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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 21 Nov 2011 15:10:27 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321906253g87p1kl9h630gtv.htm/, Retrieved Wed, 05 Nov 2025 08:02:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145961, Retrieved Wed, 05 Nov 2025 08:02:43 +0000

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-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [run sequence plot...] [2010-11-19 12:01:57] [74deae64b71f9d77c839af86f7c687b5]
- R PD      [Multiple Regression] [] [2011-11-21 20:10:27] [4be1b05f688f7fa8db5b9e9e4d3a7e33] [Current]

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6	101.82	107.34	93.63	99.85	101.76
6	101.68	107.34	93.63	99.91	102.37
6	101.68	107.34	93.63	99.87	102.38
6	102.45	107.34	96.13	99.86	102.86
6	102.45	107.34	96.13	100.10	102.87
6	102.45	107.34	96.13	100.10	102.92
6	102.45	107.34	96.13	100.12	102.95
6	102.45	107.34	96.13	99.95	103.02
6	102.45	112.60	96.13	99.94	104.08
6	102.52	112.60	96.13	100.18	104.16
6	102.52	112.60	96.13	100.31	104.24
6	102.85	112.60	96.13	100.65	104.33
7	102.85	112.61	96.13	100.65	104.73
7	102.85	112.61	96.13	100.69	104.86
7	103.25	112.61	96.13	101.26	105.03
7	103.25	112.61	98.73	101.26	105.62
7	103.25	112.61	98.73	101.38	105.63
7	103.25	112.61	98.73	101.38	105.63
7	104.45	112.61	98.73	101.38	105.94
7	104.45	112.61	98.73	101.44	106.61
7	104.45	118.65	98.73	101.40	107.69
7	104.80	118.65	98.73	101.40	107.78
7	104.80	118.65	98.73	100.58	107.93
7	105.29	118.65	98.73	100.58	108.48
8	105.29	114.29	98.73	100.58	108.14
8	105.29	114.29	98.73	100.59	108.48
8	105.29	114.29	98.73	100.81	108.48
8	106.04	114.29	101.67	100.75	108.89
8	105.94	114.29	101.67	100.75	108.93
8	105.94	114.29	101.67	100.96	109.21
8	105.94	114.29	101.67	101.31	109.47
8	106.28	114.29	101.67	101.64	109.80
8	106.48	123.33	101.67	101.46	111.73
8	107.19	123.33	101.67	101.73	111.85
8	108.14	123.33	101.67	101.73	112.12
8	108.22	123.33	101.67	101.64	112.15
9	108.22	123.33	101.67	101.77	112.17
9	108.61	123.33	101.67	101.74	112.67
9	108.61	123.33	101.67	101.89	112.80
9	108.61	123.33	107.94	101.89	113.44
9	108.61	123.33	107.94	101.93	113.53
9	109.06	123.33	107.94	101.93	114.53
9	109.06	123.33	107.94	102.32	114.51
9	112.93	123.33	107.94	102.41	115.05
9	115.84	129.03	107.94	103.58	116.67
9	118.57	128.76	107.94	104.12	117.07
9	118.57	128.76	107.94	104.10	116.92
9	118.86	128.76	107.94	104.15	117.00
10	118.98	128.76	107.94	104.15	117.02
10	119.27	128.76	107.94	104.16	117.35
10	119.39	128.76	107.94	102.94	117.36
10	119.49	128.76	110.30	103.07	117.82
10	119.59	128.76	110.30	103.04	117.88
10	120.12	128.76	110.30	103.06	118.24
10	120.14	128.76	110.30	103.05	118.50
10	120.14	128.76	110.30	102.95	118.80
10	120.14	132.63	110.30	102.95	119.76
10	120.14	132.63	110.30	103.05	120.09

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145961&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145961&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145961&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Cultuuruitgaven[t] = + 43.1196518887865 + 1.06911178353785JAAR[t] + 0.0955092964682526Bioscoop[t] + 0.293827546929861Schouwburgabonnement[t] + 0.271011310340612Daguitstap[t] -0.140287440271962HuurDVD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cultuuruitgaven[t] =  +  43.1196518887865 +  1.06911178353785JAAR[t] +  0.0955092964682526Bioscoop[t] +  0.293827546929861Schouwburgabonnement[t] +  0.271011310340612Daguitstap[t] -0.140287440271962HuurDVD[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145961&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cultuuruitgaven[t] =  +  43.1196518887865 +  1.06911178353785JAAR[t] +  0.0955092964682526Bioscoop[t] +  0.293827546929861Schouwburgabonnement[t] +  0.271011310340612Daguitstap[t] -0.140287440271962HuurDVD[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145961&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145961&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
Cultuuruitgaven[t] = + 43.1196518887865 + 1.06911178353785JAAR[t] + 0.0955092964682526Bioscoop[t] + 0.293827546929861Schouwburgabonnement[t] + 0.271011310340612Daguitstap[t] -0.140287440271962HuurDVD[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)	43.1196518887865	11.20716	3.8475	0.000328	0.000164
JAAR	1.06911178353785	0.142107	7.5233	0	0
Bioscoop	0.0955092964682526	0.030258	3.1565	0.002656	0.001328
Schouwburgabonnement	0.293827546929861	0.024122	12.1808	0	0
Daguitstap	0.271011310340612	0.040701	6.6585	0	0
HuurDVD	-0.140287440271962	0.134669	-1.0417	0.302359	0.15118

\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) & 43.1196518887865 & 11.20716 & 3.8475 & 0.000328 & 0.000164 \tabularnewline
JAAR & 1.06911178353785 & 0.142107 & 7.5233 & 0 & 0 \tabularnewline
Bioscoop & 0.0955092964682526 & 0.030258 & 3.1565 & 0.002656 & 0.001328 \tabularnewline
Schouwburgabonnement & 0.293827546929861 & 0.024122 & 12.1808 & 0 & 0 \tabularnewline
Daguitstap & 0.271011310340612 & 0.040701 & 6.6585 & 0 & 0 \tabularnewline
HuurDVD & -0.140287440271962 & 0.134669 & -1.0417 & 0.302359 & 0.15118 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145961&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]43.1196518887865[/C][C]11.20716[/C][C]3.8475[/C][C]0.000328[/C][C]0.000164[/C][/ROW]
[ROW][C]JAAR[/C][C]1.06911178353785[/C][C]0.142107[/C][C]7.5233[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bioscoop[/C][C]0.0955092964682526[/C][C]0.030258[/C][C]3.1565[/C][C]0.002656[/C][C]0.001328[/C][/ROW]
[ROW][C]Schouwburgabonnement[/C][C]0.293827546929861[/C][C]0.024122[/C][C]12.1808[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Daguitstap[/C][C]0.271011310340612[/C][C]0.040701[/C][C]6.6585[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]HuurDVD[/C][C]-0.140287440271962[/C][C]0.134669[/C][C]-1.0417[/C][C]0.302359[/C][C]0.15118[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145961&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145961&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)	43.1196518887865	11.20716	3.8475	0.000328	0.000164
JAAR	1.06911178353785	0.142107	7.5233	0	0
Bioscoop	0.0955092964682526	0.030258	3.1565	0.002656	0.001328
Schouwburgabonnement	0.293827546929861	0.024122	12.1808	0	0
Daguitstap	0.271011310340612	0.040701	6.6585	0	0
HuurDVD	-0.140287440271962	0.134669	-1.0417	0.302359	0.15118

Multiple Linear Regression - Regression Statistics
Multiple R	0.996983707496076
R-squared	0.993976513012621
Adjusted R-squared	0.993397331571527
F-TEST (value)	1716.17466045679
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.452997679974217
Sum Squared Residuals	10.6707586992252

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.996983707496076 \tabularnewline
R-squared & 0.993976513012621 \tabularnewline
Adjusted R-squared & 0.993397331571527 \tabularnewline
F-TEST (value) & 1716.17466045679 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.452997679974217 \tabularnewline
Sum Squared Residuals & 10.6707586992252 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145961&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.996983707496076[/C][/ROW]
[ROW][C]R-squared[/C][C]0.993976513012621[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.993397331571527[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1716.17466045679[/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]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.452997679974217[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10.6707586992252[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145961&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145961&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	0.996983707496076
R-squared	0.993976513012621
Adjusted R-squared	0.993397331571527
F-TEST (value)	1716.17466045679
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.452997679974217
Sum Squared Residuals	10.6707586992252

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	102.165616119899	-0.405616119898497
2	102.37	102.143827571977	0.226172428023485
3	102.38	102.149439069587	0.230560930412596
4	102.86	102.901912378122	-0.0419123781222031
5	102.87	102.868243392457	0.0017566075430726
6	102.92	102.868243392457	0.0517566075430694
7	102.95	102.865437643651	0.0845623563485112
8	103.02	102.889286508498	0.130713491502271
9	104.08	104.436222279752	-0.356222279751516
10	104.16	104.409238944839	-0.249238944839022
11	104.24	104.391001577604	-0.151001577603669
12	104.33	104.374821915746	-0.0448219157457216
13	104.73	105.446871974753	-0.716871974752862
14	104.86	105.441260477142	-0.581260477141989
15	105.03	105.399500354774	-0.369500354774269
16	105.62	106.10412976166	-0.484129761659859
17	105.63	106.087295268827	-0.457295268827234
18	105.63	106.087295268827	-0.457295268827234
19	105.94	106.201906424589	-0.261906424589135
20	106.61	106.193489178173	0.416510821827185
21	107.69	107.97381905924	-0.283819059240059
22	107.78	108.007247313004	-0.227247313003943
23	107.93	108.122283014027	-0.192283014026947
24	108.48	108.169082569296	0.310917430703605
25	108.14	107.95710624822	0.182893751779952
26	108.48	107.955703373817	0.524296626182676
27	108.48	107.924840136957	0.555159863042507
28	108.89	108.801662608126	0.0883373918735986
29	108.93	108.79211167848	0.137888321520431
30	109.21	108.762651316022	0.447348683977529
31	109.47	108.713550711927	0.756449288072722
32	109.8	108.699729017437	1.10027098256326
33	111.73	111.400283640225	0.329716359774719
34	111.85	111.430217631844	0.419782368155682
35	112.12	111.520951463489	0.599048536510851
36	112.15	111.541218076831	0.608781923168915
37	112.17	112.592092493134	-0.42209249313358
38	112.67	112.633549741964	0.0364502580356428
39	112.8	112.612506625924	0.187493374076433
40	113.44	114.311747541759	-0.871747541759199
41	113.53	114.306136044148	-0.776136044148316
42	114.53	114.349115227559	0.18088477244097
43	114.51	114.294403125853	0.215596874147037
44	115.05	114.651398233561	0.398601766439368
45	116.67	116.440010998665	0.229989001334743
46	117.07	116.545662722606	0.524337277394332
47	116.92	116.548468471411	0.3715315285889
48	117	116.569151795373	0.430848204626704
49	117.02	117.649724694487	-0.629724694487336
50	117.35	117.67601951606	-0.326019516060412
51	117.36	117.858631308768	-0.498631308768392
52	117.82	118.489531563584	-0.669531563583711
53	117.88	118.503291116439	-0.623291116438692
54	118.24	118.551105294761	-0.311105294761428
55	118.5	118.554418355094	-0.0544183550935074
56	118.8	118.568447099121	0.231552900879294
57	119.76	119.705559705739	0.0544402942607372
58	120.09	119.691530961712	0.398469038287931

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 102.165616119899 & -0.405616119898497 \tabularnewline
2 & 102.37 & 102.143827571977 & 0.226172428023485 \tabularnewline
3 & 102.38 & 102.149439069587 & 0.230560930412596 \tabularnewline
4 & 102.86 & 102.901912378122 & -0.0419123781222031 \tabularnewline
5 & 102.87 & 102.868243392457 & 0.0017566075430726 \tabularnewline
6 & 102.92 & 102.868243392457 & 0.0517566075430694 \tabularnewline
7 & 102.95 & 102.865437643651 & 0.0845623563485112 \tabularnewline
8 & 103.02 & 102.889286508498 & 0.130713491502271 \tabularnewline
9 & 104.08 & 104.436222279752 & -0.356222279751516 \tabularnewline
10 & 104.16 & 104.409238944839 & -0.249238944839022 \tabularnewline
11 & 104.24 & 104.391001577604 & -0.151001577603669 \tabularnewline
12 & 104.33 & 104.374821915746 & -0.0448219157457216 \tabularnewline
13 & 104.73 & 105.446871974753 & -0.716871974752862 \tabularnewline
14 & 104.86 & 105.441260477142 & -0.581260477141989 \tabularnewline
15 & 105.03 & 105.399500354774 & -0.369500354774269 \tabularnewline
16 & 105.62 & 106.10412976166 & -0.484129761659859 \tabularnewline
17 & 105.63 & 106.087295268827 & -0.457295268827234 \tabularnewline
18 & 105.63 & 106.087295268827 & -0.457295268827234 \tabularnewline
19 & 105.94 & 106.201906424589 & -0.261906424589135 \tabularnewline
20 & 106.61 & 106.193489178173 & 0.416510821827185 \tabularnewline
21 & 107.69 & 107.97381905924 & -0.283819059240059 \tabularnewline
22 & 107.78 & 108.007247313004 & -0.227247313003943 \tabularnewline
23 & 107.93 & 108.122283014027 & -0.192283014026947 \tabularnewline
24 & 108.48 & 108.169082569296 & 0.310917430703605 \tabularnewline
25 & 108.14 & 107.95710624822 & 0.182893751779952 \tabularnewline
26 & 108.48 & 107.955703373817 & 0.524296626182676 \tabularnewline
27 & 108.48 & 107.924840136957 & 0.555159863042507 \tabularnewline
28 & 108.89 & 108.801662608126 & 0.0883373918735986 \tabularnewline
29 & 108.93 & 108.79211167848 & 0.137888321520431 \tabularnewline
30 & 109.21 & 108.762651316022 & 0.447348683977529 \tabularnewline
31 & 109.47 & 108.713550711927 & 0.756449288072722 \tabularnewline
32 & 109.8 & 108.699729017437 & 1.10027098256326 \tabularnewline
33 & 111.73 & 111.400283640225 & 0.329716359774719 \tabularnewline
34 & 111.85 & 111.430217631844 & 0.419782368155682 \tabularnewline
35 & 112.12 & 111.520951463489 & 0.599048536510851 \tabularnewline
36 & 112.15 & 111.541218076831 & 0.608781923168915 \tabularnewline
37 & 112.17 & 112.592092493134 & -0.42209249313358 \tabularnewline
38 & 112.67 & 112.633549741964 & 0.0364502580356428 \tabularnewline
39 & 112.8 & 112.612506625924 & 0.187493374076433 \tabularnewline
40 & 113.44 & 114.311747541759 & -0.871747541759199 \tabularnewline
41 & 113.53 & 114.306136044148 & -0.776136044148316 \tabularnewline
42 & 114.53 & 114.349115227559 & 0.18088477244097 \tabularnewline
43 & 114.51 & 114.294403125853 & 0.215596874147037 \tabularnewline
44 & 115.05 & 114.651398233561 & 0.398601766439368 \tabularnewline
45 & 116.67 & 116.440010998665 & 0.229989001334743 \tabularnewline
46 & 117.07 & 116.545662722606 & 0.524337277394332 \tabularnewline
47 & 116.92 & 116.548468471411 & 0.3715315285889 \tabularnewline
48 & 117 & 116.569151795373 & 0.430848204626704 \tabularnewline
49 & 117.02 & 117.649724694487 & -0.629724694487336 \tabularnewline
50 & 117.35 & 117.67601951606 & -0.326019516060412 \tabularnewline
51 & 117.36 & 117.858631308768 & -0.498631308768392 \tabularnewline
52 & 117.82 & 118.489531563584 & -0.669531563583711 \tabularnewline
53 & 117.88 & 118.503291116439 & -0.623291116438692 \tabularnewline
54 & 118.24 & 118.551105294761 & -0.311105294761428 \tabularnewline
55 & 118.5 & 118.554418355094 & -0.0544183550935074 \tabularnewline
56 & 118.8 & 118.568447099121 & 0.231552900879294 \tabularnewline
57 & 119.76 & 119.705559705739 & 0.0544402942607372 \tabularnewline
58 & 120.09 & 119.691530961712 & 0.398469038287931 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145961&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]102.165616119899[/C][C]-0.405616119898497[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]102.143827571977[/C][C]0.226172428023485[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]102.149439069587[/C][C]0.230560930412596[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]102.901912378122[/C][C]-0.0419123781222031[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]102.868243392457[/C][C]0.0017566075430726[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]102.868243392457[/C][C]0.0517566075430694[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.865437643651[/C][C]0.0845623563485112[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]102.889286508498[/C][C]0.130713491502271[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.436222279752[/C][C]-0.356222279751516[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.409238944839[/C][C]-0.249238944839022[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.391001577604[/C][C]-0.151001577603669[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.374821915746[/C][C]-0.0448219157457216[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]105.446871974753[/C][C]-0.716871974752862[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.441260477142[/C][C]-0.581260477141989[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]105.399500354774[/C][C]-0.369500354774269[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]106.10412976166[/C][C]-0.484129761659859[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]106.087295268827[/C][C]-0.457295268827234[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.087295268827[/C][C]-0.457295268827234[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.201906424589[/C][C]-0.261906424589135[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.193489178173[/C][C]0.416510821827185[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]107.97381905924[/C][C]-0.283819059240059[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.007247313004[/C][C]-0.227247313003943[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.122283014027[/C][C]-0.192283014026947[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.169082569296[/C][C]0.310917430703605[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.95710624822[/C][C]0.182893751779952[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]107.955703373817[/C][C]0.524296626182676[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]107.924840136957[/C][C]0.555159863042507[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.801662608126[/C][C]0.0883373918735986[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]108.79211167848[/C][C]0.137888321520431[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]108.762651316022[/C][C]0.447348683977529[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]108.713550711927[/C][C]0.756449288072722[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]108.699729017437[/C][C]1.10027098256326[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.400283640225[/C][C]0.329716359774719[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.430217631844[/C][C]0.419782368155682[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.520951463489[/C][C]0.599048536510851[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]111.541218076831[/C][C]0.608781923168915[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.592092493134[/C][C]-0.42209249313358[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.633549741964[/C][C]0.0364502580356428[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.612506625924[/C][C]0.187493374076433[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]114.311747541759[/C][C]-0.871747541759199[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]114.306136044148[/C][C]-0.776136044148316[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.349115227559[/C][C]0.18088477244097[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.294403125853[/C][C]0.215596874147037[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.651398233561[/C][C]0.398601766439368[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.440010998665[/C][C]0.229989001334743[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.545662722606[/C][C]0.524337277394332[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.548468471411[/C][C]0.3715315285889[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]116.569151795373[/C][C]0.430848204626704[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.649724694487[/C][C]-0.629724694487336[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.67601951606[/C][C]-0.326019516060412[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.858631308768[/C][C]-0.498631308768392[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]118.489531563584[/C][C]-0.669531563583711[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.503291116439[/C][C]-0.623291116438692[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.551105294761[/C][C]-0.311105294761428[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.554418355094[/C][C]-0.0544183550935074[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.568447099121[/C][C]0.231552900879294[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.705559705739[/C][C]0.0544402942607372[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.691530961712[/C][C]0.398469038287931[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145961&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145961&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	102.165616119899	-0.405616119898497
2	102.37	102.143827571977	0.226172428023485
3	102.38	102.149439069587	0.230560930412596
4	102.86	102.901912378122	-0.0419123781222031
5	102.87	102.868243392457	0.0017566075430726
6	102.92	102.868243392457	0.0517566075430694
7	102.95	102.865437643651	0.0845623563485112
8	103.02	102.889286508498	0.130713491502271
9	104.08	104.436222279752	-0.356222279751516
10	104.16	104.409238944839	-0.249238944839022
11	104.24	104.391001577604	-0.151001577603669
12	104.33	104.374821915746	-0.0448219157457216
13	104.73	105.446871974753	-0.716871974752862
14	104.86	105.441260477142	-0.581260477141989
15	105.03	105.399500354774	-0.369500354774269
16	105.62	106.10412976166	-0.484129761659859
17	105.63	106.087295268827	-0.457295268827234
18	105.63	106.087295268827	-0.457295268827234
19	105.94	106.201906424589	-0.261906424589135
20	106.61	106.193489178173	0.416510821827185
21	107.69	107.97381905924	-0.283819059240059
22	107.78	108.007247313004	-0.227247313003943
23	107.93	108.122283014027	-0.192283014026947
24	108.48	108.169082569296	0.310917430703605
25	108.14	107.95710624822	0.182893751779952
26	108.48	107.955703373817	0.524296626182676
27	108.48	107.924840136957	0.555159863042507
28	108.89	108.801662608126	0.0883373918735986
29	108.93	108.79211167848	0.137888321520431
30	109.21	108.762651316022	0.447348683977529
31	109.47	108.713550711927	0.756449288072722
32	109.8	108.699729017437	1.10027098256326
33	111.73	111.400283640225	0.329716359774719
34	111.85	111.430217631844	0.419782368155682
35	112.12	111.520951463489	0.599048536510851
36	112.15	111.541218076831	0.608781923168915
37	112.17	112.592092493134	-0.42209249313358
38	112.67	112.633549741964	0.0364502580356428
39	112.8	112.612506625924	0.187493374076433
40	113.44	114.311747541759	-0.871747541759199
41	113.53	114.306136044148	-0.776136044148316
42	114.53	114.349115227559	0.18088477244097
43	114.51	114.294403125853	0.215596874147037
44	115.05	114.651398233561	0.398601766439368
45	116.67	116.440010998665	0.229989001334743
46	117.07	116.545662722606	0.524337277394332
47	116.92	116.548468471411	0.3715315285889
48	117	116.569151795373	0.430848204626704
49	117.02	117.649724694487	-0.629724694487336
50	117.35	117.67601951606	-0.326019516060412
51	117.36	117.858631308768	-0.498631308768392
52	117.82	118.489531563584	-0.669531563583711
53	117.88	118.503291116439	-0.623291116438692
54	118.24	118.551105294761	-0.311105294761428
55	118.5	118.554418355094	-0.0544183550935074
56	118.8	118.568447099121	0.231552900879294
57	119.76	119.705559705739	0.0544402942607372
58	120.09	119.691530961712	0.398469038287931

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.00502072491588492	0.0100414498317698	0.994979275084115
10	0.00669688092432525	0.0133937618486505	0.993303119075675
11	0.0017401581893518	0.0034803163787036	0.998259841810648
12	0.00741959649461374	0.0148391929892275	0.992580403505386
13	0.00279602970144418	0.00559205940288835	0.997203970298556
14	0.00110194305715108	0.00220388611430216	0.998898056942849
15	0.000476665719152754	0.000953331438305508	0.999523334280847
16	0.000649925724005584	0.00129985144801117	0.999350074275994
17	0.000320809571281456	0.000641619142562911	0.999679190428719
18	0.000172828219287115	0.000345656438574231	0.999827171780713
19	0.000119458066809006	0.000238916133618012	0.999880541933191
20	0.00186151063345141	0.00372302126690282	0.998138489366549
21	0.00171554117483145	0.00343108234966289	0.998284458825169
22	0.0017987286403889	0.0035974572807778	0.998201271359611
23	0.00384167320135226	0.00768334640270452	0.996158326798648
24	0.00965278179992227	0.0193055635998445	0.990347218200078
25	0.00793658829058768	0.0158731765811754	0.992063411709412
26	0.0101081415990047	0.0202162831980095	0.989891858400995
27	0.00923009430780607	0.0184601886156121	0.990769905692194
28	0.0122062673198232	0.0244125346396465	0.987793732680177
29	0.0124905485051236	0.0249810970102473	0.987509451494876
30	0.0114283557611581	0.0228567115223163	0.988571644238842
31	0.0190947554300468	0.0381895108600936	0.980905244569953
32	0.0535432595351598	0.10708651907032	0.94645674046484
33	0.0592902142808401	0.11858042856168	0.94070978571916
34	0.0416617970909453	0.0833235941818906	0.958338202909055
35	0.0409780145515398	0.0819560291030796	0.95902198544846
36	0.0371994480109748	0.0743988960219496	0.962800551989025
37	0.146917170391728	0.293834340783455	0.853082829608272
38	0.133094861081901	0.266189722163802	0.866905138918099
39	0.107505251252877	0.215010502505754	0.892494748747123
40	0.392622786231374	0.785245572462749	0.607377213768626
41	0.769805673908008	0.460388652183983	0.230194326091992
42	0.692911364555042	0.614177270889916	0.307088635444958
43	0.6016050555249	0.796789888950199	0.3983949444751
44	0.845896298593003	0.308207402813993	0.154103701406997
45	0.969961559340205	0.0600768813195902	0.0300384406597951
46	0.959485453374453	0.0810290932510944	0.0405145466255472
47	0.933478182701915	0.13304363459617	0.0665218172980849
48	0.857549983752711	0.284900032494578	0.142450016247289
49	0.777445777511202	0.445108444977597	0.222554222488798

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.00502072491588492 & 0.0100414498317698 & 0.994979275084115 \tabularnewline
10 & 0.00669688092432525 & 0.0133937618486505 & 0.993303119075675 \tabularnewline
11 & 0.0017401581893518 & 0.0034803163787036 & 0.998259841810648 \tabularnewline
12 & 0.00741959649461374 & 0.0148391929892275 & 0.992580403505386 \tabularnewline
13 & 0.00279602970144418 & 0.00559205940288835 & 0.997203970298556 \tabularnewline
14 & 0.00110194305715108 & 0.00220388611430216 & 0.998898056942849 \tabularnewline
15 & 0.000476665719152754 & 0.000953331438305508 & 0.999523334280847 \tabularnewline
16 & 0.000649925724005584 & 0.00129985144801117 & 0.999350074275994 \tabularnewline
17 & 0.000320809571281456 & 0.000641619142562911 & 0.999679190428719 \tabularnewline
18 & 0.000172828219287115 & 0.000345656438574231 & 0.999827171780713 \tabularnewline
19 & 0.000119458066809006 & 0.000238916133618012 & 0.999880541933191 \tabularnewline
20 & 0.00186151063345141 & 0.00372302126690282 & 0.998138489366549 \tabularnewline
21 & 0.00171554117483145 & 0.00343108234966289 & 0.998284458825169 \tabularnewline
22 & 0.0017987286403889 & 0.0035974572807778 & 0.998201271359611 \tabularnewline
23 & 0.00384167320135226 & 0.00768334640270452 & 0.996158326798648 \tabularnewline
24 & 0.00965278179992227 & 0.0193055635998445 & 0.990347218200078 \tabularnewline
25 & 0.00793658829058768 & 0.0158731765811754 & 0.992063411709412 \tabularnewline
26 & 0.0101081415990047 & 0.0202162831980095 & 0.989891858400995 \tabularnewline
27 & 0.00923009430780607 & 0.0184601886156121 & 0.990769905692194 \tabularnewline
28 & 0.0122062673198232 & 0.0244125346396465 & 0.987793732680177 \tabularnewline
29 & 0.0124905485051236 & 0.0249810970102473 & 0.987509451494876 \tabularnewline
30 & 0.0114283557611581 & 0.0228567115223163 & 0.988571644238842 \tabularnewline
31 & 0.0190947554300468 & 0.0381895108600936 & 0.980905244569953 \tabularnewline
32 & 0.0535432595351598 & 0.10708651907032 & 0.94645674046484 \tabularnewline
33 & 0.0592902142808401 & 0.11858042856168 & 0.94070978571916 \tabularnewline
34 & 0.0416617970909453 & 0.0833235941818906 & 0.958338202909055 \tabularnewline
35 & 0.0409780145515398 & 0.0819560291030796 & 0.95902198544846 \tabularnewline
36 & 0.0371994480109748 & 0.0743988960219496 & 0.962800551989025 \tabularnewline
37 & 0.146917170391728 & 0.293834340783455 & 0.853082829608272 \tabularnewline
38 & 0.133094861081901 & 0.266189722163802 & 0.866905138918099 \tabularnewline
39 & 0.107505251252877 & 0.215010502505754 & 0.892494748747123 \tabularnewline
40 & 0.392622786231374 & 0.785245572462749 & 0.607377213768626 \tabularnewline
41 & 0.769805673908008 & 0.460388652183983 & 0.230194326091992 \tabularnewline
42 & 0.692911364555042 & 0.614177270889916 & 0.307088635444958 \tabularnewline
43 & 0.6016050555249 & 0.796789888950199 & 0.3983949444751 \tabularnewline
44 & 0.845896298593003 & 0.308207402813993 & 0.154103701406997 \tabularnewline
45 & 0.969961559340205 & 0.0600768813195902 & 0.0300384406597951 \tabularnewline
46 & 0.959485453374453 & 0.0810290932510944 & 0.0405145466255472 \tabularnewline
47 & 0.933478182701915 & 0.13304363459617 & 0.0665218172980849 \tabularnewline
48 & 0.857549983752711 & 0.284900032494578 & 0.142450016247289 \tabularnewline
49 & 0.777445777511202 & 0.445108444977597 & 0.222554222488798 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145961&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]0.00502072491588492[/C][C]0.0100414498317698[/C][C]0.994979275084115[/C][/ROW]
[ROW][C]10[/C][C]0.00669688092432525[/C][C]0.0133937618486505[/C][C]0.993303119075675[/C][/ROW]
[ROW][C]11[/C][C]0.0017401581893518[/C][C]0.0034803163787036[/C][C]0.998259841810648[/C][/ROW]
[ROW][C]12[/C][C]0.00741959649461374[/C][C]0.0148391929892275[/C][C]0.992580403505386[/C][/ROW]
[ROW][C]13[/C][C]0.00279602970144418[/C][C]0.00559205940288835[/C][C]0.997203970298556[/C][/ROW]
[ROW][C]14[/C][C]0.00110194305715108[/C][C]0.00220388611430216[/C][C]0.998898056942849[/C][/ROW]
[ROW][C]15[/C][C]0.000476665719152754[/C][C]0.000953331438305508[/C][C]0.999523334280847[/C][/ROW]
[ROW][C]16[/C][C]0.000649925724005584[/C][C]0.00129985144801117[/C][C]0.999350074275994[/C][/ROW]
[ROW][C]17[/C][C]0.000320809571281456[/C][C]0.000641619142562911[/C][C]0.999679190428719[/C][/ROW]
[ROW][C]18[/C][C]0.000172828219287115[/C][C]0.000345656438574231[/C][C]0.999827171780713[/C][/ROW]
[ROW][C]19[/C][C]0.000119458066809006[/C][C]0.000238916133618012[/C][C]0.999880541933191[/C][/ROW]
[ROW][C]20[/C][C]0.00186151063345141[/C][C]0.00372302126690282[/C][C]0.998138489366549[/C][/ROW]
[ROW][C]21[/C][C]0.00171554117483145[/C][C]0.00343108234966289[/C][C]0.998284458825169[/C][/ROW]
[ROW][C]22[/C][C]0.0017987286403889[/C][C]0.0035974572807778[/C][C]0.998201271359611[/C][/ROW]
[ROW][C]23[/C][C]0.00384167320135226[/C][C]0.00768334640270452[/C][C]0.996158326798648[/C][/ROW]
[ROW][C]24[/C][C]0.00965278179992227[/C][C]0.0193055635998445[/C][C]0.990347218200078[/C][/ROW]
[ROW][C]25[/C][C]0.00793658829058768[/C][C]0.0158731765811754[/C][C]0.992063411709412[/C][/ROW]
[ROW][C]26[/C][C]0.0101081415990047[/C][C]0.0202162831980095[/C][C]0.989891858400995[/C][/ROW]
[ROW][C]27[/C][C]0.00923009430780607[/C][C]0.0184601886156121[/C][C]0.990769905692194[/C][/ROW]
[ROW][C]28[/C][C]0.0122062673198232[/C][C]0.0244125346396465[/C][C]0.987793732680177[/C][/ROW]
[ROW][C]29[/C][C]0.0124905485051236[/C][C]0.0249810970102473[/C][C]0.987509451494876[/C][/ROW]
[ROW][C]30[/C][C]0.0114283557611581[/C][C]0.0228567115223163[/C][C]0.988571644238842[/C][/ROW]
[ROW][C]31[/C][C]0.0190947554300468[/C][C]0.0381895108600936[/C][C]0.980905244569953[/C][/ROW]
[ROW][C]32[/C][C]0.0535432595351598[/C][C]0.10708651907032[/C][C]0.94645674046484[/C][/ROW]
[ROW][C]33[/C][C]0.0592902142808401[/C][C]0.11858042856168[/C][C]0.94070978571916[/C][/ROW]
[ROW][C]34[/C][C]0.0416617970909453[/C][C]0.0833235941818906[/C][C]0.958338202909055[/C][/ROW]
[ROW][C]35[/C][C]0.0409780145515398[/C][C]0.0819560291030796[/C][C]0.95902198544846[/C][/ROW]
[ROW][C]36[/C][C]0.0371994480109748[/C][C]0.0743988960219496[/C][C]0.962800551989025[/C][/ROW]
[ROW][C]37[/C][C]0.146917170391728[/C][C]0.293834340783455[/C][C]0.853082829608272[/C][/ROW]
[ROW][C]38[/C][C]0.133094861081901[/C][C]0.266189722163802[/C][C]0.866905138918099[/C][/ROW]
[ROW][C]39[/C][C]0.107505251252877[/C][C]0.215010502505754[/C][C]0.892494748747123[/C][/ROW]
[ROW][C]40[/C][C]0.392622786231374[/C][C]0.785245572462749[/C][C]0.607377213768626[/C][/ROW]
[ROW][C]41[/C][C]0.769805673908008[/C][C]0.460388652183983[/C][C]0.230194326091992[/C][/ROW]
[ROW][C]42[/C][C]0.692911364555042[/C][C]0.614177270889916[/C][C]0.307088635444958[/C][/ROW]
[ROW][C]43[/C][C]0.6016050555249[/C][C]0.796789888950199[/C][C]0.3983949444751[/C][/ROW]
[ROW][C]44[/C][C]0.845896298593003[/C][C]0.308207402813993[/C][C]0.154103701406997[/C][/ROW]
[ROW][C]45[/C][C]0.969961559340205[/C][C]0.0600768813195902[/C][C]0.0300384406597951[/C][/ROW]
[ROW][C]46[/C][C]0.959485453374453[/C][C]0.0810290932510944[/C][C]0.0405145466255472[/C][/ROW]
[ROW][C]47[/C][C]0.933478182701915[/C][C]0.13304363459617[/C][C]0.0665218172980849[/C][/ROW]
[ROW][C]48[/C][C]0.857549983752711[/C][C]0.284900032494578[/C][C]0.142450016247289[/C][/ROW]
[ROW][C]49[/C][C]0.777445777511202[/C][C]0.445108444977597[/C][C]0.222554222488798[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145961&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145961&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	0.00502072491588492	0.0100414498317698	0.994979275084115
10	0.00669688092432525	0.0133937618486505	0.993303119075675
11	0.0017401581893518	0.0034803163787036	0.998259841810648
12	0.00741959649461374	0.0148391929892275	0.992580403505386
13	0.00279602970144418	0.00559205940288835	0.997203970298556
14	0.00110194305715108	0.00220388611430216	0.998898056942849
15	0.000476665719152754	0.000953331438305508	0.999523334280847
16	0.000649925724005584	0.00129985144801117	0.999350074275994
17	0.000320809571281456	0.000641619142562911	0.999679190428719
18	0.000172828219287115	0.000345656438574231	0.999827171780713
19	0.000119458066809006	0.000238916133618012	0.999880541933191
20	0.00186151063345141	0.00372302126690282	0.998138489366549
21	0.00171554117483145	0.00343108234966289	0.998284458825169
22	0.0017987286403889	0.0035974572807778	0.998201271359611
23	0.00384167320135226	0.00768334640270452	0.996158326798648
24	0.00965278179992227	0.0193055635998445	0.990347218200078
25	0.00793658829058768	0.0158731765811754	0.992063411709412
26	0.0101081415990047	0.0202162831980095	0.989891858400995
27	0.00923009430780607	0.0184601886156121	0.990769905692194
28	0.0122062673198232	0.0244125346396465	0.987793732680177
29	0.0124905485051236	0.0249810970102473	0.987509451494876
30	0.0114283557611581	0.0228567115223163	0.988571644238842
31	0.0190947554300468	0.0381895108600936	0.980905244569953
32	0.0535432595351598	0.10708651907032	0.94645674046484
33	0.0592902142808401	0.11858042856168	0.94070978571916
34	0.0416617970909453	0.0833235941818906	0.958338202909055
35	0.0409780145515398	0.0819560291030796	0.95902198544846
36	0.0371994480109748	0.0743988960219496	0.962800551989025
37	0.146917170391728	0.293834340783455	0.853082829608272
38	0.133094861081901	0.266189722163802	0.866905138918099
39	0.107505251252877	0.215010502505754	0.892494748747123
40	0.392622786231374	0.785245572462749	0.607377213768626
41	0.769805673908008	0.460388652183983	0.230194326091992
42	0.692911364555042	0.614177270889916	0.307088635444958
43	0.6016050555249	0.796789888950199	0.3983949444751
44	0.845896298593003	0.308207402813993	0.154103701406997
45	0.969961559340205	0.0600768813195902	0.0300384406597951
46	0.959485453374453	0.0810290932510944	0.0405145466255472
47	0.933478182701915	0.13304363459617	0.0665218172980849
48	0.857549983752711	0.284900032494578	0.142450016247289
49	0.777445777511202	0.445108444977597	0.222554222488798

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	12	0.292682926829268	NOK
5% type I error level	23	0.560975609756098	NOK
10% type I error level	28	0.682926829268293	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 & 12 & 0.292682926829268 & NOK \tabularnewline
5% type I error level & 23 & 0.560975609756098 & NOK \tabularnewline
10% type I error level & 28 & 0.682926829268293 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145961&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]12[/C][C]0.292682926829268[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.560975609756098[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.682926829268293[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145961&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145961&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	12	0.292682926829268	NOK
5% type I error level	23	0.560975609756098	NOK
10% type I error level	28	0.682926829268293	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 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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