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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 03 Sep 2015 08:55:36 +0100
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/Sep/03/t1441266955y59n3ecq793jzkk.htm/, Retrieved Thu, 31 Oct 2024 23:42:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280570, Retrieved Thu, 31 Oct 2024 23:42:25 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [] [2015-09-03 07:41:44] [5b73c907efdd91cd8d467e65f28ae5f1]
- RM D    [Multiple Regression] [laatste vraag] [2015-09-03 07:55:36] [14340d7aca2fd6511ec0e21a84917c95] [Current]
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Dataseries X:
46.4 392 0.4 68.5
45.7 118 0.61 87.8
45.3 44 0.53 115.8
38.6 158 0.53 106.8
37.2 81 0.53 71.6
35 374 0.37 60.2
34 187 0.3 118.7
28.3 993 0.19 33.7
24.7 1723 0.12 27.2
24.7 287 0.2 62
24.4 970 0.19 24.9
22.7 885 0.12 22.9
22.3 200 0.53 65.7
21.7 575 0.14 21.6
21.6 688 0.34 32.4
21.3 48 0.69 108.7
21.2 572 0.49 38.6
20.8 239 0.42 46.7
20.3 244 0.48 56.5
18.9 472 0.25 44.4
18.8 134 0.52 47.4
18.6 633 0.19 21.7
18 295 0.44 55.7
17.6 906 0.24 27.1
17 1045 0.16 28.5
16.7 775 0.1 41.6
15.9 619 0.15 44.6
15.3 901 0.05 26.1
15 910 0.24 18.7
14.8 556 0.22 49.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280570&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]4 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=280570&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280570&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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
HIV_Risk[t] = + 5.55387 + 0.0065663Per_Capita_Income[t] + 9.10475Prop_Population_on_Farms[t] + 0.24269Homicides[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HIV_Risk[t] =  +  5.55387 +  0.0065663Per_Capita_Income[t] +  9.10475Prop_Population_on_Farms[t] +  0.24269Homicides[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280570&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HIV_Risk[t] =  +  5.55387 +  0.0065663Per_Capita_Income[t] +  9.10475Prop_Population_on_Farms[t] +  0.24269Homicides[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280570&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280570&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
HIV_Risk[t] = + 5.55387 + 0.0065663Per_Capita_Income[t] + 9.10475Prop_Population_on_Farms[t] + 0.24269Homicides[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+5.554 7.898+7.0320e-01 0.4882 0.2441
Per_Capita_Income+0.006566 0.006239+1.0520e+00 0.3023 0.1511
Prop_Population_on_Farms+9.105 12.83+7.0970e-01 0.4842 0.2421
Homicides+0.2427 0.07284+3.3320e+00 0.002595 0.001297

\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) & +5.554 &  7.898 & +7.0320e-01 &  0.4882 &  0.2441 \tabularnewline
Per_Capita_Income & +0.006566 &  0.006239 & +1.0520e+00 &  0.3023 &  0.1511 \tabularnewline
Prop_Population_on_Farms & +9.105 &  12.83 & +7.0970e-01 &  0.4842 &  0.2421 \tabularnewline
Homicides & +0.2427 &  0.07284 & +3.3320e+00 &  0.002595 &  0.001297 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280570&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]+5.554[/C][C] 7.898[/C][C]+7.0320e-01[/C][C] 0.4882[/C][C] 0.2441[/C][/ROW]
[ROW][C]Per_Capita_Income[/C][C]+0.006566[/C][C] 0.006239[/C][C]+1.0520e+00[/C][C] 0.3023[/C][C] 0.1511[/C][/ROW]
[ROW][C]Prop_Population_on_Farms[/C][C]+9.105[/C][C] 12.83[/C][C]+7.0970e-01[/C][C] 0.4842[/C][C] 0.2421[/C][/ROW]
[ROW][C]Homicides[/C][C]+0.2427[/C][C] 0.07284[/C][C]+3.3320e+00[/C][C] 0.002595[/C][C] 0.001297[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280570&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280570&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)+5.554 7.898+7.0320e-01 0.4882 0.2441
Per_Capita_Income+0.006566 0.006239+1.0520e+00 0.3023 0.1511
Prop_Population_on_Farms+9.105 12.83+7.0970e-01 0.4842 0.2421
Homicides+0.2427 0.07284+3.3320e+00 0.002595 0.001297







Multiple Linear Regression - Regression Statistics
Multiple R 0.6817
R-squared 0.4647
Adjusted R-squared 0.403
F-TEST (value) 7.525
F-TEST (DF numerator)3
F-TEST (DF denominator)26
p-value 0.0008779
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 7.4
Sum Squared Residuals 1424

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6817 \tabularnewline
R-squared &  0.4647 \tabularnewline
Adjusted R-squared &  0.403 \tabularnewline
F-TEST (value) &  7.525 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 26 \tabularnewline
p-value &  0.0008779 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  7.4 \tabularnewline
Sum Squared Residuals &  1424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280570&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6817[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.4647[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.403[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 7.525[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]26[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0008779[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 7.4[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280570&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280570&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 0.6817
R-squared 0.4647
Adjusted R-squared 0.403
F-TEST (value) 7.525
F-TEST (DF numerator)3
F-TEST (DF denominator)26
p-value 0.0008779
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 7.4
Sum Squared Residuals 1424







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 46.4 28.39 18.01
2 45.7 33.19 12.51
3 45.3 38.77 6.528
4 38.6 37.34 1.264
5 37.2 28.29 8.912
6 35 25.99 9.012
7 34 38.32-4.32
8 28.3 21.98 6.317
9 24.7 24.56 0.1387
10 24.7 24.31 0.3939
11 24.4 19.7 4.704
12 22.7 18.02 4.685
13 22.3 27.64-5.337
14 21.7 15.85 5.854
15 21.6 21.03 0.5697
16 21.3 38.53-17.23
17 21.2 23.14-1.939
18 20.8 22.28-1.481
19 20.3 25.24-4.938
20 18.9 21.7-2.805
21 18.8 22.67-3.872
22 18.6 16.71 1.893
23 18 25.01-7.015
24 17.6 20.27-2.665
25 17 20.79-3.789
26 16.7 21.65-4.949
27 15.9 21.81-5.908
28 15.3 18.26-2.96
29 15 18.25-3.253
30 14.8 23.12-8.324

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  46.4 &  28.39 &  18.01 \tabularnewline
2 &  45.7 &  33.19 &  12.51 \tabularnewline
3 &  45.3 &  38.77 &  6.528 \tabularnewline
4 &  38.6 &  37.34 &  1.264 \tabularnewline
5 &  37.2 &  28.29 &  8.912 \tabularnewline
6 &  35 &  25.99 &  9.012 \tabularnewline
7 &  34 &  38.32 & -4.32 \tabularnewline
8 &  28.3 &  21.98 &  6.317 \tabularnewline
9 &  24.7 &  24.56 &  0.1387 \tabularnewline
10 &  24.7 &  24.31 &  0.3939 \tabularnewline
11 &  24.4 &  19.7 &  4.704 \tabularnewline
12 &  22.7 &  18.02 &  4.685 \tabularnewline
13 &  22.3 &  27.64 & -5.337 \tabularnewline
14 &  21.7 &  15.85 &  5.854 \tabularnewline
15 &  21.6 &  21.03 &  0.5697 \tabularnewline
16 &  21.3 &  38.53 & -17.23 \tabularnewline
17 &  21.2 &  23.14 & -1.939 \tabularnewline
18 &  20.8 &  22.28 & -1.481 \tabularnewline
19 &  20.3 &  25.24 & -4.938 \tabularnewline
20 &  18.9 &  21.7 & -2.805 \tabularnewline
21 &  18.8 &  22.67 & -3.872 \tabularnewline
22 &  18.6 &  16.71 &  1.893 \tabularnewline
23 &  18 &  25.01 & -7.015 \tabularnewline
24 &  17.6 &  20.27 & -2.665 \tabularnewline
25 &  17 &  20.79 & -3.789 \tabularnewline
26 &  16.7 &  21.65 & -4.949 \tabularnewline
27 &  15.9 &  21.81 & -5.908 \tabularnewline
28 &  15.3 &  18.26 & -2.96 \tabularnewline
29 &  15 &  18.25 & -3.253 \tabularnewline
30 &  14.8 &  23.12 & -8.324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280570&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] 46.4[/C][C] 28.39[/C][C] 18.01[/C][/ROW]
[ROW][C]2[/C][C] 45.7[/C][C] 33.19[/C][C] 12.51[/C][/ROW]
[ROW][C]3[/C][C] 45.3[/C][C] 38.77[/C][C] 6.528[/C][/ROW]
[ROW][C]4[/C][C] 38.6[/C][C] 37.34[/C][C] 1.264[/C][/ROW]
[ROW][C]5[/C][C] 37.2[/C][C] 28.29[/C][C] 8.912[/C][/ROW]
[ROW][C]6[/C][C] 35[/C][C] 25.99[/C][C] 9.012[/C][/ROW]
[ROW][C]7[/C][C] 34[/C][C] 38.32[/C][C]-4.32[/C][/ROW]
[ROW][C]8[/C][C] 28.3[/C][C] 21.98[/C][C] 6.317[/C][/ROW]
[ROW][C]9[/C][C] 24.7[/C][C] 24.56[/C][C] 0.1387[/C][/ROW]
[ROW][C]10[/C][C] 24.7[/C][C] 24.31[/C][C] 0.3939[/C][/ROW]
[ROW][C]11[/C][C] 24.4[/C][C] 19.7[/C][C] 4.704[/C][/ROW]
[ROW][C]12[/C][C] 22.7[/C][C] 18.02[/C][C] 4.685[/C][/ROW]
[ROW][C]13[/C][C] 22.3[/C][C] 27.64[/C][C]-5.337[/C][/ROW]
[ROW][C]14[/C][C] 21.7[/C][C] 15.85[/C][C] 5.854[/C][/ROW]
[ROW][C]15[/C][C] 21.6[/C][C] 21.03[/C][C] 0.5697[/C][/ROW]
[ROW][C]16[/C][C] 21.3[/C][C] 38.53[/C][C]-17.23[/C][/ROW]
[ROW][C]17[/C][C] 21.2[/C][C] 23.14[/C][C]-1.939[/C][/ROW]
[ROW][C]18[/C][C] 20.8[/C][C] 22.28[/C][C]-1.481[/C][/ROW]
[ROW][C]19[/C][C] 20.3[/C][C] 25.24[/C][C]-4.938[/C][/ROW]
[ROW][C]20[/C][C] 18.9[/C][C] 21.7[/C][C]-2.805[/C][/ROW]
[ROW][C]21[/C][C] 18.8[/C][C] 22.67[/C][C]-3.872[/C][/ROW]
[ROW][C]22[/C][C] 18.6[/C][C] 16.71[/C][C] 1.893[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 25.01[/C][C]-7.015[/C][/ROW]
[ROW][C]24[/C][C] 17.6[/C][C] 20.27[/C][C]-2.665[/C][/ROW]
[ROW][C]25[/C][C] 17[/C][C] 20.79[/C][C]-3.789[/C][/ROW]
[ROW][C]26[/C][C] 16.7[/C][C] 21.65[/C][C]-4.949[/C][/ROW]
[ROW][C]27[/C][C] 15.9[/C][C] 21.81[/C][C]-5.908[/C][/ROW]
[ROW][C]28[/C][C] 15.3[/C][C] 18.26[/C][C]-2.96[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 18.25[/C][C]-3.253[/C][/ROW]
[ROW][C]30[/C][C] 14.8[/C][C] 23.12[/C][C]-8.324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280570&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280570&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 46.4 28.39 18.01
2 45.7 33.19 12.51
3 45.3 38.77 6.528
4 38.6 37.34 1.264
5 37.2 28.29 8.912
6 35 25.99 9.012
7 34 38.32-4.32
8 28.3 21.98 6.317
9 24.7 24.56 0.1387
10 24.7 24.31 0.3939
11 24.4 19.7 4.704
12 22.7 18.02 4.685
13 22.3 27.64-5.337
14 21.7 15.85 5.854
15 21.6 21.03 0.5697
16 21.3 38.53-17.23
17 21.2 23.14-1.939
18 20.8 22.28-1.481
19 20.3 25.24-4.938
20 18.9 21.7-2.805
21 18.8 22.67-3.872
22 18.6 16.71 1.893
23 18 25.01-7.015
24 17.6 20.27-2.665
25 17 20.79-3.789
26 16.7 21.65-4.949
27 15.9 21.81-5.908
28 15.3 18.26-2.96
29 15 18.25-3.253
30 14.8 23.12-8.324







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7 0.8709 0.2583 0.1291
8 0.9562 0.08767 0.04384
9 0.9478 0.1044 0.05222
10 0.973 0.05398 0.02699
11 0.987 0.026 0.013
12 0.9953 0.009465 0.004732
13 0.9999 0.0001367 6.836e-05
14 1 5.608e-05 2.804e-05
15 1 3.137e-05 1.568e-05
16 1 7.154e-06 3.577e-06
17 1 1.759e-05 8.796e-06
18 1 4.844e-05 2.422e-05
19 0.9999 0.0001214 6.071e-05
20 0.9998 0.0003135 0.0001567
21 0.9991 0.001808 0.0009038
22 0.999 0.002041 0.001021
23 0.9969 0.006171 0.003086

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.8709 &  0.2583 &  0.1291 \tabularnewline
8 &  0.9562 &  0.08767 &  0.04384 \tabularnewline
9 &  0.9478 &  0.1044 &  0.05222 \tabularnewline
10 &  0.973 &  0.05398 &  0.02699 \tabularnewline
11 &  0.987 &  0.026 &  0.013 \tabularnewline
12 &  0.9953 &  0.009465 &  0.004732 \tabularnewline
13 &  0.9999 &  0.0001367 &  6.836e-05 \tabularnewline
14 &  1 &  5.608e-05 &  2.804e-05 \tabularnewline
15 &  1 &  3.137e-05 &  1.568e-05 \tabularnewline
16 &  1 &  7.154e-06 &  3.577e-06 \tabularnewline
17 &  1 &  1.759e-05 &  8.796e-06 \tabularnewline
18 &  1 &  4.844e-05 &  2.422e-05 \tabularnewline
19 &  0.9999 &  0.0001214 &  6.071e-05 \tabularnewline
20 &  0.9998 &  0.0003135 &  0.0001567 \tabularnewline
21 &  0.9991 &  0.001808 &  0.0009038 \tabularnewline
22 &  0.999 &  0.002041 &  0.001021 \tabularnewline
23 &  0.9969 &  0.006171 &  0.003086 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280570&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]7[/C][C] 0.8709[/C][C] 0.2583[/C][C] 0.1291[/C][/ROW]
[ROW][C]8[/C][C] 0.9562[/C][C] 0.08767[/C][C] 0.04384[/C][/ROW]
[ROW][C]9[/C][C] 0.9478[/C][C] 0.1044[/C][C] 0.05222[/C][/ROW]
[ROW][C]10[/C][C] 0.973[/C][C] 0.05398[/C][C] 0.02699[/C][/ROW]
[ROW][C]11[/C][C] 0.987[/C][C] 0.026[/C][C] 0.013[/C][/ROW]
[ROW][C]12[/C][C] 0.9953[/C][C] 0.009465[/C][C] 0.004732[/C][/ROW]
[ROW][C]13[/C][C] 0.9999[/C][C] 0.0001367[/C][C] 6.836e-05[/C][/ROW]
[ROW][C]14[/C][C] 1[/C][C] 5.608e-05[/C][C] 2.804e-05[/C][/ROW]
[ROW][C]15[/C][C] 1[/C][C] 3.137e-05[/C][C] 1.568e-05[/C][/ROW]
[ROW][C]16[/C][C] 1[/C][C] 7.154e-06[/C][C] 3.577e-06[/C][/ROW]
[ROW][C]17[/C][C] 1[/C][C] 1.759e-05[/C][C] 8.796e-06[/C][/ROW]
[ROW][C]18[/C][C] 1[/C][C] 4.844e-05[/C][C] 2.422e-05[/C][/ROW]
[ROW][C]19[/C][C] 0.9999[/C][C] 0.0001214[/C][C] 6.071e-05[/C][/ROW]
[ROW][C]20[/C][C] 0.9998[/C][C] 0.0003135[/C][C] 0.0001567[/C][/ROW]
[ROW][C]21[/C][C] 0.9991[/C][C] 0.001808[/C][C] 0.0009038[/C][/ROW]
[ROW][C]22[/C][C] 0.999[/C][C] 0.002041[/C][C] 0.001021[/C][/ROW]
[ROW][C]23[/C][C] 0.9969[/C][C] 0.006171[/C][C] 0.003086[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280570&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280570&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
7 0.8709 0.2583 0.1291
8 0.9562 0.08767 0.04384
9 0.9478 0.1044 0.05222
10 0.973 0.05398 0.02699
11 0.987 0.026 0.013
12 0.9953 0.009465 0.004732
13 0.9999 0.0001367 6.836e-05
14 1 5.608e-05 2.804e-05
15 1 3.137e-05 1.568e-05
16 1 7.154e-06 3.577e-06
17 1 1.759e-05 8.796e-06
18 1 4.844e-05 2.422e-05
19 0.9999 0.0001214 6.071e-05
20 0.9998 0.0003135 0.0001567
21 0.9991 0.001808 0.0009038
22 0.999 0.002041 0.001021
23 0.9969 0.006171 0.003086







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level12 0.7059NOK
5% type I error level130.764706NOK
10% type I error level150.882353NOK

\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 & 12 &  0.7059 & NOK \tabularnewline
5% type I error level & 13 & 0.764706 & NOK \tabularnewline
10% type I error level & 15 & 0.882353 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280570&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]12[/C][C] 0.7059[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.764706[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.882353[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280570&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280570&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 level12 0.7059NOK
5% type I error level130.764706NOK
10% type I error level150.882353NOK



Parameters (Session):
Parameters (R input):
par1 = 0 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
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.'
}
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}