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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 18 Dec 2014 14:56:00 +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/2014/Dec/18/t1418914610xpz7qm0hk3mxf6l.htm/, Retrieved Sun, 19 May 2024 18:05:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271026, Retrieved Sun, 19 May 2024 18:05:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2014-12-18 14:22:41] [8e3afc5508de37bed770d90d46857754]
-    D    [Multiple Regression] [] [2014-12-18 14:56:00] [ce2f801bda31f4b58163e4bbe4fada83] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9 8 12 18 68 1.8
12.2 18 8 31 39 2.1
12.8 12 11 39 32 2.2
7.4 24 13 46 62 2.3
6.7 16 11 31 33 2.1
12.6 19 10 67 52 2.7
14.8 16 7 35 62 2.1
13.3 15 10 52 77 2.4
11.1 28 15 77 76 2.9
8.2 21 12 37 41 2.2
11.4 18 12 32 48 2.1
6.4 22 10 36 63 2.2
10.6 19 10 38 30 2.2
12 22 14 69 78 2.7
6.3 25 6 21 19 1.9
11.9 16 14 54 66 2.5
9.3 19 11 36 35 2.2
10 26 12 23 45 1.9
6.4 24 15 34 21 2.1
13.8 20 13 112 25 3.5
10.8 19 11 35 44 2.1
13.8 19 12 47 69 2.3
11.7 23 7 47 54 2.3
10.9 18 11 37 74 2.2
9.9 21 12 20 61 1.9
11.5 20 13 22 41 1.9
8.3 15 9 23 46 1.9
11.7 19 11 32 39 2.1
9 19 12 30 34 2
9.7 7 15 92 51 3.2
10.8 20 12 43 42 2.3
10.3 20 6 55 31 2.5
10.4 19 5 16 39 1.8
9.3 20 11 71 49 2.8
11.8 18 6 43 53 2.3
5.9 14 12 29 31 2
11.4 17 10 56 39 2.5
13 17 6 46 54 2.3
10.8 8 12 19 49 1.8
11.3 22 6 59 46 2.6
11.8 20 12 30 55 2
12.7 22 8 7 50 1.6
10.9 14 12 19 30 1.8
13.3 21 14 48 45 2.4
10.1 20 12 23 35 1.9
14.3 18 14 33 41 2.1
9.3 24 11 34 73 2.1
12.5 19 10 48 17 2.4
7.6 16 7 18 40 1.8
15.9 16 12 43 64 2.3
9.2 16 7 33 37 2.1
11.1 22 12 71 65 2.8
13 21 10 26 100 2
14.5 15 10 67 28 2.7
12.3 15 12 80 56 2.9
11.4 14 12 29 29 2
12.6 14 5 32 50 2.1
NA 19 10 47 3 2.3
13 16 10 43 59 2.3
13.2 26 11 29 61 2
7.7 18 12 32 51 2.1
4.35 17 9 23 12 1
12.7 6 11 16 45 1
18.1 22 12 33 37 4
17.85 20 12 32 37 4
17.1 17 12 52 68 4
19.1 20 12 75 72 4
16.1 23 10 72 143 4
13.35 18 15 15 9 2
18.4 13 10 29 55 4
14.7 22 15 13 17 1
10.6 20 10 40 37 3
12.6 20 15 19 27 3
16.2 13 9 24 37 4
13.6 16 15 121 58 3
14.1 16 13 36 21 3
14.5 15 12 23 19 3
16.15 19 12 85 78 4
14.75 19 8 41 35 3
14.8 24 9 46 48 3
12.45 9 15 18 27 2
12.65 22 12 35 43 2
17.35 15 12 17 30 3
8.6 22 15 4 25 1
18.4 22 11 28 69 4
16.1 24 12 44 72 3
17.75 21 14 38 13 4
15.25 25 12 57 61 4
17.65 26 12 23 43 4
16.35 21 12 36 51 4
17.65 14 11 22 67 4
13.6 28 12 40 36 3
14.35 21 12 31 44 3
14.75 16 12 11 45 4
18.25 16 12 38 34 4
9.9 25 8 24 36 4
16 21 8 37 72 3
18.25 22 12 37 39 4
16.85 9 12 22 43 4
18.95 24 11 43 80 4
15.6 22 12 31 40 3
17.1 10 10 31 61 4
16.1 22 11 -4 23 1
15.4 21 11 21 29 4
15.4 20 11 21 29 4
13.35 17 13 32 54 3
19.1 7 7 26 43 4
7.6 14 8 32 20 1
19.1 23 11 33 61 4
14.75 18 8 30 57 4
19.25 17 14 67 54 4
13.6 20 9 22 36 4
12.75 19 13 33 16 4
9.85 19 13 24 40 1
15.25 23 11 28 27 4
11.9 20 9 41 61 3
16.35 19 12 31 69 4
12.4 16 12 33 34 3
18.15 21 13 21 34 4
17.75 20 11 52 34 4
12.35 20 11 29 13 3
15.6 19 9 11 12 4
19.3 19 12 26 51 4
17.1 20 15 7 19 4
18.4 22 14 13 81 3
19.05 19 12 20 42 4
18.55 23 9 52 22 4
19.1 16 9 28 85 4
12.85 18 13 39 25 4
9.5 23 15 9 22 2
4.5 20 11 19 19 1
13.6 23 10 60 45 4
11.7 13 11 19 45 2
13.35 26 14 14 51 3
17.6 13 12 -2 73 4
14.05 10 13 51 24 3
16.1 21 11 2 61 4
13.35 24 11 24 23 4
11.85 21 13 40 14 4
11.95 23 12 20 54 2
13.2 16 9 20 36 4
7.7 26 13 25 26 2
14.6 16 12 38 30 3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271026&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271026&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271026&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 3.55844 -0.0759365AMS.I2[t] + 0.151153CONFSOFTTOT[t] -0.0322286PRH[t] + 0.0344235CH[t] + 2.68198PR[t] + 0.988535M1[t] + 1.71979M2[t] + 1.6331M3[t] + 1.1142M4[t] + 1.07968M5[t] + 1.56956M6[t] + 2.29616M7[t] + 1.74967M8[t] + 0.907119M9[t] + 0.636423M10[t] + 2.47879M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  3.55844 -0.0759365AMS.I2[t] +  0.151153CONFSOFTTOT[t] -0.0322286PRH[t] +  0.0344235CH[t] +  2.68198PR[t] +  0.988535M1[t] +  1.71979M2[t] +  1.6331M3[t] +  1.1142M4[t] +  1.07968M5[t] +  1.56956M6[t] +  2.29616M7[t] +  1.74967M8[t] +  0.907119M9[t] +  0.636423M10[t] +  2.47879M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271026&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  3.55844 -0.0759365AMS.I2[t] +  0.151153CONFSOFTTOT[t] -0.0322286PRH[t] +  0.0344235CH[t] +  2.68198PR[t] +  0.988535M1[t] +  1.71979M2[t] +  1.6331M3[t] +  1.1142M4[t] +  1.07968M5[t] +  1.56956M6[t] +  2.29616M7[t] +  1.74967M8[t] +  0.907119M9[t] +  0.636423M10[t] +  2.47879M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271026&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271026&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 3.55844 -0.0759365AMS.I2[t] + 0.151153CONFSOFTTOT[t] -0.0322286PRH[t] + 0.0344235CH[t] + 2.68198PR[t] + 0.988535M1[t] + 1.71979M2[t] + 1.6331M3[t] + 1.1142M4[t] + 1.07968M5[t] + 1.56956M6[t] + 2.29616M7[t] + 1.74967M8[t] + 0.907119M9[t] + 0.636423M10[t] + 2.47879M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.558441.555452.2880.02383360.0119168
AMS.I2-0.07593650.0471453-1.6110.1097690.0548846
CONFSOFTTOT0.1511530.08796421.7180.08820890.0441045
PRH-0.03222860.0106126-3.0370.002910740.00145537
CH0.03442350.01024543.360.001034330.000517163
PR2.681980.21308712.595.71073e-242.85536e-24
M10.9885350.9639091.0260.3070850.153543
M21.719790.9564171.7980.07456610.0372831
M31.63310.9919651.6460.1022080.0511041
M41.11420.9672571.1520.2515510.125776
M51.079680.9721261.1110.2688570.134428
M61.569560.9707411.6170.1084270.0542136
M72.296160.9565462.40.01784850.00892425
M81.749670.9664511.810.07263480.0363174
M90.9071190.9612070.94370.3471290.173564
M100.6364230.9880170.64410.5206630.260332
M112.478790.957722.5880.01078990.00539493

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 3.55844 & 1.55545 & 2.288 & 0.0238336 & 0.0119168 \tabularnewline
AMS.I2 & -0.0759365 & 0.0471453 & -1.611 & 0.109769 & 0.0548846 \tabularnewline
CONFSOFTTOT & 0.151153 & 0.0879642 & 1.718 & 0.0882089 & 0.0441045 \tabularnewline
PRH & -0.0322286 & 0.0106126 & -3.037 & 0.00291074 & 0.00145537 \tabularnewline
CH & 0.0344235 & 0.0102454 & 3.36 & 0.00103433 & 0.000517163 \tabularnewline
PR & 2.68198 & 0.213087 & 12.59 & 5.71073e-24 & 2.85536e-24 \tabularnewline
M1 & 0.988535 & 0.963909 & 1.026 & 0.307085 & 0.153543 \tabularnewline
M2 & 1.71979 & 0.956417 & 1.798 & 0.0745661 & 0.0372831 \tabularnewline
M3 & 1.6331 & 0.991965 & 1.646 & 0.102208 & 0.0511041 \tabularnewline
M4 & 1.1142 & 0.967257 & 1.152 & 0.251551 & 0.125776 \tabularnewline
M5 & 1.07968 & 0.972126 & 1.111 & 0.268857 & 0.134428 \tabularnewline
M6 & 1.56956 & 0.970741 & 1.617 & 0.108427 & 0.0542136 \tabularnewline
M7 & 2.29616 & 0.956546 & 2.4 & 0.0178485 & 0.00892425 \tabularnewline
M8 & 1.74967 & 0.966451 & 1.81 & 0.0726348 & 0.0363174 \tabularnewline
M9 & 0.907119 & 0.961207 & 0.9437 & 0.347129 & 0.173564 \tabularnewline
M10 & 0.636423 & 0.988017 & 0.6441 & 0.520663 & 0.260332 \tabularnewline
M11 & 2.47879 & 0.95772 & 2.588 & 0.0107899 & 0.00539493 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271026&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]3.55844[/C][C]1.55545[/C][C]2.288[/C][C]0.0238336[/C][C]0.0119168[/C][/ROW]
[ROW][C]AMS.I2[/C][C]-0.0759365[/C][C]0.0471453[/C][C]-1.611[/C][C]0.109769[/C][C]0.0548846[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.151153[/C][C]0.0879642[/C][C]1.718[/C][C]0.0882089[/C][C]0.0441045[/C][/ROW]
[ROW][C]PRH[/C][C]-0.0322286[/C][C]0.0106126[/C][C]-3.037[/C][C]0.00291074[/C][C]0.00145537[/C][/ROW]
[ROW][C]CH[/C][C]0.0344235[/C][C]0.0102454[/C][C]3.36[/C][C]0.00103433[/C][C]0.000517163[/C][/ROW]
[ROW][C]PR[/C][C]2.68198[/C][C]0.213087[/C][C]12.59[/C][C]5.71073e-24[/C][C]2.85536e-24[/C][/ROW]
[ROW][C]M1[/C][C]0.988535[/C][C]0.963909[/C][C]1.026[/C][C]0.307085[/C][C]0.153543[/C][/ROW]
[ROW][C]M2[/C][C]1.71979[/C][C]0.956417[/C][C]1.798[/C][C]0.0745661[/C][C]0.0372831[/C][/ROW]
[ROW][C]M3[/C][C]1.6331[/C][C]0.991965[/C][C]1.646[/C][C]0.102208[/C][C]0.0511041[/C][/ROW]
[ROW][C]M4[/C][C]1.1142[/C][C]0.967257[/C][C]1.152[/C][C]0.251551[/C][C]0.125776[/C][/ROW]
[ROW][C]M5[/C][C]1.07968[/C][C]0.972126[/C][C]1.111[/C][C]0.268857[/C][C]0.134428[/C][/ROW]
[ROW][C]M6[/C][C]1.56956[/C][C]0.970741[/C][C]1.617[/C][C]0.108427[/C][C]0.0542136[/C][/ROW]
[ROW][C]M7[/C][C]2.29616[/C][C]0.956546[/C][C]2.4[/C][C]0.0178485[/C][C]0.00892425[/C][/ROW]
[ROW][C]M8[/C][C]1.74967[/C][C]0.966451[/C][C]1.81[/C][C]0.0726348[/C][C]0.0363174[/C][/ROW]
[ROW][C]M9[/C][C]0.907119[/C][C]0.961207[/C][C]0.9437[/C][C]0.347129[/C][C]0.173564[/C][/ROW]
[ROW][C]M10[/C][C]0.636423[/C][C]0.988017[/C][C]0.6441[/C][C]0.520663[/C][C]0.260332[/C][/ROW]
[ROW][C]M11[/C][C]2.47879[/C][C]0.95772[/C][C]2.588[/C][C]0.0107899[/C][C]0.00539493[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271026&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271026&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.558441.555452.2880.02383360.0119168
AMS.I2-0.07593650.0471453-1.6110.1097690.0548846
CONFSOFTTOT0.1511530.08796421.7180.08820890.0441045
PRH-0.03222860.0106126-3.0370.002910740.00145537
CH0.03442350.01024543.360.001034330.000517163
PR2.681980.21308712.595.71073e-242.85536e-24
M10.9885350.9639091.0260.3070850.153543
M21.719790.9564171.7980.07456610.0372831
M31.63310.9919651.6460.1022080.0511041
M41.11420.9672571.1520.2515510.125776
M51.079680.9721261.1110.2688570.134428
M61.569560.9707411.6170.1084270.0542136
M72.296160.9565462.40.01784850.00892425
M81.749670.9664511.810.07263480.0363174
M90.9071190.9612070.94370.3471290.173564
M100.6364230.9880170.64410.5206630.260332
M112.478790.957722.5880.01078990.00539493






Multiple Linear Regression - Regression Statistics
Multiple R0.78875
R-squared0.622126
Adjusted R-squared0.573759
F-TEST (value)12.8624
F-TEST (DF numerator)16
F-TEST (DF denominator)125
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.27509
Sum Squared Residuals647.003

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.78875 \tabularnewline
R-squared & 0.622126 \tabularnewline
Adjusted R-squared & 0.573759 \tabularnewline
F-TEST (value) & 12.8624 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 125 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.27509 \tabularnewline
Sum Squared Residuals & 647.003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271026&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.78875[/C][/ROW]
[ROW][C]R-squared[/C][C]0.622126[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.573759[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.8624[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]125[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.27509[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]647.003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271026&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271026&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.78875
R-squared0.622126
Adjusted R-squared0.573759
F-TEST (value)12.8624
F-TEST (DF numerator)16
F-TEST (DF denominator)125
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.27509
Sum Squared Residuals647.003






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.34160.558433
212.211.09621.10382
312.811.6881.11202
47.411.6355-4.23545
56.710.8549-4.15486
612.612.06880.531211
714.812.33612.4639
813.313.09210.207935
911.112.519-1.41896
108.210.5333-2.33329
1111.412.7374-1.33738
126.410.3082-3.90817
1310.610.32410.275915
141213.4264-1.42637
156.39.27305-2.97305
1611.912.8104-0.910359
179.310.803-1.50296
181010.8711-0.87105
196.411.5587-5.15869
2013.812.39231.40772
2110.810.70420.0957614
2213.811.59492.20506
2311.711.8614-0.161438
2410.911.1095-0.209503
259.911.3172-1.41717
2611.511.5226-0.0225806
278.311.3509-3.05085
2811.710.83590.864108
29910.5767-1.57666
309.714.2366-4.53664
3110.812.3782-1.57821
3210.310.6958-0.3958
3310.49.432950.967049
349.311.2469-1.94688
3511.812.1845-0.384458
365.99.80562-3.90562
3711.411.01020.389751
381311.43911.56087
3910.812.2999-1.49986
4011.310.56410.735901
4111.811.22360.576381
4212.710.45342.24663
4310.911.8532-0.953246
4413.312.26841.03158
4510.110.32-0.219991
4614.310.92413.37587
479.312.9267-3.62674
4812.59.102153.39785
497.610.0145-2.41445
5015.912.86293.03709
519.210.8769-1.67691
5211.112.2747-1.17472
531312.52330.476652
5414.511.54642.95363
5512.313.6566-1.35655
5611.411.4864-0.086445
5712.610.48022.11977
58NANA-0.147485
59139.575943.42406
6013.216.8504-3.6504
617.711.0515-3.35149
624.351.763992.58601
6312.710.35392.34608
6418.116.15351.94651
6517.8517.79370.0562551
6617.114.9392.16103
6719.121.4031-2.30312
6816.113.30632.79366
6913.3511.35581.9942
7018.413.18215.21787
7114.715.6817-0.981703
7210.611.6586-1.05857
7312.613.2795-0.679526
7416.214.98671.21331
7513.612.53131.06875
7614.112.87161.22837
7714.514.5226-0.0225642
7816.1514.95041.19959
7914.7513.01181.73824
8014.814.11270.687295
8112.459.854272.59573
8212.6510.54282.10723
8317.3516.31881.03121
848.66.939791.66021
8518.416.6761.72404
8616.114.01372.08625
8717.7518.0788-0.328788
8815.2513.54451.70553
8917.6517.9705-0.320456
9016.3517.4794-1.12943
9117.6517.04180.608238
9213.612.49621.10379
9314.3516.3162-1.96617
9414.7513.80970.940292
9518.2522.4129-4.16293
969.97.39352.5065
971614.04941.95057
9818.2519.221-0.971045
9916.8514.50882.34118
10018.9516.55512.39486
10115.616.2088-0.608829
10217.110.44936.65069
10316.117.1255-1.02552
10415.415.6589-0.258907
10515.415.7924-0.392424
10613.3512.18391.16607
10719.117.54371.55631
1087.64.727322.87268
10919.121.1938-2.09379
11014.7511.94422.80577
11119.2521.4224-2.17242
11213.616.2255-2.62546
11312.7511.83560.914367
1149.8511.1257-1.27569
11515.2517.3242-2.07415
11611.912.4907-0.590652
11716.3516.8965-0.546508
11812.411.87910.520935
11918.1514.32483.82518
12017.7517.64970.100259
12112.3512.7323-0.38229
12215.613.50822.09185
12319.318.77760.52243
12417.114.19892.90107
12518.416.37822.02183
12619.0515.77783.27222
12718.5517.6550.894986
12819.121.6453-2.54528
12912.8513.8968-1.04684
1309.513.9049-4.40487
1314.54.56669-0.0666875
13213.613.42320.176843
13311.713.1204-1.42035
13413.3515.0735-1.72349
13517.616.65670.943288
13614.0515.4194-1.36943
13716.118.4644-2.36438
13813.3517.6456-4.29562
13911.8511.8537-0.00366152
14011.9514.6835-2.73354
14113.215.1388-1.93876
1427.77.590040.109964
14314.6NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.3416 & 0.558433 \tabularnewline
2 & 12.2 & 11.0962 & 1.10382 \tabularnewline
3 & 12.8 & 11.688 & 1.11202 \tabularnewline
4 & 7.4 & 11.6355 & -4.23545 \tabularnewline
5 & 6.7 & 10.8549 & -4.15486 \tabularnewline
6 & 12.6 & 12.0688 & 0.531211 \tabularnewline
7 & 14.8 & 12.3361 & 2.4639 \tabularnewline
8 & 13.3 & 13.0921 & 0.207935 \tabularnewline
9 & 11.1 & 12.519 & -1.41896 \tabularnewline
10 & 8.2 & 10.5333 & -2.33329 \tabularnewline
11 & 11.4 & 12.7374 & -1.33738 \tabularnewline
12 & 6.4 & 10.3082 & -3.90817 \tabularnewline
13 & 10.6 & 10.3241 & 0.275915 \tabularnewline
14 & 12 & 13.4264 & -1.42637 \tabularnewline
15 & 6.3 & 9.27305 & -2.97305 \tabularnewline
16 & 11.9 & 12.8104 & -0.910359 \tabularnewline
17 & 9.3 & 10.803 & -1.50296 \tabularnewline
18 & 10 & 10.8711 & -0.87105 \tabularnewline
19 & 6.4 & 11.5587 & -5.15869 \tabularnewline
20 & 13.8 & 12.3923 & 1.40772 \tabularnewline
21 & 10.8 & 10.7042 & 0.0957614 \tabularnewline
22 & 13.8 & 11.5949 & 2.20506 \tabularnewline
23 & 11.7 & 11.8614 & -0.161438 \tabularnewline
24 & 10.9 & 11.1095 & -0.209503 \tabularnewline
25 & 9.9 & 11.3172 & -1.41717 \tabularnewline
26 & 11.5 & 11.5226 & -0.0225806 \tabularnewline
27 & 8.3 & 11.3509 & -3.05085 \tabularnewline
28 & 11.7 & 10.8359 & 0.864108 \tabularnewline
29 & 9 & 10.5767 & -1.57666 \tabularnewline
30 & 9.7 & 14.2366 & -4.53664 \tabularnewline
31 & 10.8 & 12.3782 & -1.57821 \tabularnewline
32 & 10.3 & 10.6958 & -0.3958 \tabularnewline
33 & 10.4 & 9.43295 & 0.967049 \tabularnewline
34 & 9.3 & 11.2469 & -1.94688 \tabularnewline
35 & 11.8 & 12.1845 & -0.384458 \tabularnewline
36 & 5.9 & 9.80562 & -3.90562 \tabularnewline
37 & 11.4 & 11.0102 & 0.389751 \tabularnewline
38 & 13 & 11.4391 & 1.56087 \tabularnewline
39 & 10.8 & 12.2999 & -1.49986 \tabularnewline
40 & 11.3 & 10.5641 & 0.735901 \tabularnewline
41 & 11.8 & 11.2236 & 0.576381 \tabularnewline
42 & 12.7 & 10.4534 & 2.24663 \tabularnewline
43 & 10.9 & 11.8532 & -0.953246 \tabularnewline
44 & 13.3 & 12.2684 & 1.03158 \tabularnewline
45 & 10.1 & 10.32 & -0.219991 \tabularnewline
46 & 14.3 & 10.9241 & 3.37587 \tabularnewline
47 & 9.3 & 12.9267 & -3.62674 \tabularnewline
48 & 12.5 & 9.10215 & 3.39785 \tabularnewline
49 & 7.6 & 10.0145 & -2.41445 \tabularnewline
50 & 15.9 & 12.8629 & 3.03709 \tabularnewline
51 & 9.2 & 10.8769 & -1.67691 \tabularnewline
52 & 11.1 & 12.2747 & -1.17472 \tabularnewline
53 & 13 & 12.5233 & 0.476652 \tabularnewline
54 & 14.5 & 11.5464 & 2.95363 \tabularnewline
55 & 12.3 & 13.6566 & -1.35655 \tabularnewline
56 & 11.4 & 11.4864 & -0.086445 \tabularnewline
57 & 12.6 & 10.4802 & 2.11977 \tabularnewline
58 & NA & NA & -0.147485 \tabularnewline
59 & 13 & 9.57594 & 3.42406 \tabularnewline
60 & 13.2 & 16.8504 & -3.6504 \tabularnewline
61 & 7.7 & 11.0515 & -3.35149 \tabularnewline
62 & 4.35 & 1.76399 & 2.58601 \tabularnewline
63 & 12.7 & 10.3539 & 2.34608 \tabularnewline
64 & 18.1 & 16.1535 & 1.94651 \tabularnewline
65 & 17.85 & 17.7937 & 0.0562551 \tabularnewline
66 & 17.1 & 14.939 & 2.16103 \tabularnewline
67 & 19.1 & 21.4031 & -2.30312 \tabularnewline
68 & 16.1 & 13.3063 & 2.79366 \tabularnewline
69 & 13.35 & 11.3558 & 1.9942 \tabularnewline
70 & 18.4 & 13.1821 & 5.21787 \tabularnewline
71 & 14.7 & 15.6817 & -0.981703 \tabularnewline
72 & 10.6 & 11.6586 & -1.05857 \tabularnewline
73 & 12.6 & 13.2795 & -0.679526 \tabularnewline
74 & 16.2 & 14.9867 & 1.21331 \tabularnewline
75 & 13.6 & 12.5313 & 1.06875 \tabularnewline
76 & 14.1 & 12.8716 & 1.22837 \tabularnewline
77 & 14.5 & 14.5226 & -0.0225642 \tabularnewline
78 & 16.15 & 14.9504 & 1.19959 \tabularnewline
79 & 14.75 & 13.0118 & 1.73824 \tabularnewline
80 & 14.8 & 14.1127 & 0.687295 \tabularnewline
81 & 12.45 & 9.85427 & 2.59573 \tabularnewline
82 & 12.65 & 10.5428 & 2.10723 \tabularnewline
83 & 17.35 & 16.3188 & 1.03121 \tabularnewline
84 & 8.6 & 6.93979 & 1.66021 \tabularnewline
85 & 18.4 & 16.676 & 1.72404 \tabularnewline
86 & 16.1 & 14.0137 & 2.08625 \tabularnewline
87 & 17.75 & 18.0788 & -0.328788 \tabularnewline
88 & 15.25 & 13.5445 & 1.70553 \tabularnewline
89 & 17.65 & 17.9705 & -0.320456 \tabularnewline
90 & 16.35 & 17.4794 & -1.12943 \tabularnewline
91 & 17.65 & 17.0418 & 0.608238 \tabularnewline
92 & 13.6 & 12.4962 & 1.10379 \tabularnewline
93 & 14.35 & 16.3162 & -1.96617 \tabularnewline
94 & 14.75 & 13.8097 & 0.940292 \tabularnewline
95 & 18.25 & 22.4129 & -4.16293 \tabularnewline
96 & 9.9 & 7.3935 & 2.5065 \tabularnewline
97 & 16 & 14.0494 & 1.95057 \tabularnewline
98 & 18.25 & 19.221 & -0.971045 \tabularnewline
99 & 16.85 & 14.5088 & 2.34118 \tabularnewline
100 & 18.95 & 16.5551 & 2.39486 \tabularnewline
101 & 15.6 & 16.2088 & -0.608829 \tabularnewline
102 & 17.1 & 10.4493 & 6.65069 \tabularnewline
103 & 16.1 & 17.1255 & -1.02552 \tabularnewline
104 & 15.4 & 15.6589 & -0.258907 \tabularnewline
105 & 15.4 & 15.7924 & -0.392424 \tabularnewline
106 & 13.35 & 12.1839 & 1.16607 \tabularnewline
107 & 19.1 & 17.5437 & 1.55631 \tabularnewline
108 & 7.6 & 4.72732 & 2.87268 \tabularnewline
109 & 19.1 & 21.1938 & -2.09379 \tabularnewline
110 & 14.75 & 11.9442 & 2.80577 \tabularnewline
111 & 19.25 & 21.4224 & -2.17242 \tabularnewline
112 & 13.6 & 16.2255 & -2.62546 \tabularnewline
113 & 12.75 & 11.8356 & 0.914367 \tabularnewline
114 & 9.85 & 11.1257 & -1.27569 \tabularnewline
115 & 15.25 & 17.3242 & -2.07415 \tabularnewline
116 & 11.9 & 12.4907 & -0.590652 \tabularnewline
117 & 16.35 & 16.8965 & -0.546508 \tabularnewline
118 & 12.4 & 11.8791 & 0.520935 \tabularnewline
119 & 18.15 & 14.3248 & 3.82518 \tabularnewline
120 & 17.75 & 17.6497 & 0.100259 \tabularnewline
121 & 12.35 & 12.7323 & -0.38229 \tabularnewline
122 & 15.6 & 13.5082 & 2.09185 \tabularnewline
123 & 19.3 & 18.7776 & 0.52243 \tabularnewline
124 & 17.1 & 14.1989 & 2.90107 \tabularnewline
125 & 18.4 & 16.3782 & 2.02183 \tabularnewline
126 & 19.05 & 15.7778 & 3.27222 \tabularnewline
127 & 18.55 & 17.655 & 0.894986 \tabularnewline
128 & 19.1 & 21.6453 & -2.54528 \tabularnewline
129 & 12.85 & 13.8968 & -1.04684 \tabularnewline
130 & 9.5 & 13.9049 & -4.40487 \tabularnewline
131 & 4.5 & 4.56669 & -0.0666875 \tabularnewline
132 & 13.6 & 13.4232 & 0.176843 \tabularnewline
133 & 11.7 & 13.1204 & -1.42035 \tabularnewline
134 & 13.35 & 15.0735 & -1.72349 \tabularnewline
135 & 17.6 & 16.6567 & 0.943288 \tabularnewline
136 & 14.05 & 15.4194 & -1.36943 \tabularnewline
137 & 16.1 & 18.4644 & -2.36438 \tabularnewline
138 & 13.35 & 17.6456 & -4.29562 \tabularnewline
139 & 11.85 & 11.8537 & -0.00366152 \tabularnewline
140 & 11.95 & 14.6835 & -2.73354 \tabularnewline
141 & 13.2 & 15.1388 & -1.93876 \tabularnewline
142 & 7.7 & 7.59004 & 0.109964 \tabularnewline
143 & 14.6 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271026&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]12.9[/C][C]12.3416[/C][C]0.558433[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]11.0962[/C][C]1.10382[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]11.688[/C][C]1.11202[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]11.6355[/C][C]-4.23545[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.8549[/C][C]-4.15486[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]12.0688[/C][C]0.531211[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]12.3361[/C][C]2.4639[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]13.0921[/C][C]0.207935[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]12.519[/C][C]-1.41896[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.5333[/C][C]-2.33329[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]12.7374[/C][C]-1.33738[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.3082[/C][C]-3.90817[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]10.3241[/C][C]0.275915[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.4264[/C][C]-1.42637[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]9.27305[/C][C]-2.97305[/C][/ROW]
[ROW][C]16[/C][C]11.9[/C][C]12.8104[/C][C]-0.910359[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]10.803[/C][C]-1.50296[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]10.8711[/C][C]-0.87105[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]11.5587[/C][C]-5.15869[/C][/ROW]
[ROW][C]20[/C][C]13.8[/C][C]12.3923[/C][C]1.40772[/C][/ROW]
[ROW][C]21[/C][C]10.8[/C][C]10.7042[/C][C]0.0957614[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.5949[/C][C]2.20506[/C][/ROW]
[ROW][C]23[/C][C]11.7[/C][C]11.8614[/C][C]-0.161438[/C][/ROW]
[ROW][C]24[/C][C]10.9[/C][C]11.1095[/C][C]-0.209503[/C][/ROW]
[ROW][C]25[/C][C]9.9[/C][C]11.3172[/C][C]-1.41717[/C][/ROW]
[ROW][C]26[/C][C]11.5[/C][C]11.5226[/C][C]-0.0225806[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]11.3509[/C][C]-3.05085[/C][/ROW]
[ROW][C]28[/C][C]11.7[/C][C]10.8359[/C][C]0.864108[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]10.5767[/C][C]-1.57666[/C][/ROW]
[ROW][C]30[/C][C]9.7[/C][C]14.2366[/C][C]-4.53664[/C][/ROW]
[ROW][C]31[/C][C]10.8[/C][C]12.3782[/C][C]-1.57821[/C][/ROW]
[ROW][C]32[/C][C]10.3[/C][C]10.6958[/C][C]-0.3958[/C][/ROW]
[ROW][C]33[/C][C]10.4[/C][C]9.43295[/C][C]0.967049[/C][/ROW]
[ROW][C]34[/C][C]9.3[/C][C]11.2469[/C][C]-1.94688[/C][/ROW]
[ROW][C]35[/C][C]11.8[/C][C]12.1845[/C][C]-0.384458[/C][/ROW]
[ROW][C]36[/C][C]5.9[/C][C]9.80562[/C][C]-3.90562[/C][/ROW]
[ROW][C]37[/C][C]11.4[/C][C]11.0102[/C][C]0.389751[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]11.4391[/C][C]1.56087[/C][/ROW]
[ROW][C]39[/C][C]10.8[/C][C]12.2999[/C][C]-1.49986[/C][/ROW]
[ROW][C]40[/C][C]11.3[/C][C]10.5641[/C][C]0.735901[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]11.2236[/C][C]0.576381[/C][/ROW]
[ROW][C]42[/C][C]12.7[/C][C]10.4534[/C][C]2.24663[/C][/ROW]
[ROW][C]43[/C][C]10.9[/C][C]11.8532[/C][C]-0.953246[/C][/ROW]
[ROW][C]44[/C][C]13.3[/C][C]12.2684[/C][C]1.03158[/C][/ROW]
[ROW][C]45[/C][C]10.1[/C][C]10.32[/C][C]-0.219991[/C][/ROW]
[ROW][C]46[/C][C]14.3[/C][C]10.9241[/C][C]3.37587[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]12.9267[/C][C]-3.62674[/C][/ROW]
[ROW][C]48[/C][C]12.5[/C][C]9.10215[/C][C]3.39785[/C][/ROW]
[ROW][C]49[/C][C]7.6[/C][C]10.0145[/C][C]-2.41445[/C][/ROW]
[ROW][C]50[/C][C]15.9[/C][C]12.8629[/C][C]3.03709[/C][/ROW]
[ROW][C]51[/C][C]9.2[/C][C]10.8769[/C][C]-1.67691[/C][/ROW]
[ROW][C]52[/C][C]11.1[/C][C]12.2747[/C][C]-1.17472[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]12.5233[/C][C]0.476652[/C][/ROW]
[ROW][C]54[/C][C]14.5[/C][C]11.5464[/C][C]2.95363[/C][/ROW]
[ROW][C]55[/C][C]12.3[/C][C]13.6566[/C][C]-1.35655[/C][/ROW]
[ROW][C]56[/C][C]11.4[/C][C]11.4864[/C][C]-0.086445[/C][/ROW]
[ROW][C]57[/C][C]12.6[/C][C]10.4802[/C][C]2.11977[/C][/ROW]
[ROW][C]58[/C][C]NA[/C][C]NA[/C][C]-0.147485[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]9.57594[/C][C]3.42406[/C][/ROW]
[ROW][C]60[/C][C]13.2[/C][C]16.8504[/C][C]-3.6504[/C][/ROW]
[ROW][C]61[/C][C]7.7[/C][C]11.0515[/C][C]-3.35149[/C][/ROW]
[ROW][C]62[/C][C]4.35[/C][C]1.76399[/C][C]2.58601[/C][/ROW]
[ROW][C]63[/C][C]12.7[/C][C]10.3539[/C][C]2.34608[/C][/ROW]
[ROW][C]64[/C][C]18.1[/C][C]16.1535[/C][C]1.94651[/C][/ROW]
[ROW][C]65[/C][C]17.85[/C][C]17.7937[/C][C]0.0562551[/C][/ROW]
[ROW][C]66[/C][C]17.1[/C][C]14.939[/C][C]2.16103[/C][/ROW]
[ROW][C]67[/C][C]19.1[/C][C]21.4031[/C][C]-2.30312[/C][/ROW]
[ROW][C]68[/C][C]16.1[/C][C]13.3063[/C][C]2.79366[/C][/ROW]
[ROW][C]69[/C][C]13.35[/C][C]11.3558[/C][C]1.9942[/C][/ROW]
[ROW][C]70[/C][C]18.4[/C][C]13.1821[/C][C]5.21787[/C][/ROW]
[ROW][C]71[/C][C]14.7[/C][C]15.6817[/C][C]-0.981703[/C][/ROW]
[ROW][C]72[/C][C]10.6[/C][C]11.6586[/C][C]-1.05857[/C][/ROW]
[ROW][C]73[/C][C]12.6[/C][C]13.2795[/C][C]-0.679526[/C][/ROW]
[ROW][C]74[/C][C]16.2[/C][C]14.9867[/C][C]1.21331[/C][/ROW]
[ROW][C]75[/C][C]13.6[/C][C]12.5313[/C][C]1.06875[/C][/ROW]
[ROW][C]76[/C][C]14.1[/C][C]12.8716[/C][C]1.22837[/C][/ROW]
[ROW][C]77[/C][C]14.5[/C][C]14.5226[/C][C]-0.0225642[/C][/ROW]
[ROW][C]78[/C][C]16.15[/C][C]14.9504[/C][C]1.19959[/C][/ROW]
[ROW][C]79[/C][C]14.75[/C][C]13.0118[/C][C]1.73824[/C][/ROW]
[ROW][C]80[/C][C]14.8[/C][C]14.1127[/C][C]0.687295[/C][/ROW]
[ROW][C]81[/C][C]12.45[/C][C]9.85427[/C][C]2.59573[/C][/ROW]
[ROW][C]82[/C][C]12.65[/C][C]10.5428[/C][C]2.10723[/C][/ROW]
[ROW][C]83[/C][C]17.35[/C][C]16.3188[/C][C]1.03121[/C][/ROW]
[ROW][C]84[/C][C]8.6[/C][C]6.93979[/C][C]1.66021[/C][/ROW]
[ROW][C]85[/C][C]18.4[/C][C]16.676[/C][C]1.72404[/C][/ROW]
[ROW][C]86[/C][C]16.1[/C][C]14.0137[/C][C]2.08625[/C][/ROW]
[ROW][C]87[/C][C]17.75[/C][C]18.0788[/C][C]-0.328788[/C][/ROW]
[ROW][C]88[/C][C]15.25[/C][C]13.5445[/C][C]1.70553[/C][/ROW]
[ROW][C]89[/C][C]17.65[/C][C]17.9705[/C][C]-0.320456[/C][/ROW]
[ROW][C]90[/C][C]16.35[/C][C]17.4794[/C][C]-1.12943[/C][/ROW]
[ROW][C]91[/C][C]17.65[/C][C]17.0418[/C][C]0.608238[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]12.4962[/C][C]1.10379[/C][/ROW]
[ROW][C]93[/C][C]14.35[/C][C]16.3162[/C][C]-1.96617[/C][/ROW]
[ROW][C]94[/C][C]14.75[/C][C]13.8097[/C][C]0.940292[/C][/ROW]
[ROW][C]95[/C][C]18.25[/C][C]22.4129[/C][C]-4.16293[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]7.3935[/C][C]2.5065[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.0494[/C][C]1.95057[/C][/ROW]
[ROW][C]98[/C][C]18.25[/C][C]19.221[/C][C]-0.971045[/C][/ROW]
[ROW][C]99[/C][C]16.85[/C][C]14.5088[/C][C]2.34118[/C][/ROW]
[ROW][C]100[/C][C]18.95[/C][C]16.5551[/C][C]2.39486[/C][/ROW]
[ROW][C]101[/C][C]15.6[/C][C]16.2088[/C][C]-0.608829[/C][/ROW]
[ROW][C]102[/C][C]17.1[/C][C]10.4493[/C][C]6.65069[/C][/ROW]
[ROW][C]103[/C][C]16.1[/C][C]17.1255[/C][C]-1.02552[/C][/ROW]
[ROW][C]104[/C][C]15.4[/C][C]15.6589[/C][C]-0.258907[/C][/ROW]
[ROW][C]105[/C][C]15.4[/C][C]15.7924[/C][C]-0.392424[/C][/ROW]
[ROW][C]106[/C][C]13.35[/C][C]12.1839[/C][C]1.16607[/C][/ROW]
[ROW][C]107[/C][C]19.1[/C][C]17.5437[/C][C]1.55631[/C][/ROW]
[ROW][C]108[/C][C]7.6[/C][C]4.72732[/C][C]2.87268[/C][/ROW]
[ROW][C]109[/C][C]19.1[/C][C]21.1938[/C][C]-2.09379[/C][/ROW]
[ROW][C]110[/C][C]14.75[/C][C]11.9442[/C][C]2.80577[/C][/ROW]
[ROW][C]111[/C][C]19.25[/C][C]21.4224[/C][C]-2.17242[/C][/ROW]
[ROW][C]112[/C][C]13.6[/C][C]16.2255[/C][C]-2.62546[/C][/ROW]
[ROW][C]113[/C][C]12.75[/C][C]11.8356[/C][C]0.914367[/C][/ROW]
[ROW][C]114[/C][C]9.85[/C][C]11.1257[/C][C]-1.27569[/C][/ROW]
[ROW][C]115[/C][C]15.25[/C][C]17.3242[/C][C]-2.07415[/C][/ROW]
[ROW][C]116[/C][C]11.9[/C][C]12.4907[/C][C]-0.590652[/C][/ROW]
[ROW][C]117[/C][C]16.35[/C][C]16.8965[/C][C]-0.546508[/C][/ROW]
[ROW][C]118[/C][C]12.4[/C][C]11.8791[/C][C]0.520935[/C][/ROW]
[ROW][C]119[/C][C]18.15[/C][C]14.3248[/C][C]3.82518[/C][/ROW]
[ROW][C]120[/C][C]17.75[/C][C]17.6497[/C][C]0.100259[/C][/ROW]
[ROW][C]121[/C][C]12.35[/C][C]12.7323[/C][C]-0.38229[/C][/ROW]
[ROW][C]122[/C][C]15.6[/C][C]13.5082[/C][C]2.09185[/C][/ROW]
[ROW][C]123[/C][C]19.3[/C][C]18.7776[/C][C]0.52243[/C][/ROW]
[ROW][C]124[/C][C]17.1[/C][C]14.1989[/C][C]2.90107[/C][/ROW]
[ROW][C]125[/C][C]18.4[/C][C]16.3782[/C][C]2.02183[/C][/ROW]
[ROW][C]126[/C][C]19.05[/C][C]15.7778[/C][C]3.27222[/C][/ROW]
[ROW][C]127[/C][C]18.55[/C][C]17.655[/C][C]0.894986[/C][/ROW]
[ROW][C]128[/C][C]19.1[/C][C]21.6453[/C][C]-2.54528[/C][/ROW]
[ROW][C]129[/C][C]12.85[/C][C]13.8968[/C][C]-1.04684[/C][/ROW]
[ROW][C]130[/C][C]9.5[/C][C]13.9049[/C][C]-4.40487[/C][/ROW]
[ROW][C]131[/C][C]4.5[/C][C]4.56669[/C][C]-0.0666875[/C][/ROW]
[ROW][C]132[/C][C]13.6[/C][C]13.4232[/C][C]0.176843[/C][/ROW]
[ROW][C]133[/C][C]11.7[/C][C]13.1204[/C][C]-1.42035[/C][/ROW]
[ROW][C]134[/C][C]13.35[/C][C]15.0735[/C][C]-1.72349[/C][/ROW]
[ROW][C]135[/C][C]17.6[/C][C]16.6567[/C][C]0.943288[/C][/ROW]
[ROW][C]136[/C][C]14.05[/C][C]15.4194[/C][C]-1.36943[/C][/ROW]
[ROW][C]137[/C][C]16.1[/C][C]18.4644[/C][C]-2.36438[/C][/ROW]
[ROW][C]138[/C][C]13.35[/C][C]17.6456[/C][C]-4.29562[/C][/ROW]
[ROW][C]139[/C][C]11.85[/C][C]11.8537[/C][C]-0.00366152[/C][/ROW]
[ROW][C]140[/C][C]11.95[/C][C]14.6835[/C][C]-2.73354[/C][/ROW]
[ROW][C]141[/C][C]13.2[/C][C]15.1388[/C][C]-1.93876[/C][/ROW]
[ROW][C]142[/C][C]7.7[/C][C]7.59004[/C][C]0.109964[/C][/ROW]
[ROW][C]143[/C][C]14.6[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271026&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271026&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.34160.558433
212.211.09621.10382
312.811.6881.11202
47.411.6355-4.23545
56.710.8549-4.15486
612.612.06880.531211
714.812.33612.4639
813.313.09210.207935
911.112.519-1.41896
108.210.5333-2.33329
1111.412.7374-1.33738
126.410.3082-3.90817
1310.610.32410.275915
141213.4264-1.42637
156.39.27305-2.97305
1611.912.8104-0.910359
179.310.803-1.50296
181010.8711-0.87105
196.411.5587-5.15869
2013.812.39231.40772
2110.810.70420.0957614
2213.811.59492.20506
2311.711.8614-0.161438
2410.911.1095-0.209503
259.911.3172-1.41717
2611.511.5226-0.0225806
278.311.3509-3.05085
2811.710.83590.864108
29910.5767-1.57666
309.714.2366-4.53664
3110.812.3782-1.57821
3210.310.6958-0.3958
3310.49.432950.967049
349.311.2469-1.94688
3511.812.1845-0.384458
365.99.80562-3.90562
3711.411.01020.389751
381311.43911.56087
3910.812.2999-1.49986
4011.310.56410.735901
4111.811.22360.576381
4212.710.45342.24663
4310.911.8532-0.953246
4413.312.26841.03158
4510.110.32-0.219991
4614.310.92413.37587
479.312.9267-3.62674
4812.59.102153.39785
497.610.0145-2.41445
5015.912.86293.03709
519.210.8769-1.67691
5211.112.2747-1.17472
531312.52330.476652
5414.511.54642.95363
5512.313.6566-1.35655
5611.411.4864-0.086445
5712.610.48022.11977
58NANA-0.147485
59139.575943.42406
6013.216.8504-3.6504
617.711.0515-3.35149
624.351.763992.58601
6312.710.35392.34608
6418.116.15351.94651
6517.8517.79370.0562551
6617.114.9392.16103
6719.121.4031-2.30312
6816.113.30632.79366
6913.3511.35581.9942
7018.413.18215.21787
7114.715.6817-0.981703
7210.611.6586-1.05857
7312.613.2795-0.679526
7416.214.98671.21331
7513.612.53131.06875
7614.112.87161.22837
7714.514.5226-0.0225642
7816.1514.95041.19959
7914.7513.01181.73824
8014.814.11270.687295
8112.459.854272.59573
8212.6510.54282.10723
8317.3516.31881.03121
848.66.939791.66021
8518.416.6761.72404
8616.114.01372.08625
8717.7518.0788-0.328788
8815.2513.54451.70553
8917.6517.9705-0.320456
9016.3517.4794-1.12943
9117.6517.04180.608238
9213.612.49621.10379
9314.3516.3162-1.96617
9414.7513.80970.940292
9518.2522.4129-4.16293
969.97.39352.5065
971614.04941.95057
9818.2519.221-0.971045
9916.8514.50882.34118
10018.9516.55512.39486
10115.616.2088-0.608829
10217.110.44936.65069
10316.117.1255-1.02552
10415.415.6589-0.258907
10515.415.7924-0.392424
10613.3512.18391.16607
10719.117.54371.55631
1087.64.727322.87268
10919.121.1938-2.09379
11014.7511.94422.80577
11119.2521.4224-2.17242
11213.616.2255-2.62546
11312.7511.83560.914367
1149.8511.1257-1.27569
11515.2517.3242-2.07415
11611.912.4907-0.590652
11716.3516.8965-0.546508
11812.411.87910.520935
11918.1514.32483.82518
12017.7517.64970.100259
12112.3512.7323-0.38229
12215.613.50822.09185
12319.318.77760.52243
12417.114.19892.90107
12518.416.37822.02183
12619.0515.77783.27222
12718.5517.6550.894986
12819.121.6453-2.54528
12912.8513.8968-1.04684
1309.513.9049-4.40487
1314.54.56669-0.0666875
13213.613.42320.176843
13311.713.1204-1.42035
13413.3515.0735-1.72349
13517.616.65670.943288
13614.0515.4194-1.36943
13716.118.4644-2.36438
13813.3517.6456-4.29562
13911.8511.8537-0.00366152
14011.9514.6835-2.73354
14113.215.1388-1.93876
1427.77.590040.109964
14314.6NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.6414680.7170640.358532
210.4779960.9559910.522004
220.5176360.9647270.482364
230.3910910.7821810.608909
240.3692810.7385630.630719
250.2821040.5642080.717896
260.2574960.5149910.742504
270.2830820.5661650.716918
280.2970560.5941120.702944
290.2615050.523010.738495
300.5466380.9067230.453362
310.4828590.9657180.517141
320.4592160.9184320.540784
330.3941840.7883680.605816
340.3678130.7356260.632187
350.2997920.5995850.700208
360.3381080.6762160.661892
370.2738090.5476180.726191
380.2282460.4564920.771754
390.1882230.3764460.811777
400.1465350.2930690.853465
410.1387760.2775510.861224
420.1350270.2700540.864973
430.106760.2135210.89324
440.1113730.2227470.888627
450.08433950.1686790.91566
460.1620930.3241860.837907
470.1980730.3961460.801927
480.4139850.827970.586015
490.4864970.9729930.513503
500.5262740.9474520.473726
510.4991330.9982660.500867
520.466560.9331210.53344
530.4452770.8905550.554723
540.4803160.9606320.519684
550.4522580.9045170.547742
560.401710.8034190.59829
570.363320.7266390.63668
580.3282740.6565490.671726
590.4260180.8520350.573982
600.5155610.9688780.484439
610.6035920.7928170.396408
620.6907360.6185280.309264
630.6757810.6484380.324219
640.6368450.7263090.363155
650.6151330.7697350.384867
660.5839780.8320430.416022
670.6811730.6376530.318827
680.7029040.5941930.297096
690.7169430.5661140.283057
700.8811510.2376980.118849
710.8624870.2750250.137513
720.8470540.3058930.152946
730.8323990.3352020.167601
740.8493880.3012250.150612
750.8178150.3643690.182185
760.7856690.4286620.214331
770.7616260.4767490.238374
780.7231790.5536410.276821
790.6975070.6049860.302493
800.6525970.6948070.347403
810.6592490.6815020.340751
820.6472680.7054640.352732
830.6027730.7944530.397227
840.5656160.8687670.434384
850.5228680.9542630.477132
860.4957850.991570.504215
870.464290.9285790.53571
880.4290160.8580310.570984
890.381920.763840.61808
900.3917410.7834810.608259
910.3474490.6948980.652551
920.3117040.6234090.688296
930.3071470.6142940.692853
940.2678220.5356440.732178
950.4001140.8002270.599886
960.3746430.7492860.625357
970.3765150.7530290.623485
980.3326480.6652950.667352
990.2969650.5939310.703035
1000.3087610.6175230.691239
1010.2718040.5436080.728196
1020.7370370.5259260.262963
1030.6889040.6221930.311096
1040.6880370.6239260.311963
1050.6333080.7333840.366692
1060.593210.813580.40679
1070.5816740.8366530.418326
1080.5288590.9422830.471141
1090.517270.965460.48273
1100.4593860.9187720.540614
1110.462720.925440.53728
1120.4276380.8552760.572362
1130.3926340.7852680.607366
1140.3155520.6311040.684448
1150.3204910.6409810.679509
1160.2428210.4856410.757179
1170.1779740.3559470.822026
1180.1276080.2552150.872392
1190.1985040.3970080.801496
1200.1274530.2549060.872547
1210.1754860.3509710.824514
1220.1350530.2701060.864947
1230.1172150.234430.882785

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.641468 & 0.717064 & 0.358532 \tabularnewline
21 & 0.477996 & 0.955991 & 0.522004 \tabularnewline
22 & 0.517636 & 0.964727 & 0.482364 \tabularnewline
23 & 0.391091 & 0.782181 & 0.608909 \tabularnewline
24 & 0.369281 & 0.738563 & 0.630719 \tabularnewline
25 & 0.282104 & 0.564208 & 0.717896 \tabularnewline
26 & 0.257496 & 0.514991 & 0.742504 \tabularnewline
27 & 0.283082 & 0.566165 & 0.716918 \tabularnewline
28 & 0.297056 & 0.594112 & 0.702944 \tabularnewline
29 & 0.261505 & 0.52301 & 0.738495 \tabularnewline
30 & 0.546638 & 0.906723 & 0.453362 \tabularnewline
31 & 0.482859 & 0.965718 & 0.517141 \tabularnewline
32 & 0.459216 & 0.918432 & 0.540784 \tabularnewline
33 & 0.394184 & 0.788368 & 0.605816 \tabularnewline
34 & 0.367813 & 0.735626 & 0.632187 \tabularnewline
35 & 0.299792 & 0.599585 & 0.700208 \tabularnewline
36 & 0.338108 & 0.676216 & 0.661892 \tabularnewline
37 & 0.273809 & 0.547618 & 0.726191 \tabularnewline
38 & 0.228246 & 0.456492 & 0.771754 \tabularnewline
39 & 0.188223 & 0.376446 & 0.811777 \tabularnewline
40 & 0.146535 & 0.293069 & 0.853465 \tabularnewline
41 & 0.138776 & 0.277551 & 0.861224 \tabularnewline
42 & 0.135027 & 0.270054 & 0.864973 \tabularnewline
43 & 0.10676 & 0.213521 & 0.89324 \tabularnewline
44 & 0.111373 & 0.222747 & 0.888627 \tabularnewline
45 & 0.0843395 & 0.168679 & 0.91566 \tabularnewline
46 & 0.162093 & 0.324186 & 0.837907 \tabularnewline
47 & 0.198073 & 0.396146 & 0.801927 \tabularnewline
48 & 0.413985 & 0.82797 & 0.586015 \tabularnewline
49 & 0.486497 & 0.972993 & 0.513503 \tabularnewline
50 & 0.526274 & 0.947452 & 0.473726 \tabularnewline
51 & 0.499133 & 0.998266 & 0.500867 \tabularnewline
52 & 0.46656 & 0.933121 & 0.53344 \tabularnewline
53 & 0.445277 & 0.890555 & 0.554723 \tabularnewline
54 & 0.480316 & 0.960632 & 0.519684 \tabularnewline
55 & 0.452258 & 0.904517 & 0.547742 \tabularnewline
56 & 0.40171 & 0.803419 & 0.59829 \tabularnewline
57 & 0.36332 & 0.726639 & 0.63668 \tabularnewline
58 & 0.328274 & 0.656549 & 0.671726 \tabularnewline
59 & 0.426018 & 0.852035 & 0.573982 \tabularnewline
60 & 0.515561 & 0.968878 & 0.484439 \tabularnewline
61 & 0.603592 & 0.792817 & 0.396408 \tabularnewline
62 & 0.690736 & 0.618528 & 0.309264 \tabularnewline
63 & 0.675781 & 0.648438 & 0.324219 \tabularnewline
64 & 0.636845 & 0.726309 & 0.363155 \tabularnewline
65 & 0.615133 & 0.769735 & 0.384867 \tabularnewline
66 & 0.583978 & 0.832043 & 0.416022 \tabularnewline
67 & 0.681173 & 0.637653 & 0.318827 \tabularnewline
68 & 0.702904 & 0.594193 & 0.297096 \tabularnewline
69 & 0.716943 & 0.566114 & 0.283057 \tabularnewline
70 & 0.881151 & 0.237698 & 0.118849 \tabularnewline
71 & 0.862487 & 0.275025 & 0.137513 \tabularnewline
72 & 0.847054 & 0.305893 & 0.152946 \tabularnewline
73 & 0.832399 & 0.335202 & 0.167601 \tabularnewline
74 & 0.849388 & 0.301225 & 0.150612 \tabularnewline
75 & 0.817815 & 0.364369 & 0.182185 \tabularnewline
76 & 0.785669 & 0.428662 & 0.214331 \tabularnewline
77 & 0.761626 & 0.476749 & 0.238374 \tabularnewline
78 & 0.723179 & 0.553641 & 0.276821 \tabularnewline
79 & 0.697507 & 0.604986 & 0.302493 \tabularnewline
80 & 0.652597 & 0.694807 & 0.347403 \tabularnewline
81 & 0.659249 & 0.681502 & 0.340751 \tabularnewline
82 & 0.647268 & 0.705464 & 0.352732 \tabularnewline
83 & 0.602773 & 0.794453 & 0.397227 \tabularnewline
84 & 0.565616 & 0.868767 & 0.434384 \tabularnewline
85 & 0.522868 & 0.954263 & 0.477132 \tabularnewline
86 & 0.495785 & 0.99157 & 0.504215 \tabularnewline
87 & 0.46429 & 0.928579 & 0.53571 \tabularnewline
88 & 0.429016 & 0.858031 & 0.570984 \tabularnewline
89 & 0.38192 & 0.76384 & 0.61808 \tabularnewline
90 & 0.391741 & 0.783481 & 0.608259 \tabularnewline
91 & 0.347449 & 0.694898 & 0.652551 \tabularnewline
92 & 0.311704 & 0.623409 & 0.688296 \tabularnewline
93 & 0.307147 & 0.614294 & 0.692853 \tabularnewline
94 & 0.267822 & 0.535644 & 0.732178 \tabularnewline
95 & 0.400114 & 0.800227 & 0.599886 \tabularnewline
96 & 0.374643 & 0.749286 & 0.625357 \tabularnewline
97 & 0.376515 & 0.753029 & 0.623485 \tabularnewline
98 & 0.332648 & 0.665295 & 0.667352 \tabularnewline
99 & 0.296965 & 0.593931 & 0.703035 \tabularnewline
100 & 0.308761 & 0.617523 & 0.691239 \tabularnewline
101 & 0.271804 & 0.543608 & 0.728196 \tabularnewline
102 & 0.737037 & 0.525926 & 0.262963 \tabularnewline
103 & 0.688904 & 0.622193 & 0.311096 \tabularnewline
104 & 0.688037 & 0.623926 & 0.311963 \tabularnewline
105 & 0.633308 & 0.733384 & 0.366692 \tabularnewline
106 & 0.59321 & 0.81358 & 0.40679 \tabularnewline
107 & 0.581674 & 0.836653 & 0.418326 \tabularnewline
108 & 0.528859 & 0.942283 & 0.471141 \tabularnewline
109 & 0.51727 & 0.96546 & 0.48273 \tabularnewline
110 & 0.459386 & 0.918772 & 0.540614 \tabularnewline
111 & 0.46272 & 0.92544 & 0.53728 \tabularnewline
112 & 0.427638 & 0.855276 & 0.572362 \tabularnewline
113 & 0.392634 & 0.785268 & 0.607366 \tabularnewline
114 & 0.315552 & 0.631104 & 0.684448 \tabularnewline
115 & 0.320491 & 0.640981 & 0.679509 \tabularnewline
116 & 0.242821 & 0.485641 & 0.757179 \tabularnewline
117 & 0.177974 & 0.355947 & 0.822026 \tabularnewline
118 & 0.127608 & 0.255215 & 0.872392 \tabularnewline
119 & 0.198504 & 0.397008 & 0.801496 \tabularnewline
120 & 0.127453 & 0.254906 & 0.872547 \tabularnewline
121 & 0.175486 & 0.350971 & 0.824514 \tabularnewline
122 & 0.135053 & 0.270106 & 0.864947 \tabularnewline
123 & 0.117215 & 0.23443 & 0.882785 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271026&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]20[/C][C]0.641468[/C][C]0.717064[/C][C]0.358532[/C][/ROW]
[ROW][C]21[/C][C]0.477996[/C][C]0.955991[/C][C]0.522004[/C][/ROW]
[ROW][C]22[/C][C]0.517636[/C][C]0.964727[/C][C]0.482364[/C][/ROW]
[ROW][C]23[/C][C]0.391091[/C][C]0.782181[/C][C]0.608909[/C][/ROW]
[ROW][C]24[/C][C]0.369281[/C][C]0.738563[/C][C]0.630719[/C][/ROW]
[ROW][C]25[/C][C]0.282104[/C][C]0.564208[/C][C]0.717896[/C][/ROW]
[ROW][C]26[/C][C]0.257496[/C][C]0.514991[/C][C]0.742504[/C][/ROW]
[ROW][C]27[/C][C]0.283082[/C][C]0.566165[/C][C]0.716918[/C][/ROW]
[ROW][C]28[/C][C]0.297056[/C][C]0.594112[/C][C]0.702944[/C][/ROW]
[ROW][C]29[/C][C]0.261505[/C][C]0.52301[/C][C]0.738495[/C][/ROW]
[ROW][C]30[/C][C]0.546638[/C][C]0.906723[/C][C]0.453362[/C][/ROW]
[ROW][C]31[/C][C]0.482859[/C][C]0.965718[/C][C]0.517141[/C][/ROW]
[ROW][C]32[/C][C]0.459216[/C][C]0.918432[/C][C]0.540784[/C][/ROW]
[ROW][C]33[/C][C]0.394184[/C][C]0.788368[/C][C]0.605816[/C][/ROW]
[ROW][C]34[/C][C]0.367813[/C][C]0.735626[/C][C]0.632187[/C][/ROW]
[ROW][C]35[/C][C]0.299792[/C][C]0.599585[/C][C]0.700208[/C][/ROW]
[ROW][C]36[/C][C]0.338108[/C][C]0.676216[/C][C]0.661892[/C][/ROW]
[ROW][C]37[/C][C]0.273809[/C][C]0.547618[/C][C]0.726191[/C][/ROW]
[ROW][C]38[/C][C]0.228246[/C][C]0.456492[/C][C]0.771754[/C][/ROW]
[ROW][C]39[/C][C]0.188223[/C][C]0.376446[/C][C]0.811777[/C][/ROW]
[ROW][C]40[/C][C]0.146535[/C][C]0.293069[/C][C]0.853465[/C][/ROW]
[ROW][C]41[/C][C]0.138776[/C][C]0.277551[/C][C]0.861224[/C][/ROW]
[ROW][C]42[/C][C]0.135027[/C][C]0.270054[/C][C]0.864973[/C][/ROW]
[ROW][C]43[/C][C]0.10676[/C][C]0.213521[/C][C]0.89324[/C][/ROW]
[ROW][C]44[/C][C]0.111373[/C][C]0.222747[/C][C]0.888627[/C][/ROW]
[ROW][C]45[/C][C]0.0843395[/C][C]0.168679[/C][C]0.91566[/C][/ROW]
[ROW][C]46[/C][C]0.162093[/C][C]0.324186[/C][C]0.837907[/C][/ROW]
[ROW][C]47[/C][C]0.198073[/C][C]0.396146[/C][C]0.801927[/C][/ROW]
[ROW][C]48[/C][C]0.413985[/C][C]0.82797[/C][C]0.586015[/C][/ROW]
[ROW][C]49[/C][C]0.486497[/C][C]0.972993[/C][C]0.513503[/C][/ROW]
[ROW][C]50[/C][C]0.526274[/C][C]0.947452[/C][C]0.473726[/C][/ROW]
[ROW][C]51[/C][C]0.499133[/C][C]0.998266[/C][C]0.500867[/C][/ROW]
[ROW][C]52[/C][C]0.46656[/C][C]0.933121[/C][C]0.53344[/C][/ROW]
[ROW][C]53[/C][C]0.445277[/C][C]0.890555[/C][C]0.554723[/C][/ROW]
[ROW][C]54[/C][C]0.480316[/C][C]0.960632[/C][C]0.519684[/C][/ROW]
[ROW][C]55[/C][C]0.452258[/C][C]0.904517[/C][C]0.547742[/C][/ROW]
[ROW][C]56[/C][C]0.40171[/C][C]0.803419[/C][C]0.59829[/C][/ROW]
[ROW][C]57[/C][C]0.36332[/C][C]0.726639[/C][C]0.63668[/C][/ROW]
[ROW][C]58[/C][C]0.328274[/C][C]0.656549[/C][C]0.671726[/C][/ROW]
[ROW][C]59[/C][C]0.426018[/C][C]0.852035[/C][C]0.573982[/C][/ROW]
[ROW][C]60[/C][C]0.515561[/C][C]0.968878[/C][C]0.484439[/C][/ROW]
[ROW][C]61[/C][C]0.603592[/C][C]0.792817[/C][C]0.396408[/C][/ROW]
[ROW][C]62[/C][C]0.690736[/C][C]0.618528[/C][C]0.309264[/C][/ROW]
[ROW][C]63[/C][C]0.675781[/C][C]0.648438[/C][C]0.324219[/C][/ROW]
[ROW][C]64[/C][C]0.636845[/C][C]0.726309[/C][C]0.363155[/C][/ROW]
[ROW][C]65[/C][C]0.615133[/C][C]0.769735[/C][C]0.384867[/C][/ROW]
[ROW][C]66[/C][C]0.583978[/C][C]0.832043[/C][C]0.416022[/C][/ROW]
[ROW][C]67[/C][C]0.681173[/C][C]0.637653[/C][C]0.318827[/C][/ROW]
[ROW][C]68[/C][C]0.702904[/C][C]0.594193[/C][C]0.297096[/C][/ROW]
[ROW][C]69[/C][C]0.716943[/C][C]0.566114[/C][C]0.283057[/C][/ROW]
[ROW][C]70[/C][C]0.881151[/C][C]0.237698[/C][C]0.118849[/C][/ROW]
[ROW][C]71[/C][C]0.862487[/C][C]0.275025[/C][C]0.137513[/C][/ROW]
[ROW][C]72[/C][C]0.847054[/C][C]0.305893[/C][C]0.152946[/C][/ROW]
[ROW][C]73[/C][C]0.832399[/C][C]0.335202[/C][C]0.167601[/C][/ROW]
[ROW][C]74[/C][C]0.849388[/C][C]0.301225[/C][C]0.150612[/C][/ROW]
[ROW][C]75[/C][C]0.817815[/C][C]0.364369[/C][C]0.182185[/C][/ROW]
[ROW][C]76[/C][C]0.785669[/C][C]0.428662[/C][C]0.214331[/C][/ROW]
[ROW][C]77[/C][C]0.761626[/C][C]0.476749[/C][C]0.238374[/C][/ROW]
[ROW][C]78[/C][C]0.723179[/C][C]0.553641[/C][C]0.276821[/C][/ROW]
[ROW][C]79[/C][C]0.697507[/C][C]0.604986[/C][C]0.302493[/C][/ROW]
[ROW][C]80[/C][C]0.652597[/C][C]0.694807[/C][C]0.347403[/C][/ROW]
[ROW][C]81[/C][C]0.659249[/C][C]0.681502[/C][C]0.340751[/C][/ROW]
[ROW][C]82[/C][C]0.647268[/C][C]0.705464[/C][C]0.352732[/C][/ROW]
[ROW][C]83[/C][C]0.602773[/C][C]0.794453[/C][C]0.397227[/C][/ROW]
[ROW][C]84[/C][C]0.565616[/C][C]0.868767[/C][C]0.434384[/C][/ROW]
[ROW][C]85[/C][C]0.522868[/C][C]0.954263[/C][C]0.477132[/C][/ROW]
[ROW][C]86[/C][C]0.495785[/C][C]0.99157[/C][C]0.504215[/C][/ROW]
[ROW][C]87[/C][C]0.46429[/C][C]0.928579[/C][C]0.53571[/C][/ROW]
[ROW][C]88[/C][C]0.429016[/C][C]0.858031[/C][C]0.570984[/C][/ROW]
[ROW][C]89[/C][C]0.38192[/C][C]0.76384[/C][C]0.61808[/C][/ROW]
[ROW][C]90[/C][C]0.391741[/C][C]0.783481[/C][C]0.608259[/C][/ROW]
[ROW][C]91[/C][C]0.347449[/C][C]0.694898[/C][C]0.652551[/C][/ROW]
[ROW][C]92[/C][C]0.311704[/C][C]0.623409[/C][C]0.688296[/C][/ROW]
[ROW][C]93[/C][C]0.307147[/C][C]0.614294[/C][C]0.692853[/C][/ROW]
[ROW][C]94[/C][C]0.267822[/C][C]0.535644[/C][C]0.732178[/C][/ROW]
[ROW][C]95[/C][C]0.400114[/C][C]0.800227[/C][C]0.599886[/C][/ROW]
[ROW][C]96[/C][C]0.374643[/C][C]0.749286[/C][C]0.625357[/C][/ROW]
[ROW][C]97[/C][C]0.376515[/C][C]0.753029[/C][C]0.623485[/C][/ROW]
[ROW][C]98[/C][C]0.332648[/C][C]0.665295[/C][C]0.667352[/C][/ROW]
[ROW][C]99[/C][C]0.296965[/C][C]0.593931[/C][C]0.703035[/C][/ROW]
[ROW][C]100[/C][C]0.308761[/C][C]0.617523[/C][C]0.691239[/C][/ROW]
[ROW][C]101[/C][C]0.271804[/C][C]0.543608[/C][C]0.728196[/C][/ROW]
[ROW][C]102[/C][C]0.737037[/C][C]0.525926[/C][C]0.262963[/C][/ROW]
[ROW][C]103[/C][C]0.688904[/C][C]0.622193[/C][C]0.311096[/C][/ROW]
[ROW][C]104[/C][C]0.688037[/C][C]0.623926[/C][C]0.311963[/C][/ROW]
[ROW][C]105[/C][C]0.633308[/C][C]0.733384[/C][C]0.366692[/C][/ROW]
[ROW][C]106[/C][C]0.59321[/C][C]0.81358[/C][C]0.40679[/C][/ROW]
[ROW][C]107[/C][C]0.581674[/C][C]0.836653[/C][C]0.418326[/C][/ROW]
[ROW][C]108[/C][C]0.528859[/C][C]0.942283[/C][C]0.471141[/C][/ROW]
[ROW][C]109[/C][C]0.51727[/C][C]0.96546[/C][C]0.48273[/C][/ROW]
[ROW][C]110[/C][C]0.459386[/C][C]0.918772[/C][C]0.540614[/C][/ROW]
[ROW][C]111[/C][C]0.46272[/C][C]0.92544[/C][C]0.53728[/C][/ROW]
[ROW][C]112[/C][C]0.427638[/C][C]0.855276[/C][C]0.572362[/C][/ROW]
[ROW][C]113[/C][C]0.392634[/C][C]0.785268[/C][C]0.607366[/C][/ROW]
[ROW][C]114[/C][C]0.315552[/C][C]0.631104[/C][C]0.684448[/C][/ROW]
[ROW][C]115[/C][C]0.320491[/C][C]0.640981[/C][C]0.679509[/C][/ROW]
[ROW][C]116[/C][C]0.242821[/C][C]0.485641[/C][C]0.757179[/C][/ROW]
[ROW][C]117[/C][C]0.177974[/C][C]0.355947[/C][C]0.822026[/C][/ROW]
[ROW][C]118[/C][C]0.127608[/C][C]0.255215[/C][C]0.872392[/C][/ROW]
[ROW][C]119[/C][C]0.198504[/C][C]0.397008[/C][C]0.801496[/C][/ROW]
[ROW][C]120[/C][C]0.127453[/C][C]0.254906[/C][C]0.872547[/C][/ROW]
[ROW][C]121[/C][C]0.175486[/C][C]0.350971[/C][C]0.824514[/C][/ROW]
[ROW][C]122[/C][C]0.135053[/C][C]0.270106[/C][C]0.864947[/C][/ROW]
[ROW][C]123[/C][C]0.117215[/C][C]0.23443[/C][C]0.882785[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271026&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271026&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.6414680.7170640.358532
210.4779960.9559910.522004
220.5176360.9647270.482364
230.3910910.7821810.608909
240.3692810.7385630.630719
250.2821040.5642080.717896
260.2574960.5149910.742504
270.2830820.5661650.716918
280.2970560.5941120.702944
290.2615050.523010.738495
300.5466380.9067230.453362
310.4828590.9657180.517141
320.4592160.9184320.540784
330.3941840.7883680.605816
340.3678130.7356260.632187
350.2997920.5995850.700208
360.3381080.6762160.661892
370.2738090.5476180.726191
380.2282460.4564920.771754
390.1882230.3764460.811777
400.1465350.2930690.853465
410.1387760.2775510.861224
420.1350270.2700540.864973
430.106760.2135210.89324
440.1113730.2227470.888627
450.08433950.1686790.91566
460.1620930.3241860.837907
470.1980730.3961460.801927
480.4139850.827970.586015
490.4864970.9729930.513503
500.5262740.9474520.473726
510.4991330.9982660.500867
520.466560.9331210.53344
530.4452770.8905550.554723
540.4803160.9606320.519684
550.4522580.9045170.547742
560.401710.8034190.59829
570.363320.7266390.63668
580.3282740.6565490.671726
590.4260180.8520350.573982
600.5155610.9688780.484439
610.6035920.7928170.396408
620.6907360.6185280.309264
630.6757810.6484380.324219
640.6368450.7263090.363155
650.6151330.7697350.384867
660.5839780.8320430.416022
670.6811730.6376530.318827
680.7029040.5941930.297096
690.7169430.5661140.283057
700.8811510.2376980.118849
710.8624870.2750250.137513
720.8470540.3058930.152946
730.8323990.3352020.167601
740.8493880.3012250.150612
750.8178150.3643690.182185
760.7856690.4286620.214331
770.7616260.4767490.238374
780.7231790.5536410.276821
790.6975070.6049860.302493
800.6525970.6948070.347403
810.6592490.6815020.340751
820.6472680.7054640.352732
830.6027730.7944530.397227
840.5656160.8687670.434384
850.5228680.9542630.477132
860.4957850.991570.504215
870.464290.9285790.53571
880.4290160.8580310.570984
890.381920.763840.61808
900.3917410.7834810.608259
910.3474490.6948980.652551
920.3117040.6234090.688296
930.3071470.6142940.692853
940.2678220.5356440.732178
950.4001140.8002270.599886
960.3746430.7492860.625357
970.3765150.7530290.623485
980.3326480.6652950.667352
990.2969650.5939310.703035
1000.3087610.6175230.691239
1010.2718040.5436080.728196
1020.7370370.5259260.262963
1030.6889040.6221930.311096
1040.6880370.6239260.311963
1050.6333080.7333840.366692
1060.593210.813580.40679
1070.5816740.8366530.418326
1080.5288590.9422830.471141
1090.517270.965460.48273
1100.4593860.9187720.540614
1110.462720.925440.53728
1120.4276380.8552760.572362
1130.3926340.7852680.607366
1140.3155520.6311040.684448
1150.3204910.6409810.679509
1160.2428210.4856410.757179
1170.1779740.3559470.822026
1180.1276080.2552150.872392
1190.1985040.3970080.801496
1200.1274530.2549060.872547
1210.1754860.3509710.824514
1220.1350530.2701060.864947
1230.1172150.234430.882785






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error 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=271026&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=271026&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271026&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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