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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 21 Dec 2010 11:38:58 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292931463mqjj5xwmk1ld3pn.htm/, Retrieved Sun, 19 May 2024 18:06:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113301, Retrieved Sun, 19 May 2024 18:06:20 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

108

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [b-r0245787] [2010-12-21 11:38:58] [4c92126b39409bf78ea2674c8170c829] [Current]
-   P     [Multiple Regression] [b-r0245787] [2010-12-21 11:49:46] [ebb35fb07def4d07c0eb7ec8d2fd3b0e] 

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Dataseries X:

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Histogram

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0.86	2.0
0.88	2.3
0.93	2.8
0.98	2.4
0.97	2.3
1.03	2.7
1.06	2.7
1.06	2.9
1.08	3.0
1.09	2.2
1.04	2.3
1.00	2.8
1.01	2.8
1.02	2.8
1.04	2.2
1.06	2.6
1.06	2.8
1.06	2.5
1.06	2.4
1.06	2.3
1.02	1.9
0.98	1.7
0.99	2.0
0.99	2.1
0.94	1.7
0.96	1.8
0.98	1.8
1.01	1.8
1.01	1.3
1.02	1.3
1.04	1.3
1.03	1.2
1.05	1.4
1.08	2.2
1.17	2.9
1.11	3.1
1.11	3.5
1.11	3.6
1.11	4.4
1.21	4.1
1.31	5.1
1.37	5.8
1.37	5.9
1.26	5.4
1.23	5.5
1.17	4.8
1.06	3.2
0.95	2.7
0.92	2.1
0.92	1.9
0.90	0.6
0.93	0.7
0.93	-0.2
0.97	-1.0
0.96	-1.7
0.99	-0.7
0.98	-1.0
0.96	-0.9
1.00	0.0
0.99	0.3
1.03	0.8

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113301&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113301&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113301&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation
Dieselprijs[t] = + 0.932633739673073 + 0.0486637713013503Inflatie[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Dieselprijs[t] =  +  0.932633739673073 +  0.0486637713013503Inflatie[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113301&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Dieselprijs[t] =  +  0.932633739673073 +  0.0486637713013503Inflatie[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113301&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113301&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
Dieselprijs[t] = + 0.932633739673073 + 0.0486637713013503Inflatie[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.932633739673073	0.015581	59.858	0	0
Inflatie	0.0486637713013503	0.005667	8.5868	0	0

\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) & 0.932633739673073 & 0.015581 & 59.858 & 0 & 0 \tabularnewline
Inflatie & 0.0486637713013503 & 0.005667 & 8.5868 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113301&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]0.932633739673073[/C][C]0.015581[/C][C]59.858[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Inflatie[/C][C]0.0486637713013503[/C][C]0.005667[/C][C]8.5868[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113301&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113301&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.932633739673073	0.015581	59.858	0	0
Inflatie	0.0486637713013503	0.005667	8.5868	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.745317394384877
R-squared	0.555498018372663
Adjusted R-squared	0.547964086480674
F-TEST (value)	73.7328165872263
F-TEST (DF numerator)	1
F-TEST (DF denominator)	59
p-value	5.61151125566539e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0719999123506779
Sum Squared Residuals	0.305855255331813

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.745317394384877 \tabularnewline
R-squared & 0.555498018372663 \tabularnewline
Adjusted R-squared & 0.547964086480674 \tabularnewline
F-TEST (value) & 73.7328165872263 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 5.61151125566539e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0719999123506779 \tabularnewline
Sum Squared Residuals & 0.305855255331813 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113301&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.745317394384877[/C][/ROW]
[ROW][C]R-squared[/C][C]0.555498018372663[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.547964086480674[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]73.7328165872263[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]5.61151125566539e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0719999123506779[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.305855255331813[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113301&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113301&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.745317394384877
R-squared	0.555498018372663
Adjusted R-squared	0.547964086480674
F-TEST (value)	73.7328165872263
F-TEST (DF numerator)	1
F-TEST (DF denominator)	59
p-value	5.61151125566539e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0719999123506779
Sum Squared Residuals	0.305855255331813

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.86	1.02996128227577	-0.169961282275773
2	0.88	1.04456041366618	-0.164560413666178
3	0.93	1.06889229931685	-0.138892299316854
4	0.98	1.04942679079631	-0.0694267907963135
5	0.97	1.04456041366618	-0.0745604136661785
6	1.03	1.06402592218672	-0.0340259221867186
7	1.06	1.06402592218672	-0.00402592218671855
8	1.06	1.07375867644699	-0.0137586764469886
9	1.08	1.07862505357712	0.00137494642287640
10	1.09	1.03969403653604	0.0503059634639566
11	1.04	1.04456041366618	-0.00456041366617844
12	1	1.06889229931685	-0.0688922993168536
13	1.01	1.06889229931685	-0.0588922993168536
14	1.02	1.06889229931685	-0.0488922993168536
15	1.04	1.03969403653604	0.000305963463956566
16	1.06	1.05915954505658	0.000840454943416483
17	1.06	1.06889229931685	-0.00889229931685356
18	1.06	1.05429316792645	0.00570683207355152
19	1.06	1.04942679079631	0.0105732092036865
20	1.06	1.04456041366618	0.0154395863338216
21	1.02	1.02509490514564	-0.00509490514563836
22	0.98	1.01536215088537	-0.0353621508853683
23	0.99	1.02996128227577	-0.0399612822757734
24	0.99	1.03482765940591	-0.0448276594059084
25	0.94	1.01536215088537	-0.0753621508853684
26	0.96	1.02022852801550	-0.0602285280155034
27	0.98	1.02022852801550	-0.0402285280155034
28	1.01	1.02022852801550	-0.0102285280155034
29	1.01	0.995896642364828	0.0141033576351718
30	1.02	0.995896642364828	0.0241033576351718
31	1.04	0.995896642364828	0.0441033576351718
32	1.03	0.991030265234693	0.0389697347653068
33	1.05	1.00076301949496	0.0492369805050368
34	1.08	1.03969403653604	0.0403059634639566
35	1.17	1.07375867644699	0.0962413235530113
36	1.11	1.08349143070726	0.0265085692927414
37	1.11	1.10295693922780	0.00704306077220132
38	1.11	1.10782331635793	0.00217668364206628
39	1.11	1.14675433339901	-0.0367543333990140
40	1.21	1.13215520200861	0.077844797991391
41	1.31	1.18081897330996	0.129181026690041
42	1.37	1.21488361322090	0.155116386779096
43	1.37	1.21974999035104	0.150250009648961
44	1.26	1.19541810470036	0.0645818952996357
45	1.23	1.2002844818305	0.0297155181695007
46	1.17	1.16621984191955	0.00378015808044583
47	1.06	1.08835780783739	-0.0283578078373937
48	0.95	1.06402592218672	-0.114025922186719
49	0.92	1.03482765940591	-0.114827659405908
50	0.92	1.02509490514564	-0.105094905145638
51	0.9	0.961832002453883	-0.061832002453883
52	0.93	0.966698379584018	-0.036698379584018
53	0.93	0.922900985412803	0.00709901458719719
54	0.97	0.883969968371723	0.0860300316282773
55	0.96	0.849905328460777	0.110094671539223
56	0.99	0.898569099762128	0.0914309002378723
57	0.98	0.883969968371723	0.0960300316282773
58	0.96	0.888836345501858	0.0711636544981423
59	1	0.932633739673073	0.0673662603269271
60	0.99	0.947232871063478	0.042767128936522
61	1.03	0.971564756714153	0.0584352432858469

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.86 & 1.02996128227577 & -0.169961282275773 \tabularnewline
2 & 0.88 & 1.04456041366618 & -0.164560413666178 \tabularnewline
3 & 0.93 & 1.06889229931685 & -0.138892299316854 \tabularnewline
4 & 0.98 & 1.04942679079631 & -0.0694267907963135 \tabularnewline
5 & 0.97 & 1.04456041366618 & -0.0745604136661785 \tabularnewline
6 & 1.03 & 1.06402592218672 & -0.0340259221867186 \tabularnewline
7 & 1.06 & 1.06402592218672 & -0.00402592218671855 \tabularnewline
8 & 1.06 & 1.07375867644699 & -0.0137586764469886 \tabularnewline
9 & 1.08 & 1.07862505357712 & 0.00137494642287640 \tabularnewline
10 & 1.09 & 1.03969403653604 & 0.0503059634639566 \tabularnewline
11 & 1.04 & 1.04456041366618 & -0.00456041366617844 \tabularnewline
12 & 1 & 1.06889229931685 & -0.0688922993168536 \tabularnewline
13 & 1.01 & 1.06889229931685 & -0.0588922993168536 \tabularnewline
14 & 1.02 & 1.06889229931685 & -0.0488922993168536 \tabularnewline
15 & 1.04 & 1.03969403653604 & 0.000305963463956566 \tabularnewline
16 & 1.06 & 1.05915954505658 & 0.000840454943416483 \tabularnewline
17 & 1.06 & 1.06889229931685 & -0.00889229931685356 \tabularnewline
18 & 1.06 & 1.05429316792645 & 0.00570683207355152 \tabularnewline
19 & 1.06 & 1.04942679079631 & 0.0105732092036865 \tabularnewline
20 & 1.06 & 1.04456041366618 & 0.0154395863338216 \tabularnewline
21 & 1.02 & 1.02509490514564 & -0.00509490514563836 \tabularnewline
22 & 0.98 & 1.01536215088537 & -0.0353621508853683 \tabularnewline
23 & 0.99 & 1.02996128227577 & -0.0399612822757734 \tabularnewline
24 & 0.99 & 1.03482765940591 & -0.0448276594059084 \tabularnewline
25 & 0.94 & 1.01536215088537 & -0.0753621508853684 \tabularnewline
26 & 0.96 & 1.02022852801550 & -0.0602285280155034 \tabularnewline
27 & 0.98 & 1.02022852801550 & -0.0402285280155034 \tabularnewline
28 & 1.01 & 1.02022852801550 & -0.0102285280155034 \tabularnewline
29 & 1.01 & 0.995896642364828 & 0.0141033576351718 \tabularnewline
30 & 1.02 & 0.995896642364828 & 0.0241033576351718 \tabularnewline
31 & 1.04 & 0.995896642364828 & 0.0441033576351718 \tabularnewline
32 & 1.03 & 0.991030265234693 & 0.0389697347653068 \tabularnewline
33 & 1.05 & 1.00076301949496 & 0.0492369805050368 \tabularnewline
34 & 1.08 & 1.03969403653604 & 0.0403059634639566 \tabularnewline
35 & 1.17 & 1.07375867644699 & 0.0962413235530113 \tabularnewline
36 & 1.11 & 1.08349143070726 & 0.0265085692927414 \tabularnewline
37 & 1.11 & 1.10295693922780 & 0.00704306077220132 \tabularnewline
38 & 1.11 & 1.10782331635793 & 0.00217668364206628 \tabularnewline
39 & 1.11 & 1.14675433339901 & -0.0367543333990140 \tabularnewline
40 & 1.21 & 1.13215520200861 & 0.077844797991391 \tabularnewline
41 & 1.31 & 1.18081897330996 & 0.129181026690041 \tabularnewline
42 & 1.37 & 1.21488361322090 & 0.155116386779096 \tabularnewline
43 & 1.37 & 1.21974999035104 & 0.150250009648961 \tabularnewline
44 & 1.26 & 1.19541810470036 & 0.0645818952996357 \tabularnewline
45 & 1.23 & 1.2002844818305 & 0.0297155181695007 \tabularnewline
46 & 1.17 & 1.16621984191955 & 0.00378015808044583 \tabularnewline
47 & 1.06 & 1.08835780783739 & -0.0283578078373937 \tabularnewline
48 & 0.95 & 1.06402592218672 & -0.114025922186719 \tabularnewline
49 & 0.92 & 1.03482765940591 & -0.114827659405908 \tabularnewline
50 & 0.92 & 1.02509490514564 & -0.105094905145638 \tabularnewline
51 & 0.9 & 0.961832002453883 & -0.061832002453883 \tabularnewline
52 & 0.93 & 0.966698379584018 & -0.036698379584018 \tabularnewline
53 & 0.93 & 0.922900985412803 & 0.00709901458719719 \tabularnewline
54 & 0.97 & 0.883969968371723 & 0.0860300316282773 \tabularnewline
55 & 0.96 & 0.849905328460777 & 0.110094671539223 \tabularnewline
56 & 0.99 & 0.898569099762128 & 0.0914309002378723 \tabularnewline
57 & 0.98 & 0.883969968371723 & 0.0960300316282773 \tabularnewline
58 & 0.96 & 0.888836345501858 & 0.0711636544981423 \tabularnewline
59 & 1 & 0.932633739673073 & 0.0673662603269271 \tabularnewline
60 & 0.99 & 0.947232871063478 & 0.042767128936522 \tabularnewline
61 & 1.03 & 0.971564756714153 & 0.0584352432858469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113301&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]0.86[/C][C]1.02996128227577[/C][C]-0.169961282275773[/C][/ROW]
[ROW][C]2[/C][C]0.88[/C][C]1.04456041366618[/C][C]-0.164560413666178[/C][/ROW]
[ROW][C]3[/C][C]0.93[/C][C]1.06889229931685[/C][C]-0.138892299316854[/C][/ROW]
[ROW][C]4[/C][C]0.98[/C][C]1.04942679079631[/C][C]-0.0694267907963135[/C][/ROW]
[ROW][C]5[/C][C]0.97[/C][C]1.04456041366618[/C][C]-0.0745604136661785[/C][/ROW]
[ROW][C]6[/C][C]1.03[/C][C]1.06402592218672[/C][C]-0.0340259221867186[/C][/ROW]
[ROW][C]7[/C][C]1.06[/C][C]1.06402592218672[/C][C]-0.00402592218671855[/C][/ROW]
[ROW][C]8[/C][C]1.06[/C][C]1.07375867644699[/C][C]-0.0137586764469886[/C][/ROW]
[ROW][C]9[/C][C]1.08[/C][C]1.07862505357712[/C][C]0.00137494642287640[/C][/ROW]
[ROW][C]10[/C][C]1.09[/C][C]1.03969403653604[/C][C]0.0503059634639566[/C][/ROW]
[ROW][C]11[/C][C]1.04[/C][C]1.04456041366618[/C][C]-0.00456041366617844[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.06889229931685[/C][C]-0.0688922993168536[/C][/ROW]
[ROW][C]13[/C][C]1.01[/C][C]1.06889229931685[/C][C]-0.0588922993168536[/C][/ROW]
[ROW][C]14[/C][C]1.02[/C][C]1.06889229931685[/C][C]-0.0488922993168536[/C][/ROW]
[ROW][C]15[/C][C]1.04[/C][C]1.03969403653604[/C][C]0.000305963463956566[/C][/ROW]
[ROW][C]16[/C][C]1.06[/C][C]1.05915954505658[/C][C]0.000840454943416483[/C][/ROW]
[ROW][C]17[/C][C]1.06[/C][C]1.06889229931685[/C][C]-0.00889229931685356[/C][/ROW]
[ROW][C]18[/C][C]1.06[/C][C]1.05429316792645[/C][C]0.00570683207355152[/C][/ROW]
[ROW][C]19[/C][C]1.06[/C][C]1.04942679079631[/C][C]0.0105732092036865[/C][/ROW]
[ROW][C]20[/C][C]1.06[/C][C]1.04456041366618[/C][C]0.0154395863338216[/C][/ROW]
[ROW][C]21[/C][C]1.02[/C][C]1.02509490514564[/C][C]-0.00509490514563836[/C][/ROW]
[ROW][C]22[/C][C]0.98[/C][C]1.01536215088537[/C][C]-0.0353621508853683[/C][/ROW]
[ROW][C]23[/C][C]0.99[/C][C]1.02996128227577[/C][C]-0.0399612822757734[/C][/ROW]
[ROW][C]24[/C][C]0.99[/C][C]1.03482765940591[/C][C]-0.0448276594059084[/C][/ROW]
[ROW][C]25[/C][C]0.94[/C][C]1.01536215088537[/C][C]-0.0753621508853684[/C][/ROW]
[ROW][C]26[/C][C]0.96[/C][C]1.02022852801550[/C][C]-0.0602285280155034[/C][/ROW]
[ROW][C]27[/C][C]0.98[/C][C]1.02022852801550[/C][C]-0.0402285280155034[/C][/ROW]
[ROW][C]28[/C][C]1.01[/C][C]1.02022852801550[/C][C]-0.0102285280155034[/C][/ROW]
[ROW][C]29[/C][C]1.01[/C][C]0.995896642364828[/C][C]0.0141033576351718[/C][/ROW]
[ROW][C]30[/C][C]1.02[/C][C]0.995896642364828[/C][C]0.0241033576351718[/C][/ROW]
[ROW][C]31[/C][C]1.04[/C][C]0.995896642364828[/C][C]0.0441033576351718[/C][/ROW]
[ROW][C]32[/C][C]1.03[/C][C]0.991030265234693[/C][C]0.0389697347653068[/C][/ROW]
[ROW][C]33[/C][C]1.05[/C][C]1.00076301949496[/C][C]0.0492369805050368[/C][/ROW]
[ROW][C]34[/C][C]1.08[/C][C]1.03969403653604[/C][C]0.0403059634639566[/C][/ROW]
[ROW][C]35[/C][C]1.17[/C][C]1.07375867644699[/C][C]0.0962413235530113[/C][/ROW]
[ROW][C]36[/C][C]1.11[/C][C]1.08349143070726[/C][C]0.0265085692927414[/C][/ROW]
[ROW][C]37[/C][C]1.11[/C][C]1.10295693922780[/C][C]0.00704306077220132[/C][/ROW]
[ROW][C]38[/C][C]1.11[/C][C]1.10782331635793[/C][C]0.00217668364206628[/C][/ROW]
[ROW][C]39[/C][C]1.11[/C][C]1.14675433339901[/C][C]-0.0367543333990140[/C][/ROW]
[ROW][C]40[/C][C]1.21[/C][C]1.13215520200861[/C][C]0.077844797991391[/C][/ROW]
[ROW][C]41[/C][C]1.31[/C][C]1.18081897330996[/C][C]0.129181026690041[/C][/ROW]
[ROW][C]42[/C][C]1.37[/C][C]1.21488361322090[/C][C]0.155116386779096[/C][/ROW]
[ROW][C]43[/C][C]1.37[/C][C]1.21974999035104[/C][C]0.150250009648961[/C][/ROW]
[ROW][C]44[/C][C]1.26[/C][C]1.19541810470036[/C][C]0.0645818952996357[/C][/ROW]
[ROW][C]45[/C][C]1.23[/C][C]1.2002844818305[/C][C]0.0297155181695007[/C][/ROW]
[ROW][C]46[/C][C]1.17[/C][C]1.16621984191955[/C][C]0.00378015808044583[/C][/ROW]
[ROW][C]47[/C][C]1.06[/C][C]1.08835780783739[/C][C]-0.0283578078373937[/C][/ROW]
[ROW][C]48[/C][C]0.95[/C][C]1.06402592218672[/C][C]-0.114025922186719[/C][/ROW]
[ROW][C]49[/C][C]0.92[/C][C]1.03482765940591[/C][C]-0.114827659405908[/C][/ROW]
[ROW][C]50[/C][C]0.92[/C][C]1.02509490514564[/C][C]-0.105094905145638[/C][/ROW]
[ROW][C]51[/C][C]0.9[/C][C]0.961832002453883[/C][C]-0.061832002453883[/C][/ROW]
[ROW][C]52[/C][C]0.93[/C][C]0.966698379584018[/C][C]-0.036698379584018[/C][/ROW]
[ROW][C]53[/C][C]0.93[/C][C]0.922900985412803[/C][C]0.00709901458719719[/C][/ROW]
[ROW][C]54[/C][C]0.97[/C][C]0.883969968371723[/C][C]0.0860300316282773[/C][/ROW]
[ROW][C]55[/C][C]0.96[/C][C]0.849905328460777[/C][C]0.110094671539223[/C][/ROW]
[ROW][C]56[/C][C]0.99[/C][C]0.898569099762128[/C][C]0.0914309002378723[/C][/ROW]
[ROW][C]57[/C][C]0.98[/C][C]0.883969968371723[/C][C]0.0960300316282773[/C][/ROW]
[ROW][C]58[/C][C]0.96[/C][C]0.888836345501858[/C][C]0.0711636544981423[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.932633739673073[/C][C]0.0673662603269271[/C][/ROW]
[ROW][C]60[/C][C]0.99[/C][C]0.947232871063478[/C][C]0.042767128936522[/C][/ROW]
[ROW][C]61[/C][C]1.03[/C][C]0.971564756714153[/C][C]0.0584352432858469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113301&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113301&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.86	1.02996128227577	-0.169961282275773
2	0.88	1.04456041366618	-0.164560413666178
3	0.93	1.06889229931685	-0.138892299316854
4	0.98	1.04942679079631	-0.0694267907963135
5	0.97	1.04456041366618	-0.0745604136661785
6	1.03	1.06402592218672	-0.0340259221867186
7	1.06	1.06402592218672	-0.00402592218671855
8	1.06	1.07375867644699	-0.0137586764469886
9	1.08	1.07862505357712	0.00137494642287640
10	1.09	1.03969403653604	0.0503059634639566
11	1.04	1.04456041366618	-0.00456041366617844
12	1	1.06889229931685	-0.0688922993168536
13	1.01	1.06889229931685	-0.0588922993168536
14	1.02	1.06889229931685	-0.0488922993168536
15	1.04	1.03969403653604	0.000305963463956566
16	1.06	1.05915954505658	0.000840454943416483
17	1.06	1.06889229931685	-0.00889229931685356
18	1.06	1.05429316792645	0.00570683207355152
19	1.06	1.04942679079631	0.0105732092036865
20	1.06	1.04456041366618	0.0154395863338216
21	1.02	1.02509490514564	-0.00509490514563836
22	0.98	1.01536215088537	-0.0353621508853683
23	0.99	1.02996128227577	-0.0399612822757734
24	0.99	1.03482765940591	-0.0448276594059084
25	0.94	1.01536215088537	-0.0753621508853684
26	0.96	1.02022852801550	-0.0602285280155034
27	0.98	1.02022852801550	-0.0402285280155034
28	1.01	1.02022852801550	-0.0102285280155034
29	1.01	0.995896642364828	0.0141033576351718
30	1.02	0.995896642364828	0.0241033576351718
31	1.04	0.995896642364828	0.0441033576351718
32	1.03	0.991030265234693	0.0389697347653068
33	1.05	1.00076301949496	0.0492369805050368
34	1.08	1.03969403653604	0.0403059634639566
35	1.17	1.07375867644699	0.0962413235530113
36	1.11	1.08349143070726	0.0265085692927414
37	1.11	1.10295693922780	0.00704306077220132
38	1.11	1.10782331635793	0.00217668364206628
39	1.11	1.14675433339901	-0.0367543333990140
40	1.21	1.13215520200861	0.077844797991391
41	1.31	1.18081897330996	0.129181026690041
42	1.37	1.21488361322090	0.155116386779096
43	1.37	1.21974999035104	0.150250009648961
44	1.26	1.19541810470036	0.0645818952996357
45	1.23	1.2002844818305	0.0297155181695007
46	1.17	1.16621984191955	0.00378015808044583
47	1.06	1.08835780783739	-0.0283578078373937
48	0.95	1.06402592218672	-0.114025922186719
49	0.92	1.03482765940591	-0.114827659405908
50	0.92	1.02509490514564	-0.105094905145638
51	0.9	0.961832002453883	-0.061832002453883
52	0.93	0.966698379584018	-0.036698379584018
53	0.93	0.922900985412803	0.00709901458719719
54	0.97	0.883969968371723	0.0860300316282773
55	0.96	0.849905328460777	0.110094671539223
56	0.99	0.898569099762128	0.0914309002378723
57	0.98	0.883969968371723	0.0960300316282773
58	0.96	0.888836345501858	0.0711636544981423
59	1	0.932633739673073	0.0673662603269271
60	0.99	0.947232871063478	0.042767128936522
61	1.03	0.971564756714153	0.0584352432858469

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.477746124739975	0.95549224947995	0.522253875260025
6	0.474017292176766	0.948034584353531	0.525982707823234
7	0.491040821096978	0.982081642193956	0.508959178903022
8	0.370363013049602	0.740726026099204	0.629636986950398
9	0.261767504112556	0.523535008225112	0.738232495887444
10	0.722107938221049	0.555784123557902	0.277892061778951
11	0.709504857182107	0.580990285635787	0.290495142817893
12	0.657952401687157	0.684095196625686	0.342047598312843
13	0.59202970306113	0.815940593877739	0.407970296938870
14	0.51743383102294	0.96513233795412	0.48256616897706
15	0.514994636416432	0.970010727167136	0.485005363583568
16	0.465820391847396	0.931640783694792	0.534179608152604
17	0.395280711680036	0.790561423360073	0.604719288319963
18	0.356565576734254	0.713131153468507	0.643434423265746
19	0.327164642002635	0.654329284005271	0.672835357997365
20	0.303324586917944	0.606649173835887	0.696675413082056
21	0.259128499400883	0.518256998801766	0.740871500599117
22	0.209311905891885	0.418623811783770	0.790688094108115
23	0.168238088052152	0.336476176104304	0.831761911947848
24	0.136250494150307	0.272500988300614	0.863749505849693
25	0.130637991404905	0.26127598280981	0.869362008595095
26	0.115647208993161	0.231294417986322	0.884352791006839
27	0.0950970283010272	0.190194056602054	0.904902971698973
28	0.0765199998926208	0.153039999785242	0.92348000010738
29	0.0658478530638383	0.131695706127677	0.934152146936162
30	0.0546125684746073	0.109225136949215	0.945387431525393
31	0.0481325944966161	0.0962651889932322	0.951867405503384
32	0.0371387035759472	0.0742774071518943	0.962861296424053
33	0.0302140020019767	0.0604280040039533	0.969785997998023
34	0.0263329805709205	0.0526659611418409	0.97366701942908
35	0.0578524063206091	0.115704812641218	0.94214759367939
36	0.0471650031141178	0.0943300062282356	0.952834996885882
37	0.0348594129577996	0.0697188259155992	0.9651405870422
38	0.0246780435249802	0.0493560870499604	0.97532195647502
39	0.0200535062448543	0.0401070124897085	0.979946493755146
40	0.0221783796352896	0.0443567592705792	0.97782162036471
41	0.0390342018360532	0.0780684036721064	0.960965798163947
42	0.0847145639015494	0.169429127803099	0.91528543609845
43	0.232374419818654	0.464748839637308	0.767625580181346
44	0.320761217522758	0.641522435045515	0.679238782477242
45	0.516333318336688	0.967333363326624	0.483666681663312
46	0.872236666495514	0.255526667008972	0.127763333504486
47	0.973916584394017	0.0521668312119668	0.0260834156059834
48	0.96606054399965	0.0678789120007005	0.0339394560003502
49	0.951177516835946	0.0976449663281076	0.0488224831640538
50	0.936346005775489	0.127307988449023	0.0636539942245114
51	0.973473592093525	0.0530528158129507	0.0265264079064753
52	0.99150086022197	0.0169982795560610	0.00849913977803051
53	0.999812824002673	0.000374351994654194	0.000187175997327097
54	0.999262697098218	0.00147460580356452	0.000737302901782262
55	0.997012603397599	0.00597479320480232	0.00298739660240116
56	0.989042593382306	0.0219148132353871	0.0109574066176936

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.477746124739975 & 0.95549224947995 & 0.522253875260025 \tabularnewline
6 & 0.474017292176766 & 0.948034584353531 & 0.525982707823234 \tabularnewline
7 & 0.491040821096978 & 0.982081642193956 & 0.508959178903022 \tabularnewline
8 & 0.370363013049602 & 0.740726026099204 & 0.629636986950398 \tabularnewline
9 & 0.261767504112556 & 0.523535008225112 & 0.738232495887444 \tabularnewline
10 & 0.722107938221049 & 0.555784123557902 & 0.277892061778951 \tabularnewline
11 & 0.709504857182107 & 0.580990285635787 & 0.290495142817893 \tabularnewline
12 & 0.657952401687157 & 0.684095196625686 & 0.342047598312843 \tabularnewline
13 & 0.59202970306113 & 0.815940593877739 & 0.407970296938870 \tabularnewline
14 & 0.51743383102294 & 0.96513233795412 & 0.48256616897706 \tabularnewline
15 & 0.514994636416432 & 0.970010727167136 & 0.485005363583568 \tabularnewline
16 & 0.465820391847396 & 0.931640783694792 & 0.534179608152604 \tabularnewline
17 & 0.395280711680036 & 0.790561423360073 & 0.604719288319963 \tabularnewline
18 & 0.356565576734254 & 0.713131153468507 & 0.643434423265746 \tabularnewline
19 & 0.327164642002635 & 0.654329284005271 & 0.672835357997365 \tabularnewline
20 & 0.303324586917944 & 0.606649173835887 & 0.696675413082056 \tabularnewline
21 & 0.259128499400883 & 0.518256998801766 & 0.740871500599117 \tabularnewline
22 & 0.209311905891885 & 0.418623811783770 & 0.790688094108115 \tabularnewline
23 & 0.168238088052152 & 0.336476176104304 & 0.831761911947848 \tabularnewline
24 & 0.136250494150307 & 0.272500988300614 & 0.863749505849693 \tabularnewline
25 & 0.130637991404905 & 0.26127598280981 & 0.869362008595095 \tabularnewline
26 & 0.115647208993161 & 0.231294417986322 & 0.884352791006839 \tabularnewline
27 & 0.0950970283010272 & 0.190194056602054 & 0.904902971698973 \tabularnewline
28 & 0.0765199998926208 & 0.153039999785242 & 0.92348000010738 \tabularnewline
29 & 0.0658478530638383 & 0.131695706127677 & 0.934152146936162 \tabularnewline
30 & 0.0546125684746073 & 0.109225136949215 & 0.945387431525393 \tabularnewline
31 & 0.0481325944966161 & 0.0962651889932322 & 0.951867405503384 \tabularnewline
32 & 0.0371387035759472 & 0.0742774071518943 & 0.962861296424053 \tabularnewline
33 & 0.0302140020019767 & 0.0604280040039533 & 0.969785997998023 \tabularnewline
34 & 0.0263329805709205 & 0.0526659611418409 & 0.97366701942908 \tabularnewline
35 & 0.0578524063206091 & 0.115704812641218 & 0.94214759367939 \tabularnewline
36 & 0.0471650031141178 & 0.0943300062282356 & 0.952834996885882 \tabularnewline
37 & 0.0348594129577996 & 0.0697188259155992 & 0.9651405870422 \tabularnewline
38 & 0.0246780435249802 & 0.0493560870499604 & 0.97532195647502 \tabularnewline
39 & 0.0200535062448543 & 0.0401070124897085 & 0.979946493755146 \tabularnewline
40 & 0.0221783796352896 & 0.0443567592705792 & 0.97782162036471 \tabularnewline
41 & 0.0390342018360532 & 0.0780684036721064 & 0.960965798163947 \tabularnewline
42 & 0.0847145639015494 & 0.169429127803099 & 0.91528543609845 \tabularnewline
43 & 0.232374419818654 & 0.464748839637308 & 0.767625580181346 \tabularnewline
44 & 0.320761217522758 & 0.641522435045515 & 0.679238782477242 \tabularnewline
45 & 0.516333318336688 & 0.967333363326624 & 0.483666681663312 \tabularnewline
46 & 0.872236666495514 & 0.255526667008972 & 0.127763333504486 \tabularnewline
47 & 0.973916584394017 & 0.0521668312119668 & 0.0260834156059834 \tabularnewline
48 & 0.96606054399965 & 0.0678789120007005 & 0.0339394560003502 \tabularnewline
49 & 0.951177516835946 & 0.0976449663281076 & 0.0488224831640538 \tabularnewline
50 & 0.936346005775489 & 0.127307988449023 & 0.0636539942245114 \tabularnewline
51 & 0.973473592093525 & 0.0530528158129507 & 0.0265264079064753 \tabularnewline
52 & 0.99150086022197 & 0.0169982795560610 & 0.00849913977803051 \tabularnewline
53 & 0.999812824002673 & 0.000374351994654194 & 0.000187175997327097 \tabularnewline
54 & 0.999262697098218 & 0.00147460580356452 & 0.000737302901782262 \tabularnewline
55 & 0.997012603397599 & 0.00597479320480232 & 0.00298739660240116 \tabularnewline
56 & 0.989042593382306 & 0.0219148132353871 & 0.0109574066176936 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113301&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]0.477746124739975[/C][C]0.95549224947995[/C][C]0.522253875260025[/C][/ROW]
[ROW][C]6[/C][C]0.474017292176766[/C][C]0.948034584353531[/C][C]0.525982707823234[/C][/ROW]
[ROW][C]7[/C][C]0.491040821096978[/C][C]0.982081642193956[/C][C]0.508959178903022[/C][/ROW]
[ROW][C]8[/C][C]0.370363013049602[/C][C]0.740726026099204[/C][C]0.629636986950398[/C][/ROW]
[ROW][C]9[/C][C]0.261767504112556[/C][C]0.523535008225112[/C][C]0.738232495887444[/C][/ROW]
[ROW][C]10[/C][C]0.722107938221049[/C][C]0.555784123557902[/C][C]0.277892061778951[/C][/ROW]
[ROW][C]11[/C][C]0.709504857182107[/C][C]0.580990285635787[/C][C]0.290495142817893[/C][/ROW]
[ROW][C]12[/C][C]0.657952401687157[/C][C]0.684095196625686[/C][C]0.342047598312843[/C][/ROW]
[ROW][C]13[/C][C]0.59202970306113[/C][C]0.815940593877739[/C][C]0.407970296938870[/C][/ROW]
[ROW][C]14[/C][C]0.51743383102294[/C][C]0.96513233795412[/C][C]0.48256616897706[/C][/ROW]
[ROW][C]15[/C][C]0.514994636416432[/C][C]0.970010727167136[/C][C]0.485005363583568[/C][/ROW]
[ROW][C]16[/C][C]0.465820391847396[/C][C]0.931640783694792[/C][C]0.534179608152604[/C][/ROW]
[ROW][C]17[/C][C]0.395280711680036[/C][C]0.790561423360073[/C][C]0.604719288319963[/C][/ROW]
[ROW][C]18[/C][C]0.356565576734254[/C][C]0.713131153468507[/C][C]0.643434423265746[/C][/ROW]
[ROW][C]19[/C][C]0.327164642002635[/C][C]0.654329284005271[/C][C]0.672835357997365[/C][/ROW]
[ROW][C]20[/C][C]0.303324586917944[/C][C]0.606649173835887[/C][C]0.696675413082056[/C][/ROW]
[ROW][C]21[/C][C]0.259128499400883[/C][C]0.518256998801766[/C][C]0.740871500599117[/C][/ROW]
[ROW][C]22[/C][C]0.209311905891885[/C][C]0.418623811783770[/C][C]0.790688094108115[/C][/ROW]
[ROW][C]23[/C][C]0.168238088052152[/C][C]0.336476176104304[/C][C]0.831761911947848[/C][/ROW]
[ROW][C]24[/C][C]0.136250494150307[/C][C]0.272500988300614[/C][C]0.863749505849693[/C][/ROW]
[ROW][C]25[/C][C]0.130637991404905[/C][C]0.26127598280981[/C][C]0.869362008595095[/C][/ROW]
[ROW][C]26[/C][C]0.115647208993161[/C][C]0.231294417986322[/C][C]0.884352791006839[/C][/ROW]
[ROW][C]27[/C][C]0.0950970283010272[/C][C]0.190194056602054[/C][C]0.904902971698973[/C][/ROW]
[ROW][C]28[/C][C]0.0765199998926208[/C][C]0.153039999785242[/C][C]0.92348000010738[/C][/ROW]
[ROW][C]29[/C][C]0.0658478530638383[/C][C]0.131695706127677[/C][C]0.934152146936162[/C][/ROW]
[ROW][C]30[/C][C]0.0546125684746073[/C][C]0.109225136949215[/C][C]0.945387431525393[/C][/ROW]
[ROW][C]31[/C][C]0.0481325944966161[/C][C]0.0962651889932322[/C][C]0.951867405503384[/C][/ROW]
[ROW][C]32[/C][C]0.0371387035759472[/C][C]0.0742774071518943[/C][C]0.962861296424053[/C][/ROW]
[ROW][C]33[/C][C]0.0302140020019767[/C][C]0.0604280040039533[/C][C]0.969785997998023[/C][/ROW]
[ROW][C]34[/C][C]0.0263329805709205[/C][C]0.0526659611418409[/C][C]0.97366701942908[/C][/ROW]
[ROW][C]35[/C][C]0.0578524063206091[/C][C]0.115704812641218[/C][C]0.94214759367939[/C][/ROW]
[ROW][C]36[/C][C]0.0471650031141178[/C][C]0.0943300062282356[/C][C]0.952834996885882[/C][/ROW]
[ROW][C]37[/C][C]0.0348594129577996[/C][C]0.0697188259155992[/C][C]0.9651405870422[/C][/ROW]
[ROW][C]38[/C][C]0.0246780435249802[/C][C]0.0493560870499604[/C][C]0.97532195647502[/C][/ROW]
[ROW][C]39[/C][C]0.0200535062448543[/C][C]0.0401070124897085[/C][C]0.979946493755146[/C][/ROW]
[ROW][C]40[/C][C]0.0221783796352896[/C][C]0.0443567592705792[/C][C]0.97782162036471[/C][/ROW]
[ROW][C]41[/C][C]0.0390342018360532[/C][C]0.0780684036721064[/C][C]0.960965798163947[/C][/ROW]
[ROW][C]42[/C][C]0.0847145639015494[/C][C]0.169429127803099[/C][C]0.91528543609845[/C][/ROW]
[ROW][C]43[/C][C]0.232374419818654[/C][C]0.464748839637308[/C][C]0.767625580181346[/C][/ROW]
[ROW][C]44[/C][C]0.320761217522758[/C][C]0.641522435045515[/C][C]0.679238782477242[/C][/ROW]
[ROW][C]45[/C][C]0.516333318336688[/C][C]0.967333363326624[/C][C]0.483666681663312[/C][/ROW]
[ROW][C]46[/C][C]0.872236666495514[/C][C]0.255526667008972[/C][C]0.127763333504486[/C][/ROW]
[ROW][C]47[/C][C]0.973916584394017[/C][C]0.0521668312119668[/C][C]0.0260834156059834[/C][/ROW]
[ROW][C]48[/C][C]0.96606054399965[/C][C]0.0678789120007005[/C][C]0.0339394560003502[/C][/ROW]
[ROW][C]49[/C][C]0.951177516835946[/C][C]0.0976449663281076[/C][C]0.0488224831640538[/C][/ROW]
[ROW][C]50[/C][C]0.936346005775489[/C][C]0.127307988449023[/C][C]0.0636539942245114[/C][/ROW]
[ROW][C]51[/C][C]0.973473592093525[/C][C]0.0530528158129507[/C][C]0.0265264079064753[/C][/ROW]
[ROW][C]52[/C][C]0.99150086022197[/C][C]0.0169982795560610[/C][C]0.00849913977803051[/C][/ROW]
[ROW][C]53[/C][C]0.999812824002673[/C][C]0.000374351994654194[/C][C]0.000187175997327097[/C][/ROW]
[ROW][C]54[/C][C]0.999262697098218[/C][C]0.00147460580356452[/C][C]0.000737302901782262[/C][/ROW]
[ROW][C]55[/C][C]0.997012603397599[/C][C]0.00597479320480232[/C][C]0.00298739660240116[/C][/ROW]
[ROW][C]56[/C][C]0.989042593382306[/C][C]0.0219148132353871[/C][C]0.0109574066176936[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113301&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113301&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.477746124739975	0.95549224947995	0.522253875260025
6	0.474017292176766	0.948034584353531	0.525982707823234
7	0.491040821096978	0.982081642193956	0.508959178903022
8	0.370363013049602	0.740726026099204	0.629636986950398
9	0.261767504112556	0.523535008225112	0.738232495887444
10	0.722107938221049	0.555784123557902	0.277892061778951
11	0.709504857182107	0.580990285635787	0.290495142817893
12	0.657952401687157	0.684095196625686	0.342047598312843
13	0.59202970306113	0.815940593877739	0.407970296938870
14	0.51743383102294	0.96513233795412	0.48256616897706
15	0.514994636416432	0.970010727167136	0.485005363583568
16	0.465820391847396	0.931640783694792	0.534179608152604
17	0.395280711680036	0.790561423360073	0.604719288319963
18	0.356565576734254	0.713131153468507	0.643434423265746
19	0.327164642002635	0.654329284005271	0.672835357997365
20	0.303324586917944	0.606649173835887	0.696675413082056
21	0.259128499400883	0.518256998801766	0.740871500599117
22	0.209311905891885	0.418623811783770	0.790688094108115
23	0.168238088052152	0.336476176104304	0.831761911947848
24	0.136250494150307	0.272500988300614	0.863749505849693
25	0.130637991404905	0.26127598280981	0.869362008595095
26	0.115647208993161	0.231294417986322	0.884352791006839
27	0.0950970283010272	0.190194056602054	0.904902971698973
28	0.0765199998926208	0.153039999785242	0.92348000010738
29	0.0658478530638383	0.131695706127677	0.934152146936162
30	0.0546125684746073	0.109225136949215	0.945387431525393
31	0.0481325944966161	0.0962651889932322	0.951867405503384
32	0.0371387035759472	0.0742774071518943	0.962861296424053
33	0.0302140020019767	0.0604280040039533	0.969785997998023
34	0.0263329805709205	0.0526659611418409	0.97366701942908
35	0.0578524063206091	0.115704812641218	0.94214759367939
36	0.0471650031141178	0.0943300062282356	0.952834996885882
37	0.0348594129577996	0.0697188259155992	0.9651405870422
38	0.0246780435249802	0.0493560870499604	0.97532195647502
39	0.0200535062448543	0.0401070124897085	0.979946493755146
40	0.0221783796352896	0.0443567592705792	0.97782162036471
41	0.0390342018360532	0.0780684036721064	0.960965798163947
42	0.0847145639015494	0.169429127803099	0.91528543609845
43	0.232374419818654	0.464748839637308	0.767625580181346
44	0.320761217522758	0.641522435045515	0.679238782477242
45	0.516333318336688	0.967333363326624	0.483666681663312
46	0.872236666495514	0.255526667008972	0.127763333504486
47	0.973916584394017	0.0521668312119668	0.0260834156059834
48	0.96606054399965	0.0678789120007005	0.0339394560003502
49	0.951177516835946	0.0976449663281076	0.0488224831640538
50	0.936346005775489	0.127307988449023	0.0636539942245114
51	0.973473592093525	0.0530528158129507	0.0265264079064753
52	0.99150086022197	0.0169982795560610	0.00849913977803051
53	0.999812824002673	0.000374351994654194	0.000187175997327097
54	0.999262697098218	0.00147460580356452	0.000737302901782262
55	0.997012603397599	0.00597479320480232	0.00298739660240116
56	0.989042593382306	0.0219148132353871	0.0109574066176936

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.0576923076923077	NOK
5% type I error level	8	0.153846153846154	NOK
10% type I error level	19	0.365384615384615	NOK

\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 & 3 & 0.0576923076923077 & NOK \tabularnewline
5% type I error level & 8 & 0.153846153846154 & NOK \tabularnewline
10% type I error level & 19 & 0.365384615384615 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113301&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]3[/C][C]0.0576923076923077[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.153846153846154[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.365384615384615[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113301&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113301&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 tests	OK/NOK
1% type I error level	3	0.0576923076923077	NOK
5% type I error level	8	0.153846153846154	NOK
10% type I error level	19	0.365384615384615	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code