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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 22 Dec 2011 08:27:56 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t1324560882v16702w1uj61zf5.htm/, Retrieved Fri, 03 May 2024 05:48:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159440, Retrieved Fri, 03 May 2024 05:48:59 +0000

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-       [Multiple Regression] [] [2011-12-22 13:27:56] [75a32e1bc492240bc1028714aca23077] [Current]

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1.0622	2.1	8.93	2.60	5.8
1.0773	2.4	8.96	2.98	5.9
1.0807	2.5	8.99	3.02	5.9
1.0848	2.1	8.98	2.22	6
1.1582	1.8	9	2.06	6.1
1.1663	1.9	9.03	2.11	6.3
1.1372	1.9	9.02	2.11	6.2
1.1139	2.1	9	2.16	6.1
1.1222	2.2	9.03	2.32	6.1
1.1692	2	9.03	2.04	6
1.1702	2.2	9.03	1.77	5.8
1.2286	2	9.07	1.88	5.7
1.2613	1.9	9.15	1.93	5.7
1.2646	1.6	9.1	1.69	5.6
1.2262	1.7	9.15	1.74	5.8
1.1985	2	9.15	2.29	5.6
1.2007	2.5	9.22	3.05	5.6
1.2138	2.4	9.22	3.27	5.6
1.2266	2.3	9.24	2.99	5.5
1.2176	2.3	9.26	2.65	5.4
1.2218	2.1	9.3	2.54	5.4
1.249	2.4	9.27	3.19	5.5
1.2991	2.2	9.32	3.52	5.4
1.3408	2.4	9.33	3.26	5.4
1.3119	1.9	9.32	2.97	5.3
1.3014	2.1	9.34	3.01	5.4
1.3201	2.1	9.32	3.15	5.2
1.2938	2.1	9.32	3.51	5.2
1.2694	2	9.24	2.80	5.1
1.2165	2.1	9.24	2.53	5
1.2037	2.2	9.15	3.17	5
1.2292	2.2	9.17	3.64	4.9
1.2256	2.6	9.14	4.69	5
1.2015	2.5	9.11	4.35	5
1.1786	2.3	9.04	3.46	5
1.1856	2.2	8.96	3.42	4.9
1.2103	2.4	8.86	3.99	4.7
1.1938	2.3	8.85	3.60	4.8
1.202	2.2	8.75	3.36	4.7
1.2271	2.5	8.65	3.55	4.7
1.277	2.5	8.61	4.17	4.6
1.265	2.5	8.46	4.32	4.6
1.2684	2.4	8.38	4.15	4.7
1.2811	2.3	8.33	3.82	4.7
1.2727	1.7	8.27	2.06	4.5
1.2611	1.6	8.21	1.31	4.4
1.2881	1.9	8.18	1.97	4.5
1.3213	1.9	8.04	2.54	4.4
1.2999	1.8	7.97	2.08	4.6
1.3074	1.8	7.86	2.42	4.5
1.3242	1.9	7.75	2.78	4.4
1.3516	1.9	7.65	2.57	4.5
1.3511	1.9	7.62	2.69	4.4
1.3419	1.9	7.55	2.69	4.6
1.3716	1.8	7.6	2.36	4.7
1.3622	1.7	7.54	1.97	4.6
1.3896	2.1	7.48	2.76	4.7
1.4227	2.6	7.44	3.54	4.7
1.4684	3.1	7.41	4.31	4.7
1.457	3.1	7.45	4.08	5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159440&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159440&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159440&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	4 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Dollar/Euro[t] = -0.291886267292158 -0.00446981564425716`Infl-Eu`[t] + 0.0497838811131522`Werkl-Eu`[t] -0.0118898354240754`Infl-VS`[t] + 0.16022758300618`Werkl-VS`[t] + 0.0106104700270044t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Dollar/Euro[t] =  -0.291886267292158 -0.00446981564425716`Infl-Eu`[t] +  0.0497838811131522`Werkl-Eu`[t] -0.0118898354240754`Infl-VS`[t] +  0.16022758300618`Werkl-VS`[t] +  0.0106104700270044t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159440&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Dollar/Euro[t] =  -0.291886267292158 -0.00446981564425716`Infl-Eu`[t] +  0.0497838811131522`Werkl-Eu`[t] -0.0118898354240754`Infl-VS`[t] +  0.16022758300618`Werkl-VS`[t] +  0.0106104700270044t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159440&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159440&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
Dollar/Euro[t] = -0.291886267292158 -0.00446981564425716`Infl-Eu`[t] + 0.0497838811131522`Werkl-Eu`[t] -0.0118898354240754`Infl-VS`[t] + 0.16022758300618`Werkl-VS`[t] + 0.0106104700270044t + 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)	-0.291886267292158	0.329991	-0.8845	0.380333	0.190166
`Infl-Eu`	-0.00446981564425716	0.039383	-0.1135	0.910058	0.455029
`Werkl-Eu`	0.0497838811131522	0.020767	2.3973	0.020007	0.010003
`Infl-VS`	-0.0118898354240754	0.017536	-0.678	0.500663	0.250332
`Werkl-VS`	0.16022758300618	0.035757	4.4811	3.9e-05	1.9e-05
t	0.0106104700270044	0.001455	7.2934	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) & -0.291886267292158 & 0.329991 & -0.8845 & 0.380333 & 0.190166 \tabularnewline
`Infl-Eu` & -0.00446981564425716 & 0.039383 & -0.1135 & 0.910058 & 0.455029 \tabularnewline
`Werkl-Eu` & 0.0497838811131522 & 0.020767 & 2.3973 & 0.020007 & 0.010003 \tabularnewline
`Infl-VS` & -0.0118898354240754 & 0.017536 & -0.678 & 0.500663 & 0.250332 \tabularnewline
`Werkl-VS` & 0.16022758300618 & 0.035757 & 4.4811 & 3.9e-05 & 1.9e-05 \tabularnewline
t & 0.0106104700270044 & 0.001455 & 7.2934 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159440&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]-0.291886267292158[/C][C]0.329991[/C][C]-0.8845[/C][C]0.380333[/C][C]0.190166[/C][/ROW]
[ROW][C]`Infl-Eu`[/C][C]-0.00446981564425716[/C][C]0.039383[/C][C]-0.1135[/C][C]0.910058[/C][C]0.455029[/C][/ROW]
[ROW][C]`Werkl-Eu`[/C][C]0.0497838811131522[/C][C]0.020767[/C][C]2.3973[/C][C]0.020007[/C][C]0.010003[/C][/ROW]
[ROW][C]`Infl-VS`[/C][C]-0.0118898354240754[/C][C]0.017536[/C][C]-0.678[/C][C]0.500663[/C][C]0.250332[/C][/ROW]
[ROW][C]`Werkl-VS`[/C][C]0.16022758300618[/C][C]0.035757[/C][C]4.4811[/C][C]3.9e-05[/C][C]1.9e-05[/C][/ROW]
[ROW][C]t[/C][C]0.0106104700270044[/C][C]0.001455[/C][C]7.2934[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159440&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159440&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)	-0.291886267292158	0.329991	-0.8845	0.380333	0.190166
`Infl-Eu`	-0.00446981564425716	0.039383	-0.1135	0.910058	0.455029
`Werkl-Eu`	0.0497838811131522	0.020767	2.3973	0.020007	0.010003
`Infl-VS`	-0.0118898354240754	0.017536	-0.678	0.500663	0.250332
`Werkl-VS`	0.16022758300618	0.035757	4.4811	3.9e-05	1.9e-05
t	0.0106104700270044	0.001455	7.2934	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.873434909037978
R-squared	0.762888540326181
Adjusted R-squared	0.740933775541568
F-TEST (value)	34.7481992091692
F-TEST (DF numerator)	5
F-TEST (DF denominator)	54
p-value	9.9920072216264e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0455153120510615
Sum Squared Residuals	0.111868756079697

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.873434909037978 \tabularnewline
R-squared & 0.762888540326181 \tabularnewline
Adjusted R-squared & 0.740933775541568 \tabularnewline
F-TEST (value) & 34.7481992091692 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 9.9920072216264e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0455153120510615 \tabularnewline
Sum Squared Residuals & 0.111868756079697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159440&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.873434909037978[/C][/ROW]
[ROW][C]R-squared[/C][C]0.762888540326181[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.740933775541568[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]34.7481992091692[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]9.9920072216264e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0455153120510615[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.111868756079697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159440&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159440&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	0.873434909037978
R-squared	0.762888540326181
Adjusted R-squared	0.740933775541568
F-TEST (value)	34.7481992091692
F-TEST (DF numerator)	5
F-TEST (DF denominator)	54
p-value	9.9920072216264e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0455153120510615
Sum Squared Residuals	0.111868756079697

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.0622	1.0523140575556	0.00988594244439663
2	1.0773	1.07458172016219	0.00271827983780483
3	1.0807	1.08576313164121	-0.00506313164120515
4	1.0848	1.12319831575466	-0.0383983157546593
5	1.1582	1.15407054006567	0.00412945993432607
6	1.1663	1.19717856979168	-0.0308785697916795
7	1.1372	1.19126844270693	-0.0540684427069343
8	1.1139	1.183372021911	-0.0694720219110025
9	1.1222	1.19312665313912	-0.0709266531391235
10	1.1692	1.1919374819131	-0.0227374819131025
11	1.1702	1.17281872777452	-0.00261872777451986
12	1.2286	1.16898387597764	0.0596161240223644
13	1.2613	1.18342954628691	0.0778704537130861
14	1.2646	1.1797225691527	0.0848774308473021
15	1.2262	1.22382627650097	0.00237372349903346
16	1.1985	1.19451087575022	0.00398912424978365
17	1.2007	1.19733503471072	0.00336496528928464
18	1.2138	1.20577672250885	0.008023277491151
19	1.2266	1.20513624734067	0.0214637526593346
20	1.2176	1.2047621807335	0.0128378192664996
21	1.2218	1.21956585103053	0.00223414896946936
22	1.249	1.23563622520583	0.0133637747941679
23	1.2991	1.22968344842678	0.0694165515732171
24	1.3408	1.24298915134633	0.097810848653673
25	1.3119	1.24276198435669	0.069138015643308
26	1.3014	1.26902133376076	0.0323786662392367
27	1.3201	1.2449260326049	0.075173967395102
28	1.2938	1.25125616187924	0.0425438381207648
29	1.2694	1.25074992783209	0.0186500721679115
30	1.2165	1.24810091355855	-0.0316009135585498
31	1.2037	1.24617435804954	-0.0424743580495364
32	1.2292	1.23616952474887	-0.00696952474887039
33	1.2256	1.24703698319012	-0.0214369831901163
34	1.2015	1.26064346239234	-0.0591434623923374
35	1.1786	1.2792449773977	-0.1006449773977
36	1.1856	1.27077255361642	-0.0851725536164227
37	1.2103	1.2366879496103	-0.0263879496103015
38	1.1938	1.26790735650661	-0.0741073565066074
39	1.202	1.26081722218788	-0.0588172221878824
40	1.2271	1.26284929067972	-0.0357492906797201
41	1.277	1.24807394919865	0.0289260508013463
42	1.265	1.24943336174507	0.015566638254926
43	1.2684	1.27455213317016	-0.00615213317016278
44	1.2811	1.28704403639588	-0.00594403639588019
45	1.2727	1.28622995668779	-0.0135299566877863
46	1.2611	1.28719499367987	-0.0260949936798658
47	1.2881	1.30314646950093	-0.0150464695009267
48	1.3213	1.28398723167975	0.037312768320251
49	1.2999	1.32907465248957	-0.0291746524895689
50	1.3074	1.31414359324932	-0.00674359324932318
51	1.3242	1.29852775573617	0.0256722442638302
52	1.3516	1.32267946139153	0.028920538608467
53	1.3511	1.31434687643364	0.0367531235663643
54	1.3419	1.35351799138396	-0.0116179913839552
55	1.3716	1.38701104102161	-0.0154110410216061
56	1.3622	1.38369573726102	-0.0214957372610183
57	1.3896	1.39616103647913	-0.00656103647912936
58	1.4227	1.3932711718087	0.0294288281912998
59	1.4684	1.39099804430364	0.0774019556963564
60	1.457	1.45440280662457	0.00259719337543472

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.0622 & 1.0523140575556 & 0.00988594244439663 \tabularnewline
2 & 1.0773 & 1.07458172016219 & 0.00271827983780483 \tabularnewline
3 & 1.0807 & 1.08576313164121 & -0.00506313164120515 \tabularnewline
4 & 1.0848 & 1.12319831575466 & -0.0383983157546593 \tabularnewline
5 & 1.1582 & 1.15407054006567 & 0.00412945993432607 \tabularnewline
6 & 1.1663 & 1.19717856979168 & -0.0308785697916795 \tabularnewline
7 & 1.1372 & 1.19126844270693 & -0.0540684427069343 \tabularnewline
8 & 1.1139 & 1.183372021911 & -0.0694720219110025 \tabularnewline
9 & 1.1222 & 1.19312665313912 & -0.0709266531391235 \tabularnewline
10 & 1.1692 & 1.1919374819131 & -0.0227374819131025 \tabularnewline
11 & 1.1702 & 1.17281872777452 & -0.00261872777451986 \tabularnewline
12 & 1.2286 & 1.16898387597764 & 0.0596161240223644 \tabularnewline
13 & 1.2613 & 1.18342954628691 & 0.0778704537130861 \tabularnewline
14 & 1.2646 & 1.1797225691527 & 0.0848774308473021 \tabularnewline
15 & 1.2262 & 1.22382627650097 & 0.00237372349903346 \tabularnewline
16 & 1.1985 & 1.19451087575022 & 0.00398912424978365 \tabularnewline
17 & 1.2007 & 1.19733503471072 & 0.00336496528928464 \tabularnewline
18 & 1.2138 & 1.20577672250885 & 0.008023277491151 \tabularnewline
19 & 1.2266 & 1.20513624734067 & 0.0214637526593346 \tabularnewline
20 & 1.2176 & 1.2047621807335 & 0.0128378192664996 \tabularnewline
21 & 1.2218 & 1.21956585103053 & 0.00223414896946936 \tabularnewline
22 & 1.249 & 1.23563622520583 & 0.0133637747941679 \tabularnewline
23 & 1.2991 & 1.22968344842678 & 0.0694165515732171 \tabularnewline
24 & 1.3408 & 1.24298915134633 & 0.097810848653673 \tabularnewline
25 & 1.3119 & 1.24276198435669 & 0.069138015643308 \tabularnewline
26 & 1.3014 & 1.26902133376076 & 0.0323786662392367 \tabularnewline
27 & 1.3201 & 1.2449260326049 & 0.075173967395102 \tabularnewline
28 & 1.2938 & 1.25125616187924 & 0.0425438381207648 \tabularnewline
29 & 1.2694 & 1.25074992783209 & 0.0186500721679115 \tabularnewline
30 & 1.2165 & 1.24810091355855 & -0.0316009135585498 \tabularnewline
31 & 1.2037 & 1.24617435804954 & -0.0424743580495364 \tabularnewline
32 & 1.2292 & 1.23616952474887 & -0.00696952474887039 \tabularnewline
33 & 1.2256 & 1.24703698319012 & -0.0214369831901163 \tabularnewline
34 & 1.2015 & 1.26064346239234 & -0.0591434623923374 \tabularnewline
35 & 1.1786 & 1.2792449773977 & -0.1006449773977 \tabularnewline
36 & 1.1856 & 1.27077255361642 & -0.0851725536164227 \tabularnewline
37 & 1.2103 & 1.2366879496103 & -0.0263879496103015 \tabularnewline
38 & 1.1938 & 1.26790735650661 & -0.0741073565066074 \tabularnewline
39 & 1.202 & 1.26081722218788 & -0.0588172221878824 \tabularnewline
40 & 1.2271 & 1.26284929067972 & -0.0357492906797201 \tabularnewline
41 & 1.277 & 1.24807394919865 & 0.0289260508013463 \tabularnewline
42 & 1.265 & 1.24943336174507 & 0.015566638254926 \tabularnewline
43 & 1.2684 & 1.27455213317016 & -0.00615213317016278 \tabularnewline
44 & 1.2811 & 1.28704403639588 & -0.00594403639588019 \tabularnewline
45 & 1.2727 & 1.28622995668779 & -0.0135299566877863 \tabularnewline
46 & 1.2611 & 1.28719499367987 & -0.0260949936798658 \tabularnewline
47 & 1.2881 & 1.30314646950093 & -0.0150464695009267 \tabularnewline
48 & 1.3213 & 1.28398723167975 & 0.037312768320251 \tabularnewline
49 & 1.2999 & 1.32907465248957 & -0.0291746524895689 \tabularnewline
50 & 1.3074 & 1.31414359324932 & -0.00674359324932318 \tabularnewline
51 & 1.3242 & 1.29852775573617 & 0.0256722442638302 \tabularnewline
52 & 1.3516 & 1.32267946139153 & 0.028920538608467 \tabularnewline
53 & 1.3511 & 1.31434687643364 & 0.0367531235663643 \tabularnewline
54 & 1.3419 & 1.35351799138396 & -0.0116179913839552 \tabularnewline
55 & 1.3716 & 1.38701104102161 & -0.0154110410216061 \tabularnewline
56 & 1.3622 & 1.38369573726102 & -0.0214957372610183 \tabularnewline
57 & 1.3896 & 1.39616103647913 & -0.00656103647912936 \tabularnewline
58 & 1.4227 & 1.3932711718087 & 0.0294288281912998 \tabularnewline
59 & 1.4684 & 1.39099804430364 & 0.0774019556963564 \tabularnewline
60 & 1.457 & 1.45440280662457 & 0.00259719337543472 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159440&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1.0622[/C][C]1.0523140575556[/C][C]0.00988594244439663[/C][/ROW]
[ROW][C]2[/C][C]1.0773[/C][C]1.07458172016219[/C][C]0.00271827983780483[/C][/ROW]
[ROW][C]3[/C][C]1.0807[/C][C]1.08576313164121[/C][C]-0.00506313164120515[/C][/ROW]
[ROW][C]4[/C][C]1.0848[/C][C]1.12319831575466[/C][C]-0.0383983157546593[/C][/ROW]
[ROW][C]5[/C][C]1.1582[/C][C]1.15407054006567[/C][C]0.00412945993432607[/C][/ROW]
[ROW][C]6[/C][C]1.1663[/C][C]1.19717856979168[/C][C]-0.0308785697916795[/C][/ROW]
[ROW][C]7[/C][C]1.1372[/C][C]1.19126844270693[/C][C]-0.0540684427069343[/C][/ROW]
[ROW][C]8[/C][C]1.1139[/C][C]1.183372021911[/C][C]-0.0694720219110025[/C][/ROW]
[ROW][C]9[/C][C]1.1222[/C][C]1.19312665313912[/C][C]-0.0709266531391235[/C][/ROW]
[ROW][C]10[/C][C]1.1692[/C][C]1.1919374819131[/C][C]-0.0227374819131025[/C][/ROW]
[ROW][C]11[/C][C]1.1702[/C][C]1.17281872777452[/C][C]-0.00261872777451986[/C][/ROW]
[ROW][C]12[/C][C]1.2286[/C][C]1.16898387597764[/C][C]0.0596161240223644[/C][/ROW]
[ROW][C]13[/C][C]1.2613[/C][C]1.18342954628691[/C][C]0.0778704537130861[/C][/ROW]
[ROW][C]14[/C][C]1.2646[/C][C]1.1797225691527[/C][C]0.0848774308473021[/C][/ROW]
[ROW][C]15[/C][C]1.2262[/C][C]1.22382627650097[/C][C]0.00237372349903346[/C][/ROW]
[ROW][C]16[/C][C]1.1985[/C][C]1.19451087575022[/C][C]0.00398912424978365[/C][/ROW]
[ROW][C]17[/C][C]1.2007[/C][C]1.19733503471072[/C][C]0.00336496528928464[/C][/ROW]
[ROW][C]18[/C][C]1.2138[/C][C]1.20577672250885[/C][C]0.008023277491151[/C][/ROW]
[ROW][C]19[/C][C]1.2266[/C][C]1.20513624734067[/C][C]0.0214637526593346[/C][/ROW]
[ROW][C]20[/C][C]1.2176[/C][C]1.2047621807335[/C][C]0.0128378192664996[/C][/ROW]
[ROW][C]21[/C][C]1.2218[/C][C]1.21956585103053[/C][C]0.00223414896946936[/C][/ROW]
[ROW][C]22[/C][C]1.249[/C][C]1.23563622520583[/C][C]0.0133637747941679[/C][/ROW]
[ROW][C]23[/C][C]1.2991[/C][C]1.22968344842678[/C][C]0.0694165515732171[/C][/ROW]
[ROW][C]24[/C][C]1.3408[/C][C]1.24298915134633[/C][C]0.097810848653673[/C][/ROW]
[ROW][C]25[/C][C]1.3119[/C][C]1.24276198435669[/C][C]0.069138015643308[/C][/ROW]
[ROW][C]26[/C][C]1.3014[/C][C]1.26902133376076[/C][C]0.0323786662392367[/C][/ROW]
[ROW][C]27[/C][C]1.3201[/C][C]1.2449260326049[/C][C]0.075173967395102[/C][/ROW]
[ROW][C]28[/C][C]1.2938[/C][C]1.25125616187924[/C][C]0.0425438381207648[/C][/ROW]
[ROW][C]29[/C][C]1.2694[/C][C]1.25074992783209[/C][C]0.0186500721679115[/C][/ROW]
[ROW][C]30[/C][C]1.2165[/C][C]1.24810091355855[/C][C]-0.0316009135585498[/C][/ROW]
[ROW][C]31[/C][C]1.2037[/C][C]1.24617435804954[/C][C]-0.0424743580495364[/C][/ROW]
[ROW][C]32[/C][C]1.2292[/C][C]1.23616952474887[/C][C]-0.00696952474887039[/C][/ROW]
[ROW][C]33[/C][C]1.2256[/C][C]1.24703698319012[/C][C]-0.0214369831901163[/C][/ROW]
[ROW][C]34[/C][C]1.2015[/C][C]1.26064346239234[/C][C]-0.0591434623923374[/C][/ROW]
[ROW][C]35[/C][C]1.1786[/C][C]1.2792449773977[/C][C]-0.1006449773977[/C][/ROW]
[ROW][C]36[/C][C]1.1856[/C][C]1.27077255361642[/C][C]-0.0851725536164227[/C][/ROW]
[ROW][C]37[/C][C]1.2103[/C][C]1.2366879496103[/C][C]-0.0263879496103015[/C][/ROW]
[ROW][C]38[/C][C]1.1938[/C][C]1.26790735650661[/C][C]-0.0741073565066074[/C][/ROW]
[ROW][C]39[/C][C]1.202[/C][C]1.26081722218788[/C][C]-0.0588172221878824[/C][/ROW]
[ROW][C]40[/C][C]1.2271[/C][C]1.26284929067972[/C][C]-0.0357492906797201[/C][/ROW]
[ROW][C]41[/C][C]1.277[/C][C]1.24807394919865[/C][C]0.0289260508013463[/C][/ROW]
[ROW][C]42[/C][C]1.265[/C][C]1.24943336174507[/C][C]0.015566638254926[/C][/ROW]
[ROW][C]43[/C][C]1.2684[/C][C]1.27455213317016[/C][C]-0.00615213317016278[/C][/ROW]
[ROW][C]44[/C][C]1.2811[/C][C]1.28704403639588[/C][C]-0.00594403639588019[/C][/ROW]
[ROW][C]45[/C][C]1.2727[/C][C]1.28622995668779[/C][C]-0.0135299566877863[/C][/ROW]
[ROW][C]46[/C][C]1.2611[/C][C]1.28719499367987[/C][C]-0.0260949936798658[/C][/ROW]
[ROW][C]47[/C][C]1.2881[/C][C]1.30314646950093[/C][C]-0.0150464695009267[/C][/ROW]
[ROW][C]48[/C][C]1.3213[/C][C]1.28398723167975[/C][C]0.037312768320251[/C][/ROW]
[ROW][C]49[/C][C]1.2999[/C][C]1.32907465248957[/C][C]-0.0291746524895689[/C][/ROW]
[ROW][C]50[/C][C]1.3074[/C][C]1.31414359324932[/C][C]-0.00674359324932318[/C][/ROW]
[ROW][C]51[/C][C]1.3242[/C][C]1.29852775573617[/C][C]0.0256722442638302[/C][/ROW]
[ROW][C]52[/C][C]1.3516[/C][C]1.32267946139153[/C][C]0.028920538608467[/C][/ROW]
[ROW][C]53[/C][C]1.3511[/C][C]1.31434687643364[/C][C]0.0367531235663643[/C][/ROW]
[ROW][C]54[/C][C]1.3419[/C][C]1.35351799138396[/C][C]-0.0116179913839552[/C][/ROW]
[ROW][C]55[/C][C]1.3716[/C][C]1.38701104102161[/C][C]-0.0154110410216061[/C][/ROW]
[ROW][C]56[/C][C]1.3622[/C][C]1.38369573726102[/C][C]-0.0214957372610183[/C][/ROW]
[ROW][C]57[/C][C]1.3896[/C][C]1.39616103647913[/C][C]-0.00656103647912936[/C][/ROW]
[ROW][C]58[/C][C]1.4227[/C][C]1.3932711718087[/C][C]0.0294288281912998[/C][/ROW]
[ROW][C]59[/C][C]1.4684[/C][C]1.39099804430364[/C][C]0.0774019556963564[/C][/ROW]
[ROW][C]60[/C][C]1.457[/C][C]1.45440280662457[/C][C]0.00259719337543472[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159440&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159440&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	1.0622	1.0523140575556	0.00988594244439663
2	1.0773	1.07458172016219	0.00271827983780483
3	1.0807	1.08576313164121	-0.00506313164120515
4	1.0848	1.12319831575466	-0.0383983157546593
5	1.1582	1.15407054006567	0.00412945993432607
6	1.1663	1.19717856979168	-0.0308785697916795
7	1.1372	1.19126844270693	-0.0540684427069343
8	1.1139	1.183372021911	-0.0694720219110025
9	1.1222	1.19312665313912	-0.0709266531391235
10	1.1692	1.1919374819131	-0.0227374819131025
11	1.1702	1.17281872777452	-0.00261872777451986
12	1.2286	1.16898387597764	0.0596161240223644
13	1.2613	1.18342954628691	0.0778704537130861
14	1.2646	1.1797225691527	0.0848774308473021
15	1.2262	1.22382627650097	0.00237372349903346
16	1.1985	1.19451087575022	0.00398912424978365
17	1.2007	1.19733503471072	0.00336496528928464
18	1.2138	1.20577672250885	0.008023277491151
19	1.2266	1.20513624734067	0.0214637526593346
20	1.2176	1.2047621807335	0.0128378192664996
21	1.2218	1.21956585103053	0.00223414896946936
22	1.249	1.23563622520583	0.0133637747941679
23	1.2991	1.22968344842678	0.0694165515732171
24	1.3408	1.24298915134633	0.097810848653673
25	1.3119	1.24276198435669	0.069138015643308
26	1.3014	1.26902133376076	0.0323786662392367
27	1.3201	1.2449260326049	0.075173967395102
28	1.2938	1.25125616187924	0.0425438381207648
29	1.2694	1.25074992783209	0.0186500721679115
30	1.2165	1.24810091355855	-0.0316009135585498
31	1.2037	1.24617435804954	-0.0424743580495364
32	1.2292	1.23616952474887	-0.00696952474887039
33	1.2256	1.24703698319012	-0.0214369831901163
34	1.2015	1.26064346239234	-0.0591434623923374
35	1.1786	1.2792449773977	-0.1006449773977
36	1.1856	1.27077255361642	-0.0851725536164227
37	1.2103	1.2366879496103	-0.0263879496103015
38	1.1938	1.26790735650661	-0.0741073565066074
39	1.202	1.26081722218788	-0.0588172221878824
40	1.2271	1.26284929067972	-0.0357492906797201
41	1.277	1.24807394919865	0.0289260508013463
42	1.265	1.24943336174507	0.015566638254926
43	1.2684	1.27455213317016	-0.00615213317016278
44	1.2811	1.28704403639588	-0.00594403639588019
45	1.2727	1.28622995668779	-0.0135299566877863
46	1.2611	1.28719499367987	-0.0260949936798658
47	1.2881	1.30314646950093	-0.0150464695009267
48	1.3213	1.28398723167975	0.037312768320251
49	1.2999	1.32907465248957	-0.0291746524895689
50	1.3074	1.31414359324932	-0.00674359324932318
51	1.3242	1.29852775573617	0.0256722442638302
52	1.3516	1.32267946139153	0.028920538608467
53	1.3511	1.31434687643364	0.0367531235663643
54	1.3419	1.35351799138396	-0.0116179913839552
55	1.3716	1.38701104102161	-0.0154110410216061
56	1.3622	1.38369573726102	-0.0214957372610183
57	1.3896	1.39616103647913	-0.00656103647912936
58	1.4227	1.3932711718087	0.0294288281912998
59	1.4684	1.39099804430364	0.0774019556963564
60	1.457	1.45440280662457	0.00259719337543472

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0426342117869752	0.0852684235739504	0.957365788213025
10	0.0319160444025115	0.063832088805023	0.968083955597488
11	0.0252475927776254	0.0504951855552509	0.974752407222375
12	0.0115104439041542	0.0230208878083084	0.988489556095846
13	0.0198455075073316	0.0396910150146631	0.980154492492668
14	0.00900694450737895	0.0180138890147579	0.99099305549262
15	0.0431838977669037	0.0863677955338075	0.956816102233096
16	0.0440084786157899	0.0880169572315797	0.95599152138421
17	0.0277080883756481	0.0554161767512961	0.972291911624352
18	0.0260432714667353	0.0520865429334705	0.973956728533265
19	0.0184158520160773	0.0368317040321547	0.981584147983923
20	0.0382510474465934	0.0765020948931867	0.961748952553407
21	0.137262811579933	0.274525623159867	0.862737188420067
22	0.398925582407694	0.797851164815389	0.601074417592306
23	0.486006726437807	0.972013452875614	0.513993273562193
24	0.739069840031117	0.521860319937765	0.260930159968883
25	0.679318259636451	0.641363480727098	0.320681740363549
26	0.62078820968512	0.75842358062976	0.37921179031488
27	0.655039263243558	0.689921473512884	0.344960736756442
28	0.83604007325979	0.32791985348042	0.16395992674021
29	0.944376542719852	0.111246914560297	0.0556234572801483
30	0.97215859558603	0.0556828088279399	0.02784140441397
31	0.964606152671196	0.0707876946576086	0.0353938473288043
32	0.998599545791536	0.00280090841692794	0.00140045420846397
33	0.999958069665223	8.38606695538136e-05	4.19303347769068e-05
34	0.999982820478115	3.43590437690103e-05	1.71795218845052e-05
35	0.99997650290056	4.69941988799165e-05	2.34970994399583e-05
36	0.999975117727455	4.97645450904618e-05	2.48822725452309e-05
37	0.999988178691288	2.36426174243101e-05	1.1821308712155e-05
38	0.999982737118813	3.45257623740219e-05	1.72628811870109e-05
39	0.999990928851338	1.81422973230288e-05	9.07114866151438e-06
40	0.999996248215742	7.50356851685284e-06	3.75178425842642e-06
41	0.999998258760203	3.48247959436799e-06	1.741239797184e-06
42	0.999997715437355	4.56912529040335e-06	2.28456264520167e-06
43	0.999994924978891	1.01500422172292e-05	5.07502110861458e-06
44	0.999985967463453	2.80650730935237e-05	1.40325365467619e-05
45	0.99994235059174	0.000115298816521783	5.76494082608917e-05
46	0.999778015815264	0.000443968369471556	0.000221984184735778
47	0.999331887654037	0.00133622469192667	0.000668112345963333
48	0.998994021192814	0.0020119576143715	0.00100597880718575
49	0.99578861084746	0.00842277830508071	0.00421138915254035
50	0.986102521957052	0.0277949560858952	0.0138974780429476
51	0.97378692651448	0.052426146971038	0.026213073485519

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0426342117869752 & 0.0852684235739504 & 0.957365788213025 \tabularnewline
10 & 0.0319160444025115 & 0.063832088805023 & 0.968083955597488 \tabularnewline
11 & 0.0252475927776254 & 0.0504951855552509 & 0.974752407222375 \tabularnewline
12 & 0.0115104439041542 & 0.0230208878083084 & 0.988489556095846 \tabularnewline
13 & 0.0198455075073316 & 0.0396910150146631 & 0.980154492492668 \tabularnewline
14 & 0.00900694450737895 & 0.0180138890147579 & 0.99099305549262 \tabularnewline
15 & 0.0431838977669037 & 0.0863677955338075 & 0.956816102233096 \tabularnewline
16 & 0.0440084786157899 & 0.0880169572315797 & 0.95599152138421 \tabularnewline
17 & 0.0277080883756481 & 0.0554161767512961 & 0.972291911624352 \tabularnewline
18 & 0.0260432714667353 & 0.0520865429334705 & 0.973956728533265 \tabularnewline
19 & 0.0184158520160773 & 0.0368317040321547 & 0.981584147983923 \tabularnewline
20 & 0.0382510474465934 & 0.0765020948931867 & 0.961748952553407 \tabularnewline
21 & 0.137262811579933 & 0.274525623159867 & 0.862737188420067 \tabularnewline
22 & 0.398925582407694 & 0.797851164815389 & 0.601074417592306 \tabularnewline
23 & 0.486006726437807 & 0.972013452875614 & 0.513993273562193 \tabularnewline
24 & 0.739069840031117 & 0.521860319937765 & 0.260930159968883 \tabularnewline
25 & 0.679318259636451 & 0.641363480727098 & 0.320681740363549 \tabularnewline
26 & 0.62078820968512 & 0.75842358062976 & 0.37921179031488 \tabularnewline
27 & 0.655039263243558 & 0.689921473512884 & 0.344960736756442 \tabularnewline
28 & 0.83604007325979 & 0.32791985348042 & 0.16395992674021 \tabularnewline
29 & 0.944376542719852 & 0.111246914560297 & 0.0556234572801483 \tabularnewline
30 & 0.97215859558603 & 0.0556828088279399 & 0.02784140441397 \tabularnewline
31 & 0.964606152671196 & 0.0707876946576086 & 0.0353938473288043 \tabularnewline
32 & 0.998599545791536 & 0.00280090841692794 & 0.00140045420846397 \tabularnewline
33 & 0.999958069665223 & 8.38606695538136e-05 & 4.19303347769068e-05 \tabularnewline
34 & 0.999982820478115 & 3.43590437690103e-05 & 1.71795218845052e-05 \tabularnewline
35 & 0.99997650290056 & 4.69941988799165e-05 & 2.34970994399583e-05 \tabularnewline
36 & 0.999975117727455 & 4.97645450904618e-05 & 2.48822725452309e-05 \tabularnewline
37 & 0.999988178691288 & 2.36426174243101e-05 & 1.1821308712155e-05 \tabularnewline
38 & 0.999982737118813 & 3.45257623740219e-05 & 1.72628811870109e-05 \tabularnewline
39 & 0.999990928851338 & 1.81422973230288e-05 & 9.07114866151438e-06 \tabularnewline
40 & 0.999996248215742 & 7.50356851685284e-06 & 3.75178425842642e-06 \tabularnewline
41 & 0.999998258760203 & 3.48247959436799e-06 & 1.741239797184e-06 \tabularnewline
42 & 0.999997715437355 & 4.56912529040335e-06 & 2.28456264520167e-06 \tabularnewline
43 & 0.999994924978891 & 1.01500422172292e-05 & 5.07502110861458e-06 \tabularnewline
44 & 0.999985967463453 & 2.80650730935237e-05 & 1.40325365467619e-05 \tabularnewline
45 & 0.99994235059174 & 0.000115298816521783 & 5.76494082608917e-05 \tabularnewline
46 & 0.999778015815264 & 0.000443968369471556 & 0.000221984184735778 \tabularnewline
47 & 0.999331887654037 & 0.00133622469192667 & 0.000668112345963333 \tabularnewline
48 & 0.998994021192814 & 0.0020119576143715 & 0.00100597880718575 \tabularnewline
49 & 0.99578861084746 & 0.00842277830508071 & 0.00421138915254035 \tabularnewline
50 & 0.986102521957052 & 0.0277949560858952 & 0.0138974780429476 \tabularnewline
51 & 0.97378692651448 & 0.052426146971038 & 0.026213073485519 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159440&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]9[/C][C]0.0426342117869752[/C][C]0.0852684235739504[/C][C]0.957365788213025[/C][/ROW]
[ROW][C]10[/C][C]0.0319160444025115[/C][C]0.063832088805023[/C][C]0.968083955597488[/C][/ROW]
[ROW][C]11[/C][C]0.0252475927776254[/C][C]0.0504951855552509[/C][C]0.974752407222375[/C][/ROW]
[ROW][C]12[/C][C]0.0115104439041542[/C][C]0.0230208878083084[/C][C]0.988489556095846[/C][/ROW]
[ROW][C]13[/C][C]0.0198455075073316[/C][C]0.0396910150146631[/C][C]0.980154492492668[/C][/ROW]
[ROW][C]14[/C][C]0.00900694450737895[/C][C]0.0180138890147579[/C][C]0.99099305549262[/C][/ROW]
[ROW][C]15[/C][C]0.0431838977669037[/C][C]0.0863677955338075[/C][C]0.956816102233096[/C][/ROW]
[ROW][C]16[/C][C]0.0440084786157899[/C][C]0.0880169572315797[/C][C]0.95599152138421[/C][/ROW]
[ROW][C]17[/C][C]0.0277080883756481[/C][C]0.0554161767512961[/C][C]0.972291911624352[/C][/ROW]
[ROW][C]18[/C][C]0.0260432714667353[/C][C]0.0520865429334705[/C][C]0.973956728533265[/C][/ROW]
[ROW][C]19[/C][C]0.0184158520160773[/C][C]0.0368317040321547[/C][C]0.981584147983923[/C][/ROW]
[ROW][C]20[/C][C]0.0382510474465934[/C][C]0.0765020948931867[/C][C]0.961748952553407[/C][/ROW]
[ROW][C]21[/C][C]0.137262811579933[/C][C]0.274525623159867[/C][C]0.862737188420067[/C][/ROW]
[ROW][C]22[/C][C]0.398925582407694[/C][C]0.797851164815389[/C][C]0.601074417592306[/C][/ROW]
[ROW][C]23[/C][C]0.486006726437807[/C][C]0.972013452875614[/C][C]0.513993273562193[/C][/ROW]
[ROW][C]24[/C][C]0.739069840031117[/C][C]0.521860319937765[/C][C]0.260930159968883[/C][/ROW]
[ROW][C]25[/C][C]0.679318259636451[/C][C]0.641363480727098[/C][C]0.320681740363549[/C][/ROW]
[ROW][C]26[/C][C]0.62078820968512[/C][C]0.75842358062976[/C][C]0.37921179031488[/C][/ROW]
[ROW][C]27[/C][C]0.655039263243558[/C][C]0.689921473512884[/C][C]0.344960736756442[/C][/ROW]
[ROW][C]28[/C][C]0.83604007325979[/C][C]0.32791985348042[/C][C]0.16395992674021[/C][/ROW]
[ROW][C]29[/C][C]0.944376542719852[/C][C]0.111246914560297[/C][C]0.0556234572801483[/C][/ROW]
[ROW][C]30[/C][C]0.97215859558603[/C][C]0.0556828088279399[/C][C]0.02784140441397[/C][/ROW]
[ROW][C]31[/C][C]0.964606152671196[/C][C]0.0707876946576086[/C][C]0.0353938473288043[/C][/ROW]
[ROW][C]32[/C][C]0.998599545791536[/C][C]0.00280090841692794[/C][C]0.00140045420846397[/C][/ROW]
[ROW][C]33[/C][C]0.999958069665223[/C][C]8.38606695538136e-05[/C][C]4.19303347769068e-05[/C][/ROW]
[ROW][C]34[/C][C]0.999982820478115[/C][C]3.43590437690103e-05[/C][C]1.71795218845052e-05[/C][/ROW]
[ROW][C]35[/C][C]0.99997650290056[/C][C]4.69941988799165e-05[/C][C]2.34970994399583e-05[/C][/ROW]
[ROW][C]36[/C][C]0.999975117727455[/C][C]4.97645450904618e-05[/C][C]2.48822725452309e-05[/C][/ROW]
[ROW][C]37[/C][C]0.999988178691288[/C][C]2.36426174243101e-05[/C][C]1.1821308712155e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999982737118813[/C][C]3.45257623740219e-05[/C][C]1.72628811870109e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999990928851338[/C][C]1.81422973230288e-05[/C][C]9.07114866151438e-06[/C][/ROW]
[ROW][C]40[/C][C]0.999996248215742[/C][C]7.50356851685284e-06[/C][C]3.75178425842642e-06[/C][/ROW]
[ROW][C]41[/C][C]0.999998258760203[/C][C]3.48247959436799e-06[/C][C]1.741239797184e-06[/C][/ROW]
[ROW][C]42[/C][C]0.999997715437355[/C][C]4.56912529040335e-06[/C][C]2.28456264520167e-06[/C][/ROW]
[ROW][C]43[/C][C]0.999994924978891[/C][C]1.01500422172292e-05[/C][C]5.07502110861458e-06[/C][/ROW]
[ROW][C]44[/C][C]0.999985967463453[/C][C]2.80650730935237e-05[/C][C]1.40325365467619e-05[/C][/ROW]
[ROW][C]45[/C][C]0.99994235059174[/C][C]0.000115298816521783[/C][C]5.76494082608917e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999778015815264[/C][C]0.000443968369471556[/C][C]0.000221984184735778[/C][/ROW]
[ROW][C]47[/C][C]0.999331887654037[/C][C]0.00133622469192667[/C][C]0.000668112345963333[/C][/ROW]
[ROW][C]48[/C][C]0.998994021192814[/C][C]0.0020119576143715[/C][C]0.00100597880718575[/C][/ROW]
[ROW][C]49[/C][C]0.99578861084746[/C][C]0.00842277830508071[/C][C]0.00421138915254035[/C][/ROW]
[ROW][C]50[/C][C]0.986102521957052[/C][C]0.0277949560858952[/C][C]0.0138974780429476[/C][/ROW]
[ROW][C]51[/C][C]0.97378692651448[/C][C]0.052426146971038[/C][C]0.026213073485519[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159440&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159440&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
9	0.0426342117869752	0.0852684235739504	0.957365788213025
10	0.0319160444025115	0.063832088805023	0.968083955597488
11	0.0252475927776254	0.0504951855552509	0.974752407222375
12	0.0115104439041542	0.0230208878083084	0.988489556095846
13	0.0198455075073316	0.0396910150146631	0.980154492492668
14	0.00900694450737895	0.0180138890147579	0.99099305549262
15	0.0431838977669037	0.0863677955338075	0.956816102233096
16	0.0440084786157899	0.0880169572315797	0.95599152138421
17	0.0277080883756481	0.0554161767512961	0.972291911624352
18	0.0260432714667353	0.0520865429334705	0.973956728533265
19	0.0184158520160773	0.0368317040321547	0.981584147983923
20	0.0382510474465934	0.0765020948931867	0.961748952553407
21	0.137262811579933	0.274525623159867	0.862737188420067
22	0.398925582407694	0.797851164815389	0.601074417592306
23	0.486006726437807	0.972013452875614	0.513993273562193
24	0.739069840031117	0.521860319937765	0.260930159968883
25	0.679318259636451	0.641363480727098	0.320681740363549
26	0.62078820968512	0.75842358062976	0.37921179031488
27	0.655039263243558	0.689921473512884	0.344960736756442
28	0.83604007325979	0.32791985348042	0.16395992674021
29	0.944376542719852	0.111246914560297	0.0556234572801483
30	0.97215859558603	0.0556828088279399	0.02784140441397
31	0.964606152671196	0.0707876946576086	0.0353938473288043
32	0.998599545791536	0.00280090841692794	0.00140045420846397
33	0.999958069665223	8.38606695538136e-05	4.19303347769068e-05
34	0.999982820478115	3.43590437690103e-05	1.71795218845052e-05
35	0.99997650290056	4.69941988799165e-05	2.34970994399583e-05
36	0.999975117727455	4.97645450904618e-05	2.48822725452309e-05
37	0.999988178691288	2.36426174243101e-05	1.1821308712155e-05
38	0.999982737118813	3.45257623740219e-05	1.72628811870109e-05
39	0.999990928851338	1.81422973230288e-05	9.07114866151438e-06
40	0.999996248215742	7.50356851685284e-06	3.75178425842642e-06
41	0.999998258760203	3.48247959436799e-06	1.741239797184e-06
42	0.999997715437355	4.56912529040335e-06	2.28456264520167e-06
43	0.999994924978891	1.01500422172292e-05	5.07502110861458e-06
44	0.999985967463453	2.80650730935237e-05	1.40325365467619e-05
45	0.99994235059174	0.000115298816521783	5.76494082608917e-05
46	0.999778015815264	0.000443968369471556	0.000221984184735778
47	0.999331887654037	0.00133622469192667	0.000668112345963333
48	0.998994021192814	0.0020119576143715	0.00100597880718575
49	0.99578861084746	0.00842277830508071	0.00421138915254035
50	0.986102521957052	0.0277949560858952	0.0138974780429476
51	0.97378692651448	0.052426146971038	0.026213073485519

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	18	0.418604651162791	NOK
5% type I error level	23	0.534883720930233	NOK
10% type I error level	34	0.790697674418605	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 & 18 & 0.418604651162791 & NOK \tabularnewline
5% type I error level & 23 & 0.534883720930233 & NOK \tabularnewline
10% type I error level & 34 & 0.790697674418605 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159440&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]18[/C][C]0.418604651162791[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.534883720930233[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.790697674418605[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159440&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159440&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	18	0.418604651162791	NOK
5% type I error level	23	0.534883720930233	NOK
10% type I error level	34	0.790697674418605	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