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Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:37:55 -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/t1258731523d9di3flyuex2ndm.htm/, Retrieved Fri, 29 Mar 2024 08:36:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58271, Retrieved Fri, 29 Mar 2024 08:36:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
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]
F   PD      [Multiple Regression] [WS 7.1] [2009-11-20 15:37:55] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2009-11-27 11:42:53 [f115a54dbe4716ad97e5a9a6f3e33a85] [reply
Je hebt steeds bij verified en belongs to author no. Probeer de linken toch goed en correct te posten.

Post a new message
Dataseries X:
9.9	8.2
9.8	8
9.3	7.5
8.3	6.8
8	6.5
8.5	6.6
10.4	7.6
11.1	8
10.9	8.1
10	7.7
9.2	7.5
9.2	7.6
9.5	7.8
9.6	7.8
9.5	7.8
9.1	7.5
8.9	7.5
9	7.1
10.1	7.5
10.3	7.5
10.2	7.6
9.6	7.7
9.2	7.7
9.3	7.9
9.4	8.1
9.4	8.2
9.2	8.2
9	8.2
9	7.9
9	7.3
9.8	6.9
10	6.6
9.8	6.7
9.3	6.9
9	7
9	7.1
9.1	7.2
9.1	7.1
9.1	6.9
9.2	7
8.8	6.8
8.3	6.4
8.4	6.7
8.1	6.6
7.7	6.4
7.9	6.3
7.9	6.2
8	6.5
7.9	6.8
7.6	6.8
7.1	6.4
6.8	6.1
6.5	5.8
6.9	6.1
8.2	7.2
8.7	7.3
8.3	6.9
7.9	6.1
7.5	5.8
7.8	6.2




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=58271&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=58271&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58271&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
WLMan[t] = + 2.46122394441475 + 0.525724444031325WLVrouw[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WLMan[t] =  +  2.46122394441475 +  0.525724444031325WLVrouw[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58271&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WLMan[t] =  +  2.46122394441475 +  0.525724444031325WLVrouw[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58271&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58271&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
WLMan[t] = + 2.46122394441475 + 0.525724444031325WLVrouw[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.461223944414750.5049684.8749e-064e-06
WLVrouw0.5257244440313250.056449.314700

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 2.46122394441475 & 0.504968 & 4.874 & 9e-06 & 4e-06 \tabularnewline
WLVrouw & 0.525724444031325 & 0.05644 & 9.3147 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58271&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]2.46122394441475[/C][C]0.504968[/C][C]4.874[/C][C]9e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]WLVrouw[/C][C]0.525724444031325[/C][C]0.05644[/C][C]9.3147[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58271&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58271&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.461223944414750.5049684.8749e-064e-06
WLVrouw0.5257244440313250.056449.314700







Multiple Linear Regression - Regression Statistics
Multiple R0.77417457022882
R-squared0.599346265188978
Adjusted R-squared0.592438442174995
F-TEST (value)86.7634078023937
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value4.02788913334007e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.427523556935347
Sum Squared Residuals10.6010307206098

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.77417457022882 \tabularnewline
R-squared & 0.599346265188978 \tabularnewline
Adjusted R-squared & 0.592438442174995 \tabularnewline
F-TEST (value) & 86.7634078023937 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 4.02788913334007e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.427523556935347 \tabularnewline
Sum Squared Residuals & 10.6010307206098 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58271&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.77417457022882[/C][/ROW]
[ROW][C]R-squared[/C][C]0.599346265188978[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.592438442174995[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]86.7634078023937[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]4.02788913334007e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.427523556935347[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.6010307206098[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58271&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58271&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 R0.77417457022882
R-squared0.599346265188978
Adjusted R-squared0.592438442174995
F-TEST (value)86.7634078023937
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value4.02788913334007e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.427523556935347
Sum Squared Residuals10.6010307206098







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.27.665895940324880.534104059675124
287.613323495921730.386676504078266
37.57.350461273906070.149538726093928
46.86.82473682987475-0.0247368298747477
56.56.66701949666535-0.167019496665350
66.66.92988171868101-0.329881718681012
77.67.92875816234053-0.32875816234053
888.29676527316246-0.296765273162456
98.18.19162038435619-0.091620384356192
107.77.718468384728-0.0184683847279990
117.57.297888829502940.202111170497061
127.67.297888829502940.302111170497061
137.87.455606162712340.344393837287663
147.87.508178607115470.291821392884531
157.87.455606162712340.344393837287663
167.57.24531638509980.254683614900193
177.57.140171496293540.359828503706458
187.17.19274394069667-0.0927439406966748
197.57.77104082913113-0.271040829131131
207.57.8761857179374-0.376185717937397
217.67.82361327353426-0.223613273534264
227.77.508178607115470.191821392884531
237.77.297888829502940.402111170497061
247.97.350461273906070.549538726093928
258.17.40303371830920.696966281690795
268.27.40303371830920.796966281690795
278.27.297888829502940.90211117049706
288.27.192743940696671.00725605930332
297.97.192743940696670.707256059303326
307.37.192743940696670.107256059303325
316.97.61332349592173-0.713323495921734
326.67.718468384728-1.118468384728
336.77.61332349592173-0.913323495921734
346.97.35046127390607-0.450461273906072
3577.19274394069667-0.192743940696674
367.17.19274394069667-0.0927439406966748
377.27.2453163850998-0.0453163850998065
387.17.2453163850998-0.145316385099807
396.97.2453163850998-0.345316385099806
4077.29788882950294-0.297888829502939
416.87.08759905189041-0.28759905189041
426.46.82473682987475-0.424736829874747
436.76.87730927427788-0.177309274277880
446.66.71959194106848-0.119591941068482
456.46.50930216345595-0.109302163455952
466.36.61444705226222-0.314447052262217
476.26.61444705226222-0.414447052262217
486.56.66701949666535-0.167019496665350
496.86.614447052262220.185552947737783
506.86.456729719052820.34327028094718
516.46.193867497037160.206132502962843
526.16.036150163827760.0638498361722397
535.85.87843283061836-0.0784328306183626
546.16.088722608230890.0112773917691069
557.26.772164385471610.427835614528386
567.37.035026607487280.264973392512723
576.96.824736829874750.0752631701252529
586.16.61444705226222-0.514447052262218
595.86.40415727464969-0.604157274649687
606.26.56187460785908-0.361874607859084

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.2 & 7.66589594032488 & 0.534104059675124 \tabularnewline
2 & 8 & 7.61332349592173 & 0.386676504078266 \tabularnewline
3 & 7.5 & 7.35046127390607 & 0.149538726093928 \tabularnewline
4 & 6.8 & 6.82473682987475 & -0.0247368298747477 \tabularnewline
5 & 6.5 & 6.66701949666535 & -0.167019496665350 \tabularnewline
6 & 6.6 & 6.92988171868101 & -0.329881718681012 \tabularnewline
7 & 7.6 & 7.92875816234053 & -0.32875816234053 \tabularnewline
8 & 8 & 8.29676527316246 & -0.296765273162456 \tabularnewline
9 & 8.1 & 8.19162038435619 & -0.091620384356192 \tabularnewline
10 & 7.7 & 7.718468384728 & -0.0184683847279990 \tabularnewline
11 & 7.5 & 7.29788882950294 & 0.202111170497061 \tabularnewline
12 & 7.6 & 7.29788882950294 & 0.302111170497061 \tabularnewline
13 & 7.8 & 7.45560616271234 & 0.344393837287663 \tabularnewline
14 & 7.8 & 7.50817860711547 & 0.291821392884531 \tabularnewline
15 & 7.8 & 7.45560616271234 & 0.344393837287663 \tabularnewline
16 & 7.5 & 7.2453163850998 & 0.254683614900193 \tabularnewline
17 & 7.5 & 7.14017149629354 & 0.359828503706458 \tabularnewline
18 & 7.1 & 7.19274394069667 & -0.0927439406966748 \tabularnewline
19 & 7.5 & 7.77104082913113 & -0.271040829131131 \tabularnewline
20 & 7.5 & 7.8761857179374 & -0.376185717937397 \tabularnewline
21 & 7.6 & 7.82361327353426 & -0.223613273534264 \tabularnewline
22 & 7.7 & 7.50817860711547 & 0.191821392884531 \tabularnewline
23 & 7.7 & 7.29788882950294 & 0.402111170497061 \tabularnewline
24 & 7.9 & 7.35046127390607 & 0.549538726093928 \tabularnewline
25 & 8.1 & 7.4030337183092 & 0.696966281690795 \tabularnewline
26 & 8.2 & 7.4030337183092 & 0.796966281690795 \tabularnewline
27 & 8.2 & 7.29788882950294 & 0.90211117049706 \tabularnewline
28 & 8.2 & 7.19274394069667 & 1.00725605930332 \tabularnewline
29 & 7.9 & 7.19274394069667 & 0.707256059303326 \tabularnewline
30 & 7.3 & 7.19274394069667 & 0.107256059303325 \tabularnewline
31 & 6.9 & 7.61332349592173 & -0.713323495921734 \tabularnewline
32 & 6.6 & 7.718468384728 & -1.118468384728 \tabularnewline
33 & 6.7 & 7.61332349592173 & -0.913323495921734 \tabularnewline
34 & 6.9 & 7.35046127390607 & -0.450461273906072 \tabularnewline
35 & 7 & 7.19274394069667 & -0.192743940696674 \tabularnewline
36 & 7.1 & 7.19274394069667 & -0.0927439406966748 \tabularnewline
37 & 7.2 & 7.2453163850998 & -0.0453163850998065 \tabularnewline
38 & 7.1 & 7.2453163850998 & -0.145316385099807 \tabularnewline
39 & 6.9 & 7.2453163850998 & -0.345316385099806 \tabularnewline
40 & 7 & 7.29788882950294 & -0.297888829502939 \tabularnewline
41 & 6.8 & 7.08759905189041 & -0.28759905189041 \tabularnewline
42 & 6.4 & 6.82473682987475 & -0.424736829874747 \tabularnewline
43 & 6.7 & 6.87730927427788 & -0.177309274277880 \tabularnewline
44 & 6.6 & 6.71959194106848 & -0.119591941068482 \tabularnewline
45 & 6.4 & 6.50930216345595 & -0.109302163455952 \tabularnewline
46 & 6.3 & 6.61444705226222 & -0.314447052262217 \tabularnewline
47 & 6.2 & 6.61444705226222 & -0.414447052262217 \tabularnewline
48 & 6.5 & 6.66701949666535 & -0.167019496665350 \tabularnewline
49 & 6.8 & 6.61444705226222 & 0.185552947737783 \tabularnewline
50 & 6.8 & 6.45672971905282 & 0.34327028094718 \tabularnewline
51 & 6.4 & 6.19386749703716 & 0.206132502962843 \tabularnewline
52 & 6.1 & 6.03615016382776 & 0.0638498361722397 \tabularnewline
53 & 5.8 & 5.87843283061836 & -0.0784328306183626 \tabularnewline
54 & 6.1 & 6.08872260823089 & 0.0112773917691069 \tabularnewline
55 & 7.2 & 6.77216438547161 & 0.427835614528386 \tabularnewline
56 & 7.3 & 7.03502660748728 & 0.264973392512723 \tabularnewline
57 & 6.9 & 6.82473682987475 & 0.0752631701252529 \tabularnewline
58 & 6.1 & 6.61444705226222 & -0.514447052262218 \tabularnewline
59 & 5.8 & 6.40415727464969 & -0.604157274649687 \tabularnewline
60 & 6.2 & 6.56187460785908 & -0.361874607859084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58271&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]8.2[/C][C]7.66589594032488[/C][C]0.534104059675124[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]7.61332349592173[/C][C]0.386676504078266[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]7.35046127390607[/C][C]0.149538726093928[/C][/ROW]
[ROW][C]4[/C][C]6.8[/C][C]6.82473682987475[/C][C]-0.0247368298747477[/C][/ROW]
[ROW][C]5[/C][C]6.5[/C][C]6.66701949666535[/C][C]-0.167019496665350[/C][/ROW]
[ROW][C]6[/C][C]6.6[/C][C]6.92988171868101[/C][C]-0.329881718681012[/C][/ROW]
[ROW][C]7[/C][C]7.6[/C][C]7.92875816234053[/C][C]-0.32875816234053[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]8.29676527316246[/C][C]-0.296765273162456[/C][/ROW]
[ROW][C]9[/C][C]8.1[/C][C]8.19162038435619[/C][C]-0.091620384356192[/C][/ROW]
[ROW][C]10[/C][C]7.7[/C][C]7.718468384728[/C][C]-0.0184683847279990[/C][/ROW]
[ROW][C]11[/C][C]7.5[/C][C]7.29788882950294[/C][C]0.202111170497061[/C][/ROW]
[ROW][C]12[/C][C]7.6[/C][C]7.29788882950294[/C][C]0.302111170497061[/C][/ROW]
[ROW][C]13[/C][C]7.8[/C][C]7.45560616271234[/C][C]0.344393837287663[/C][/ROW]
[ROW][C]14[/C][C]7.8[/C][C]7.50817860711547[/C][C]0.291821392884531[/C][/ROW]
[ROW][C]15[/C][C]7.8[/C][C]7.45560616271234[/C][C]0.344393837287663[/C][/ROW]
[ROW][C]16[/C][C]7.5[/C][C]7.2453163850998[/C][C]0.254683614900193[/C][/ROW]
[ROW][C]17[/C][C]7.5[/C][C]7.14017149629354[/C][C]0.359828503706458[/C][/ROW]
[ROW][C]18[/C][C]7.1[/C][C]7.19274394069667[/C][C]-0.0927439406966748[/C][/ROW]
[ROW][C]19[/C][C]7.5[/C][C]7.77104082913113[/C][C]-0.271040829131131[/C][/ROW]
[ROW][C]20[/C][C]7.5[/C][C]7.8761857179374[/C][C]-0.376185717937397[/C][/ROW]
[ROW][C]21[/C][C]7.6[/C][C]7.82361327353426[/C][C]-0.223613273534264[/C][/ROW]
[ROW][C]22[/C][C]7.7[/C][C]7.50817860711547[/C][C]0.191821392884531[/C][/ROW]
[ROW][C]23[/C][C]7.7[/C][C]7.29788882950294[/C][C]0.402111170497061[/C][/ROW]
[ROW][C]24[/C][C]7.9[/C][C]7.35046127390607[/C][C]0.549538726093928[/C][/ROW]
[ROW][C]25[/C][C]8.1[/C][C]7.4030337183092[/C][C]0.696966281690795[/C][/ROW]
[ROW][C]26[/C][C]8.2[/C][C]7.4030337183092[/C][C]0.796966281690795[/C][/ROW]
[ROW][C]27[/C][C]8.2[/C][C]7.29788882950294[/C][C]0.90211117049706[/C][/ROW]
[ROW][C]28[/C][C]8.2[/C][C]7.19274394069667[/C][C]1.00725605930332[/C][/ROW]
[ROW][C]29[/C][C]7.9[/C][C]7.19274394069667[/C][C]0.707256059303326[/C][/ROW]
[ROW][C]30[/C][C]7.3[/C][C]7.19274394069667[/C][C]0.107256059303325[/C][/ROW]
[ROW][C]31[/C][C]6.9[/C][C]7.61332349592173[/C][C]-0.713323495921734[/C][/ROW]
[ROW][C]32[/C][C]6.6[/C][C]7.718468384728[/C][C]-1.118468384728[/C][/ROW]
[ROW][C]33[/C][C]6.7[/C][C]7.61332349592173[/C][C]-0.913323495921734[/C][/ROW]
[ROW][C]34[/C][C]6.9[/C][C]7.35046127390607[/C][C]-0.450461273906072[/C][/ROW]
[ROW][C]35[/C][C]7[/C][C]7.19274394069667[/C][C]-0.192743940696674[/C][/ROW]
[ROW][C]36[/C][C]7.1[/C][C]7.19274394069667[/C][C]-0.0927439406966748[/C][/ROW]
[ROW][C]37[/C][C]7.2[/C][C]7.2453163850998[/C][C]-0.0453163850998065[/C][/ROW]
[ROW][C]38[/C][C]7.1[/C][C]7.2453163850998[/C][C]-0.145316385099807[/C][/ROW]
[ROW][C]39[/C][C]6.9[/C][C]7.2453163850998[/C][C]-0.345316385099806[/C][/ROW]
[ROW][C]40[/C][C]7[/C][C]7.29788882950294[/C][C]-0.297888829502939[/C][/ROW]
[ROW][C]41[/C][C]6.8[/C][C]7.08759905189041[/C][C]-0.28759905189041[/C][/ROW]
[ROW][C]42[/C][C]6.4[/C][C]6.82473682987475[/C][C]-0.424736829874747[/C][/ROW]
[ROW][C]43[/C][C]6.7[/C][C]6.87730927427788[/C][C]-0.177309274277880[/C][/ROW]
[ROW][C]44[/C][C]6.6[/C][C]6.71959194106848[/C][C]-0.119591941068482[/C][/ROW]
[ROW][C]45[/C][C]6.4[/C][C]6.50930216345595[/C][C]-0.109302163455952[/C][/ROW]
[ROW][C]46[/C][C]6.3[/C][C]6.61444705226222[/C][C]-0.314447052262217[/C][/ROW]
[ROW][C]47[/C][C]6.2[/C][C]6.61444705226222[/C][C]-0.414447052262217[/C][/ROW]
[ROW][C]48[/C][C]6.5[/C][C]6.66701949666535[/C][C]-0.167019496665350[/C][/ROW]
[ROW][C]49[/C][C]6.8[/C][C]6.61444705226222[/C][C]0.185552947737783[/C][/ROW]
[ROW][C]50[/C][C]6.8[/C][C]6.45672971905282[/C][C]0.34327028094718[/C][/ROW]
[ROW][C]51[/C][C]6.4[/C][C]6.19386749703716[/C][C]0.206132502962843[/C][/ROW]
[ROW][C]52[/C][C]6.1[/C][C]6.03615016382776[/C][C]0.0638498361722397[/C][/ROW]
[ROW][C]53[/C][C]5.8[/C][C]5.87843283061836[/C][C]-0.0784328306183626[/C][/ROW]
[ROW][C]54[/C][C]6.1[/C][C]6.08872260823089[/C][C]0.0112773917691069[/C][/ROW]
[ROW][C]55[/C][C]7.2[/C][C]6.77216438547161[/C][C]0.427835614528386[/C][/ROW]
[ROW][C]56[/C][C]7.3[/C][C]7.03502660748728[/C][C]0.264973392512723[/C][/ROW]
[ROW][C]57[/C][C]6.9[/C][C]6.82473682987475[/C][C]0.0752631701252529[/C][/ROW]
[ROW][C]58[/C][C]6.1[/C][C]6.61444705226222[/C][C]-0.514447052262218[/C][/ROW]
[ROW][C]59[/C][C]5.8[/C][C]6.40415727464969[/C][C]-0.604157274649687[/C][/ROW]
[ROW][C]60[/C][C]6.2[/C][C]6.56187460785908[/C][C]-0.361874607859084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58271&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58271&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
18.27.665895940324880.534104059675124
287.613323495921730.386676504078266
37.57.350461273906070.149538726093928
46.86.82473682987475-0.0247368298747477
56.56.66701949666535-0.167019496665350
66.66.92988171868101-0.329881718681012
77.67.92875816234053-0.32875816234053
888.29676527316246-0.296765273162456
98.18.19162038435619-0.091620384356192
107.77.718468384728-0.0184683847279990
117.57.297888829502940.202111170497061
127.67.297888829502940.302111170497061
137.87.455606162712340.344393837287663
147.87.508178607115470.291821392884531
157.87.455606162712340.344393837287663
167.57.24531638509980.254683614900193
177.57.140171496293540.359828503706458
187.17.19274394069667-0.0927439406966748
197.57.77104082913113-0.271040829131131
207.57.8761857179374-0.376185717937397
217.67.82361327353426-0.223613273534264
227.77.508178607115470.191821392884531
237.77.297888829502940.402111170497061
247.97.350461273906070.549538726093928
258.17.40303371830920.696966281690795
268.27.40303371830920.796966281690795
278.27.297888829502940.90211117049706
288.27.192743940696671.00725605930332
297.97.192743940696670.707256059303326
307.37.192743940696670.107256059303325
316.97.61332349592173-0.713323495921734
326.67.718468384728-1.118468384728
336.77.61332349592173-0.913323495921734
346.97.35046127390607-0.450461273906072
3577.19274394069667-0.192743940696674
367.17.19274394069667-0.0927439406966748
377.27.2453163850998-0.0453163850998065
387.17.2453163850998-0.145316385099807
396.97.2453163850998-0.345316385099806
4077.29788882950294-0.297888829502939
416.87.08759905189041-0.28759905189041
426.46.82473682987475-0.424736829874747
436.76.87730927427788-0.177309274277880
446.66.71959194106848-0.119591941068482
456.46.50930216345595-0.109302163455952
466.36.61444705226222-0.314447052262217
476.26.61444705226222-0.414447052262217
486.56.66701949666535-0.167019496665350
496.86.614447052262220.185552947737783
506.86.456729719052820.34327028094718
516.46.193867497037160.206132502962843
526.16.036150163827760.0638498361722397
535.85.87843283061836-0.0784328306183626
546.16.088722608230890.0112773917691069
557.26.772164385471610.427835614528386
567.37.035026607487280.264973392512723
576.96.824736829874750.0752631701252529
586.16.61444705226222-0.514447052262218
595.86.40415727464969-0.604157274649687
606.26.56187460785908-0.361874607859084







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.009649902375378070.01929980475075610.990350097624622
60.03074334218857420.06148668437714840.969256657811426
70.3157938411561030.6315876823122070.684206158843897
80.325157402556030.650314805112060.67484259744397
90.2221105804808680.4442211609617360.777889419519132
100.1407501016827310.2815002033654630.859249898317269
110.09950659237932720.1990131847586540.900493407620673
120.08015847311083660.1603169462216730.919841526889163
130.06807540225376120.1361508045075220.931924597746239
140.05034534611568660.1006906922313730.949654653884313
150.0400801048103490.0801602096206980.959919895189651
160.02618427749665160.05236855499330310.973815722503348
170.01988846088623510.03977692177247010.980111539113765
180.01324930906999370.02649861813998740.986750690930006
190.01170557166850910.02341114333701810.98829442833149
200.01246504993431620.02493009986863230.987534950065684
210.008530844035787160.01706168807157430.991469155964213
220.005205514542207880.01041102908441580.994794485457792
230.004673223555442110.009346447110884210.995326776444558
240.006977506467584770.01395501293516950.993022493532415
250.01826772696598010.03653545393196020.98173227303402
260.06094170434997720.1218834086999540.939058295650023
270.2164710477741970.4329420955483950.783528952225803
280.6356371280676630.7287257438646740.364362871932337
290.8614839033285670.2770321933428660.138516096671433
300.8658931353112930.2682137293774140.134106864688707
310.9180471011088630.1639057977822740.0819528988911368
320.9893858977619570.02122820447608510.0106141022380426
330.9980466076120980.003906784775804030.00195339238790202
340.99800608018540.003987839629199930.00199391981459996
350.9967596067179030.006480786564193770.00324039328209688
360.9945696009397010.01086079812059740.00543039906029869
370.991313372417270.01737325516546050.00868662758273023
380.9859963960638560.02800720787228780.0140036039361439
390.9810131410984120.0379737178031760.018986858901588
400.972583705568290.05483258886341850.0274162944317093
410.9629613901353320.0740772197293360.037038609864668
420.965287787253410.06942442549318140.0347122127465907
430.948518808975090.1029623820498220.051481191024911
440.9220520904180580.1558958191638840.077947909581942
450.8845307218514710.2309385562970590.115469278148529
460.8623957954380170.2752084091239660.137604204561983
470.8671959767437520.2656080465124960.132804023256248
480.8185892216790420.3628215566419160.181410778320958
490.7524826497046170.4950347005907660.247517350295383
500.7347190159350510.5305619681298980.265280984064949
510.6878633408970280.6242733182059440.312136659102972
520.6146559265648770.7706881468702450.385344073435123
530.5511465857320880.8977068285358240.448853414267912
540.8240957273555110.3518085452889770.175904272644489
550.9676798296689180.06464034066216360.0323201703310818

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.00964990237537807 & 0.0192998047507561 & 0.990350097624622 \tabularnewline
6 & 0.0307433421885742 & 0.0614866843771484 & 0.969256657811426 \tabularnewline
7 & 0.315793841156103 & 0.631587682312207 & 0.684206158843897 \tabularnewline
8 & 0.32515740255603 & 0.65031480511206 & 0.67484259744397 \tabularnewline
9 & 0.222110580480868 & 0.444221160961736 & 0.777889419519132 \tabularnewline
10 & 0.140750101682731 & 0.281500203365463 & 0.859249898317269 \tabularnewline
11 & 0.0995065923793272 & 0.199013184758654 & 0.900493407620673 \tabularnewline
12 & 0.0801584731108366 & 0.160316946221673 & 0.919841526889163 \tabularnewline
13 & 0.0680754022537612 & 0.136150804507522 & 0.931924597746239 \tabularnewline
14 & 0.0503453461156866 & 0.100690692231373 & 0.949654653884313 \tabularnewline
15 & 0.040080104810349 & 0.080160209620698 & 0.959919895189651 \tabularnewline
16 & 0.0261842774966516 & 0.0523685549933031 & 0.973815722503348 \tabularnewline
17 & 0.0198884608862351 & 0.0397769217724701 & 0.980111539113765 \tabularnewline
18 & 0.0132493090699937 & 0.0264986181399874 & 0.986750690930006 \tabularnewline
19 & 0.0117055716685091 & 0.0234111433370181 & 0.98829442833149 \tabularnewline
20 & 0.0124650499343162 & 0.0249300998686323 & 0.987534950065684 \tabularnewline
21 & 0.00853084403578716 & 0.0170616880715743 & 0.991469155964213 \tabularnewline
22 & 0.00520551454220788 & 0.0104110290844158 & 0.994794485457792 \tabularnewline
23 & 0.00467322355544211 & 0.00934644711088421 & 0.995326776444558 \tabularnewline
24 & 0.00697750646758477 & 0.0139550129351695 & 0.993022493532415 \tabularnewline
25 & 0.0182677269659801 & 0.0365354539319602 & 0.98173227303402 \tabularnewline
26 & 0.0609417043499772 & 0.121883408699954 & 0.939058295650023 \tabularnewline
27 & 0.216471047774197 & 0.432942095548395 & 0.783528952225803 \tabularnewline
28 & 0.635637128067663 & 0.728725743864674 & 0.364362871932337 \tabularnewline
29 & 0.861483903328567 & 0.277032193342866 & 0.138516096671433 \tabularnewline
30 & 0.865893135311293 & 0.268213729377414 & 0.134106864688707 \tabularnewline
31 & 0.918047101108863 & 0.163905797782274 & 0.0819528988911368 \tabularnewline
32 & 0.989385897761957 & 0.0212282044760851 & 0.0106141022380426 \tabularnewline
33 & 0.998046607612098 & 0.00390678477580403 & 0.00195339238790202 \tabularnewline
34 & 0.9980060801854 & 0.00398783962919993 & 0.00199391981459996 \tabularnewline
35 & 0.996759606717903 & 0.00648078656419377 & 0.00324039328209688 \tabularnewline
36 & 0.994569600939701 & 0.0108607981205974 & 0.00543039906029869 \tabularnewline
37 & 0.99131337241727 & 0.0173732551654605 & 0.00868662758273023 \tabularnewline
38 & 0.985996396063856 & 0.0280072078722878 & 0.0140036039361439 \tabularnewline
39 & 0.981013141098412 & 0.037973717803176 & 0.018986858901588 \tabularnewline
40 & 0.97258370556829 & 0.0548325888634185 & 0.0274162944317093 \tabularnewline
41 & 0.962961390135332 & 0.074077219729336 & 0.037038609864668 \tabularnewline
42 & 0.96528778725341 & 0.0694244254931814 & 0.0347122127465907 \tabularnewline
43 & 0.94851880897509 & 0.102962382049822 & 0.051481191024911 \tabularnewline
44 & 0.922052090418058 & 0.155895819163884 & 0.077947909581942 \tabularnewline
45 & 0.884530721851471 & 0.230938556297059 & 0.115469278148529 \tabularnewline
46 & 0.862395795438017 & 0.275208409123966 & 0.137604204561983 \tabularnewline
47 & 0.867195976743752 & 0.265608046512496 & 0.132804023256248 \tabularnewline
48 & 0.818589221679042 & 0.362821556641916 & 0.181410778320958 \tabularnewline
49 & 0.752482649704617 & 0.495034700590766 & 0.247517350295383 \tabularnewline
50 & 0.734719015935051 & 0.530561968129898 & 0.265280984064949 \tabularnewline
51 & 0.687863340897028 & 0.624273318205944 & 0.312136659102972 \tabularnewline
52 & 0.614655926564877 & 0.770688146870245 & 0.385344073435123 \tabularnewline
53 & 0.551146585732088 & 0.897706828535824 & 0.448853414267912 \tabularnewline
54 & 0.824095727355511 & 0.351808545288977 & 0.175904272644489 \tabularnewline
55 & 0.967679829668918 & 0.0646403406621636 & 0.0323201703310818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58271&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]0.00964990237537807[/C][C]0.0192998047507561[/C][C]0.990350097624622[/C][/ROW]
[ROW][C]6[/C][C]0.0307433421885742[/C][C]0.0614866843771484[/C][C]0.969256657811426[/C][/ROW]
[ROW][C]7[/C][C]0.315793841156103[/C][C]0.631587682312207[/C][C]0.684206158843897[/C][/ROW]
[ROW][C]8[/C][C]0.32515740255603[/C][C]0.65031480511206[/C][C]0.67484259744397[/C][/ROW]
[ROW][C]9[/C][C]0.222110580480868[/C][C]0.444221160961736[/C][C]0.777889419519132[/C][/ROW]
[ROW][C]10[/C][C]0.140750101682731[/C][C]0.281500203365463[/C][C]0.859249898317269[/C][/ROW]
[ROW][C]11[/C][C]0.0995065923793272[/C][C]0.199013184758654[/C][C]0.900493407620673[/C][/ROW]
[ROW][C]12[/C][C]0.0801584731108366[/C][C]0.160316946221673[/C][C]0.919841526889163[/C][/ROW]
[ROW][C]13[/C][C]0.0680754022537612[/C][C]0.136150804507522[/C][C]0.931924597746239[/C][/ROW]
[ROW][C]14[/C][C]0.0503453461156866[/C][C]0.100690692231373[/C][C]0.949654653884313[/C][/ROW]
[ROW][C]15[/C][C]0.040080104810349[/C][C]0.080160209620698[/C][C]0.959919895189651[/C][/ROW]
[ROW][C]16[/C][C]0.0261842774966516[/C][C]0.0523685549933031[/C][C]0.973815722503348[/C][/ROW]
[ROW][C]17[/C][C]0.0198884608862351[/C][C]0.0397769217724701[/C][C]0.980111539113765[/C][/ROW]
[ROW][C]18[/C][C]0.0132493090699937[/C][C]0.0264986181399874[/C][C]0.986750690930006[/C][/ROW]
[ROW][C]19[/C][C]0.0117055716685091[/C][C]0.0234111433370181[/C][C]0.98829442833149[/C][/ROW]
[ROW][C]20[/C][C]0.0124650499343162[/C][C]0.0249300998686323[/C][C]0.987534950065684[/C][/ROW]
[ROW][C]21[/C][C]0.00853084403578716[/C][C]0.0170616880715743[/C][C]0.991469155964213[/C][/ROW]
[ROW][C]22[/C][C]0.00520551454220788[/C][C]0.0104110290844158[/C][C]0.994794485457792[/C][/ROW]
[ROW][C]23[/C][C]0.00467322355544211[/C][C]0.00934644711088421[/C][C]0.995326776444558[/C][/ROW]
[ROW][C]24[/C][C]0.00697750646758477[/C][C]0.0139550129351695[/C][C]0.993022493532415[/C][/ROW]
[ROW][C]25[/C][C]0.0182677269659801[/C][C]0.0365354539319602[/C][C]0.98173227303402[/C][/ROW]
[ROW][C]26[/C][C]0.0609417043499772[/C][C]0.121883408699954[/C][C]0.939058295650023[/C][/ROW]
[ROW][C]27[/C][C]0.216471047774197[/C][C]0.432942095548395[/C][C]0.783528952225803[/C][/ROW]
[ROW][C]28[/C][C]0.635637128067663[/C][C]0.728725743864674[/C][C]0.364362871932337[/C][/ROW]
[ROW][C]29[/C][C]0.861483903328567[/C][C]0.277032193342866[/C][C]0.138516096671433[/C][/ROW]
[ROW][C]30[/C][C]0.865893135311293[/C][C]0.268213729377414[/C][C]0.134106864688707[/C][/ROW]
[ROW][C]31[/C][C]0.918047101108863[/C][C]0.163905797782274[/C][C]0.0819528988911368[/C][/ROW]
[ROW][C]32[/C][C]0.989385897761957[/C][C]0.0212282044760851[/C][C]0.0106141022380426[/C][/ROW]
[ROW][C]33[/C][C]0.998046607612098[/C][C]0.00390678477580403[/C][C]0.00195339238790202[/C][/ROW]
[ROW][C]34[/C][C]0.9980060801854[/C][C]0.00398783962919993[/C][C]0.00199391981459996[/C][/ROW]
[ROW][C]35[/C][C]0.996759606717903[/C][C]0.00648078656419377[/C][C]0.00324039328209688[/C][/ROW]
[ROW][C]36[/C][C]0.994569600939701[/C][C]0.0108607981205974[/C][C]0.00543039906029869[/C][/ROW]
[ROW][C]37[/C][C]0.99131337241727[/C][C]0.0173732551654605[/C][C]0.00868662758273023[/C][/ROW]
[ROW][C]38[/C][C]0.985996396063856[/C][C]0.0280072078722878[/C][C]0.0140036039361439[/C][/ROW]
[ROW][C]39[/C][C]0.981013141098412[/C][C]0.037973717803176[/C][C]0.018986858901588[/C][/ROW]
[ROW][C]40[/C][C]0.97258370556829[/C][C]0.0548325888634185[/C][C]0.0274162944317093[/C][/ROW]
[ROW][C]41[/C][C]0.962961390135332[/C][C]0.074077219729336[/C][C]0.037038609864668[/C][/ROW]
[ROW][C]42[/C][C]0.96528778725341[/C][C]0.0694244254931814[/C][C]0.0347122127465907[/C][/ROW]
[ROW][C]43[/C][C]0.94851880897509[/C][C]0.102962382049822[/C][C]0.051481191024911[/C][/ROW]
[ROW][C]44[/C][C]0.922052090418058[/C][C]0.155895819163884[/C][C]0.077947909581942[/C][/ROW]
[ROW][C]45[/C][C]0.884530721851471[/C][C]0.230938556297059[/C][C]0.115469278148529[/C][/ROW]
[ROW][C]46[/C][C]0.862395795438017[/C][C]0.275208409123966[/C][C]0.137604204561983[/C][/ROW]
[ROW][C]47[/C][C]0.867195976743752[/C][C]0.265608046512496[/C][C]0.132804023256248[/C][/ROW]
[ROW][C]48[/C][C]0.818589221679042[/C][C]0.362821556641916[/C][C]0.181410778320958[/C][/ROW]
[ROW][C]49[/C][C]0.752482649704617[/C][C]0.495034700590766[/C][C]0.247517350295383[/C][/ROW]
[ROW][C]50[/C][C]0.734719015935051[/C][C]0.530561968129898[/C][C]0.265280984064949[/C][/ROW]
[ROW][C]51[/C][C]0.687863340897028[/C][C]0.624273318205944[/C][C]0.312136659102972[/C][/ROW]
[ROW][C]52[/C][C]0.614655926564877[/C][C]0.770688146870245[/C][C]0.385344073435123[/C][/ROW]
[ROW][C]53[/C][C]0.551146585732088[/C][C]0.897706828535824[/C][C]0.448853414267912[/C][/ROW]
[ROW][C]54[/C][C]0.824095727355511[/C][C]0.351808545288977[/C][C]0.175904272644489[/C][/ROW]
[ROW][C]55[/C][C]0.967679829668918[/C][C]0.0646403406621636[/C][C]0.0323201703310818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58271&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58271&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
50.009649902375378070.01929980475075610.990350097624622
60.03074334218857420.06148668437714840.969256657811426
70.3157938411561030.6315876823122070.684206158843897
80.325157402556030.650314805112060.67484259744397
90.2221105804808680.4442211609617360.777889419519132
100.1407501016827310.2815002033654630.859249898317269
110.09950659237932720.1990131847586540.900493407620673
120.08015847311083660.1603169462216730.919841526889163
130.06807540225376120.1361508045075220.931924597746239
140.05034534611568660.1006906922313730.949654653884313
150.0400801048103490.0801602096206980.959919895189651
160.02618427749665160.05236855499330310.973815722503348
170.01988846088623510.03977692177247010.980111539113765
180.01324930906999370.02649861813998740.986750690930006
190.01170557166850910.02341114333701810.98829442833149
200.01246504993431620.02493009986863230.987534950065684
210.008530844035787160.01706168807157430.991469155964213
220.005205514542207880.01041102908441580.994794485457792
230.004673223555442110.009346447110884210.995326776444558
240.006977506467584770.01395501293516950.993022493532415
250.01826772696598010.03653545393196020.98173227303402
260.06094170434997720.1218834086999540.939058295650023
270.2164710477741970.4329420955483950.783528952225803
280.6356371280676630.7287257438646740.364362871932337
290.8614839033285670.2770321933428660.138516096671433
300.8658931353112930.2682137293774140.134106864688707
310.9180471011088630.1639057977822740.0819528988911368
320.9893858977619570.02122820447608510.0106141022380426
330.9980466076120980.003906784775804030.00195339238790202
340.99800608018540.003987839629199930.00199391981459996
350.9967596067179030.006480786564193770.00324039328209688
360.9945696009397010.01086079812059740.00543039906029869
370.991313372417270.01737325516546050.00868662758273023
380.9859963960638560.02800720787228780.0140036039361439
390.9810131410984120.0379737178031760.018986858901588
400.972583705568290.05483258886341850.0274162944317093
410.9629613901353320.0740772197293360.037038609864668
420.965287787253410.06942442549318140.0347122127465907
430.948518808975090.1029623820498220.051481191024911
440.9220520904180580.1558958191638840.077947909581942
450.8845307218514710.2309385562970590.115469278148529
460.8623957954380170.2752084091239660.137604204561983
470.8671959767437520.2656080465124960.132804023256248
480.8185892216790420.3628215566419160.181410778320958
490.7524826497046170.4950347005907660.247517350295383
500.7347190159350510.5305619681298980.265280984064949
510.6878633408970280.6242733182059440.312136659102972
520.6146559265648770.7706881468702450.385344073435123
530.5511465857320880.8977068285358240.448853414267912
540.8240957273555110.3518085452889770.175904272644489
550.9676798296689180.06464034066216360.0323201703310818







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0784313725490196NOK
5% type I error level180.352941176470588NOK
10% type I error level250.490196078431373NOK

\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 & 4 & 0.0784313725490196 & NOK \tabularnewline
5% type I error level & 18 & 0.352941176470588 & NOK \tabularnewline
10% type I error level & 25 & 0.490196078431373 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58271&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]4[/C][C]0.0784313725490196[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.352941176470588[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.490196078431373[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58271&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58271&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 level40.0784313725490196NOK
5% type I error level180.352941176470588NOK
10% type I error level250.490196078431373NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}