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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 04:51:25 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258718092r0idgn312ifqyvc.htm/, Retrieved Thu, 28 Mar 2024 20:53:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58048, Retrieved Thu, 28 Mar 2024 20:53:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact240
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
F    D      [Multiple Regression] [ws77] [2009-11-20 11:51:25] [9a1fef436e1d399a5ecd6808bfbd8489] [Current]
-    D        [Multiple Regression] [w7] [2009-11-22 13:02:44] [0a7d38ad9c7f1a2c46637c75a8a0e083]
Feedback Forum
2009-11-25 19:02:49 [Nick Aerts] [reply
Voorbeeld van een beter compendium:
Model1:
http://www.freestatistics.org/blog/index.php?v=date/2009/Nov/25/t1259174661rk8ftst492o9qga.htm/

Yt = Werkloosheidscijfer van de mannen (%)
Xt (in dit geval geen dummy, maar een tijdreeks) = economische groei(%)

H0 = er verandert niets door de toevoeging van de economische groei
HA = er is een verandering door de toevoeging van de economische groei

Analyse van de cijfergegevens:
De economische groei zorgt voor een daling van het percentage werkloze mannen met 0,02. Deze daling is significant omdat de alfa-fout (p-waarde 1-tail) bijna gelijk is aan 0.
We zullen dus de nulhypothese verwerpen. Om te weten hoeveel schommelingen van dit model kunnen bepaald worden bekijken we de adjusted R².
R² = 33,3%; Deze waarde is significant want de p-waarde is bijna 0.
De standaardfout is: 0,62

Analyse van de grafieken:
Het is ook visueel zichtbaar dat onze voorspellingen nog niet goed zijn. Dit is te zien in de 'actuals and interpolation' grafiek. De bolletjes zijn de voorspelde waarden, en de grafiek is de tijdreeks.
Bij de residu's zien we een licht dalende trend. We zien ook dat de gegevens niet mooi rond 0 schommelen.
In de autocorrelatiefunctie wordt het vermoeden van de lange termijn trend bevestigd. We gaan dus in het volgende model deze trend uitfilteren.

2009-11-26 09:33:55 [Angelo Stuer] [reply
Volgens mij bedoel je dat de residual deviation niet 0, in plaats van de autocorrelation function. Je kan ook afleiden uit de golvende beweging van de residu's dat dit model nog niet goed is, hoewel er niet echt autocorrelatie meer aanwezig is.

Post a new message
Dataseries X:
100	0
95,84395716	0
105,5073942	1
118,1540031	1
101,8612953	1
109,8419174	1
105,6348802	1
112,927078	1
133,0698623	1
125,6756757	1
146,736359	1
142,5803162	1
106,1448241	1
126,5170831	1
132,7893932	1
121,2391637	1
114,5079041	1
146,1499235	1
146,1244263	1
128,5058644	1
155,5838858	1
125,0382458	1
136,8944416	1
142,2233554	1
117,7715451	1
120,627231	1
127,7664457	1
135,1096379	1
105,7113717	1
117,9245283	1
120,754717	1
107,572667	1
130,4436512	1
107,2157063	1
105,0739419	1
130,1121877	1
109,6379398	1
116,7261601	1
97,11881693	0
140,8975013	1
108,2865885	1
97,65425803	0
112,0346762	1
123,0494646	1
112,4171341	1
116,4966854	1
104,6914839	1
122,2335543	1
99,79602244	0
96,71086181	0
112,3151453	1
102,5497195	1
104,5385008	1
122,0805711	1
80,64762876	0
91,40744518	0
99,51555329	0
106,527282	1
98,49566548	0
106,7567568	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58048&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58048&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58048&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 95.719020908 + 24.90158083X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  95.719020908 +  24.90158083X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58048&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  95.719020908 +  24.90158083X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58048&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58048&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
Y[t] = + 95.719020908 + 24.90158083X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)95.7190209084.12143223.224700
X24.901580834.5148035.51551e-060

\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) & 95.719020908 & 4.121432 & 23.2247 & 0 & 0 \tabularnewline
X & 24.90158083 & 4.514803 & 5.5155 & 1e-06 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58048&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]95.719020908[/C][C]4.121432[/C][C]23.2247[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]24.90158083[/C][C]4.514803[/C][C]5.5155[/C][C]1e-06[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58048&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58048&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)95.7190209084.12143223.224700
X24.901580834.5148035.51551e-060







Multiple Linear Regression - Regression Statistics
Multiple R0.586556645237301
R-squared0.344048698072037
Adjusted R-squared0.332739192866383
F-TEST (value)30.4211980821247
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value8.44200816363383e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13.0331123291101
Sum Squared Residuals9851.99698502568

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.586556645237301 \tabularnewline
R-squared & 0.344048698072037 \tabularnewline
Adjusted R-squared & 0.332739192866383 \tabularnewline
F-TEST (value) & 30.4211980821247 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 8.44200816363383e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 13.0331123291101 \tabularnewline
Sum Squared Residuals & 9851.99698502568 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58048&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.586556645237301[/C][/ROW]
[ROW][C]R-squared[/C][C]0.344048698072037[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.332739192866383[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]30.4211980821247[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]8.44200816363383e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]13.0331123291101[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9851.99698502568[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58048&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58048&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 R0.586556645237301
R-squared0.344048698072037
Adjusted R-squared0.332739192866383
F-TEST (value)30.4211980821247
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value8.44200816363383e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13.0331123291101
Sum Squared Residuals9851.99698502568







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110095.71902090800024.28097909199981
295.8439571695.7190209080.124936252000047
3105.5073942120.620601738-15.113207538
4118.1540031120.620601738-2.466598638
5101.8612953120.620601738-18.759306438
6109.8419174120.620601738-10.778684338
7105.6348802120.620601738-14.985721538
8112.927078120.620601738-7.693523738
9133.0698623120.62060173812.449260562
10125.6756757120.6206017385.055073962
11146.736359120.62060173826.115757262
12142.5803162120.62060173821.959714462
13106.1448241120.620601738-14.475777638
14126.5170831120.6206017385.896481362
15132.7893932120.62060173812.168791462
16121.2391637120.6206017380.618561962000007
17114.5079041120.620601738-6.112697638
18146.1499235120.62060173825.529321762
19146.1244263120.62060173825.503824562
20128.5058644120.6206017387.88526266200001
21155.5838858120.62060173834.963284062
22125.0382458120.6206017384.417644062
23136.8944416120.62060173816.273839862
24142.2233554120.62060173821.602753662
25117.7715451120.620601738-2.849056638
26120.627231120.6206017380.00662926199999536
27127.7664457120.6206017387.145843962
28135.1096379120.62060173814.489036162
29105.7113717120.620601738-14.909230038
30117.9245283120.620601738-2.69607343799999
31120.754717120.6206017380.134115262
32107.572667120.620601738-13.047934738
33130.4436512120.6206017389.823049462
34107.2157063120.620601738-13.404895438
35105.0739419120.620601738-15.546659838
36130.1121877120.6206017389.491585962
37109.6379398120.620601738-10.982661938
38116.7261601120.620601738-3.894441638
3997.1188169395.7190209081.39979602200002
40140.8975013120.62060173820.276899562
41108.2865885120.620601738-12.334013238
4297.6542580395.7190209081.93523712200002
43112.0346762120.620601738-8.585925538
44123.0494646120.6206017382.42886286199999
45112.4171341120.620601738-8.203467638
46116.4966854120.620601738-4.12391633800000
47104.6914839120.620601738-15.929117838
48122.2335543120.6206017381.61295256199999
4999.7960224495.7190209084.07700153200002
5096.7108618195.7190209080.991840902000019
51112.3151453120.620601738-8.305456438
52102.5497195120.620601738-18.070882238
53104.5385008120.620601738-16.082100938
54122.0805711120.6206017381.459969362
5580.6476287695.719020908-15.0713921480000
5691.4074451895.719020908-4.31157572799998
5799.5155532995.7190209083.79653238200002
58106.527282120.620601738-14.093319738
5998.4956654895.7190209082.77664457200002
60106.7567568120.620601738-13.863844938

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100 & 95.7190209080002 & 4.28097909199981 \tabularnewline
2 & 95.84395716 & 95.719020908 & 0.124936252000047 \tabularnewline
3 & 105.5073942 & 120.620601738 & -15.113207538 \tabularnewline
4 & 118.1540031 & 120.620601738 & -2.466598638 \tabularnewline
5 & 101.8612953 & 120.620601738 & -18.759306438 \tabularnewline
6 & 109.8419174 & 120.620601738 & -10.778684338 \tabularnewline
7 & 105.6348802 & 120.620601738 & -14.985721538 \tabularnewline
8 & 112.927078 & 120.620601738 & -7.693523738 \tabularnewline
9 & 133.0698623 & 120.620601738 & 12.449260562 \tabularnewline
10 & 125.6756757 & 120.620601738 & 5.055073962 \tabularnewline
11 & 146.736359 & 120.620601738 & 26.115757262 \tabularnewline
12 & 142.5803162 & 120.620601738 & 21.959714462 \tabularnewline
13 & 106.1448241 & 120.620601738 & -14.475777638 \tabularnewline
14 & 126.5170831 & 120.620601738 & 5.896481362 \tabularnewline
15 & 132.7893932 & 120.620601738 & 12.168791462 \tabularnewline
16 & 121.2391637 & 120.620601738 & 0.618561962000007 \tabularnewline
17 & 114.5079041 & 120.620601738 & -6.112697638 \tabularnewline
18 & 146.1499235 & 120.620601738 & 25.529321762 \tabularnewline
19 & 146.1244263 & 120.620601738 & 25.503824562 \tabularnewline
20 & 128.5058644 & 120.620601738 & 7.88526266200001 \tabularnewline
21 & 155.5838858 & 120.620601738 & 34.963284062 \tabularnewline
22 & 125.0382458 & 120.620601738 & 4.417644062 \tabularnewline
23 & 136.8944416 & 120.620601738 & 16.273839862 \tabularnewline
24 & 142.2233554 & 120.620601738 & 21.602753662 \tabularnewline
25 & 117.7715451 & 120.620601738 & -2.849056638 \tabularnewline
26 & 120.627231 & 120.620601738 & 0.00662926199999536 \tabularnewline
27 & 127.7664457 & 120.620601738 & 7.145843962 \tabularnewline
28 & 135.1096379 & 120.620601738 & 14.489036162 \tabularnewline
29 & 105.7113717 & 120.620601738 & -14.909230038 \tabularnewline
30 & 117.9245283 & 120.620601738 & -2.69607343799999 \tabularnewline
31 & 120.754717 & 120.620601738 & 0.134115262 \tabularnewline
32 & 107.572667 & 120.620601738 & -13.047934738 \tabularnewline
33 & 130.4436512 & 120.620601738 & 9.823049462 \tabularnewline
34 & 107.2157063 & 120.620601738 & -13.404895438 \tabularnewline
35 & 105.0739419 & 120.620601738 & -15.546659838 \tabularnewline
36 & 130.1121877 & 120.620601738 & 9.491585962 \tabularnewline
37 & 109.6379398 & 120.620601738 & -10.982661938 \tabularnewline
38 & 116.7261601 & 120.620601738 & -3.894441638 \tabularnewline
39 & 97.11881693 & 95.719020908 & 1.39979602200002 \tabularnewline
40 & 140.8975013 & 120.620601738 & 20.276899562 \tabularnewline
41 & 108.2865885 & 120.620601738 & -12.334013238 \tabularnewline
42 & 97.65425803 & 95.719020908 & 1.93523712200002 \tabularnewline
43 & 112.0346762 & 120.620601738 & -8.585925538 \tabularnewline
44 & 123.0494646 & 120.620601738 & 2.42886286199999 \tabularnewline
45 & 112.4171341 & 120.620601738 & -8.203467638 \tabularnewline
46 & 116.4966854 & 120.620601738 & -4.12391633800000 \tabularnewline
47 & 104.6914839 & 120.620601738 & -15.929117838 \tabularnewline
48 & 122.2335543 & 120.620601738 & 1.61295256199999 \tabularnewline
49 & 99.79602244 & 95.719020908 & 4.07700153200002 \tabularnewline
50 & 96.71086181 & 95.719020908 & 0.991840902000019 \tabularnewline
51 & 112.3151453 & 120.620601738 & -8.305456438 \tabularnewline
52 & 102.5497195 & 120.620601738 & -18.070882238 \tabularnewline
53 & 104.5385008 & 120.620601738 & -16.082100938 \tabularnewline
54 & 122.0805711 & 120.620601738 & 1.459969362 \tabularnewline
55 & 80.64762876 & 95.719020908 & -15.0713921480000 \tabularnewline
56 & 91.40744518 & 95.719020908 & -4.31157572799998 \tabularnewline
57 & 99.51555329 & 95.719020908 & 3.79653238200002 \tabularnewline
58 & 106.527282 & 120.620601738 & -14.093319738 \tabularnewline
59 & 98.49566548 & 95.719020908 & 2.77664457200002 \tabularnewline
60 & 106.7567568 & 120.620601738 & -13.863844938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58048&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]100[/C][C]95.7190209080002[/C][C]4.28097909199981[/C][/ROW]
[ROW][C]2[/C][C]95.84395716[/C][C]95.719020908[/C][C]0.124936252000047[/C][/ROW]
[ROW][C]3[/C][C]105.5073942[/C][C]120.620601738[/C][C]-15.113207538[/C][/ROW]
[ROW][C]4[/C][C]118.1540031[/C][C]120.620601738[/C][C]-2.466598638[/C][/ROW]
[ROW][C]5[/C][C]101.8612953[/C][C]120.620601738[/C][C]-18.759306438[/C][/ROW]
[ROW][C]6[/C][C]109.8419174[/C][C]120.620601738[/C][C]-10.778684338[/C][/ROW]
[ROW][C]7[/C][C]105.6348802[/C][C]120.620601738[/C][C]-14.985721538[/C][/ROW]
[ROW][C]8[/C][C]112.927078[/C][C]120.620601738[/C][C]-7.693523738[/C][/ROW]
[ROW][C]9[/C][C]133.0698623[/C][C]120.620601738[/C][C]12.449260562[/C][/ROW]
[ROW][C]10[/C][C]125.6756757[/C][C]120.620601738[/C][C]5.055073962[/C][/ROW]
[ROW][C]11[/C][C]146.736359[/C][C]120.620601738[/C][C]26.115757262[/C][/ROW]
[ROW][C]12[/C][C]142.5803162[/C][C]120.620601738[/C][C]21.959714462[/C][/ROW]
[ROW][C]13[/C][C]106.1448241[/C][C]120.620601738[/C][C]-14.475777638[/C][/ROW]
[ROW][C]14[/C][C]126.5170831[/C][C]120.620601738[/C][C]5.896481362[/C][/ROW]
[ROW][C]15[/C][C]132.7893932[/C][C]120.620601738[/C][C]12.168791462[/C][/ROW]
[ROW][C]16[/C][C]121.2391637[/C][C]120.620601738[/C][C]0.618561962000007[/C][/ROW]
[ROW][C]17[/C][C]114.5079041[/C][C]120.620601738[/C][C]-6.112697638[/C][/ROW]
[ROW][C]18[/C][C]146.1499235[/C][C]120.620601738[/C][C]25.529321762[/C][/ROW]
[ROW][C]19[/C][C]146.1244263[/C][C]120.620601738[/C][C]25.503824562[/C][/ROW]
[ROW][C]20[/C][C]128.5058644[/C][C]120.620601738[/C][C]7.88526266200001[/C][/ROW]
[ROW][C]21[/C][C]155.5838858[/C][C]120.620601738[/C][C]34.963284062[/C][/ROW]
[ROW][C]22[/C][C]125.0382458[/C][C]120.620601738[/C][C]4.417644062[/C][/ROW]
[ROW][C]23[/C][C]136.8944416[/C][C]120.620601738[/C][C]16.273839862[/C][/ROW]
[ROW][C]24[/C][C]142.2233554[/C][C]120.620601738[/C][C]21.602753662[/C][/ROW]
[ROW][C]25[/C][C]117.7715451[/C][C]120.620601738[/C][C]-2.849056638[/C][/ROW]
[ROW][C]26[/C][C]120.627231[/C][C]120.620601738[/C][C]0.00662926199999536[/C][/ROW]
[ROW][C]27[/C][C]127.7664457[/C][C]120.620601738[/C][C]7.145843962[/C][/ROW]
[ROW][C]28[/C][C]135.1096379[/C][C]120.620601738[/C][C]14.489036162[/C][/ROW]
[ROW][C]29[/C][C]105.7113717[/C][C]120.620601738[/C][C]-14.909230038[/C][/ROW]
[ROW][C]30[/C][C]117.9245283[/C][C]120.620601738[/C][C]-2.69607343799999[/C][/ROW]
[ROW][C]31[/C][C]120.754717[/C][C]120.620601738[/C][C]0.134115262[/C][/ROW]
[ROW][C]32[/C][C]107.572667[/C][C]120.620601738[/C][C]-13.047934738[/C][/ROW]
[ROW][C]33[/C][C]130.4436512[/C][C]120.620601738[/C][C]9.823049462[/C][/ROW]
[ROW][C]34[/C][C]107.2157063[/C][C]120.620601738[/C][C]-13.404895438[/C][/ROW]
[ROW][C]35[/C][C]105.0739419[/C][C]120.620601738[/C][C]-15.546659838[/C][/ROW]
[ROW][C]36[/C][C]130.1121877[/C][C]120.620601738[/C][C]9.491585962[/C][/ROW]
[ROW][C]37[/C][C]109.6379398[/C][C]120.620601738[/C][C]-10.982661938[/C][/ROW]
[ROW][C]38[/C][C]116.7261601[/C][C]120.620601738[/C][C]-3.894441638[/C][/ROW]
[ROW][C]39[/C][C]97.11881693[/C][C]95.719020908[/C][C]1.39979602200002[/C][/ROW]
[ROW][C]40[/C][C]140.8975013[/C][C]120.620601738[/C][C]20.276899562[/C][/ROW]
[ROW][C]41[/C][C]108.2865885[/C][C]120.620601738[/C][C]-12.334013238[/C][/ROW]
[ROW][C]42[/C][C]97.65425803[/C][C]95.719020908[/C][C]1.93523712200002[/C][/ROW]
[ROW][C]43[/C][C]112.0346762[/C][C]120.620601738[/C][C]-8.585925538[/C][/ROW]
[ROW][C]44[/C][C]123.0494646[/C][C]120.620601738[/C][C]2.42886286199999[/C][/ROW]
[ROW][C]45[/C][C]112.4171341[/C][C]120.620601738[/C][C]-8.203467638[/C][/ROW]
[ROW][C]46[/C][C]116.4966854[/C][C]120.620601738[/C][C]-4.12391633800000[/C][/ROW]
[ROW][C]47[/C][C]104.6914839[/C][C]120.620601738[/C][C]-15.929117838[/C][/ROW]
[ROW][C]48[/C][C]122.2335543[/C][C]120.620601738[/C][C]1.61295256199999[/C][/ROW]
[ROW][C]49[/C][C]99.79602244[/C][C]95.719020908[/C][C]4.07700153200002[/C][/ROW]
[ROW][C]50[/C][C]96.71086181[/C][C]95.719020908[/C][C]0.991840902000019[/C][/ROW]
[ROW][C]51[/C][C]112.3151453[/C][C]120.620601738[/C][C]-8.305456438[/C][/ROW]
[ROW][C]52[/C][C]102.5497195[/C][C]120.620601738[/C][C]-18.070882238[/C][/ROW]
[ROW][C]53[/C][C]104.5385008[/C][C]120.620601738[/C][C]-16.082100938[/C][/ROW]
[ROW][C]54[/C][C]122.0805711[/C][C]120.620601738[/C][C]1.459969362[/C][/ROW]
[ROW][C]55[/C][C]80.64762876[/C][C]95.719020908[/C][C]-15.0713921480000[/C][/ROW]
[ROW][C]56[/C][C]91.40744518[/C][C]95.719020908[/C][C]-4.31157572799998[/C][/ROW]
[ROW][C]57[/C][C]99.51555329[/C][C]95.719020908[/C][C]3.79653238200002[/C][/ROW]
[ROW][C]58[/C][C]106.527282[/C][C]120.620601738[/C][C]-14.093319738[/C][/ROW]
[ROW][C]59[/C][C]98.49566548[/C][C]95.719020908[/C][C]2.77664457200002[/C][/ROW]
[ROW][C]60[/C][C]106.7567568[/C][C]120.620601738[/C][C]-13.863844938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58048&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58048&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
110095.71902090800024.28097909199981
295.8439571695.7190209080.124936252000047
3105.5073942120.620601738-15.113207538
4118.1540031120.620601738-2.466598638
5101.8612953120.620601738-18.759306438
6109.8419174120.620601738-10.778684338
7105.6348802120.620601738-14.985721538
8112.927078120.620601738-7.693523738
9133.0698623120.62060173812.449260562
10125.6756757120.6206017385.055073962
11146.736359120.62060173826.115757262
12142.5803162120.62060173821.959714462
13106.1448241120.620601738-14.475777638
14126.5170831120.6206017385.896481362
15132.7893932120.62060173812.168791462
16121.2391637120.6206017380.618561962000007
17114.5079041120.620601738-6.112697638
18146.1499235120.62060173825.529321762
19146.1244263120.62060173825.503824562
20128.5058644120.6206017387.88526266200001
21155.5838858120.62060173834.963284062
22125.0382458120.6206017384.417644062
23136.8944416120.62060173816.273839862
24142.2233554120.62060173821.602753662
25117.7715451120.620601738-2.849056638
26120.627231120.6206017380.00662926199999536
27127.7664457120.6206017387.145843962
28135.1096379120.62060173814.489036162
29105.7113717120.620601738-14.909230038
30117.9245283120.620601738-2.69607343799999
31120.754717120.6206017380.134115262
32107.572667120.620601738-13.047934738
33130.4436512120.6206017389.823049462
34107.2157063120.620601738-13.404895438
35105.0739419120.620601738-15.546659838
36130.1121877120.6206017389.491585962
37109.6379398120.620601738-10.982661938
38116.7261601120.620601738-3.894441638
3997.1188169395.7190209081.39979602200002
40140.8975013120.62060173820.276899562
41108.2865885120.620601738-12.334013238
4297.6542580395.7190209081.93523712200002
43112.0346762120.620601738-8.585925538
44123.0494646120.6206017382.42886286199999
45112.4171341120.620601738-8.203467638
46116.4966854120.620601738-4.12391633800000
47104.6914839120.620601738-15.929117838
48122.2335543120.6206017381.61295256199999
4999.7960224495.7190209084.07700153200002
5096.7108618195.7190209080.991840902000019
51112.3151453120.620601738-8.305456438
52102.5497195120.620601738-18.070882238
53104.5385008120.620601738-16.082100938
54122.0805711120.6206017381.459969362
5580.6476287695.719020908-15.0713921480000
5691.4074451895.719020908-4.31157572799998
5799.5155532995.7190209083.79653238200002
58106.527282120.620601738-14.093319738
5998.4956654895.7190209082.77664457200002
60106.7567568120.620601738-13.863844938







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1724945728656950.3449891457313890.827505427134305
60.07597932243501850.1519586448700370.924020677564982
70.03501412254789430.07002824509578860.964985877452106
80.01751471770125650.03502943540251300.982485282298744
90.2099235451488880.4198470902977760.790076454851112
100.2121926233870860.4243852467741720.787807376612914
110.6629158663090320.6741682673819370.337084133690968
120.804057981959870.391884036080260.19594201804013
130.8002525860556090.3994948278887830.199747413944391
140.7470310745595120.5059378508809760.252968925440488
150.7331878440376140.5336243119247710.266812155962386
160.6556670745398650.6886658509202710.344332925460135
170.5887793376601660.8224413246796680.411220662339834
180.7705365435739840.4589269128520320.229463456426016
190.88507009573120.2298598085375990.114929904268800
200.8540986924951330.2918026150097340.145901307504867
210.984214754506150.03157049098770040.0157852454938502
220.9765592571180940.04688148576381170.0234407428819058
230.9827278751930710.03454424961385780.0172721248069289
240.9946224516257360.01075509674852850.00537754837426423
250.9917001069480050.01659978610398990.00829989305199493
260.987313896847360.02537220630528120.0126861031526406
270.9851094006729760.02978119865404790.0148905993270239
280.991821063308410.01635787338317990.00817893669158996
290.9932003361493860.01359932770122870.00679966385061434
300.9894798218182720.02104035636345550.0105201781817277
310.9846271242837530.03074575143249360.0153728757162468
320.9835959191603370.03280816167932710.0164040808396635
330.9872837347956840.0254325304086320.012716265204316
340.9860686450337350.02786270993253060.0139313549662653
350.986958126860090.02608374627981790.0130418731399090
360.9905405512267120.01891889754657550.00945944877328774
370.9869935492067550.02601290158649070.0130064507932454
380.9792671732246470.04146565355070510.0207328267753525
390.9665265024684180.06694699506316390.0334734975315819
400.9986358821415120.002728235716977020.00136411785848851
410.997836554148910.004326891702177860.00216344585108893
420.9958201482550260.008359703489948750.00417985174497438
430.9923860140741650.01522797185166980.00761398592583492
440.9932772976690740.01344540466185250.00672270233092624
450.9876725473667730.02465490526645440.0123274526332272
460.981083190904830.03783361819033920.0189168090951696
470.9746171121583290.05076577568334260.0253828878416713
480.9800976819786750.03980463604264990.0199023180213250
490.9684935855701610.06301282885967740.0315064144298387
500.944075739601760.1118485207964790.0559242603982395
510.9057641063002820.1884717873994350.0942358936997177
520.8757496677277080.2485006645445840.124250332272292
530.8255360631908330.3489278736183350.174463936809167
540.8617467524652150.276506495069570.138253247534785
550.974028131208370.05194373758325830.0259718687916292

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.172494572865695 & 0.344989145731389 & 0.827505427134305 \tabularnewline
6 & 0.0759793224350185 & 0.151958644870037 & 0.924020677564982 \tabularnewline
7 & 0.0350141225478943 & 0.0700282450957886 & 0.964985877452106 \tabularnewline
8 & 0.0175147177012565 & 0.0350294354025130 & 0.982485282298744 \tabularnewline
9 & 0.209923545148888 & 0.419847090297776 & 0.790076454851112 \tabularnewline
10 & 0.212192623387086 & 0.424385246774172 & 0.787807376612914 \tabularnewline
11 & 0.662915866309032 & 0.674168267381937 & 0.337084133690968 \tabularnewline
12 & 0.80405798195987 & 0.39188403608026 & 0.19594201804013 \tabularnewline
13 & 0.800252586055609 & 0.399494827888783 & 0.199747413944391 \tabularnewline
14 & 0.747031074559512 & 0.505937850880976 & 0.252968925440488 \tabularnewline
15 & 0.733187844037614 & 0.533624311924771 & 0.266812155962386 \tabularnewline
16 & 0.655667074539865 & 0.688665850920271 & 0.344332925460135 \tabularnewline
17 & 0.588779337660166 & 0.822441324679668 & 0.411220662339834 \tabularnewline
18 & 0.770536543573984 & 0.458926912852032 & 0.229463456426016 \tabularnewline
19 & 0.8850700957312 & 0.229859808537599 & 0.114929904268800 \tabularnewline
20 & 0.854098692495133 & 0.291802615009734 & 0.145901307504867 \tabularnewline
21 & 0.98421475450615 & 0.0315704909877004 & 0.0157852454938502 \tabularnewline
22 & 0.976559257118094 & 0.0468814857638117 & 0.0234407428819058 \tabularnewline
23 & 0.982727875193071 & 0.0345442496138578 & 0.0172721248069289 \tabularnewline
24 & 0.994622451625736 & 0.0107550967485285 & 0.00537754837426423 \tabularnewline
25 & 0.991700106948005 & 0.0165997861039899 & 0.00829989305199493 \tabularnewline
26 & 0.98731389684736 & 0.0253722063052812 & 0.0126861031526406 \tabularnewline
27 & 0.985109400672976 & 0.0297811986540479 & 0.0148905993270239 \tabularnewline
28 & 0.99182106330841 & 0.0163578733831799 & 0.00817893669158996 \tabularnewline
29 & 0.993200336149386 & 0.0135993277012287 & 0.00679966385061434 \tabularnewline
30 & 0.989479821818272 & 0.0210403563634555 & 0.0105201781817277 \tabularnewline
31 & 0.984627124283753 & 0.0307457514324936 & 0.0153728757162468 \tabularnewline
32 & 0.983595919160337 & 0.0328081616793271 & 0.0164040808396635 \tabularnewline
33 & 0.987283734795684 & 0.025432530408632 & 0.012716265204316 \tabularnewline
34 & 0.986068645033735 & 0.0278627099325306 & 0.0139313549662653 \tabularnewline
35 & 0.98695812686009 & 0.0260837462798179 & 0.0130418731399090 \tabularnewline
36 & 0.990540551226712 & 0.0189188975465755 & 0.00945944877328774 \tabularnewline
37 & 0.986993549206755 & 0.0260129015864907 & 0.0130064507932454 \tabularnewline
38 & 0.979267173224647 & 0.0414656535507051 & 0.0207328267753525 \tabularnewline
39 & 0.966526502468418 & 0.0669469950631639 & 0.0334734975315819 \tabularnewline
40 & 0.998635882141512 & 0.00272823571697702 & 0.00136411785848851 \tabularnewline
41 & 0.99783655414891 & 0.00432689170217786 & 0.00216344585108893 \tabularnewline
42 & 0.995820148255026 & 0.00835970348994875 & 0.00417985174497438 \tabularnewline
43 & 0.992386014074165 & 0.0152279718516698 & 0.00761398592583492 \tabularnewline
44 & 0.993277297669074 & 0.0134454046618525 & 0.00672270233092624 \tabularnewline
45 & 0.987672547366773 & 0.0246549052664544 & 0.0123274526332272 \tabularnewline
46 & 0.98108319090483 & 0.0378336181903392 & 0.0189168090951696 \tabularnewline
47 & 0.974617112158329 & 0.0507657756833426 & 0.0253828878416713 \tabularnewline
48 & 0.980097681978675 & 0.0398046360426499 & 0.0199023180213250 \tabularnewline
49 & 0.968493585570161 & 0.0630128288596774 & 0.0315064144298387 \tabularnewline
50 & 0.94407573960176 & 0.111848520796479 & 0.0559242603982395 \tabularnewline
51 & 0.905764106300282 & 0.188471787399435 & 0.0942358936997177 \tabularnewline
52 & 0.875749667727708 & 0.248500664544584 & 0.124250332272292 \tabularnewline
53 & 0.825536063190833 & 0.348927873618335 & 0.174463936809167 \tabularnewline
54 & 0.861746752465215 & 0.27650649506957 & 0.138253247534785 \tabularnewline
55 & 0.97402813120837 & 0.0519437375832583 & 0.0259718687916292 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58048&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]5[/C][C]0.172494572865695[/C][C]0.344989145731389[/C][C]0.827505427134305[/C][/ROW]
[ROW][C]6[/C][C]0.0759793224350185[/C][C]0.151958644870037[/C][C]0.924020677564982[/C][/ROW]
[ROW][C]7[/C][C]0.0350141225478943[/C][C]0.0700282450957886[/C][C]0.964985877452106[/C][/ROW]
[ROW][C]8[/C][C]0.0175147177012565[/C][C]0.0350294354025130[/C][C]0.982485282298744[/C][/ROW]
[ROW][C]9[/C][C]0.209923545148888[/C][C]0.419847090297776[/C][C]0.790076454851112[/C][/ROW]
[ROW][C]10[/C][C]0.212192623387086[/C][C]0.424385246774172[/C][C]0.787807376612914[/C][/ROW]
[ROW][C]11[/C][C]0.662915866309032[/C][C]0.674168267381937[/C][C]0.337084133690968[/C][/ROW]
[ROW][C]12[/C][C]0.80405798195987[/C][C]0.39188403608026[/C][C]0.19594201804013[/C][/ROW]
[ROW][C]13[/C][C]0.800252586055609[/C][C]0.399494827888783[/C][C]0.199747413944391[/C][/ROW]
[ROW][C]14[/C][C]0.747031074559512[/C][C]0.505937850880976[/C][C]0.252968925440488[/C][/ROW]
[ROW][C]15[/C][C]0.733187844037614[/C][C]0.533624311924771[/C][C]0.266812155962386[/C][/ROW]
[ROW][C]16[/C][C]0.655667074539865[/C][C]0.688665850920271[/C][C]0.344332925460135[/C][/ROW]
[ROW][C]17[/C][C]0.588779337660166[/C][C]0.822441324679668[/C][C]0.411220662339834[/C][/ROW]
[ROW][C]18[/C][C]0.770536543573984[/C][C]0.458926912852032[/C][C]0.229463456426016[/C][/ROW]
[ROW][C]19[/C][C]0.8850700957312[/C][C]0.229859808537599[/C][C]0.114929904268800[/C][/ROW]
[ROW][C]20[/C][C]0.854098692495133[/C][C]0.291802615009734[/C][C]0.145901307504867[/C][/ROW]
[ROW][C]21[/C][C]0.98421475450615[/C][C]0.0315704909877004[/C][C]0.0157852454938502[/C][/ROW]
[ROW][C]22[/C][C]0.976559257118094[/C][C]0.0468814857638117[/C][C]0.0234407428819058[/C][/ROW]
[ROW][C]23[/C][C]0.982727875193071[/C][C]0.0345442496138578[/C][C]0.0172721248069289[/C][/ROW]
[ROW][C]24[/C][C]0.994622451625736[/C][C]0.0107550967485285[/C][C]0.00537754837426423[/C][/ROW]
[ROW][C]25[/C][C]0.991700106948005[/C][C]0.0165997861039899[/C][C]0.00829989305199493[/C][/ROW]
[ROW][C]26[/C][C]0.98731389684736[/C][C]0.0253722063052812[/C][C]0.0126861031526406[/C][/ROW]
[ROW][C]27[/C][C]0.985109400672976[/C][C]0.0297811986540479[/C][C]0.0148905993270239[/C][/ROW]
[ROW][C]28[/C][C]0.99182106330841[/C][C]0.0163578733831799[/C][C]0.00817893669158996[/C][/ROW]
[ROW][C]29[/C][C]0.993200336149386[/C][C]0.0135993277012287[/C][C]0.00679966385061434[/C][/ROW]
[ROW][C]30[/C][C]0.989479821818272[/C][C]0.0210403563634555[/C][C]0.0105201781817277[/C][/ROW]
[ROW][C]31[/C][C]0.984627124283753[/C][C]0.0307457514324936[/C][C]0.0153728757162468[/C][/ROW]
[ROW][C]32[/C][C]0.983595919160337[/C][C]0.0328081616793271[/C][C]0.0164040808396635[/C][/ROW]
[ROW][C]33[/C][C]0.987283734795684[/C][C]0.025432530408632[/C][C]0.012716265204316[/C][/ROW]
[ROW][C]34[/C][C]0.986068645033735[/C][C]0.0278627099325306[/C][C]0.0139313549662653[/C][/ROW]
[ROW][C]35[/C][C]0.98695812686009[/C][C]0.0260837462798179[/C][C]0.0130418731399090[/C][/ROW]
[ROW][C]36[/C][C]0.990540551226712[/C][C]0.0189188975465755[/C][C]0.00945944877328774[/C][/ROW]
[ROW][C]37[/C][C]0.986993549206755[/C][C]0.0260129015864907[/C][C]0.0130064507932454[/C][/ROW]
[ROW][C]38[/C][C]0.979267173224647[/C][C]0.0414656535507051[/C][C]0.0207328267753525[/C][/ROW]
[ROW][C]39[/C][C]0.966526502468418[/C][C]0.0669469950631639[/C][C]0.0334734975315819[/C][/ROW]
[ROW][C]40[/C][C]0.998635882141512[/C][C]0.00272823571697702[/C][C]0.00136411785848851[/C][/ROW]
[ROW][C]41[/C][C]0.99783655414891[/C][C]0.00432689170217786[/C][C]0.00216344585108893[/C][/ROW]
[ROW][C]42[/C][C]0.995820148255026[/C][C]0.00835970348994875[/C][C]0.00417985174497438[/C][/ROW]
[ROW][C]43[/C][C]0.992386014074165[/C][C]0.0152279718516698[/C][C]0.00761398592583492[/C][/ROW]
[ROW][C]44[/C][C]0.993277297669074[/C][C]0.0134454046618525[/C][C]0.00672270233092624[/C][/ROW]
[ROW][C]45[/C][C]0.987672547366773[/C][C]0.0246549052664544[/C][C]0.0123274526332272[/C][/ROW]
[ROW][C]46[/C][C]0.98108319090483[/C][C]0.0378336181903392[/C][C]0.0189168090951696[/C][/ROW]
[ROW][C]47[/C][C]0.974617112158329[/C][C]0.0507657756833426[/C][C]0.0253828878416713[/C][/ROW]
[ROW][C]48[/C][C]0.980097681978675[/C][C]0.0398046360426499[/C][C]0.0199023180213250[/C][/ROW]
[ROW][C]49[/C][C]0.968493585570161[/C][C]0.0630128288596774[/C][C]0.0315064144298387[/C][/ROW]
[ROW][C]50[/C][C]0.94407573960176[/C][C]0.111848520796479[/C][C]0.0559242603982395[/C][/ROW]
[ROW][C]51[/C][C]0.905764106300282[/C][C]0.188471787399435[/C][C]0.0942358936997177[/C][/ROW]
[ROW][C]52[/C][C]0.875749667727708[/C][C]0.248500664544584[/C][C]0.124250332272292[/C][/ROW]
[ROW][C]53[/C][C]0.825536063190833[/C][C]0.348927873618335[/C][C]0.174463936809167[/C][/ROW]
[ROW][C]54[/C][C]0.861746752465215[/C][C]0.27650649506957[/C][C]0.138253247534785[/C][/ROW]
[ROW][C]55[/C][C]0.97402813120837[/C][C]0.0519437375832583[/C][C]0.0259718687916292[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58048&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58048&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
50.1724945728656950.3449891457313890.827505427134305
60.07597932243501850.1519586448700370.924020677564982
70.03501412254789430.07002824509578860.964985877452106
80.01751471770125650.03502943540251300.982485282298744
90.2099235451488880.4198470902977760.790076454851112
100.2121926233870860.4243852467741720.787807376612914
110.6629158663090320.6741682673819370.337084133690968
120.804057981959870.391884036080260.19594201804013
130.8002525860556090.3994948278887830.199747413944391
140.7470310745595120.5059378508809760.252968925440488
150.7331878440376140.5336243119247710.266812155962386
160.6556670745398650.6886658509202710.344332925460135
170.5887793376601660.8224413246796680.411220662339834
180.7705365435739840.4589269128520320.229463456426016
190.88507009573120.2298598085375990.114929904268800
200.8540986924951330.2918026150097340.145901307504867
210.984214754506150.03157049098770040.0157852454938502
220.9765592571180940.04688148576381170.0234407428819058
230.9827278751930710.03454424961385780.0172721248069289
240.9946224516257360.01075509674852850.00537754837426423
250.9917001069480050.01659978610398990.00829989305199493
260.987313896847360.02537220630528120.0126861031526406
270.9851094006729760.02978119865404790.0148905993270239
280.991821063308410.01635787338317990.00817893669158996
290.9932003361493860.01359932770122870.00679966385061434
300.9894798218182720.02104035636345550.0105201781817277
310.9846271242837530.03074575143249360.0153728757162468
320.9835959191603370.03280816167932710.0164040808396635
330.9872837347956840.0254325304086320.012716265204316
340.9860686450337350.02786270993253060.0139313549662653
350.986958126860090.02608374627981790.0130418731399090
360.9905405512267120.01891889754657550.00945944877328774
370.9869935492067550.02601290158649070.0130064507932454
380.9792671732246470.04146565355070510.0207328267753525
390.9665265024684180.06694699506316390.0334734975315819
400.9986358821415120.002728235716977020.00136411785848851
410.997836554148910.004326891702177860.00216344585108893
420.9958201482550260.008359703489948750.00417985174497438
430.9923860140741650.01522797185166980.00761398592583492
440.9932772976690740.01344540466185250.00672270233092624
450.9876725473667730.02465490526645440.0123274526332272
460.981083190904830.03783361819033920.0189168090951696
470.9746171121583290.05076577568334260.0253828878416713
480.9800976819786750.03980463604264990.0199023180213250
490.9684935855701610.06301282885967740.0315064144298387
500.944075739601760.1118485207964790.0559242603982395
510.9057641063002820.1884717873994350.0942358936997177
520.8757496677277080.2485006645445840.124250332272292
530.8255360631908330.3489278736183350.174463936809167
540.8617467524652150.276506495069570.138253247534785
550.974028131208370.05194373758325830.0259718687916292







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0588235294117647NOK
5% type I error level270.529411764705882NOK
10% type I error level320.627450980392157NOK

\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 & 3 & 0.0588235294117647 & NOK \tabularnewline
5% type I error level & 27 & 0.529411764705882 & NOK \tabularnewline
10% type I error level & 32 & 0.627450980392157 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58048&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]3[/C][C]0.0588235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.529411764705882[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.627450980392157[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58048&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58048&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 level30.0588235294117647NOK
5% type I error level270.529411764705882NOK
10% type I error level320.627450980392157NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}