Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 14:39:40 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321904395o62y7c7tmwp4b03.htm/, Retrieved Thu, 31 Oct 2024 22:49:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145939, Retrieved Thu, 31 Oct 2024 22:49:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact163
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [vrijetijdsbesteding] [2010-11-19 11:37:04] [74deae64b71f9d77c839af86f7c687b5]
- R       [Multiple Regression] [] [2011-11-21 19:04:37] [46d7ccc24e5d35a2decd922dfb3b3a39]
-           [Multiple Regression] [] [2011-11-21 19:08:46] [46d7ccc24e5d35a2decd922dfb3b3a39]
-             [Multiple Regression] [] [2011-11-21 19:21:49] [46d7ccc24e5d35a2decd922dfb3b3a39]
-                 [Multiple Regression] [Cultuuruitgaven] [2011-11-21 19:39:40] [4be1b05f688f7fa8db5b9e9e4d3a7e33] [Current]
Feedback Forum

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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145939&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145939&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145939&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Cultuuruitgaven[t] = + 29.7574433780093 + 0.117381875008497Bioscoop[t] + 0.351226236672808Schouwburgabonn[t] + 0.441727025825751Daguitstap[t] -0.18690047239566HuurDVD[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cultuuruitgaven[t] =  +  29.7574433780093 +  0.117381875008497Bioscoop[t] +  0.351226236672808Schouwburgabonn[t] +  0.441727025825751Daguitstap[t] -0.18690047239566HuurDVD[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145939&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cultuuruitgaven[t] =  +  29.7574433780093 +  0.117381875008497Bioscoop[t] +  0.351226236672808Schouwburgabonn[t] +  0.441727025825751Daguitstap[t] -0.18690047239566HuurDVD[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145939&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145939&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] = + 29.7574433780093 + 0.117381875008497Bioscoop[t] + 0.351226236672808Schouwburgabonn[t] + 0.441727025825751Daguitstap[t] -0.18690047239566HuurDVD[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)29.757443378009315.8397751.87870.0657970.032898
Bioscoop0.1173818750084970.0431122.72270.0087460.004373
Schouwburgabonn0.3512262366728080.03275710.722100
Daguitstap0.4417270258257510.0483679.132800
HuurDVD-0.186900472395660.192568-0.97060.3361710.168085

\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) & 29.7574433780093 & 15.839775 & 1.8787 & 0.065797 & 0.032898 \tabularnewline
Bioscoop & 0.117381875008497 & 0.043112 & 2.7227 & 0.008746 & 0.004373 \tabularnewline
Schouwburgabonn & 0.351226236672808 & 0.032757 & 10.7221 & 0 & 0 \tabularnewline
Daguitstap & 0.441727025825751 & 0.048367 & 9.1328 & 0 & 0 \tabularnewline
HuurDVD & -0.18690047239566 & 0.192568 & -0.9706 & 0.336171 & 0.168085 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145939&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]29.7574433780093[/C][C]15.839775[/C][C]1.8787[/C][C]0.065797[/C][C]0.032898[/C][/ROW]
[ROW][C]Bioscoop[/C][C]0.117381875008497[/C][C]0.043112[/C][C]2.7227[/C][C]0.008746[/C][C]0.004373[/C][/ROW]
[ROW][C]Schouwburgabonn[/C][C]0.351226236672808[/C][C]0.032757[/C][C]10.7221[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Daguitstap[/C][C]0.441727025825751[/C][C]0.048367[/C][C]9.1328[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]HuurDVD[/C][C]-0.18690047239566[/C][C]0.192568[/C][C]-0.9706[/C][C]0.336171[/C][C]0.168085[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145939&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145939&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)29.757443378009315.8397751.87870.0657970.032898
Bioscoop0.1173818750084970.0431122.72270.0087460.004373
Schouwburgabonn0.3512262366728080.03275710.722100
Daguitstap0.4417270258257510.0483679.132800
HuurDVD-0.186900472395660.192568-0.97060.3361710.168085







Multiple Linear Regression - Regression Statistics
Multiple R0.993690183423486
R-squared0.987420180632202
Adjusted R-squared0.986470760302557
F-TEST (value)1040.02426512322
F-TEST (DF numerator)4
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.64844467642285
Sum Squared Residuals22.2854664142002

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.993690183423486 \tabularnewline
R-squared & 0.987420180632202 \tabularnewline
Adjusted R-squared & 0.986470760302557 \tabularnewline
F-TEST (value) & 1040.02426512322 \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 & 0.64844467642285 \tabularnewline
Sum Squared Residuals & 22.2854664142002 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145939&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.993690183423486[/C][/ROW]
[ROW][C]R-squared[/C][C]0.987420180632202[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.986470760302557[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1040.02426512322[/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]0.64844467642285[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]22.2854664142002[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145939&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145939&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 R0.993690183423486
R-squared0.987420180632202
Adjusted R-squared0.986470760302557
F-TEST (value)1040.02426512322
F-TEST (DF numerator)4
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.64844467642285
Sum Squared Residuals22.2854664142002







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1101.76102.106779395192-0.346779395192159
2102.37102.0791319043470.290868095652856
3102.38102.0866079232430.293392076757025
4102.86103.283178536288-0.423178536287848
5102.87103.238322422913-0.368322422912886
6102.92103.238322422913-0.318322422912889
7102.95103.234584413465-0.284584413464973
8103.02103.266357493772-0.246357493772242
9104.08105.115676503395-1.03567650339517
10104.16105.079037121271-0.919037121270802
11104.24105.054740059859-0.814740059859369
12104.33105.029929917998-0.699929917997644
13104.73105.033442180364-0.303442180364368
14104.86105.025966161469-0.165966161468548
15105.03104.9663856422060.063614357793582
16105.62106.114875909353-0.494875909353371
17105.63106.092447852666-0.462447852665902
18105.63106.092447852666-0.462447852665902
19105.94106.233306102676-0.293306102676097
20106.61106.2220920743320.387907925667645
21107.69108.350974562732-0.660974562731947
22107.78108.392058218985-0.612058218984917
23107.93108.545316606349-0.615316606349354
24108.48108.602833725104-0.122833725103521
25108.14107.071487333211.06851266678992
26108.48107.0696183284861.41038167151388
27108.48107.0285002245591.45149977544093
28108.89108.4264281150870.463571884913104
29108.93108.4146899275860.515310072413961
30109.21108.3754408283830.834559171617035
31109.47108.3100256630441.15997433695552
32109.8108.2882583446571.5117416553432
33111.73111.5204619842120.2095380157881
34111.85111.5533399879210.296660012078889
35112.12111.6648527691790.455147230820827
36112.15111.6910643616950.458935638304538
37112.17111.6667673002840.503232699715969
38112.67111.7181532457090.951846754290785
39112.8111.690118174851.10988182515013
40113.44114.459746626777-1.01974662677732
41113.53114.452270607881-0.922270607881493
42114.53114.5050924516350.024907548364684
43114.51114.4322012674010.077798732598993
44115.05114.8696480811680.180351918831711
45116.67116.994545333775-0.324545333775096
46117.07117.119240513553-0.0492405135529805
47116.92117.122978523001-0.202978523000887
48117117.147674243134-0.147674243133568
49117.02117.161760068135-0.141760068134593
50117.35117.1939318071630.156068192836897
51117.36117.436036208487-0.076036208486823
52117.82118.465953115525-0.645953115525015
53117.88118.483298317198-0.60329831719773
54118.24118.541772701504-0.301772701504322
55118.5118.545989343728-0.045989343728444
56118.8118.5646793909680.235320609031988
57119.76119.923924926892-0.163924926891774
58120.09119.9052348796520.184765120347789

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 102.106779395192 & -0.346779395192159 \tabularnewline
2 & 102.37 & 102.079131904347 & 0.290868095652856 \tabularnewline
3 & 102.38 & 102.086607923243 & 0.293392076757025 \tabularnewline
4 & 102.86 & 103.283178536288 & -0.423178536287848 \tabularnewline
5 & 102.87 & 103.238322422913 & -0.368322422912886 \tabularnewline
6 & 102.92 & 103.238322422913 & -0.318322422912889 \tabularnewline
7 & 102.95 & 103.234584413465 & -0.284584413464973 \tabularnewline
8 & 103.02 & 103.266357493772 & -0.246357493772242 \tabularnewline
9 & 104.08 & 105.115676503395 & -1.03567650339517 \tabularnewline
10 & 104.16 & 105.079037121271 & -0.919037121270802 \tabularnewline
11 & 104.24 & 105.054740059859 & -0.814740059859369 \tabularnewline
12 & 104.33 & 105.029929917998 & -0.699929917997644 \tabularnewline
13 & 104.73 & 105.033442180364 & -0.303442180364368 \tabularnewline
14 & 104.86 & 105.025966161469 & -0.165966161468548 \tabularnewline
15 & 105.03 & 104.966385642206 & 0.063614357793582 \tabularnewline
16 & 105.62 & 106.114875909353 & -0.494875909353371 \tabularnewline
17 & 105.63 & 106.092447852666 & -0.462447852665902 \tabularnewline
18 & 105.63 & 106.092447852666 & -0.462447852665902 \tabularnewline
19 & 105.94 & 106.233306102676 & -0.293306102676097 \tabularnewline
20 & 106.61 & 106.222092074332 & 0.387907925667645 \tabularnewline
21 & 107.69 & 108.350974562732 & -0.660974562731947 \tabularnewline
22 & 107.78 & 108.392058218985 & -0.612058218984917 \tabularnewline
23 & 107.93 & 108.545316606349 & -0.615316606349354 \tabularnewline
24 & 108.48 & 108.602833725104 & -0.122833725103521 \tabularnewline
25 & 108.14 & 107.07148733321 & 1.06851266678992 \tabularnewline
26 & 108.48 & 107.069618328486 & 1.41038167151388 \tabularnewline
27 & 108.48 & 107.028500224559 & 1.45149977544093 \tabularnewline
28 & 108.89 & 108.426428115087 & 0.463571884913104 \tabularnewline
29 & 108.93 & 108.414689927586 & 0.515310072413961 \tabularnewline
30 & 109.21 & 108.375440828383 & 0.834559171617035 \tabularnewline
31 & 109.47 & 108.310025663044 & 1.15997433695552 \tabularnewline
32 & 109.8 & 108.288258344657 & 1.5117416553432 \tabularnewline
33 & 111.73 & 111.520461984212 & 0.2095380157881 \tabularnewline
34 & 111.85 & 111.553339987921 & 0.296660012078889 \tabularnewline
35 & 112.12 & 111.664852769179 & 0.455147230820827 \tabularnewline
36 & 112.15 & 111.691064361695 & 0.458935638304538 \tabularnewline
37 & 112.17 & 111.666767300284 & 0.503232699715969 \tabularnewline
38 & 112.67 & 111.718153245709 & 0.951846754290785 \tabularnewline
39 & 112.8 & 111.69011817485 & 1.10988182515013 \tabularnewline
40 & 113.44 & 114.459746626777 & -1.01974662677732 \tabularnewline
41 & 113.53 & 114.452270607881 & -0.922270607881493 \tabularnewline
42 & 114.53 & 114.505092451635 & 0.024907548364684 \tabularnewline
43 & 114.51 & 114.432201267401 & 0.077798732598993 \tabularnewline
44 & 115.05 & 114.869648081168 & 0.180351918831711 \tabularnewline
45 & 116.67 & 116.994545333775 & -0.324545333775096 \tabularnewline
46 & 117.07 & 117.119240513553 & -0.0492405135529805 \tabularnewline
47 & 116.92 & 117.122978523001 & -0.202978523000887 \tabularnewline
48 & 117 & 117.147674243134 & -0.147674243133568 \tabularnewline
49 & 117.02 & 117.161760068135 & -0.141760068134593 \tabularnewline
50 & 117.35 & 117.193931807163 & 0.156068192836897 \tabularnewline
51 & 117.36 & 117.436036208487 & -0.076036208486823 \tabularnewline
52 & 117.82 & 118.465953115525 & -0.645953115525015 \tabularnewline
53 & 117.88 & 118.483298317198 & -0.60329831719773 \tabularnewline
54 & 118.24 & 118.541772701504 & -0.301772701504322 \tabularnewline
55 & 118.5 & 118.545989343728 & -0.045989343728444 \tabularnewline
56 & 118.8 & 118.564679390968 & 0.235320609031988 \tabularnewline
57 & 119.76 & 119.923924926892 & -0.163924926891774 \tabularnewline
58 & 120.09 & 119.905234879652 & 0.184765120347789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145939&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]102.106779395192[/C][C]-0.346779395192159[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]102.079131904347[/C][C]0.290868095652856[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]102.086607923243[/C][C]0.293392076757025[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]103.283178536288[/C][C]-0.423178536287848[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]103.238322422913[/C][C]-0.368322422912886[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]103.238322422913[/C][C]-0.318322422912889[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]103.234584413465[/C][C]-0.284584413464973[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]103.266357493772[/C][C]-0.246357493772242[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]105.115676503395[/C][C]-1.03567650339517[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]105.079037121271[/C][C]-0.919037121270802[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]105.054740059859[/C][C]-0.814740059859369[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]105.029929917998[/C][C]-0.699929917997644[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]105.033442180364[/C][C]-0.303442180364368[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.025966161469[/C][C]-0.165966161468548[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]104.966385642206[/C][C]0.063614357793582[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]106.114875909353[/C][C]-0.494875909353371[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]106.092447852666[/C][C]-0.462447852665902[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.092447852666[/C][C]-0.462447852665902[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.233306102676[/C][C]-0.293306102676097[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.222092074332[/C][C]0.387907925667645[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]108.350974562732[/C][C]-0.660974562731947[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.392058218985[/C][C]-0.612058218984917[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.545316606349[/C][C]-0.615316606349354[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.602833725104[/C][C]-0.122833725103521[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.07148733321[/C][C]1.06851266678992[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]107.069618328486[/C][C]1.41038167151388[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]107.028500224559[/C][C]1.45149977544093[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.426428115087[/C][C]0.463571884913104[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]108.414689927586[/C][C]0.515310072413961[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]108.375440828383[/C][C]0.834559171617035[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]108.310025663044[/C][C]1.15997433695552[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]108.288258344657[/C][C]1.5117416553432[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.520461984212[/C][C]0.2095380157881[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.553339987921[/C][C]0.296660012078889[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.664852769179[/C][C]0.455147230820827[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]111.691064361695[/C][C]0.458935638304538[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]111.666767300284[/C][C]0.503232699715969[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]111.718153245709[/C][C]0.951846754290785[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]111.69011817485[/C][C]1.10988182515013[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]114.459746626777[/C][C]-1.01974662677732[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]114.452270607881[/C][C]-0.922270607881493[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.505092451635[/C][C]0.024907548364684[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.432201267401[/C][C]0.077798732598993[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.869648081168[/C][C]0.180351918831711[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.994545333775[/C][C]-0.324545333775096[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]117.119240513553[/C][C]-0.0492405135529805[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]117.122978523001[/C][C]-0.202978523000887[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]117.147674243134[/C][C]-0.147674243133568[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.161760068135[/C][C]-0.141760068134593[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.193931807163[/C][C]0.156068192836897[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.436036208487[/C][C]-0.076036208486823[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]118.465953115525[/C][C]-0.645953115525015[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.483298317198[/C][C]-0.60329831719773[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.541772701504[/C][C]-0.301772701504322[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.545989343728[/C][C]-0.045989343728444[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.564679390968[/C][C]0.235320609031988[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.923924926892[/C][C]-0.163924926891774[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.905234879652[/C][C]0.184765120347789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145939&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145939&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 IndexActualsInterpolationForecastResidualsPrediction Error
1101.76102.106779395192-0.346779395192159
2102.37102.0791319043470.290868095652856
3102.38102.0866079232430.293392076757025
4102.86103.283178536288-0.423178536287848
5102.87103.238322422913-0.368322422912886
6102.92103.238322422913-0.318322422912889
7102.95103.234584413465-0.284584413464973
8103.02103.266357493772-0.246357493772242
9104.08105.115676503395-1.03567650339517
10104.16105.079037121271-0.919037121270802
11104.24105.054740059859-0.814740059859369
12104.33105.029929917998-0.699929917997644
13104.73105.033442180364-0.303442180364368
14104.86105.025966161469-0.165966161468548
15105.03104.9663856422060.063614357793582
16105.62106.114875909353-0.494875909353371
17105.63106.092447852666-0.462447852665902
18105.63106.092447852666-0.462447852665902
19105.94106.233306102676-0.293306102676097
20106.61106.2220920743320.387907925667645
21107.69108.350974562732-0.660974562731947
22107.78108.392058218985-0.612058218984917
23107.93108.545316606349-0.615316606349354
24108.48108.602833725104-0.122833725103521
25108.14107.071487333211.06851266678992
26108.48107.0696183284861.41038167151388
27108.48107.0285002245591.45149977544093
28108.89108.4264281150870.463571884913104
29108.93108.4146899275860.515310072413961
30109.21108.3754408283830.834559171617035
31109.47108.3100256630441.15997433695552
32109.8108.2882583446571.5117416553432
33111.73111.5204619842120.2095380157881
34111.85111.5533399879210.296660012078889
35112.12111.6648527691790.455147230820827
36112.15111.6910643616950.458935638304538
37112.17111.6667673002840.503232699715969
38112.67111.7181532457090.951846754290785
39112.8111.690118174851.10988182515013
40113.44114.459746626777-1.01974662677732
41113.53114.452270607881-0.922270607881493
42114.53114.5050924516350.024907548364684
43114.51114.4322012674010.077798732598993
44115.05114.8696480811680.180351918831711
45116.67116.994545333775-0.324545333775096
46117.07117.119240513553-0.0492405135529805
47116.92117.122978523001-0.202978523000887
48117117.147674243134-0.147674243133568
49117.02117.161760068135-0.141760068134593
50117.35117.1939318071630.156068192836897
51117.36117.436036208487-0.076036208486823
52117.82118.465953115525-0.645953115525015
53117.88118.483298317198-0.60329831719773
54118.24118.541772701504-0.301772701504322
55118.5118.545989343728-0.045989343728444
56118.8118.5646793909680.235320609031988
57119.76119.923924926892-0.163924926891774
58120.09119.9052348796520.184765120347789







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.001840348386726520.003680696773453050.998159651613273
90.0001876940402266740.0003753880804533480.999812305959773
100.0003725810209627610.0007451620419255230.999627418979037
118.97282530086277e-050.0001794565060172550.999910271746991
120.0006695647144805540.001339129428961110.99933043528552
130.001408724228415410.002817448456830830.998591275771585
140.001240587244373910.002481174488747810.998759412755626
150.000417881197798350.00083576239559670.999582118802202
160.0002159165303094140.0004318330606188280.99978408346969
170.0001004502691106640.0002009005382213270.99989954973089
185.1389604324628e-050.0001027792086492560.999948610395675
193.29743811755042e-056.59487623510083e-050.999967025618824
200.0002094104361856380.0004188208723712760.999790589563814
210.0002442619592296090.0004885239184592180.99975573804077
220.0004199271708961240.0008398543417922480.999580072829104
230.005405555505620970.01081111101124190.994594444494379
240.03643363509945730.07286727019891460.963566364900543
250.1701772591926810.3403545183853630.829822740807319
260.34661128134240.69322256268480.6533887186576
270.3997314594137210.7994629188274420.600268540586279
280.3943344774668230.7886689549336450.605665522533177
290.4105483871243420.8210967742486850.589451612875658
300.3965751724437190.7931503448874380.603424827556281
310.376141823080490.7522836461609790.62385817691951
320.396739792065320.7934795841306410.60326020793468
330.3960056126914690.7920112253829380.603994387308531
340.3805606377640550.7611212755281090.619439362235945
350.5430713860436380.9138572279127240.456928613956362
360.6072943018120640.7854113963758730.392705698187936
370.6331483813959840.7337032372080310.366851618604016
380.5621935038251720.8756129923496560.437806496174828
390.4994737465609240.9989474931218480.500526253439076
400.7528878141751890.4942243716496220.247112185824811
410.9430628147221080.1138743705557840.0569371852778919
420.9075564473609110.1848871052781780.0924435526390889
430.8585824444953360.2828351110093280.141417555504664
440.9903491418380770.01930171632384530.00965085816192266
450.999085886674350.001828226651298520.00091411332564926
460.9989889691285680.002022061742863950.00101103087143197
470.998241481276370.003517037447258540.00175851872362927
480.994026628272750.01194674345449810.00597337172724906
490.9786145134763470.04277097304730620.0213854865236531
500.9372173466820380.1255653066359240.0627826533179622

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.00184034838672652 & 0.00368069677345305 & 0.998159651613273 \tabularnewline
9 & 0.000187694040226674 & 0.000375388080453348 & 0.999812305959773 \tabularnewline
10 & 0.000372581020962761 & 0.000745162041925523 & 0.999627418979037 \tabularnewline
11 & 8.97282530086277e-05 & 0.000179456506017255 & 0.999910271746991 \tabularnewline
12 & 0.000669564714480554 & 0.00133912942896111 & 0.99933043528552 \tabularnewline
13 & 0.00140872422841541 & 0.00281744845683083 & 0.998591275771585 \tabularnewline
14 & 0.00124058724437391 & 0.00248117448874781 & 0.998759412755626 \tabularnewline
15 & 0.00041788119779835 & 0.0008357623955967 & 0.999582118802202 \tabularnewline
16 & 0.000215916530309414 & 0.000431833060618828 & 0.99978408346969 \tabularnewline
17 & 0.000100450269110664 & 0.000200900538221327 & 0.99989954973089 \tabularnewline
18 & 5.1389604324628e-05 & 0.000102779208649256 & 0.999948610395675 \tabularnewline
19 & 3.29743811755042e-05 & 6.59487623510083e-05 & 0.999967025618824 \tabularnewline
20 & 0.000209410436185638 & 0.000418820872371276 & 0.999790589563814 \tabularnewline
21 & 0.000244261959229609 & 0.000488523918459218 & 0.99975573804077 \tabularnewline
22 & 0.000419927170896124 & 0.000839854341792248 & 0.999580072829104 \tabularnewline
23 & 0.00540555550562097 & 0.0108111110112419 & 0.994594444494379 \tabularnewline
24 & 0.0364336350994573 & 0.0728672701989146 & 0.963566364900543 \tabularnewline
25 & 0.170177259192681 & 0.340354518385363 & 0.829822740807319 \tabularnewline
26 & 0.3466112813424 & 0.6932225626848 & 0.6533887186576 \tabularnewline
27 & 0.399731459413721 & 0.799462918827442 & 0.600268540586279 \tabularnewline
28 & 0.394334477466823 & 0.788668954933645 & 0.605665522533177 \tabularnewline
29 & 0.410548387124342 & 0.821096774248685 & 0.589451612875658 \tabularnewline
30 & 0.396575172443719 & 0.793150344887438 & 0.603424827556281 \tabularnewline
31 & 0.37614182308049 & 0.752283646160979 & 0.62385817691951 \tabularnewline
32 & 0.39673979206532 & 0.793479584130641 & 0.60326020793468 \tabularnewline
33 & 0.396005612691469 & 0.792011225382938 & 0.603994387308531 \tabularnewline
34 & 0.380560637764055 & 0.761121275528109 & 0.619439362235945 \tabularnewline
35 & 0.543071386043638 & 0.913857227912724 & 0.456928613956362 \tabularnewline
36 & 0.607294301812064 & 0.785411396375873 & 0.392705698187936 \tabularnewline
37 & 0.633148381395984 & 0.733703237208031 & 0.366851618604016 \tabularnewline
38 & 0.562193503825172 & 0.875612992349656 & 0.437806496174828 \tabularnewline
39 & 0.499473746560924 & 0.998947493121848 & 0.500526253439076 \tabularnewline
40 & 0.752887814175189 & 0.494224371649622 & 0.247112185824811 \tabularnewline
41 & 0.943062814722108 & 0.113874370555784 & 0.0569371852778919 \tabularnewline
42 & 0.907556447360911 & 0.184887105278178 & 0.0924435526390889 \tabularnewline
43 & 0.858582444495336 & 0.282835111009328 & 0.141417555504664 \tabularnewline
44 & 0.990349141838077 & 0.0193017163238453 & 0.00965085816192266 \tabularnewline
45 & 0.99908588667435 & 0.00182822665129852 & 0.00091411332564926 \tabularnewline
46 & 0.998988969128568 & 0.00202206174286395 & 0.00101103087143197 \tabularnewline
47 & 0.99824148127637 & 0.00351703744725854 & 0.00175851872362927 \tabularnewline
48 & 0.99402662827275 & 0.0119467434544981 & 0.00597337172724906 \tabularnewline
49 & 0.978614513476347 & 0.0427709730473062 & 0.0213854865236531 \tabularnewline
50 & 0.937217346682038 & 0.125565306635924 & 0.0627826533179622 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145939&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]0.00184034838672652[/C][C]0.00368069677345305[/C][C]0.998159651613273[/C][/ROW]
[ROW][C]9[/C][C]0.000187694040226674[/C][C]0.000375388080453348[/C][C]0.999812305959773[/C][/ROW]
[ROW][C]10[/C][C]0.000372581020962761[/C][C]0.000745162041925523[/C][C]0.999627418979037[/C][/ROW]
[ROW][C]11[/C][C]8.97282530086277e-05[/C][C]0.000179456506017255[/C][C]0.999910271746991[/C][/ROW]
[ROW][C]12[/C][C]0.000669564714480554[/C][C]0.00133912942896111[/C][C]0.99933043528552[/C][/ROW]
[ROW][C]13[/C][C]0.00140872422841541[/C][C]0.00281744845683083[/C][C]0.998591275771585[/C][/ROW]
[ROW][C]14[/C][C]0.00124058724437391[/C][C]0.00248117448874781[/C][C]0.998759412755626[/C][/ROW]
[ROW][C]15[/C][C]0.00041788119779835[/C][C]0.0008357623955967[/C][C]0.999582118802202[/C][/ROW]
[ROW][C]16[/C][C]0.000215916530309414[/C][C]0.000431833060618828[/C][C]0.99978408346969[/C][/ROW]
[ROW][C]17[/C][C]0.000100450269110664[/C][C]0.000200900538221327[/C][C]0.99989954973089[/C][/ROW]
[ROW][C]18[/C][C]5.1389604324628e-05[/C][C]0.000102779208649256[/C][C]0.999948610395675[/C][/ROW]
[ROW][C]19[/C][C]3.29743811755042e-05[/C][C]6.59487623510083e-05[/C][C]0.999967025618824[/C][/ROW]
[ROW][C]20[/C][C]0.000209410436185638[/C][C]0.000418820872371276[/C][C]0.999790589563814[/C][/ROW]
[ROW][C]21[/C][C]0.000244261959229609[/C][C]0.000488523918459218[/C][C]0.99975573804077[/C][/ROW]
[ROW][C]22[/C][C]0.000419927170896124[/C][C]0.000839854341792248[/C][C]0.999580072829104[/C][/ROW]
[ROW][C]23[/C][C]0.00540555550562097[/C][C]0.0108111110112419[/C][C]0.994594444494379[/C][/ROW]
[ROW][C]24[/C][C]0.0364336350994573[/C][C]0.0728672701989146[/C][C]0.963566364900543[/C][/ROW]
[ROW][C]25[/C][C]0.170177259192681[/C][C]0.340354518385363[/C][C]0.829822740807319[/C][/ROW]
[ROW][C]26[/C][C]0.3466112813424[/C][C]0.6932225626848[/C][C]0.6533887186576[/C][/ROW]
[ROW][C]27[/C][C]0.399731459413721[/C][C]0.799462918827442[/C][C]0.600268540586279[/C][/ROW]
[ROW][C]28[/C][C]0.394334477466823[/C][C]0.788668954933645[/C][C]0.605665522533177[/C][/ROW]
[ROW][C]29[/C][C]0.410548387124342[/C][C]0.821096774248685[/C][C]0.589451612875658[/C][/ROW]
[ROW][C]30[/C][C]0.396575172443719[/C][C]0.793150344887438[/C][C]0.603424827556281[/C][/ROW]
[ROW][C]31[/C][C]0.37614182308049[/C][C]0.752283646160979[/C][C]0.62385817691951[/C][/ROW]
[ROW][C]32[/C][C]0.39673979206532[/C][C]0.793479584130641[/C][C]0.60326020793468[/C][/ROW]
[ROW][C]33[/C][C]0.396005612691469[/C][C]0.792011225382938[/C][C]0.603994387308531[/C][/ROW]
[ROW][C]34[/C][C]0.380560637764055[/C][C]0.761121275528109[/C][C]0.619439362235945[/C][/ROW]
[ROW][C]35[/C][C]0.543071386043638[/C][C]0.913857227912724[/C][C]0.456928613956362[/C][/ROW]
[ROW][C]36[/C][C]0.607294301812064[/C][C]0.785411396375873[/C][C]0.392705698187936[/C][/ROW]
[ROW][C]37[/C][C]0.633148381395984[/C][C]0.733703237208031[/C][C]0.366851618604016[/C][/ROW]
[ROW][C]38[/C][C]0.562193503825172[/C][C]0.875612992349656[/C][C]0.437806496174828[/C][/ROW]
[ROW][C]39[/C][C]0.499473746560924[/C][C]0.998947493121848[/C][C]0.500526253439076[/C][/ROW]
[ROW][C]40[/C][C]0.752887814175189[/C][C]0.494224371649622[/C][C]0.247112185824811[/C][/ROW]
[ROW][C]41[/C][C]0.943062814722108[/C][C]0.113874370555784[/C][C]0.0569371852778919[/C][/ROW]
[ROW][C]42[/C][C]0.907556447360911[/C][C]0.184887105278178[/C][C]0.0924435526390889[/C][/ROW]
[ROW][C]43[/C][C]0.858582444495336[/C][C]0.282835111009328[/C][C]0.141417555504664[/C][/ROW]
[ROW][C]44[/C][C]0.990349141838077[/C][C]0.0193017163238453[/C][C]0.00965085816192266[/C][/ROW]
[ROW][C]45[/C][C]0.99908588667435[/C][C]0.00182822665129852[/C][C]0.00091411332564926[/C][/ROW]
[ROW][C]46[/C][C]0.998988969128568[/C][C]0.00202206174286395[/C][C]0.00101103087143197[/C][/ROW]
[ROW][C]47[/C][C]0.99824148127637[/C][C]0.00351703744725854[/C][C]0.00175851872362927[/C][/ROW]
[ROW][C]48[/C][C]0.99402662827275[/C][C]0.0119467434544981[/C][C]0.00597337172724906[/C][/ROW]
[ROW][C]49[/C][C]0.978614513476347[/C][C]0.0427709730473062[/C][C]0.0213854865236531[/C][/ROW]
[ROW][C]50[/C][C]0.937217346682038[/C][C]0.125565306635924[/C][C]0.0627826533179622[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145939&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145939&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.001840348386726520.003680696773453050.998159651613273
90.0001876940402266740.0003753880804533480.999812305959773
100.0003725810209627610.0007451620419255230.999627418979037
118.97282530086277e-050.0001794565060172550.999910271746991
120.0006695647144805540.001339129428961110.99933043528552
130.001408724228415410.002817448456830830.998591275771585
140.001240587244373910.002481174488747810.998759412755626
150.000417881197798350.00083576239559670.999582118802202
160.0002159165303094140.0004318330606188280.99978408346969
170.0001004502691106640.0002009005382213270.99989954973089
185.1389604324628e-050.0001027792086492560.999948610395675
193.29743811755042e-056.59487623510083e-050.999967025618824
200.0002094104361856380.0004188208723712760.999790589563814
210.0002442619592296090.0004885239184592180.99975573804077
220.0004199271708961240.0008398543417922480.999580072829104
230.005405555505620970.01081111101124190.994594444494379
240.03643363509945730.07286727019891460.963566364900543
250.1701772591926810.3403545183853630.829822740807319
260.34661128134240.69322256268480.6533887186576
270.3997314594137210.7994629188274420.600268540586279
280.3943344774668230.7886689549336450.605665522533177
290.4105483871243420.8210967742486850.589451612875658
300.3965751724437190.7931503448874380.603424827556281
310.376141823080490.7522836461609790.62385817691951
320.396739792065320.7934795841306410.60326020793468
330.3960056126914690.7920112253829380.603994387308531
340.3805606377640550.7611212755281090.619439362235945
350.5430713860436380.9138572279127240.456928613956362
360.6072943018120640.7854113963758730.392705698187936
370.6331483813959840.7337032372080310.366851618604016
380.5621935038251720.8756129923496560.437806496174828
390.4994737465609240.9989474931218480.500526253439076
400.7528878141751890.4942243716496220.247112185824811
410.9430628147221080.1138743705557840.0569371852778919
420.9075564473609110.1848871052781780.0924435526390889
430.8585824444953360.2828351110093280.141417555504664
440.9903491418380770.01930171632384530.00965085816192266
450.999085886674350.001828226651298520.00091411332564926
460.9989889691285680.002022061742863950.00101103087143197
470.998241481276370.003517037447258540.00175851872362927
480.994026628272750.01194674345449810.00597337172724906
490.9786145134763470.04277097304730620.0213854865236531
500.9372173466820380.1255653066359240.0627826533179622







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.418604651162791NOK
5% type I error level220.511627906976744NOK
10% type I error level230.534883720930233NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 18 & 0.418604651162791 & NOK \tabularnewline
5% type I error level & 22 & 0.511627906976744 & NOK \tabularnewline
10% type I error level & 23 & 0.534883720930233 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145939&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]18[/C][C]0.418604651162791[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.511627906976744[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.534883720930233[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145939&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145939&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 testsOK/NOK
1% type I error level180.418604651162791NOK
5% type I error level220.511627906976744NOK
10% type I error level230.534883720930233NOK



Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}