Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 24 Nov 2010 12:45:58 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/24/t12906027057srdwll8yvmzo1b.htm/, Retrieved Fri, 03 May 2024 19:33:19 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=100073, Retrieved Fri, 03 May 2024 19:33:19 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

128

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

-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [ws7] [2010-11-24 12:45:58] [b47314d83d48c7bf812ec2bcd743b159] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

1	102,89	167,16	100,70	106,88	97,69
1	106,14	196,07	100,12	109,96	100,90
1	108,26	369,59	99,10	113,15	106,83
1	112,79	239,36	99,84	118,27	110,67
1	117,31	201,19	97,90	121,81	102,75
2	102,64	179,84	99,62	107,45	101,69
2	105,85	199,98	100,40	110,49	103,91
2	108,93	425,00	99,48	113,28	112,51
2	113,87	224,69	99,68	118,88	113,28
2	117,77	194,37	97,88	122,21	107,21
3	103,33	174,44	99,83	107,65	102,72
3	106,27	199,10	100,51	111,37	103,81
3	109,43	439,72	99,74	113,83	113,61
3	114,28	230,98	99,74	119,11	112,08
3	118,37	191,08	97,56	122,82	107,24
4	103,56	180,35	100,74	107,72	101,85
4	106,51	198,31	100,70	111,56	104,59
4	109,61	362,23	100,42	114,49	114,96
4	115,51	233,47	99,71	119,29	111,41
4	117,91	192,87	96,86	123,02	106,01
5	103,60	193,17	100,84	108,10	114,94
5	106,82	195,72	100,62	111,90	104,94
5	109,74	328,76	100,80	114,76	118,66
5	116,76	256,70	99,35	119,36	113,81
5	118,12	181,61	96,86	123,14	121,36
6	104,24	195,16	100,85	108,38	106,20
6	106,53	223,04	99,70	111,96	111,64
6	110,12	348,55	100,66	114,96	116,84
6	116,91	253,41	99,21	119,48	109,16
6	118,02	157,67	96,75	123,12	120,44
7	105,31	202,43	99,71	108,62	106,76
7	107,14	238,41	99,48	112,25	111,27
7	110,16	328,18	101,03	115,41	121,19
7	116,47	224,95	99,21	120,10	105,09
7	117,77	196,14	97,12	123,42	109,40
8	105,40	189,91	100,80	108,79	107,24
8	107,39	259,73	99,36	112,39	106,82
8	110,44	329,34	101,22	115,84	117,42
8	116,94	210,37	99,16	120,30	102,23
8	117,85	246,35	97,22	123,50	111,51
9	105,89	195,98	100,06	109,03	106,50
9	107,33	326,54	99,39	112,30	106,07
9	111,23	295,55	101,23	116,31	116,88
9	117,24	191,09	99,20	120,54	101,95
9	118,68	271,90	97,52	125,77	111,97
10	105,89	212,09	100,57	109,34	106,77
10	107,53	335,15	99,45	112,49	111,35
10	112,86	237,38	100,10	117,23	115,01
10	116,82	198,85	99,08	120,86	104,75
10	118,90	270,29	97,57	125,99	114,64
11	105,54	205,81	99,79	109,73	108,24
11	107,42	321,81	99,28	112,77	112,59
11	112,77	226,85	99,98	117,97	111,81
11	117,48	211,04	98,16	121,10	107,25
12	106,15	204,31	99,90	109,76	104,43
12	108,25	368,62	99,40	113,15	108,59
12	113,04	220,14	99,91	118,08	110,61
12	117,11	206,25	98,00	121,42	105,25

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100073&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100073&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100073&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	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation
suiker[t] = + 114.597307770066 + 0.00106518222376726month[t] + 0.166950547178408Bier[t] + 0.00186452253761169Tarwe[t] -0.338281167276215minerwater[t] + 0.0455394914285356fruit[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
suiker[t] =  +  114.597307770066 +  0.00106518222376726month[t] +  0.166950547178408Bier[t] +  0.00186452253761169Tarwe[t] -0.338281167276215minerwater[t] +  0.0455394914285356fruit[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100073&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]suiker[t] =  +  114.597307770066 +  0.00106518222376726month[t] +  0.166950547178408Bier[t] +  0.00186452253761169Tarwe[t] -0.338281167276215minerwater[t] +  0.0455394914285356fruit[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100073&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100073&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
suiker[t] = + 114.597307770066 + 0.00106518222376726month[t] + 0.166950547178408Bier[t] + 0.00186452253761169Tarwe[t] -0.338281167276215minerwater[t] + 0.0455394914285356fruit[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)	114.597307770066	2.694921	42.5234	0	0
month	0.00106518222376726	0.030571	0.0348	0.972338	0.486169
Bier	0.166950547178408	0.128996	1.2942	0.201304	0.100652
Tarwe	0.00186452253761169	0.001782	1.046	0.300388	0.150194
minerwater	-0.338281167276215	0.126346	-2.6774	0.009904	0.004952
fruit	0.0455394914285356	0.023818	1.912	0.061397	0.030698

\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) & 114.597307770066 & 2.694921 & 42.5234 & 0 & 0 \tabularnewline
month & 0.00106518222376726 & 0.030571 & 0.0348 & 0.972338 & 0.486169 \tabularnewline
Bier & 0.166950547178408 & 0.128996 & 1.2942 & 0.201304 & 0.100652 \tabularnewline
Tarwe & 0.00186452253761169 & 0.001782 & 1.046 & 0.300388 & 0.150194 \tabularnewline
minerwater & -0.338281167276215 & 0.126346 & -2.6774 & 0.009904 & 0.004952 \tabularnewline
fruit & 0.0455394914285356 & 0.023818 & 1.912 & 0.061397 & 0.030698 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100073&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]114.597307770066[/C][C]2.694921[/C][C]42.5234[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]month[/C][C]0.00106518222376726[/C][C]0.030571[/C][C]0.0348[/C][C]0.972338[/C][C]0.486169[/C][/ROW]
[ROW][C]Bier[/C][C]0.166950547178408[/C][C]0.128996[/C][C]1.2942[/C][C]0.201304[/C][C]0.100652[/C][/ROW]
[ROW][C]Tarwe[/C][C]0.00186452253761169[/C][C]0.001782[/C][C]1.046[/C][C]0.300388[/C][C]0.150194[/C][/ROW]
[ROW][C]minerwater[/C][C]-0.338281167276215[/C][C]0.126346[/C][C]-2.6774[/C][C]0.009904[/C][C]0.004952[/C][/ROW]
[ROW][C]fruit[/C][C]0.0455394914285356[/C][C]0.023818[/C][C]1.912[/C][C]0.061397[/C][C]0.030698[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100073&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100073&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)	114.597307770066	2.694921	42.5234	0	0
month	0.00106518222376726	0.030571	0.0348	0.972338	0.486169
Bier	0.166950547178408	0.128996	1.2942	0.201304	0.100652
Tarwe	0.00186452253761169	0.001782	1.046	0.300388	0.150194
minerwater	-0.338281167276215	0.126346	-2.6774	0.009904	0.004952
fruit	0.0455394914285356	0.023818	1.912	0.061397	0.030698

Multiple Linear Regression - Regression Statistics
Multiple R	0.78793835686911
R-squared	0.620846854225593
Adjusted R-squared	0.584389820978054
F-TEST (value)	17.0295495524859
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	6.06275363246311e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.773754810920322
Sum Squared Residuals	31.1322183859618

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.78793835686911 \tabularnewline
R-squared & 0.620846854225593 \tabularnewline
Adjusted R-squared & 0.584389820978054 \tabularnewline
F-TEST (value) & 17.0295495524859 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 6.06275363246311e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.773754810920322 \tabularnewline
Sum Squared Residuals & 31.1322183859618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100073&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.78793835686911[/C][/ROW]
[ROW][C]R-squared[/C][C]0.620846854225593[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.584389820978054[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.0295495524859[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]6.06275363246311e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.773754810920322[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]31.1322183859618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100073&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100073&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	0.78793835686911
R-squared	0.620846854225593
Adjusted R-squared	0.584389820978054
F-TEST (value)	17.0295495524859
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	6.06275363246311e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.773754810920322
Sum Squared Residuals	31.1322183859618

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.7	100.380850098035	0.319149901965114
2	100.12	100.081618495202	0.0383815047983082
3	99.1	99.9500178665064	-0.850017866506393
4	99.84	98.9063591457828	0.933640854217241
5	97.9	98.0316186894967	-0.131618689496714
6	99.62	100.353157489607	-0.733157489607435
7	100.4	99.9993431524093	0.400656847590722
8	99.48	100.380940868717	-0.900940868716924
9	99.68	98.9728849339224	0.707115066077574
10	97.88	98.1645587445768	-0.284558744576831
11	99.83	100.438599570397	-0.608599570397354
12	100.51	99.766645408269	0.743354591731047
13	99.74	100.356965894853	-0.61696589485302
14	99.74	98.9216756290631	0.818324370936845
15	97.56	98.0546746486633	-0.494674648663261
16	100.74	100.425783667417	0.314216332582715
17	100.7	99.7775531305426	0.922446869457393
18	100.42	100.081813067156	0.338186932844415
19	99.71	99.0413305760682	0.668669423931821
20	96.86	97.858610266615	-0.998610266614945
21	100.84	100.924995149695	-0.084995149694932
22	100.62	99.7264670941453	0.893532905854665
23	100.8	100.1193364543	0.680663545700315
24	99.35	99.3800118985328	-0.0300118985328279
25	96.86	98.5321779933276	-1.67217799332755
26	100.85	100.54388520004	0.306114799960005
27	99.7	100.01487309595	-0.314873095949539
28	100.66	100.070203637615	0.589796362384588
29	99.21	99.1476330084688	0.0623669915312252
30	96.75	98.4367807425143	-1.68678074251432
31	99.71	100.681457181647	-0.971457181646782
32	99.48	100.031484673017	-0.551484673016562
33	101.03	100.085836780075	0.94416321992501
34	99.21	98.6270955846882	0.582904415311775
35	97.12	97.8635961344115	-0.743596134411503
36	100.8	100.638555248394	0.161444751605555
37	99.36	99.8640290122612	-0.504029012261163
38	101.22	99.818666177038	1.40133382296201
39	99.16	98.4815436065466	0.678456393453394
40	97.22	98.0406608705551	-0.820660870555144
41	100.06	100.617857146736	-0.557857146735523
42	99.39	99.9759365988755	-0.58593659887552
43	101.23	99.7050366009956	1.52496339900443
44	99.2	98.4028074206525	0.79719257934755
45	97.52	97.480983474113	0.0390165258869077
46	100.57	100.55638828787	0.0136117121296998
47	99.45	100.202620522544	-0.752620522543992
48	100.1	99.4733943762418	0.626605623758204
49	99.08	98.3674826704247	0.712517329575329
50	97.57	97.562944480744	0.00705551925601923
51	99.79	100.422324974208	-0.632324974207634
52	99.28	100.12219865646	-0.84219865646044
53	99.98	99.0437461505427	0.936253849457267
54	98.16	98.5341249919447	-0.374124991944726
55	99.9	100.338779309043	-0.438779309042806
56	99.4	100.038406283549	-0.638406283548776
57	99.91	98.9855187161627	0.924481283837321
58	98	98.2651584523718	-0.265158452371856

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.7 & 100.380850098035 & 0.319149901965114 \tabularnewline
2 & 100.12 & 100.081618495202 & 0.0383815047983082 \tabularnewline
3 & 99.1 & 99.9500178665064 & -0.850017866506393 \tabularnewline
4 & 99.84 & 98.9063591457828 & 0.933640854217241 \tabularnewline
5 & 97.9 & 98.0316186894967 & -0.131618689496714 \tabularnewline
6 & 99.62 & 100.353157489607 & -0.733157489607435 \tabularnewline
7 & 100.4 & 99.9993431524093 & 0.400656847590722 \tabularnewline
8 & 99.48 & 100.380940868717 & -0.900940868716924 \tabularnewline
9 & 99.68 & 98.9728849339224 & 0.707115066077574 \tabularnewline
10 & 97.88 & 98.1645587445768 & -0.284558744576831 \tabularnewline
11 & 99.83 & 100.438599570397 & -0.608599570397354 \tabularnewline
12 & 100.51 & 99.766645408269 & 0.743354591731047 \tabularnewline
13 & 99.74 & 100.356965894853 & -0.61696589485302 \tabularnewline
14 & 99.74 & 98.9216756290631 & 0.818324370936845 \tabularnewline
15 & 97.56 & 98.0546746486633 & -0.494674648663261 \tabularnewline
16 & 100.74 & 100.425783667417 & 0.314216332582715 \tabularnewline
17 & 100.7 & 99.7775531305426 & 0.922446869457393 \tabularnewline
18 & 100.42 & 100.081813067156 & 0.338186932844415 \tabularnewline
19 & 99.71 & 99.0413305760682 & 0.668669423931821 \tabularnewline
20 & 96.86 & 97.858610266615 & -0.998610266614945 \tabularnewline
21 & 100.84 & 100.924995149695 & -0.084995149694932 \tabularnewline
22 & 100.62 & 99.7264670941453 & 0.893532905854665 \tabularnewline
23 & 100.8 & 100.1193364543 & 0.680663545700315 \tabularnewline
24 & 99.35 & 99.3800118985328 & -0.0300118985328279 \tabularnewline
25 & 96.86 & 98.5321779933276 & -1.67217799332755 \tabularnewline
26 & 100.85 & 100.54388520004 & 0.306114799960005 \tabularnewline
27 & 99.7 & 100.01487309595 & -0.314873095949539 \tabularnewline
28 & 100.66 & 100.070203637615 & 0.589796362384588 \tabularnewline
29 & 99.21 & 99.1476330084688 & 0.0623669915312252 \tabularnewline
30 & 96.75 & 98.4367807425143 & -1.68678074251432 \tabularnewline
31 & 99.71 & 100.681457181647 & -0.971457181646782 \tabularnewline
32 & 99.48 & 100.031484673017 & -0.551484673016562 \tabularnewline
33 & 101.03 & 100.085836780075 & 0.94416321992501 \tabularnewline
34 & 99.21 & 98.6270955846882 & 0.582904415311775 \tabularnewline
35 & 97.12 & 97.8635961344115 & -0.743596134411503 \tabularnewline
36 & 100.8 & 100.638555248394 & 0.161444751605555 \tabularnewline
37 & 99.36 & 99.8640290122612 & -0.504029012261163 \tabularnewline
38 & 101.22 & 99.818666177038 & 1.40133382296201 \tabularnewline
39 & 99.16 & 98.4815436065466 & 0.678456393453394 \tabularnewline
40 & 97.22 & 98.0406608705551 & -0.820660870555144 \tabularnewline
41 & 100.06 & 100.617857146736 & -0.557857146735523 \tabularnewline
42 & 99.39 & 99.9759365988755 & -0.58593659887552 \tabularnewline
43 & 101.23 & 99.7050366009956 & 1.52496339900443 \tabularnewline
44 & 99.2 & 98.4028074206525 & 0.79719257934755 \tabularnewline
45 & 97.52 & 97.480983474113 & 0.0390165258869077 \tabularnewline
46 & 100.57 & 100.55638828787 & 0.0136117121296998 \tabularnewline
47 & 99.45 & 100.202620522544 & -0.752620522543992 \tabularnewline
48 & 100.1 & 99.4733943762418 & 0.626605623758204 \tabularnewline
49 & 99.08 & 98.3674826704247 & 0.712517329575329 \tabularnewline
50 & 97.57 & 97.562944480744 & 0.00705551925601923 \tabularnewline
51 & 99.79 & 100.422324974208 & -0.632324974207634 \tabularnewline
52 & 99.28 & 100.12219865646 & -0.84219865646044 \tabularnewline
53 & 99.98 & 99.0437461505427 & 0.936253849457267 \tabularnewline
54 & 98.16 & 98.5341249919447 & -0.374124991944726 \tabularnewline
55 & 99.9 & 100.338779309043 & -0.438779309042806 \tabularnewline
56 & 99.4 & 100.038406283549 & -0.638406283548776 \tabularnewline
57 & 99.91 & 98.9855187161627 & 0.924481283837321 \tabularnewline
58 & 98 & 98.2651584523718 & -0.265158452371856 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100073&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]100.7[/C][C]100.380850098035[/C][C]0.319149901965114[/C][/ROW]
[ROW][C]2[/C][C]100.12[/C][C]100.081618495202[/C][C]0.0383815047983082[/C][/ROW]
[ROW][C]3[/C][C]99.1[/C][C]99.9500178665064[/C][C]-0.850017866506393[/C][/ROW]
[ROW][C]4[/C][C]99.84[/C][C]98.9063591457828[/C][C]0.933640854217241[/C][/ROW]
[ROW][C]5[/C][C]97.9[/C][C]98.0316186894967[/C][C]-0.131618689496714[/C][/ROW]
[ROW][C]6[/C][C]99.62[/C][C]100.353157489607[/C][C]-0.733157489607435[/C][/ROW]
[ROW][C]7[/C][C]100.4[/C][C]99.9993431524093[/C][C]0.400656847590722[/C][/ROW]
[ROW][C]8[/C][C]99.48[/C][C]100.380940868717[/C][C]-0.900940868716924[/C][/ROW]
[ROW][C]9[/C][C]99.68[/C][C]98.9728849339224[/C][C]0.707115066077574[/C][/ROW]
[ROW][C]10[/C][C]97.88[/C][C]98.1645587445768[/C][C]-0.284558744576831[/C][/ROW]
[ROW][C]11[/C][C]99.83[/C][C]100.438599570397[/C][C]-0.608599570397354[/C][/ROW]
[ROW][C]12[/C][C]100.51[/C][C]99.766645408269[/C][C]0.743354591731047[/C][/ROW]
[ROW][C]13[/C][C]99.74[/C][C]100.356965894853[/C][C]-0.61696589485302[/C][/ROW]
[ROW][C]14[/C][C]99.74[/C][C]98.9216756290631[/C][C]0.818324370936845[/C][/ROW]
[ROW][C]15[/C][C]97.56[/C][C]98.0546746486633[/C][C]-0.494674648663261[/C][/ROW]
[ROW][C]16[/C][C]100.74[/C][C]100.425783667417[/C][C]0.314216332582715[/C][/ROW]
[ROW][C]17[/C][C]100.7[/C][C]99.7775531305426[/C][C]0.922446869457393[/C][/ROW]
[ROW][C]18[/C][C]100.42[/C][C]100.081813067156[/C][C]0.338186932844415[/C][/ROW]
[ROW][C]19[/C][C]99.71[/C][C]99.0413305760682[/C][C]0.668669423931821[/C][/ROW]
[ROW][C]20[/C][C]96.86[/C][C]97.858610266615[/C][C]-0.998610266614945[/C][/ROW]
[ROW][C]21[/C][C]100.84[/C][C]100.924995149695[/C][C]-0.084995149694932[/C][/ROW]
[ROW][C]22[/C][C]100.62[/C][C]99.7264670941453[/C][C]0.893532905854665[/C][/ROW]
[ROW][C]23[/C][C]100.8[/C][C]100.1193364543[/C][C]0.680663545700315[/C][/ROW]
[ROW][C]24[/C][C]99.35[/C][C]99.3800118985328[/C][C]-0.0300118985328279[/C][/ROW]
[ROW][C]25[/C][C]96.86[/C][C]98.5321779933276[/C][C]-1.67217799332755[/C][/ROW]
[ROW][C]26[/C][C]100.85[/C][C]100.54388520004[/C][C]0.306114799960005[/C][/ROW]
[ROW][C]27[/C][C]99.7[/C][C]100.01487309595[/C][C]-0.314873095949539[/C][/ROW]
[ROW][C]28[/C][C]100.66[/C][C]100.070203637615[/C][C]0.589796362384588[/C][/ROW]
[ROW][C]29[/C][C]99.21[/C][C]99.1476330084688[/C][C]0.0623669915312252[/C][/ROW]
[ROW][C]30[/C][C]96.75[/C][C]98.4367807425143[/C][C]-1.68678074251432[/C][/ROW]
[ROW][C]31[/C][C]99.71[/C][C]100.681457181647[/C][C]-0.971457181646782[/C][/ROW]
[ROW][C]32[/C][C]99.48[/C][C]100.031484673017[/C][C]-0.551484673016562[/C][/ROW]
[ROW][C]33[/C][C]101.03[/C][C]100.085836780075[/C][C]0.94416321992501[/C][/ROW]
[ROW][C]34[/C][C]99.21[/C][C]98.6270955846882[/C][C]0.582904415311775[/C][/ROW]
[ROW][C]35[/C][C]97.12[/C][C]97.8635961344115[/C][C]-0.743596134411503[/C][/ROW]
[ROW][C]36[/C][C]100.8[/C][C]100.638555248394[/C][C]0.161444751605555[/C][/ROW]
[ROW][C]37[/C][C]99.36[/C][C]99.8640290122612[/C][C]-0.504029012261163[/C][/ROW]
[ROW][C]38[/C][C]101.22[/C][C]99.818666177038[/C][C]1.40133382296201[/C][/ROW]
[ROW][C]39[/C][C]99.16[/C][C]98.4815436065466[/C][C]0.678456393453394[/C][/ROW]
[ROW][C]40[/C][C]97.22[/C][C]98.0406608705551[/C][C]-0.820660870555144[/C][/ROW]
[ROW][C]41[/C][C]100.06[/C][C]100.617857146736[/C][C]-0.557857146735523[/C][/ROW]
[ROW][C]42[/C][C]99.39[/C][C]99.9759365988755[/C][C]-0.58593659887552[/C][/ROW]
[ROW][C]43[/C][C]101.23[/C][C]99.7050366009956[/C][C]1.52496339900443[/C][/ROW]
[ROW][C]44[/C][C]99.2[/C][C]98.4028074206525[/C][C]0.79719257934755[/C][/ROW]
[ROW][C]45[/C][C]97.52[/C][C]97.480983474113[/C][C]0.0390165258869077[/C][/ROW]
[ROW][C]46[/C][C]100.57[/C][C]100.55638828787[/C][C]0.0136117121296998[/C][/ROW]
[ROW][C]47[/C][C]99.45[/C][C]100.202620522544[/C][C]-0.752620522543992[/C][/ROW]
[ROW][C]48[/C][C]100.1[/C][C]99.4733943762418[/C][C]0.626605623758204[/C][/ROW]
[ROW][C]49[/C][C]99.08[/C][C]98.3674826704247[/C][C]0.712517329575329[/C][/ROW]
[ROW][C]50[/C][C]97.57[/C][C]97.562944480744[/C][C]0.00705551925601923[/C][/ROW]
[ROW][C]51[/C][C]99.79[/C][C]100.422324974208[/C][C]-0.632324974207634[/C][/ROW]
[ROW][C]52[/C][C]99.28[/C][C]100.12219865646[/C][C]-0.84219865646044[/C][/ROW]
[ROW][C]53[/C][C]99.98[/C][C]99.0437461505427[/C][C]0.936253849457267[/C][/ROW]
[ROW][C]54[/C][C]98.16[/C][C]98.5341249919447[/C][C]-0.374124991944726[/C][/ROW]
[ROW][C]55[/C][C]99.9[/C][C]100.338779309043[/C][C]-0.438779309042806[/C][/ROW]
[ROW][C]56[/C][C]99.4[/C][C]100.038406283549[/C][C]-0.638406283548776[/C][/ROW]
[ROW][C]57[/C][C]99.91[/C][C]98.9855187161627[/C][C]0.924481283837321[/C][/ROW]
[ROW][C]58[/C][C]98[/C][C]98.2651584523718[/C][C]-0.265158452371856[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100073&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100073&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	100.7	100.380850098035	0.319149901965114
2	100.12	100.081618495202	0.0383815047983082
3	99.1	99.9500178665064	-0.850017866506393
4	99.84	98.9063591457828	0.933640854217241
5	97.9	98.0316186894967	-0.131618689496714
6	99.62	100.353157489607	-0.733157489607435
7	100.4	99.9993431524093	0.400656847590722
8	99.48	100.380940868717	-0.900940868716924
9	99.68	98.9728849339224	0.707115066077574
10	97.88	98.1645587445768	-0.284558744576831
11	99.83	100.438599570397	-0.608599570397354
12	100.51	99.766645408269	0.743354591731047
13	99.74	100.356965894853	-0.61696589485302
14	99.74	98.9216756290631	0.818324370936845
15	97.56	98.0546746486633	-0.494674648663261
16	100.74	100.425783667417	0.314216332582715
17	100.7	99.7775531305426	0.922446869457393
18	100.42	100.081813067156	0.338186932844415
19	99.71	99.0413305760682	0.668669423931821
20	96.86	97.858610266615	-0.998610266614945
21	100.84	100.924995149695	-0.084995149694932
22	100.62	99.7264670941453	0.893532905854665
23	100.8	100.1193364543	0.680663545700315
24	99.35	99.3800118985328	-0.0300118985328279
25	96.86	98.5321779933276	-1.67217799332755
26	100.85	100.54388520004	0.306114799960005
27	99.7	100.01487309595	-0.314873095949539
28	100.66	100.070203637615	0.589796362384588
29	99.21	99.1476330084688	0.0623669915312252
30	96.75	98.4367807425143	-1.68678074251432
31	99.71	100.681457181647	-0.971457181646782
32	99.48	100.031484673017	-0.551484673016562
33	101.03	100.085836780075	0.94416321992501
34	99.21	98.6270955846882	0.582904415311775
35	97.12	97.8635961344115	-0.743596134411503
36	100.8	100.638555248394	0.161444751605555
37	99.36	99.8640290122612	-0.504029012261163
38	101.22	99.818666177038	1.40133382296201
39	99.16	98.4815436065466	0.678456393453394
40	97.22	98.0406608705551	-0.820660870555144
41	100.06	100.617857146736	-0.557857146735523
42	99.39	99.9759365988755	-0.58593659887552
43	101.23	99.7050366009956	1.52496339900443
44	99.2	98.4028074206525	0.79719257934755
45	97.52	97.480983474113	0.0390165258869077
46	100.57	100.55638828787	0.0136117121296998
47	99.45	100.202620522544	-0.752620522543992
48	100.1	99.4733943762418	0.626605623758204
49	99.08	98.3674826704247	0.712517329575329
50	97.57	97.562944480744	0.00705551925601923
51	99.79	100.422324974208	-0.632324974207634
52	99.28	100.12219865646	-0.84219865646044
53	99.98	99.0437461505427	0.936253849457267
54	98.16	98.5341249919447	-0.374124991944726
55	99.9	100.338779309043	-0.438779309042806
56	99.4	100.038406283549	-0.638406283548776
57	99.91	98.9855187161627	0.924481283837321
58	98	98.2651584523718	-0.265158452371856

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.217491514081586	0.434983028163172	0.782508485918414
10	0.112980212900156	0.225960425800311	0.887019787099844
11	0.0518187612263096	0.103637522452619	0.94818123877369
12	0.241710515661302	0.483421031322604	0.758289484338698
13	0.209427070728857	0.418854141457714	0.790572929271143
14	0.168998591592344	0.337997183184687	0.831001408407656
15	0.127961300208441	0.255922600416882	0.87203869979156
16	0.113090322250809	0.226180644501619	0.88690967774919
17	0.117391285518436	0.234782571036871	0.882608714481564
18	0.0784448391371442	0.156889678274288	0.921555160862856
19	0.0681014820938346	0.136202964187669	0.931898517906165
20	0.0945466341298567	0.189093268259713	0.905453365870143
21	0.171629712814677	0.343259425629355	0.828370287185323
22	0.205150397948714	0.410300795897429	0.794849602051286
23	0.16256961557763	0.32513923115526	0.83743038442237
24	0.119949604656611	0.239899209313223	0.880050395343389
25	0.52280685732696	0.95438628534608	0.47719314267304
26	0.492793773375854	0.985587546751707	0.507206226624146
27	0.446963337895411	0.893926675790822	0.553036662104589
28	0.397744961796961	0.795489923593921	0.602255038203039
29	0.352112398994944	0.704224797989887	0.647887601005056
30	0.819372882938005	0.36125423412399	0.180627117061995
31	0.851809464631993	0.296381070736015	0.148190535368007
32	0.81339375318673	0.373212493626539	0.18660624681327
33	0.804099238788498	0.391801522423004	0.195900761211502
34	0.758136434133106	0.483727131733787	0.241863565866894
35	0.78895818839555	0.4220836232089	0.21104181160445
36	0.71876447443205	0.562471051135898	0.281235525567949
37	0.651517205553297	0.696965588893407	0.348482794446703
38	0.72652639266393	0.546947214672141	0.27347360733607
39	0.692803285464031	0.614393429071938	0.307196714535969
40	0.824213506001612	0.351572987996777	0.175786493998388
41	0.832416983075452	0.335166033849096	0.167583016924548
42	0.772679197532607	0.454641604934785	0.227320802467393
43	0.868726290322214	0.262547419355573	0.131273709677786
44	0.856338840514395	0.28732231897121	0.143661159485605
45	0.773253386530192	0.453493226939615	0.226746613469808
46	0.681127909572446	0.637744180855108	0.318872090427554
47	0.566753787873911	0.866492424252178	0.433246212126089
48	0.438447303637501	0.876894607275002	0.561552696362499
49	0.795705428215611	0.408589143568778	0.204294571784389

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.217491514081586 & 0.434983028163172 & 0.782508485918414 \tabularnewline
10 & 0.112980212900156 & 0.225960425800311 & 0.887019787099844 \tabularnewline
11 & 0.0518187612263096 & 0.103637522452619 & 0.94818123877369 \tabularnewline
12 & 0.241710515661302 & 0.483421031322604 & 0.758289484338698 \tabularnewline
13 & 0.209427070728857 & 0.418854141457714 & 0.790572929271143 \tabularnewline
14 & 0.168998591592344 & 0.337997183184687 & 0.831001408407656 \tabularnewline
15 & 0.127961300208441 & 0.255922600416882 & 0.87203869979156 \tabularnewline
16 & 0.113090322250809 & 0.226180644501619 & 0.88690967774919 \tabularnewline
17 & 0.117391285518436 & 0.234782571036871 & 0.882608714481564 \tabularnewline
18 & 0.0784448391371442 & 0.156889678274288 & 0.921555160862856 \tabularnewline
19 & 0.0681014820938346 & 0.136202964187669 & 0.931898517906165 \tabularnewline
20 & 0.0945466341298567 & 0.189093268259713 & 0.905453365870143 \tabularnewline
21 & 0.171629712814677 & 0.343259425629355 & 0.828370287185323 \tabularnewline
22 & 0.205150397948714 & 0.410300795897429 & 0.794849602051286 \tabularnewline
23 & 0.16256961557763 & 0.32513923115526 & 0.83743038442237 \tabularnewline
24 & 0.119949604656611 & 0.239899209313223 & 0.880050395343389 \tabularnewline
25 & 0.52280685732696 & 0.95438628534608 & 0.47719314267304 \tabularnewline
26 & 0.492793773375854 & 0.985587546751707 & 0.507206226624146 \tabularnewline
27 & 0.446963337895411 & 0.893926675790822 & 0.553036662104589 \tabularnewline
28 & 0.397744961796961 & 0.795489923593921 & 0.602255038203039 \tabularnewline
29 & 0.352112398994944 & 0.704224797989887 & 0.647887601005056 \tabularnewline
30 & 0.819372882938005 & 0.36125423412399 & 0.180627117061995 \tabularnewline
31 & 0.851809464631993 & 0.296381070736015 & 0.148190535368007 \tabularnewline
32 & 0.81339375318673 & 0.373212493626539 & 0.18660624681327 \tabularnewline
33 & 0.804099238788498 & 0.391801522423004 & 0.195900761211502 \tabularnewline
34 & 0.758136434133106 & 0.483727131733787 & 0.241863565866894 \tabularnewline
35 & 0.78895818839555 & 0.4220836232089 & 0.21104181160445 \tabularnewline
36 & 0.71876447443205 & 0.562471051135898 & 0.281235525567949 \tabularnewline
37 & 0.651517205553297 & 0.696965588893407 & 0.348482794446703 \tabularnewline
38 & 0.72652639266393 & 0.546947214672141 & 0.27347360733607 \tabularnewline
39 & 0.692803285464031 & 0.614393429071938 & 0.307196714535969 \tabularnewline
40 & 0.824213506001612 & 0.351572987996777 & 0.175786493998388 \tabularnewline
41 & 0.832416983075452 & 0.335166033849096 & 0.167583016924548 \tabularnewline
42 & 0.772679197532607 & 0.454641604934785 & 0.227320802467393 \tabularnewline
43 & 0.868726290322214 & 0.262547419355573 & 0.131273709677786 \tabularnewline
44 & 0.856338840514395 & 0.28732231897121 & 0.143661159485605 \tabularnewline
45 & 0.773253386530192 & 0.453493226939615 & 0.226746613469808 \tabularnewline
46 & 0.681127909572446 & 0.637744180855108 & 0.318872090427554 \tabularnewline
47 & 0.566753787873911 & 0.866492424252178 & 0.433246212126089 \tabularnewline
48 & 0.438447303637501 & 0.876894607275002 & 0.561552696362499 \tabularnewline
49 & 0.795705428215611 & 0.408589143568778 & 0.204294571784389 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100073&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]0.217491514081586[/C][C]0.434983028163172[/C][C]0.782508485918414[/C][/ROW]
[ROW][C]10[/C][C]0.112980212900156[/C][C]0.225960425800311[/C][C]0.887019787099844[/C][/ROW]
[ROW][C]11[/C][C]0.0518187612263096[/C][C]0.103637522452619[/C][C]0.94818123877369[/C][/ROW]
[ROW][C]12[/C][C]0.241710515661302[/C][C]0.483421031322604[/C][C]0.758289484338698[/C][/ROW]
[ROW][C]13[/C][C]0.209427070728857[/C][C]0.418854141457714[/C][C]0.790572929271143[/C][/ROW]
[ROW][C]14[/C][C]0.168998591592344[/C][C]0.337997183184687[/C][C]0.831001408407656[/C][/ROW]
[ROW][C]15[/C][C]0.127961300208441[/C][C]0.255922600416882[/C][C]0.87203869979156[/C][/ROW]
[ROW][C]16[/C][C]0.113090322250809[/C][C]0.226180644501619[/C][C]0.88690967774919[/C][/ROW]
[ROW][C]17[/C][C]0.117391285518436[/C][C]0.234782571036871[/C][C]0.882608714481564[/C][/ROW]
[ROW][C]18[/C][C]0.0784448391371442[/C][C]0.156889678274288[/C][C]0.921555160862856[/C][/ROW]
[ROW][C]19[/C][C]0.0681014820938346[/C][C]0.136202964187669[/C][C]0.931898517906165[/C][/ROW]
[ROW][C]20[/C][C]0.0945466341298567[/C][C]0.189093268259713[/C][C]0.905453365870143[/C][/ROW]
[ROW][C]21[/C][C]0.171629712814677[/C][C]0.343259425629355[/C][C]0.828370287185323[/C][/ROW]
[ROW][C]22[/C][C]0.205150397948714[/C][C]0.410300795897429[/C][C]0.794849602051286[/C][/ROW]
[ROW][C]23[/C][C]0.16256961557763[/C][C]0.32513923115526[/C][C]0.83743038442237[/C][/ROW]
[ROW][C]24[/C][C]0.119949604656611[/C][C]0.239899209313223[/C][C]0.880050395343389[/C][/ROW]
[ROW][C]25[/C][C]0.52280685732696[/C][C]0.95438628534608[/C][C]0.47719314267304[/C][/ROW]
[ROW][C]26[/C][C]0.492793773375854[/C][C]0.985587546751707[/C][C]0.507206226624146[/C][/ROW]
[ROW][C]27[/C][C]0.446963337895411[/C][C]0.893926675790822[/C][C]0.553036662104589[/C][/ROW]
[ROW][C]28[/C][C]0.397744961796961[/C][C]0.795489923593921[/C][C]0.602255038203039[/C][/ROW]
[ROW][C]29[/C][C]0.352112398994944[/C][C]0.704224797989887[/C][C]0.647887601005056[/C][/ROW]
[ROW][C]30[/C][C]0.819372882938005[/C][C]0.36125423412399[/C][C]0.180627117061995[/C][/ROW]
[ROW][C]31[/C][C]0.851809464631993[/C][C]0.296381070736015[/C][C]0.148190535368007[/C][/ROW]
[ROW][C]32[/C][C]0.81339375318673[/C][C]0.373212493626539[/C][C]0.18660624681327[/C][/ROW]
[ROW][C]33[/C][C]0.804099238788498[/C][C]0.391801522423004[/C][C]0.195900761211502[/C][/ROW]
[ROW][C]34[/C][C]0.758136434133106[/C][C]0.483727131733787[/C][C]0.241863565866894[/C][/ROW]
[ROW][C]35[/C][C]0.78895818839555[/C][C]0.4220836232089[/C][C]0.21104181160445[/C][/ROW]
[ROW][C]36[/C][C]0.71876447443205[/C][C]0.562471051135898[/C][C]0.281235525567949[/C][/ROW]
[ROW][C]37[/C][C]0.651517205553297[/C][C]0.696965588893407[/C][C]0.348482794446703[/C][/ROW]
[ROW][C]38[/C][C]0.72652639266393[/C][C]0.546947214672141[/C][C]0.27347360733607[/C][/ROW]
[ROW][C]39[/C][C]0.692803285464031[/C][C]0.614393429071938[/C][C]0.307196714535969[/C][/ROW]
[ROW][C]40[/C][C]0.824213506001612[/C][C]0.351572987996777[/C][C]0.175786493998388[/C][/ROW]
[ROW][C]41[/C][C]0.832416983075452[/C][C]0.335166033849096[/C][C]0.167583016924548[/C][/ROW]
[ROW][C]42[/C][C]0.772679197532607[/C][C]0.454641604934785[/C][C]0.227320802467393[/C][/ROW]
[ROW][C]43[/C][C]0.868726290322214[/C][C]0.262547419355573[/C][C]0.131273709677786[/C][/ROW]
[ROW][C]44[/C][C]0.856338840514395[/C][C]0.28732231897121[/C][C]0.143661159485605[/C][/ROW]
[ROW][C]45[/C][C]0.773253386530192[/C][C]0.453493226939615[/C][C]0.226746613469808[/C][/ROW]
[ROW][C]46[/C][C]0.681127909572446[/C][C]0.637744180855108[/C][C]0.318872090427554[/C][/ROW]
[ROW][C]47[/C][C]0.566753787873911[/C][C]0.866492424252178[/C][C]0.433246212126089[/C][/ROW]
[ROW][C]48[/C][C]0.438447303637501[/C][C]0.876894607275002[/C][C]0.561552696362499[/C][/ROW]
[ROW][C]49[/C][C]0.795705428215611[/C][C]0.408589143568778[/C][C]0.204294571784389[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100073&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100073&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	0.217491514081586	0.434983028163172	0.782508485918414
10	0.112980212900156	0.225960425800311	0.887019787099844
11	0.0518187612263096	0.103637522452619	0.94818123877369
12	0.241710515661302	0.483421031322604	0.758289484338698
13	0.209427070728857	0.418854141457714	0.790572929271143
14	0.168998591592344	0.337997183184687	0.831001408407656
15	0.127961300208441	0.255922600416882	0.87203869979156
16	0.113090322250809	0.226180644501619	0.88690967774919
17	0.117391285518436	0.234782571036871	0.882608714481564
18	0.0784448391371442	0.156889678274288	0.921555160862856
19	0.0681014820938346	0.136202964187669	0.931898517906165
20	0.0945466341298567	0.189093268259713	0.905453365870143
21	0.171629712814677	0.343259425629355	0.828370287185323
22	0.205150397948714	0.410300795897429	0.794849602051286
23	0.16256961557763	0.32513923115526	0.83743038442237
24	0.119949604656611	0.239899209313223	0.880050395343389
25	0.52280685732696	0.95438628534608	0.47719314267304
26	0.492793773375854	0.985587546751707	0.507206226624146
27	0.446963337895411	0.893926675790822	0.553036662104589
28	0.397744961796961	0.795489923593921	0.602255038203039
29	0.352112398994944	0.704224797989887	0.647887601005056
30	0.819372882938005	0.36125423412399	0.180627117061995
31	0.851809464631993	0.296381070736015	0.148190535368007
32	0.81339375318673	0.373212493626539	0.18660624681327
33	0.804099238788498	0.391801522423004	0.195900761211502
34	0.758136434133106	0.483727131733787	0.241863565866894
35	0.78895818839555	0.4220836232089	0.21104181160445
36	0.71876447443205	0.562471051135898	0.281235525567949
37	0.651517205553297	0.696965588893407	0.348482794446703
38	0.72652639266393	0.546947214672141	0.27347360733607
39	0.692803285464031	0.614393429071938	0.307196714535969
40	0.824213506001612	0.351572987996777	0.175786493998388
41	0.832416983075452	0.335166033849096	0.167583016924548
42	0.772679197532607	0.454641604934785	0.227320802467393
43	0.868726290322214	0.262547419355573	0.131273709677786
44	0.856338840514395	0.28732231897121	0.143661159485605
45	0.773253386530192	0.453493226939615	0.226746613469808
46	0.681127909572446	0.637744180855108	0.318872090427554
47	0.566753787873911	0.866492424252178	0.433246212126089
48	0.438447303637501	0.876894607275002	0.561552696362499
49	0.795705428215611	0.408589143568778	0.204294571784389

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=100073&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=100073&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100073&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

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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