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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 19 Nov 2010 14:23:14 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/19/t12901765178kndu6v4qjzx0wg.htm/, Retrieved Fri, 26 Apr 2024 03:33:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97997, Retrieved Fri, 26 Apr 2024 03:33:42 +0000

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Estimated Impact

165

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

-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-11-19 14:23:14] [df17410ebb98883e83037e1662207ccb] [Current]

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101,82	107,34	93,63	101,76
101,68	107,34	93,63	102,37
101,68	107,34	93,63	102,38
102,45	107,34	96,13	102,86
102,45	107,34	96,13	102,87
102,45	107,34	96,13	102,92
102,45	107,34	96,13	102,95
102,45	107,34	96,13	103,02
102,45	112,60	96,13	104,08
102,52	112,60	96,13	104,16
102,52	112,60	96,13	104,24
102,85	112,60	96,13	104,33
102,85	112,61	96,13	104,73
102,85	112,61	96,13	104,86
103,25	112,61	96,13	105,03
103,25	112,61	98,73	105,62
103,25	112,61	98,73	105,63
103,25	112,61	98,73	105,63
104,45	112,61	98,73	105,94
104,45	112,61	98,73	106,61
104,45	118,65	98,73	107,69
104,80	118,65	98,73	107,78
104,80	118,65	98,73	107,93
105,29	118,65	98,73	108,48
105,29	114,29	98,73	108,14
105,29	114,29	98,73	108,48
105,29	114,29	98,73	108,48
106,04	114,29	101,67	108,89
105,94	114,29	101,67	108,93
105,94	114,29	101,67	109,21
105,94	114,29	101,67	109,47
106,28	114,29	101,67	109,80
106,48	123,33	101,67	111,73
107,19	123,33	101,67	111,85
108,14	123,33	101,67	112,12
108,22	123,33	101,67	112,15
108,22	123,33	101,67	112,17
108,61	123,33	101,67	112,67
108,61	123,33	101,67	112,80
108,61	123,33	107,94	113,44
108,61	123,33	107,94	113,53
109,06	123,33	107,94	114,53
109,06	123,33	107,94	114,51
112,93	123,33	107,94	115,05
115,84	129,03	107,94	116,67
118,57	128,76	107,94	117,07
118,57	128,76	107,94	116,92
118,86	128,76	107,94	117,00
118,98	128,76	107,94	117,02
119,27	128,76	107,94	117,35
119,39	128,76	107,94	117,36
119,49	128,76	110,30	117,82
119,59	128,76	110,30	117,88
120,12	128,76	110,30	118,24
120,14	128,76	110,30	118,50
120,14	128,76	110,30	118,80
120,14	132,63	110,30	119,76
120,14	132,63	110,30	120,09

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	'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97997&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97997&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97997&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	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
And.dienstenrecr.&cultuur[t] = + 61.3522977549418 + 0.104860262173398Bioscoop[t] + 0.167292816816152Schouwburgabonnement[t] + 0.121128872847212Eendagsattracties[t] + 0.178769949566188t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
And.dienstenrecr.&cultuur[t] =  +  61.3522977549418 +  0.104860262173398Bioscoop[t] +  0.167292816816152Schouwburgabonnement[t] +  0.121128872847212Eendagsattracties[t] +  0.178769949566188t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97997&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]And.dienstenrecr.&cultuur[t] =  +  61.3522977549418 +  0.104860262173398Bioscoop[t] +  0.167292816816152Schouwburgabonnement[t] +  0.121128872847212Eendagsattracties[t] +  0.178769949566188t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97997&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97997&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
And.dienstenrecr.&cultuur[t] = + 61.3522977549418 + 0.104860262173398Bioscoop[t] + 0.167292816816152Schouwburgabonnement[t] + 0.121128872847212Eendagsattracties[t] + 0.178769949566188t + 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)	61.3522977549418	3.419552	17.9416	0	0
Bioscoop	0.104860262173398	0.017876	5.866	0	0
Schouwburgabonnement	0.167292816816152	0.018921	8.8416	0	0
Eendagsattracties	0.121128872847212	0.031641	3.8282	0.000343	0.000171
t	0.178769949566188	0.012691	14.0861	0	0

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 61.3522977549418 & 3.419552 & 17.9416 & 0 & 0 \tabularnewline
Bioscoop & 0.104860262173398 & 0.017876 & 5.866 & 0 & 0 \tabularnewline
Schouwburgabonnement & 0.167292816816152 & 0.018921 & 8.8416 & 0 & 0 \tabularnewline
Eendagsattracties & 0.121128872847212 & 0.031641 & 3.8282 & 0.000343 & 0.000171 \tabularnewline
t & 0.178769949566188 & 0.012691 & 14.0861 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97997&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]61.3522977549418[/C][C]3.419552[/C][C]17.9416[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bioscoop[/C][C]0.104860262173398[/C][C]0.017876[/C][C]5.866[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Schouwburgabonnement[/C][C]0.167292816816152[/C][C]0.018921[/C][C]8.8416[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Eendagsattracties[/C][C]0.121128872847212[/C][C]0.031641[/C][C]3.8282[/C][C]0.000343[/C][C]0.000171[/C][/ROW]
[ROW][C]t[/C][C]0.178769949566188[/C][C]0.012691[/C][C]14.0861[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97997&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97997&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)	61.3522977549418	3.419552	17.9416	0	0
Bioscoop	0.104860262173398	0.017876	5.866	0	0
Schouwburgabonnement	0.167292816816152	0.018921	8.8416	0	0
Eendagsattracties	0.121128872847212	0.031641	3.8282	0.000343	0.000171
t	0.178769949566188	0.012691	14.0861	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.998649583453835
R-squared	0.997300990532518
Adjusted R-squared	0.997097291704784
F-TEST (value)	4895.95841873246
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.300357119404692
Sum Squared Residuals	4.78136315638548

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998649583453835 \tabularnewline
R-squared & 0.997300990532518 \tabularnewline
Adjusted R-squared & 0.997097291704784 \tabularnewline
F-TEST (value) & 4895.95841873246 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.300357119404692 \tabularnewline
Sum Squared Residuals & 4.78136315638548 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97997&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998649583453835[/C][/ROW]
[ROW][C]R-squared[/C][C]0.997300990532518[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997097291704784[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4895.95841873246[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.300357119404692[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.78136315638548[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97997&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97997&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	0.998649583453835
R-squared	0.997300990532518
Adjusted R-squared	0.997097291704784
F-TEST (value)	4895.95841873246
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.300357119404692
Sum Squared Residuals	4.78136315638548

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	101.506446920734	0.253553079266132
2	102.37	101.670536433596	0.699463566404492
3	102.38	101.849306383162	0.530693616838284
4	102.86	102.411640916719	0.448359083280561
5	102.87	102.590410866286	0.279589133714374
6	102.92	102.769180815852	0.150819184148186
7	102.95	102.947950765418	0.00204923458199948
8	103.02	103.126720714984	-0.106720714984195
9	104.08	104.185450881003	-0.105450881003340
10	104.16	104.371561048922	-0.211561048921668
11	104.24	104.550330998488	-0.310330998487857
12	104.33	104.763704834571	-0.433704834571263
13	104.73	104.944147712306	-0.214147712305608
14	104.86	105.122917661872	-0.2629176618718
15	105.03	105.343631716307	-0.313631716307346
16	105.62	105.837336735276	-0.217336735276282
17	105.63	106.016106684842	-0.386106684842479
18	105.63	106.194876634409	-0.564876634408667
19	105.94	106.499478898583	-0.559478898582932
20	106.61	106.678248848149	-0.068248848149118
21	107.69	107.867467411285	-0.177467411284868
22	107.78	108.082938452612	-0.302938452611742
23	107.93	108.261708402178	-0.331708402177924
24	108.48	108.491859880209	-0.0118598802090809
25	108.14	107.941233148457	0.198766851543152
26	108.48	108.120003098023	0.359996901976967
27	108.48	108.298773047589	0.181226952410779
28	108.89	108.912307079956	-0.0223070799562636
29	108.93	109.080591003305	-0.150591003305104
30	109.21	109.259360952871	-0.0493609528713051
31	109.47	109.438130902437	0.031869097562512
32	109.8	109.652553341143	0.147446658857367
33	111.73	111.364622407162	0.365377592838490
34	111.85	111.617843142871	0.23215685712918
35	112.12	111.896230341502	0.223769658498273
36	112.15	112.083389112042	0.0666108879582143
37	112.17	112.262159061608	-0.0921590616079776
38	112.67	112.481824513422	0.188175486578210
39	112.8	112.660594462988	0.139405537012017
40	113.44	113.598842445306	-0.158842445306188
41	113.53	113.777612394872	-0.247612394872372
42	114.53	114.003569462417	0.526430537583410
43	114.51	114.182339411983	0.327660588017226
44	115.05	114.76691857616	0.283081423839978
45	116.67	116.204400944503	0.465599055497137
46	117.07	116.624270349262	0.445729650737927
47	116.92	116.803040298828	0.116959701171748
48	117	117.012219724425	-0.0122197244247277
49	117.02	117.203572905452	-0.183572905451728
50	117.35	117.412752331048	-0.0627523310482026
51	117.36	117.604105512075	-0.244105512075195
52	117.82	118.079225627778	-0.259225627778146
53	117.88	118.268481603562	-0.388481603561673
54	118.24	118.502827492080	-0.262827492079763
55	118.5	118.683694646889	-0.183694646889414
56	118.8	118.862464596456	-0.0624645964556048
57	119.76	119.688657747100	0.0713422528997051
58	120.09	119.867427696666	0.222572303333515

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 101.506446920734 & 0.253553079266132 \tabularnewline
2 & 102.37 & 101.670536433596 & 0.699463566404492 \tabularnewline
3 & 102.38 & 101.849306383162 & 0.530693616838284 \tabularnewline
4 & 102.86 & 102.411640916719 & 0.448359083280561 \tabularnewline
5 & 102.87 & 102.590410866286 & 0.279589133714374 \tabularnewline
6 & 102.92 & 102.769180815852 & 0.150819184148186 \tabularnewline
7 & 102.95 & 102.947950765418 & 0.00204923458199948 \tabularnewline
8 & 103.02 & 103.126720714984 & -0.106720714984195 \tabularnewline
9 & 104.08 & 104.185450881003 & -0.105450881003340 \tabularnewline
10 & 104.16 & 104.371561048922 & -0.211561048921668 \tabularnewline
11 & 104.24 & 104.550330998488 & -0.310330998487857 \tabularnewline
12 & 104.33 & 104.763704834571 & -0.433704834571263 \tabularnewline
13 & 104.73 & 104.944147712306 & -0.214147712305608 \tabularnewline
14 & 104.86 & 105.122917661872 & -0.2629176618718 \tabularnewline
15 & 105.03 & 105.343631716307 & -0.313631716307346 \tabularnewline
16 & 105.62 & 105.837336735276 & -0.217336735276282 \tabularnewline
17 & 105.63 & 106.016106684842 & -0.386106684842479 \tabularnewline
18 & 105.63 & 106.194876634409 & -0.564876634408667 \tabularnewline
19 & 105.94 & 106.499478898583 & -0.559478898582932 \tabularnewline
20 & 106.61 & 106.678248848149 & -0.068248848149118 \tabularnewline
21 & 107.69 & 107.867467411285 & -0.177467411284868 \tabularnewline
22 & 107.78 & 108.082938452612 & -0.302938452611742 \tabularnewline
23 & 107.93 & 108.261708402178 & -0.331708402177924 \tabularnewline
24 & 108.48 & 108.491859880209 & -0.0118598802090809 \tabularnewline
25 & 108.14 & 107.941233148457 & 0.198766851543152 \tabularnewline
26 & 108.48 & 108.120003098023 & 0.359996901976967 \tabularnewline
27 & 108.48 & 108.298773047589 & 0.181226952410779 \tabularnewline
28 & 108.89 & 108.912307079956 & -0.0223070799562636 \tabularnewline
29 & 108.93 & 109.080591003305 & -0.150591003305104 \tabularnewline
30 & 109.21 & 109.259360952871 & -0.0493609528713051 \tabularnewline
31 & 109.47 & 109.438130902437 & 0.031869097562512 \tabularnewline
32 & 109.8 & 109.652553341143 & 0.147446658857367 \tabularnewline
33 & 111.73 & 111.364622407162 & 0.365377592838490 \tabularnewline
34 & 111.85 & 111.617843142871 & 0.23215685712918 \tabularnewline
35 & 112.12 & 111.896230341502 & 0.223769658498273 \tabularnewline
36 & 112.15 & 112.083389112042 & 0.0666108879582143 \tabularnewline
37 & 112.17 & 112.262159061608 & -0.0921590616079776 \tabularnewline
38 & 112.67 & 112.481824513422 & 0.188175486578210 \tabularnewline
39 & 112.8 & 112.660594462988 & 0.139405537012017 \tabularnewline
40 & 113.44 & 113.598842445306 & -0.158842445306188 \tabularnewline
41 & 113.53 & 113.777612394872 & -0.247612394872372 \tabularnewline
42 & 114.53 & 114.003569462417 & 0.526430537583410 \tabularnewline
43 & 114.51 & 114.182339411983 & 0.327660588017226 \tabularnewline
44 & 115.05 & 114.76691857616 & 0.283081423839978 \tabularnewline
45 & 116.67 & 116.204400944503 & 0.465599055497137 \tabularnewline
46 & 117.07 & 116.624270349262 & 0.445729650737927 \tabularnewline
47 & 116.92 & 116.803040298828 & 0.116959701171748 \tabularnewline
48 & 117 & 117.012219724425 & -0.0122197244247277 \tabularnewline
49 & 117.02 & 117.203572905452 & -0.183572905451728 \tabularnewline
50 & 117.35 & 117.412752331048 & -0.0627523310482026 \tabularnewline
51 & 117.36 & 117.604105512075 & -0.244105512075195 \tabularnewline
52 & 117.82 & 118.079225627778 & -0.259225627778146 \tabularnewline
53 & 117.88 & 118.268481603562 & -0.388481603561673 \tabularnewline
54 & 118.24 & 118.502827492080 & -0.262827492079763 \tabularnewline
55 & 118.5 & 118.683694646889 & -0.183694646889414 \tabularnewline
56 & 118.8 & 118.862464596456 & -0.0624645964556048 \tabularnewline
57 & 119.76 & 119.688657747100 & 0.0713422528997051 \tabularnewline
58 & 120.09 & 119.867427696666 & 0.222572303333515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97997&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]101.76[/C][C]101.506446920734[/C][C]0.253553079266132[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]101.670536433596[/C][C]0.699463566404492[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]101.849306383162[/C][C]0.530693616838284[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]102.411640916719[/C][C]0.448359083280561[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]102.590410866286[/C][C]0.279589133714374[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]102.769180815852[/C][C]0.150819184148186[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.947950765418[/C][C]0.00204923458199948[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]103.126720714984[/C][C]-0.106720714984195[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.185450881003[/C][C]-0.105450881003340[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.371561048922[/C][C]-0.211561048921668[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.550330998488[/C][C]-0.310330998487857[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.763704834571[/C][C]-0.433704834571263[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]104.944147712306[/C][C]-0.214147712305608[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.122917661872[/C][C]-0.2629176618718[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]105.343631716307[/C][C]-0.313631716307346[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]105.837336735276[/C][C]-0.217336735276282[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]106.016106684842[/C][C]-0.386106684842479[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.194876634409[/C][C]-0.564876634408667[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.499478898583[/C][C]-0.559478898582932[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.678248848149[/C][C]-0.068248848149118[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]107.867467411285[/C][C]-0.177467411284868[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.082938452612[/C][C]-0.302938452611742[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.261708402178[/C][C]-0.331708402177924[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.491859880209[/C][C]-0.0118598802090809[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.941233148457[/C][C]0.198766851543152[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]108.120003098023[/C][C]0.359996901976967[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]108.298773047589[/C][C]0.181226952410779[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.912307079956[/C][C]-0.0223070799562636[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]109.080591003305[/C][C]-0.150591003305104[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]109.259360952871[/C][C]-0.0493609528713051[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]109.438130902437[/C][C]0.031869097562512[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]109.652553341143[/C][C]0.147446658857367[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.364622407162[/C][C]0.365377592838490[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.617843142871[/C][C]0.23215685712918[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.896230341502[/C][C]0.223769658498273[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]112.083389112042[/C][C]0.0666108879582143[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.262159061608[/C][C]-0.0921590616079776[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.481824513422[/C][C]0.188175486578210[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.660594462988[/C][C]0.139405537012017[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]113.598842445306[/C][C]-0.158842445306188[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]113.777612394872[/C][C]-0.247612394872372[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.003569462417[/C][C]0.526430537583410[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.182339411983[/C][C]0.327660588017226[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.76691857616[/C][C]0.283081423839978[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.204400944503[/C][C]0.465599055497137[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.624270349262[/C][C]0.445729650737927[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.803040298828[/C][C]0.116959701171748[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]117.012219724425[/C][C]-0.0122197244247277[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.203572905452[/C][C]-0.183572905451728[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.412752331048[/C][C]-0.0627523310482026[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.604105512075[/C][C]-0.244105512075195[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]118.079225627778[/C][C]-0.259225627778146[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.268481603562[/C][C]-0.388481603561673[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.502827492080[/C][C]-0.262827492079763[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.683694646889[/C][C]-0.183694646889414[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.862464596456[/C][C]-0.0624645964556048[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.688657747100[/C][C]0.0713422528997051[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.867427696666[/C][C]0.222572303333515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97997&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97997&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	101.76	101.506446920734	0.253553079266132
2	102.37	101.670536433596	0.699463566404492
3	102.38	101.849306383162	0.530693616838284
4	102.86	102.411640916719	0.448359083280561
5	102.87	102.590410866286	0.279589133714374
6	102.92	102.769180815852	0.150819184148186
7	102.95	102.947950765418	0.00204923458199948
8	103.02	103.126720714984	-0.106720714984195
9	104.08	104.185450881003	-0.105450881003340
10	104.16	104.371561048922	-0.211561048921668
11	104.24	104.550330998488	-0.310330998487857
12	104.33	104.763704834571	-0.433704834571263
13	104.73	104.944147712306	-0.214147712305608
14	104.86	105.122917661872	-0.2629176618718
15	105.03	105.343631716307	-0.313631716307346
16	105.62	105.837336735276	-0.217336735276282
17	105.63	106.016106684842	-0.386106684842479
18	105.63	106.194876634409	-0.564876634408667
19	105.94	106.499478898583	-0.559478898582932
20	106.61	106.678248848149	-0.068248848149118
21	107.69	107.867467411285	-0.177467411284868
22	107.78	108.082938452612	-0.302938452611742
23	107.93	108.261708402178	-0.331708402177924
24	108.48	108.491859880209	-0.0118598802090809
25	108.14	107.941233148457	0.198766851543152
26	108.48	108.120003098023	0.359996901976967
27	108.48	108.298773047589	0.181226952410779
28	108.89	108.912307079956	-0.0223070799562636
29	108.93	109.080591003305	-0.150591003305104
30	109.21	109.259360952871	-0.0493609528713051
31	109.47	109.438130902437	0.031869097562512
32	109.8	109.652553341143	0.147446658857367
33	111.73	111.364622407162	0.365377592838490
34	111.85	111.617843142871	0.23215685712918
35	112.12	111.896230341502	0.223769658498273
36	112.15	112.083389112042	0.0666108879582143
37	112.17	112.262159061608	-0.0921590616079776
38	112.67	112.481824513422	0.188175486578210
39	112.8	112.660594462988	0.139405537012017
40	113.44	113.598842445306	-0.158842445306188
41	113.53	113.777612394872	-0.247612394872372
42	114.53	114.003569462417	0.526430537583410
43	114.51	114.182339411983	0.327660588017226
44	115.05	114.76691857616	0.283081423839978
45	116.67	116.204400944503	0.465599055497137
46	117.07	116.624270349262	0.445729650737927
47	116.92	116.803040298828	0.116959701171748
48	117	117.012219724425	-0.0122197244247277
49	117.02	117.203572905452	-0.183572905451728
50	117.35	117.412752331048	-0.0627523310482026
51	117.36	117.604105512075	-0.244105512075195
52	117.82	118.079225627778	-0.259225627778146
53	117.88	118.268481603562	-0.388481603561673
54	118.24	118.502827492080	-0.262827492079763
55	118.5	118.683694646889	-0.183694646889414
56	118.8	118.862464596456	-0.0624645964556048
57	119.76	119.688657747100	0.0713422528997051
58	120.09	119.867427696666	0.222572303333515

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.00106771540000924	0.00213543080001848	0.99893228459999
9	8.29172946740711e-05	0.000165834589348142	0.999917082705326
10	0.0202576939307905	0.040515387861581	0.97974230606921
11	0.0126024341060529	0.0252048682121058	0.987397565893947
12	0.109040383060299	0.218080766120599	0.8909596169397
13	0.172617050094518	0.345234100189036	0.827382949905482
14	0.143086676535707	0.286173353071413	0.856913323464293
15	0.0979231660618269	0.195846332123654	0.902076833938173
16	0.138726408742863	0.277452817485726	0.861273591257137
17	0.0936401032911739	0.187280206582348	0.906359896708826
18	0.0775673263447698	0.155134652689540	0.92243267365523
19	0.0676213465226017	0.135242693045203	0.932378653477398
20	0.173807302023062	0.347614604046124	0.826192697976938
21	0.234267235231381	0.468534470462762	0.765732764768619
22	0.22173611529168	0.44347223058336	0.77826388470832
23	0.284629378857554	0.569258757715108	0.715370621142446
24	0.331591495396557	0.663182990793113	0.668408504603443
25	0.414759987357777	0.829519974715554	0.585240012642223
26	0.54527944896475	0.9094411020705	0.45472055103525
27	0.504071627341166	0.991856745317667	0.495928372658834
28	0.424631387716153	0.849262775432305	0.575368612283848
29	0.365112399557182	0.730224799114364	0.634887600442818
30	0.305597259502631	0.611194519005262	0.694402740497369
31	0.265512919455488	0.531025838910976	0.734487080544512
32	0.280294367856035	0.56058873571207	0.719705632143965
33	0.530698358582418	0.938603282835164	0.469301641417582
34	0.452862877824313	0.905725755648625	0.547137122175687
35	0.421833990343651	0.843667980687303	0.578166009656349
36	0.401252951931182	0.802505903862363	0.598747048068818
37	0.449286159598301	0.898572319196603	0.550713840401699
38	0.372923504996769	0.745847009993538	0.627076495003231
39	0.332927668002649	0.665855336005298	0.667072331997351
40	0.403951625790861	0.807903251581722	0.596048374209139
41	0.869209695252928	0.261580609494143	0.130790304747072
42	0.870847204475137	0.258305591049725	0.129152795524863
43	0.88811077111343	0.223778457773140	0.111889228886570
44	0.89853710617474	0.202925787650521	0.101462893825260
45	0.858229888192529	0.283540223614943	0.141770111807471
46	0.968357375520594	0.0632852489588116	0.0316426244794058
47	0.97991817473241	0.0401636505351799	0.0200818252675900
48	0.985794984605446	0.0284100307891076	0.0142050153945538
49	0.962950697620354	0.0740986047592917	0.0370493023796458
50	0.966943886863082	0.0661122262738351	0.0330561131369175

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.00106771540000924 & 0.00213543080001848 & 0.99893228459999 \tabularnewline
9 & 8.29172946740711e-05 & 0.000165834589348142 & 0.999917082705326 \tabularnewline
10 & 0.0202576939307905 & 0.040515387861581 & 0.97974230606921 \tabularnewline
11 & 0.0126024341060529 & 0.0252048682121058 & 0.987397565893947 \tabularnewline
12 & 0.109040383060299 & 0.218080766120599 & 0.8909596169397 \tabularnewline
13 & 0.172617050094518 & 0.345234100189036 & 0.827382949905482 \tabularnewline
14 & 0.143086676535707 & 0.286173353071413 & 0.856913323464293 \tabularnewline
15 & 0.0979231660618269 & 0.195846332123654 & 0.902076833938173 \tabularnewline
16 & 0.138726408742863 & 0.277452817485726 & 0.861273591257137 \tabularnewline
17 & 0.0936401032911739 & 0.187280206582348 & 0.906359896708826 \tabularnewline
18 & 0.0775673263447698 & 0.155134652689540 & 0.92243267365523 \tabularnewline
19 & 0.0676213465226017 & 0.135242693045203 & 0.932378653477398 \tabularnewline
20 & 0.173807302023062 & 0.347614604046124 & 0.826192697976938 \tabularnewline
21 & 0.234267235231381 & 0.468534470462762 & 0.765732764768619 \tabularnewline
22 & 0.22173611529168 & 0.44347223058336 & 0.77826388470832 \tabularnewline
23 & 0.284629378857554 & 0.569258757715108 & 0.715370621142446 \tabularnewline
24 & 0.331591495396557 & 0.663182990793113 & 0.668408504603443 \tabularnewline
25 & 0.414759987357777 & 0.829519974715554 & 0.585240012642223 \tabularnewline
26 & 0.54527944896475 & 0.9094411020705 & 0.45472055103525 \tabularnewline
27 & 0.504071627341166 & 0.991856745317667 & 0.495928372658834 \tabularnewline
28 & 0.424631387716153 & 0.849262775432305 & 0.575368612283848 \tabularnewline
29 & 0.365112399557182 & 0.730224799114364 & 0.634887600442818 \tabularnewline
30 & 0.305597259502631 & 0.611194519005262 & 0.694402740497369 \tabularnewline
31 & 0.265512919455488 & 0.531025838910976 & 0.734487080544512 \tabularnewline
32 & 0.280294367856035 & 0.56058873571207 & 0.719705632143965 \tabularnewline
33 & 0.530698358582418 & 0.938603282835164 & 0.469301641417582 \tabularnewline
34 & 0.452862877824313 & 0.905725755648625 & 0.547137122175687 \tabularnewline
35 & 0.421833990343651 & 0.843667980687303 & 0.578166009656349 \tabularnewline
36 & 0.401252951931182 & 0.802505903862363 & 0.598747048068818 \tabularnewline
37 & 0.449286159598301 & 0.898572319196603 & 0.550713840401699 \tabularnewline
38 & 0.372923504996769 & 0.745847009993538 & 0.627076495003231 \tabularnewline
39 & 0.332927668002649 & 0.665855336005298 & 0.667072331997351 \tabularnewline
40 & 0.403951625790861 & 0.807903251581722 & 0.596048374209139 \tabularnewline
41 & 0.869209695252928 & 0.261580609494143 & 0.130790304747072 \tabularnewline
42 & 0.870847204475137 & 0.258305591049725 & 0.129152795524863 \tabularnewline
43 & 0.88811077111343 & 0.223778457773140 & 0.111889228886570 \tabularnewline
44 & 0.89853710617474 & 0.202925787650521 & 0.101462893825260 \tabularnewline
45 & 0.858229888192529 & 0.283540223614943 & 0.141770111807471 \tabularnewline
46 & 0.968357375520594 & 0.0632852489588116 & 0.0316426244794058 \tabularnewline
47 & 0.97991817473241 & 0.0401636505351799 & 0.0200818252675900 \tabularnewline
48 & 0.985794984605446 & 0.0284100307891076 & 0.0142050153945538 \tabularnewline
49 & 0.962950697620354 & 0.0740986047592917 & 0.0370493023796458 \tabularnewline
50 & 0.966943886863082 & 0.0661122262738351 & 0.0330561131369175 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97997&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]8[/C][C]0.00106771540000924[/C][C]0.00213543080001848[/C][C]0.99893228459999[/C][/ROW]
[ROW][C]9[/C][C]8.29172946740711e-05[/C][C]0.000165834589348142[/C][C]0.999917082705326[/C][/ROW]
[ROW][C]10[/C][C]0.0202576939307905[/C][C]0.040515387861581[/C][C]0.97974230606921[/C][/ROW]
[ROW][C]11[/C][C]0.0126024341060529[/C][C]0.0252048682121058[/C][C]0.987397565893947[/C][/ROW]
[ROW][C]12[/C][C]0.109040383060299[/C][C]0.218080766120599[/C][C]0.8909596169397[/C][/ROW]
[ROW][C]13[/C][C]0.172617050094518[/C][C]0.345234100189036[/C][C]0.827382949905482[/C][/ROW]
[ROW][C]14[/C][C]0.143086676535707[/C][C]0.286173353071413[/C][C]0.856913323464293[/C][/ROW]
[ROW][C]15[/C][C]0.0979231660618269[/C][C]0.195846332123654[/C][C]0.902076833938173[/C][/ROW]
[ROW][C]16[/C][C]0.138726408742863[/C][C]0.277452817485726[/C][C]0.861273591257137[/C][/ROW]
[ROW][C]17[/C][C]0.0936401032911739[/C][C]0.187280206582348[/C][C]0.906359896708826[/C][/ROW]
[ROW][C]18[/C][C]0.0775673263447698[/C][C]0.155134652689540[/C][C]0.92243267365523[/C][/ROW]
[ROW][C]19[/C][C]0.0676213465226017[/C][C]0.135242693045203[/C][C]0.932378653477398[/C][/ROW]
[ROW][C]20[/C][C]0.173807302023062[/C][C]0.347614604046124[/C][C]0.826192697976938[/C][/ROW]
[ROW][C]21[/C][C]0.234267235231381[/C][C]0.468534470462762[/C][C]0.765732764768619[/C][/ROW]
[ROW][C]22[/C][C]0.22173611529168[/C][C]0.44347223058336[/C][C]0.77826388470832[/C][/ROW]
[ROW][C]23[/C][C]0.284629378857554[/C][C]0.569258757715108[/C][C]0.715370621142446[/C][/ROW]
[ROW][C]24[/C][C]0.331591495396557[/C][C]0.663182990793113[/C][C]0.668408504603443[/C][/ROW]
[ROW][C]25[/C][C]0.414759987357777[/C][C]0.829519974715554[/C][C]0.585240012642223[/C][/ROW]
[ROW][C]26[/C][C]0.54527944896475[/C][C]0.9094411020705[/C][C]0.45472055103525[/C][/ROW]
[ROW][C]27[/C][C]0.504071627341166[/C][C]0.991856745317667[/C][C]0.495928372658834[/C][/ROW]
[ROW][C]28[/C][C]0.424631387716153[/C][C]0.849262775432305[/C][C]0.575368612283848[/C][/ROW]
[ROW][C]29[/C][C]0.365112399557182[/C][C]0.730224799114364[/C][C]0.634887600442818[/C][/ROW]
[ROW][C]30[/C][C]0.305597259502631[/C][C]0.611194519005262[/C][C]0.694402740497369[/C][/ROW]
[ROW][C]31[/C][C]0.265512919455488[/C][C]0.531025838910976[/C][C]0.734487080544512[/C][/ROW]
[ROW][C]32[/C][C]0.280294367856035[/C][C]0.56058873571207[/C][C]0.719705632143965[/C][/ROW]
[ROW][C]33[/C][C]0.530698358582418[/C][C]0.938603282835164[/C][C]0.469301641417582[/C][/ROW]
[ROW][C]34[/C][C]0.452862877824313[/C][C]0.905725755648625[/C][C]0.547137122175687[/C][/ROW]
[ROW][C]35[/C][C]0.421833990343651[/C][C]0.843667980687303[/C][C]0.578166009656349[/C][/ROW]
[ROW][C]36[/C][C]0.401252951931182[/C][C]0.802505903862363[/C][C]0.598747048068818[/C][/ROW]
[ROW][C]37[/C][C]0.449286159598301[/C][C]0.898572319196603[/C][C]0.550713840401699[/C][/ROW]
[ROW][C]38[/C][C]0.372923504996769[/C][C]0.745847009993538[/C][C]0.627076495003231[/C][/ROW]
[ROW][C]39[/C][C]0.332927668002649[/C][C]0.665855336005298[/C][C]0.667072331997351[/C][/ROW]
[ROW][C]40[/C][C]0.403951625790861[/C][C]0.807903251581722[/C][C]0.596048374209139[/C][/ROW]
[ROW][C]41[/C][C]0.869209695252928[/C][C]0.261580609494143[/C][C]0.130790304747072[/C][/ROW]
[ROW][C]42[/C][C]0.870847204475137[/C][C]0.258305591049725[/C][C]0.129152795524863[/C][/ROW]
[ROW][C]43[/C][C]0.88811077111343[/C][C]0.223778457773140[/C][C]0.111889228886570[/C][/ROW]
[ROW][C]44[/C][C]0.89853710617474[/C][C]0.202925787650521[/C][C]0.101462893825260[/C][/ROW]
[ROW][C]45[/C][C]0.858229888192529[/C][C]0.283540223614943[/C][C]0.141770111807471[/C][/ROW]
[ROW][C]46[/C][C]0.968357375520594[/C][C]0.0632852489588116[/C][C]0.0316426244794058[/C][/ROW]
[ROW][C]47[/C][C]0.97991817473241[/C][C]0.0401636505351799[/C][C]0.0200818252675900[/C][/ROW]
[ROW][C]48[/C][C]0.985794984605446[/C][C]0.0284100307891076[/C][C]0.0142050153945538[/C][/ROW]
[ROW][C]49[/C][C]0.962950697620354[/C][C]0.0740986047592917[/C][C]0.0370493023796458[/C][/ROW]
[ROW][C]50[/C][C]0.966943886863082[/C][C]0.0661122262738351[/C][C]0.0330561131369175[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97997&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97997&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
8	0.00106771540000924	0.00213543080001848	0.99893228459999
9	8.29172946740711e-05	0.000165834589348142	0.999917082705326
10	0.0202576939307905	0.040515387861581	0.97974230606921
11	0.0126024341060529	0.0252048682121058	0.987397565893947
12	0.109040383060299	0.218080766120599	0.8909596169397
13	0.172617050094518	0.345234100189036	0.827382949905482
14	0.143086676535707	0.286173353071413	0.856913323464293
15	0.0979231660618269	0.195846332123654	0.902076833938173
16	0.138726408742863	0.277452817485726	0.861273591257137
17	0.0936401032911739	0.187280206582348	0.906359896708826
18	0.0775673263447698	0.155134652689540	0.92243267365523
19	0.0676213465226017	0.135242693045203	0.932378653477398
20	0.173807302023062	0.347614604046124	0.826192697976938
21	0.234267235231381	0.468534470462762	0.765732764768619
22	0.22173611529168	0.44347223058336	0.77826388470832
23	0.284629378857554	0.569258757715108	0.715370621142446
24	0.331591495396557	0.663182990793113	0.668408504603443
25	0.414759987357777	0.829519974715554	0.585240012642223
26	0.54527944896475	0.9094411020705	0.45472055103525
27	0.504071627341166	0.991856745317667	0.495928372658834
28	0.424631387716153	0.849262775432305	0.575368612283848
29	0.365112399557182	0.730224799114364	0.634887600442818
30	0.305597259502631	0.611194519005262	0.694402740497369
31	0.265512919455488	0.531025838910976	0.734487080544512
32	0.280294367856035	0.56058873571207	0.719705632143965
33	0.530698358582418	0.938603282835164	0.469301641417582
34	0.452862877824313	0.905725755648625	0.547137122175687
35	0.421833990343651	0.843667980687303	0.578166009656349
36	0.401252951931182	0.802505903862363	0.598747048068818
37	0.449286159598301	0.898572319196603	0.550713840401699
38	0.372923504996769	0.745847009993538	0.627076495003231
39	0.332927668002649	0.665855336005298	0.667072331997351
40	0.403951625790861	0.807903251581722	0.596048374209139
41	0.869209695252928	0.261580609494143	0.130790304747072
42	0.870847204475137	0.258305591049725	0.129152795524863
43	0.88811077111343	0.223778457773140	0.111889228886570
44	0.89853710617474	0.202925787650521	0.101462893825260
45	0.858229888192529	0.283540223614943	0.141770111807471
46	0.968357375520594	0.0632852489588116	0.0316426244794058
47	0.97991817473241	0.0401636505351799	0.0200818252675900
48	0.985794984605446	0.0284100307891076	0.0142050153945538
49	0.962950697620354	0.0740986047592917	0.0370493023796458
50	0.966943886863082	0.0661122262738351	0.0330561131369175

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0465116279069767	NOK
5% type I error level	6	0.139534883720930	NOK
10% type I error level	9	0.209302325581395	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 2 & 0.0465116279069767 & NOK \tabularnewline
5% type I error level & 6 & 0.139534883720930 & NOK \tabularnewline
10% type I error level & 9 & 0.209302325581395 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97997&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]2[/C][C]0.0465116279069767[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.139534883720930[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.209302325581395[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97997&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97997&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	2	0.0465116279069767	NOK
5% type I error level	6	0.139534883720930	NOK
10% type I error level	9	0.209302325581395	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code