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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 21 Dec 2010 11:46:21 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292931876ulejpwhrh2d7yga.htm/, Retrieved Fri, 17 May 2024 22:39:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113312, Retrieved Fri, 17 May 2024 22:39:18 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

109

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

-     [Multiple Regression] [b-r0245095] [2010-12-21 11:43:06] [ec8d68d52c1e9c5e97bb689b42436a8c]
-   P     [Multiple Regression] [b-r0245095] [2010-12-21 11:46:21] [4bfaadb29d89ff24ebcdd4f425066435] [Current]

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Dataseries X:

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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	'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113312&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113312&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113312&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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation
Dieselprijs[t] = + 0.850881296135343 + 0.0551712842435391Inflatie[t] + 0.00217191079112509t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Dieselprijs[t] =  +  0.850881296135343 +  0.0551712842435391Inflatie[t] +  0.00217191079112509t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113312&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Dieselprijs[t] =  +  0.850881296135343 +  0.0551712842435391Inflatie[t] +  0.00217191079112509t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113312&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113312&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] = + 0.850881296135343 + 0.0551712842435391Inflatie[t] + 0.00217191079112509t + 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)	0.850881296135343	0.022206	38.3177	0	0
Inflatie	0.0551712842435391	0.005085	10.8504	0	0
t	0.00217191079112509	0.00047	4.6234	2.2e-05	1.1e-05

\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) & 0.850881296135343 & 0.022206 & 38.3177 & 0 & 0 \tabularnewline
Inflatie & 0.0551712842435391 & 0.005085 & 10.8504 & 0 & 0 \tabularnewline
t & 0.00217191079112509 & 0.00047 & 4.6234 & 2.2e-05 & 1.1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113312&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]0.850881296135343[/C][C]0.022206[/C][C]38.3177[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Inflatie[/C][C]0.0551712842435391[/C][C]0.005085[/C][C]10.8504[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.00217191079112509[/C][C]0.00047[/C][C]4.6234[/C][C]2.2e-05[/C][C]1.1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113312&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113312&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)	0.850881296135343	0.022206	38.3177	0	0
Inflatie	0.0551712842435391	0.005085	10.8504	0	0
t	0.00217191079112509	0.00047	4.6234	2.2e-05	1.1e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.82170726122344
R-squared	0.675202823147327
Adjusted R-squared	0.664002920497235
F-TEST (value)	60.2864903599649
F-TEST (DF numerator)	2
F-TEST (DF denominator)	58
p-value	6.88338275267597e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0620745026925834
Sum Squared Residuals	0.22348814530283

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.82170726122344 \tabularnewline
R-squared & 0.675202823147327 \tabularnewline
Adjusted R-squared & 0.664002920497235 \tabularnewline
F-TEST (value) & 60.2864903599649 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 6.88338275267597e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0620745026925834 \tabularnewline
Sum Squared Residuals & 0.22348814530283 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113312&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.82170726122344[/C][/ROW]
[ROW][C]R-squared[/C][C]0.675202823147327[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.664002920497235[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]60.2864903599649[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]6.88338275267597e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0620745026925834[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.22348814530283[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113312&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113312&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	0.82170726122344
R-squared	0.675202823147327
Adjusted R-squared	0.664002920497235
F-TEST (value)	60.2864903599649
F-TEST (DF numerator)	2
F-TEST (DF denominator)	58
p-value	6.88338275267597e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0620745026925834
Sum Squared Residuals	0.22348814530283

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.86	0.963395775413547	-0.103395775413547
2	0.88	0.982119071477734	-0.102119071477734
3	0.93	1.01187662439063	-0.0818766243906284
4	0.98	0.991980021484338	-0.0119800214843379
5	0.97	0.98863480385111	-0.0186348038511091
6	1.03	1.01287522833965	0.0171247716603502
7	1.06	1.01504713913077	0.0449528608692251
8	1.06	1.02825330677061	0.0317466932293922
9	1.08	1.03594234598609	0.0440576540139132
10	1.09	0.99397722938238	0.0960227706176195
11	1.04	1.00166626859786	0.0383337314021404
12	1	1.03142382151075	-0.0314238215107543
13	1.01	1.03359573230188	-0.0235957323018794
14	1.02	1.035767643093	-0.0157676430930044
15	1.04	1.00483678333801	0.0351632166619939
16	1.06	1.02907720782655	0.0309227921734532
17	1.06	1.04228337546638	0.0177166245336203
18	1.06	1.02790390098444	0.0320960990155569
19	1.06	1.02455868335121	0.0354413166487858
20	1.06	1.02121346571799	0.0387865342820146
21	1.02	1.00131686281169	0.0186831371883051
22	0.98	0.992454516754112	-0.0124545167541122
23	0.99	1.0111778128183	-0.021177812818299
24	0.99	1.01886685203378	-0.028866852033778
25	0.94	0.998970249127487	-0.0589702491274875
26	0.96	1.00665928834297	-0.0466592883429665
27	0.98	1.00883119913409	-0.0288311991340916
28	1.01	1.01100310992522	-0.00100310992521663
29	1.01	0.985589378594572	0.0244106214054279
30	1.02	0.987761289385697	0.0322387106143028
31	1.04	0.989933200176822	0.0500667998231777
32	1.03	0.986587982543593	0.0434120174564065
33	1.05	0.999794150183426	0.0502058498165736
34	1.08	1.04610308836938	0.0338969116306172
35	1.17	1.08689489813099	0.0831051018690146
36	1.11	1.10010106577082	0.0098989342291818
37	1.11	1.12434149025936	-0.0143414902593589
38	1.11	1.13203052947484	-0.022030529474838
39	1.11	1.17833946766079	-0.0683394676607944
40	1.21	1.16395999317886	0.0460400068211422
41	1.31	1.22130318821352	0.088696811786478
42	1.37	1.26209499797512	0.107905002024876
43	1.37	1.2697840371906	0.100215962809397
44	1.26	1.24437030585996	0.0156296941400409
45	1.23	1.25205934507544	-0.0220593450754381
46	1.17	1.21561135689609	-0.0456113568960858
47	1.06	1.12950921289755	-0.0695092128975482
48	0.95	1.1040954815669	-0.154095481566904
49	0.92	1.07316462181191	-0.153164621811905
50	0.92	1.06430227575432	-0.144302275754323
51	0.9	0.994751517028847	-0.0947515170288468
52	0.93	1.00244055624433	-0.0724405562443258
53	0.93	0.954958311216266	-0.0249583112162656
54	0.97	0.91299319461256	0.0570068053874405
55	0.96	0.876545206433207	0.0834547935667928
56	0.99	0.933888401467871	0.0561115985321286
57	0.98	0.919508926985935	0.0604910730140652
58	0.96	0.927197966201414	0.0328020337985862
59	1	0.979024032811724	0.0209759671882759
60	0.99	0.99774732887591	-0.00774732887591091
61	1.03	1.02750488178881	0.00249511821119444

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.86 & 0.963395775413547 & -0.103395775413547 \tabularnewline
2 & 0.88 & 0.982119071477734 & -0.102119071477734 \tabularnewline
3 & 0.93 & 1.01187662439063 & -0.0818766243906284 \tabularnewline
4 & 0.98 & 0.991980021484338 & -0.0119800214843379 \tabularnewline
5 & 0.97 & 0.98863480385111 & -0.0186348038511091 \tabularnewline
6 & 1.03 & 1.01287522833965 & 0.0171247716603502 \tabularnewline
7 & 1.06 & 1.01504713913077 & 0.0449528608692251 \tabularnewline
8 & 1.06 & 1.02825330677061 & 0.0317466932293922 \tabularnewline
9 & 1.08 & 1.03594234598609 & 0.0440576540139132 \tabularnewline
10 & 1.09 & 0.99397722938238 & 0.0960227706176195 \tabularnewline
11 & 1.04 & 1.00166626859786 & 0.0383337314021404 \tabularnewline
12 & 1 & 1.03142382151075 & -0.0314238215107543 \tabularnewline
13 & 1.01 & 1.03359573230188 & -0.0235957323018794 \tabularnewline
14 & 1.02 & 1.035767643093 & -0.0157676430930044 \tabularnewline
15 & 1.04 & 1.00483678333801 & 0.0351632166619939 \tabularnewline
16 & 1.06 & 1.02907720782655 & 0.0309227921734532 \tabularnewline
17 & 1.06 & 1.04228337546638 & 0.0177166245336203 \tabularnewline
18 & 1.06 & 1.02790390098444 & 0.0320960990155569 \tabularnewline
19 & 1.06 & 1.02455868335121 & 0.0354413166487858 \tabularnewline
20 & 1.06 & 1.02121346571799 & 0.0387865342820146 \tabularnewline
21 & 1.02 & 1.00131686281169 & 0.0186831371883051 \tabularnewline
22 & 0.98 & 0.992454516754112 & -0.0124545167541122 \tabularnewline
23 & 0.99 & 1.0111778128183 & -0.021177812818299 \tabularnewline
24 & 0.99 & 1.01886685203378 & -0.028866852033778 \tabularnewline
25 & 0.94 & 0.998970249127487 & -0.0589702491274875 \tabularnewline
26 & 0.96 & 1.00665928834297 & -0.0466592883429665 \tabularnewline
27 & 0.98 & 1.00883119913409 & -0.0288311991340916 \tabularnewline
28 & 1.01 & 1.01100310992522 & -0.00100310992521663 \tabularnewline
29 & 1.01 & 0.985589378594572 & 0.0244106214054279 \tabularnewline
30 & 1.02 & 0.987761289385697 & 0.0322387106143028 \tabularnewline
31 & 1.04 & 0.989933200176822 & 0.0500667998231777 \tabularnewline
32 & 1.03 & 0.986587982543593 & 0.0434120174564065 \tabularnewline
33 & 1.05 & 0.999794150183426 & 0.0502058498165736 \tabularnewline
34 & 1.08 & 1.04610308836938 & 0.0338969116306172 \tabularnewline
35 & 1.17 & 1.08689489813099 & 0.0831051018690146 \tabularnewline
36 & 1.11 & 1.10010106577082 & 0.0098989342291818 \tabularnewline
37 & 1.11 & 1.12434149025936 & -0.0143414902593589 \tabularnewline
38 & 1.11 & 1.13203052947484 & -0.022030529474838 \tabularnewline
39 & 1.11 & 1.17833946766079 & -0.0683394676607944 \tabularnewline
40 & 1.21 & 1.16395999317886 & 0.0460400068211422 \tabularnewline
41 & 1.31 & 1.22130318821352 & 0.088696811786478 \tabularnewline
42 & 1.37 & 1.26209499797512 & 0.107905002024876 \tabularnewline
43 & 1.37 & 1.2697840371906 & 0.100215962809397 \tabularnewline
44 & 1.26 & 1.24437030585996 & 0.0156296941400409 \tabularnewline
45 & 1.23 & 1.25205934507544 & -0.0220593450754381 \tabularnewline
46 & 1.17 & 1.21561135689609 & -0.0456113568960858 \tabularnewline
47 & 1.06 & 1.12950921289755 & -0.0695092128975482 \tabularnewline
48 & 0.95 & 1.1040954815669 & -0.154095481566904 \tabularnewline
49 & 0.92 & 1.07316462181191 & -0.153164621811905 \tabularnewline
50 & 0.92 & 1.06430227575432 & -0.144302275754323 \tabularnewline
51 & 0.9 & 0.994751517028847 & -0.0947515170288468 \tabularnewline
52 & 0.93 & 1.00244055624433 & -0.0724405562443258 \tabularnewline
53 & 0.93 & 0.954958311216266 & -0.0249583112162656 \tabularnewline
54 & 0.97 & 0.91299319461256 & 0.0570068053874405 \tabularnewline
55 & 0.96 & 0.876545206433207 & 0.0834547935667928 \tabularnewline
56 & 0.99 & 0.933888401467871 & 0.0561115985321286 \tabularnewline
57 & 0.98 & 0.919508926985935 & 0.0604910730140652 \tabularnewline
58 & 0.96 & 0.927197966201414 & 0.0328020337985862 \tabularnewline
59 & 1 & 0.979024032811724 & 0.0209759671882759 \tabularnewline
60 & 0.99 & 0.99774732887591 & -0.00774732887591091 \tabularnewline
61 & 1.03 & 1.02750488178881 & 0.00249511821119444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113312&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]0.963395775413547[/C][C]-0.103395775413547[/C][/ROW]
[ROW][C]2[/C][C]0.88[/C][C]0.982119071477734[/C][C]-0.102119071477734[/C][/ROW]
[ROW][C]3[/C][C]0.93[/C][C]1.01187662439063[/C][C]-0.0818766243906284[/C][/ROW]
[ROW][C]4[/C][C]0.98[/C][C]0.991980021484338[/C][C]-0.0119800214843379[/C][/ROW]
[ROW][C]5[/C][C]0.97[/C][C]0.98863480385111[/C][C]-0.0186348038511091[/C][/ROW]
[ROW][C]6[/C][C]1.03[/C][C]1.01287522833965[/C][C]0.0171247716603502[/C][/ROW]
[ROW][C]7[/C][C]1.06[/C][C]1.01504713913077[/C][C]0.0449528608692251[/C][/ROW]
[ROW][C]8[/C][C]1.06[/C][C]1.02825330677061[/C][C]0.0317466932293922[/C][/ROW]
[ROW][C]9[/C][C]1.08[/C][C]1.03594234598609[/C][C]0.0440576540139132[/C][/ROW]
[ROW][C]10[/C][C]1.09[/C][C]0.99397722938238[/C][C]0.0960227706176195[/C][/ROW]
[ROW][C]11[/C][C]1.04[/C][C]1.00166626859786[/C][C]0.0383337314021404[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.03142382151075[/C][C]-0.0314238215107543[/C][/ROW]
[ROW][C]13[/C][C]1.01[/C][C]1.03359573230188[/C][C]-0.0235957323018794[/C][/ROW]
[ROW][C]14[/C][C]1.02[/C][C]1.035767643093[/C][C]-0.0157676430930044[/C][/ROW]
[ROW][C]15[/C][C]1.04[/C][C]1.00483678333801[/C][C]0.0351632166619939[/C][/ROW]
[ROW][C]16[/C][C]1.06[/C][C]1.02907720782655[/C][C]0.0309227921734532[/C][/ROW]
[ROW][C]17[/C][C]1.06[/C][C]1.04228337546638[/C][C]0.0177166245336203[/C][/ROW]
[ROW][C]18[/C][C]1.06[/C][C]1.02790390098444[/C][C]0.0320960990155569[/C][/ROW]
[ROW][C]19[/C][C]1.06[/C][C]1.02455868335121[/C][C]0.0354413166487858[/C][/ROW]
[ROW][C]20[/C][C]1.06[/C][C]1.02121346571799[/C][C]0.0387865342820146[/C][/ROW]
[ROW][C]21[/C][C]1.02[/C][C]1.00131686281169[/C][C]0.0186831371883051[/C][/ROW]
[ROW][C]22[/C][C]0.98[/C][C]0.992454516754112[/C][C]-0.0124545167541122[/C][/ROW]
[ROW][C]23[/C][C]0.99[/C][C]1.0111778128183[/C][C]-0.021177812818299[/C][/ROW]
[ROW][C]24[/C][C]0.99[/C][C]1.01886685203378[/C][C]-0.028866852033778[/C][/ROW]
[ROW][C]25[/C][C]0.94[/C][C]0.998970249127487[/C][C]-0.0589702491274875[/C][/ROW]
[ROW][C]26[/C][C]0.96[/C][C]1.00665928834297[/C][C]-0.0466592883429665[/C][/ROW]
[ROW][C]27[/C][C]0.98[/C][C]1.00883119913409[/C][C]-0.0288311991340916[/C][/ROW]
[ROW][C]28[/C][C]1.01[/C][C]1.01100310992522[/C][C]-0.00100310992521663[/C][/ROW]
[ROW][C]29[/C][C]1.01[/C][C]0.985589378594572[/C][C]0.0244106214054279[/C][/ROW]
[ROW][C]30[/C][C]1.02[/C][C]0.987761289385697[/C][C]0.0322387106143028[/C][/ROW]
[ROW][C]31[/C][C]1.04[/C][C]0.989933200176822[/C][C]0.0500667998231777[/C][/ROW]
[ROW][C]32[/C][C]1.03[/C][C]0.986587982543593[/C][C]0.0434120174564065[/C][/ROW]
[ROW][C]33[/C][C]1.05[/C][C]0.999794150183426[/C][C]0.0502058498165736[/C][/ROW]
[ROW][C]34[/C][C]1.08[/C][C]1.04610308836938[/C][C]0.0338969116306172[/C][/ROW]
[ROW][C]35[/C][C]1.17[/C][C]1.08689489813099[/C][C]0.0831051018690146[/C][/ROW]
[ROW][C]36[/C][C]1.11[/C][C]1.10010106577082[/C][C]0.0098989342291818[/C][/ROW]
[ROW][C]37[/C][C]1.11[/C][C]1.12434149025936[/C][C]-0.0143414902593589[/C][/ROW]
[ROW][C]38[/C][C]1.11[/C][C]1.13203052947484[/C][C]-0.022030529474838[/C][/ROW]
[ROW][C]39[/C][C]1.11[/C][C]1.17833946766079[/C][C]-0.0683394676607944[/C][/ROW]
[ROW][C]40[/C][C]1.21[/C][C]1.16395999317886[/C][C]0.0460400068211422[/C][/ROW]
[ROW][C]41[/C][C]1.31[/C][C]1.22130318821352[/C][C]0.088696811786478[/C][/ROW]
[ROW][C]42[/C][C]1.37[/C][C]1.26209499797512[/C][C]0.107905002024876[/C][/ROW]
[ROW][C]43[/C][C]1.37[/C][C]1.2697840371906[/C][C]0.100215962809397[/C][/ROW]
[ROW][C]44[/C][C]1.26[/C][C]1.24437030585996[/C][C]0.0156296941400409[/C][/ROW]
[ROW][C]45[/C][C]1.23[/C][C]1.25205934507544[/C][C]-0.0220593450754381[/C][/ROW]
[ROW][C]46[/C][C]1.17[/C][C]1.21561135689609[/C][C]-0.0456113568960858[/C][/ROW]
[ROW][C]47[/C][C]1.06[/C][C]1.12950921289755[/C][C]-0.0695092128975482[/C][/ROW]
[ROW][C]48[/C][C]0.95[/C][C]1.1040954815669[/C][C]-0.154095481566904[/C][/ROW]
[ROW][C]49[/C][C]0.92[/C][C]1.07316462181191[/C][C]-0.153164621811905[/C][/ROW]
[ROW][C]50[/C][C]0.92[/C][C]1.06430227575432[/C][C]-0.144302275754323[/C][/ROW]
[ROW][C]51[/C][C]0.9[/C][C]0.994751517028847[/C][C]-0.0947515170288468[/C][/ROW]
[ROW][C]52[/C][C]0.93[/C][C]1.00244055624433[/C][C]-0.0724405562443258[/C][/ROW]
[ROW][C]53[/C][C]0.93[/C][C]0.954958311216266[/C][C]-0.0249583112162656[/C][/ROW]
[ROW][C]54[/C][C]0.97[/C][C]0.91299319461256[/C][C]0.0570068053874405[/C][/ROW]
[ROW][C]55[/C][C]0.96[/C][C]0.876545206433207[/C][C]0.0834547935667928[/C][/ROW]
[ROW][C]56[/C][C]0.99[/C][C]0.933888401467871[/C][C]0.0561115985321286[/C][/ROW]
[ROW][C]57[/C][C]0.98[/C][C]0.919508926985935[/C][C]0.0604910730140652[/C][/ROW]
[ROW][C]58[/C][C]0.96[/C][C]0.927197966201414[/C][C]0.0328020337985862[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]0.979024032811724[/C][C]0.0209759671882759[/C][/ROW]
[ROW][C]60[/C][C]0.99[/C][C]0.99774732887591[/C][C]-0.00774732887591091[/C][/ROW]
[ROW][C]61[/C][C]1.03[/C][C]1.02750488178881[/C][C]0.00249511821119444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113312&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113312&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	0.963395775413547	-0.103395775413547
2	0.88	0.982119071477734	-0.102119071477734
3	0.93	1.01187662439063	-0.0818766243906284
4	0.98	0.991980021484338	-0.0119800214843379
5	0.97	0.98863480385111	-0.0186348038511091
6	1.03	1.01287522833965	0.0171247716603502
7	1.06	1.01504713913077	0.0449528608692251
8	1.06	1.02825330677061	0.0317466932293922
9	1.08	1.03594234598609	0.0440576540139132
10	1.09	0.99397722938238	0.0960227706176195
11	1.04	1.00166626859786	0.0383337314021404
12	1	1.03142382151075	-0.0314238215107543
13	1.01	1.03359573230188	-0.0235957323018794
14	1.02	1.035767643093	-0.0157676430930044
15	1.04	1.00483678333801	0.0351632166619939
16	1.06	1.02907720782655	0.0309227921734532
17	1.06	1.04228337546638	0.0177166245336203
18	1.06	1.02790390098444	0.0320960990155569
19	1.06	1.02455868335121	0.0354413166487858
20	1.06	1.02121346571799	0.0387865342820146
21	1.02	1.00131686281169	0.0186831371883051
22	0.98	0.992454516754112	-0.0124545167541122
23	0.99	1.0111778128183	-0.021177812818299
24	0.99	1.01886685203378	-0.028866852033778
25	0.94	0.998970249127487	-0.0589702491274875
26	0.96	1.00665928834297	-0.0466592883429665
27	0.98	1.00883119913409	-0.0288311991340916
28	1.01	1.01100310992522	-0.00100310992521663
29	1.01	0.985589378594572	0.0244106214054279
30	1.02	0.987761289385697	0.0322387106143028
31	1.04	0.989933200176822	0.0500667998231777
32	1.03	0.986587982543593	0.0434120174564065
33	1.05	0.999794150183426	0.0502058498165736
34	1.08	1.04610308836938	0.0338969116306172
35	1.17	1.08689489813099	0.0831051018690146
36	1.11	1.10010106577082	0.0098989342291818
37	1.11	1.12434149025936	-0.0143414902593589
38	1.11	1.13203052947484	-0.022030529474838
39	1.11	1.17833946766079	-0.0683394676607944
40	1.21	1.16395999317886	0.0460400068211422
41	1.31	1.22130318821352	0.088696811786478
42	1.37	1.26209499797512	0.107905002024876
43	1.37	1.2697840371906	0.100215962809397
44	1.26	1.24437030585996	0.0156296941400409
45	1.23	1.25205934507544	-0.0220593450754381
46	1.17	1.21561135689609	-0.0456113568960858
47	1.06	1.12950921289755	-0.0695092128975482
48	0.95	1.1040954815669	-0.154095481566904
49	0.92	1.07316462181191	-0.153164621811905
50	0.92	1.06430227575432	-0.144302275754323
51	0.9	0.994751517028847	-0.0947515170288468
52	0.93	1.00244055624433	-0.0724405562443258
53	0.93	0.954958311216266	-0.0249583112162656
54	0.97	0.91299319461256	0.0570068053874405
55	0.96	0.876545206433207	0.0834547935667928
56	0.99	0.933888401467871	0.0561115985321286
57	0.98	0.919508926985935	0.0604910730140652
58	0.96	0.927197966201414	0.0328020337985862
59	1	0.979024032811724	0.0209759671882759
60	0.99	0.99774732887591	-0.00774732887591091
61	1.03	1.02750488178881	0.00249511821119444

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.0366856739294994	0.0733713478589987	0.9633143260705
7	0.00898036395765953	0.0179607279153191	0.99101963604234
8	0.00682979986328786	0.0136595997265757	0.993170200136712
9	0.00407747199028291	0.00815494398056583	0.995922528009717
10	0.00168762134593224	0.00337524269186447	0.998312378654068
11	0.0137429942485713	0.0274859884971426	0.986257005751429
12	0.194242714214088	0.388485428428176	0.805757285785912
13	0.29038044363278	0.58076088726556	0.70961955636722
14	0.300707473918224	0.601414947836449	0.699292526081776
15	0.22533373976902	0.45066747953804	0.77466626023098
16	0.16557923661166	0.331158473223319	0.83442076338834
17	0.125998736156377	0.251997472312754	0.874001263843623
18	0.0882251535272633	0.176450307054527	0.911774846472737
19	0.0599792935415953	0.119958587083191	0.940020706458405
20	0.0397503326292219	0.0795006652584437	0.960249667370778
21	0.0273015140949613	0.0546030281899226	0.97269848590504
22	0.021960374935781	0.043920749871562	0.978039625064219
23	0.01969215504709	0.03938431009418	0.98030784495291
24	0.0189725699372776	0.0379451398745551	0.981027430062722
25	0.0218616206287006	0.0437232412574012	0.9781383793713
26	0.0201352962749438	0.0402705925498876	0.979864703725056
27	0.0148800774846816	0.0297601549693631	0.985119922515318
28	0.00930921733746631	0.0186184346749326	0.990690782662534
29	0.00740445659157383	0.0148089131831477	0.992595543408426
30	0.00542702452938614	0.0108540490587723	0.994572975470614
31	0.00425758630161036	0.00851517260322073	0.99574241369839
32	0.00283273148059577	0.00566546296119154	0.997167268519404
33	0.00177560259218237	0.00355120518436473	0.998224397407818
34	0.000980797226130476	0.00196159445226095	0.99901920277387
35	0.000779152458295567	0.00155830491659113	0.999220847541704
36	0.000707336928375294	0.00141467385675059	0.999292663071625
37	0.000682656016385478	0.00136531203277096	0.999317343983614
38	0.000517886807018705	0.00103577361403741	0.999482113192981
39	0.000671998985148108	0.00134399797029622	0.999328001014852
40	0.000499827747177994	0.000999655494355989	0.999500172252822
41	0.0010300365346152	0.0020600730692304	0.998969963465385
42	0.00408380927322877	0.00816761854645754	0.995916190726771
43	0.0288220897502902	0.0576441795005804	0.97117791024971
44	0.073422920801071	0.146845841602142	0.926577079198929
45	0.22795001848288	0.45590003696576	0.77204998151712
46	0.782290298795542	0.435419402408916	0.217709701204458
47	0.998851493830339	0.00229701233932212	0.00114850616966106
48	0.99981901961442	0.000361960771161294	0.000180980385580647
49	0.99974158914474	0.000516821710519051	0.000258410855259526
50	0.99947085778815	0.0010582844236992	0.0005291422118496
51	0.998861081457499	0.00227783708500173	0.00113891854250086
52	0.996174299229436	0.00765140154112842	0.00382570077056421
53	0.998963340483594	0.00207331903281151	0.00103665951640575
54	0.99795604464667	0.00408791070666003	0.00204395535333002
55	0.989728874100148	0.0205422517997041	0.0102711258998521

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.0366856739294994 & 0.0733713478589987 & 0.9633143260705 \tabularnewline
7 & 0.00898036395765953 & 0.0179607279153191 & 0.99101963604234 \tabularnewline
8 & 0.00682979986328786 & 0.0136595997265757 & 0.993170200136712 \tabularnewline
9 & 0.00407747199028291 & 0.00815494398056583 & 0.995922528009717 \tabularnewline
10 & 0.00168762134593224 & 0.00337524269186447 & 0.998312378654068 \tabularnewline
11 & 0.0137429942485713 & 0.0274859884971426 & 0.986257005751429 \tabularnewline
12 & 0.194242714214088 & 0.388485428428176 & 0.805757285785912 \tabularnewline
13 & 0.29038044363278 & 0.58076088726556 & 0.70961955636722 \tabularnewline
14 & 0.300707473918224 & 0.601414947836449 & 0.699292526081776 \tabularnewline
15 & 0.22533373976902 & 0.45066747953804 & 0.77466626023098 \tabularnewline
16 & 0.16557923661166 & 0.331158473223319 & 0.83442076338834 \tabularnewline
17 & 0.125998736156377 & 0.251997472312754 & 0.874001263843623 \tabularnewline
18 & 0.0882251535272633 & 0.176450307054527 & 0.911774846472737 \tabularnewline
19 & 0.0599792935415953 & 0.119958587083191 & 0.940020706458405 \tabularnewline
20 & 0.0397503326292219 & 0.0795006652584437 & 0.960249667370778 \tabularnewline
21 & 0.0273015140949613 & 0.0546030281899226 & 0.97269848590504 \tabularnewline
22 & 0.021960374935781 & 0.043920749871562 & 0.978039625064219 \tabularnewline
23 & 0.01969215504709 & 0.03938431009418 & 0.98030784495291 \tabularnewline
24 & 0.0189725699372776 & 0.0379451398745551 & 0.981027430062722 \tabularnewline
25 & 0.0218616206287006 & 0.0437232412574012 & 0.9781383793713 \tabularnewline
26 & 0.0201352962749438 & 0.0402705925498876 & 0.979864703725056 \tabularnewline
27 & 0.0148800774846816 & 0.0297601549693631 & 0.985119922515318 \tabularnewline
28 & 0.00930921733746631 & 0.0186184346749326 & 0.990690782662534 \tabularnewline
29 & 0.00740445659157383 & 0.0148089131831477 & 0.992595543408426 \tabularnewline
30 & 0.00542702452938614 & 0.0108540490587723 & 0.994572975470614 \tabularnewline
31 & 0.00425758630161036 & 0.00851517260322073 & 0.99574241369839 \tabularnewline
32 & 0.00283273148059577 & 0.00566546296119154 & 0.997167268519404 \tabularnewline
33 & 0.00177560259218237 & 0.00355120518436473 & 0.998224397407818 \tabularnewline
34 & 0.000980797226130476 & 0.00196159445226095 & 0.99901920277387 \tabularnewline
35 & 0.000779152458295567 & 0.00155830491659113 & 0.999220847541704 \tabularnewline
36 & 0.000707336928375294 & 0.00141467385675059 & 0.999292663071625 \tabularnewline
37 & 0.000682656016385478 & 0.00136531203277096 & 0.999317343983614 \tabularnewline
38 & 0.000517886807018705 & 0.00103577361403741 & 0.999482113192981 \tabularnewline
39 & 0.000671998985148108 & 0.00134399797029622 & 0.999328001014852 \tabularnewline
40 & 0.000499827747177994 & 0.000999655494355989 & 0.999500172252822 \tabularnewline
41 & 0.0010300365346152 & 0.0020600730692304 & 0.998969963465385 \tabularnewline
42 & 0.00408380927322877 & 0.00816761854645754 & 0.995916190726771 \tabularnewline
43 & 0.0288220897502902 & 0.0576441795005804 & 0.97117791024971 \tabularnewline
44 & 0.073422920801071 & 0.146845841602142 & 0.926577079198929 \tabularnewline
45 & 0.22795001848288 & 0.45590003696576 & 0.77204998151712 \tabularnewline
46 & 0.782290298795542 & 0.435419402408916 & 0.217709701204458 \tabularnewline
47 & 0.998851493830339 & 0.00229701233932212 & 0.00114850616966106 \tabularnewline
48 & 0.99981901961442 & 0.000361960771161294 & 0.000180980385580647 \tabularnewline
49 & 0.99974158914474 & 0.000516821710519051 & 0.000258410855259526 \tabularnewline
50 & 0.99947085778815 & 0.0010582844236992 & 0.0005291422118496 \tabularnewline
51 & 0.998861081457499 & 0.00227783708500173 & 0.00113891854250086 \tabularnewline
52 & 0.996174299229436 & 0.00765140154112842 & 0.00382570077056421 \tabularnewline
53 & 0.998963340483594 & 0.00207331903281151 & 0.00103665951640575 \tabularnewline
54 & 0.99795604464667 & 0.00408791070666003 & 0.00204395535333002 \tabularnewline
55 & 0.989728874100148 & 0.0205422517997041 & 0.0102711258998521 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113312&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]6[/C][C]0.0366856739294994[/C][C]0.0733713478589987[/C][C]0.9633143260705[/C][/ROW]
[ROW][C]7[/C][C]0.00898036395765953[/C][C]0.0179607279153191[/C][C]0.99101963604234[/C][/ROW]
[ROW][C]8[/C][C]0.00682979986328786[/C][C]0.0136595997265757[/C][C]0.993170200136712[/C][/ROW]
[ROW][C]9[/C][C]0.00407747199028291[/C][C]0.00815494398056583[/C][C]0.995922528009717[/C][/ROW]
[ROW][C]10[/C][C]0.00168762134593224[/C][C]0.00337524269186447[/C][C]0.998312378654068[/C][/ROW]
[ROW][C]11[/C][C]0.0137429942485713[/C][C]0.0274859884971426[/C][C]0.986257005751429[/C][/ROW]
[ROW][C]12[/C][C]0.194242714214088[/C][C]0.388485428428176[/C][C]0.805757285785912[/C][/ROW]
[ROW][C]13[/C][C]0.29038044363278[/C][C]0.58076088726556[/C][C]0.70961955636722[/C][/ROW]
[ROW][C]14[/C][C]0.300707473918224[/C][C]0.601414947836449[/C][C]0.699292526081776[/C][/ROW]
[ROW][C]15[/C][C]0.22533373976902[/C][C]0.45066747953804[/C][C]0.77466626023098[/C][/ROW]
[ROW][C]16[/C][C]0.16557923661166[/C][C]0.331158473223319[/C][C]0.83442076338834[/C][/ROW]
[ROW][C]17[/C][C]0.125998736156377[/C][C]0.251997472312754[/C][C]0.874001263843623[/C][/ROW]
[ROW][C]18[/C][C]0.0882251535272633[/C][C]0.176450307054527[/C][C]0.911774846472737[/C][/ROW]
[ROW][C]19[/C][C]0.0599792935415953[/C][C]0.119958587083191[/C][C]0.940020706458405[/C][/ROW]
[ROW][C]20[/C][C]0.0397503326292219[/C][C]0.0795006652584437[/C][C]0.960249667370778[/C][/ROW]
[ROW][C]21[/C][C]0.0273015140949613[/C][C]0.0546030281899226[/C][C]0.97269848590504[/C][/ROW]
[ROW][C]22[/C][C]0.021960374935781[/C][C]0.043920749871562[/C][C]0.978039625064219[/C][/ROW]
[ROW][C]23[/C][C]0.01969215504709[/C][C]0.03938431009418[/C][C]0.98030784495291[/C][/ROW]
[ROW][C]24[/C][C]0.0189725699372776[/C][C]0.0379451398745551[/C][C]0.981027430062722[/C][/ROW]
[ROW][C]25[/C][C]0.0218616206287006[/C][C]0.0437232412574012[/C][C]0.9781383793713[/C][/ROW]
[ROW][C]26[/C][C]0.0201352962749438[/C][C]0.0402705925498876[/C][C]0.979864703725056[/C][/ROW]
[ROW][C]27[/C][C]0.0148800774846816[/C][C]0.0297601549693631[/C][C]0.985119922515318[/C][/ROW]
[ROW][C]28[/C][C]0.00930921733746631[/C][C]0.0186184346749326[/C][C]0.990690782662534[/C][/ROW]
[ROW][C]29[/C][C]0.00740445659157383[/C][C]0.0148089131831477[/C][C]0.992595543408426[/C][/ROW]
[ROW][C]30[/C][C]0.00542702452938614[/C][C]0.0108540490587723[/C][C]0.994572975470614[/C][/ROW]
[ROW][C]31[/C][C]0.00425758630161036[/C][C]0.00851517260322073[/C][C]0.99574241369839[/C][/ROW]
[ROW][C]32[/C][C]0.00283273148059577[/C][C]0.00566546296119154[/C][C]0.997167268519404[/C][/ROW]
[ROW][C]33[/C][C]0.00177560259218237[/C][C]0.00355120518436473[/C][C]0.998224397407818[/C][/ROW]
[ROW][C]34[/C][C]0.000980797226130476[/C][C]0.00196159445226095[/C][C]0.99901920277387[/C][/ROW]
[ROW][C]35[/C][C]0.000779152458295567[/C][C]0.00155830491659113[/C][C]0.999220847541704[/C][/ROW]
[ROW][C]36[/C][C]0.000707336928375294[/C][C]0.00141467385675059[/C][C]0.999292663071625[/C][/ROW]
[ROW][C]37[/C][C]0.000682656016385478[/C][C]0.00136531203277096[/C][C]0.999317343983614[/C][/ROW]
[ROW][C]38[/C][C]0.000517886807018705[/C][C]0.00103577361403741[/C][C]0.999482113192981[/C][/ROW]
[ROW][C]39[/C][C]0.000671998985148108[/C][C]0.00134399797029622[/C][C]0.999328001014852[/C][/ROW]
[ROW][C]40[/C][C]0.000499827747177994[/C][C]0.000999655494355989[/C][C]0.999500172252822[/C][/ROW]
[ROW][C]41[/C][C]0.0010300365346152[/C][C]0.0020600730692304[/C][C]0.998969963465385[/C][/ROW]
[ROW][C]42[/C][C]0.00408380927322877[/C][C]0.00816761854645754[/C][C]0.995916190726771[/C][/ROW]
[ROW][C]43[/C][C]0.0288220897502902[/C][C]0.0576441795005804[/C][C]0.97117791024971[/C][/ROW]
[ROW][C]44[/C][C]0.073422920801071[/C][C]0.146845841602142[/C][C]0.926577079198929[/C][/ROW]
[ROW][C]45[/C][C]0.22795001848288[/C][C]0.45590003696576[/C][C]0.77204998151712[/C][/ROW]
[ROW][C]46[/C][C]0.782290298795542[/C][C]0.435419402408916[/C][C]0.217709701204458[/C][/ROW]
[ROW][C]47[/C][C]0.998851493830339[/C][C]0.00229701233932212[/C][C]0.00114850616966106[/C][/ROW]
[ROW][C]48[/C][C]0.99981901961442[/C][C]0.000361960771161294[/C][C]0.000180980385580647[/C][/ROW]
[ROW][C]49[/C][C]0.99974158914474[/C][C]0.000516821710519051[/C][C]0.000258410855259526[/C][/ROW]
[ROW][C]50[/C][C]0.99947085778815[/C][C]0.0010582844236992[/C][C]0.0005291422118496[/C][/ROW]
[ROW][C]51[/C][C]0.998861081457499[/C][C]0.00227783708500173[/C][C]0.00113891854250086[/C][/ROW]
[ROW][C]52[/C][C]0.996174299229436[/C][C]0.00765140154112842[/C][C]0.00382570077056421[/C][/ROW]
[ROW][C]53[/C][C]0.998963340483594[/C][C]0.00207331903281151[/C][C]0.00103665951640575[/C][/ROW]
[ROW][C]54[/C][C]0.99795604464667[/C][C]0.00408791070666003[/C][C]0.00204395535333002[/C][/ROW]
[ROW][C]55[/C][C]0.989728874100148[/C][C]0.0205422517997041[/C][C]0.0102711258998521[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113312&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113312&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
6	0.0366856739294994	0.0733713478589987	0.9633143260705
7	0.00898036395765953	0.0179607279153191	0.99101963604234
8	0.00682979986328786	0.0136595997265757	0.993170200136712
9	0.00407747199028291	0.00815494398056583	0.995922528009717
10	0.00168762134593224	0.00337524269186447	0.998312378654068
11	0.0137429942485713	0.0274859884971426	0.986257005751429
12	0.194242714214088	0.388485428428176	0.805757285785912
13	0.29038044363278	0.58076088726556	0.70961955636722
14	0.300707473918224	0.601414947836449	0.699292526081776
15	0.22533373976902	0.45066747953804	0.77466626023098
16	0.16557923661166	0.331158473223319	0.83442076338834
17	0.125998736156377	0.251997472312754	0.874001263843623
18	0.0882251535272633	0.176450307054527	0.911774846472737
19	0.0599792935415953	0.119958587083191	0.940020706458405
20	0.0397503326292219	0.0795006652584437	0.960249667370778
21	0.0273015140949613	0.0546030281899226	0.97269848590504
22	0.021960374935781	0.043920749871562	0.978039625064219
23	0.01969215504709	0.03938431009418	0.98030784495291
24	0.0189725699372776	0.0379451398745551	0.981027430062722
25	0.0218616206287006	0.0437232412574012	0.9781383793713
26	0.0201352962749438	0.0402705925498876	0.979864703725056
27	0.0148800774846816	0.0297601549693631	0.985119922515318
28	0.00930921733746631	0.0186184346749326	0.990690782662534
29	0.00740445659157383	0.0148089131831477	0.992595543408426
30	0.00542702452938614	0.0108540490587723	0.994572975470614
31	0.00425758630161036	0.00851517260322073	0.99574241369839
32	0.00283273148059577	0.00566546296119154	0.997167268519404
33	0.00177560259218237	0.00355120518436473	0.998224397407818
34	0.000980797226130476	0.00196159445226095	0.99901920277387
35	0.000779152458295567	0.00155830491659113	0.999220847541704
36	0.000707336928375294	0.00141467385675059	0.999292663071625
37	0.000682656016385478	0.00136531203277096	0.999317343983614
38	0.000517886807018705	0.00103577361403741	0.999482113192981
39	0.000671998985148108	0.00134399797029622	0.999328001014852
40	0.000499827747177994	0.000999655494355989	0.999500172252822
41	0.0010300365346152	0.0020600730692304	0.998969963465385
42	0.00408380927322877	0.00816761854645754	0.995916190726771
43	0.0288220897502902	0.0576441795005804	0.97117791024971
44	0.073422920801071	0.146845841602142	0.926577079198929
45	0.22795001848288	0.45590003696576	0.77204998151712
46	0.782290298795542	0.435419402408916	0.217709701204458
47	0.998851493830339	0.00229701233932212	0.00114850616966106
48	0.99981901961442	0.000361960771161294	0.000180980385580647
49	0.99974158914474	0.000516821710519051	0.000258410855259526
50	0.99947085778815	0.0010582844236992	0.0005291422118496
51	0.998861081457499	0.00227783708500173	0.00113891854250086
52	0.996174299229436	0.00765140154112842	0.00382570077056421
53	0.998963340483594	0.00207331903281151	0.00103665951640575
54	0.99795604464667	0.00408791070666003	0.00204395535333002
55	0.989728874100148	0.0205422517997041	0.0102711258998521

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	22	0.44	NOK
5% type I error level	35	0.7	NOK
10% type I error level	39	0.78	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 & 22 & 0.44 & NOK \tabularnewline
5% type I error level & 35 & 0.7 & NOK \tabularnewline
10% type I error level & 39 & 0.78 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113312&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]22[/C][C]0.44[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.7[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]39[/C][C]0.78[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113312&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113312&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	22	0.44	NOK
5% type I error level	35	0.7	NOK
10% type I error level	39	0.78	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 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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