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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 04:18:26 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258715972dfgjrb2zhfcev8z.htm/, Retrieved Thu, 28 Mar 2024 23:13:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58036, Retrieved Thu, 28 Mar 2024 23:13:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact98
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 11:18:26] [fbab597368601c68e80be601720d8ff9] [Current]
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Dataseries X:
1,4	2
1,2	2
1	2
1,7	2
2,4	2
2	2
2,1	2
2	2
1,8	2
2,7	2
2,3	2
1,9	2
2	2
2,3	2
2,8	2
2,4	2
2,3	2
2,7	2
2,7	2
2,9	2
3	2
2,2	2
2,3	2
2,8	2,21
2,8	2,25
2,8	2,25
2,2	2,45
2,6	2,5
2,8	2,5
2,5	2,64
2,4	2,75
2,3	2,93
1,9	3
1,7	3,17
2	3,25
2,1	3,39
1,7	3,5
1,8	3,5
1,8	3,65
1,8	3,75
1,3	3,75
1,3	3,9
1,3	4
1,2	4
1,4	4
2,2	4
2,9	4
3,1	4
3,5	4
3,6	4
4,4	4
4,1	4
5,1	4
5,8	4
5,9	4,18
5,4	4,25
5,5	4,25
4,8	3,97
3,2	3,42
2,7	2,75
2,1	2,31
1,9	2
0,6	1,66
0,7	1,31
-0,2	1,09
-1	1
-1,7	1
-0,7	1
-1	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' @ 72.249.127.135

\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' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58036&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58036&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58036&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' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Inflatie[t] = -0.207439244018707 + 0.921102813688366rente[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Inflatie[t] =  -0.207439244018707 +  0.921102813688366rente[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58036&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Inflatie[t] =  -0.207439244018707 +  0.921102813688366rente[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58036&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58036&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Inflatie[t] = -0.207439244018707 + 0.921102813688366rente[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2074392440187070.406456-0.51040.6114760.305738
rente0.9211028136883660.1413546.516300

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.207439244018707 & 0.406456 & -0.5104 & 0.611476 & 0.305738 \tabularnewline
rente & 0.921102813688366 & 0.141354 & 6.5163 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58036&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.207439244018707[/C][C]0.406456[/C][C]-0.5104[/C][C]0.611476[/C][C]0.305738[/C][/ROW]
[ROW][C]rente[/C][C]0.921102813688366[/C][C]0.141354[/C][C]6.5163[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58036&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58036&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.2074392440187070.406456-0.51040.6114760.305738
rente0.9211028136883660.1413546.516300







Multiple Linear Regression - Regression Statistics
Multiple R0.6228277326174
R-squared0.387914384517331
Adjusted R-squared0.378778778316097
F-TEST (value)42.461811068973
F-TEST (DF numerator)1
F-TEST (DF denominator)67
p-value1.10074940273819e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.15053961016272
Sum Squared Residuals88.6906734350775

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.6228277326174 \tabularnewline
R-squared & 0.387914384517331 \tabularnewline
Adjusted R-squared & 0.378778778316097 \tabularnewline
F-TEST (value) & 42.461811068973 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value & 1.10074940273819e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.15053961016272 \tabularnewline
Sum Squared Residuals & 88.6906734350775 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58036&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.6228277326174[/C][/ROW]
[ROW][C]R-squared[/C][C]0.387914384517331[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.378778778316097[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]42.461811068973[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/C][/ROW]
[ROW][C]p-value[/C][C]1.10074940273819e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.15053961016272[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]88.6906734350775[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58036&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58036&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.6228277326174
R-squared0.387914384517331
Adjusted R-squared0.378778778316097
F-TEST (value)42.461811068973
F-TEST (DF numerator)1
F-TEST (DF denominator)67
p-value1.10074940273819e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.15053961016272
Sum Squared Residuals88.6906734350775







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.41.63476638335802-0.234766383358022
21.21.63476638335802-0.434766383358021
311.63476638335802-0.634766383358023
41.71.634766383358020.0652336166419766
52.41.634766383358020.765233616641977
621.634766383358020.365233616641977
72.11.634766383358020.465233616641977
821.634766383358020.365233616641977
91.81.634766383358020.165233616641977
102.71.634766383358021.06523361664198
112.31.634766383358020.665233616641977
121.91.634766383358020.265233616641977
1321.634766383358020.365233616641977
142.31.634766383358020.665233616641977
152.81.634766383358021.16523361664198
162.41.634766383358020.765233616641977
172.31.634766383358020.665233616641977
182.71.634766383358021.06523361664198
192.71.634766383358021.06523361664198
202.91.634766383358021.26523361664198
2131.634766383358021.36523361664198
222.21.634766383358020.565233616641977
232.31.634766383358020.665233616641977
242.81.828197974232580.97180202576742
252.81.865042086780110.934957913219885
262.81.865042086780110.934957913219885
272.22.049262649517790.150737350482212
282.62.095317790202210.504682209797794
292.82.095317790202210.704682209797794
302.52.224272184118580.275727815881422
312.42.325593493624300.0744065063757022
322.32.49139200008820-0.191392000088204
331.92.55586919704639-0.655869197046389
341.72.71245667537341-1.01245667537341
3522.78614490046848-0.78614490046848
362.12.91509929438485-0.815099294384852
371.73.01642060389057-1.31642060389057
381.83.01642060389057-1.21642060389057
391.83.15458602594383-1.35458602594383
401.83.24669630731266-1.44669630731266
411.33.24669630731266-1.94669630731266
421.33.38486172936592-2.08486172936592
431.33.47697201073476-2.17697201073475
441.23.47697201073475-2.27697201073475
451.43.47697201073475-2.07697201073475
462.23.47697201073476-1.27697201073475
472.93.47697201073475-0.576972010734755
483.13.47697201073475-0.376972010734755
493.53.476972010734750.023027989265245
503.63.476972010734750.123027989265245
514.43.476972010734750.923027989265245
524.13.476972010734750.623027989265245
535.13.476972010734761.62302798926524
545.83.476972010734752.32302798926525
555.93.642770517198662.25722948280134
565.43.707247714156851.69275228584315
575.53.707247714156851.79275228584315
584.83.449338926324101.35066107367590
593.22.942732378795500.257267621204497
602.72.325593493624300.374406506375702
612.11.920308255601420.179691744398583
621.91.634766383358020.265233616641977
630.61.32159142670398-0.721591426703979
640.70.999205441913051-0.299205441913051
65-0.20.79656282290161-0.99656282290161
66-10.713663569669658-1.71366356966966
67-1.70.713663569669657-2.41366356966966
68-0.70.713663569669657-1.41366356966966
69-10.713663569669658-1.71366356966966

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.63476638335802 & -0.234766383358022 \tabularnewline
2 & 1.2 & 1.63476638335802 & -0.434766383358021 \tabularnewline
3 & 1 & 1.63476638335802 & -0.634766383358023 \tabularnewline
4 & 1.7 & 1.63476638335802 & 0.0652336166419766 \tabularnewline
5 & 2.4 & 1.63476638335802 & 0.765233616641977 \tabularnewline
6 & 2 & 1.63476638335802 & 0.365233616641977 \tabularnewline
7 & 2.1 & 1.63476638335802 & 0.465233616641977 \tabularnewline
8 & 2 & 1.63476638335802 & 0.365233616641977 \tabularnewline
9 & 1.8 & 1.63476638335802 & 0.165233616641977 \tabularnewline
10 & 2.7 & 1.63476638335802 & 1.06523361664198 \tabularnewline
11 & 2.3 & 1.63476638335802 & 0.665233616641977 \tabularnewline
12 & 1.9 & 1.63476638335802 & 0.265233616641977 \tabularnewline
13 & 2 & 1.63476638335802 & 0.365233616641977 \tabularnewline
14 & 2.3 & 1.63476638335802 & 0.665233616641977 \tabularnewline
15 & 2.8 & 1.63476638335802 & 1.16523361664198 \tabularnewline
16 & 2.4 & 1.63476638335802 & 0.765233616641977 \tabularnewline
17 & 2.3 & 1.63476638335802 & 0.665233616641977 \tabularnewline
18 & 2.7 & 1.63476638335802 & 1.06523361664198 \tabularnewline
19 & 2.7 & 1.63476638335802 & 1.06523361664198 \tabularnewline
20 & 2.9 & 1.63476638335802 & 1.26523361664198 \tabularnewline
21 & 3 & 1.63476638335802 & 1.36523361664198 \tabularnewline
22 & 2.2 & 1.63476638335802 & 0.565233616641977 \tabularnewline
23 & 2.3 & 1.63476638335802 & 0.665233616641977 \tabularnewline
24 & 2.8 & 1.82819797423258 & 0.97180202576742 \tabularnewline
25 & 2.8 & 1.86504208678011 & 0.934957913219885 \tabularnewline
26 & 2.8 & 1.86504208678011 & 0.934957913219885 \tabularnewline
27 & 2.2 & 2.04926264951779 & 0.150737350482212 \tabularnewline
28 & 2.6 & 2.09531779020221 & 0.504682209797794 \tabularnewline
29 & 2.8 & 2.09531779020221 & 0.704682209797794 \tabularnewline
30 & 2.5 & 2.22427218411858 & 0.275727815881422 \tabularnewline
31 & 2.4 & 2.32559349362430 & 0.0744065063757022 \tabularnewline
32 & 2.3 & 2.49139200008820 & -0.191392000088204 \tabularnewline
33 & 1.9 & 2.55586919704639 & -0.655869197046389 \tabularnewline
34 & 1.7 & 2.71245667537341 & -1.01245667537341 \tabularnewline
35 & 2 & 2.78614490046848 & -0.78614490046848 \tabularnewline
36 & 2.1 & 2.91509929438485 & -0.815099294384852 \tabularnewline
37 & 1.7 & 3.01642060389057 & -1.31642060389057 \tabularnewline
38 & 1.8 & 3.01642060389057 & -1.21642060389057 \tabularnewline
39 & 1.8 & 3.15458602594383 & -1.35458602594383 \tabularnewline
40 & 1.8 & 3.24669630731266 & -1.44669630731266 \tabularnewline
41 & 1.3 & 3.24669630731266 & -1.94669630731266 \tabularnewline
42 & 1.3 & 3.38486172936592 & -2.08486172936592 \tabularnewline
43 & 1.3 & 3.47697201073476 & -2.17697201073475 \tabularnewline
44 & 1.2 & 3.47697201073475 & -2.27697201073475 \tabularnewline
45 & 1.4 & 3.47697201073475 & -2.07697201073475 \tabularnewline
46 & 2.2 & 3.47697201073476 & -1.27697201073475 \tabularnewline
47 & 2.9 & 3.47697201073475 & -0.576972010734755 \tabularnewline
48 & 3.1 & 3.47697201073475 & -0.376972010734755 \tabularnewline
49 & 3.5 & 3.47697201073475 & 0.023027989265245 \tabularnewline
50 & 3.6 & 3.47697201073475 & 0.123027989265245 \tabularnewline
51 & 4.4 & 3.47697201073475 & 0.923027989265245 \tabularnewline
52 & 4.1 & 3.47697201073475 & 0.623027989265245 \tabularnewline
53 & 5.1 & 3.47697201073476 & 1.62302798926524 \tabularnewline
54 & 5.8 & 3.47697201073475 & 2.32302798926525 \tabularnewline
55 & 5.9 & 3.64277051719866 & 2.25722948280134 \tabularnewline
56 & 5.4 & 3.70724771415685 & 1.69275228584315 \tabularnewline
57 & 5.5 & 3.70724771415685 & 1.79275228584315 \tabularnewline
58 & 4.8 & 3.44933892632410 & 1.35066107367590 \tabularnewline
59 & 3.2 & 2.94273237879550 & 0.257267621204497 \tabularnewline
60 & 2.7 & 2.32559349362430 & 0.374406506375702 \tabularnewline
61 & 2.1 & 1.92030825560142 & 0.179691744398583 \tabularnewline
62 & 1.9 & 1.63476638335802 & 0.265233616641977 \tabularnewline
63 & 0.6 & 1.32159142670398 & -0.721591426703979 \tabularnewline
64 & 0.7 & 0.999205441913051 & -0.299205441913051 \tabularnewline
65 & -0.2 & 0.79656282290161 & -0.99656282290161 \tabularnewline
66 & -1 & 0.713663569669658 & -1.71366356966966 \tabularnewline
67 & -1.7 & 0.713663569669657 & -2.41366356966966 \tabularnewline
68 & -0.7 & 0.713663569669657 & -1.41366356966966 \tabularnewline
69 & -1 & 0.713663569669658 & -1.71366356966966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58036&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1.4[/C][C]1.63476638335802[/C][C]-0.234766383358022[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]1.63476638335802[/C][C]-0.434766383358021[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.63476638335802[/C][C]-0.634766383358023[/C][/ROW]
[ROW][C]4[/C][C]1.7[/C][C]1.63476638335802[/C][C]0.0652336166419766[/C][/ROW]
[ROW][C]5[/C][C]2.4[/C][C]1.63476638335802[/C][C]0.765233616641977[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.63476638335802[/C][C]0.365233616641977[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]1.63476638335802[/C][C]0.465233616641977[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.63476638335802[/C][C]0.365233616641977[/C][/ROW]
[ROW][C]9[/C][C]1.8[/C][C]1.63476638335802[/C][C]0.165233616641977[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]1.63476638335802[/C][C]1.06523361664198[/C][/ROW]
[ROW][C]11[/C][C]2.3[/C][C]1.63476638335802[/C][C]0.665233616641977[/C][/ROW]
[ROW][C]12[/C][C]1.9[/C][C]1.63476638335802[/C][C]0.265233616641977[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]1.63476638335802[/C][C]0.365233616641977[/C][/ROW]
[ROW][C]14[/C][C]2.3[/C][C]1.63476638335802[/C][C]0.665233616641977[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]1.63476638335802[/C][C]1.16523361664198[/C][/ROW]
[ROW][C]16[/C][C]2.4[/C][C]1.63476638335802[/C][C]0.765233616641977[/C][/ROW]
[ROW][C]17[/C][C]2.3[/C][C]1.63476638335802[/C][C]0.665233616641977[/C][/ROW]
[ROW][C]18[/C][C]2.7[/C][C]1.63476638335802[/C][C]1.06523361664198[/C][/ROW]
[ROW][C]19[/C][C]2.7[/C][C]1.63476638335802[/C][C]1.06523361664198[/C][/ROW]
[ROW][C]20[/C][C]2.9[/C][C]1.63476638335802[/C][C]1.26523361664198[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]1.63476638335802[/C][C]1.36523361664198[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]1.63476638335802[/C][C]0.565233616641977[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]1.63476638335802[/C][C]0.665233616641977[/C][/ROW]
[ROW][C]24[/C][C]2.8[/C][C]1.82819797423258[/C][C]0.97180202576742[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]1.86504208678011[/C][C]0.934957913219885[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]1.86504208678011[/C][C]0.934957913219885[/C][/ROW]
[ROW][C]27[/C][C]2.2[/C][C]2.04926264951779[/C][C]0.150737350482212[/C][/ROW]
[ROW][C]28[/C][C]2.6[/C][C]2.09531779020221[/C][C]0.504682209797794[/C][/ROW]
[ROW][C]29[/C][C]2.8[/C][C]2.09531779020221[/C][C]0.704682209797794[/C][/ROW]
[ROW][C]30[/C][C]2.5[/C][C]2.22427218411858[/C][C]0.275727815881422[/C][/ROW]
[ROW][C]31[/C][C]2.4[/C][C]2.32559349362430[/C][C]0.0744065063757022[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]2.49139200008820[/C][C]-0.191392000088204[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]2.55586919704639[/C][C]-0.655869197046389[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]2.71245667537341[/C][C]-1.01245667537341[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.78614490046848[/C][C]-0.78614490046848[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.91509929438485[/C][C]-0.815099294384852[/C][/ROW]
[ROW][C]37[/C][C]1.7[/C][C]3.01642060389057[/C][C]-1.31642060389057[/C][/ROW]
[ROW][C]38[/C][C]1.8[/C][C]3.01642060389057[/C][C]-1.21642060389057[/C][/ROW]
[ROW][C]39[/C][C]1.8[/C][C]3.15458602594383[/C][C]-1.35458602594383[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]3.24669630731266[/C][C]-1.44669630731266[/C][/ROW]
[ROW][C]41[/C][C]1.3[/C][C]3.24669630731266[/C][C]-1.94669630731266[/C][/ROW]
[ROW][C]42[/C][C]1.3[/C][C]3.38486172936592[/C][C]-2.08486172936592[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]3.47697201073476[/C][C]-2.17697201073475[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]3.47697201073475[/C][C]-2.27697201073475[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]3.47697201073475[/C][C]-2.07697201073475[/C][/ROW]
[ROW][C]46[/C][C]2.2[/C][C]3.47697201073476[/C][C]-1.27697201073475[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]3.47697201073475[/C][C]-0.576972010734755[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]3.47697201073475[/C][C]-0.376972010734755[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]3.47697201073475[/C][C]0.023027989265245[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]3.47697201073475[/C][C]0.123027989265245[/C][/ROW]
[ROW][C]51[/C][C]4.4[/C][C]3.47697201073475[/C][C]0.923027989265245[/C][/ROW]
[ROW][C]52[/C][C]4.1[/C][C]3.47697201073475[/C][C]0.623027989265245[/C][/ROW]
[ROW][C]53[/C][C]5.1[/C][C]3.47697201073476[/C][C]1.62302798926524[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]3.47697201073475[/C][C]2.32302798926525[/C][/ROW]
[ROW][C]55[/C][C]5.9[/C][C]3.64277051719866[/C][C]2.25722948280134[/C][/ROW]
[ROW][C]56[/C][C]5.4[/C][C]3.70724771415685[/C][C]1.69275228584315[/C][/ROW]
[ROW][C]57[/C][C]5.5[/C][C]3.70724771415685[/C][C]1.79275228584315[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]3.44933892632410[/C][C]1.35066107367590[/C][/ROW]
[ROW][C]59[/C][C]3.2[/C][C]2.94273237879550[/C][C]0.257267621204497[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]2.32559349362430[/C][C]0.374406506375702[/C][/ROW]
[ROW][C]61[/C][C]2.1[/C][C]1.92030825560142[/C][C]0.179691744398583[/C][/ROW]
[ROW][C]62[/C][C]1.9[/C][C]1.63476638335802[/C][C]0.265233616641977[/C][/ROW]
[ROW][C]63[/C][C]0.6[/C][C]1.32159142670398[/C][C]-0.721591426703979[/C][/ROW]
[ROW][C]64[/C][C]0.7[/C][C]0.999205441913051[/C][C]-0.299205441913051[/C][/ROW]
[ROW][C]65[/C][C]-0.2[/C][C]0.79656282290161[/C][C]-0.99656282290161[/C][/ROW]
[ROW][C]66[/C][C]-1[/C][C]0.713663569669658[/C][C]-1.71366356966966[/C][/ROW]
[ROW][C]67[/C][C]-1.7[/C][C]0.713663569669657[/C][C]-2.41366356966966[/C][/ROW]
[ROW][C]68[/C][C]-0.7[/C][C]0.713663569669657[/C][C]-1.41366356966966[/C][/ROW]
[ROW][C]69[/C][C]-1[/C][C]0.713663569669658[/C][C]-1.71366356966966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58036&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58036&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
11.41.63476638335802-0.234766383358022
21.21.63476638335802-0.434766383358021
311.63476638335802-0.634766383358023
41.71.634766383358020.0652336166419766
52.41.634766383358020.765233616641977
621.634766383358020.365233616641977
72.11.634766383358020.465233616641977
821.634766383358020.365233616641977
91.81.634766383358020.165233616641977
102.71.634766383358021.06523361664198
112.31.634766383358020.665233616641977
121.91.634766383358020.265233616641977
1321.634766383358020.365233616641977
142.31.634766383358020.665233616641977
152.81.634766383358021.16523361664198
162.41.634766383358020.765233616641977
172.31.634766383358020.665233616641977
182.71.634766383358021.06523361664198
192.71.634766383358021.06523361664198
202.91.634766383358021.26523361664198
2131.634766383358021.36523361664198
222.21.634766383358020.565233616641977
232.31.634766383358020.665233616641977
242.81.828197974232580.97180202576742
252.81.865042086780110.934957913219885
262.81.865042086780110.934957913219885
272.22.049262649517790.150737350482212
282.62.095317790202210.504682209797794
292.82.095317790202210.704682209797794
302.52.224272184118580.275727815881422
312.42.325593493624300.0744065063757022
322.32.49139200008820-0.191392000088204
331.92.55586919704639-0.655869197046389
341.72.71245667537341-1.01245667537341
3522.78614490046848-0.78614490046848
362.12.91509929438485-0.815099294384852
371.73.01642060389057-1.31642060389057
381.83.01642060389057-1.21642060389057
391.83.15458602594383-1.35458602594383
401.83.24669630731266-1.44669630731266
411.33.24669630731266-1.94669630731266
421.33.38486172936592-2.08486172936592
431.33.47697201073476-2.17697201073475
441.23.47697201073475-2.27697201073475
451.43.47697201073475-2.07697201073475
462.23.47697201073476-1.27697201073475
472.93.47697201073475-0.576972010734755
483.13.47697201073475-0.376972010734755
493.53.476972010734750.023027989265245
503.63.476972010734750.123027989265245
514.43.476972010734750.923027989265245
524.13.476972010734750.623027989265245
535.13.476972010734761.62302798926524
545.83.476972010734752.32302798926525
555.93.642770517198662.25722948280134
565.43.707247714156851.69275228584315
575.53.707247714156851.79275228584315
584.83.449338926324101.35066107367590
593.22.942732378795500.257267621204497
602.72.325593493624300.374406506375702
612.11.920308255601420.179691744398583
621.91.634766383358020.265233616641977
630.61.32159142670398-0.721591426703979
640.70.999205441913051-0.299205441913051
65-0.20.79656282290161-0.99656282290161
66-10.713663569669658-1.71366356966966
67-1.70.713663569669657-2.41366356966966
68-0.70.713663569669657-1.41366356966966
69-10.713663569669658-1.71366356966966







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1615945242680470.3231890485360950.838405475731953
60.08477745895758920.1695549179151780.915222541042411
70.0456654127790860.0913308255581720.954334587220914
80.02069904964120500.04139809928240990.979300950358795
90.007837241481198870.01567448296239770.992162758518801
100.01167521094418080.02335042188836150.98832478905582
110.006458034250199470.01291606850039890.9935419657498
120.002593122189065690.005186244378131380.997406877810934
130.001018800912698240.002037601825396490.998981199087302
140.0005132215201869910.001026443040373980.999486778479813
150.0007077065158751940.001415413031750390.999292293484125
160.0003795822694046540.0007591645388093080.999620417730595
170.0001756234488131350.0003512468976262710.999824376551187
180.0001532945846078310.0003065891692156630.999846705415392
190.0001260897117098680.0002521794234197350.99987391028829
200.0001539314107506430.0003078628215012870.99984606858925
210.0002199933186166560.0004399866372333110.999780006681383
220.0001048736339487590.0002097472678975170.999895126366051
235.21118188679677e-050.0001042236377359350.999947888181132
242.78884727553605e-055.57769455107209e-050.999972111527245
251.52077590766730e-053.04155181533461e-050.999984792240923
268.5563622785853e-061.71127245571706e-050.999991443637721
278.95087227484142e-061.79017445496828e-050.999991049127725
284.44112337581569e-068.88224675163139e-060.999995558876624
292.39567838302280e-064.79135676604559e-060.999997604321617
301.24355448277283e-062.48710896554566e-060.999998756445517
316.41559802630996e-071.28311960526199e-060.999999358440197
323.30520310454488e-076.61040620908977e-070.99999966947969
332.48909037153164e-074.97818074306327e-070.999999751090963
342.01382843070875e-074.0276568614175e-070.999999798617157
358.27061479452403e-081.65412295890481e-070.999999917293852
363.12397390421145e-086.2479478084229e-080.999999968760261
371.88120398222335e-083.7624079644467e-080.99999998118796
389.06790128969788e-091.81358025793958e-080.999999990932099
394.79180007751672e-099.58360015503343e-090.9999999952082
402.87388881849845e-095.74777763699689e-090.99999999712611
415.56287434702816e-091.11257486940563e-080.999999994437126
421.57685481131404e-083.15370962262808e-080.999999984231452
438.80750051018436e-081.76150010203687e-070.999999911924995
441.67573717102815e-063.35147434205631e-060.999998324262829
456.21467321689057e-050.0001242934643378110.99993785326783
460.001093785342212620.002187570684425250.998906214657787
470.01283296907218420.02566593814436830.987167030927816
480.09178370140439810.1835674028087960.908216298595602
490.3173804236494010.6347608472988010.6826195763506
500.6583570872551450.683285825489710.341642912744855
510.8272309187413170.3455381625173670.172769081258683
520.9345397376755360.1309205246489270.0654602623244635
530.9609885409171160.07802291816576720.0390114590828836
540.987779893286790.02444021342642120.0122201067132106
550.9933642434604480.01327151307910360.00663575653955181
560.9908665213079780.01826695738404310.00913347869202156
570.9864983410445950.02700331791081010.0135016589554051
580.9759596488403810.04808070231923720.0240403511596186
590.9844406911842280.03111861763154460.0155593088157723
600.978305440009740.04338911998052040.0216945599902602
610.964543675651670.070912648696660.03545632434833
620.9209381371211220.1581237257577550.0790618628788775
630.9631038060162370.07379238796752510.0368961939837626
640.9209028157265020.1581943685469950.0790971842734976

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.161594524268047 & 0.323189048536095 & 0.838405475731953 \tabularnewline
6 & 0.0847774589575892 & 0.169554917915178 & 0.915222541042411 \tabularnewline
7 & 0.045665412779086 & 0.091330825558172 & 0.954334587220914 \tabularnewline
8 & 0.0206990496412050 & 0.0413980992824099 & 0.979300950358795 \tabularnewline
9 & 0.00783724148119887 & 0.0156744829623977 & 0.992162758518801 \tabularnewline
10 & 0.0116752109441808 & 0.0233504218883615 & 0.98832478905582 \tabularnewline
11 & 0.00645803425019947 & 0.0129160685003989 & 0.9935419657498 \tabularnewline
12 & 0.00259312218906569 & 0.00518624437813138 & 0.997406877810934 \tabularnewline
13 & 0.00101880091269824 & 0.00203760182539649 & 0.998981199087302 \tabularnewline
14 & 0.000513221520186991 & 0.00102644304037398 & 0.999486778479813 \tabularnewline
15 & 0.000707706515875194 & 0.00141541303175039 & 0.999292293484125 \tabularnewline
16 & 0.000379582269404654 & 0.000759164538809308 & 0.999620417730595 \tabularnewline
17 & 0.000175623448813135 & 0.000351246897626271 & 0.999824376551187 \tabularnewline
18 & 0.000153294584607831 & 0.000306589169215663 & 0.999846705415392 \tabularnewline
19 & 0.000126089711709868 & 0.000252179423419735 & 0.99987391028829 \tabularnewline
20 & 0.000153931410750643 & 0.000307862821501287 & 0.99984606858925 \tabularnewline
21 & 0.000219993318616656 & 0.000439986637233311 & 0.999780006681383 \tabularnewline
22 & 0.000104873633948759 & 0.000209747267897517 & 0.999895126366051 \tabularnewline
23 & 5.21118188679677e-05 & 0.000104223637735935 & 0.999947888181132 \tabularnewline
24 & 2.78884727553605e-05 & 5.57769455107209e-05 & 0.999972111527245 \tabularnewline
25 & 1.52077590766730e-05 & 3.04155181533461e-05 & 0.999984792240923 \tabularnewline
26 & 8.5563622785853e-06 & 1.71127245571706e-05 & 0.999991443637721 \tabularnewline
27 & 8.95087227484142e-06 & 1.79017445496828e-05 & 0.999991049127725 \tabularnewline
28 & 4.44112337581569e-06 & 8.88224675163139e-06 & 0.999995558876624 \tabularnewline
29 & 2.39567838302280e-06 & 4.79135676604559e-06 & 0.999997604321617 \tabularnewline
30 & 1.24355448277283e-06 & 2.48710896554566e-06 & 0.999998756445517 \tabularnewline
31 & 6.41559802630996e-07 & 1.28311960526199e-06 & 0.999999358440197 \tabularnewline
32 & 3.30520310454488e-07 & 6.61040620908977e-07 & 0.99999966947969 \tabularnewline
33 & 2.48909037153164e-07 & 4.97818074306327e-07 & 0.999999751090963 \tabularnewline
34 & 2.01382843070875e-07 & 4.0276568614175e-07 & 0.999999798617157 \tabularnewline
35 & 8.27061479452403e-08 & 1.65412295890481e-07 & 0.999999917293852 \tabularnewline
36 & 3.12397390421145e-08 & 6.2479478084229e-08 & 0.999999968760261 \tabularnewline
37 & 1.88120398222335e-08 & 3.7624079644467e-08 & 0.99999998118796 \tabularnewline
38 & 9.06790128969788e-09 & 1.81358025793958e-08 & 0.999999990932099 \tabularnewline
39 & 4.79180007751672e-09 & 9.58360015503343e-09 & 0.9999999952082 \tabularnewline
40 & 2.87388881849845e-09 & 5.74777763699689e-09 & 0.99999999712611 \tabularnewline
41 & 5.56287434702816e-09 & 1.11257486940563e-08 & 0.999999994437126 \tabularnewline
42 & 1.57685481131404e-08 & 3.15370962262808e-08 & 0.999999984231452 \tabularnewline
43 & 8.80750051018436e-08 & 1.76150010203687e-07 & 0.999999911924995 \tabularnewline
44 & 1.67573717102815e-06 & 3.35147434205631e-06 & 0.999998324262829 \tabularnewline
45 & 6.21467321689057e-05 & 0.000124293464337811 & 0.99993785326783 \tabularnewline
46 & 0.00109378534221262 & 0.00218757068442525 & 0.998906214657787 \tabularnewline
47 & 0.0128329690721842 & 0.0256659381443683 & 0.987167030927816 \tabularnewline
48 & 0.0917837014043981 & 0.183567402808796 & 0.908216298595602 \tabularnewline
49 & 0.317380423649401 & 0.634760847298801 & 0.6826195763506 \tabularnewline
50 & 0.658357087255145 & 0.68328582548971 & 0.341642912744855 \tabularnewline
51 & 0.827230918741317 & 0.345538162517367 & 0.172769081258683 \tabularnewline
52 & 0.934539737675536 & 0.130920524648927 & 0.0654602623244635 \tabularnewline
53 & 0.960988540917116 & 0.0780229181657672 & 0.0390114590828836 \tabularnewline
54 & 0.98777989328679 & 0.0244402134264212 & 0.0122201067132106 \tabularnewline
55 & 0.993364243460448 & 0.0132715130791036 & 0.00663575653955181 \tabularnewline
56 & 0.990866521307978 & 0.0182669573840431 & 0.00913347869202156 \tabularnewline
57 & 0.986498341044595 & 0.0270033179108101 & 0.0135016589554051 \tabularnewline
58 & 0.975959648840381 & 0.0480807023192372 & 0.0240403511596186 \tabularnewline
59 & 0.984440691184228 & 0.0311186176315446 & 0.0155593088157723 \tabularnewline
60 & 0.97830544000974 & 0.0433891199805204 & 0.0216945599902602 \tabularnewline
61 & 0.96454367565167 & 0.07091264869666 & 0.03545632434833 \tabularnewline
62 & 0.920938137121122 & 0.158123725757755 & 0.0790618628788775 \tabularnewline
63 & 0.963103806016237 & 0.0737923879675251 & 0.0368961939837626 \tabularnewline
64 & 0.920902815726502 & 0.158194368546995 & 0.0790971842734976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58036&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]5[/C][C]0.161594524268047[/C][C]0.323189048536095[/C][C]0.838405475731953[/C][/ROW]
[ROW][C]6[/C][C]0.0847774589575892[/C][C]0.169554917915178[/C][C]0.915222541042411[/C][/ROW]
[ROW][C]7[/C][C]0.045665412779086[/C][C]0.091330825558172[/C][C]0.954334587220914[/C][/ROW]
[ROW][C]8[/C][C]0.0206990496412050[/C][C]0.0413980992824099[/C][C]0.979300950358795[/C][/ROW]
[ROW][C]9[/C][C]0.00783724148119887[/C][C]0.0156744829623977[/C][C]0.992162758518801[/C][/ROW]
[ROW][C]10[/C][C]0.0116752109441808[/C][C]0.0233504218883615[/C][C]0.98832478905582[/C][/ROW]
[ROW][C]11[/C][C]0.00645803425019947[/C][C]0.0129160685003989[/C][C]0.9935419657498[/C][/ROW]
[ROW][C]12[/C][C]0.00259312218906569[/C][C]0.00518624437813138[/C][C]0.997406877810934[/C][/ROW]
[ROW][C]13[/C][C]0.00101880091269824[/C][C]0.00203760182539649[/C][C]0.998981199087302[/C][/ROW]
[ROW][C]14[/C][C]0.000513221520186991[/C][C]0.00102644304037398[/C][C]0.999486778479813[/C][/ROW]
[ROW][C]15[/C][C]0.000707706515875194[/C][C]0.00141541303175039[/C][C]0.999292293484125[/C][/ROW]
[ROW][C]16[/C][C]0.000379582269404654[/C][C]0.000759164538809308[/C][C]0.999620417730595[/C][/ROW]
[ROW][C]17[/C][C]0.000175623448813135[/C][C]0.000351246897626271[/C][C]0.999824376551187[/C][/ROW]
[ROW][C]18[/C][C]0.000153294584607831[/C][C]0.000306589169215663[/C][C]0.999846705415392[/C][/ROW]
[ROW][C]19[/C][C]0.000126089711709868[/C][C]0.000252179423419735[/C][C]0.99987391028829[/C][/ROW]
[ROW][C]20[/C][C]0.000153931410750643[/C][C]0.000307862821501287[/C][C]0.99984606858925[/C][/ROW]
[ROW][C]21[/C][C]0.000219993318616656[/C][C]0.000439986637233311[/C][C]0.999780006681383[/C][/ROW]
[ROW][C]22[/C][C]0.000104873633948759[/C][C]0.000209747267897517[/C][C]0.999895126366051[/C][/ROW]
[ROW][C]23[/C][C]5.21118188679677e-05[/C][C]0.000104223637735935[/C][C]0.999947888181132[/C][/ROW]
[ROW][C]24[/C][C]2.78884727553605e-05[/C][C]5.57769455107209e-05[/C][C]0.999972111527245[/C][/ROW]
[ROW][C]25[/C][C]1.52077590766730e-05[/C][C]3.04155181533461e-05[/C][C]0.999984792240923[/C][/ROW]
[ROW][C]26[/C][C]8.5563622785853e-06[/C][C]1.71127245571706e-05[/C][C]0.999991443637721[/C][/ROW]
[ROW][C]27[/C][C]8.95087227484142e-06[/C][C]1.79017445496828e-05[/C][C]0.999991049127725[/C][/ROW]
[ROW][C]28[/C][C]4.44112337581569e-06[/C][C]8.88224675163139e-06[/C][C]0.999995558876624[/C][/ROW]
[ROW][C]29[/C][C]2.39567838302280e-06[/C][C]4.79135676604559e-06[/C][C]0.999997604321617[/C][/ROW]
[ROW][C]30[/C][C]1.24355448277283e-06[/C][C]2.48710896554566e-06[/C][C]0.999998756445517[/C][/ROW]
[ROW][C]31[/C][C]6.41559802630996e-07[/C][C]1.28311960526199e-06[/C][C]0.999999358440197[/C][/ROW]
[ROW][C]32[/C][C]3.30520310454488e-07[/C][C]6.61040620908977e-07[/C][C]0.99999966947969[/C][/ROW]
[ROW][C]33[/C][C]2.48909037153164e-07[/C][C]4.97818074306327e-07[/C][C]0.999999751090963[/C][/ROW]
[ROW][C]34[/C][C]2.01382843070875e-07[/C][C]4.0276568614175e-07[/C][C]0.999999798617157[/C][/ROW]
[ROW][C]35[/C][C]8.27061479452403e-08[/C][C]1.65412295890481e-07[/C][C]0.999999917293852[/C][/ROW]
[ROW][C]36[/C][C]3.12397390421145e-08[/C][C]6.2479478084229e-08[/C][C]0.999999968760261[/C][/ROW]
[ROW][C]37[/C][C]1.88120398222335e-08[/C][C]3.7624079644467e-08[/C][C]0.99999998118796[/C][/ROW]
[ROW][C]38[/C][C]9.06790128969788e-09[/C][C]1.81358025793958e-08[/C][C]0.999999990932099[/C][/ROW]
[ROW][C]39[/C][C]4.79180007751672e-09[/C][C]9.58360015503343e-09[/C][C]0.9999999952082[/C][/ROW]
[ROW][C]40[/C][C]2.87388881849845e-09[/C][C]5.74777763699689e-09[/C][C]0.99999999712611[/C][/ROW]
[ROW][C]41[/C][C]5.56287434702816e-09[/C][C]1.11257486940563e-08[/C][C]0.999999994437126[/C][/ROW]
[ROW][C]42[/C][C]1.57685481131404e-08[/C][C]3.15370962262808e-08[/C][C]0.999999984231452[/C][/ROW]
[ROW][C]43[/C][C]8.80750051018436e-08[/C][C]1.76150010203687e-07[/C][C]0.999999911924995[/C][/ROW]
[ROW][C]44[/C][C]1.67573717102815e-06[/C][C]3.35147434205631e-06[/C][C]0.999998324262829[/C][/ROW]
[ROW][C]45[/C][C]6.21467321689057e-05[/C][C]0.000124293464337811[/C][C]0.99993785326783[/C][/ROW]
[ROW][C]46[/C][C]0.00109378534221262[/C][C]0.00218757068442525[/C][C]0.998906214657787[/C][/ROW]
[ROW][C]47[/C][C]0.0128329690721842[/C][C]0.0256659381443683[/C][C]0.987167030927816[/C][/ROW]
[ROW][C]48[/C][C]0.0917837014043981[/C][C]0.183567402808796[/C][C]0.908216298595602[/C][/ROW]
[ROW][C]49[/C][C]0.317380423649401[/C][C]0.634760847298801[/C][C]0.6826195763506[/C][/ROW]
[ROW][C]50[/C][C]0.658357087255145[/C][C]0.68328582548971[/C][C]0.341642912744855[/C][/ROW]
[ROW][C]51[/C][C]0.827230918741317[/C][C]0.345538162517367[/C][C]0.172769081258683[/C][/ROW]
[ROW][C]52[/C][C]0.934539737675536[/C][C]0.130920524648927[/C][C]0.0654602623244635[/C][/ROW]
[ROW][C]53[/C][C]0.960988540917116[/C][C]0.0780229181657672[/C][C]0.0390114590828836[/C][/ROW]
[ROW][C]54[/C][C]0.98777989328679[/C][C]0.0244402134264212[/C][C]0.0122201067132106[/C][/ROW]
[ROW][C]55[/C][C]0.993364243460448[/C][C]0.0132715130791036[/C][C]0.00663575653955181[/C][/ROW]
[ROW][C]56[/C][C]0.990866521307978[/C][C]0.0182669573840431[/C][C]0.00913347869202156[/C][/ROW]
[ROW][C]57[/C][C]0.986498341044595[/C][C]0.0270033179108101[/C][C]0.0135016589554051[/C][/ROW]
[ROW][C]58[/C][C]0.975959648840381[/C][C]0.0480807023192372[/C][C]0.0240403511596186[/C][/ROW]
[ROW][C]59[/C][C]0.984440691184228[/C][C]0.0311186176315446[/C][C]0.0155593088157723[/C][/ROW]
[ROW][C]60[/C][C]0.97830544000974[/C][C]0.0433891199805204[/C][C]0.0216945599902602[/C][/ROW]
[ROW][C]61[/C][C]0.96454367565167[/C][C]0.07091264869666[/C][C]0.03545632434833[/C][/ROW]
[ROW][C]62[/C][C]0.920938137121122[/C][C]0.158123725757755[/C][C]0.0790618628788775[/C][/ROW]
[ROW][C]63[/C][C]0.963103806016237[/C][C]0.0737923879675251[/C][C]0.0368961939837626[/C][/ROW]
[ROW][C]64[/C][C]0.920902815726502[/C][C]0.158194368546995[/C][C]0.0790971842734976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58036&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58036&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
50.1615945242680470.3231890485360950.838405475731953
60.08477745895758920.1695549179151780.915222541042411
70.0456654127790860.0913308255581720.954334587220914
80.02069904964120500.04139809928240990.979300950358795
90.007837241481198870.01567448296239770.992162758518801
100.01167521094418080.02335042188836150.98832478905582
110.006458034250199470.01291606850039890.9935419657498
120.002593122189065690.005186244378131380.997406877810934
130.001018800912698240.002037601825396490.998981199087302
140.0005132215201869910.001026443040373980.999486778479813
150.0007077065158751940.001415413031750390.999292293484125
160.0003795822694046540.0007591645388093080.999620417730595
170.0001756234488131350.0003512468976262710.999824376551187
180.0001532945846078310.0003065891692156630.999846705415392
190.0001260897117098680.0002521794234197350.99987391028829
200.0001539314107506430.0003078628215012870.99984606858925
210.0002199933186166560.0004399866372333110.999780006681383
220.0001048736339487590.0002097472678975170.999895126366051
235.21118188679677e-050.0001042236377359350.999947888181132
242.78884727553605e-055.57769455107209e-050.999972111527245
251.52077590766730e-053.04155181533461e-050.999984792240923
268.5563622785853e-061.71127245571706e-050.999991443637721
278.95087227484142e-061.79017445496828e-050.999991049127725
284.44112337581569e-068.88224675163139e-060.999995558876624
292.39567838302280e-064.79135676604559e-060.999997604321617
301.24355448277283e-062.48710896554566e-060.999998756445517
316.41559802630996e-071.28311960526199e-060.999999358440197
323.30520310454488e-076.61040620908977e-070.99999966947969
332.48909037153164e-074.97818074306327e-070.999999751090963
342.01382843070875e-074.0276568614175e-070.999999798617157
358.27061479452403e-081.65412295890481e-070.999999917293852
363.12397390421145e-086.2479478084229e-080.999999968760261
371.88120398222335e-083.7624079644467e-080.99999998118796
389.06790128969788e-091.81358025793958e-080.999999990932099
394.79180007751672e-099.58360015503343e-090.9999999952082
402.87388881849845e-095.74777763699689e-090.99999999712611
415.56287434702816e-091.11257486940563e-080.999999994437126
421.57685481131404e-083.15370962262808e-080.999999984231452
438.80750051018436e-081.76150010203687e-070.999999911924995
441.67573717102815e-063.35147434205631e-060.999998324262829
456.21467321689057e-050.0001242934643378110.99993785326783
460.001093785342212620.002187570684425250.998906214657787
470.01283296907218420.02566593814436830.987167030927816
480.09178370140439810.1835674028087960.908216298595602
490.3173804236494010.6347608472988010.6826195763506
500.6583570872551450.683285825489710.341642912744855
510.8272309187413170.3455381625173670.172769081258683
520.9345397376755360.1309205246489270.0654602623244635
530.9609885409171160.07802291816576720.0390114590828836
540.987779893286790.02444021342642120.0122201067132106
550.9933642434604480.01327151307910360.00663575653955181
560.9908665213079780.01826695738404310.00913347869202156
570.9864983410445950.02700331791081010.0135016589554051
580.9759596488403810.04808070231923720.0240403511596186
590.9844406911842280.03111861763154460.0155593088157723
600.978305440009740.04338911998052040.0216945599902602
610.964543675651670.070912648696660.03545632434833
620.9209381371211220.1581237257577550.0790618628788775
630.9631038060162370.07379238796752510.0368961939837626
640.9209028157265020.1581943685469950.0790971842734976







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level350.583333333333333NOK
5% type I error level470.783333333333333NOK
10% type I error level510.85NOK

\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 & 35 & 0.583333333333333 & NOK \tabularnewline
5% type I error level & 47 & 0.783333333333333 & NOK \tabularnewline
10% type I error level & 51 & 0.85 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58036&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]35[/C][C]0.583333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]47[/C][C]0.783333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]51[/C][C]0.85[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58036&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58036&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 level350.583333333333333NOK
5% type I error level470.783333333333333NOK
10% type I error level510.85NOK



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