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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 18 Nov 2009 10:06:03 -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/18/t1258564008btnboe7p57vzyaz.htm/, Retrieved Sun, 05 May 2024 09:45:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57536, Retrieved Sun, 05 May 2024 09:45:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108

Tree of Dependent Computations

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]
-   PD      [Multiple Regression] [] [2009-11-18 17:06:03] [7dd0431c761b876151627bfbf92230c8] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8.9	1.6
8.8	1.8
8.3	1.6
7.5	1.5
7.2	1.5
7.4	1.3
8.8	1.4
9.3	1.4
9.3	1.3
8.7	1.3
8.2	1.2
8.3	1.1
8.5	1.4
8.6	1.2
8.5	1.5
8.2	1.1
8.1	1.3
7.9	1.5
8.6	1.1
8.7	1.4
8.7	1.3
8.5	1.5
8.4	1.6
8.5	1.7
8.7	1.1
8.7	1.6
8.6	1.3
8.5	1.7
8.3	1.6
8	1.7
8.2	1.9
8.1	1.8
8.1	1.9
8	1.6
7.9	1.5
7.9	1.6
8	1.6
8	1.7
7.9	2
8	2
7.7	1.9
7.2	1.7
7.5	1.8
7.3	1.9
7	1.7
7	2
7	2.1
7.2	2.4
7.3	2.5
7.1	2.5
6.8	2.6
6.4	2.2
6.1	2.5
6.5	2.8
7.7	2.8
7.9	2.9
7.5	3
6.9	3.1
6.6	2.9
6.9	2.7

Tables (Output of Computation)





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=57536&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=57536&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57536&T=0

As an alternative you can also use a QR Code:  
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
inflatie[t] = + 5.7573134816754 -0.500081806282723graad[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
inflatie[t] =  +  5.7573134816754 -0.500081806282723graad[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57536&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]inflatie[t] =  +  5.7573134816754 -0.500081806282723graad[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57536&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57536&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
inflatie[t] = + 5.7573134816754 -0.500081806282723graad[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.75731348167540.54260310.610500
graad-0.5000818062827230.06833-7.318700

\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) & 5.7573134816754 & 0.542603 & 10.6105 & 0 & 0 \tabularnewline
graad & -0.500081806282723 & 0.06833 & -7.3187 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57536&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]5.7573134816754[/C][C]0.542603[/C][C]10.6105[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]graad[/C][C]-0.500081806282723[/C][C]0.06833[/C][C]-7.3187[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57536&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57536&T=2

As an alternative you can also use a QR Code:  
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)5.75731348167540.54260310.610500
graad-0.5000818062827230.06833-7.318700






Multiple Linear Regression - Regression Statistics
Multiple R0.692901934975365
R-squared0.480113091492605
Adjusted R-squared0.471149524104546
F-TEST (value)53.5627246058552
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value8.54115778103903e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.390122286645665
Sum Squared Residuals8.82733311518324

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.692901934975365 \tabularnewline
R-squared & 0.480113091492605 \tabularnewline
Adjusted R-squared & 0.471149524104546 \tabularnewline
F-TEST (value) & 53.5627246058552 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 8.54115778103903e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.390122286645665 \tabularnewline
Sum Squared Residuals & 8.82733311518324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57536&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.692901934975365[/C][/ROW]
[ROW][C]R-squared[/C][C]0.480113091492605[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.471149524104546[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]53.5627246058552[/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]8.54115778103903e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.390122286645665[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8.82733311518324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57536&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57536&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.692901934975365
R-squared0.480113091492605
Adjusted R-squared0.471149524104546
F-TEST (value)53.5627246058552
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value8.54115778103903e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.390122286645665
Sum Squared Residuals8.82733311518324






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.61.306585405759180.293414594240823
21.81.356593586387430.443406413612566
31.61.60663448952879-0.00663448952879472
41.52.00669993455497-0.506699934554974
51.52.15672447643979-0.656724476439791
61.32.05670811518325-0.756708115183246
71.41.356593586387430.0434064136125667
81.41.106552683246070.293447316753928
91.31.106552683246070.193447316753929
101.31.40660176701571-0.106601767015706
111.21.65664267015707-0.456642670157068
121.11.60663448952879-0.506634489528795
131.41.50661812827225-0.106618128272251
141.21.45660994764398-0.256609947643978
151.51.50661812827225-0.00661812827225051
161.11.65664267015707-0.556642670157068
171.31.70665085078534-0.40665085078534
181.51.80666721204188-0.306667212041884
191.11.45660994764398-0.356609947643978
201.41.40660176701571-0.00660176701570632
211.31.40660176701571-0.106601767015706
221.51.50661812827225-0.00661812827225051
231.61.556626308900520.0433736910994774
241.71.506618128272250.193381871727749
251.11.40660176701571-0.306601767015706
261.61.406601767015710.193398232984294
271.31.45660994764398-0.156609947643978
281.71.506618128272250.193381871727749
291.61.60663448952879-0.00663448952879472
301.71.75665903141361-0.0566590314136122
311.91.656642670157070.243357329842932
321.81.706650850785340.09334914921466
331.91.706650850785340.19334914921466
341.61.75665903141361-0.156659031413612
351.51.80666721204188-0.306667212041884
361.61.80666721204188-0.206667212041884
371.61.75665903141361-0.156659031413612
381.71.75665903141361-0.0566590314136122
3921.806667212041880.193332787958116
4021.756659031413610.243340968586388
411.91.90668357329843-0.00668357329842911
421.72.15672447643979-0.456724476439791
431.82.00669993455497-0.206699934554974
441.92.10671629581152-0.206716295811519
451.72.25674083769634-0.556740837696335
4622.25674083769634-0.256740837696335
472.12.25674083769634-0.156740837696335
482.42.156724476439790.243275523560209
492.52.106716295811520.393283704188482
502.52.206732657068060.293267342931937
512.62.356757198952880.24324280104712
522.22.55678992146597-0.356789921465969
532.52.70681446335079-0.206814463350786
542.82.506781740837700.293218259162303
552.81.906683573298430.89331642670157
562.91.806667212041881.09333278795812
5732.006699934554970.993300065445026
583.12.306749018324610.793250981675393
592.92.456773560209420.443226439790575
602.72.306749018324610.393250981675393

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.6 & 1.30658540575918 & 0.293414594240823 \tabularnewline
2 & 1.8 & 1.35659358638743 & 0.443406413612566 \tabularnewline
3 & 1.6 & 1.60663448952879 & -0.00663448952879472 \tabularnewline
4 & 1.5 & 2.00669993455497 & -0.506699934554974 \tabularnewline
5 & 1.5 & 2.15672447643979 & -0.656724476439791 \tabularnewline
6 & 1.3 & 2.05670811518325 & -0.756708115183246 \tabularnewline
7 & 1.4 & 1.35659358638743 & 0.0434064136125667 \tabularnewline
8 & 1.4 & 1.10655268324607 & 0.293447316753928 \tabularnewline
9 & 1.3 & 1.10655268324607 & 0.193447316753929 \tabularnewline
10 & 1.3 & 1.40660176701571 & -0.106601767015706 \tabularnewline
11 & 1.2 & 1.65664267015707 & -0.456642670157068 \tabularnewline
12 & 1.1 & 1.60663448952879 & -0.506634489528795 \tabularnewline
13 & 1.4 & 1.50661812827225 & -0.106618128272251 \tabularnewline
14 & 1.2 & 1.45660994764398 & -0.256609947643978 \tabularnewline
15 & 1.5 & 1.50661812827225 & -0.00661812827225051 \tabularnewline
16 & 1.1 & 1.65664267015707 & -0.556642670157068 \tabularnewline
17 & 1.3 & 1.70665085078534 & -0.40665085078534 \tabularnewline
18 & 1.5 & 1.80666721204188 & -0.306667212041884 \tabularnewline
19 & 1.1 & 1.45660994764398 & -0.356609947643978 \tabularnewline
20 & 1.4 & 1.40660176701571 & -0.00660176701570632 \tabularnewline
21 & 1.3 & 1.40660176701571 & -0.106601767015706 \tabularnewline
22 & 1.5 & 1.50661812827225 & -0.00661812827225051 \tabularnewline
23 & 1.6 & 1.55662630890052 & 0.0433736910994774 \tabularnewline
24 & 1.7 & 1.50661812827225 & 0.193381871727749 \tabularnewline
25 & 1.1 & 1.40660176701571 & -0.306601767015706 \tabularnewline
26 & 1.6 & 1.40660176701571 & 0.193398232984294 \tabularnewline
27 & 1.3 & 1.45660994764398 & -0.156609947643978 \tabularnewline
28 & 1.7 & 1.50661812827225 & 0.193381871727749 \tabularnewline
29 & 1.6 & 1.60663448952879 & -0.00663448952879472 \tabularnewline
30 & 1.7 & 1.75665903141361 & -0.0566590314136122 \tabularnewline
31 & 1.9 & 1.65664267015707 & 0.243357329842932 \tabularnewline
32 & 1.8 & 1.70665085078534 & 0.09334914921466 \tabularnewline
33 & 1.9 & 1.70665085078534 & 0.19334914921466 \tabularnewline
34 & 1.6 & 1.75665903141361 & -0.156659031413612 \tabularnewline
35 & 1.5 & 1.80666721204188 & -0.306667212041884 \tabularnewline
36 & 1.6 & 1.80666721204188 & -0.206667212041884 \tabularnewline
37 & 1.6 & 1.75665903141361 & -0.156659031413612 \tabularnewline
38 & 1.7 & 1.75665903141361 & -0.0566590314136122 \tabularnewline
39 & 2 & 1.80666721204188 & 0.193332787958116 \tabularnewline
40 & 2 & 1.75665903141361 & 0.243340968586388 \tabularnewline
41 & 1.9 & 1.90668357329843 & -0.00668357329842911 \tabularnewline
42 & 1.7 & 2.15672447643979 & -0.456724476439791 \tabularnewline
43 & 1.8 & 2.00669993455497 & -0.206699934554974 \tabularnewline
44 & 1.9 & 2.10671629581152 & -0.206716295811519 \tabularnewline
45 & 1.7 & 2.25674083769634 & -0.556740837696335 \tabularnewline
46 & 2 & 2.25674083769634 & -0.256740837696335 \tabularnewline
47 & 2.1 & 2.25674083769634 & -0.156740837696335 \tabularnewline
48 & 2.4 & 2.15672447643979 & 0.243275523560209 \tabularnewline
49 & 2.5 & 2.10671629581152 & 0.393283704188482 \tabularnewline
50 & 2.5 & 2.20673265706806 & 0.293267342931937 \tabularnewline
51 & 2.6 & 2.35675719895288 & 0.24324280104712 \tabularnewline
52 & 2.2 & 2.55678992146597 & -0.356789921465969 \tabularnewline
53 & 2.5 & 2.70681446335079 & -0.206814463350786 \tabularnewline
54 & 2.8 & 2.50678174083770 & 0.293218259162303 \tabularnewline
55 & 2.8 & 1.90668357329843 & 0.89331642670157 \tabularnewline
56 & 2.9 & 1.80666721204188 & 1.09333278795812 \tabularnewline
57 & 3 & 2.00669993455497 & 0.993300065445026 \tabularnewline
58 & 3.1 & 2.30674901832461 & 0.793250981675393 \tabularnewline
59 & 2.9 & 2.45677356020942 & 0.443226439790575 \tabularnewline
60 & 2.7 & 2.30674901832461 & 0.393250981675393 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57536&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]1.6[/C][C]1.30658540575918[/C][C]0.293414594240823[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]1.35659358638743[/C][C]0.443406413612566[/C][/ROW]
[ROW][C]3[/C][C]1.6[/C][C]1.60663448952879[/C][C]-0.00663448952879472[/C][/ROW]
[ROW][C]4[/C][C]1.5[/C][C]2.00669993455497[/C][C]-0.506699934554974[/C][/ROW]
[ROW][C]5[/C][C]1.5[/C][C]2.15672447643979[/C][C]-0.656724476439791[/C][/ROW]
[ROW][C]6[/C][C]1.3[/C][C]2.05670811518325[/C][C]-0.756708115183246[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]1.35659358638743[/C][C]0.0434064136125667[/C][/ROW]
[ROW][C]8[/C][C]1.4[/C][C]1.10655268324607[/C][C]0.293447316753928[/C][/ROW]
[ROW][C]9[/C][C]1.3[/C][C]1.10655268324607[/C][C]0.193447316753929[/C][/ROW]
[ROW][C]10[/C][C]1.3[/C][C]1.40660176701571[/C][C]-0.106601767015706[/C][/ROW]
[ROW][C]11[/C][C]1.2[/C][C]1.65664267015707[/C][C]-0.456642670157068[/C][/ROW]
[ROW][C]12[/C][C]1.1[/C][C]1.60663448952879[/C][C]-0.506634489528795[/C][/ROW]
[ROW][C]13[/C][C]1.4[/C][C]1.50661812827225[/C][C]-0.106618128272251[/C][/ROW]
[ROW][C]14[/C][C]1.2[/C][C]1.45660994764398[/C][C]-0.256609947643978[/C][/ROW]
[ROW][C]15[/C][C]1.5[/C][C]1.50661812827225[/C][C]-0.00661812827225051[/C][/ROW]
[ROW][C]16[/C][C]1.1[/C][C]1.65664267015707[/C][C]-0.556642670157068[/C][/ROW]
[ROW][C]17[/C][C]1.3[/C][C]1.70665085078534[/C][C]-0.40665085078534[/C][/ROW]
[ROW][C]18[/C][C]1.5[/C][C]1.80666721204188[/C][C]-0.306667212041884[/C][/ROW]
[ROW][C]19[/C][C]1.1[/C][C]1.45660994764398[/C][C]-0.356609947643978[/C][/ROW]
[ROW][C]20[/C][C]1.4[/C][C]1.40660176701571[/C][C]-0.00660176701570632[/C][/ROW]
[ROW][C]21[/C][C]1.3[/C][C]1.40660176701571[/C][C]-0.106601767015706[/C][/ROW]
[ROW][C]22[/C][C]1.5[/C][C]1.50661812827225[/C][C]-0.00661812827225051[/C][/ROW]
[ROW][C]23[/C][C]1.6[/C][C]1.55662630890052[/C][C]0.0433736910994774[/C][/ROW]
[ROW][C]24[/C][C]1.7[/C][C]1.50661812827225[/C][C]0.193381871727749[/C][/ROW]
[ROW][C]25[/C][C]1.1[/C][C]1.40660176701571[/C][C]-0.306601767015706[/C][/ROW]
[ROW][C]26[/C][C]1.6[/C][C]1.40660176701571[/C][C]0.193398232984294[/C][/ROW]
[ROW][C]27[/C][C]1.3[/C][C]1.45660994764398[/C][C]-0.156609947643978[/C][/ROW]
[ROW][C]28[/C][C]1.7[/C][C]1.50661812827225[/C][C]0.193381871727749[/C][/ROW]
[ROW][C]29[/C][C]1.6[/C][C]1.60663448952879[/C][C]-0.00663448952879472[/C][/ROW]
[ROW][C]30[/C][C]1.7[/C][C]1.75665903141361[/C][C]-0.0566590314136122[/C][/ROW]
[ROW][C]31[/C][C]1.9[/C][C]1.65664267015707[/C][C]0.243357329842932[/C][/ROW]
[ROW][C]32[/C][C]1.8[/C][C]1.70665085078534[/C][C]0.09334914921466[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]1.70665085078534[/C][C]0.19334914921466[/C][/ROW]
[ROW][C]34[/C][C]1.6[/C][C]1.75665903141361[/C][C]-0.156659031413612[/C][/ROW]
[ROW][C]35[/C][C]1.5[/C][C]1.80666721204188[/C][C]-0.306667212041884[/C][/ROW]
[ROW][C]36[/C][C]1.6[/C][C]1.80666721204188[/C][C]-0.206667212041884[/C][/ROW]
[ROW][C]37[/C][C]1.6[/C][C]1.75665903141361[/C][C]-0.156659031413612[/C][/ROW]
[ROW][C]38[/C][C]1.7[/C][C]1.75665903141361[/C][C]-0.0566590314136122[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.80666721204188[/C][C]0.193332787958116[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]1.75665903141361[/C][C]0.243340968586388[/C][/ROW]
[ROW][C]41[/C][C]1.9[/C][C]1.90668357329843[/C][C]-0.00668357329842911[/C][/ROW]
[ROW][C]42[/C][C]1.7[/C][C]2.15672447643979[/C][C]-0.456724476439791[/C][/ROW]
[ROW][C]43[/C][C]1.8[/C][C]2.00669993455497[/C][C]-0.206699934554974[/C][/ROW]
[ROW][C]44[/C][C]1.9[/C][C]2.10671629581152[/C][C]-0.206716295811519[/C][/ROW]
[ROW][C]45[/C][C]1.7[/C][C]2.25674083769634[/C][C]-0.556740837696335[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]2.25674083769634[/C][C]-0.256740837696335[/C][/ROW]
[ROW][C]47[/C][C]2.1[/C][C]2.25674083769634[/C][C]-0.156740837696335[/C][/ROW]
[ROW][C]48[/C][C]2.4[/C][C]2.15672447643979[/C][C]0.243275523560209[/C][/ROW]
[ROW][C]49[/C][C]2.5[/C][C]2.10671629581152[/C][C]0.393283704188482[/C][/ROW]
[ROW][C]50[/C][C]2.5[/C][C]2.20673265706806[/C][C]0.293267342931937[/C][/ROW]
[ROW][C]51[/C][C]2.6[/C][C]2.35675719895288[/C][C]0.24324280104712[/C][/ROW]
[ROW][C]52[/C][C]2.2[/C][C]2.55678992146597[/C][C]-0.356789921465969[/C][/ROW]
[ROW][C]53[/C][C]2.5[/C][C]2.70681446335079[/C][C]-0.206814463350786[/C][/ROW]
[ROW][C]54[/C][C]2.8[/C][C]2.50678174083770[/C][C]0.293218259162303[/C][/ROW]
[ROW][C]55[/C][C]2.8[/C][C]1.90668357329843[/C][C]0.89331642670157[/C][/ROW]
[ROW][C]56[/C][C]2.9[/C][C]1.80666721204188[/C][C]1.09333278795812[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.00669993455497[/C][C]0.993300065445026[/C][/ROW]
[ROW][C]58[/C][C]3.1[/C][C]2.30674901832461[/C][C]0.793250981675393[/C][/ROW]
[ROW][C]59[/C][C]2.9[/C][C]2.45677356020942[/C][C]0.443226439790575[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]2.30674901832461[/C][C]0.393250981675393[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57536&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57536&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.61.306585405759180.293414594240823
21.81.356593586387430.443406413612566
31.61.60663448952879-0.00663448952879472
41.52.00669993455497-0.506699934554974
51.52.15672447643979-0.656724476439791
61.32.05670811518325-0.756708115183246
71.41.356593586387430.0434064136125667
81.41.106552683246070.293447316753928
91.31.106552683246070.193447316753929
101.31.40660176701571-0.106601767015706
111.21.65664267015707-0.456642670157068
121.11.60663448952879-0.506634489528795
131.41.50661812827225-0.106618128272251
141.21.45660994764398-0.256609947643978
151.51.50661812827225-0.00661812827225051
161.11.65664267015707-0.556642670157068
171.31.70665085078534-0.40665085078534
181.51.80666721204188-0.306667212041884
191.11.45660994764398-0.356609947643978
201.41.40660176701571-0.00660176701570632
211.31.40660176701571-0.106601767015706
221.51.50661812827225-0.00661812827225051
231.61.556626308900520.0433736910994774
241.71.506618128272250.193381871727749
251.11.40660176701571-0.306601767015706
261.61.406601767015710.193398232984294
271.31.45660994764398-0.156609947643978
281.71.506618128272250.193381871727749
291.61.60663448952879-0.00663448952879472
301.71.75665903141361-0.0566590314136122
311.91.656642670157070.243357329842932
321.81.706650850785340.09334914921466
331.91.706650850785340.19334914921466
341.61.75665903141361-0.156659031413612
351.51.80666721204188-0.306667212041884
361.61.80666721204188-0.206667212041884
371.61.75665903141361-0.156659031413612
381.71.75665903141361-0.0566590314136122
3921.806667212041880.193332787958116
4021.756659031413610.243340968586388
411.91.90668357329843-0.00668357329842911
421.72.15672447643979-0.456724476439791
431.82.00669993455497-0.206699934554974
441.92.10671629581152-0.206716295811519
451.72.25674083769634-0.556740837696335
4622.25674083769634-0.256740837696335
472.12.25674083769634-0.156740837696335
482.42.156724476439790.243275523560209
492.52.106716295811520.393283704188482
502.52.206732657068060.293267342931937
512.62.356757198952880.24324280104712
522.22.55678992146597-0.356789921465969
532.52.70681446335079-0.206814463350786
542.82.506781740837700.293218259162303
552.81.906683573298430.89331642670157
562.91.806667212041881.09333278795812
5732.006699934554970.993300065445026
583.12.306749018324610.793250981675393
592.92.456773560209420.443226439790575
602.72.306749018324610.393250981675393






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.01683005789638130.03366011579276270.983169942103619
60.01743115978249250.03486231956498500.982568840217507
70.02397287759684210.04794575519368430.976027122403158
80.02032985279061850.0406597055812370.979670147209382
90.01867060922785590.03734121845571180.981329390772144
100.01214451060889780.02428902121779560.987855489391102
110.01314260029465080.02628520058930160.98685739970535
120.02108487291838320.04216974583676630.978915127081617
130.01031216180796820.02062432361593640.989687838192032
140.007813243330567550.01562648666113510.992186756669432
150.00400222749853210.00800445499706420.995997772501468
160.005727197596520530.01145439519304110.99427280240348
170.003507959231909560.007015918463819120.99649204076809
180.002212175041181040.004424350082362070.997787824958819
190.002710630621352050.00542126124270410.997289369378648
200.001316186379942980.002632372759885960.998683813620057
210.0006721215072443380.001344243014488680.999327878492756
220.0003624509720464420.0007249019440928830.999637549027954
230.0002673681437061410.0005347362874122820.999732631856294
240.0003081883872923280.0006163767745846560.999691811612708
250.0003997715820345620.0007995431640691240.999600228417965
260.0002718410521242050.000543682104248410.999728158947876
270.0001558615551157620.0003117231102315240.999844138444884
280.0001554329603744230.0003108659207488460.999844567039626
290.0001007750029328780.0002015500058657560.999899224997067
309.33516591240936e-050.0001867033182481870.999906648340876
310.0002077801195497030.0004155602390994060.99979221988045
320.00021169784276440.00042339568552880.999788302157236
330.0002913649443402920.0005827298886805840.99970863505566
340.0001845645274053410.0003691290548106830.999815435472595
350.0001486493096782460.0002972986193564920.999851350690322
360.0001167721142380060.0002335442284760110.999883227885762
370.0001000896538038750.0002001793076077510.999899910346196
389.73333001080194e-050.0001946666002160390.999902666699892
390.0001700233264227770.0003400466528455540.999829976673577
400.000264015400707060.000528030801414120.999735984599293
410.000330347899617640.000660695799235280.999669652100382
420.0007337629668886250.001467525933777250.999266237033111
430.001891753649126690.003783507298253380.998108246350873
440.004914313508394030.009828627016788050.995085686491606
450.04340256452871090.08680512905742170.95659743547129
460.1310303939778460.2620607879556920.868969606022154
470.326961226507020.653922453014040.67303877349298
480.4772685806868620.9545371613737230.522731419313138
490.602287413093260.7954251738134810.397712586906740
500.6669100012552480.6661799974895050.333089998744752
510.6230935377816720.7538129244366570.376906462218328
520.8819708683964180.2360582632071640.118029131603582
530.9219983615465480.1560032769069050.0780016384534524
540.8716818574075410.2566362851849170.128318142592459
550.8287180322568920.3425639354862150.171281967743108

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0168300578963813 & 0.0336601157927627 & 0.983169942103619 \tabularnewline
6 & 0.0174311597824925 & 0.0348623195649850 & 0.982568840217507 \tabularnewline
7 & 0.0239728775968421 & 0.0479457551936843 & 0.976027122403158 \tabularnewline
8 & 0.0203298527906185 & 0.040659705581237 & 0.979670147209382 \tabularnewline
9 & 0.0186706092278559 & 0.0373412184557118 & 0.981329390772144 \tabularnewline
10 & 0.0121445106088978 & 0.0242890212177956 & 0.987855489391102 \tabularnewline
11 & 0.0131426002946508 & 0.0262852005893016 & 0.98685739970535 \tabularnewline
12 & 0.0210848729183832 & 0.0421697458367663 & 0.978915127081617 \tabularnewline
13 & 0.0103121618079682 & 0.0206243236159364 & 0.989687838192032 \tabularnewline
14 & 0.00781324333056755 & 0.0156264866611351 & 0.992186756669432 \tabularnewline
15 & 0.0040022274985321 & 0.0080044549970642 & 0.995997772501468 \tabularnewline
16 & 0.00572719759652053 & 0.0114543951930411 & 0.99427280240348 \tabularnewline
17 & 0.00350795923190956 & 0.00701591846381912 & 0.99649204076809 \tabularnewline
18 & 0.00221217504118104 & 0.00442435008236207 & 0.997787824958819 \tabularnewline
19 & 0.00271063062135205 & 0.0054212612427041 & 0.997289369378648 \tabularnewline
20 & 0.00131618637994298 & 0.00263237275988596 & 0.998683813620057 \tabularnewline
21 & 0.000672121507244338 & 0.00134424301448868 & 0.999327878492756 \tabularnewline
22 & 0.000362450972046442 & 0.000724901944092883 & 0.999637549027954 \tabularnewline
23 & 0.000267368143706141 & 0.000534736287412282 & 0.999732631856294 \tabularnewline
24 & 0.000308188387292328 & 0.000616376774584656 & 0.999691811612708 \tabularnewline
25 & 0.000399771582034562 & 0.000799543164069124 & 0.999600228417965 \tabularnewline
26 & 0.000271841052124205 & 0.00054368210424841 & 0.999728158947876 \tabularnewline
27 & 0.000155861555115762 & 0.000311723110231524 & 0.999844138444884 \tabularnewline
28 & 0.000155432960374423 & 0.000310865920748846 & 0.999844567039626 \tabularnewline
29 & 0.000100775002932878 & 0.000201550005865756 & 0.999899224997067 \tabularnewline
30 & 9.33516591240936e-05 & 0.000186703318248187 & 0.999906648340876 \tabularnewline
31 & 0.000207780119549703 & 0.000415560239099406 & 0.99979221988045 \tabularnewline
32 & 0.0002116978427644 & 0.0004233956855288 & 0.999788302157236 \tabularnewline
33 & 0.000291364944340292 & 0.000582729888680584 & 0.99970863505566 \tabularnewline
34 & 0.000184564527405341 & 0.000369129054810683 & 0.999815435472595 \tabularnewline
35 & 0.000148649309678246 & 0.000297298619356492 & 0.999851350690322 \tabularnewline
36 & 0.000116772114238006 & 0.000233544228476011 & 0.999883227885762 \tabularnewline
37 & 0.000100089653803875 & 0.000200179307607751 & 0.999899910346196 \tabularnewline
38 & 9.73333001080194e-05 & 0.000194666600216039 & 0.999902666699892 \tabularnewline
39 & 0.000170023326422777 & 0.000340046652845554 & 0.999829976673577 \tabularnewline
40 & 0.00026401540070706 & 0.00052803080141412 & 0.999735984599293 \tabularnewline
41 & 0.00033034789961764 & 0.00066069579923528 & 0.999669652100382 \tabularnewline
42 & 0.000733762966888625 & 0.00146752593377725 & 0.999266237033111 \tabularnewline
43 & 0.00189175364912669 & 0.00378350729825338 & 0.998108246350873 \tabularnewline
44 & 0.00491431350839403 & 0.00982862701678805 & 0.995085686491606 \tabularnewline
45 & 0.0434025645287109 & 0.0868051290574217 & 0.95659743547129 \tabularnewline
46 & 0.131030393977846 & 0.262060787955692 & 0.868969606022154 \tabularnewline
47 & 0.32696122650702 & 0.65392245301404 & 0.67303877349298 \tabularnewline
48 & 0.477268580686862 & 0.954537161373723 & 0.522731419313138 \tabularnewline
49 & 0.60228741309326 & 0.795425173813481 & 0.397712586906740 \tabularnewline
50 & 0.666910001255248 & 0.666179997489505 & 0.333089998744752 \tabularnewline
51 & 0.623093537781672 & 0.753812924436657 & 0.376906462218328 \tabularnewline
52 & 0.881970868396418 & 0.236058263207164 & 0.118029131603582 \tabularnewline
53 & 0.921998361546548 & 0.156003276906905 & 0.0780016384534524 \tabularnewline
54 & 0.871681857407541 & 0.256636285184917 & 0.128318142592459 \tabularnewline
55 & 0.828718032256892 & 0.342563935486215 & 0.171281967743108 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57536&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]0.0168300578963813[/C][C]0.0336601157927627[/C][C]0.983169942103619[/C][/ROW]
[ROW][C]6[/C][C]0.0174311597824925[/C][C]0.0348623195649850[/C][C]0.982568840217507[/C][/ROW]
[ROW][C]7[/C][C]0.0239728775968421[/C][C]0.0479457551936843[/C][C]0.976027122403158[/C][/ROW]
[ROW][C]8[/C][C]0.0203298527906185[/C][C]0.040659705581237[/C][C]0.979670147209382[/C][/ROW]
[ROW][C]9[/C][C]0.0186706092278559[/C][C]0.0373412184557118[/C][C]0.981329390772144[/C][/ROW]
[ROW][C]10[/C][C]0.0121445106088978[/C][C]0.0242890212177956[/C][C]0.987855489391102[/C][/ROW]
[ROW][C]11[/C][C]0.0131426002946508[/C][C]0.0262852005893016[/C][C]0.98685739970535[/C][/ROW]
[ROW][C]12[/C][C]0.0210848729183832[/C][C]0.0421697458367663[/C][C]0.978915127081617[/C][/ROW]
[ROW][C]13[/C][C]0.0103121618079682[/C][C]0.0206243236159364[/C][C]0.989687838192032[/C][/ROW]
[ROW][C]14[/C][C]0.00781324333056755[/C][C]0.0156264866611351[/C][C]0.992186756669432[/C][/ROW]
[ROW][C]15[/C][C]0.0040022274985321[/C][C]0.0080044549970642[/C][C]0.995997772501468[/C][/ROW]
[ROW][C]16[/C][C]0.00572719759652053[/C][C]0.0114543951930411[/C][C]0.99427280240348[/C][/ROW]
[ROW][C]17[/C][C]0.00350795923190956[/C][C]0.00701591846381912[/C][C]0.99649204076809[/C][/ROW]
[ROW][C]18[/C][C]0.00221217504118104[/C][C]0.00442435008236207[/C][C]0.997787824958819[/C][/ROW]
[ROW][C]19[/C][C]0.00271063062135205[/C][C]0.0054212612427041[/C][C]0.997289369378648[/C][/ROW]
[ROW][C]20[/C][C]0.00131618637994298[/C][C]0.00263237275988596[/C][C]0.998683813620057[/C][/ROW]
[ROW][C]21[/C][C]0.000672121507244338[/C][C]0.00134424301448868[/C][C]0.999327878492756[/C][/ROW]
[ROW][C]22[/C][C]0.000362450972046442[/C][C]0.000724901944092883[/C][C]0.999637549027954[/C][/ROW]
[ROW][C]23[/C][C]0.000267368143706141[/C][C]0.000534736287412282[/C][C]0.999732631856294[/C][/ROW]
[ROW][C]24[/C][C]0.000308188387292328[/C][C]0.000616376774584656[/C][C]0.999691811612708[/C][/ROW]
[ROW][C]25[/C][C]0.000399771582034562[/C][C]0.000799543164069124[/C][C]0.999600228417965[/C][/ROW]
[ROW][C]26[/C][C]0.000271841052124205[/C][C]0.00054368210424841[/C][C]0.999728158947876[/C][/ROW]
[ROW][C]27[/C][C]0.000155861555115762[/C][C]0.000311723110231524[/C][C]0.999844138444884[/C][/ROW]
[ROW][C]28[/C][C]0.000155432960374423[/C][C]0.000310865920748846[/C][C]0.999844567039626[/C][/ROW]
[ROW][C]29[/C][C]0.000100775002932878[/C][C]0.000201550005865756[/C][C]0.999899224997067[/C][/ROW]
[ROW][C]30[/C][C]9.33516591240936e-05[/C][C]0.000186703318248187[/C][C]0.999906648340876[/C][/ROW]
[ROW][C]31[/C][C]0.000207780119549703[/C][C]0.000415560239099406[/C][C]0.99979221988045[/C][/ROW]
[ROW][C]32[/C][C]0.0002116978427644[/C][C]0.0004233956855288[/C][C]0.999788302157236[/C][/ROW]
[ROW][C]33[/C][C]0.000291364944340292[/C][C]0.000582729888680584[/C][C]0.99970863505566[/C][/ROW]
[ROW][C]34[/C][C]0.000184564527405341[/C][C]0.000369129054810683[/C][C]0.999815435472595[/C][/ROW]
[ROW][C]35[/C][C]0.000148649309678246[/C][C]0.000297298619356492[/C][C]0.999851350690322[/C][/ROW]
[ROW][C]36[/C][C]0.000116772114238006[/C][C]0.000233544228476011[/C][C]0.999883227885762[/C][/ROW]
[ROW][C]37[/C][C]0.000100089653803875[/C][C]0.000200179307607751[/C][C]0.999899910346196[/C][/ROW]
[ROW][C]38[/C][C]9.73333001080194e-05[/C][C]0.000194666600216039[/C][C]0.999902666699892[/C][/ROW]
[ROW][C]39[/C][C]0.000170023326422777[/C][C]0.000340046652845554[/C][C]0.999829976673577[/C][/ROW]
[ROW][C]40[/C][C]0.00026401540070706[/C][C]0.00052803080141412[/C][C]0.999735984599293[/C][/ROW]
[ROW][C]41[/C][C]0.00033034789961764[/C][C]0.00066069579923528[/C][C]0.999669652100382[/C][/ROW]
[ROW][C]42[/C][C]0.000733762966888625[/C][C]0.00146752593377725[/C][C]0.999266237033111[/C][/ROW]
[ROW][C]43[/C][C]0.00189175364912669[/C][C]0.00378350729825338[/C][C]0.998108246350873[/C][/ROW]
[ROW][C]44[/C][C]0.00491431350839403[/C][C]0.00982862701678805[/C][C]0.995085686491606[/C][/ROW]
[ROW][C]45[/C][C]0.0434025645287109[/C][C]0.0868051290574217[/C][C]0.95659743547129[/C][/ROW]
[ROW][C]46[/C][C]0.131030393977846[/C][C]0.262060787955692[/C][C]0.868969606022154[/C][/ROW]
[ROW][C]47[/C][C]0.32696122650702[/C][C]0.65392245301404[/C][C]0.67303877349298[/C][/ROW]
[ROW][C]48[/C][C]0.477268580686862[/C][C]0.954537161373723[/C][C]0.522731419313138[/C][/ROW]
[ROW][C]49[/C][C]0.60228741309326[/C][C]0.795425173813481[/C][C]0.397712586906740[/C][/ROW]
[ROW][C]50[/C][C]0.666910001255248[/C][C]0.666179997489505[/C][C]0.333089998744752[/C][/ROW]
[ROW][C]51[/C][C]0.623093537781672[/C][C]0.753812924436657[/C][C]0.376906462218328[/C][/ROW]
[ROW][C]52[/C][C]0.881970868396418[/C][C]0.236058263207164[/C][C]0.118029131603582[/C][/ROW]
[ROW][C]53[/C][C]0.921998361546548[/C][C]0.156003276906905[/C][C]0.0780016384534524[/C][/ROW]
[ROW][C]54[/C][C]0.871681857407541[/C][C]0.256636285184917[/C][C]0.128318142592459[/C][/ROW]
[ROW][C]55[/C][C]0.828718032256892[/C][C]0.342563935486215[/C][C]0.171281967743108[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57536&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57536&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.01683005789638130.03366011579276270.983169942103619
60.01743115978249250.03486231956498500.982568840217507
70.02397287759684210.04794575519368430.976027122403158
80.02032985279061850.0406597055812370.979670147209382
90.01867060922785590.03734121845571180.981329390772144
100.01214451060889780.02428902121779560.987855489391102
110.01314260029465080.02628520058930160.98685739970535
120.02108487291838320.04216974583676630.978915127081617
130.01031216180796820.02062432361593640.989687838192032
140.007813243330567550.01562648666113510.992186756669432
150.00400222749853210.00800445499706420.995997772501468
160.005727197596520530.01145439519304110.99427280240348
170.003507959231909560.007015918463819120.99649204076809
180.002212175041181040.004424350082362070.997787824958819
190.002710630621352050.00542126124270410.997289369378648
200.001316186379942980.002632372759885960.998683813620057
210.0006721215072443380.001344243014488680.999327878492756
220.0003624509720464420.0007249019440928830.999637549027954
230.0002673681437061410.0005347362874122820.999732631856294
240.0003081883872923280.0006163767745846560.999691811612708
250.0003997715820345620.0007995431640691240.999600228417965
260.0002718410521242050.000543682104248410.999728158947876
270.0001558615551157620.0003117231102315240.999844138444884
280.0001554329603744230.0003108659207488460.999844567039626
290.0001007750029328780.0002015500058657560.999899224997067
309.33516591240936e-050.0001867033182481870.999906648340876
310.0002077801195497030.0004155602390994060.99979221988045
320.00021169784276440.00042339568552880.999788302157236
330.0002913649443402920.0005827298886805840.99970863505566
340.0001845645274053410.0003691290548106830.999815435472595
350.0001486493096782460.0002972986193564920.999851350690322
360.0001167721142380060.0002335442284760110.999883227885762
370.0001000896538038750.0002001793076077510.999899910346196
389.73333001080194e-050.0001946666002160390.999902666699892
390.0001700233264227770.0003400466528455540.999829976673577
400.000264015400707060.000528030801414120.999735984599293
410.000330347899617640.000660695799235280.999669652100382
420.0007337629668886250.001467525933777250.999266237033111
430.001891753649126690.003783507298253380.998108246350873
440.004914313508394030.009828627016788050.995085686491606
450.04340256452871090.08680512905742170.95659743547129
460.1310303939778460.2620607879556920.868969606022154
470.326961226507020.653922453014040.67303877349298
480.4772685806868620.9545371613737230.522731419313138
490.602287413093260.7954251738134810.397712586906740
500.6669100012552480.6661799974895050.333089998744752
510.6230935377816720.7538129244366570.376906462218328
520.8819708683964180.2360582632071640.118029131603582
530.9219983615465480.1560032769069050.0780016384534524
540.8716818574075410.2566362851849170.128318142592459
550.8287180322568920.3425639354862150.171281967743108






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level290.568627450980392NOK
5% type I error level400.784313725490196NOK
10% type I error level410.80392156862745NOK

\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 & 29 & 0.568627450980392 & NOK \tabularnewline
5% type I error level & 40 & 0.784313725490196 & NOK \tabularnewline
10% type I error level & 41 & 0.80392156862745 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57536&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]29[/C][C]0.568627450980392[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]40[/C][C]0.784313725490196[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.80392156862745[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57536&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57536&T=6

As an alternative you can also use a QR Code:  
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 level290.568627450980392NOK
5% type I error level400.784313725490196NOK
10% type I error level410.80392156862745NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}