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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 10:26:37 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321889240m64xckns3x3xvrt.htm/, Retrieved Thu, 31 Oct 2024 23:25:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145771, Retrieved Thu, 31 Oct 2024 23:25:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Mini-Tutorial WS 7] [2011-11-21 15:26:37] [13d85cac30d4a10947636c080219d4f4] [Current]
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Dataseries X:
15	40	0
16	42	0
15	38	0
14	34	0
13	32	0
16	40	0
18	50	0
14	25	0
11	16	0
10	12	0
9	4	0
11	7	0
13	16	0
18	50	0
21	60	1
15	35	0
14	32	0
15	33	0
16	39	0
15	33	0
16	35	0
17	40	0
14	25	0
13	19	0
12	12	0
15	19	0
16	25	0
18	29	0
19	41	0
17	50	1
18	70	1
18	65	1
18	50	1
19	45	0
20	62	1
22	82	1
21	62	1
20	42	0
18	39	0
17	35	0
16	30	0
19	40	0
21	45	0
20	42	0
20	41	0
21	45	0
20	43	0
19	30	0
16	20	0
18	25	0
19	27	0
21	38	1
22	40	1
25	60	1
24	61	1
23	55	1
22	43	1
21	34	1
20	20	0
22	38	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.wessa.net
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 & 'George Udny Yule' @ yule.wessa.net \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=145771&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]'George Udny Yule' @ yule.wessa.net[/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=145771&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145771&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.wessa.net
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
Gem_Graden[t] = + 12.1342791396536 + 0.127221061677616Gem_Fietsers[t] + 1.88557563162603`Geslacht `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Gem_Graden[t] =  +  12.1342791396536 +  0.127221061677616Gem_Fietsers[t] +  1.88557563162603`Geslacht
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145771&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Gem_Graden[t] =  +  12.1342791396536 +  0.127221061677616Gem_Fietsers[t] +  1.88557563162603`Geslacht
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145771&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145771&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Gem_Graden[t] = + 12.1342791396536 + 0.127221061677616Gem_Fietsers[t] + 1.88557563162603`Geslacht `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.13427913965360.91501813.261200
Gem_Fietsers0.1272210616776160.0265034.80031.2e-056e-06
`Geslacht `1.885575631626030.9309262.02550.0475090.023755

\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) & 12.1342791396536 & 0.915018 & 13.2612 & 0 & 0 \tabularnewline
Gem_Fietsers & 0.127221061677616 & 0.026503 & 4.8003 & 1.2e-05 & 6e-06 \tabularnewline
`Geslacht
` & 1.88557563162603 & 0.930926 & 2.0255 & 0.047509 & 0.023755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145771&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]12.1342791396536[/C][C]0.915018[/C][C]13.2612[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gem_Fietsers[/C][C]0.127221061677616[/C][C]0.026503[/C][C]4.8003[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]`Geslacht
`[/C][C]1.88557563162603[/C][C]0.930926[/C][C]2.0255[/C][C]0.047509[/C][C]0.023755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145771&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145771&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.13427913965360.91501813.261200
Gem_Fietsers0.1272210616776160.0265034.80031.2e-056e-06
`Geslacht `1.885575631626030.9309262.02550.0475090.023755







Multiple Linear Regression - Regression Statistics
Multiple R0.738539369486175
R-squared0.545440400281037
Adjusted R-squared0.529490940641775
F-TEST (value)34.198048875484
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value1.74316561185606e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.43046254892519
Sum Squared Residuals336.707447498493

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.738539369486175 \tabularnewline
R-squared & 0.545440400281037 \tabularnewline
Adjusted R-squared & 0.529490940641775 \tabularnewline
F-TEST (value) & 34.198048875484 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 1.74316561185606e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.43046254892519 \tabularnewline
Sum Squared Residuals & 336.707447498493 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145771&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.738539369486175[/C][/ROW]
[ROW][C]R-squared[/C][C]0.545440400281037[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.529490940641775[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]34.198048875484[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]1.74316561185606e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.43046254892519[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]336.707447498493[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145771&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145771&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.738539369486175
R-squared0.545440400281037
Adjusted R-squared0.529490940641775
F-TEST (value)34.198048875484
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value1.74316561185606e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.43046254892519
Sum Squared Residuals336.707447498493







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11517.2231216067582-2.22312160675821
21617.4775637301135-1.47756373011347
31516.968679483403-1.96867948340301
41416.4597952366925-2.45979523669255
51316.2053531133373-3.20535311333732
61617.2231216067582-1.22312160675824
71818.4953322235344-0.495332223534404
81415.314805681594-1.314805681594
91114.1698161264955-3.16981612649546
101013.660931879785-3.660931879785
11912.6431633863641-3.64316338636407
121113.0248265713969-2.02482657139692
131314.1698161264955-1.16981612649546
141818.4953322235344-0.495332223534404
152121.6531184719366-0.65311847193659
161516.5870162983702-1.58701629837016
171416.2053531133373-2.20535311333732
181516.3325741750149-1.33257417501493
191617.0959005450806-1.09590054508063
201516.3325741750149-1.33257417501493
211616.5870162983702-0.587016298370164
221717.2231216067582-0.223121606758244
231415.314805681594-1.314805681594
241314.5514793115283-1.55147931152831
251213.660931879785-1.660931879785
261514.55147931152830.448520688471692
271615.3148056815940.685194318405997
281815.82368992830452.17631007169553
291917.35034266843591.64965733156414
301720.3809078551604-3.38090785516043
311822.9253290887128-4.92532908871275
321822.2892237803247-4.28922378032467
331820.3809078551604-2.38090785516043
341917.85922691514631.14077308485368
352021.9075605952918-1.90756059529182
362224.4519818288441-2.45198182884414
372121.9075605952918-0.907560595291822
382017.47756373011352.52243626988652
391817.09590054508060.904099454919372
401716.58701629837020.412983701629836
411615.95091098998210.0490890100179163
421917.22312160675821.77687839324176
432117.85922691514633.14077308485368
442017.47756373011352.52243626988652
452017.35034266843592.64965733156414
462117.85922691514633.14077308485368
472017.60478479179112.39521520820891
481915.95091098998213.04908901001792
491614.67870037320591.32129962679408
501815.3148056815942.685194318406
511915.56924780494923.43075219505076
522118.8542551150292.14574488497096
532219.10869723838432.89130276161573
542521.65311847193663.34688152806341
552421.78033953361422.21966046638579
562321.01701316354851.98298683645149
572219.49036042341712.50963957658288
582118.34537086831862.65462913168143
592014.67870037320595.32129962679408
602218.8542551150293.14574488497096

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 17.2231216067582 & -2.22312160675821 \tabularnewline
2 & 16 & 17.4775637301135 & -1.47756373011347 \tabularnewline
3 & 15 & 16.968679483403 & -1.96867948340301 \tabularnewline
4 & 14 & 16.4597952366925 & -2.45979523669255 \tabularnewline
5 & 13 & 16.2053531133373 & -3.20535311333732 \tabularnewline
6 & 16 & 17.2231216067582 & -1.22312160675824 \tabularnewline
7 & 18 & 18.4953322235344 & -0.495332223534404 \tabularnewline
8 & 14 & 15.314805681594 & -1.314805681594 \tabularnewline
9 & 11 & 14.1698161264955 & -3.16981612649546 \tabularnewline
10 & 10 & 13.660931879785 & -3.660931879785 \tabularnewline
11 & 9 & 12.6431633863641 & -3.64316338636407 \tabularnewline
12 & 11 & 13.0248265713969 & -2.02482657139692 \tabularnewline
13 & 13 & 14.1698161264955 & -1.16981612649546 \tabularnewline
14 & 18 & 18.4953322235344 & -0.495332223534404 \tabularnewline
15 & 21 & 21.6531184719366 & -0.65311847193659 \tabularnewline
16 & 15 & 16.5870162983702 & -1.58701629837016 \tabularnewline
17 & 14 & 16.2053531133373 & -2.20535311333732 \tabularnewline
18 & 15 & 16.3325741750149 & -1.33257417501493 \tabularnewline
19 & 16 & 17.0959005450806 & -1.09590054508063 \tabularnewline
20 & 15 & 16.3325741750149 & -1.33257417501493 \tabularnewline
21 & 16 & 16.5870162983702 & -0.587016298370164 \tabularnewline
22 & 17 & 17.2231216067582 & -0.223121606758244 \tabularnewline
23 & 14 & 15.314805681594 & -1.314805681594 \tabularnewline
24 & 13 & 14.5514793115283 & -1.55147931152831 \tabularnewline
25 & 12 & 13.660931879785 & -1.660931879785 \tabularnewline
26 & 15 & 14.5514793115283 & 0.448520688471692 \tabularnewline
27 & 16 & 15.314805681594 & 0.685194318405997 \tabularnewline
28 & 18 & 15.8236899283045 & 2.17631007169553 \tabularnewline
29 & 19 & 17.3503426684359 & 1.64965733156414 \tabularnewline
30 & 17 & 20.3809078551604 & -3.38090785516043 \tabularnewline
31 & 18 & 22.9253290887128 & -4.92532908871275 \tabularnewline
32 & 18 & 22.2892237803247 & -4.28922378032467 \tabularnewline
33 & 18 & 20.3809078551604 & -2.38090785516043 \tabularnewline
34 & 19 & 17.8592269151463 & 1.14077308485368 \tabularnewline
35 & 20 & 21.9075605952918 & -1.90756059529182 \tabularnewline
36 & 22 & 24.4519818288441 & -2.45198182884414 \tabularnewline
37 & 21 & 21.9075605952918 & -0.907560595291822 \tabularnewline
38 & 20 & 17.4775637301135 & 2.52243626988652 \tabularnewline
39 & 18 & 17.0959005450806 & 0.904099454919372 \tabularnewline
40 & 17 & 16.5870162983702 & 0.412983701629836 \tabularnewline
41 & 16 & 15.9509109899821 & 0.0490890100179163 \tabularnewline
42 & 19 & 17.2231216067582 & 1.77687839324176 \tabularnewline
43 & 21 & 17.8592269151463 & 3.14077308485368 \tabularnewline
44 & 20 & 17.4775637301135 & 2.52243626988652 \tabularnewline
45 & 20 & 17.3503426684359 & 2.64965733156414 \tabularnewline
46 & 21 & 17.8592269151463 & 3.14077308485368 \tabularnewline
47 & 20 & 17.6047847917911 & 2.39521520820891 \tabularnewline
48 & 19 & 15.9509109899821 & 3.04908901001792 \tabularnewline
49 & 16 & 14.6787003732059 & 1.32129962679408 \tabularnewline
50 & 18 & 15.314805681594 & 2.685194318406 \tabularnewline
51 & 19 & 15.5692478049492 & 3.43075219505076 \tabularnewline
52 & 21 & 18.854255115029 & 2.14574488497096 \tabularnewline
53 & 22 & 19.1086972383843 & 2.89130276161573 \tabularnewline
54 & 25 & 21.6531184719366 & 3.34688152806341 \tabularnewline
55 & 24 & 21.7803395336142 & 2.21966046638579 \tabularnewline
56 & 23 & 21.0170131635485 & 1.98298683645149 \tabularnewline
57 & 22 & 19.4903604234171 & 2.50963957658288 \tabularnewline
58 & 21 & 18.3453708683186 & 2.65462913168143 \tabularnewline
59 & 20 & 14.6787003732059 & 5.32129962679408 \tabularnewline
60 & 22 & 18.854255115029 & 3.14574488497096 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145771&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]15[/C][C]17.2231216067582[/C][C]-2.22312160675821[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]17.4775637301135[/C][C]-1.47756373011347[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]16.968679483403[/C][C]-1.96867948340301[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]16.4597952366925[/C][C]-2.45979523669255[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]16.2053531133373[/C][C]-3.20535311333732[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]17.2231216067582[/C][C]-1.22312160675824[/C][/ROW]
[ROW][C]7[/C][C]18[/C][C]18.4953322235344[/C][C]-0.495332223534404[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]15.314805681594[/C][C]-1.314805681594[/C][/ROW]
[ROW][C]9[/C][C]11[/C][C]14.1698161264955[/C][C]-3.16981612649546[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]13.660931879785[/C][C]-3.660931879785[/C][/ROW]
[ROW][C]11[/C][C]9[/C][C]12.6431633863641[/C][C]-3.64316338636407[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]13.0248265713969[/C][C]-2.02482657139692[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]14.1698161264955[/C][C]-1.16981612649546[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]18.4953322235344[/C][C]-0.495332223534404[/C][/ROW]
[ROW][C]15[/C][C]21[/C][C]21.6531184719366[/C][C]-0.65311847193659[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]16.5870162983702[/C][C]-1.58701629837016[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]16.2053531133373[/C][C]-2.20535311333732[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]16.3325741750149[/C][C]-1.33257417501493[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]17.0959005450806[/C][C]-1.09590054508063[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]16.3325741750149[/C][C]-1.33257417501493[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]16.5870162983702[/C][C]-0.587016298370164[/C][/ROW]
[ROW][C]22[/C][C]17[/C][C]17.2231216067582[/C][C]-0.223121606758244[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]15.314805681594[/C][C]-1.314805681594[/C][/ROW]
[ROW][C]24[/C][C]13[/C][C]14.5514793115283[/C][C]-1.55147931152831[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]13.660931879785[/C][C]-1.660931879785[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.5514793115283[/C][C]0.448520688471692[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.314805681594[/C][C]0.685194318405997[/C][/ROW]
[ROW][C]28[/C][C]18[/C][C]15.8236899283045[/C][C]2.17631007169553[/C][/ROW]
[ROW][C]29[/C][C]19[/C][C]17.3503426684359[/C][C]1.64965733156414[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]20.3809078551604[/C][C]-3.38090785516043[/C][/ROW]
[ROW][C]31[/C][C]18[/C][C]22.9253290887128[/C][C]-4.92532908871275[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]22.2892237803247[/C][C]-4.28922378032467[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]20.3809078551604[/C][C]-2.38090785516043[/C][/ROW]
[ROW][C]34[/C][C]19[/C][C]17.8592269151463[/C][C]1.14077308485368[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]21.9075605952918[/C][C]-1.90756059529182[/C][/ROW]
[ROW][C]36[/C][C]22[/C][C]24.4519818288441[/C][C]-2.45198182884414[/C][/ROW]
[ROW][C]37[/C][C]21[/C][C]21.9075605952918[/C][C]-0.907560595291822[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]17.4775637301135[/C][C]2.52243626988652[/C][/ROW]
[ROW][C]39[/C][C]18[/C][C]17.0959005450806[/C][C]0.904099454919372[/C][/ROW]
[ROW][C]40[/C][C]17[/C][C]16.5870162983702[/C][C]0.412983701629836[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.9509109899821[/C][C]0.0490890100179163[/C][/ROW]
[ROW][C]42[/C][C]19[/C][C]17.2231216067582[/C][C]1.77687839324176[/C][/ROW]
[ROW][C]43[/C][C]21[/C][C]17.8592269151463[/C][C]3.14077308485368[/C][/ROW]
[ROW][C]44[/C][C]20[/C][C]17.4775637301135[/C][C]2.52243626988652[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]17.3503426684359[/C][C]2.64965733156414[/C][/ROW]
[ROW][C]46[/C][C]21[/C][C]17.8592269151463[/C][C]3.14077308485368[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]17.6047847917911[/C][C]2.39521520820891[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]15.9509109899821[/C][C]3.04908901001792[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.6787003732059[/C][C]1.32129962679408[/C][/ROW]
[ROW][C]50[/C][C]18[/C][C]15.314805681594[/C][C]2.685194318406[/C][/ROW]
[ROW][C]51[/C][C]19[/C][C]15.5692478049492[/C][C]3.43075219505076[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]18.854255115029[/C][C]2.14574488497096[/C][/ROW]
[ROW][C]53[/C][C]22[/C][C]19.1086972383843[/C][C]2.89130276161573[/C][/ROW]
[ROW][C]54[/C][C]25[/C][C]21.6531184719366[/C][C]3.34688152806341[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]21.7803395336142[/C][C]2.21966046638579[/C][/ROW]
[ROW][C]56[/C][C]23[/C][C]21.0170131635485[/C][C]1.98298683645149[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]19.4903604234171[/C][C]2.50963957658288[/C][/ROW]
[ROW][C]58[/C][C]21[/C][C]18.3453708683186[/C][C]2.65462913168143[/C][/ROW]
[ROW][C]59[/C][C]20[/C][C]14.6787003732059[/C][C]5.32129962679408[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]18.854255115029[/C][C]3.14574488497096[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145771&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145771&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11517.2231216067582-2.22312160675821
21617.4775637301135-1.47756373011347
31516.968679483403-1.96867948340301
41416.4597952366925-2.45979523669255
51316.2053531133373-3.20535311333732
61617.2231216067582-1.22312160675824
71818.4953322235344-0.495332223534404
81415.314805681594-1.314805681594
91114.1698161264955-3.16981612649546
101013.660931879785-3.660931879785
11912.6431633863641-3.64316338636407
121113.0248265713969-2.02482657139692
131314.1698161264955-1.16981612649546
141818.4953322235344-0.495332223534404
152121.6531184719366-0.65311847193659
161516.5870162983702-1.58701629837016
171416.2053531133373-2.20535311333732
181516.3325741750149-1.33257417501493
191617.0959005450806-1.09590054508063
201516.3325741750149-1.33257417501493
211616.5870162983702-0.587016298370164
221717.2231216067582-0.223121606758244
231415.314805681594-1.314805681594
241314.5514793115283-1.55147931152831
251213.660931879785-1.660931879785
261514.55147931152830.448520688471692
271615.3148056815940.685194318405997
281815.82368992830452.17631007169553
291917.35034266843591.64965733156414
301720.3809078551604-3.38090785516043
311822.9253290887128-4.92532908871275
321822.2892237803247-4.28922378032467
331820.3809078551604-2.38090785516043
341917.85922691514631.14077308485368
352021.9075605952918-1.90756059529182
362224.4519818288441-2.45198182884414
372121.9075605952918-0.907560595291822
382017.47756373011352.52243626988652
391817.09590054508060.904099454919372
401716.58701629837020.412983701629836
411615.95091098998210.0490890100179163
421917.22312160675821.77687839324176
432117.85922691514633.14077308485368
442017.47756373011352.52243626988652
452017.35034266843592.64965733156414
462117.85922691514633.14077308485368
472017.60478479179112.39521520820891
481915.95091098998213.04908901001792
491614.67870037320591.32129962679408
501815.3148056815942.685194318406
511915.56924780494923.43075219505076
522118.8542551150292.14574488497096
532219.10869723838432.89130276161573
542521.65311847193663.34688152806341
552421.78033953361422.21966046638579
562321.01701316354851.98298683645149
572219.49036042341712.50963957658288
582118.34537086831862.65462913168143
592014.67870037320595.32129962679408
602218.8542551150293.14574488497096







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.008423263128015280.01684652625603060.991576736871985
70.001437000075198620.002874000150397230.998562999924801
80.0132708204406620.02654164088132410.986729179559338
90.004470225656984430.008940451313968860.995529774343016
100.001625541973714750.003251083947429510.998374458026285
110.0006622335345529350.001324467069105870.999337766465447
120.001219720354485220.002439440708970440.998780279645515
130.00198488500622180.003969770012443610.998015114993778
140.0009343251214923880.001868650242984780.999065674878508
150.0003326380077953530.0006652760155907060.999667361992205
160.0001340260095115460.0002680520190230910.999865973990488
176.44210253776287e-050.0001288420507552570.999935578974622
183.09499812001851e-056.18999624003703e-050.9999690500188
191.33495764193398e-052.66991528386795e-050.99998665042358
206.50580915857155e-061.30116183171431e-050.999993494190841
215.66154772386642e-061.13230954477328e-050.999994338452276
224.81757058334286e-069.63514116668571e-060.999995182429417
233.82689476113788e-067.65378952227576e-060.999996173105239
244.53430418915674e-069.06860837831347e-060.99999546569581
251.4007273282116e-052.8014546564232e-050.999985992726718
260.0002692762419487270.0005385524838974530.999730723758051
270.001396292718383240.002792585436766480.998603707281617
280.01325032177508280.02650064355016570.986749678224917
290.02028669989161230.04057339978322450.979713300108388
300.04192602762612920.08385205525225840.95807397237387
310.1321450389452540.2642900778905080.867854961054746
320.2646862256527850.5293724513055710.735313774347215
330.4571944861141620.9143889722283240.542805513885838
340.4392742728377970.8785485456755940.560725727162203
350.5434927777367390.9130144445265210.456507222263261
360.6534362383065770.6931275233868460.346563761693423
370.8462475257477230.3075049485045550.153752474252277
380.8739235171975640.2521529656048730.126076482802436
390.883672494130110.232655011739780.11632750586989
400.9300687107194270.1398625785611460.0699312892805731
410.9889928761386340.02201424772273190.011007123861366
420.990635738398110.01872852320377880.00936426160188942
430.9899534358874050.02009312822519060.0100465641125953
440.9862485159445440.02750296811091260.0137514840554563
450.9805730641687520.03885387166249530.0194269358312477
460.9724849854231310.05503002915373870.0275150145768694
470.958735887736720.08252822452655950.0412641122632798
480.9452601153491930.1094797693016150.0547398846508074
490.9877727237498740.02445455250025290.0122272762501265
500.9938603262530030.01227934749399420.00613967374699708
510.9979978301945380.004004339610923190.0020021698054616
520.996965259050890.006069481898218560.00303474094910928
530.99048686030290.01902627939419840.0095131396970992
540.9960411415570730.007917716885854130.00395885844292706

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.00842326312801528 & 0.0168465262560306 & 0.991576736871985 \tabularnewline
7 & 0.00143700007519862 & 0.00287400015039723 & 0.998562999924801 \tabularnewline
8 & 0.013270820440662 & 0.0265416408813241 & 0.986729179559338 \tabularnewline
9 & 0.00447022565698443 & 0.00894045131396886 & 0.995529774343016 \tabularnewline
10 & 0.00162554197371475 & 0.00325108394742951 & 0.998374458026285 \tabularnewline
11 & 0.000662233534552935 & 0.00132446706910587 & 0.999337766465447 \tabularnewline
12 & 0.00121972035448522 & 0.00243944070897044 & 0.998780279645515 \tabularnewline
13 & 0.0019848850062218 & 0.00396977001244361 & 0.998015114993778 \tabularnewline
14 & 0.000934325121492388 & 0.00186865024298478 & 0.999065674878508 \tabularnewline
15 & 0.000332638007795353 & 0.000665276015590706 & 0.999667361992205 \tabularnewline
16 & 0.000134026009511546 & 0.000268052019023091 & 0.999865973990488 \tabularnewline
17 & 6.44210253776287e-05 & 0.000128842050755257 & 0.999935578974622 \tabularnewline
18 & 3.09499812001851e-05 & 6.18999624003703e-05 & 0.9999690500188 \tabularnewline
19 & 1.33495764193398e-05 & 2.66991528386795e-05 & 0.99998665042358 \tabularnewline
20 & 6.50580915857155e-06 & 1.30116183171431e-05 & 0.999993494190841 \tabularnewline
21 & 5.66154772386642e-06 & 1.13230954477328e-05 & 0.999994338452276 \tabularnewline
22 & 4.81757058334286e-06 & 9.63514116668571e-06 & 0.999995182429417 \tabularnewline
23 & 3.82689476113788e-06 & 7.65378952227576e-06 & 0.999996173105239 \tabularnewline
24 & 4.53430418915674e-06 & 9.06860837831347e-06 & 0.99999546569581 \tabularnewline
25 & 1.4007273282116e-05 & 2.8014546564232e-05 & 0.999985992726718 \tabularnewline
26 & 0.000269276241948727 & 0.000538552483897453 & 0.999730723758051 \tabularnewline
27 & 0.00139629271838324 & 0.00279258543676648 & 0.998603707281617 \tabularnewline
28 & 0.0132503217750828 & 0.0265006435501657 & 0.986749678224917 \tabularnewline
29 & 0.0202866998916123 & 0.0405733997832245 & 0.979713300108388 \tabularnewline
30 & 0.0419260276261292 & 0.0838520552522584 & 0.95807397237387 \tabularnewline
31 & 0.132145038945254 & 0.264290077890508 & 0.867854961054746 \tabularnewline
32 & 0.264686225652785 & 0.529372451305571 & 0.735313774347215 \tabularnewline
33 & 0.457194486114162 & 0.914388972228324 & 0.542805513885838 \tabularnewline
34 & 0.439274272837797 & 0.878548545675594 & 0.560725727162203 \tabularnewline
35 & 0.543492777736739 & 0.913014444526521 & 0.456507222263261 \tabularnewline
36 & 0.653436238306577 & 0.693127523386846 & 0.346563761693423 \tabularnewline
37 & 0.846247525747723 & 0.307504948504555 & 0.153752474252277 \tabularnewline
38 & 0.873923517197564 & 0.252152965604873 & 0.126076482802436 \tabularnewline
39 & 0.88367249413011 & 0.23265501173978 & 0.11632750586989 \tabularnewline
40 & 0.930068710719427 & 0.139862578561146 & 0.0699312892805731 \tabularnewline
41 & 0.988992876138634 & 0.0220142477227319 & 0.011007123861366 \tabularnewline
42 & 0.99063573839811 & 0.0187285232037788 & 0.00936426160188942 \tabularnewline
43 & 0.989953435887405 & 0.0200931282251906 & 0.0100465641125953 \tabularnewline
44 & 0.986248515944544 & 0.0275029681109126 & 0.0137514840554563 \tabularnewline
45 & 0.980573064168752 & 0.0388538716624953 & 0.0194269358312477 \tabularnewline
46 & 0.972484985423131 & 0.0550300291537387 & 0.0275150145768694 \tabularnewline
47 & 0.95873588773672 & 0.0825282245265595 & 0.0412641122632798 \tabularnewline
48 & 0.945260115349193 & 0.109479769301615 & 0.0547398846508074 \tabularnewline
49 & 0.987772723749874 & 0.0244545525002529 & 0.0122272762501265 \tabularnewline
50 & 0.993860326253003 & 0.0122793474939942 & 0.00613967374699708 \tabularnewline
51 & 0.997997830194538 & 0.00400433961092319 & 0.0020021698054616 \tabularnewline
52 & 0.99696525905089 & 0.00606948189821856 & 0.00303474094910928 \tabularnewline
53 & 0.9904868603029 & 0.0190262793941984 & 0.0095131396970992 \tabularnewline
54 & 0.996041141557073 & 0.00791771688585413 & 0.00395885844292706 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145771&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]6[/C][C]0.00842326312801528[/C][C]0.0168465262560306[/C][C]0.991576736871985[/C][/ROW]
[ROW][C]7[/C][C]0.00143700007519862[/C][C]0.00287400015039723[/C][C]0.998562999924801[/C][/ROW]
[ROW][C]8[/C][C]0.013270820440662[/C][C]0.0265416408813241[/C][C]0.986729179559338[/C][/ROW]
[ROW][C]9[/C][C]0.00447022565698443[/C][C]0.00894045131396886[/C][C]0.995529774343016[/C][/ROW]
[ROW][C]10[/C][C]0.00162554197371475[/C][C]0.00325108394742951[/C][C]0.998374458026285[/C][/ROW]
[ROW][C]11[/C][C]0.000662233534552935[/C][C]0.00132446706910587[/C][C]0.999337766465447[/C][/ROW]
[ROW][C]12[/C][C]0.00121972035448522[/C][C]0.00243944070897044[/C][C]0.998780279645515[/C][/ROW]
[ROW][C]13[/C][C]0.0019848850062218[/C][C]0.00396977001244361[/C][C]0.998015114993778[/C][/ROW]
[ROW][C]14[/C][C]0.000934325121492388[/C][C]0.00186865024298478[/C][C]0.999065674878508[/C][/ROW]
[ROW][C]15[/C][C]0.000332638007795353[/C][C]0.000665276015590706[/C][C]0.999667361992205[/C][/ROW]
[ROW][C]16[/C][C]0.000134026009511546[/C][C]0.000268052019023091[/C][C]0.999865973990488[/C][/ROW]
[ROW][C]17[/C][C]6.44210253776287e-05[/C][C]0.000128842050755257[/C][C]0.999935578974622[/C][/ROW]
[ROW][C]18[/C][C]3.09499812001851e-05[/C][C]6.18999624003703e-05[/C][C]0.9999690500188[/C][/ROW]
[ROW][C]19[/C][C]1.33495764193398e-05[/C][C]2.66991528386795e-05[/C][C]0.99998665042358[/C][/ROW]
[ROW][C]20[/C][C]6.50580915857155e-06[/C][C]1.30116183171431e-05[/C][C]0.999993494190841[/C][/ROW]
[ROW][C]21[/C][C]5.66154772386642e-06[/C][C]1.13230954477328e-05[/C][C]0.999994338452276[/C][/ROW]
[ROW][C]22[/C][C]4.81757058334286e-06[/C][C]9.63514116668571e-06[/C][C]0.999995182429417[/C][/ROW]
[ROW][C]23[/C][C]3.82689476113788e-06[/C][C]7.65378952227576e-06[/C][C]0.999996173105239[/C][/ROW]
[ROW][C]24[/C][C]4.53430418915674e-06[/C][C]9.06860837831347e-06[/C][C]0.99999546569581[/C][/ROW]
[ROW][C]25[/C][C]1.4007273282116e-05[/C][C]2.8014546564232e-05[/C][C]0.999985992726718[/C][/ROW]
[ROW][C]26[/C][C]0.000269276241948727[/C][C]0.000538552483897453[/C][C]0.999730723758051[/C][/ROW]
[ROW][C]27[/C][C]0.00139629271838324[/C][C]0.00279258543676648[/C][C]0.998603707281617[/C][/ROW]
[ROW][C]28[/C][C]0.0132503217750828[/C][C]0.0265006435501657[/C][C]0.986749678224917[/C][/ROW]
[ROW][C]29[/C][C]0.0202866998916123[/C][C]0.0405733997832245[/C][C]0.979713300108388[/C][/ROW]
[ROW][C]30[/C][C]0.0419260276261292[/C][C]0.0838520552522584[/C][C]0.95807397237387[/C][/ROW]
[ROW][C]31[/C][C]0.132145038945254[/C][C]0.264290077890508[/C][C]0.867854961054746[/C][/ROW]
[ROW][C]32[/C][C]0.264686225652785[/C][C]0.529372451305571[/C][C]0.735313774347215[/C][/ROW]
[ROW][C]33[/C][C]0.457194486114162[/C][C]0.914388972228324[/C][C]0.542805513885838[/C][/ROW]
[ROW][C]34[/C][C]0.439274272837797[/C][C]0.878548545675594[/C][C]0.560725727162203[/C][/ROW]
[ROW][C]35[/C][C]0.543492777736739[/C][C]0.913014444526521[/C][C]0.456507222263261[/C][/ROW]
[ROW][C]36[/C][C]0.653436238306577[/C][C]0.693127523386846[/C][C]0.346563761693423[/C][/ROW]
[ROW][C]37[/C][C]0.846247525747723[/C][C]0.307504948504555[/C][C]0.153752474252277[/C][/ROW]
[ROW][C]38[/C][C]0.873923517197564[/C][C]0.252152965604873[/C][C]0.126076482802436[/C][/ROW]
[ROW][C]39[/C][C]0.88367249413011[/C][C]0.23265501173978[/C][C]0.11632750586989[/C][/ROW]
[ROW][C]40[/C][C]0.930068710719427[/C][C]0.139862578561146[/C][C]0.0699312892805731[/C][/ROW]
[ROW][C]41[/C][C]0.988992876138634[/C][C]0.0220142477227319[/C][C]0.011007123861366[/C][/ROW]
[ROW][C]42[/C][C]0.99063573839811[/C][C]0.0187285232037788[/C][C]0.00936426160188942[/C][/ROW]
[ROW][C]43[/C][C]0.989953435887405[/C][C]0.0200931282251906[/C][C]0.0100465641125953[/C][/ROW]
[ROW][C]44[/C][C]0.986248515944544[/C][C]0.0275029681109126[/C][C]0.0137514840554563[/C][/ROW]
[ROW][C]45[/C][C]0.980573064168752[/C][C]0.0388538716624953[/C][C]0.0194269358312477[/C][/ROW]
[ROW][C]46[/C][C]0.972484985423131[/C][C]0.0550300291537387[/C][C]0.0275150145768694[/C][/ROW]
[ROW][C]47[/C][C]0.95873588773672[/C][C]0.0825282245265595[/C][C]0.0412641122632798[/C][/ROW]
[ROW][C]48[/C][C]0.945260115349193[/C][C]0.109479769301615[/C][C]0.0547398846508074[/C][/ROW]
[ROW][C]49[/C][C]0.987772723749874[/C][C]0.0244545525002529[/C][C]0.0122272762501265[/C][/ROW]
[ROW][C]50[/C][C]0.993860326253003[/C][C]0.0122793474939942[/C][C]0.00613967374699708[/C][/ROW]
[ROW][C]51[/C][C]0.997997830194538[/C][C]0.00400433961092319[/C][C]0.0020021698054616[/C][/ROW]
[ROW][C]52[/C][C]0.99696525905089[/C][C]0.00606948189821856[/C][C]0.00303474094910928[/C][/ROW]
[ROW][C]53[/C][C]0.9904868603029[/C][C]0.0190262793941984[/C][C]0.0095131396970992[/C][/ROW]
[ROW][C]54[/C][C]0.996041141557073[/C][C]0.00791771688585413[/C][C]0.00395885844292706[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145771&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145771&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.008423263128015280.01684652625603060.991576736871985
70.001437000075198620.002874000150397230.998562999924801
80.0132708204406620.02654164088132410.986729179559338
90.004470225656984430.008940451313968860.995529774343016
100.001625541973714750.003251083947429510.998374458026285
110.0006622335345529350.001324467069105870.999337766465447
120.001219720354485220.002439440708970440.998780279645515
130.00198488500622180.003969770012443610.998015114993778
140.0009343251214923880.001868650242984780.999065674878508
150.0003326380077953530.0006652760155907060.999667361992205
160.0001340260095115460.0002680520190230910.999865973990488
176.44210253776287e-050.0001288420507552570.999935578974622
183.09499812001851e-056.18999624003703e-050.9999690500188
191.33495764193398e-052.66991528386795e-050.99998665042358
206.50580915857155e-061.30116183171431e-050.999993494190841
215.66154772386642e-061.13230954477328e-050.999994338452276
224.81757058334286e-069.63514116668571e-060.999995182429417
233.82689476113788e-067.65378952227576e-060.999996173105239
244.53430418915674e-069.06860837831347e-060.99999546569581
251.4007273282116e-052.8014546564232e-050.999985992726718
260.0002692762419487270.0005385524838974530.999730723758051
270.001396292718383240.002792585436766480.998603707281617
280.01325032177508280.02650064355016570.986749678224917
290.02028669989161230.04057339978322450.979713300108388
300.04192602762612920.08385205525225840.95807397237387
310.1321450389452540.2642900778905080.867854961054746
320.2646862256527850.5293724513055710.735313774347215
330.4571944861141620.9143889722283240.542805513885838
340.4392742728377970.8785485456755940.560725727162203
350.5434927777367390.9130144445265210.456507222263261
360.6534362383065770.6931275233868460.346563761693423
370.8462475257477230.3075049485045550.153752474252277
380.8739235171975640.2521529656048730.126076482802436
390.883672494130110.232655011739780.11632750586989
400.9300687107194270.1398625785611460.0699312892805731
410.9889928761386340.02201424772273190.011007123861366
420.990635738398110.01872852320377880.00936426160188942
430.9899534358874050.02009312822519060.0100465641125953
440.9862485159445440.02750296811091260.0137514840554563
450.9805730641687520.03885387166249530.0194269358312477
460.9724849854231310.05503002915373870.0275150145768694
470.958735887736720.08252822452655950.0412641122632798
480.9452601153491930.1094797693016150.0547398846508074
490.9877727237498740.02445455250025290.0122272762501265
500.9938603262530030.01227934749399420.00613967374699708
510.9979978301945380.004004339610923190.0020021698054616
520.996965259050890.006069481898218560.00303474094910928
530.99048686030290.01902627939419840.0095131396970992
540.9960411415570730.007917716885854130.00395885844292706







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level230.469387755102041NOK
5% type I error level350.714285714285714NOK
10% type I error level380.775510204081633NOK

\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 & 23 & 0.469387755102041 & NOK \tabularnewline
5% type I error level & 35 & 0.714285714285714 & NOK \tabularnewline
10% type I error level & 38 & 0.775510204081633 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145771&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]23[/C][C]0.469387755102041[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.714285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]38[/C][C]0.775510204081633[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145771&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145771&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level230.469387755102041NOK
5% type I error level350.714285714285714NOK
10% type I error level380.775510204081633NOK



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