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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 computationFri, 11 Dec 2009 09:03:36 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/11/t1260547571ri7eqkh41tshrd1.htm/, Retrieved Mon, 29 Apr 2024 04:17:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66444, Retrieved Mon, 29 Apr 2024 04:17:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsETP(35)
Estimated Impact109

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Paper statistiek:...] [2009-12-11 16:03:36] [af31b947d6acaef3c71f428c4bb503e9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.43	0	0	0	0	0.51
1.43	0	0	0	0	0.51
1.43	0	0	0	0	0.51
1.43	0	0	0	0	0.51
1.43	0	0	0	0	0.52
1.43	0	0	0	0	0.52
1.44	0	0	0	0	0.52
1.48	1	0	0	0	0.53
1.48	1	0	0	0	0.53
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.52
1.48	1	0	0	0	0.53
1.48	1	0	0	0	0.53
1.48	1	0	0	0	0.53
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.54
1.48	1	0	0	0	0.53
1.48	1	0	0	0	0.53
1.48	1	0	0	0	0.53
1.48	1	0	0	0	0.53
1.48	1	0	0	0	0.53
1.57	1	1	0	0	0.54
1.58	1	1	0	0	0.55
1.58	1	1	0	0	0.55
1.58	1	1	0	0	0.55
1.58	1	1	0	0	0.55
1.59	1	1	1	1	0.55
1.6	1	1	1	2	0.55
1.6	1	1	1	3	0.55
1.61	1	1	1	4	0.55
1.61	1	1	1	5	0.56
1.61	1	1	1	6	0.56
1.62	1	1	1	7	0.56
1.63	1	1	1	8	0.56
1.63	1	1	1	9	0.56
1.64	1	1	1	10	0.55
1.64	1	1	1	11	0.56
1.64	1	1	1	12	0.55
1.64	1	1	1	13	0.55
1.64	1	1	1	14	0.56
1.65	1	1	1	15	0.55
1.65	1	1	1	16	0.55
1.65	1	1	1	17	0.55
1.65	1	1	1	18	0.55

Tables (Output of Computation)





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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66444&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66444&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66444&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
Broodprijzen[t] = + 1.37674903761831 + 0.0476820908725279Dummy1[t] + 0.0968243660889128Dummy2[t] + 0.0149811800172971Dummy3[t] + 0.00364815973913286Dummy4[t] + 0.106652028005743Bakmeel[t] -4.25201481723485e-05t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Broodprijzen[t] =  +  1.37674903761831 +  0.0476820908725279Dummy1[t] +  0.0968243660889128Dummy2[t] +  0.0149811800172971Dummy3[t] +  0.00364815973913286Dummy4[t] +  0.106652028005743Bakmeel[t] -4.25201481723485e-05t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66444&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Broodprijzen[t] =  +  1.37674903761831 +  0.0476820908725279Dummy1[t] +  0.0968243660889128Dummy2[t] +  0.0149811800172971Dummy3[t] +  0.00364815973913286Dummy4[t] +  0.106652028005743Bakmeel[t] -4.25201481723485e-05t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66444&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66444&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
Broodprijzen[t] = + 1.37674903761831 + 0.0476820908725279Dummy1[t] + 0.0968243660889128Dummy2[t] + 0.0149811800172971Dummy3[t] + 0.00364815973913286Dummy4[t] + 0.106652028005743Bakmeel[t] -4.25201481723485e-05t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.376749037618310.04039934.078500
Dummy10.04768209087252790.00196324.286100
Dummy20.09682436608891280.00213245.425400
Dummy30.01498118001729710.00236.512400
Dummy40.003648159739132860.00017920.397900
Bakmeel0.1066520280057430.0788841.3520.1821110.091056
t-4.25201481723485e-058.5e-05-0.49870.6200830.310042

\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) & 1.37674903761831 & 0.040399 & 34.0785 & 0 & 0 \tabularnewline
Dummy1 & 0.0476820908725279 & 0.001963 & 24.2861 & 0 & 0 \tabularnewline
Dummy2 & 0.0968243660889128 & 0.002132 & 45.4254 & 0 & 0 \tabularnewline
Dummy3 & 0.0149811800172971 & 0.0023 & 6.5124 & 0 & 0 \tabularnewline
Dummy4 & 0.00364815973913286 & 0.000179 & 20.3979 & 0 & 0 \tabularnewline
Bakmeel & 0.106652028005743 & 0.078884 & 1.352 & 0.182111 & 0.091056 \tabularnewline
t & -4.25201481723485e-05 & 8.5e-05 & -0.4987 & 0.620083 & 0.310042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66444&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]1.37674903761831[/C][C]0.040399[/C][C]34.0785[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy1[/C][C]0.0476820908725279[/C][C]0.001963[/C][C]24.2861[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy2[/C][C]0.0968243660889128[/C][C]0.002132[/C][C]45.4254[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy3[/C][C]0.0149811800172971[/C][C]0.0023[/C][C]6.5124[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy4[/C][C]0.00364815973913286[/C][C]0.000179[/C][C]20.3979[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bakmeel[/C][C]0.106652028005743[/C][C]0.078884[/C][C]1.352[/C][C]0.182111[/C][C]0.091056[/C][/ROW]
[ROW][C]t[/C][C]-4.25201481723485e-05[/C][C]8.5e-05[/C][C]-0.4987[/C][C]0.620083[/C][C]0.310042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66444&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66444&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)1.376749037618310.04039934.078500
Dummy10.04768209087252790.00196324.286100
Dummy20.09682436608891280.00213245.425400
Dummy30.01498118001729710.00236.512400
Dummy40.003648159739132860.00017920.397900
Bakmeel0.1066520280057430.0788841.3520.1821110.091056
t-4.25201481723485e-058.5e-05-0.49870.6200830.310042






Multiple Linear Regression - Regression Statistics
Multiple R0.999089522014467
R-squared0.998179872999095
Adjusted R-squared0.997973820885786
F-TEST (value)4844.30786447532
F-TEST (DF numerator)6
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0033811833505616
Sum Squared Residuals0.000605917245056093

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999089522014467 \tabularnewline
R-squared & 0.998179872999095 \tabularnewline
Adjusted R-squared & 0.997973820885786 \tabularnewline
F-TEST (value) & 4844.30786447532 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0033811833505616 \tabularnewline
Sum Squared Residuals & 0.000605917245056093 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66444&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999089522014467[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998179872999095[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997973820885786[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4844.30786447532[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/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]0.0033811833505616[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.000605917245056093[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66444&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66444&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 R0.999089522014467
R-squared0.998179872999095
Adjusted R-squared0.997973820885786
F-TEST (value)4844.30786447532
F-TEST (DF numerator)6
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0033811833505616
Sum Squared Residuals0.000605917245056093






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.431.43109905175307-0.00109905175307049
21.431.43105653160489-0.00105653160489032
31.431.43101401145672-0.00101401145671809
41.431.43097149130855-0.000971491308545764
51.431.43199549144043-0.00199549144043077
61.431.43195297129226-0.00195297129225846
71.441.431910451144090.00808954885591388
81.481.4806165421485-0.000616542148499051
91.481.48057402200033-0.000574022000326703
101.481.479464981572100.000535018427903076
111.481.479422461423920.000577538576075424
121.481.479379941275750.000620058724247772
131.481.479337421127580.000662578872420121
141.481.479294900979410.000705099020592469
151.481.479252380831240.000747619168764818
161.481.479209860683060.000790139316937166
171.481.479167340534890.000832659465109514
181.481.479124820386720.000875179613281863
191.481.479082300238550.000917699761454211
201.481.48010630037043-0.000106300370430871
211.481.48006378022226-6.37802222585223e-05
221.481.48002126007409-2.12600740861740e-05
231.481.48104526020597-0.00104526020597126
241.481.4810027400578-0.00100274005779891
251.481.48096021990963-0.00096021990962656
261.481.48091769976145-0.00091769976145421
271.481.48087517961328-0.000875179613281862
281.481.48083265946511-0.000832659465109514
291.481.48079013931694-0.000790139316937166
301.481.48074761916876-0.000747619168764817
311.481.48070509902059-0.000705099020592469
321.481.48066257887242-0.00066257887242012
331.481.479553538444190.000446461555809658
341.481.479511018296020.000488981703982006
351.481.479468498147850.000531501852154354
361.481.479425977999670.000574022000326703
371.481.47938345785150.000616542148499051
381.571.5772318240723-0.00723182407229876
391.581.578255824204180.00174417579581617
401.581.578213304056010.00178669594398852
411.581.578170783907840.00182921609216086
421.581.578128263759670.00187173624033321
431.591.59671508336792-0.00671508336792435
441.61.60032072295888-0.000320722958884853
451.61.60392636254985-0.00392636254984536
461.611.607532002140810.00246799785919413
471.611.61220416201182-0.00220416201182380
481.611.61580980160278-0.00580980160278431
491.621.619415441193740.000584558806255193
501.631.623021080784710.00697891921529447
511.631.626626720375670.00337327962433396
521.641.629165839686570.0108341603134309
531.641.633837999557590.00616200044241296
541.641.636377118868490.00362288113150988
551.641.639982758459451.72415405493680e-05
561.641.64465491833047-0.00465491833046857
571.651.647194037641370.00280596235862836
581.651.65079967723233-0.000799677232332149
591.651.65440531682329-0.00440531682329266
601.651.65801095641425-0.00801095641425316

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.43 & 1.43109905175307 & -0.00109905175307049 \tabularnewline
2 & 1.43 & 1.43105653160489 & -0.00105653160489032 \tabularnewline
3 & 1.43 & 1.43101401145672 & -0.00101401145671809 \tabularnewline
4 & 1.43 & 1.43097149130855 & -0.000971491308545764 \tabularnewline
5 & 1.43 & 1.43199549144043 & -0.00199549144043077 \tabularnewline
6 & 1.43 & 1.43195297129226 & -0.00195297129225846 \tabularnewline
7 & 1.44 & 1.43191045114409 & 0.00808954885591388 \tabularnewline
8 & 1.48 & 1.4806165421485 & -0.000616542148499051 \tabularnewline
9 & 1.48 & 1.48057402200033 & -0.000574022000326703 \tabularnewline
10 & 1.48 & 1.47946498157210 & 0.000535018427903076 \tabularnewline
11 & 1.48 & 1.47942246142392 & 0.000577538576075424 \tabularnewline
12 & 1.48 & 1.47937994127575 & 0.000620058724247772 \tabularnewline
13 & 1.48 & 1.47933742112758 & 0.000662578872420121 \tabularnewline
14 & 1.48 & 1.47929490097941 & 0.000705099020592469 \tabularnewline
15 & 1.48 & 1.47925238083124 & 0.000747619168764818 \tabularnewline
16 & 1.48 & 1.47920986068306 & 0.000790139316937166 \tabularnewline
17 & 1.48 & 1.47916734053489 & 0.000832659465109514 \tabularnewline
18 & 1.48 & 1.47912482038672 & 0.000875179613281863 \tabularnewline
19 & 1.48 & 1.47908230023855 & 0.000917699761454211 \tabularnewline
20 & 1.48 & 1.48010630037043 & -0.000106300370430871 \tabularnewline
21 & 1.48 & 1.48006378022226 & -6.37802222585223e-05 \tabularnewline
22 & 1.48 & 1.48002126007409 & -2.12600740861740e-05 \tabularnewline
23 & 1.48 & 1.48104526020597 & -0.00104526020597126 \tabularnewline
24 & 1.48 & 1.4810027400578 & -0.00100274005779891 \tabularnewline
25 & 1.48 & 1.48096021990963 & -0.00096021990962656 \tabularnewline
26 & 1.48 & 1.48091769976145 & -0.00091769976145421 \tabularnewline
27 & 1.48 & 1.48087517961328 & -0.000875179613281862 \tabularnewline
28 & 1.48 & 1.48083265946511 & -0.000832659465109514 \tabularnewline
29 & 1.48 & 1.48079013931694 & -0.000790139316937166 \tabularnewline
30 & 1.48 & 1.48074761916876 & -0.000747619168764817 \tabularnewline
31 & 1.48 & 1.48070509902059 & -0.000705099020592469 \tabularnewline
32 & 1.48 & 1.48066257887242 & -0.00066257887242012 \tabularnewline
33 & 1.48 & 1.47955353844419 & 0.000446461555809658 \tabularnewline
34 & 1.48 & 1.47951101829602 & 0.000488981703982006 \tabularnewline
35 & 1.48 & 1.47946849814785 & 0.000531501852154354 \tabularnewline
36 & 1.48 & 1.47942597799967 & 0.000574022000326703 \tabularnewline
37 & 1.48 & 1.4793834578515 & 0.000616542148499051 \tabularnewline
38 & 1.57 & 1.5772318240723 & -0.00723182407229876 \tabularnewline
39 & 1.58 & 1.57825582420418 & 0.00174417579581617 \tabularnewline
40 & 1.58 & 1.57821330405601 & 0.00178669594398852 \tabularnewline
41 & 1.58 & 1.57817078390784 & 0.00182921609216086 \tabularnewline
42 & 1.58 & 1.57812826375967 & 0.00187173624033321 \tabularnewline
43 & 1.59 & 1.59671508336792 & -0.00671508336792435 \tabularnewline
44 & 1.6 & 1.60032072295888 & -0.000320722958884853 \tabularnewline
45 & 1.6 & 1.60392636254985 & -0.00392636254984536 \tabularnewline
46 & 1.61 & 1.60753200214081 & 0.00246799785919413 \tabularnewline
47 & 1.61 & 1.61220416201182 & -0.00220416201182380 \tabularnewline
48 & 1.61 & 1.61580980160278 & -0.00580980160278431 \tabularnewline
49 & 1.62 & 1.61941544119374 & 0.000584558806255193 \tabularnewline
50 & 1.63 & 1.62302108078471 & 0.00697891921529447 \tabularnewline
51 & 1.63 & 1.62662672037567 & 0.00337327962433396 \tabularnewline
52 & 1.64 & 1.62916583968657 & 0.0108341603134309 \tabularnewline
53 & 1.64 & 1.63383799955759 & 0.00616200044241296 \tabularnewline
54 & 1.64 & 1.63637711886849 & 0.00362288113150988 \tabularnewline
55 & 1.64 & 1.63998275845945 & 1.72415405493680e-05 \tabularnewline
56 & 1.64 & 1.64465491833047 & -0.00465491833046857 \tabularnewline
57 & 1.65 & 1.64719403764137 & 0.00280596235862836 \tabularnewline
58 & 1.65 & 1.65079967723233 & -0.000799677232332149 \tabularnewline
59 & 1.65 & 1.65440531682329 & -0.00440531682329266 \tabularnewline
60 & 1.65 & 1.65801095641425 & -0.00801095641425316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66444&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1.43[/C][C]1.43109905175307[/C][C]-0.00109905175307049[/C][/ROW]
[ROW][C]2[/C][C]1.43[/C][C]1.43105653160489[/C][C]-0.00105653160489032[/C][/ROW]
[ROW][C]3[/C][C]1.43[/C][C]1.43101401145672[/C][C]-0.00101401145671809[/C][/ROW]
[ROW][C]4[/C][C]1.43[/C][C]1.43097149130855[/C][C]-0.000971491308545764[/C][/ROW]
[ROW][C]5[/C][C]1.43[/C][C]1.43199549144043[/C][C]-0.00199549144043077[/C][/ROW]
[ROW][C]6[/C][C]1.43[/C][C]1.43195297129226[/C][C]-0.00195297129225846[/C][/ROW]
[ROW][C]7[/C][C]1.44[/C][C]1.43191045114409[/C][C]0.00808954885591388[/C][/ROW]
[ROW][C]8[/C][C]1.48[/C][C]1.4806165421485[/C][C]-0.000616542148499051[/C][/ROW]
[ROW][C]9[/C][C]1.48[/C][C]1.48057402200033[/C][C]-0.000574022000326703[/C][/ROW]
[ROW][C]10[/C][C]1.48[/C][C]1.47946498157210[/C][C]0.000535018427903076[/C][/ROW]
[ROW][C]11[/C][C]1.48[/C][C]1.47942246142392[/C][C]0.000577538576075424[/C][/ROW]
[ROW][C]12[/C][C]1.48[/C][C]1.47937994127575[/C][C]0.000620058724247772[/C][/ROW]
[ROW][C]13[/C][C]1.48[/C][C]1.47933742112758[/C][C]0.000662578872420121[/C][/ROW]
[ROW][C]14[/C][C]1.48[/C][C]1.47929490097941[/C][C]0.000705099020592469[/C][/ROW]
[ROW][C]15[/C][C]1.48[/C][C]1.47925238083124[/C][C]0.000747619168764818[/C][/ROW]
[ROW][C]16[/C][C]1.48[/C][C]1.47920986068306[/C][C]0.000790139316937166[/C][/ROW]
[ROW][C]17[/C][C]1.48[/C][C]1.47916734053489[/C][C]0.000832659465109514[/C][/ROW]
[ROW][C]18[/C][C]1.48[/C][C]1.47912482038672[/C][C]0.000875179613281863[/C][/ROW]
[ROW][C]19[/C][C]1.48[/C][C]1.47908230023855[/C][C]0.000917699761454211[/C][/ROW]
[ROW][C]20[/C][C]1.48[/C][C]1.48010630037043[/C][C]-0.000106300370430871[/C][/ROW]
[ROW][C]21[/C][C]1.48[/C][C]1.48006378022226[/C][C]-6.37802222585223e-05[/C][/ROW]
[ROW][C]22[/C][C]1.48[/C][C]1.48002126007409[/C][C]-2.12600740861740e-05[/C][/ROW]
[ROW][C]23[/C][C]1.48[/C][C]1.48104526020597[/C][C]-0.00104526020597126[/C][/ROW]
[ROW][C]24[/C][C]1.48[/C][C]1.4810027400578[/C][C]-0.00100274005779891[/C][/ROW]
[ROW][C]25[/C][C]1.48[/C][C]1.48096021990963[/C][C]-0.00096021990962656[/C][/ROW]
[ROW][C]26[/C][C]1.48[/C][C]1.48091769976145[/C][C]-0.00091769976145421[/C][/ROW]
[ROW][C]27[/C][C]1.48[/C][C]1.48087517961328[/C][C]-0.000875179613281862[/C][/ROW]
[ROW][C]28[/C][C]1.48[/C][C]1.48083265946511[/C][C]-0.000832659465109514[/C][/ROW]
[ROW][C]29[/C][C]1.48[/C][C]1.48079013931694[/C][C]-0.000790139316937166[/C][/ROW]
[ROW][C]30[/C][C]1.48[/C][C]1.48074761916876[/C][C]-0.000747619168764817[/C][/ROW]
[ROW][C]31[/C][C]1.48[/C][C]1.48070509902059[/C][C]-0.000705099020592469[/C][/ROW]
[ROW][C]32[/C][C]1.48[/C][C]1.48066257887242[/C][C]-0.00066257887242012[/C][/ROW]
[ROW][C]33[/C][C]1.48[/C][C]1.47955353844419[/C][C]0.000446461555809658[/C][/ROW]
[ROW][C]34[/C][C]1.48[/C][C]1.47951101829602[/C][C]0.000488981703982006[/C][/ROW]
[ROW][C]35[/C][C]1.48[/C][C]1.47946849814785[/C][C]0.000531501852154354[/C][/ROW]
[ROW][C]36[/C][C]1.48[/C][C]1.47942597799967[/C][C]0.000574022000326703[/C][/ROW]
[ROW][C]37[/C][C]1.48[/C][C]1.4793834578515[/C][C]0.000616542148499051[/C][/ROW]
[ROW][C]38[/C][C]1.57[/C][C]1.5772318240723[/C][C]-0.00723182407229876[/C][/ROW]
[ROW][C]39[/C][C]1.58[/C][C]1.57825582420418[/C][C]0.00174417579581617[/C][/ROW]
[ROW][C]40[/C][C]1.58[/C][C]1.57821330405601[/C][C]0.00178669594398852[/C][/ROW]
[ROW][C]41[/C][C]1.58[/C][C]1.57817078390784[/C][C]0.00182921609216086[/C][/ROW]
[ROW][C]42[/C][C]1.58[/C][C]1.57812826375967[/C][C]0.00187173624033321[/C][/ROW]
[ROW][C]43[/C][C]1.59[/C][C]1.59671508336792[/C][C]-0.00671508336792435[/C][/ROW]
[ROW][C]44[/C][C]1.6[/C][C]1.60032072295888[/C][C]-0.000320722958884853[/C][/ROW]
[ROW][C]45[/C][C]1.6[/C][C]1.60392636254985[/C][C]-0.00392636254984536[/C][/ROW]
[ROW][C]46[/C][C]1.61[/C][C]1.60753200214081[/C][C]0.00246799785919413[/C][/ROW]
[ROW][C]47[/C][C]1.61[/C][C]1.61220416201182[/C][C]-0.00220416201182380[/C][/ROW]
[ROW][C]48[/C][C]1.61[/C][C]1.61580980160278[/C][C]-0.00580980160278431[/C][/ROW]
[ROW][C]49[/C][C]1.62[/C][C]1.61941544119374[/C][C]0.000584558806255193[/C][/ROW]
[ROW][C]50[/C][C]1.63[/C][C]1.62302108078471[/C][C]0.00697891921529447[/C][/ROW]
[ROW][C]51[/C][C]1.63[/C][C]1.62662672037567[/C][C]0.00337327962433396[/C][/ROW]
[ROW][C]52[/C][C]1.64[/C][C]1.62916583968657[/C][C]0.0108341603134309[/C][/ROW]
[ROW][C]53[/C][C]1.64[/C][C]1.63383799955759[/C][C]0.00616200044241296[/C][/ROW]
[ROW][C]54[/C][C]1.64[/C][C]1.63637711886849[/C][C]0.00362288113150988[/C][/ROW]
[ROW][C]55[/C][C]1.64[/C][C]1.63998275845945[/C][C]1.72415405493680e-05[/C][/ROW]
[ROW][C]56[/C][C]1.64[/C][C]1.64465491833047[/C][C]-0.00465491833046857[/C][/ROW]
[ROW][C]57[/C][C]1.65[/C][C]1.64719403764137[/C][C]0.00280596235862836[/C][/ROW]
[ROW][C]58[/C][C]1.65[/C][C]1.65079967723233[/C][C]-0.000799677232332149[/C][/ROW]
[ROW][C]59[/C][C]1.65[/C][C]1.65440531682329[/C][C]-0.00440531682329266[/C][/ROW]
[ROW][C]60[/C][C]1.65[/C][C]1.65801095641425[/C][C]-0.00801095641425316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66444&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66444&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.431.43109905175307-0.00109905175307049
21.431.43105653160489-0.00105653160489032
31.431.43101401145672-0.00101401145671809
41.431.43097149130855-0.000971491308545764
51.431.43199549144043-0.00199549144043077
61.431.43195297129226-0.00195297129225846
71.441.431910451144090.00808954885591388
81.481.4806165421485-0.000616542148499051
91.481.48057402200033-0.000574022000326703
101.481.479464981572100.000535018427903076
111.481.479422461423920.000577538576075424
121.481.479379941275750.000620058724247772
131.481.479337421127580.000662578872420121
141.481.479294900979410.000705099020592469
151.481.479252380831240.000747619168764818
161.481.479209860683060.000790139316937166
171.481.479167340534890.000832659465109514
181.481.479124820386720.000875179613281863
191.481.479082300238550.000917699761454211
201.481.48010630037043-0.000106300370430871
211.481.48006378022226-6.37802222585223e-05
221.481.48002126007409-2.12600740861740e-05
231.481.48104526020597-0.00104526020597126
241.481.4810027400578-0.00100274005779891
251.481.48096021990963-0.00096021990962656
261.481.48091769976145-0.00091769976145421
271.481.48087517961328-0.000875179613281862
281.481.48083265946511-0.000832659465109514
291.481.48079013931694-0.000790139316937166
301.481.48074761916876-0.000747619168764817
311.481.48070509902059-0.000705099020592469
321.481.48066257887242-0.00066257887242012
331.481.479553538444190.000446461555809658
341.481.479511018296020.000488981703982006
351.481.479468498147850.000531501852154354
361.481.479425977999670.000574022000326703
371.481.47938345785150.000616542148499051
381.571.5772318240723-0.00723182407229876
391.581.578255824204180.00174417579581617
401.581.578213304056010.00178669594398852
411.581.578170783907840.00182921609216086
421.581.578128263759670.00187173624033321
431.591.59671508336792-0.00671508336792435
441.61.60032072295888-0.000320722958884853
451.61.60392636254985-0.00392636254984536
461.611.607532002140810.00246799785919413
471.611.61220416201182-0.00220416201182380
481.611.61580980160278-0.00580980160278431
491.621.619415441193740.000584558806255193
501.631.623021080784710.00697891921529447
511.631.626626720375670.00337327962433396
521.641.629165839686570.0108341603134309
531.641.633837999557590.00616200044241296
541.641.636377118868490.00362288113150988
551.641.639982758459451.72415405493680e-05
561.641.64465491833047-0.00465491833046857
571.651.647194037641370.00280596235862836
581.651.65079967723233-0.000799677232332149
591.651.65440531682329-0.00440531682329266
601.651.65801095641425-0.00801095641425316






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.7739180696886990.4521638606226030.226081930311301
110.6432323218519690.7135353562960610.356767678148031
120.5105203789750680.9789592420498650.489479621024932
130.389162207489990.778324414979980.61083779251001
140.2857981949037080.5715963898074160.714201805096292
150.2023856939342800.4047713878685610.79761430606572
160.1382340571729120.2764681143458230.861765942827088
170.09122407570236390.1824481514047280.908775924297636
180.0585448736016780.1170897472033560.941455126398322
190.037372241739650.07474448347930.96262775826035
200.02954007348549210.05908014697098420.970459926514508
210.02007019617481580.04014039234963160.979929803825184
220.01326481905767650.02652963811535310.986735180942323
230.00841173269725450.0168234653945090.991588267302746
240.004733299465470610.009466598930941220.99526670053453
250.002495822349729560.004991644699459120.99750417765027
260.001250505470524840.002501010941049670.998749494529475
270.000597824803303810.001195649606607620.999402175196696
280.0002727144522704780.0005454289045409550.99972728554773
290.0001184383650154380.0002368767300308760.999881561634985
304.88151434487691e-059.76302868975382e-050.999951184856551
311.90910209652536e-053.81820419305071e-050.999980908979035
327.21299868379037e-061.44259973675807e-050.999992787001316
332.75396185755998e-065.50792371511996e-060.999997246038142
341.01473542438012e-062.02947084876023e-060.999998985264576
353.52732125553602e-077.05464251107204e-070.999999647267874
361.13701449030775e-072.2740289806155e-070.99999988629855
373.41751354709079e-086.83502709418157e-080.999999965824865
381.79021519541505e-083.58043039083009e-080.999999982097848
398.58522447591033e-071.71704489518207e-060.999999141477552
409.57314447626582e-071.91462889525316e-060.999999042685552
415.56025034460861e-071.11205006892172e-060.999999443974966
422.47800894435532e-074.95601788871065e-070.999999752199106
433.35096191529875e-076.7019238305975e-070.999999664903809
441.17036784044181e-072.34073568088362e-070.999999882963216
458.8004149935026e-071.76008299870052e-060.9999991199585
466.79646240553768e-071.35929248110754e-060.99999932035376
472.54632883548039e-065.09265767096078e-060.999997453671164
480.002784042807335890.005568085614671780.997215957192664
490.03794769834171060.07589539668342120.96205230165829
500.02893882386138170.05787764772276340.971061176138618

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.773918069688699 & 0.452163860622603 & 0.226081930311301 \tabularnewline
11 & 0.643232321851969 & 0.713535356296061 & 0.356767678148031 \tabularnewline
12 & 0.510520378975068 & 0.978959242049865 & 0.489479621024932 \tabularnewline
13 & 0.38916220748999 & 0.77832441497998 & 0.61083779251001 \tabularnewline
14 & 0.285798194903708 & 0.571596389807416 & 0.714201805096292 \tabularnewline
15 & 0.202385693934280 & 0.404771387868561 & 0.79761430606572 \tabularnewline
16 & 0.138234057172912 & 0.276468114345823 & 0.861765942827088 \tabularnewline
17 & 0.0912240757023639 & 0.182448151404728 & 0.908775924297636 \tabularnewline
18 & 0.058544873601678 & 0.117089747203356 & 0.941455126398322 \tabularnewline
19 & 0.03737224173965 & 0.0747444834793 & 0.96262775826035 \tabularnewline
20 & 0.0295400734854921 & 0.0590801469709842 & 0.970459926514508 \tabularnewline
21 & 0.0200701961748158 & 0.0401403923496316 & 0.979929803825184 \tabularnewline
22 & 0.0132648190576765 & 0.0265296381153531 & 0.986735180942323 \tabularnewline
23 & 0.0084117326972545 & 0.016823465394509 & 0.991588267302746 \tabularnewline
24 & 0.00473329946547061 & 0.00946659893094122 & 0.99526670053453 \tabularnewline
25 & 0.00249582234972956 & 0.00499164469945912 & 0.99750417765027 \tabularnewline
26 & 0.00125050547052484 & 0.00250101094104967 & 0.998749494529475 \tabularnewline
27 & 0.00059782480330381 & 0.00119564960660762 & 0.999402175196696 \tabularnewline
28 & 0.000272714452270478 & 0.000545428904540955 & 0.99972728554773 \tabularnewline
29 & 0.000118438365015438 & 0.000236876730030876 & 0.999881561634985 \tabularnewline
30 & 4.88151434487691e-05 & 9.76302868975382e-05 & 0.999951184856551 \tabularnewline
31 & 1.90910209652536e-05 & 3.81820419305071e-05 & 0.999980908979035 \tabularnewline
32 & 7.21299868379037e-06 & 1.44259973675807e-05 & 0.999992787001316 \tabularnewline
33 & 2.75396185755998e-06 & 5.50792371511996e-06 & 0.999997246038142 \tabularnewline
34 & 1.01473542438012e-06 & 2.02947084876023e-06 & 0.999998985264576 \tabularnewline
35 & 3.52732125553602e-07 & 7.05464251107204e-07 & 0.999999647267874 \tabularnewline
36 & 1.13701449030775e-07 & 2.2740289806155e-07 & 0.99999988629855 \tabularnewline
37 & 3.41751354709079e-08 & 6.83502709418157e-08 & 0.999999965824865 \tabularnewline
38 & 1.79021519541505e-08 & 3.58043039083009e-08 & 0.999999982097848 \tabularnewline
39 & 8.58522447591033e-07 & 1.71704489518207e-06 & 0.999999141477552 \tabularnewline
40 & 9.57314447626582e-07 & 1.91462889525316e-06 & 0.999999042685552 \tabularnewline
41 & 5.56025034460861e-07 & 1.11205006892172e-06 & 0.999999443974966 \tabularnewline
42 & 2.47800894435532e-07 & 4.95601788871065e-07 & 0.999999752199106 \tabularnewline
43 & 3.35096191529875e-07 & 6.7019238305975e-07 & 0.999999664903809 \tabularnewline
44 & 1.17036784044181e-07 & 2.34073568088362e-07 & 0.999999882963216 \tabularnewline
45 & 8.8004149935026e-07 & 1.76008299870052e-06 & 0.9999991199585 \tabularnewline
46 & 6.79646240553768e-07 & 1.35929248110754e-06 & 0.99999932035376 \tabularnewline
47 & 2.54632883548039e-06 & 5.09265767096078e-06 & 0.999997453671164 \tabularnewline
48 & 0.00278404280733589 & 0.00556808561467178 & 0.997215957192664 \tabularnewline
49 & 0.0379476983417106 & 0.0758953966834212 & 0.96205230165829 \tabularnewline
50 & 0.0289388238613817 & 0.0578776477227634 & 0.971061176138618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66444&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]10[/C][C]0.773918069688699[/C][C]0.452163860622603[/C][C]0.226081930311301[/C][/ROW]
[ROW][C]11[/C][C]0.643232321851969[/C][C]0.713535356296061[/C][C]0.356767678148031[/C][/ROW]
[ROW][C]12[/C][C]0.510520378975068[/C][C]0.978959242049865[/C][C]0.489479621024932[/C][/ROW]
[ROW][C]13[/C][C]0.38916220748999[/C][C]0.77832441497998[/C][C]0.61083779251001[/C][/ROW]
[ROW][C]14[/C][C]0.285798194903708[/C][C]0.571596389807416[/C][C]0.714201805096292[/C][/ROW]
[ROW][C]15[/C][C]0.202385693934280[/C][C]0.404771387868561[/C][C]0.79761430606572[/C][/ROW]
[ROW][C]16[/C][C]0.138234057172912[/C][C]0.276468114345823[/C][C]0.861765942827088[/C][/ROW]
[ROW][C]17[/C][C]0.0912240757023639[/C][C]0.182448151404728[/C][C]0.908775924297636[/C][/ROW]
[ROW][C]18[/C][C]0.058544873601678[/C][C]0.117089747203356[/C][C]0.941455126398322[/C][/ROW]
[ROW][C]19[/C][C]0.03737224173965[/C][C]0.0747444834793[/C][C]0.96262775826035[/C][/ROW]
[ROW][C]20[/C][C]0.0295400734854921[/C][C]0.0590801469709842[/C][C]0.970459926514508[/C][/ROW]
[ROW][C]21[/C][C]0.0200701961748158[/C][C]0.0401403923496316[/C][C]0.979929803825184[/C][/ROW]
[ROW][C]22[/C][C]0.0132648190576765[/C][C]0.0265296381153531[/C][C]0.986735180942323[/C][/ROW]
[ROW][C]23[/C][C]0.0084117326972545[/C][C]0.016823465394509[/C][C]0.991588267302746[/C][/ROW]
[ROW][C]24[/C][C]0.00473329946547061[/C][C]0.00946659893094122[/C][C]0.99526670053453[/C][/ROW]
[ROW][C]25[/C][C]0.00249582234972956[/C][C]0.00499164469945912[/C][C]0.99750417765027[/C][/ROW]
[ROW][C]26[/C][C]0.00125050547052484[/C][C]0.00250101094104967[/C][C]0.998749494529475[/C][/ROW]
[ROW][C]27[/C][C]0.00059782480330381[/C][C]0.00119564960660762[/C][C]0.999402175196696[/C][/ROW]
[ROW][C]28[/C][C]0.000272714452270478[/C][C]0.000545428904540955[/C][C]0.99972728554773[/C][/ROW]
[ROW][C]29[/C][C]0.000118438365015438[/C][C]0.000236876730030876[/C][C]0.999881561634985[/C][/ROW]
[ROW][C]30[/C][C]4.88151434487691e-05[/C][C]9.76302868975382e-05[/C][C]0.999951184856551[/C][/ROW]
[ROW][C]31[/C][C]1.90910209652536e-05[/C][C]3.81820419305071e-05[/C][C]0.999980908979035[/C][/ROW]
[ROW][C]32[/C][C]7.21299868379037e-06[/C][C]1.44259973675807e-05[/C][C]0.999992787001316[/C][/ROW]
[ROW][C]33[/C][C]2.75396185755998e-06[/C][C]5.50792371511996e-06[/C][C]0.999997246038142[/C][/ROW]
[ROW][C]34[/C][C]1.01473542438012e-06[/C][C]2.02947084876023e-06[/C][C]0.999998985264576[/C][/ROW]
[ROW][C]35[/C][C]3.52732125553602e-07[/C][C]7.05464251107204e-07[/C][C]0.999999647267874[/C][/ROW]
[ROW][C]36[/C][C]1.13701449030775e-07[/C][C]2.2740289806155e-07[/C][C]0.99999988629855[/C][/ROW]
[ROW][C]37[/C][C]3.41751354709079e-08[/C][C]6.83502709418157e-08[/C][C]0.999999965824865[/C][/ROW]
[ROW][C]38[/C][C]1.79021519541505e-08[/C][C]3.58043039083009e-08[/C][C]0.999999982097848[/C][/ROW]
[ROW][C]39[/C][C]8.58522447591033e-07[/C][C]1.71704489518207e-06[/C][C]0.999999141477552[/C][/ROW]
[ROW][C]40[/C][C]9.57314447626582e-07[/C][C]1.91462889525316e-06[/C][C]0.999999042685552[/C][/ROW]
[ROW][C]41[/C][C]5.56025034460861e-07[/C][C]1.11205006892172e-06[/C][C]0.999999443974966[/C][/ROW]
[ROW][C]42[/C][C]2.47800894435532e-07[/C][C]4.95601788871065e-07[/C][C]0.999999752199106[/C][/ROW]
[ROW][C]43[/C][C]3.35096191529875e-07[/C][C]6.7019238305975e-07[/C][C]0.999999664903809[/C][/ROW]
[ROW][C]44[/C][C]1.17036784044181e-07[/C][C]2.34073568088362e-07[/C][C]0.999999882963216[/C][/ROW]
[ROW][C]45[/C][C]8.8004149935026e-07[/C][C]1.76008299870052e-06[/C][C]0.9999991199585[/C][/ROW]
[ROW][C]46[/C][C]6.79646240553768e-07[/C][C]1.35929248110754e-06[/C][C]0.99999932035376[/C][/ROW]
[ROW][C]47[/C][C]2.54632883548039e-06[/C][C]5.09265767096078e-06[/C][C]0.999997453671164[/C][/ROW]
[ROW][C]48[/C][C]0.00278404280733589[/C][C]0.00556808561467178[/C][C]0.997215957192664[/C][/ROW]
[ROW][C]49[/C][C]0.0379476983417106[/C][C]0.0758953966834212[/C][C]0.96205230165829[/C][/ROW]
[ROW][C]50[/C][C]0.0289388238613817[/C][C]0.0578776477227634[/C][C]0.971061176138618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66444&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66444&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
100.7739180696886990.4521638606226030.226081930311301
110.6432323218519690.7135353562960610.356767678148031
120.5105203789750680.9789592420498650.489479621024932
130.389162207489990.778324414979980.61083779251001
140.2857981949037080.5715963898074160.714201805096292
150.2023856939342800.4047713878685610.79761430606572
160.1382340571729120.2764681143458230.861765942827088
170.09122407570236390.1824481514047280.908775924297636
180.0585448736016780.1170897472033560.941455126398322
190.037372241739650.07474448347930.96262775826035
200.02954007348549210.05908014697098420.970459926514508
210.02007019617481580.04014039234963160.979929803825184
220.01326481905767650.02652963811535310.986735180942323
230.00841173269725450.0168234653945090.991588267302746
240.004733299465470610.009466598930941220.99526670053453
250.002495822349729560.004991644699459120.99750417765027
260.001250505470524840.002501010941049670.998749494529475
270.000597824803303810.001195649606607620.999402175196696
280.0002727144522704780.0005454289045409550.99972728554773
290.0001184383650154380.0002368767300308760.999881561634985
304.88151434487691e-059.76302868975382e-050.999951184856551
311.90910209652536e-053.81820419305071e-050.999980908979035
327.21299868379037e-061.44259973675807e-050.999992787001316
332.75396185755998e-065.50792371511996e-060.999997246038142
341.01473542438012e-062.02947084876023e-060.999998985264576
353.52732125553602e-077.05464251107204e-070.999999647267874
361.13701449030775e-072.2740289806155e-070.99999988629855
373.41751354709079e-086.83502709418157e-080.999999965824865
381.79021519541505e-083.58043039083009e-080.999999982097848
398.58522447591033e-071.71704489518207e-060.999999141477552
409.57314447626582e-071.91462889525316e-060.999999042685552
415.56025034460861e-071.11205006892172e-060.999999443974966
422.47800894435532e-074.95601788871065e-070.999999752199106
433.35096191529875e-076.7019238305975e-070.999999664903809
441.17036784044181e-072.34073568088362e-070.999999882963216
458.8004149935026e-071.76008299870052e-060.9999991199585
466.79646240553768e-071.35929248110754e-060.99999932035376
472.54632883548039e-065.09265767096078e-060.999997453671164
480.002784042807335890.005568085614671780.997215957192664
490.03794769834171060.07589539668342120.96205230165829
500.02893882386138170.05787764772276340.971061176138618






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level250.609756097560976NOK
5% type I error level280.682926829268293NOK
10% type I error level320.780487804878049NOK

\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 & 25 & 0.609756097560976 & NOK \tabularnewline
5% type I error level & 28 & 0.682926829268293 & NOK \tabularnewline
10% type I error level & 32 & 0.780487804878049 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66444&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]25[/C][C]0.609756097560976[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.682926829268293[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.780487804878049[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66444&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66444&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 level250.609756097560976NOK
5% type I error level280.682926829268293NOK
10% type I error level320.780487804878049NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}