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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, 22 Dec 2010 20:01:00 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/22/t12930480845lbe9xbwagloxvu.htm/, Retrieved Sun, 05 May 2024 22:08:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114546, Retrieved Sun, 05 May 2024 22:08:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multiple regression] [2010-12-22 20:01:00] [ea05999e24dc6223e14cc730e7a15b1e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-999,00	-999,00	38,60	6654,00	5712,00	645,00	3,00	5,00	3,00
6,30	2,00	4,50	1,00	6600,00	42,00	3,00	1,00	3,00
-999,00	-999,00	14,00	3,39	44,50	60,00	1,00	1,00	1,00
-999,00	-999,00	-999,00	0,92	5,70	25,00	5,00	2,00	3,00
2,10	1,80	69,00	2547,00	4603,00	624,00	3,00	5,00	4,00
9,10	0,70	27,00	10,55	179,50	180,00	4,00	4,00	4,00
15,80	3,90	19,00	0,02	0,30	35,00	1,00	1,00	1,00
5,20	1,00	30,40	160,00	169,00	392,00	4,00	5,00	4,00
10,90	3,60	28,00	3,30	25,60	63,00	1,00	2,00	1,00
8,30	1,40	50,00	52,16	440,00	230,00	1,00	1,00	1,00
11,00	1,50	7,00	0,43	6,40	112,00	5,00	4,00	4,00
3,20	0,70	30,00	465,00	423,00	281,00	5,00	5,00	5,00
7,60	2,70	-999,00	0,55	2,40	-999,00	2,00	1,00	2,00
-999,00	-999,00	40,00	187,10	419,00	365,00	5,00	5,00	5,00
6,30	2,10	3,50	0,08	1,20	42,00	1,00	1,00	1,00
8,60	0,00	50,00	3,00	25,00	28,00	2,00	2,00	2,00
6,60	4,10	6,00	0,79	3500,00	42,00	2,00	2,00	2,00
9,50	1,20	10,40	0,20	5,00	120,00	2,00	2,00	2,00
4,80	1,30	34,00	1,41	17,50	-999,00	1,00	2,00	1,00
12,00	6,10	7,00	60,00	81,00	-999,00	1,00	1,00	1,00
-999,00	0,30	28,00	529,00	680,00	400,00	5,00	5,00	5,00
3,30	0,50	20,00	27,66	115,00	148,00	5,00	5,00	5,00
11,00	3,40	3,90	0,12	1,00	16,00	3,00	1,00	2,00
-999,00	-999,00	39,30	207,00	406,00	252,00	1,00	4,00	1,00
4,70	1,50	41,00	85,00	325,00	310,00	1,00	3,00	1,00
-999,00	-999,00	16,20	36,33	119,50	63,00	1,00	1,00	1,00
10,40	3,40	9,00	0,10	4,00	28,00	5,00	1,00	3,00
7,40	0,80	7,60	1,04	5,50	68,00	5,00	3,00	4,00
2,10	0,80	46,00	521,00	655,00	336,00	5,00	5,00	5,00
-999,00	-999,00	22,40	100,00	157,00	100,00	1,00	1,00	1,00
-999,00	-999,00	16,30	35,00	56,00	33,00	3,00	5,00	4,00
7,70	1,40	2,60	0,01	0,14	21,50	5,00	2,00	4,00
17,90	2,00	24,00	0,01	0,25	50,00	1,00	1,00	1,00
6,10	1,90	100,00	62,00	1320,00	267,00	1,00	1,00	1,00
8,20	2,40	-999,00	0,12	3,00	30,00	2,00	1,00	1,00
8,40	2,80	-999,00	1,35	8,10	45,00	3,00	1,00	3,00
11,90	1,30	3,20	0,02	0,40	19,00	4,00	1,00	3,00
10,80	2,00	2,00	0,05	0,33	30,00	4,00	1,00	3,00
13,80	5,60	5,00	1,70	6,30	12,00	2,00	1,00	1,00
14,30	3,10	6,50	3,50	10,80	120,00	2,00	1,00	1,00
-999,00	1,00	23,60	250,00	490,00	440,00	5,00	5,00	5,00
15,20	1,80	12,00	0,48	15,50	140,00	2,00	2,00	2,00
10,00	0,90	20,20	10,00	115,00	170,00	4,00	4,00	4,00
11,90	1,80	13,00	1,62	11,40	17,00	2,00	1,00	2,00
6,50	1,90	27,00	192,00	180,00	115,00	4,00	4,00	4,00
7,50	0,90	18,00	2,50	12,10	31,00	5,00	5,00	5,00
-999,00	-999,00	13,70	4,29	39,20	63,00	2,00	2,00	2,00
10,60	2,60	4,70	0,28	1,90	21,00	3,00	1,00	3,00
7,40	2,40	9,80	4,24	50,40	52,00	1,00	1,00	1,00
8,40	1,20	29,00	6,80	179,00	164,00	2,00	3,00	2,00
5,70	0,90	7,00	0,75	12,30	225,00	2,00	2,00	2,00
4,90	0,50	6,00	3,60	21,00	225,00	3,00	2,00	3,00
-999,00	-999,00	17,00	14,83	98,20	150,00	5,00	5,00	5,00
3,20	0,60	20,00	55,50	175,00	151,00	5,00	5,00	5,00
-999,00	-999,00	12,70	1,40	12,50	90,00	2,00	2,00	2,00
8,10	2,20	3,50	0,06	1,00	-999,00	3,00	1,00	2,00
11,00	2,30	4,50	0,90	2,60	60,00	2,00	1,00	2,00
4,90	0,50	7,50	2,00	12,30	200,00	3,00	1,00	3,00
13,20	2,60	2,30	0,10	2,50	46,00	3,00	2,00	2,00
9,70	0,60	24,00	4,19	58,00	210,00	4,00	3,00	4,00
12,80	6,60	3,00	3,50	3,90	14,00	2,00	1,00	1,00
-999,00	-999,00	13,00	4,05	17,00	38,00	3,00	1,00	1,00

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114546&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114546&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114546&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 time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
D [t] = -0.101608832209852 -6.09551076358242e-05SWS[t] + 0.000261740059678429PS[t] + 7.3273090252969e-06L[t] -0.000138734472492483Wb[t] + 0.00010510687851621Wbr[t] + 1.26344683983161e-05Tg[t] + 0.660225760372188P[t] + 0.34557106541433S[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
D
[t] =  -0.101608832209852 -6.09551076358242e-05SWS[t] +  0.000261740059678429PS[t] +  7.3273090252969e-06L[t] -0.000138734472492483Wb[t] +  0.00010510687851621Wbr[t] +  1.26344683983161e-05Tg[t] +  0.660225760372188P[t] +  0.34557106541433S[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114546&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]D
[t] =  -0.101608832209852 -6.09551076358242e-05SWS[t] +  0.000261740059678429PS[t] +  7.3273090252969e-06L[t] -0.000138734472492483Wb[t] +  0.00010510687851621Wbr[t] +  1.26344683983161e-05Tg[t] +  0.660225760372188P[t] +  0.34557106541433S[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114546&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114546&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
D [t] = -0.101608832209852 -6.09551076358242e-05SWS[t] + 0.000261740059678429PS[t] + 7.3273090252969e-06L[t] -0.000138734472492483Wb[t] + 0.00010510687851621Wbr[t] + 1.26344683983161e-05Tg[t] + 0.660225760372188P[t] + 0.34557106541433S[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.1016088322098520.128875-0.78840.4339590.21698
SWS-6.09551076358242e-050.000316-0.19270.8479480.423974
PS0.0002617400596784290.0003330.78680.4348860.217443
L7.3273090252969e-060.0002270.03230.9743780.487189
Wb-0.0001387344724924838.4e-05-1.64550.1057860.052893
Wbr0.000105106878516215.5e-051.91420.0610050.030503
Tg1.26344683983161e-052e-040.06310.9499340.474967
P0.6602257603721880.04888113.506700
S0.345571065414330.0502196.881300

\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) & -0.101608832209852 & 0.128875 & -0.7884 & 0.433959 & 0.21698 \tabularnewline
SWS & -6.09551076358242e-05 & 0.000316 & -0.1927 & 0.847948 & 0.423974 \tabularnewline
PS & 0.000261740059678429 & 0.000333 & 0.7868 & 0.434886 & 0.217443 \tabularnewline
L & 7.3273090252969e-06 & 0.000227 & 0.0323 & 0.974378 & 0.487189 \tabularnewline
Wb & -0.000138734472492483 & 8.4e-05 & -1.6455 & 0.105786 & 0.052893 \tabularnewline
Wbr & 0.00010510687851621 & 5.5e-05 & 1.9142 & 0.061005 & 0.030503 \tabularnewline
Tg & 1.26344683983161e-05 & 2e-04 & 0.0631 & 0.949934 & 0.474967 \tabularnewline
P & 0.660225760372188 & 0.048881 & 13.5067 & 0 & 0 \tabularnewline
S & 0.34557106541433 & 0.050219 & 6.8813 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114546&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]-0.101608832209852[/C][C]0.128875[/C][C]-0.7884[/C][C]0.433959[/C][C]0.21698[/C][/ROW]
[ROW][C]SWS[/C][C]-6.09551076358242e-05[/C][C]0.000316[/C][C]-0.1927[/C][C]0.847948[/C][C]0.423974[/C][/ROW]
[ROW][C]PS[/C][C]0.000261740059678429[/C][C]0.000333[/C][C]0.7868[/C][C]0.434886[/C][C]0.217443[/C][/ROW]
[ROW][C]L[/C][C]7.3273090252969e-06[/C][C]0.000227[/C][C]0.0323[/C][C]0.974378[/C][C]0.487189[/C][/ROW]
[ROW][C]Wb[/C][C]-0.000138734472492483[/C][C]8.4e-05[/C][C]-1.6455[/C][C]0.105786[/C][C]0.052893[/C][/ROW]
[ROW][C]Wbr[/C][C]0.00010510687851621[/C][C]5.5e-05[/C][C]1.9142[/C][C]0.061005[/C][C]0.030503[/C][/ROW]
[ROW][C]Tg[/C][C]1.26344683983161e-05[/C][C]2e-04[/C][C]0.0631[/C][C]0.949934[/C][C]0.474967[/C][/ROW]
[ROW][C]P[/C][C]0.660225760372188[/C][C]0.048881[/C][C]13.5067[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]S[/C][C]0.34557106541433[/C][C]0.050219[/C][C]6.8813[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114546&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114546&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)-0.1016088322098520.128875-0.78840.4339590.21698
SWS-6.09551076358242e-050.000316-0.19270.8479480.423974
PS0.0002617400596784290.0003330.78680.4348860.217443
L7.3273090252969e-060.0002270.03230.9743780.487189
Wb-0.0001387344724924838.4e-05-1.64550.1057860.052893
Wbr0.000105106878516215.5e-051.91420.0610050.030503
Tg1.26344683983161e-052e-040.06310.9499340.474967
P0.6602257603721880.04888113.506700
S0.345571065414330.0502196.881300






Multiple Linear Regression - Regression Statistics
Multiple R0.963410634069038
R-squared0.928160049837306
Adjusted R-squared0.917316283775013
F-TEST (value)85.5938835737854
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.41442883884995
Sum Squared Residuals9.10281691093745

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.963410634069038 \tabularnewline
R-squared & 0.928160049837306 \tabularnewline
Adjusted R-squared & 0.917316283775013 \tabularnewline
F-TEST (value) & 85.5938835737854 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.41442883884995 \tabularnewline
Sum Squared Residuals & 9.10281691093745 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114546&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.963410634069038[/C][/ROW]
[ROW][C]R-squared[/C][C]0.928160049837306[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.917316283775013[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]85.5938835737854[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.41442883884995[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.10281691093745[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114546&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114546&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 R0.963410634069038
R-squared0.928160049837306
Adjusted R-squared0.917316283775013
F-TEST (value)85.5938835737854
F-TEST (DF numerator)8
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.41442883884995
Sum Squared Residuals9.10281691093745






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
133.0920029852527-0.0920029852527001
232.918909261560130.0810907384398717
310.7086714231485790.291328576851421
433.68354528687572-0.68354528687572
543.74610665533480.253893344665197
643.941082075162750.0589179248372471
710.9048558717482930.0951441282517073
844.26783531852748-0.267835318527475
911.2532709610252-0.253270961025201
1010.946331431911640.0536685680883593
1144.58360571503705-0.583605715037052
1254.911082243214120.0889177567858839
1321.544891330194330.455108669805667
1454.749778365253520.250221634746478
1510.9050249532746230.0949750467253773
1621.912392204549360.0876077954506383
1722.27899473599782-0.278994735997816
1821.9118099616320.0881900383679984
1911.23907778589669-0.239077785896688
2010.8926721926925980.107327807307402
2154.991689064723360.00831093527663622
2254.937571357921060.0624286420789421
2322.22517811108134-0.225178111081343
2411.7577442288818-0.757744228881802
2511.62202065571838-0.622020655718378
2610.7120385489984430.287961451001557
2733.54617328311211-0.546173283112106
2844.23734012552544-0.23734012552544
2954.928603265900980.071396734099022
3010.7076597377258980.292340262274102
3143.407906260141730.592093739858272
3243.89086320192680.109136798073202
3310.9044528454807690.095547154519231
3411.03855914984522-0.03855914984522
3511.557899823044-0.557899823043996
3632.218773007123770.78122699287623
3732.884782941340350.11521705865965
3832.88515187686640.114848123133595
3911.565652892695-0.565652892695
4011.56656683744538-0.566566837445378
4155.01108203224868-0.0110820322486828
4221.912948751188810.0870512488111864
4343.934200303488040.0657996965119633
4421.565443029825160.434556970174844
4543.915612589473830.084387410526173
4654.92860221660080.0713977833992019
4721.71382202666620.286177973333795
4832.225134533940930.774865466059068
4910.9098030544223990.0901969455776014
5022.2755132315655-0.275513231565498
5121.913955751607550.0860442483924511
5232.574637289329440.425362710670564
5354.737074927388580.262925072611418
5454.940085575839770.0599144241602345
5521.711750418973050.288249581026945
5622.21222220054232-0.212222200542322
5721.565284707755530.434715292244468
5832.228068898481580.771931101518417
5922.57093342857586-0.570933428575865
6043.583917180308080.41608281969192
6111.56548422362213-0.565484223622135
6212.02585565436812-1.02585565436812

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 3.0920029852527 & -0.0920029852527001 \tabularnewline
2 & 3 & 2.91890926156013 & 0.0810907384398717 \tabularnewline
3 & 1 & 0.708671423148579 & 0.291328576851421 \tabularnewline
4 & 3 & 3.68354528687572 & -0.68354528687572 \tabularnewline
5 & 4 & 3.7461066553348 & 0.253893344665197 \tabularnewline
6 & 4 & 3.94108207516275 & 0.0589179248372471 \tabularnewline
7 & 1 & 0.904855871748293 & 0.0951441282517073 \tabularnewline
8 & 4 & 4.26783531852748 & -0.267835318527475 \tabularnewline
9 & 1 & 1.2532709610252 & -0.253270961025201 \tabularnewline
10 & 1 & 0.94633143191164 & 0.0536685680883593 \tabularnewline
11 & 4 & 4.58360571503705 & -0.583605715037052 \tabularnewline
12 & 5 & 4.91108224321412 & 0.0889177567858839 \tabularnewline
13 & 2 & 1.54489133019433 & 0.455108669805667 \tabularnewline
14 & 5 & 4.74977836525352 & 0.250221634746478 \tabularnewline
15 & 1 & 0.905024953274623 & 0.0949750467253773 \tabularnewline
16 & 2 & 1.91239220454936 & 0.0876077954506383 \tabularnewline
17 & 2 & 2.27899473599782 & -0.278994735997816 \tabularnewline
18 & 2 & 1.911809961632 & 0.0881900383679984 \tabularnewline
19 & 1 & 1.23907778589669 & -0.239077785896688 \tabularnewline
20 & 1 & 0.892672192692598 & 0.107327807307402 \tabularnewline
21 & 5 & 4.99168906472336 & 0.00831093527663622 \tabularnewline
22 & 5 & 4.93757135792106 & 0.0624286420789421 \tabularnewline
23 & 2 & 2.22517811108134 & -0.225178111081343 \tabularnewline
24 & 1 & 1.7577442288818 & -0.757744228881802 \tabularnewline
25 & 1 & 1.62202065571838 & -0.622020655718378 \tabularnewline
26 & 1 & 0.712038548998443 & 0.287961451001557 \tabularnewline
27 & 3 & 3.54617328311211 & -0.546173283112106 \tabularnewline
28 & 4 & 4.23734012552544 & -0.23734012552544 \tabularnewline
29 & 5 & 4.92860326590098 & 0.071396734099022 \tabularnewline
30 & 1 & 0.707659737725898 & 0.292340262274102 \tabularnewline
31 & 4 & 3.40790626014173 & 0.592093739858272 \tabularnewline
32 & 4 & 3.8908632019268 & 0.109136798073202 \tabularnewline
33 & 1 & 0.904452845480769 & 0.095547154519231 \tabularnewline
34 & 1 & 1.03855914984522 & -0.03855914984522 \tabularnewline
35 & 1 & 1.557899823044 & -0.557899823043996 \tabularnewline
36 & 3 & 2.21877300712377 & 0.78122699287623 \tabularnewline
37 & 3 & 2.88478294134035 & 0.11521705865965 \tabularnewline
38 & 3 & 2.8851518768664 & 0.114848123133595 \tabularnewline
39 & 1 & 1.565652892695 & -0.565652892695 \tabularnewline
40 & 1 & 1.56656683744538 & -0.566566837445378 \tabularnewline
41 & 5 & 5.01108203224868 & -0.0110820322486828 \tabularnewline
42 & 2 & 1.91294875118881 & 0.0870512488111864 \tabularnewline
43 & 4 & 3.93420030348804 & 0.0657996965119633 \tabularnewline
44 & 2 & 1.56544302982516 & 0.434556970174844 \tabularnewline
45 & 4 & 3.91561258947383 & 0.084387410526173 \tabularnewline
46 & 5 & 4.9286022166008 & 0.0713977833992019 \tabularnewline
47 & 2 & 1.7138220266662 & 0.286177973333795 \tabularnewline
48 & 3 & 2.22513453394093 & 0.774865466059068 \tabularnewline
49 & 1 & 0.909803054422399 & 0.0901969455776014 \tabularnewline
50 & 2 & 2.2755132315655 & -0.275513231565498 \tabularnewline
51 & 2 & 1.91395575160755 & 0.0860442483924511 \tabularnewline
52 & 3 & 2.57463728932944 & 0.425362710670564 \tabularnewline
53 & 5 & 4.73707492738858 & 0.262925072611418 \tabularnewline
54 & 5 & 4.94008557583977 & 0.0599144241602345 \tabularnewline
55 & 2 & 1.71175041897305 & 0.288249581026945 \tabularnewline
56 & 2 & 2.21222220054232 & -0.212222200542322 \tabularnewline
57 & 2 & 1.56528470775553 & 0.434715292244468 \tabularnewline
58 & 3 & 2.22806889848158 & 0.771931101518417 \tabularnewline
59 & 2 & 2.57093342857586 & -0.570933428575865 \tabularnewline
60 & 4 & 3.58391718030808 & 0.41608281969192 \tabularnewline
61 & 1 & 1.56548422362213 & -0.565484223622135 \tabularnewline
62 & 1 & 2.02585565436812 & -1.02585565436812 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114546&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]3[/C][C]3.0920029852527[/C][C]-0.0920029852527001[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]2.91890926156013[/C][C]0.0810907384398717[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.708671423148579[/C][C]0.291328576851421[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]3.68354528687572[/C][C]-0.68354528687572[/C][/ROW]
[ROW][C]5[/C][C]4[/C][C]3.7461066553348[/C][C]0.253893344665197[/C][/ROW]
[ROW][C]6[/C][C]4[/C][C]3.94108207516275[/C][C]0.0589179248372471[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.904855871748293[/C][C]0.0951441282517073[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]4.26783531852748[/C][C]-0.267835318527475[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.2532709610252[/C][C]-0.253270961025201[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.94633143191164[/C][C]0.0536685680883593[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]4.58360571503705[/C][C]-0.583605715037052[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]4.91108224321412[/C][C]0.0889177567858839[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]1.54489133019433[/C][C]0.455108669805667[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]4.74977836525352[/C][C]0.250221634746478[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.905024953274623[/C][C]0.0949750467253773[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]1.91239220454936[/C][C]0.0876077954506383[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.27899473599782[/C][C]-0.278994735997816[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.911809961632[/C][C]0.0881900383679984[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.23907778589669[/C][C]-0.239077785896688[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.892672192692598[/C][C]0.107327807307402[/C][/ROW]
[ROW][C]21[/C][C]5[/C][C]4.99168906472336[/C][C]0.00831093527663622[/C][/ROW]
[ROW][C]22[/C][C]5[/C][C]4.93757135792106[/C][C]0.0624286420789421[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]2.22517811108134[/C][C]-0.225178111081343[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]1.7577442288818[/C][C]-0.757744228881802[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.62202065571838[/C][C]-0.622020655718378[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.712038548998443[/C][C]0.287961451001557[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]3.54617328311211[/C][C]-0.546173283112106[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]4.23734012552544[/C][C]-0.23734012552544[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]4.92860326590098[/C][C]0.071396734099022[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]0.707659737725898[/C][C]0.292340262274102[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]3.40790626014173[/C][C]0.592093739858272[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]3.8908632019268[/C][C]0.109136798073202[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]0.904452845480769[/C][C]0.095547154519231[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]1.03855914984522[/C][C]-0.03855914984522[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]1.557899823044[/C][C]-0.557899823043996[/C][/ROW]
[ROW][C]36[/C][C]3[/C][C]2.21877300712377[/C][C]0.78122699287623[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]2.88478294134035[/C][C]0.11521705865965[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]2.8851518768664[/C][C]0.114848123133595[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]1.565652892695[/C][C]-0.565652892695[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.56656683744538[/C][C]-0.566566837445378[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]5.01108203224868[/C][C]-0.0110820322486828[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.91294875118881[/C][C]0.0870512488111864[/C][/ROW]
[ROW][C]43[/C][C]4[/C][C]3.93420030348804[/C][C]0.0657996965119633[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]1.56544302982516[/C][C]0.434556970174844[/C][/ROW]
[ROW][C]45[/C][C]4[/C][C]3.91561258947383[/C][C]0.084387410526173[/C][/ROW]
[ROW][C]46[/C][C]5[/C][C]4.9286022166008[/C][C]0.0713977833992019[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.7138220266662[/C][C]0.286177973333795[/C][/ROW]
[ROW][C]48[/C][C]3[/C][C]2.22513453394093[/C][C]0.774865466059068[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]0.909803054422399[/C][C]0.0901969455776014[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]2.2755132315655[/C][C]-0.275513231565498[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]1.91395575160755[/C][C]0.0860442483924511[/C][/ROW]
[ROW][C]52[/C][C]3[/C][C]2.57463728932944[/C][C]0.425362710670564[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]4.73707492738858[/C][C]0.262925072611418[/C][/ROW]
[ROW][C]54[/C][C]5[/C][C]4.94008557583977[/C][C]0.0599144241602345[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]1.71175041897305[/C][C]0.288249581026945[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]2.21222220054232[/C][C]-0.212222200542322[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]1.56528470775553[/C][C]0.434715292244468[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]2.22806889848158[/C][C]0.771931101518417[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]2.57093342857586[/C][C]-0.570933428575865[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]3.58391718030808[/C][C]0.41608281969192[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.56548422362213[/C][C]-0.565484223622135[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]2.02585565436812[/C][C]-1.02585565436812[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114546&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114546&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
133.0920029852527-0.0920029852527001
232.918909261560130.0810907384398717
310.7086714231485790.291328576851421
433.68354528687572-0.68354528687572
543.74610665533480.253893344665197
643.941082075162750.0589179248372471
710.9048558717482930.0951441282517073
844.26783531852748-0.267835318527475
911.2532709610252-0.253270961025201
1010.946331431911640.0536685680883593
1144.58360571503705-0.583605715037052
1254.911082243214120.0889177567858839
1321.544891330194330.455108669805667
1454.749778365253520.250221634746478
1510.9050249532746230.0949750467253773
1621.912392204549360.0876077954506383
1722.27899473599782-0.278994735997816
1821.9118099616320.0881900383679984
1911.23907778589669-0.239077785896688
2010.8926721926925980.107327807307402
2154.991689064723360.00831093527663622
2254.937571357921060.0624286420789421
2322.22517811108134-0.225178111081343
2411.7577442288818-0.757744228881802
2511.62202065571838-0.622020655718378
2610.7120385489984430.287961451001557
2733.54617328311211-0.546173283112106
2844.23734012552544-0.23734012552544
2954.928603265900980.071396734099022
3010.7076597377258980.292340262274102
3143.407906260141730.592093739858272
3243.89086320192680.109136798073202
3310.9044528454807690.095547154519231
3411.03855914984522-0.03855914984522
3511.557899823044-0.557899823043996
3632.218773007123770.78122699287623
3732.884782941340350.11521705865965
3832.88515187686640.114848123133595
3911.565652892695-0.565652892695
4011.56656683744538-0.566566837445378
4155.01108203224868-0.0110820322486828
4221.912948751188810.0870512488111864
4343.934200303488040.0657996965119633
4421.565443029825160.434556970174844
4543.915612589473830.084387410526173
4654.92860221660080.0713977833992019
4721.71382202666620.286177973333795
4832.225134533940930.774865466059068
4910.9098030544223990.0901969455776014
5022.2755132315655-0.275513231565498
5121.913955751607550.0860442483924511
5232.574637289329440.425362710670564
5354.737074927388580.262925072611418
5454.940085575839770.0599144241602345
5521.711750418973050.288249581026945
5622.21222220054232-0.212222200542322
5721.565284707755530.434715292244468
5832.228068898481580.771931101518417
5922.57093342857586-0.570933428575865
6043.583917180308080.41608281969192
6111.56548422362213-0.565484223622135
6212.02585565436812-1.02585565436812






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3958480999340390.7916961998680770.604151900065961
130.300187436952530.600374873905060.69981256304747
140.2520554875147350.5041109750294710.747944512485265
150.1502619423243080.3005238846486170.849738057675692
160.08687055115173350.1737411023034670.913129448848267
170.1519417854285440.3038835708570870.848058214571456
180.1009000816914620.2018001633829230.899099918308539
190.1913253814979170.3826507629958340.808674618502083
200.1352068332974930.2704136665949860.864793166702507
210.08637712833435260.1727542566687050.913622871665647
220.05905021198173670.1181004239634730.940949788018263
230.03601425788078650.0720285157615730.963985742119214
240.1143938373737180.2287876747474360.885606162626282
250.1515016290885090.3030032581770170.848498370911491
260.1368031733245470.2736063466490930.863196826675453
270.1740698066511240.3481396133022490.825930193348876
280.1350833679702070.2701667359404150.864916632029793
290.102783858693380.205567717386760.89721614130662
300.08826476756556060.1765295351311210.91173523243444
310.1219676954620960.2439353909241920.878032304537904
320.09217731750979550.1843546350195910.907822682490204
330.06587314997882230.1317462999576450.934126850021178
340.04291999053205680.08583998106411360.957080009467943
350.06468023894677880.1293604778935580.935319761053221
360.1432336574481970.2864673148963950.856766342551803
370.102162834253880.2043256685077610.89783716574612
380.06953643485145510.139072869702910.930463565148545
390.08522823054139330.1704564610827870.914771769458607
400.1181250440532560.2362500881065110.881874955946744
410.0848462088134760.1696924176269520.915153791186524
420.05554392974017480.111087859480350.944456070259825
430.0340020740400470.0680041480800940.965997925959953
440.03087490985006090.06174981970012180.96912509014994
450.01873659276082980.03747318552165960.98126340723917
460.01614232050829350.03228464101658710.983857679491706
470.01525840038226820.03051680076453640.984741599617732
480.05225659314334940.1045131862866990.94774340685665
490.02817378225636110.05634756451272220.971826217743639
500.02028331452740410.04056662905480820.979716685472596

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.395848099934039 & 0.791696199868077 & 0.604151900065961 \tabularnewline
13 & 0.30018743695253 & 0.60037487390506 & 0.69981256304747 \tabularnewline
14 & 0.252055487514735 & 0.504110975029471 & 0.747944512485265 \tabularnewline
15 & 0.150261942324308 & 0.300523884648617 & 0.849738057675692 \tabularnewline
16 & 0.0868705511517335 & 0.173741102303467 & 0.913129448848267 \tabularnewline
17 & 0.151941785428544 & 0.303883570857087 & 0.848058214571456 \tabularnewline
18 & 0.100900081691462 & 0.201800163382923 & 0.899099918308539 \tabularnewline
19 & 0.191325381497917 & 0.382650762995834 & 0.808674618502083 \tabularnewline
20 & 0.135206833297493 & 0.270413666594986 & 0.864793166702507 \tabularnewline
21 & 0.0863771283343526 & 0.172754256668705 & 0.913622871665647 \tabularnewline
22 & 0.0590502119817367 & 0.118100423963473 & 0.940949788018263 \tabularnewline
23 & 0.0360142578807865 & 0.072028515761573 & 0.963985742119214 \tabularnewline
24 & 0.114393837373718 & 0.228787674747436 & 0.885606162626282 \tabularnewline
25 & 0.151501629088509 & 0.303003258177017 & 0.848498370911491 \tabularnewline
26 & 0.136803173324547 & 0.273606346649093 & 0.863196826675453 \tabularnewline
27 & 0.174069806651124 & 0.348139613302249 & 0.825930193348876 \tabularnewline
28 & 0.135083367970207 & 0.270166735940415 & 0.864916632029793 \tabularnewline
29 & 0.10278385869338 & 0.20556771738676 & 0.89721614130662 \tabularnewline
30 & 0.0882647675655606 & 0.176529535131121 & 0.91173523243444 \tabularnewline
31 & 0.121967695462096 & 0.243935390924192 & 0.878032304537904 \tabularnewline
32 & 0.0921773175097955 & 0.184354635019591 & 0.907822682490204 \tabularnewline
33 & 0.0658731499788223 & 0.131746299957645 & 0.934126850021178 \tabularnewline
34 & 0.0429199905320568 & 0.0858399810641136 & 0.957080009467943 \tabularnewline
35 & 0.0646802389467788 & 0.129360477893558 & 0.935319761053221 \tabularnewline
36 & 0.143233657448197 & 0.286467314896395 & 0.856766342551803 \tabularnewline
37 & 0.10216283425388 & 0.204325668507761 & 0.89783716574612 \tabularnewline
38 & 0.0695364348514551 & 0.13907286970291 & 0.930463565148545 \tabularnewline
39 & 0.0852282305413933 & 0.170456461082787 & 0.914771769458607 \tabularnewline
40 & 0.118125044053256 & 0.236250088106511 & 0.881874955946744 \tabularnewline
41 & 0.084846208813476 & 0.169692417626952 & 0.915153791186524 \tabularnewline
42 & 0.0555439297401748 & 0.11108785948035 & 0.944456070259825 \tabularnewline
43 & 0.034002074040047 & 0.068004148080094 & 0.965997925959953 \tabularnewline
44 & 0.0308749098500609 & 0.0617498197001218 & 0.96912509014994 \tabularnewline
45 & 0.0187365927608298 & 0.0374731855216596 & 0.98126340723917 \tabularnewline
46 & 0.0161423205082935 & 0.0322846410165871 & 0.983857679491706 \tabularnewline
47 & 0.0152584003822682 & 0.0305168007645364 & 0.984741599617732 \tabularnewline
48 & 0.0522565931433494 & 0.104513186286699 & 0.94774340685665 \tabularnewline
49 & 0.0281737822563611 & 0.0563475645127222 & 0.971826217743639 \tabularnewline
50 & 0.0202833145274041 & 0.0405666290548082 & 0.979716685472596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114546&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]12[/C][C]0.395848099934039[/C][C]0.791696199868077[/C][C]0.604151900065961[/C][/ROW]
[ROW][C]13[/C][C]0.30018743695253[/C][C]0.60037487390506[/C][C]0.69981256304747[/C][/ROW]
[ROW][C]14[/C][C]0.252055487514735[/C][C]0.504110975029471[/C][C]0.747944512485265[/C][/ROW]
[ROW][C]15[/C][C]0.150261942324308[/C][C]0.300523884648617[/C][C]0.849738057675692[/C][/ROW]
[ROW][C]16[/C][C]0.0868705511517335[/C][C]0.173741102303467[/C][C]0.913129448848267[/C][/ROW]
[ROW][C]17[/C][C]0.151941785428544[/C][C]0.303883570857087[/C][C]0.848058214571456[/C][/ROW]
[ROW][C]18[/C][C]0.100900081691462[/C][C]0.201800163382923[/C][C]0.899099918308539[/C][/ROW]
[ROW][C]19[/C][C]0.191325381497917[/C][C]0.382650762995834[/C][C]0.808674618502083[/C][/ROW]
[ROW][C]20[/C][C]0.135206833297493[/C][C]0.270413666594986[/C][C]0.864793166702507[/C][/ROW]
[ROW][C]21[/C][C]0.0863771283343526[/C][C]0.172754256668705[/C][C]0.913622871665647[/C][/ROW]
[ROW][C]22[/C][C]0.0590502119817367[/C][C]0.118100423963473[/C][C]0.940949788018263[/C][/ROW]
[ROW][C]23[/C][C]0.0360142578807865[/C][C]0.072028515761573[/C][C]0.963985742119214[/C][/ROW]
[ROW][C]24[/C][C]0.114393837373718[/C][C]0.228787674747436[/C][C]0.885606162626282[/C][/ROW]
[ROW][C]25[/C][C]0.151501629088509[/C][C]0.303003258177017[/C][C]0.848498370911491[/C][/ROW]
[ROW][C]26[/C][C]0.136803173324547[/C][C]0.273606346649093[/C][C]0.863196826675453[/C][/ROW]
[ROW][C]27[/C][C]0.174069806651124[/C][C]0.348139613302249[/C][C]0.825930193348876[/C][/ROW]
[ROW][C]28[/C][C]0.135083367970207[/C][C]0.270166735940415[/C][C]0.864916632029793[/C][/ROW]
[ROW][C]29[/C][C]0.10278385869338[/C][C]0.20556771738676[/C][C]0.89721614130662[/C][/ROW]
[ROW][C]30[/C][C]0.0882647675655606[/C][C]0.176529535131121[/C][C]0.91173523243444[/C][/ROW]
[ROW][C]31[/C][C]0.121967695462096[/C][C]0.243935390924192[/C][C]0.878032304537904[/C][/ROW]
[ROW][C]32[/C][C]0.0921773175097955[/C][C]0.184354635019591[/C][C]0.907822682490204[/C][/ROW]
[ROW][C]33[/C][C]0.0658731499788223[/C][C]0.131746299957645[/C][C]0.934126850021178[/C][/ROW]
[ROW][C]34[/C][C]0.0429199905320568[/C][C]0.0858399810641136[/C][C]0.957080009467943[/C][/ROW]
[ROW][C]35[/C][C]0.0646802389467788[/C][C]0.129360477893558[/C][C]0.935319761053221[/C][/ROW]
[ROW][C]36[/C][C]0.143233657448197[/C][C]0.286467314896395[/C][C]0.856766342551803[/C][/ROW]
[ROW][C]37[/C][C]0.10216283425388[/C][C]0.204325668507761[/C][C]0.89783716574612[/C][/ROW]
[ROW][C]38[/C][C]0.0695364348514551[/C][C]0.13907286970291[/C][C]0.930463565148545[/C][/ROW]
[ROW][C]39[/C][C]0.0852282305413933[/C][C]0.170456461082787[/C][C]0.914771769458607[/C][/ROW]
[ROW][C]40[/C][C]0.118125044053256[/C][C]0.236250088106511[/C][C]0.881874955946744[/C][/ROW]
[ROW][C]41[/C][C]0.084846208813476[/C][C]0.169692417626952[/C][C]0.915153791186524[/C][/ROW]
[ROW][C]42[/C][C]0.0555439297401748[/C][C]0.11108785948035[/C][C]0.944456070259825[/C][/ROW]
[ROW][C]43[/C][C]0.034002074040047[/C][C]0.068004148080094[/C][C]0.965997925959953[/C][/ROW]
[ROW][C]44[/C][C]0.0308749098500609[/C][C]0.0617498197001218[/C][C]0.96912509014994[/C][/ROW]
[ROW][C]45[/C][C]0.0187365927608298[/C][C]0.0374731855216596[/C][C]0.98126340723917[/C][/ROW]
[ROW][C]46[/C][C]0.0161423205082935[/C][C]0.0322846410165871[/C][C]0.983857679491706[/C][/ROW]
[ROW][C]47[/C][C]0.0152584003822682[/C][C]0.0305168007645364[/C][C]0.984741599617732[/C][/ROW]
[ROW][C]48[/C][C]0.0522565931433494[/C][C]0.104513186286699[/C][C]0.94774340685665[/C][/ROW]
[ROW][C]49[/C][C]0.0281737822563611[/C][C]0.0563475645127222[/C][C]0.971826217743639[/C][/ROW]
[ROW][C]50[/C][C]0.0202833145274041[/C][C]0.0405666290548082[/C][C]0.979716685472596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114546&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114546&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
120.3958480999340390.7916961998680770.604151900065961
130.300187436952530.600374873905060.69981256304747
140.2520554875147350.5041109750294710.747944512485265
150.1502619423243080.3005238846486170.849738057675692
160.08687055115173350.1737411023034670.913129448848267
170.1519417854285440.3038835708570870.848058214571456
180.1009000816914620.2018001633829230.899099918308539
190.1913253814979170.3826507629958340.808674618502083
200.1352068332974930.2704136665949860.864793166702507
210.08637712833435260.1727542566687050.913622871665647
220.05905021198173670.1181004239634730.940949788018263
230.03601425788078650.0720285157615730.963985742119214
240.1143938373737180.2287876747474360.885606162626282
250.1515016290885090.3030032581770170.848498370911491
260.1368031733245470.2736063466490930.863196826675453
270.1740698066511240.3481396133022490.825930193348876
280.1350833679702070.2701667359404150.864916632029793
290.102783858693380.205567717386760.89721614130662
300.08826476756556060.1765295351311210.91173523243444
310.1219676954620960.2439353909241920.878032304537904
320.09217731750979550.1843546350195910.907822682490204
330.06587314997882230.1317462999576450.934126850021178
340.04291999053205680.08583998106411360.957080009467943
350.06468023894677880.1293604778935580.935319761053221
360.1432336574481970.2864673148963950.856766342551803
370.102162834253880.2043256685077610.89783716574612
380.06953643485145510.139072869702910.930463565148545
390.08522823054139330.1704564610827870.914771769458607
400.1181250440532560.2362500881065110.881874955946744
410.0848462088134760.1696924176269520.915153791186524
420.05554392974017480.111087859480350.944456070259825
430.0340020740400470.0680041480800940.965997925959953
440.03087490985006090.06174981970012180.96912509014994
450.01873659276082980.03747318552165960.98126340723917
460.01614232050829350.03228464101658710.983857679491706
470.01525840038226820.03051680076453640.984741599617732
480.05225659314334940.1045131862866990.94774340685665
490.02817378225636110.05634756451272220.971826217743639
500.02028331452740410.04056662905480820.979716685472596






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.102564102564103NOK
10% type I error level90.230769230769231NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 4 & 0.102564102564103 & NOK \tabularnewline
10% type I error level & 9 & 0.230769230769231 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114546&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.102564102564103[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.230769230769231[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114546&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114546&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 level00OK
5% type I error level40.102564102564103NOK
10% type I error level90.230769230769231NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 9 ; 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')
}