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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 20 Nov 2012 12:10:52 -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/2012/Nov/20/t13534314978f39ja183yhbsl5.htm/, Retrieved Mon, 29 Apr 2024 19:20:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191195, Retrieved Mon, 29 Apr 2024 19:20:38 +0000

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- R  D        [Multiple Regression] [] [2012-11-20 17:10:52] [73032bb7ab3591a95ced5a483893ad29] [Current]

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Dataseries X:

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Histogram

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6	10	2	2	5	6	9	2
2	7	4	9	4	6	5	5
2	2	10	1	1	5	7	5
2	10	4	10	6	3	9	3
6	8	3	3	9	1	8	5
5	5	7	3	1	7	2	7
3	3	5	6	10	9	1	2
5	9	9	4	3	10	10	6
1	4	3	3	10	7	9	6
5	6	8	4	8	3	9	4
1	4	2	9	1	10	9	5
6	4	9	10	7	9	1	5
1	2	5	3	7	4	2	5
10	5	6	6	1	7	7	4
6	3	5	8	7	2	4	2
9	4	3	5	7	10	8	5
6	9	5	9	9	1	10	4
2	3	7	4	9	1	5	4
6	6	1	5	4	10	4	3
7	2	10	9	8	4	8	10
7	3	6	10	9	7	5	5
5	9	2	7	4	5	3	3
10	8	7	1	9	2	8	2
10	8	2	10	7	7	2	9
7	10	7	5	9	4	6	6
8	4	9	3	7	3	3	9
2	7	9	10	3	3	1	4
6	5	9	6	6	8	4	10
6	1	6	6	10	7	4	2
7	2	9	6	4	10	6	8
5	6	5	4	10	7	8	7
8	10	7	1	9	7	2	5
9	10	9	3	2	8	6	9
5	8	1	1	8	2	7	10
1	5	4	8	8	4	1	2
3	1	7	5	5	4	10	3
2	9	3	8	5	4	1	7
9	2	6	7	9	10	3	9
1	10	7	3	5	5	7	3
6	8	8	5	5	8	4	6
8	5	2	4	8	9	9	8
9	9	1	8	7	5	3	3
10	5	3	6	4	5	1	1
4	2	9	3	3	2	6	3
3	10	2	7	3	1	7	6
7	5	5	7	8	8	7	10
4	2	8	10	3	9	8	8
7	1	8	7	10	7	10	5
5	3	1	9	2	9	9	8
6	5	1	2	8	9	8	3
10	6	1	8	2	6	1	6
10	6	10	1	6	4	1	9
10	10	2	10	9	7	8	9
10	8	1	5	4	1	8	6
5	7	5	2	7	7	7	1
4	6	10	4	10	4	2	3
10	10	6	4	3	5	10	6
6	9	8	10	3	1	10	9
2	1	2	10	7	5	3	6
4	9	9	6	10	9	10	10
3	2	8	6	10	3	5	5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'George Udny Yule' @ yule.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191195&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191195&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191195&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	8 seconds
R Server	'George Udny Yule' @ yule.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
yt[t] = + 3.45629811619316 + 0.196161753058943x2t[t] -0.0759301670223675x3t[t] -0.113069511933674x4t[t] + 0.0478247101671386x5t[t] + 0.139243237474657x6t[t] -0.0750676981882023x7t[t] + 0.267814648345915`x8t\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
yt[t] =  +  3.45629811619316 +  0.196161753058943x2t[t] -0.0759301670223675x3t[t] -0.113069511933674x4t[t] +  0.0478247101671386x5t[t] +  0.139243237474657x6t[t] -0.0750676981882023x7t[t] +  0.267814648345915`x8t\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191195&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]yt[t] =  +  3.45629811619316 +  0.196161753058943x2t[t] -0.0759301670223675x3t[t] -0.113069511933674x4t[t] +  0.0478247101671386x5t[t] +  0.139243237474657x6t[t] -0.0750676981882023x7t[t] +  0.267814648345915`x8t\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191195&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191195&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
yt[t] = + 3.45629811619316 + 0.196161753058943x2t[t] -0.0759301670223675x3t[t] -0.113069511933674x4t[t] + 0.0478247101671386x5t[t] + 0.139243237474657x6t[t] -0.0750676981882023x7t[t] + 0.267814648345915`x8t\r`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	3.45629811619316	2.178667	1.5864	0.118591	0.059296
x2t	0.196161753058943	0.132418	1.4814	0.144427	0.072214
x3t	-0.0759301670223675	0.13247	-0.5732	0.568942	0.284471
x4t	-0.113069511933674	0.134229	-0.8424	0.403369	0.201684
x5t	0.0478247101671386	0.13037	0.3668	0.7152	0.3576
x6t	0.139243237474657	0.136731	1.0184	0.313125	0.156563
x7t	-0.0750676981882023	0.121729	-0.6167	0.540086	0.270043
`x8t\r`	0.267814648345915	0.150226	1.7827	0.080355	0.040178

\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) & 3.45629811619316 & 2.178667 & 1.5864 & 0.118591 & 0.059296 \tabularnewline
x2t & 0.196161753058943 & 0.132418 & 1.4814 & 0.144427 & 0.072214 \tabularnewline
x3t & -0.0759301670223675 & 0.13247 & -0.5732 & 0.568942 & 0.284471 \tabularnewline
x4t & -0.113069511933674 & 0.134229 & -0.8424 & 0.403369 & 0.201684 \tabularnewline
x5t & 0.0478247101671386 & 0.13037 & 0.3668 & 0.7152 & 0.3576 \tabularnewline
x6t & 0.139243237474657 & 0.136731 & 1.0184 & 0.313125 & 0.156563 \tabularnewline
x7t & -0.0750676981882023 & 0.121729 & -0.6167 & 0.540086 & 0.270043 \tabularnewline
`x8t\r` & 0.267814648345915 & 0.150226 & 1.7827 & 0.080355 & 0.040178 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191195&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]3.45629811619316[/C][C]2.178667[/C][C]1.5864[/C][C]0.118591[/C][C]0.059296[/C][/ROW]
[ROW][C]x2t[/C][C]0.196161753058943[/C][C]0.132418[/C][C]1.4814[/C][C]0.144427[/C][C]0.072214[/C][/ROW]
[ROW][C]x3t[/C][C]-0.0759301670223675[/C][C]0.13247[/C][C]-0.5732[/C][C]0.568942[/C][C]0.284471[/C][/ROW]
[ROW][C]x4t[/C][C]-0.113069511933674[/C][C]0.134229[/C][C]-0.8424[/C][C]0.403369[/C][C]0.201684[/C][/ROW]
[ROW][C]x5t[/C][C]0.0478247101671386[/C][C]0.13037[/C][C]0.3668[/C][C]0.7152[/C][C]0.3576[/C][/ROW]
[ROW][C]x6t[/C][C]0.139243237474657[/C][C]0.136731[/C][C]1.0184[/C][C]0.313125[/C][C]0.156563[/C][/ROW]
[ROW][C]x7t[/C][C]-0.0750676981882023[/C][C]0.121729[/C][C]-0.6167[/C][C]0.540086[/C][C]0.270043[/C][/ROW]
[ROW][C]`x8t\r`[/C][C]0.267814648345915[/C][C]0.150226[/C][C]1.7827[/C][C]0.080355[/C][C]0.040178[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191195&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191195&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)	3.45629811619316	2.178667	1.5864	0.118591	0.059296
x2t	0.196161753058943	0.132418	1.4814	0.144427	0.072214
x3t	-0.0759301670223675	0.13247	-0.5732	0.568942	0.284471
x4t	-0.113069511933674	0.134229	-0.8424	0.403369	0.201684
x5t	0.0478247101671386	0.13037	0.3668	0.7152	0.3576
x6t	0.139243237474657	0.136731	1.0184	0.313125	0.156563
x7t	-0.0750676981882023	0.121729	-0.6167	0.540086	0.270043
`x8t\r`	0.267814648345915	0.150226	1.7827	0.080355	0.040178

Multiple Linear Regression - Regression Statistics
Multiple R	0.364556124942623
R-squared	0.132901168233181
Adjusted R-squared	0.0183786810186957
F-TEST (value)	1.16048098033598
F-TEST (DF numerator)	7
F-TEST (DF denominator)	53
p-value	0.341012012633835
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.82490682301822
Sum Squared Residuals	422.94522361295

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.364556124942623 \tabularnewline
R-squared & 0.132901168233181 \tabularnewline
Adjusted R-squared & 0.0183786810186957 \tabularnewline
F-TEST (value) & 1.16048098033598 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0.341012012633835 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.82490682301822 \tabularnewline
Sum Squared Residuals & 422.94522361295 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191195&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.364556124942623[/C][/ROW]
[ROW][C]R-squared[/C][C]0.132901168233181[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0183786810186957[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.16048098033598[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C]0.341012012633835[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.82490682301822[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]422.94522361295[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191195&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191195&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	0.364556124942623
R-squared	0.132901168233181
Adjusted R-squared	0.0183786810186957
F-TEST (value)	1.16048098033598
F-TEST (DF numerator)	7
F-TEST (DF denominator)	53
p-value	0.341012012633835
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.82490682301822
Sum Squared Residuals	422.94522361295

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6	5.97451927755216	0.0254807224478388
2	2	5.49857712841829	-3.49857712841829
3	2	4.53389069210628	-2.53389069210628
4	2	4.81601249412711	-2.81601249412711
5	6	5.76679038899944	0.233209611000556
6	5	6.31348169106502	-1.31348169106502
7	3	5.17871330610304	-2.17871330610304
8	5	6.4782217562291	-1.4782217562291
9	1	6.05817446193647	-5.05817446193647
10	5	4.7695259540841	0.230474045915896
11	1	5.1751802299306	-4.1751802299306
12	6	5.27884615787414	0.721153842125858
13	1	5.21044601781996	-4.21044601781996
14	10	4.87142088630761	5.12857911369239
15	6	3.60919439484707	2.39080560515293
16	9	5.91354406983396	3.08645593016604
17	6	4.71472469168929	1.28527530831071
18	2	4.32657988990027	-2.32657988990027
19	6	6.07889527555615	-0.0788952755561467
20	7	5.08886987387357	1.91113012612643
21	7	4.82736705851445	2.17263294148555
22	5	5.74416285465818	-0.744162854658176
23	10	5.02500803721423	4.97499196278577
24	10	7.31270875951263	2.68729124048737
25	7	6.4649339603068	0.535066039693197
26	8	6.15599651356917	1.84400348643083
27	2	4.57275850318856	-2.57275850318856
28	6	6.85408815819095	-0.854088158190954
29	6	4.20677006344886	1.79322993655114
30	7	5.76267526056093	1.23732473943907
31	5	6.52845046861006	-1.52845046861006
32	8	7.36739786487237	0.632602135127633
33	9	7.56485657389582	1.43514342610418
34	5	7.65034921413682	-2.65034921413682
35	1	4.62896234766838	-3.62896234766838
36	3	3.40446460431721	-0.404464604317205
37	2	6.68513863815468	-4.68513863815468
38	9	6.60953754344057	2.39046245655943
39	1	5.76050573775431	-4.76050573775431
40	6	6.51248979277303	-0.512489792773028
41	8	6.93566322139097	1.06433677860903
42	9	5.85049764024829	3.14950235975172
43	10	4.61116128701828	5.38883871298172
44	4	3.60103994466798	0.398960055332021
45	3	5.83870009993629	-2.83870009993629
46	7	6.91518564011642	0.0848143598835802
47	4	5.0491240358304	-1.0491240358304
48	7	4.29487797337898	2.70512202662102
49	5	5.76697406162425	-0.766974061624246
50	6	5.97372686873931	0.0262731312606854
51	10	6.11571140912457	3.88428859087543
52	10	6.94008280021596	3.05991719978404
53	10	7.35027549683558	2.64972450316442
54	10	5.72120279668705	4.27879720331295
55	5	5.27545692314765	-0.275456923147651
56	4	5.11021751681981	-1.11021751681981
57	10	6.20595782298186	3.79404217701814
58	6	5.42598965941525	0.574010340584749
59	2	4.78257836992476	-2.78257836992476
60	4	7.51887105944073	-3.51887105944072
61	3	4.42247477941398	-1.42247477941398

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6 & 5.97451927755216 & 0.0254807224478388 \tabularnewline
2 & 2 & 5.49857712841829 & -3.49857712841829 \tabularnewline
3 & 2 & 4.53389069210628 & -2.53389069210628 \tabularnewline
4 & 2 & 4.81601249412711 & -2.81601249412711 \tabularnewline
5 & 6 & 5.76679038899944 & 0.233209611000556 \tabularnewline
6 & 5 & 6.31348169106502 & -1.31348169106502 \tabularnewline
7 & 3 & 5.17871330610304 & -2.17871330610304 \tabularnewline
8 & 5 & 6.4782217562291 & -1.4782217562291 \tabularnewline
9 & 1 & 6.05817446193647 & -5.05817446193647 \tabularnewline
10 & 5 & 4.7695259540841 & 0.230474045915896 \tabularnewline
11 & 1 & 5.1751802299306 & -4.1751802299306 \tabularnewline
12 & 6 & 5.27884615787414 & 0.721153842125858 \tabularnewline
13 & 1 & 5.21044601781996 & -4.21044601781996 \tabularnewline
14 & 10 & 4.87142088630761 & 5.12857911369239 \tabularnewline
15 & 6 & 3.60919439484707 & 2.39080560515293 \tabularnewline
16 & 9 & 5.91354406983396 & 3.08645593016604 \tabularnewline
17 & 6 & 4.71472469168929 & 1.28527530831071 \tabularnewline
18 & 2 & 4.32657988990027 & -2.32657988990027 \tabularnewline
19 & 6 & 6.07889527555615 & -0.0788952755561467 \tabularnewline
20 & 7 & 5.08886987387357 & 1.91113012612643 \tabularnewline
21 & 7 & 4.82736705851445 & 2.17263294148555 \tabularnewline
22 & 5 & 5.74416285465818 & -0.744162854658176 \tabularnewline
23 & 10 & 5.02500803721423 & 4.97499196278577 \tabularnewline
24 & 10 & 7.31270875951263 & 2.68729124048737 \tabularnewline
25 & 7 & 6.4649339603068 & 0.535066039693197 \tabularnewline
26 & 8 & 6.15599651356917 & 1.84400348643083 \tabularnewline
27 & 2 & 4.57275850318856 & -2.57275850318856 \tabularnewline
28 & 6 & 6.85408815819095 & -0.854088158190954 \tabularnewline
29 & 6 & 4.20677006344886 & 1.79322993655114 \tabularnewline
30 & 7 & 5.76267526056093 & 1.23732473943907 \tabularnewline
31 & 5 & 6.52845046861006 & -1.52845046861006 \tabularnewline
32 & 8 & 7.36739786487237 & 0.632602135127633 \tabularnewline
33 & 9 & 7.56485657389582 & 1.43514342610418 \tabularnewline
34 & 5 & 7.65034921413682 & -2.65034921413682 \tabularnewline
35 & 1 & 4.62896234766838 & -3.62896234766838 \tabularnewline
36 & 3 & 3.40446460431721 & -0.404464604317205 \tabularnewline
37 & 2 & 6.68513863815468 & -4.68513863815468 \tabularnewline
38 & 9 & 6.60953754344057 & 2.39046245655943 \tabularnewline
39 & 1 & 5.76050573775431 & -4.76050573775431 \tabularnewline
40 & 6 & 6.51248979277303 & -0.512489792773028 \tabularnewline
41 & 8 & 6.93566322139097 & 1.06433677860903 \tabularnewline
42 & 9 & 5.85049764024829 & 3.14950235975172 \tabularnewline
43 & 10 & 4.61116128701828 & 5.38883871298172 \tabularnewline
44 & 4 & 3.60103994466798 & 0.398960055332021 \tabularnewline
45 & 3 & 5.83870009993629 & -2.83870009993629 \tabularnewline
46 & 7 & 6.91518564011642 & 0.0848143598835802 \tabularnewline
47 & 4 & 5.0491240358304 & -1.0491240358304 \tabularnewline
48 & 7 & 4.29487797337898 & 2.70512202662102 \tabularnewline
49 & 5 & 5.76697406162425 & -0.766974061624246 \tabularnewline
50 & 6 & 5.97372686873931 & 0.0262731312606854 \tabularnewline
51 & 10 & 6.11571140912457 & 3.88428859087543 \tabularnewline
52 & 10 & 6.94008280021596 & 3.05991719978404 \tabularnewline
53 & 10 & 7.35027549683558 & 2.64972450316442 \tabularnewline
54 & 10 & 5.72120279668705 & 4.27879720331295 \tabularnewline
55 & 5 & 5.27545692314765 & -0.275456923147651 \tabularnewline
56 & 4 & 5.11021751681981 & -1.11021751681981 \tabularnewline
57 & 10 & 6.20595782298186 & 3.79404217701814 \tabularnewline
58 & 6 & 5.42598965941525 & 0.574010340584749 \tabularnewline
59 & 2 & 4.78257836992476 & -2.78257836992476 \tabularnewline
60 & 4 & 7.51887105944073 & -3.51887105944072 \tabularnewline
61 & 3 & 4.42247477941398 & -1.42247477941398 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191195&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]6[/C][C]5.97451927755216[/C][C]0.0254807224478388[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]5.49857712841829[/C][C]-3.49857712841829[/C][/ROW]
[ROW][C]3[/C][C]2[/C][C]4.53389069210628[/C][C]-2.53389069210628[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]4.81601249412711[/C][C]-2.81601249412711[/C][/ROW]
[ROW][C]5[/C][C]6[/C][C]5.76679038899944[/C][C]0.233209611000556[/C][/ROW]
[ROW][C]6[/C][C]5[/C][C]6.31348169106502[/C][C]-1.31348169106502[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]5.17871330610304[/C][C]-2.17871330610304[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]6.4782217562291[/C][C]-1.4782217562291[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]6.05817446193647[/C][C]-5.05817446193647[/C][/ROW]
[ROW][C]10[/C][C]5[/C][C]4.7695259540841[/C][C]0.230474045915896[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]5.1751802299306[/C][C]-4.1751802299306[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]5.27884615787414[/C][C]0.721153842125858[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]5.21044601781996[/C][C]-4.21044601781996[/C][/ROW]
[ROW][C]14[/C][C]10[/C][C]4.87142088630761[/C][C]5.12857911369239[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]3.60919439484707[/C][C]2.39080560515293[/C][/ROW]
[ROW][C]16[/C][C]9[/C][C]5.91354406983396[/C][C]3.08645593016604[/C][/ROW]
[ROW][C]17[/C][C]6[/C][C]4.71472469168929[/C][C]1.28527530831071[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]4.32657988990027[/C][C]-2.32657988990027[/C][/ROW]
[ROW][C]19[/C][C]6[/C][C]6.07889527555615[/C][C]-0.0788952755561467[/C][/ROW]
[ROW][C]20[/C][C]7[/C][C]5.08886987387357[/C][C]1.91113012612643[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]4.82736705851445[/C][C]2.17263294148555[/C][/ROW]
[ROW][C]22[/C][C]5[/C][C]5.74416285465818[/C][C]-0.744162854658176[/C][/ROW]
[ROW][C]23[/C][C]10[/C][C]5.02500803721423[/C][C]4.97499196278577[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]7.31270875951263[/C][C]2.68729124048737[/C][/ROW]
[ROW][C]25[/C][C]7[/C][C]6.4649339603068[/C][C]0.535066039693197[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]6.15599651356917[/C][C]1.84400348643083[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]4.57275850318856[/C][C]-2.57275850318856[/C][/ROW]
[ROW][C]28[/C][C]6[/C][C]6.85408815819095[/C][C]-0.854088158190954[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]4.20677006344886[/C][C]1.79322993655114[/C][/ROW]
[ROW][C]30[/C][C]7[/C][C]5.76267526056093[/C][C]1.23732473943907[/C][/ROW]
[ROW][C]31[/C][C]5[/C][C]6.52845046861006[/C][C]-1.52845046861006[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]7.36739786487237[/C][C]0.632602135127633[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]7.56485657389582[/C][C]1.43514342610418[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]7.65034921413682[/C][C]-2.65034921413682[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]4.62896234766838[/C][C]-3.62896234766838[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]3.40446460431721[/C][C]-0.404464604317205[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]6.68513863815468[/C][C]-4.68513863815468[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]6.60953754344057[/C][C]2.39046245655943[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]5.76050573775431[/C][C]-4.76050573775431[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]6.51248979277303[/C][C]-0.512489792773028[/C][/ROW]
[ROW][C]41[/C][C]8[/C][C]6.93566322139097[/C][C]1.06433677860903[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]5.85049764024829[/C][C]3.14950235975172[/C][/ROW]
[ROW][C]43[/C][C]10[/C][C]4.61116128701828[/C][C]5.38883871298172[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]3.60103994466798[/C][C]0.398960055332021[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]5.83870009993629[/C][C]-2.83870009993629[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]6.91518564011642[/C][C]0.0848143598835802[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]5.0491240358304[/C][C]-1.0491240358304[/C][/ROW]
[ROW][C]48[/C][C]7[/C][C]4.29487797337898[/C][C]2.70512202662102[/C][/ROW]
[ROW][C]49[/C][C]5[/C][C]5.76697406162425[/C][C]-0.766974061624246[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]5.97372686873931[/C][C]0.0262731312606854[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]6.11571140912457[/C][C]3.88428859087543[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]6.94008280021596[/C][C]3.05991719978404[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]7.35027549683558[/C][C]2.64972450316442[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]5.72120279668705[/C][C]4.27879720331295[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]5.27545692314765[/C][C]-0.275456923147651[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]5.11021751681981[/C][C]-1.11021751681981[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]6.20595782298186[/C][C]3.79404217701814[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]5.42598965941525[/C][C]0.574010340584749[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]4.78257836992476[/C][C]-2.78257836992476[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]7.51887105944073[/C][C]-3.51887105944072[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]4.42247477941398[/C][C]-1.42247477941398[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191195&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191195&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	6	5.97451927755216	0.0254807224478388
2	2	5.49857712841829	-3.49857712841829
3	2	4.53389069210628	-2.53389069210628
4	2	4.81601249412711	-2.81601249412711
5	6	5.76679038899944	0.233209611000556
6	5	6.31348169106502	-1.31348169106502
7	3	5.17871330610304	-2.17871330610304
8	5	6.4782217562291	-1.4782217562291
9	1	6.05817446193647	-5.05817446193647
10	5	4.7695259540841	0.230474045915896
11	1	5.1751802299306	-4.1751802299306
12	6	5.27884615787414	0.721153842125858
13	1	5.21044601781996	-4.21044601781996
14	10	4.87142088630761	5.12857911369239
15	6	3.60919439484707	2.39080560515293
16	9	5.91354406983396	3.08645593016604
17	6	4.71472469168929	1.28527530831071
18	2	4.32657988990027	-2.32657988990027
19	6	6.07889527555615	-0.0788952755561467
20	7	5.08886987387357	1.91113012612643
21	7	4.82736705851445	2.17263294148555
22	5	5.74416285465818	-0.744162854658176
23	10	5.02500803721423	4.97499196278577
24	10	7.31270875951263	2.68729124048737
25	7	6.4649339603068	0.535066039693197
26	8	6.15599651356917	1.84400348643083
27	2	4.57275850318856	-2.57275850318856
28	6	6.85408815819095	-0.854088158190954
29	6	4.20677006344886	1.79322993655114
30	7	5.76267526056093	1.23732473943907
31	5	6.52845046861006	-1.52845046861006
32	8	7.36739786487237	0.632602135127633
33	9	7.56485657389582	1.43514342610418
34	5	7.65034921413682	-2.65034921413682
35	1	4.62896234766838	-3.62896234766838
36	3	3.40446460431721	-0.404464604317205
37	2	6.68513863815468	-4.68513863815468
38	9	6.60953754344057	2.39046245655943
39	1	5.76050573775431	-4.76050573775431
40	6	6.51248979277303	-0.512489792773028
41	8	6.93566322139097	1.06433677860903
42	9	5.85049764024829	3.14950235975172
43	10	4.61116128701828	5.38883871298172
44	4	3.60103994466798	0.398960055332021
45	3	5.83870009993629	-2.83870009993629
46	7	6.91518564011642	0.0848143598835802
47	4	5.0491240358304	-1.0491240358304
48	7	4.29487797337898	2.70512202662102
49	5	5.76697406162425	-0.766974061624246
50	6	5.97372686873931	0.0262731312606854
51	10	6.11571140912457	3.88428859087543
52	10	6.94008280021596	3.05991719978404
53	10	7.35027549683558	2.64972450316442
54	10	5.72120279668705	4.27879720331295
55	5	5.27545692314765	-0.275456923147651
56	4	5.11021751681981	-1.11021751681981
57	10	6.20595782298186	3.79404217701814
58	6	5.42598965941525	0.574010340584749
59	2	4.78257836992476	-2.78257836992476
60	4	7.51887105944073	-3.51887105944072
61	3	4.42247477941398	-1.42247477941398

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.2514255922502	0.5028511845004	0.7485744077498
12	0.284825566910268	0.569651133820537	0.715174433089732
13	0.250028711838761	0.500057423677521	0.749971288161239
14	0.780239027916043	0.439521944167915	0.219760972083957
15	0.737191744272082	0.525616511455836	0.262808255727918
16	0.885496979969198	0.229006040061604	0.114503020030802
17	0.845884676851504	0.308230646296993	0.154115323148496
18	0.806542468511267	0.386915062977465	0.193457531488733
19	0.736823553416327	0.526352893167347	0.263176446583673
20	0.750964898695285	0.49807020260943	0.249035101304715
21	0.713314494064707	0.573371011870586	0.286685505935293
22	0.637050301376214	0.725899397247572	0.362949698623786
23	0.748871637637303	0.502256724725393	0.251128362362697
24	0.781392460060118	0.437215079879765	0.218607539939882
25	0.722843418961977	0.554313162076047	0.277156581038023
26	0.676774346499516	0.646451307000967	0.323225653500484
27	0.663865455269376	0.672269089461248	0.336134544730624
28	0.59300824955177	0.81398350089646	0.40699175044823
29	0.538080569198442	0.923838861603116	0.461919430801558
30	0.464015188923457	0.928030377846914	0.535984811076543
31	0.402641175163599	0.805282350327199	0.597358824836401
32	0.324324743272499	0.648649486544997	0.675675256727502
33	0.26010223302623	0.52020446605246	0.73989776697377
34	0.25017303057408	0.50034606114816	0.74982696942592
35	0.263459601523975	0.526919203047951	0.736540398476025
36	0.200062363605205	0.400124727210409	0.799937636394795
37	0.332267391374227	0.664534782748454	0.667732608625773
38	0.2910142157713	0.5820284315426	0.7089857842287
39	0.49859651140472	0.99719302280944	0.50140348859528
40	0.432850843625555	0.865701687251109	0.567149156374445
41	0.34999935203667	0.699998704073339	0.65000064796333
42	0.331903280400857	0.663806560801714	0.668096719599143
43	0.484792764184787	0.969585528369575	0.515207235815213
44	0.386639176899089	0.773278353798178	0.613360823100911
45	0.578858505177855	0.84228298964429	0.421141494822145
46	0.461224167451828	0.922448334903657	0.538775832548172
47	0.344375681157159	0.688751362314319	0.655624318842841
48	0.922889908614788	0.154220182770425	0.0771100913852123
49	0.85446786117229	0.29106427765542	0.14553213882771
50	0.717305599422041	0.565388801155918	0.282694400577959

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.2514255922502 & 0.5028511845004 & 0.7485744077498 \tabularnewline
12 & 0.284825566910268 & 0.569651133820537 & 0.715174433089732 \tabularnewline
13 & 0.250028711838761 & 0.500057423677521 & 0.749971288161239 \tabularnewline
14 & 0.780239027916043 & 0.439521944167915 & 0.219760972083957 \tabularnewline
15 & 0.737191744272082 & 0.525616511455836 & 0.262808255727918 \tabularnewline
16 & 0.885496979969198 & 0.229006040061604 & 0.114503020030802 \tabularnewline
17 & 0.845884676851504 & 0.308230646296993 & 0.154115323148496 \tabularnewline
18 & 0.806542468511267 & 0.386915062977465 & 0.193457531488733 \tabularnewline
19 & 0.736823553416327 & 0.526352893167347 & 0.263176446583673 \tabularnewline
20 & 0.750964898695285 & 0.49807020260943 & 0.249035101304715 \tabularnewline
21 & 0.713314494064707 & 0.573371011870586 & 0.286685505935293 \tabularnewline
22 & 0.637050301376214 & 0.725899397247572 & 0.362949698623786 \tabularnewline
23 & 0.748871637637303 & 0.502256724725393 & 0.251128362362697 \tabularnewline
24 & 0.781392460060118 & 0.437215079879765 & 0.218607539939882 \tabularnewline
25 & 0.722843418961977 & 0.554313162076047 & 0.277156581038023 \tabularnewline
26 & 0.676774346499516 & 0.646451307000967 & 0.323225653500484 \tabularnewline
27 & 0.663865455269376 & 0.672269089461248 & 0.336134544730624 \tabularnewline
28 & 0.59300824955177 & 0.81398350089646 & 0.40699175044823 \tabularnewline
29 & 0.538080569198442 & 0.923838861603116 & 0.461919430801558 \tabularnewline
30 & 0.464015188923457 & 0.928030377846914 & 0.535984811076543 \tabularnewline
31 & 0.402641175163599 & 0.805282350327199 & 0.597358824836401 \tabularnewline
32 & 0.324324743272499 & 0.648649486544997 & 0.675675256727502 \tabularnewline
33 & 0.26010223302623 & 0.52020446605246 & 0.73989776697377 \tabularnewline
34 & 0.25017303057408 & 0.50034606114816 & 0.74982696942592 \tabularnewline
35 & 0.263459601523975 & 0.526919203047951 & 0.736540398476025 \tabularnewline
36 & 0.200062363605205 & 0.400124727210409 & 0.799937636394795 \tabularnewline
37 & 0.332267391374227 & 0.664534782748454 & 0.667732608625773 \tabularnewline
38 & 0.2910142157713 & 0.5820284315426 & 0.7089857842287 \tabularnewline
39 & 0.49859651140472 & 0.99719302280944 & 0.50140348859528 \tabularnewline
40 & 0.432850843625555 & 0.865701687251109 & 0.567149156374445 \tabularnewline
41 & 0.34999935203667 & 0.699998704073339 & 0.65000064796333 \tabularnewline
42 & 0.331903280400857 & 0.663806560801714 & 0.668096719599143 \tabularnewline
43 & 0.484792764184787 & 0.969585528369575 & 0.515207235815213 \tabularnewline
44 & 0.386639176899089 & 0.773278353798178 & 0.613360823100911 \tabularnewline
45 & 0.578858505177855 & 0.84228298964429 & 0.421141494822145 \tabularnewline
46 & 0.461224167451828 & 0.922448334903657 & 0.538775832548172 \tabularnewline
47 & 0.344375681157159 & 0.688751362314319 & 0.655624318842841 \tabularnewline
48 & 0.922889908614788 & 0.154220182770425 & 0.0771100913852123 \tabularnewline
49 & 0.85446786117229 & 0.29106427765542 & 0.14553213882771 \tabularnewline
50 & 0.717305599422041 & 0.565388801155918 & 0.282694400577959 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191195&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]11[/C][C]0.2514255922502[/C][C]0.5028511845004[/C][C]0.7485744077498[/C][/ROW]
[ROW][C]12[/C][C]0.284825566910268[/C][C]0.569651133820537[/C][C]0.715174433089732[/C][/ROW]
[ROW][C]13[/C][C]0.250028711838761[/C][C]0.500057423677521[/C][C]0.749971288161239[/C][/ROW]
[ROW][C]14[/C][C]0.780239027916043[/C][C]0.439521944167915[/C][C]0.219760972083957[/C][/ROW]
[ROW][C]15[/C][C]0.737191744272082[/C][C]0.525616511455836[/C][C]0.262808255727918[/C][/ROW]
[ROW][C]16[/C][C]0.885496979969198[/C][C]0.229006040061604[/C][C]0.114503020030802[/C][/ROW]
[ROW][C]17[/C][C]0.845884676851504[/C][C]0.308230646296993[/C][C]0.154115323148496[/C][/ROW]
[ROW][C]18[/C][C]0.806542468511267[/C][C]0.386915062977465[/C][C]0.193457531488733[/C][/ROW]
[ROW][C]19[/C][C]0.736823553416327[/C][C]0.526352893167347[/C][C]0.263176446583673[/C][/ROW]
[ROW][C]20[/C][C]0.750964898695285[/C][C]0.49807020260943[/C][C]0.249035101304715[/C][/ROW]
[ROW][C]21[/C][C]0.713314494064707[/C][C]0.573371011870586[/C][C]0.286685505935293[/C][/ROW]
[ROW][C]22[/C][C]0.637050301376214[/C][C]0.725899397247572[/C][C]0.362949698623786[/C][/ROW]
[ROW][C]23[/C][C]0.748871637637303[/C][C]0.502256724725393[/C][C]0.251128362362697[/C][/ROW]
[ROW][C]24[/C][C]0.781392460060118[/C][C]0.437215079879765[/C][C]0.218607539939882[/C][/ROW]
[ROW][C]25[/C][C]0.722843418961977[/C][C]0.554313162076047[/C][C]0.277156581038023[/C][/ROW]
[ROW][C]26[/C][C]0.676774346499516[/C][C]0.646451307000967[/C][C]0.323225653500484[/C][/ROW]
[ROW][C]27[/C][C]0.663865455269376[/C][C]0.672269089461248[/C][C]0.336134544730624[/C][/ROW]
[ROW][C]28[/C][C]0.59300824955177[/C][C]0.81398350089646[/C][C]0.40699175044823[/C][/ROW]
[ROW][C]29[/C][C]0.538080569198442[/C][C]0.923838861603116[/C][C]0.461919430801558[/C][/ROW]
[ROW][C]30[/C][C]0.464015188923457[/C][C]0.928030377846914[/C][C]0.535984811076543[/C][/ROW]
[ROW][C]31[/C][C]0.402641175163599[/C][C]0.805282350327199[/C][C]0.597358824836401[/C][/ROW]
[ROW][C]32[/C][C]0.324324743272499[/C][C]0.648649486544997[/C][C]0.675675256727502[/C][/ROW]
[ROW][C]33[/C][C]0.26010223302623[/C][C]0.52020446605246[/C][C]0.73989776697377[/C][/ROW]
[ROW][C]34[/C][C]0.25017303057408[/C][C]0.50034606114816[/C][C]0.74982696942592[/C][/ROW]
[ROW][C]35[/C][C]0.263459601523975[/C][C]0.526919203047951[/C][C]0.736540398476025[/C][/ROW]
[ROW][C]36[/C][C]0.200062363605205[/C][C]0.400124727210409[/C][C]0.799937636394795[/C][/ROW]
[ROW][C]37[/C][C]0.332267391374227[/C][C]0.664534782748454[/C][C]0.667732608625773[/C][/ROW]
[ROW][C]38[/C][C]0.2910142157713[/C][C]0.5820284315426[/C][C]0.7089857842287[/C][/ROW]
[ROW][C]39[/C][C]0.49859651140472[/C][C]0.99719302280944[/C][C]0.50140348859528[/C][/ROW]
[ROW][C]40[/C][C]0.432850843625555[/C][C]0.865701687251109[/C][C]0.567149156374445[/C][/ROW]
[ROW][C]41[/C][C]0.34999935203667[/C][C]0.699998704073339[/C][C]0.65000064796333[/C][/ROW]
[ROW][C]42[/C][C]0.331903280400857[/C][C]0.663806560801714[/C][C]0.668096719599143[/C][/ROW]
[ROW][C]43[/C][C]0.484792764184787[/C][C]0.969585528369575[/C][C]0.515207235815213[/C][/ROW]
[ROW][C]44[/C][C]0.386639176899089[/C][C]0.773278353798178[/C][C]0.613360823100911[/C][/ROW]
[ROW][C]45[/C][C]0.578858505177855[/C][C]0.84228298964429[/C][C]0.421141494822145[/C][/ROW]
[ROW][C]46[/C][C]0.461224167451828[/C][C]0.922448334903657[/C][C]0.538775832548172[/C][/ROW]
[ROW][C]47[/C][C]0.344375681157159[/C][C]0.688751362314319[/C][C]0.655624318842841[/C][/ROW]
[ROW][C]48[/C][C]0.922889908614788[/C][C]0.154220182770425[/C][C]0.0771100913852123[/C][/ROW]
[ROW][C]49[/C][C]0.85446786117229[/C][C]0.29106427765542[/C][C]0.14553213882771[/C][/ROW]
[ROW][C]50[/C][C]0.717305599422041[/C][C]0.565388801155918[/C][C]0.282694400577959[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191195&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191195&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
11	0.2514255922502	0.5028511845004	0.7485744077498
12	0.284825566910268	0.569651133820537	0.715174433089732
13	0.250028711838761	0.500057423677521	0.749971288161239
14	0.780239027916043	0.439521944167915	0.219760972083957
15	0.737191744272082	0.525616511455836	0.262808255727918
16	0.885496979969198	0.229006040061604	0.114503020030802
17	0.845884676851504	0.308230646296993	0.154115323148496
18	0.806542468511267	0.386915062977465	0.193457531488733
19	0.736823553416327	0.526352893167347	0.263176446583673
20	0.750964898695285	0.49807020260943	0.249035101304715
21	0.713314494064707	0.573371011870586	0.286685505935293
22	0.637050301376214	0.725899397247572	0.362949698623786
23	0.748871637637303	0.502256724725393	0.251128362362697
24	0.781392460060118	0.437215079879765	0.218607539939882
25	0.722843418961977	0.554313162076047	0.277156581038023
26	0.676774346499516	0.646451307000967	0.323225653500484
27	0.663865455269376	0.672269089461248	0.336134544730624
28	0.59300824955177	0.81398350089646	0.40699175044823
29	0.538080569198442	0.923838861603116	0.461919430801558
30	0.464015188923457	0.928030377846914	0.535984811076543
31	0.402641175163599	0.805282350327199	0.597358824836401
32	0.324324743272499	0.648649486544997	0.675675256727502
33	0.26010223302623	0.52020446605246	0.73989776697377
34	0.25017303057408	0.50034606114816	0.74982696942592
35	0.263459601523975	0.526919203047951	0.736540398476025
36	0.200062363605205	0.400124727210409	0.799937636394795
37	0.332267391374227	0.664534782748454	0.667732608625773
38	0.2910142157713	0.5820284315426	0.7089857842287
39	0.49859651140472	0.99719302280944	0.50140348859528
40	0.432850843625555	0.865701687251109	0.567149156374445
41	0.34999935203667	0.699998704073339	0.65000064796333
42	0.331903280400857	0.663806560801714	0.668096719599143
43	0.484792764184787	0.969585528369575	0.515207235815213
44	0.386639176899089	0.773278353798178	0.613360823100911
45	0.578858505177855	0.84228298964429	0.421141494822145
46	0.461224167451828	0.922448334903657	0.538775832548172
47	0.344375681157159	0.688751362314319	0.655624318842841
48	0.922889908614788	0.154220182770425	0.0771100913852123
49	0.85446786117229	0.29106427765542	0.14553213882771
50	0.717305599422041	0.565388801155918	0.282694400577959

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191195&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191195&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191195&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

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

PNG link

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code