0.04031 0.1753-2.3000e-01 0.819 0.4095
`inflatie(t-3)`+0.03431 0.1676+2.0460e-01 0.8386 0.4193
`inflatie(t-4)`+0.1562 0.1634+9.5560e-01 0.3434 0.1717
`inflatie(t-5)`-0.3162 0.1193-2.6490e+00 0.01046 0.00523
M1-0.1371 0.1899-7.2170e-01 0.4735 0.2367
M2-0.03663 0.1996-1.8360e-01 0.855 0.4275
M3+0.1161 0.2001+5.8020e-01 0.5641 0.2821
M4-0.002875 0.2024-1.4210e-02 0.9887 0.4944
M5+0.004583 0.199+2.3030e-02 0.9817 0.4909
M6+0.05078 0.1978+2.5680e-01 0.7983 0.3991
M7-0.05024 0.1995-2.5180e-01 0.8021 0.401
M8-0.03373 0.2041-1.6520e-01 0.8694 0.4347
M9+0.01646 0.2014+8.1730e-02 0.9352 0.4676
M10+0.05032 0.1998+2.5190e-01 0.8021 0.401
M11-0.03527 0.1972-1.7890e-01 0.8587 0.4293
t-6.893e-05 0.00263-2.6210e-02 0.9792 0.4896







Multiple Linear Regression - Regression Statistics
Multiple R 0.9806
R-squared 0.9616
Adjusted R-squared 0.9493
F-TEST (value) 77.99
F-TEST (DF numerator)18
F-TEST (DF denominator)56
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.3389
Sum Squared Residuals 6.433

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9806 \tabularnewline
R-squared &  0.9616 \tabularnewline
Adjusted R-squared &  0.9493 \tabularnewline
F-TEST (value) &  77.99 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.3389 \tabularnewline
Sum Squared Residuals &  6.433 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284679&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9806[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9616[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9493[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 77.99[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.3389[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 6.433[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284679&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284679&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 R 0.9806
R-squared 0.9616
Adjusted R-squared 0.9493
F-TEST (value) 77.99
F-TEST (DF numerator)18
F-TEST (DF denominator)56
p-value 0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 0.3389
Sum Squared Residuals 6.433







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1.1-0.5267-0.5733
2-1.7-1.296-0.4045
3-0.8-1.378 0.5784
4-1.2-0.6776-0.5224
5-1-0.8968-0.1032
6-0.1-0.5029 0.4029
7 0.3 0.5292-0.2292
8 0.6 0.4853 0.1147
9 0.7 0.9827-0.2827
10 1.7 1.227 0.4726
11 1.8 1.973-0.1733
12 2.3 2.024 0.2761
13 2.5 2.346 0.1536
14 2.6 2.607-0.00677
15 2.3 2.6-0.2999
16 2.9 2.316 0.5842
17 3 2.883 0.1174
18 2.9 3.039-0.1391
19 3.1 2.805 0.2954
20 3.2 3.251-0.05079
21 3.4 3.308 0.09221
22 3.5 3.531-0.03084
23 3.4 3.666-0.2656
24 3.4 3.554-0.1543
25 3.7 3.424 0.2757
26 3.8 3.627 0.1725
27 3.6 3.762-0.162
28 3.6 3.538 0.06208
29 3.6 3.604-0.003655
30 3.9 3.594 0.3061
31 3.5 3.735-0.2351
32 3.7 3.366 0.3344
33 3.7 3.676 0.02418
34 3.4 3.735-0.3347
35 3.2 3.163 0.03682
36 2.8 3.165-0.3648
37 2.3 2.555-0.2553
38 2.3 2.049 0.251
39 2.9 2.272 0.6283
40 2.8 2.747 0.05342
41 2.8 2.586 0.2143
42 2.3 2.754-0.454
43 2.2 2.174 0.02583
44 1.5 1.843-0.3432
45 1.2 1.23-0.03012
46 1.1 0.9357 0.1643
47 1 0.9394 0.06058
48 1.2 0.8192 0.3808
49 1.6 1.091 0.5092
50 1.5 1.514-0.0145
51 1 1.572-0.5722
52 0.9 1.055-0.1554
53 0.6 0.9771-0.3771
54 0.8 0.5931 0.2069
55 1 0.6274 0.3726
56 1.1 0.9109 0.1891
57 1 1.077-0.07652
58 0.9 1.168-0.2678
59 0.6 0.9861-0.3861
60 0.4 0.6994-0.2994
61 0.3 0.29 0.009966
62 0.3 0.1512 0.1488
63 0 0.2555-0.2555
64-0.1-0.07818-0.02182
65 0.1-0.05229 0.1523
66-0.1 0.2226-0.3226
67-0.4-0.1705-0.2295
68-0.7-0.4558-0.2442
69-0.4-0.673 0.273
70-0.4-0.3964-0.003551
71 0.3-0.4276 0.7276
72 0.6 0.4384 0.1616
73 0.6 0.7198-0.1198
74 0.5 0.6465-0.1465
75 0.9 0.8171 0.08292

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -1.1 & -0.5267 & -0.5733 \tabularnewline
2 & -1.7 & -1.296 & -0.4045 \tabularnewline
3 & -0.8 & -1.378 &  0.5784 \tabularnewline
4 & -1.2 & -0.6776 & -0.5224 \tabularnewline
5 & -1 & -0.8968 & -0.1032 \tabularnewline
6 & -0.1 & -0.5029 &  0.4029 \tabularnewline
7 &  0.3 &  0.5292 & -0.2292 \tabularnewline
8 &  0.6 &  0.4853 &  0.1147 \tabularnewline
9 &  0.7 &  0.9827 & -0.2827 \tabularnewline
10 &  1.7 &  1.227 &  0.4726 \tabularnewline
11 &  1.8 &  1.973 & -0.1733 \tabularnewline
12 &  2.3 &  2.024 &  0.2761 \tabularnewline
13 &  2.5 &  2.346 &  0.1536 \tabularnewline
14 &  2.6 &  2.607 & -0.00677 \tabularnewline
15 &  2.3 &  2.6 & -0.2999 \tabularnewline
16 &  2.9 &  2.316 &  0.5842 \tabularnewline
17 &  3 &  2.883 &  0.1174 \tabularnewline
18 &  2.9 &  3.039 & -0.1391 \tabularnewline
19 &  3.1 &  2.805 &  0.2954 \tabularnewline
20 &  3.2 &  3.251 & -0.05079 \tabularnewline
21 &  3.4 &  3.308 &  0.09221 \tabularnewline
22 &  3.5 &  3.531 & -0.03084 \tabularnewline
23 &  3.4 &  3.666 & -0.2656 \tabularnewline
24 &  3.4 &  3.554 & -0.1543 \tabularnewline
25 &  3.7 &  3.424 &  0.2757 \tabularnewline
26 &  3.8 &  3.627 &  0.1725 \tabularnewline
27 &  3.6 &  3.762 & -0.162 \tabularnewline
28 &  3.6 &  3.538 &  0.06208 \tabularnewline
29 &  3.6 &  3.604 & -0.003655 \tabularnewline
30 &  3.9 &  3.594 &  0.3061 \tabularnewline
31 &  3.5 &  3.735 & -0.2351 \tabularnewline
32 &  3.7 &  3.366 &  0.3344 \tabularnewline
33 &  3.7 &  3.676 &  0.02418 \tabularnewline
34 &  3.4 &  3.735 & -0.3347 \tabularnewline
35 &  3.2 &  3.163 &  0.03682 \tabularnewline
36 &  2.8 &  3.165 & -0.3648 \tabularnewline
37 &  2.3 &  2.555 & -0.2553 \tabularnewline
38 &  2.3 &  2.049 &  0.251 \tabularnewline
39 &  2.9 &  2.272 &  0.6283 \tabularnewline
40 &  2.8 &  2.747 &  0.05342 \tabularnewline
41 &  2.8 &  2.586 &  0.2143 \tabularnewline
42 &  2.3 &  2.754 & -0.454 \tabularnewline
43 &  2.2 &  2.174 &  0.02583 \tabularnewline
44 &  1.5 &  1.843 & -0.3432 \tabularnewline
45 &  1.2 &  1.23 & -0.03012 \tabularnewline
46 &  1.1 &  0.9357 &  0.1643 \tabularnewline
47 &  1 &  0.9394 &  0.06058 \tabularnewline
48 &  1.2 &  0.8192 &  0.3808 \tabularnewline
49 &  1.6 &  1.091 &  0.5092 \tabularnewline
50 &  1.5 &  1.514 & -0.0145 \tabularnewline
51 &  1 &  1.572 & -0.5722 \tabularnewline
52 &  0.9 &  1.055 & -0.1554 \tabularnewline
53 &  0.6 &  0.9771 & -0.3771 \tabularnewline
54 &  0.8 &  0.5931 &  0.2069 \tabularnewline
55 &  1 &  0.6274 &  0.3726 \tabularnewline
56 &  1.1 &  0.9109 &  0.1891 \tabularnewline
57 &  1 &  1.077 & -0.07652 \tabularnewline
58 &  0.9 &  1.168 & -0.2678 \tabularnewline
59 &  0.6 &  0.9861 & -0.3861 \tabularnewline
60 &  0.4 &  0.6994 & -0.2994 \tabularnewline
61 &  0.3 &  0.29 &  0.009966 \tabularnewline
62 &  0.3 &  0.1512 &  0.1488 \tabularnewline
63 &  0 &  0.2555 & -0.2555 \tabularnewline
64 & -0.1 & -0.07818 & -0.02182 \tabularnewline
65 &  0.1 & -0.05229 &  0.1523 \tabularnewline
66 & -0.1 &  0.2226 & -0.3226 \tabularnewline
67 & -0.4 & -0.1705 & -0.2295 \tabularnewline
68 & -0.7 & -0.4558 & -0.2442 \tabularnewline
69 & -0.4 & -0.673 &  0.273 \tabularnewline
70 & -0.4 & -0.3964 & -0.003551 \tabularnewline
71 &  0.3 & -0.4276 &  0.7276 \tabularnewline
72 &  0.6 &  0.4384 &  0.1616 \tabularnewline
73 &  0.6 &  0.7198 & -0.1198 \tabularnewline
74 &  0.5 &  0.6465 & -0.1465 \tabularnewline
75 &  0.9 &  0.8171 &  0.08292 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284679&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]-1.1[/C][C]-0.5267[/C][C]-0.5733[/C][/ROW]
[ROW][C]2[/C][C]-1.7[/C][C]-1.296[/C][C]-0.4045[/C][/ROW]
[ROW][C]3[/C][C]-0.8[/C][C]-1.378[/C][C] 0.5784[/C][/ROW]
[ROW][C]4[/C][C]-1.2[/C][C]-0.6776[/C][C]-0.5224[/C][/ROW]
[ROW][C]5[/C][C]-1[/C][C]-0.8968[/C][C]-0.1032[/C][/ROW]
[ROW][C]6[/C][C]-0.1[/C][C]-0.5029[/C][C] 0.4029[/C][/ROW]
[ROW][C]7[/C][C] 0.3[/C][C] 0.5292[/C][C]-0.2292[/C][/ROW]
[ROW][C]8[/C][C] 0.6[/C][C] 0.4853[/C][C] 0.1147[/C][/ROW]
[ROW][C]9[/C][C] 0.7[/C][C] 0.9827[/C][C]-0.2827[/C][/ROW]
[ROW][C]10[/C][C] 1.7[/C][C] 1.227[/C][C] 0.4726[/C][/ROW]
[ROW][C]11[/C][C] 1.8[/C][C] 1.973[/C][C]-0.1733[/C][/ROW]
[ROW][C]12[/C][C] 2.3[/C][C] 2.024[/C][C] 0.2761[/C][/ROW]
[ROW][C]13[/C][C] 2.5[/C][C] 2.346[/C][C] 0.1536[/C][/ROW]
[ROW][C]14[/C][C] 2.6[/C][C] 2.607[/C][C]-0.00677[/C][/ROW]
[ROW][C]15[/C][C] 2.3[/C][C] 2.6[/C][C]-0.2999[/C][/ROW]
[ROW][C]16[/C][C] 2.9[/C][C] 2.316[/C][C] 0.5842[/C][/ROW]
[ROW][C]17[/C][C] 3[/C][C] 2.883[/C][C] 0.1174[/C][/ROW]
[ROW][C]18[/C][C] 2.9[/C][C] 3.039[/C][C]-0.1391[/C][/ROW]
[ROW][C]19[/C][C] 3.1[/C][C] 2.805[/C][C] 0.2954[/C][/ROW]
[ROW][C]20[/C][C] 3.2[/C][C] 3.251[/C][C]-0.05079[/C][/ROW]
[ROW][C]21[/C][C] 3.4[/C][C] 3.308[/C][C] 0.09221[/C][/ROW]
[ROW][C]22[/C][C] 3.5[/C][C] 3.531[/C][C]-0.03084[/C][/ROW]
[ROW][C]23[/C][C] 3.4[/C][C] 3.666[/C][C]-0.2656[/C][/ROW]
[ROW][C]24[/C][C] 3.4[/C][C] 3.554[/C][C]-0.1543[/C][/ROW]
[ROW][C]25[/C][C] 3.7[/C][C] 3.424[/C][C] 0.2757[/C][/ROW]
[ROW][C]26[/C][C] 3.8[/C][C] 3.627[/C][C] 0.1725[/C][/ROW]
[ROW][C]27[/C][C] 3.6[/C][C] 3.762[/C][C]-0.162[/C][/ROW]
[ROW][C]28[/C][C] 3.6[/C][C] 3.538[/C][C] 0.06208[/C][/ROW]
[ROW][C]29[/C][C] 3.6[/C][C] 3.604[/C][C]-0.003655[/C][/ROW]
[ROW][C]30[/C][C] 3.9[/C][C] 3.594[/C][C] 0.3061[/C][/ROW]
[ROW][C]31[/C][C] 3.5[/C][C] 3.735[/C][C]-0.2351[/C][/ROW]
[ROW][C]32[/C][C] 3.7[/C][C] 3.366[/C][C] 0.3344[/C][/ROW]
[ROW][C]33[/C][C] 3.7[/C][C] 3.676[/C][C] 0.02418[/C][/ROW]
[ROW][C]34[/C][C] 3.4[/C][C] 3.735[/C][C]-0.3347[/C][/ROW]
[ROW][C]35[/C][C] 3.2[/C][C] 3.163[/C][C] 0.03682[/C][/ROW]
[ROW][C]36[/C][C] 2.8[/C][C] 3.165[/C][C]-0.3648[/C][/ROW]
[ROW][C]37[/C][C] 2.3[/C][C] 2.555[/C][C]-0.2553[/C][/ROW]
[ROW][C]38[/C][C] 2.3[/C][C] 2.049[/C][C] 0.251[/C][/ROW]
[ROW][C]39[/C][C] 2.9[/C][C] 2.272[/C][C] 0.6283[/C][/ROW]
[ROW][C]40[/C][C] 2.8[/C][C] 2.747[/C][C] 0.05342[/C][/ROW]
[ROW][C]41[/C][C] 2.8[/C][C] 2.586[/C][C] 0.2143[/C][/ROW]
[ROW][C]42[/C][C] 2.3[/C][C] 2.754[/C][C]-0.454[/C][/ROW]
[ROW][C]43[/C][C] 2.2[/C][C] 2.174[/C][C] 0.02583[/C][/ROW]
[ROW][C]44[/C][C] 1.5[/C][C] 1.843[/C][C]-0.3432[/C][/ROW]
[ROW][C]45[/C][C] 1.2[/C][C] 1.23[/C][C]-0.03012[/C][/ROW]
[ROW][C]46[/C][C] 1.1[/C][C] 0.9357[/C][C] 0.1643[/C][/ROW]
[ROW][C]47[/C][C] 1[/C][C] 0.9394[/C][C] 0.06058[/C][/ROW]
[ROW][C]48[/C][C] 1.2[/C][C] 0.8192[/C][C] 0.3808[/C][/ROW]
[ROW][C]49[/C][C] 1.6[/C][C] 1.091[/C][C] 0.5092[/C][/ROW]
[ROW][C]50[/C][C] 1.5[/C][C] 1.514[/C][C]-0.0145[/C][/ROW]
[ROW][C]51[/C][C] 1[/C][C] 1.572[/C][C]-0.5722[/C][/ROW]
[ROW][C]52[/C][C] 0.9[/C][C] 1.055[/C][C]-0.1554[/C][/ROW]
[ROW][C]53[/C][C] 0.6[/C][C] 0.9771[/C][C]-0.3771[/C][/ROW]
[ROW][C]54[/C][C] 0.8[/C][C] 0.5931[/C][C] 0.2069[/C][/ROW]
[ROW][C]55[/C][C] 1[/C][C] 0.6274[/C][C] 0.3726[/C][/ROW]
[ROW][C]56[/C][C] 1.1[/C][C] 0.9109[/C][C] 0.1891[/C][/ROW]
[ROW][C]57[/C][C] 1[/C][C] 1.077[/C][C]-0.07652[/C][/ROW]
[ROW][C]58[/C][C] 0.9[/C][C] 1.168[/C][C]-0.2678[/C][/ROW]
[ROW][C]59[/C][C] 0.6[/C][C] 0.9861[/C][C]-0.3861[/C][/ROW]
[ROW][C]60[/C][C] 0.4[/C][C] 0.6994[/C][C]-0.2994[/C][/ROW]
[ROW][C]61[/C][C] 0.3[/C][C] 0.29[/C][C] 0.009966[/C][/ROW]
[ROW][C]62[/C][C] 0.3[/C][C] 0.1512[/C][C] 0.1488[/C][/ROW]
[ROW][C]63[/C][C] 0[/C][C] 0.2555[/C][C]-0.2555[/C][/ROW]
[ROW][C]64[/C][C]-0.1[/C][C]-0.07818[/C][C]-0.02182[/C][/ROW]
[ROW][C]65[/C][C] 0.1[/C][C]-0.05229[/C][C] 0.1523[/C][/ROW]
[ROW][C]66[/C][C]-0.1[/C][C] 0.2226[/C][C]-0.3226[/C][/ROW]
[ROW][C]67[/C][C]-0.4[/C][C]-0.1705[/C][C]-0.2295[/C][/ROW]
[ROW][C]68[/C][C]-0.7[/C][C]-0.4558[/C][C]-0.2442[/C][/ROW]
[ROW][C]69[/C][C]-0.4[/C][C]-0.673[/C][C] 0.273[/C][/ROW]
[ROW][C]70[/C][C]-0.4[/C][C]-0.3964[/C][C]-0.003551[/C][/ROW]
[ROW][C]71[/C][C] 0.3[/C][C]-0.4276[/C][C] 0.7276[/C][/ROW]
[ROW][C]72[/C][C] 0.6[/C][C] 0.4384[/C][C] 0.1616[/C][/ROW]
[ROW][C]73[/C][C] 0.6[/C][C] 0.7198[/C][C]-0.1198[/C][/ROW]
[ROW][C]74[/C][C] 0.5[/C][C] 0.6465[/C][C]-0.1465[/C][/ROW]
[ROW][C]75[/C][C] 0.9[/C][C] 0.8171[/C][C] 0.08292[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284679&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284679&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
1-1.1-0.5267-0.5733
2-1.7-1.296-0.4045
3-0.8-1.378 0.5784
4-1.2-0.6776-0.5224
5-1-0.8968-0.1032
6-0.1-0.5029 0.4029
7 0.3 0.5292-0.2292
8 0.6 0.4853 0.1147
9 0.7 0.9827-0.2827
10 1.7 1.227 0.4726
11 1.8 1.973-0.1733
12 2.3 2.024 0.2761
13 2.5 2.346 0.1536
14 2.6 2.607-0.00677
15 2.3 2.6-0.2999
16 2.9 2.316 0.5842
17 3 2.883 0.1174
18 2.9 3.039-0.1391
19 3.1 2.805 0.2954
20 3.2 3.251-0.05079
21 3.4 3.308 0.09221
22 3.5 3.531-0.03084
23 3.4 3.666-0.2656
24 3.4 3.554-0.1543
25 3.7 3.424 0.2757
26 3.8 3.627 0.1725
27 3.6 3.762-0.162
28 3.6 3.538 0.06208
29 3.6 3.604-0.003655
30 3.9 3.594 0.3061
31 3.5 3.735-0.2351
32 3.7 3.366 0.3344
33 3.7 3.676 0.02418
34 3.4 3.735-0.3347
35 3.2 3.163 0.03682
36 2.8 3.165-0.3648
37 2.3 2.555-0.2553
38 2.3 2.049 0.251
39 2.9 2.272 0.6283
40 2.8 2.747 0.05342
41 2.8 2.586 0.2143
42 2.3 2.754-0.454
43 2.2 2.174 0.02583
44 1.5 1.843-0.3432
45 1.2 1.23-0.03012
46 1.1 0.9357 0.1643
47 1 0.9394 0.06058
48 1.2 0.8192 0.3808
49 1.6 1.091 0.5092
50 1.5 1.514-0.0145
51 1 1.572-0.5722
52 0.9 1.055-0.1554
53 0.6 0.9771-0.3771
54 0.8 0.5931 0.2069
55 1 0.6274 0.3726
56 1.1 0.9109 0.1891
57 1 1.077-0.07652
58 0.9 1.168-0.2678
59 0.6 0.9861-0.3861
60 0.4 0.6994-0.2994
61 0.3 0.29 0.009966
62 0.3 0.1512 0.1488
63 0 0.2555-0.2555
64-0.1-0.07818-0.02182
65 0.1-0.05229 0.1523
66-0.1 0.2226-0.3226
67-0.4-0.1705-0.2295
68-0.7-0.4558-0.2442
69-0.4-0.673 0.273
70-0.4-0.3964-0.003551
71 0.3-0.4276 0.7276
72 0.6 0.4384 0.1616
73 0.6 0.7198-0.1198
74 0.5 0.6465-0.1465
75 0.9 0.8171 0.08292







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
22 0.5493 0.9014 0.4507
23 0.5703 0.8594 0.4297
24 0.8649 0.2702 0.1351
25 0.8248 0.3505 0.1752
26 0.7393 0.5214 0.2607
27 0.7461 0.5078 0.2539
28 0.7843 0.4314 0.2157
29 0.8058 0.3885 0.1942
30 0.7754 0.4492 0.2246
31 0.7345 0.531 0.2655
32 0.708 0.5841 0.292
33 0.6196 0.7607 0.3804
34 0.6205 0.759 0.3795
35 0.5324 0.9351 0.4676
36 0.5442 0.9117 0.4558
37 0.5211 0.9577 0.4789
38 0.4552 0.9104 0.5448
39 0.5765 0.847 0.4235
40 0.4891 0.9782 0.5109
41 0.4212 0.8423 0.5788
42 0.5099 0.9803 0.4901
43 0.5216 0.9567 0.4784
44 0.4344 0.8688 0.5656
45 0.3454 0.6909 0.6546
46 0.3266 0.6532 0.6734
47 0.2542 0.5084 0.7458
48 0.292 0.584 0.708
49 0.259 0.5181 0.741
50 0.182 0.3641 0.818
51 0.2027 0.4055 0.7973
52 0.1713 0.3427 0.8287
53 0.1069 0.2137 0.8931

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 &  0.5493 &  0.9014 &  0.4507 \tabularnewline
23 &  0.5703 &  0.8594 &  0.4297 \tabularnewline
24 &  0.8649 &  0.2702 &  0.1351 \tabularnewline
25 &  0.8248 &  0.3505 &  0.1752 \tabularnewline
26 &  0.7393 &  0.5214 &  0.2607 \tabularnewline
27 &  0.7461 &  0.5078 &  0.2539 \tabularnewline
28 &  0.7843 &  0.4314 &  0.2157 \tabularnewline
29 &  0.8058 &  0.3885 &  0.1942 \tabularnewline
30 &  0.7754 &  0.4492 &  0.2246 \tabularnewline
31 &  0.7345 &  0.531 &  0.2655 \tabularnewline
32 &  0.708 &  0.5841 &  0.292 \tabularnewline
33 &  0.6196 &  0.7607 &  0.3804 \tabularnewline
34 &  0.6205 &  0.759 &  0.3795 \tabularnewline
35 &  0.5324 &  0.9351 &  0.4676 \tabularnewline
36 &  0.5442 &  0.9117 &  0.4558 \tabularnewline
37 &  0.5211 &  0.9577 &  0.4789 \tabularnewline
38 &  0.4552 &  0.9104 &  0.5448 \tabularnewline
39 &  0.5765 &  0.847 &  0.4235 \tabularnewline
40 &  0.4891 &  0.9782 &  0.5109 \tabularnewline
41 &  0.4212 &  0.8423 &  0.5788 \tabularnewline
42 &  0.5099 &  0.9803 &  0.4901 \tabularnewline
43 &  0.5216 &  0.9567 &  0.4784 \tabularnewline
44 &  0.4344 &  0.8688 &  0.5656 \tabularnewline
45 &  0.3454 &  0.6909 &  0.6546 \tabularnewline
46 &  0.3266 &  0.6532 &  0.6734 \tabularnewline
47 &  0.2542 &  0.5084 &  0.7458 \tabularnewline
48 &  0.292 &  0.584 &  0.708 \tabularnewline
49 &  0.259 &  0.5181 &  0.741 \tabularnewline
50 &  0.182 &  0.3641 &  0.818 \tabularnewline
51 &  0.2027 &  0.4055 &  0.7973 \tabularnewline
52 &  0.1713 &  0.3427 &  0.8287 \tabularnewline
53 &  0.1069 &  0.2137 &  0.8931 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284679&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]22[/C][C] 0.5493[/C][C] 0.9014[/C][C] 0.4507[/C][/ROW]
[ROW][C]23[/C][C] 0.5703[/C][C] 0.8594[/C][C] 0.4297[/C][/ROW]
[ROW][C]24[/C][C] 0.8649[/C][C] 0.2702[/C][C] 0.1351[/C][/ROW]
[ROW][C]25[/C][C] 0.8248[/C][C] 0.3505[/C][C] 0.1752[/C][/ROW]
[ROW][C]26[/C][C] 0.7393[/C][C] 0.5214[/C][C] 0.2607[/C][/ROW]
[ROW][C]27[/C][C] 0.7461[/C][C] 0.5078[/C][C] 0.2539[/C][/ROW]
[ROW][C]28[/C][C] 0.7843[/C][C] 0.4314[/C][C] 0.2157[/C][/ROW]
[ROW][C]29[/C][C] 0.8058[/C][C] 0.3885[/C][C] 0.1942[/C][/ROW]
[ROW][C]30[/C][C] 0.7754[/C][C] 0.4492[/C][C] 0.2246[/C][/ROW]
[ROW][C]31[/C][C] 0.7345[/C][C] 0.531[/C][C] 0.2655[/C][/ROW]
[ROW][C]32[/C][C] 0.708[/C][C] 0.5841[/C][C] 0.292[/C][/ROW]
[ROW][C]33[/C][C] 0.6196[/C][C] 0.7607[/C][C] 0.3804[/C][/ROW]
[ROW][C]34[/C][C] 0.6205[/C][C] 0.759[/C][C] 0.3795[/C][/ROW]
[ROW][C]35[/C][C] 0.5324[/C][C] 0.9351[/C][C] 0.4676[/C][/ROW]
[ROW][C]36[/C][C] 0.5442[/C][C] 0.9117[/C][C] 0.4558[/C][/ROW]
[ROW][C]37[/C][C] 0.5211[/C][C] 0.9577[/C][C] 0.4789[/C][/ROW]
[ROW][C]38[/C][C] 0.4552[/C][C] 0.9104[/C][C] 0.5448[/C][/ROW]
[ROW][C]39[/C][C] 0.5765[/C][C] 0.847[/C][C] 0.4235[/C][/ROW]
[ROW][C]40[/C][C] 0.4891[/C][C] 0.9782[/C][C] 0.5109[/C][/ROW]
[ROW][C]41[/C][C] 0.4212[/C][C] 0.8423[/C][C] 0.5788[/C][/ROW]
[ROW][C]42[/C][C] 0.5099[/C][C] 0.9803[/C][C] 0.4901[/C][/ROW]
[ROW][C]43[/C][C] 0.5216[/C][C] 0.9567[/C][C] 0.4784[/C][/ROW]
[ROW][C]44[/C][C] 0.4344[/C][C] 0.8688[/C][C] 0.5656[/C][/ROW]
[ROW][C]45[/C][C] 0.3454[/C][C] 0.6909[/C][C] 0.6546[/C][/ROW]
[ROW][C]46[/C][C] 0.3266[/C][C] 0.6532[/C][C] 0.6734[/C][/ROW]
[ROW][C]47[/C][C] 0.2542[/C][C] 0.5084[/C][C] 0.7458[/C][/ROW]
[ROW][C]48[/C][C] 0.292[/C][C] 0.584[/C][C] 0.708[/C][/ROW]
[ROW][C]49[/C][C] 0.259[/C][C] 0.5181[/C][C] 0.741[/C][/ROW]
[ROW][C]50[/C][C] 0.182[/C][C] 0.3641[/C][C] 0.818[/C][/ROW]
[ROW][C]51[/C][C] 0.2027[/C][C] 0.4055[/C][C] 0.7973[/C][/ROW]
[ROW][C]52[/C][C] 0.1713[/C][C] 0.3427[/C][C] 0.8287[/C][/ROW]
[ROW][C]53[/C][C] 0.1069[/C][C] 0.2137[/C][C] 0.8931[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284679&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284679&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
22 0.5493 0.9014 0.4507
23 0.5703 0.8594 0.4297
24 0.8649 0.2702 0.1351
25 0.8248 0.3505 0.1752
26 0.7393 0.5214 0.2607
27 0.7461 0.5078 0.2539
28 0.7843 0.4314 0.2157
29 0.8058 0.3885 0.1942
30 0.7754 0.4492 0.2246
31 0.7345 0.531 0.2655
32 0.708 0.5841 0.292
33 0.6196 0.7607 0.3804
34 0.6205 0.759 0.3795
35 0.5324 0.9351 0.4676
36 0.5442 0.9117 0.4558
37 0.5211 0.9577 0.4789
38 0.4552 0.9104 0.5448
39 0.5765 0.847 0.4235
40 0.4891 0.9782 0.5109
41 0.4212 0.8423 0.5788
42 0.5099 0.9803 0.4901
43 0.5216 0.9567 0.4784
44 0.4344 0.8688 0.5656
45 0.3454 0.6909 0.6546
46 0.3266 0.6532 0.6734
47 0.2542 0.5084 0.7458
48 0.292 0.584 0.708
49 0.259 0.5181 0.741
50 0.182 0.3641 0.818
51 0.2027 0.4055 0.7973
52 0.1713 0.3427 0.8287
53 0.1069 0.2137 0.8931







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284679&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284679&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284679&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 level0 0OK
5% type I error level00OK
10% type I error level00OK



Parameters (Session):
par2 = Include Monthly Dummies ; par3 = Linear Trend ; par4 = 5 ;
Parameters (R input):
par1 = ; par2 = Include Monthly Dummies ; par3 = Linear Trend ; par4 = 5 ; par5 = ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
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+par4,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1+par4,])
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}