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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:56:42 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258732772p6s3bw3hg8i5lp8.htm/, Retrieved Thu, 28 Mar 2024 21:07:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58289, Retrieved Thu, 28 Mar 2024 21:07:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD    [Multiple Regression] [ws7] [2009-11-20 15:56:42] [b243db81ea3e1f02fb3382887fb0f701] [Current]
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Dataseries X:
2756,76	3016,70
2849,27	3052,40
2921,44	3099,60
2981,85	3103,30
3080,58	3119,80
3106,22	3093,70
3119,31	3164,90
3061,26	3311,50
3097,31	3410,60
3161,69	3392,60
3257,16	3338,20
3277,01	3285,10
3295,32	3294,80
3363,99	3611,20
3494,17	3611,30
3667,03	3521,00
3813,06	3519,30
3917,96	3438,30
3895,51	3534,90
3801,06	3705,80
3570,12	3807,60
3701,61	3663,00
3862,27	3604,50
3970,10	3563,80
4138,52	3511,40
4199,75	3546,50
4290,89	3525,40
4443,91	3529,90
4502,64	3591,60
4356,98	3668,30
4591,27	3728,80
4696,96	3853,60
4621,40	3897,70
4562,84	3640,70
4202,52	3495,50
4296,49	3495,10
4435,23	3268,00
4105,18	3479,10
4116,68	3417,80
3844,49	3521,30
3720,98	3487,10
3674,40	3529,90
3857,62	3544,30
3801,06	3710,80
3504,37	3641,90
3032,60	3447,10
3047,03	3386,80
2962,34	3438,50
2197,82	3364,30
2014,45	3462,70
1862,83	3291,90
1905,41	3550,00
1810,99	3611,00
1670,07	3708,60
1864,44	3771,10
2052,02	4042,70
2029,60	3988,40
2070,83	3851,20
2293,41	3876,70




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \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=58289&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/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=58289&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58289&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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
Bel20[t] = + 2724.04422398202 + 0.188672136013879`Zichtrekeningen `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bel20[t] =  +  2724.04422398202 +  0.188672136013879`Zichtrekeningen
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58289&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bel20[t] =  +  2724.04422398202 +  0.188672136013879`Zichtrekeningen
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58289&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58289&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
Bel20[t] = + 2724.04422398202 + 0.188672136013879`Zichtrekeningen `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2724.044223982021711.2043421.59190.1169410.058471
`Zichtrekeningen `0.1886721360138790.4863760.38790.6995250.349762

\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) & 2724.04422398202 & 1711.204342 & 1.5919 & 0.116941 & 0.058471 \tabularnewline
`Zichtrekeningen
` & 0.188672136013879 & 0.486376 & 0.3879 & 0.699525 & 0.349762 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58289&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]2724.04422398202[/C][C]1711.204342[/C][C]1.5919[/C][C]0.116941[/C][C]0.058471[/C][/ROW]
[ROW][C]`Zichtrekeningen
`[/C][C]0.188672136013879[/C][C]0.486376[/C][C]0.3879[/C][C]0.699525[/C][C]0.349762[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58289&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58289&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)2724.044223982021711.2043421.59190.1169410.058471
`Zichtrekeningen `0.1886721360138790.4863760.38790.6995250.349762







Multiple Linear Regression - Regression Statistics
Multiple R0.0513127948145057
R-squared0.00263300291167556
Adjusted R-squared-0.0148646637039092
F-TEST (value)0.150477373327620
F-TEST (DF numerator)1
F-TEST (DF denominator)57
p-value0.699524510799623
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation853.9877328034
Sum Squared Residuals41569817.7233853

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0513127948145057 \tabularnewline
R-squared & 0.00263300291167556 \tabularnewline
Adjusted R-squared & -0.0148646637039092 \tabularnewline
F-TEST (value) & 0.150477373327620 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0.699524510799623 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 853.9877328034 \tabularnewline
Sum Squared Residuals & 41569817.7233853 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58289&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0513127948145057[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00263300291167556[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0148646637039092[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.150477373327620[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]0.699524510799623[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]853.9877328034[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]41569817.7233853[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58289&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58289&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 R0.0513127948145057
R-squared0.00263300291167556
Adjusted R-squared-0.0148646637039092
F-TEST (value)0.150477373327620
F-TEST (DF numerator)1
F-TEST (DF denominator)57
p-value0.699524510799623
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation853.9877328034
Sum Squared Residuals41569817.7233853







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12756.763293.21145669509-536.451456695092
22849.273299.94705195079-450.677051950789
32921.443308.85237677064-387.412376770645
42981.853309.55046367390-327.700463673896
53080.583312.66355391813-232.083553918125
63106.223307.73921116816-201.519211168163
73119.313321.17266725235-201.862667252351
83061.263348.83200239199-287.572002391986
93097.313367.52941107096-270.219411070961
103161.693364.13331262271-202.443312622711
113257.163353.86954842356-96.7095484235565
123277.013343.85105800122-66.8410580012192
133295.323345.68117772055-50.3611777205539
143363.993405.37704155535-41.3870415553457
153494.173405.3959087689588.7740912310531
163667.033388.35881488689278.671185113107
173813.063388.03807225567425.02192774433
183917.963372.75562923855545.204370761454
193895.513390.98135757749504.528642422514
203801.063423.22542562226377.834574377741
213570.123442.43224906847127.687750931528
223701.613415.15025820086286.459741799136
233862.273404.11293824405458.157061755947
243970.13396.43398230829573.666017691712
254138.523386.54756238116751.97243761884
264199.753393.16995435525806.580045644752
274290.893389.18897228535901.701027714646
284443.913390.037996897421053.87200310258
294502.643401.679067689471100.96093231053
304356.983416.15022052174940.829779478261
314591.273427.564884750581163.70511524942
324696.963451.111167325111245.84883267489
334621.43459.431608523321161.96839147668
344562.843410.942869567751151.89713043225
354202.523383.54767541854818.97232458146
364296.493383.47220656413913.017793435866
374435.233340.624764475381094.60523552462
384105.183380.45345238791724.726547612088
394116.683368.88785045026747.792149549739
403844.493388.4154165277456.074583472302
413720.983381.96282947602339.017170523977
423674.43390.03799689742284.362003102583
433857.623392.75487565602464.865124343983
443801.063424.16878630233376.891213697672
453504.373411.1692761309793.2007238690282
463032.63374.41594403547-341.815944035468
473047.033363.03901423383-316.009014233831
482962.343372.79336366575-410.453363665748
492197.823358.79389117352-1160.97389117352
502014.453377.35922935728-1362.90922935728
511862.833345.13402852611-1482.30402852611
521905.413393.83030683130-1488.42030683130
531810.993405.33930712814-1594.34930712814
541670.073423.7537076031-1753.68370760310
551864.443435.54571610396-1571.10571610396
562052.023486.78906824533-1434.76906824533
572029.63476.54417125978-1446.94417125978
582070.833450.65835419868-1379.82835419868
592293.413455.46949366703-1162.05949366703

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2756.76 & 3293.21145669509 & -536.451456695092 \tabularnewline
2 & 2849.27 & 3299.94705195079 & -450.677051950789 \tabularnewline
3 & 2921.44 & 3308.85237677064 & -387.412376770645 \tabularnewline
4 & 2981.85 & 3309.55046367390 & -327.700463673896 \tabularnewline
5 & 3080.58 & 3312.66355391813 & -232.083553918125 \tabularnewline
6 & 3106.22 & 3307.73921116816 & -201.519211168163 \tabularnewline
7 & 3119.31 & 3321.17266725235 & -201.862667252351 \tabularnewline
8 & 3061.26 & 3348.83200239199 & -287.572002391986 \tabularnewline
9 & 3097.31 & 3367.52941107096 & -270.219411070961 \tabularnewline
10 & 3161.69 & 3364.13331262271 & -202.443312622711 \tabularnewline
11 & 3257.16 & 3353.86954842356 & -96.7095484235565 \tabularnewline
12 & 3277.01 & 3343.85105800122 & -66.8410580012192 \tabularnewline
13 & 3295.32 & 3345.68117772055 & -50.3611777205539 \tabularnewline
14 & 3363.99 & 3405.37704155535 & -41.3870415553457 \tabularnewline
15 & 3494.17 & 3405.39590876895 & 88.7740912310531 \tabularnewline
16 & 3667.03 & 3388.35881488689 & 278.671185113107 \tabularnewline
17 & 3813.06 & 3388.03807225567 & 425.02192774433 \tabularnewline
18 & 3917.96 & 3372.75562923855 & 545.204370761454 \tabularnewline
19 & 3895.51 & 3390.98135757749 & 504.528642422514 \tabularnewline
20 & 3801.06 & 3423.22542562226 & 377.834574377741 \tabularnewline
21 & 3570.12 & 3442.43224906847 & 127.687750931528 \tabularnewline
22 & 3701.61 & 3415.15025820086 & 286.459741799136 \tabularnewline
23 & 3862.27 & 3404.11293824405 & 458.157061755947 \tabularnewline
24 & 3970.1 & 3396.43398230829 & 573.666017691712 \tabularnewline
25 & 4138.52 & 3386.54756238116 & 751.97243761884 \tabularnewline
26 & 4199.75 & 3393.16995435525 & 806.580045644752 \tabularnewline
27 & 4290.89 & 3389.18897228535 & 901.701027714646 \tabularnewline
28 & 4443.91 & 3390.03799689742 & 1053.87200310258 \tabularnewline
29 & 4502.64 & 3401.67906768947 & 1100.96093231053 \tabularnewline
30 & 4356.98 & 3416.15022052174 & 940.829779478261 \tabularnewline
31 & 4591.27 & 3427.56488475058 & 1163.70511524942 \tabularnewline
32 & 4696.96 & 3451.11116732511 & 1245.84883267489 \tabularnewline
33 & 4621.4 & 3459.43160852332 & 1161.96839147668 \tabularnewline
34 & 4562.84 & 3410.94286956775 & 1151.89713043225 \tabularnewline
35 & 4202.52 & 3383.54767541854 & 818.97232458146 \tabularnewline
36 & 4296.49 & 3383.47220656413 & 913.017793435866 \tabularnewline
37 & 4435.23 & 3340.62476447538 & 1094.60523552462 \tabularnewline
38 & 4105.18 & 3380.45345238791 & 724.726547612088 \tabularnewline
39 & 4116.68 & 3368.88785045026 & 747.792149549739 \tabularnewline
40 & 3844.49 & 3388.4154165277 & 456.074583472302 \tabularnewline
41 & 3720.98 & 3381.96282947602 & 339.017170523977 \tabularnewline
42 & 3674.4 & 3390.03799689742 & 284.362003102583 \tabularnewline
43 & 3857.62 & 3392.75487565602 & 464.865124343983 \tabularnewline
44 & 3801.06 & 3424.16878630233 & 376.891213697672 \tabularnewline
45 & 3504.37 & 3411.16927613097 & 93.2007238690282 \tabularnewline
46 & 3032.6 & 3374.41594403547 & -341.815944035468 \tabularnewline
47 & 3047.03 & 3363.03901423383 & -316.009014233831 \tabularnewline
48 & 2962.34 & 3372.79336366575 & -410.453363665748 \tabularnewline
49 & 2197.82 & 3358.79389117352 & -1160.97389117352 \tabularnewline
50 & 2014.45 & 3377.35922935728 & -1362.90922935728 \tabularnewline
51 & 1862.83 & 3345.13402852611 & -1482.30402852611 \tabularnewline
52 & 1905.41 & 3393.83030683130 & -1488.42030683130 \tabularnewline
53 & 1810.99 & 3405.33930712814 & -1594.34930712814 \tabularnewline
54 & 1670.07 & 3423.7537076031 & -1753.68370760310 \tabularnewline
55 & 1864.44 & 3435.54571610396 & -1571.10571610396 \tabularnewline
56 & 2052.02 & 3486.78906824533 & -1434.76906824533 \tabularnewline
57 & 2029.6 & 3476.54417125978 & -1446.94417125978 \tabularnewline
58 & 2070.83 & 3450.65835419868 & -1379.82835419868 \tabularnewline
59 & 2293.41 & 3455.46949366703 & -1162.05949366703 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58289&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]2756.76[/C][C]3293.21145669509[/C][C]-536.451456695092[/C][/ROW]
[ROW][C]2[/C][C]2849.27[/C][C]3299.94705195079[/C][C]-450.677051950789[/C][/ROW]
[ROW][C]3[/C][C]2921.44[/C][C]3308.85237677064[/C][C]-387.412376770645[/C][/ROW]
[ROW][C]4[/C][C]2981.85[/C][C]3309.55046367390[/C][C]-327.700463673896[/C][/ROW]
[ROW][C]5[/C][C]3080.58[/C][C]3312.66355391813[/C][C]-232.083553918125[/C][/ROW]
[ROW][C]6[/C][C]3106.22[/C][C]3307.73921116816[/C][C]-201.519211168163[/C][/ROW]
[ROW][C]7[/C][C]3119.31[/C][C]3321.17266725235[/C][C]-201.862667252351[/C][/ROW]
[ROW][C]8[/C][C]3061.26[/C][C]3348.83200239199[/C][C]-287.572002391986[/C][/ROW]
[ROW][C]9[/C][C]3097.31[/C][C]3367.52941107096[/C][C]-270.219411070961[/C][/ROW]
[ROW][C]10[/C][C]3161.69[/C][C]3364.13331262271[/C][C]-202.443312622711[/C][/ROW]
[ROW][C]11[/C][C]3257.16[/C][C]3353.86954842356[/C][C]-96.7095484235565[/C][/ROW]
[ROW][C]12[/C][C]3277.01[/C][C]3343.85105800122[/C][C]-66.8410580012192[/C][/ROW]
[ROW][C]13[/C][C]3295.32[/C][C]3345.68117772055[/C][C]-50.3611777205539[/C][/ROW]
[ROW][C]14[/C][C]3363.99[/C][C]3405.37704155535[/C][C]-41.3870415553457[/C][/ROW]
[ROW][C]15[/C][C]3494.17[/C][C]3405.39590876895[/C][C]88.7740912310531[/C][/ROW]
[ROW][C]16[/C][C]3667.03[/C][C]3388.35881488689[/C][C]278.671185113107[/C][/ROW]
[ROW][C]17[/C][C]3813.06[/C][C]3388.03807225567[/C][C]425.02192774433[/C][/ROW]
[ROW][C]18[/C][C]3917.96[/C][C]3372.75562923855[/C][C]545.204370761454[/C][/ROW]
[ROW][C]19[/C][C]3895.51[/C][C]3390.98135757749[/C][C]504.528642422514[/C][/ROW]
[ROW][C]20[/C][C]3801.06[/C][C]3423.22542562226[/C][C]377.834574377741[/C][/ROW]
[ROW][C]21[/C][C]3570.12[/C][C]3442.43224906847[/C][C]127.687750931528[/C][/ROW]
[ROW][C]22[/C][C]3701.61[/C][C]3415.15025820086[/C][C]286.459741799136[/C][/ROW]
[ROW][C]23[/C][C]3862.27[/C][C]3404.11293824405[/C][C]458.157061755947[/C][/ROW]
[ROW][C]24[/C][C]3970.1[/C][C]3396.43398230829[/C][C]573.666017691712[/C][/ROW]
[ROW][C]25[/C][C]4138.52[/C][C]3386.54756238116[/C][C]751.97243761884[/C][/ROW]
[ROW][C]26[/C][C]4199.75[/C][C]3393.16995435525[/C][C]806.580045644752[/C][/ROW]
[ROW][C]27[/C][C]4290.89[/C][C]3389.18897228535[/C][C]901.701027714646[/C][/ROW]
[ROW][C]28[/C][C]4443.91[/C][C]3390.03799689742[/C][C]1053.87200310258[/C][/ROW]
[ROW][C]29[/C][C]4502.64[/C][C]3401.67906768947[/C][C]1100.96093231053[/C][/ROW]
[ROW][C]30[/C][C]4356.98[/C][C]3416.15022052174[/C][C]940.829779478261[/C][/ROW]
[ROW][C]31[/C][C]4591.27[/C][C]3427.56488475058[/C][C]1163.70511524942[/C][/ROW]
[ROW][C]32[/C][C]4696.96[/C][C]3451.11116732511[/C][C]1245.84883267489[/C][/ROW]
[ROW][C]33[/C][C]4621.4[/C][C]3459.43160852332[/C][C]1161.96839147668[/C][/ROW]
[ROW][C]34[/C][C]4562.84[/C][C]3410.94286956775[/C][C]1151.89713043225[/C][/ROW]
[ROW][C]35[/C][C]4202.52[/C][C]3383.54767541854[/C][C]818.97232458146[/C][/ROW]
[ROW][C]36[/C][C]4296.49[/C][C]3383.47220656413[/C][C]913.017793435866[/C][/ROW]
[ROW][C]37[/C][C]4435.23[/C][C]3340.62476447538[/C][C]1094.60523552462[/C][/ROW]
[ROW][C]38[/C][C]4105.18[/C][C]3380.45345238791[/C][C]724.726547612088[/C][/ROW]
[ROW][C]39[/C][C]4116.68[/C][C]3368.88785045026[/C][C]747.792149549739[/C][/ROW]
[ROW][C]40[/C][C]3844.49[/C][C]3388.4154165277[/C][C]456.074583472302[/C][/ROW]
[ROW][C]41[/C][C]3720.98[/C][C]3381.96282947602[/C][C]339.017170523977[/C][/ROW]
[ROW][C]42[/C][C]3674.4[/C][C]3390.03799689742[/C][C]284.362003102583[/C][/ROW]
[ROW][C]43[/C][C]3857.62[/C][C]3392.75487565602[/C][C]464.865124343983[/C][/ROW]
[ROW][C]44[/C][C]3801.06[/C][C]3424.16878630233[/C][C]376.891213697672[/C][/ROW]
[ROW][C]45[/C][C]3504.37[/C][C]3411.16927613097[/C][C]93.2007238690282[/C][/ROW]
[ROW][C]46[/C][C]3032.6[/C][C]3374.41594403547[/C][C]-341.815944035468[/C][/ROW]
[ROW][C]47[/C][C]3047.03[/C][C]3363.03901423383[/C][C]-316.009014233831[/C][/ROW]
[ROW][C]48[/C][C]2962.34[/C][C]3372.79336366575[/C][C]-410.453363665748[/C][/ROW]
[ROW][C]49[/C][C]2197.82[/C][C]3358.79389117352[/C][C]-1160.97389117352[/C][/ROW]
[ROW][C]50[/C][C]2014.45[/C][C]3377.35922935728[/C][C]-1362.90922935728[/C][/ROW]
[ROW][C]51[/C][C]1862.83[/C][C]3345.13402852611[/C][C]-1482.30402852611[/C][/ROW]
[ROW][C]52[/C][C]1905.41[/C][C]3393.83030683130[/C][C]-1488.42030683130[/C][/ROW]
[ROW][C]53[/C][C]1810.99[/C][C]3405.33930712814[/C][C]-1594.34930712814[/C][/ROW]
[ROW][C]54[/C][C]1670.07[/C][C]3423.7537076031[/C][C]-1753.68370760310[/C][/ROW]
[ROW][C]55[/C][C]1864.44[/C][C]3435.54571610396[/C][C]-1571.10571610396[/C][/ROW]
[ROW][C]56[/C][C]2052.02[/C][C]3486.78906824533[/C][C]-1434.76906824533[/C][/ROW]
[ROW][C]57[/C][C]2029.6[/C][C]3476.54417125978[/C][C]-1446.94417125978[/C][/ROW]
[ROW][C]58[/C][C]2070.83[/C][C]3450.65835419868[/C][C]-1379.82835419868[/C][/ROW]
[ROW][C]59[/C][C]2293.41[/C][C]3455.46949366703[/C][C]-1162.05949366703[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58289&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58289&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
12756.763293.21145669509-536.451456695092
22849.273299.94705195079-450.677051950789
32921.443308.85237677064-387.412376770645
42981.853309.55046367390-327.700463673896
53080.583312.66355391813-232.083553918125
63106.223307.73921116816-201.519211168163
73119.313321.17266725235-201.862667252351
83061.263348.83200239199-287.572002391986
93097.313367.52941107096-270.219411070961
103161.693364.13331262271-202.443312622711
113257.163353.86954842356-96.7095484235565
123277.013343.85105800122-66.8410580012192
133295.323345.68117772055-50.3611777205539
143363.993405.37704155535-41.3870415553457
153494.173405.3959087689588.7740912310531
163667.033388.35881488689278.671185113107
173813.063388.03807225567425.02192774433
183917.963372.75562923855545.204370761454
193895.513390.98135757749504.528642422514
203801.063423.22542562226377.834574377741
213570.123442.43224906847127.687750931528
223701.613415.15025820086286.459741799136
233862.273404.11293824405458.157061755947
243970.13396.43398230829573.666017691712
254138.523386.54756238116751.97243761884
264199.753393.16995435525806.580045644752
274290.893389.18897228535901.701027714646
284443.913390.037996897421053.87200310258
294502.643401.679067689471100.96093231053
304356.983416.15022052174940.829779478261
314591.273427.564884750581163.70511524942
324696.963451.111167325111245.84883267489
334621.43459.431608523321161.96839147668
344562.843410.942869567751151.89713043225
354202.523383.54767541854818.97232458146
364296.493383.47220656413913.017793435866
374435.233340.624764475381094.60523552462
384105.183380.45345238791724.726547612088
394116.683368.88785045026747.792149549739
403844.493388.4154165277456.074583472302
413720.983381.96282947602339.017170523977
423674.43390.03799689742284.362003102583
433857.623392.75487565602464.865124343983
443801.063424.16878630233376.891213697672
453504.373411.1692761309793.2007238690282
463032.63374.41594403547-341.815944035468
473047.033363.03901423383-316.009014233831
482962.343372.79336366575-410.453363665748
492197.823358.79389117352-1160.97389117352
502014.453377.35922935728-1362.90922935728
511862.833345.13402852611-1482.30402852611
521905.413393.83030683130-1488.42030683130
531810.993405.33930712814-1594.34930712814
541670.073423.7537076031-1753.68370760310
551864.443435.54571610396-1571.10571610396
562052.023486.78906824533-1434.76906824533
572029.63476.54417125978-1446.94417125978
582070.833450.65835419868-1379.82835419868
592293.413455.46949366703-1162.05949366703







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.0001602608585459910.0003205217170919810.999839739141454
60.0001075905662149350.0002151811324298690.999892409433785
71.09216463710682e-052.18432927421364e-050.999989078353629
81.75815778138549e-053.51631556277098e-050.999982418422186
92.93994683241157e-065.87989366482313e-060.999997060053168
103.24501830791238e-076.49003661582476e-070.99999967549817
117.20580726249537e-081.44116145249907e-070.999999927941927
122.41226928391766e-084.82453856783531e-080.999999975877307
136.8883202382099e-091.37766404764198e-080.99999999311168
148.60218118610691e-101.72043623722138e-090.999999999139782
151.22252677963667e-102.44505355927335e-100.999999999877747
162.46892871807879e-104.93785743615758e-100.999999999753107
179.94794726997828e-101.98958945399566e-090.999999999005205
181.00179869090714e-082.00359738181427e-080.999999989982013
197.00995827068686e-091.40199165413737e-080.999999992990042
201.46896592907789e-092.93793185815578e-090.999999998531034
219.77708595705104e-101.95541719141021e-090.999999999022291
221.99881184723772e-103.99762369447544e-100.999999999800119
237.3007192157926e-111.46014384315852e-100.999999999926993
246.10301213008156e-111.22060242601631e-100.99999999993897
252.46130638261067e-104.92261276522134e-100.99999999975387
265.82469712530865e-101.16493942506173e-090.99999999941753
272.02023073248592e-094.04046146497183e-090.99999999797977
281.12543661175767e-082.25087322351535e-080.999999988745634
293.20887868923206e-086.41775737846412e-080.999999967911213
302.62202467596579e-085.24404935193159e-080.999999973779753
314.12725668255237e-088.25451336510474e-080.999999958727433
327.99774216482774e-081.59954843296555e-070.999999920022578
332.46891102144227e-074.93782204288454e-070.999999753108898
341.11674851500439e-062.23349703000877e-060.999998883251485
351.57611303054016e-063.15222606108033e-060.99999842388697
363.68355018513957e-067.36710037027913e-060.999996316449815
374.53393797149537e-059.06787594299073e-050.999954660620285
386.76338247154399e-050.0001352676494308800.999932366175285
390.0001354492133424210.0002708984266848420.999864550786658
400.0001900917614923910.0003801835229847830.999809908238508
410.0002548625119135340.0005097250238270680.999745137488086
420.0004702210110657320.0009404420221314650.999529778988934
430.002231105965426000.004462211930852010.997768894034574
440.03083175677739840.06166351355479680.969168243222602
450.2159150403498790.4318300806997570.784084959650121
460.3782494745879410.7564989491758830.621750525412059
470.6824198829449250.635160234110150.317580117055075
480.9923098922464930.01538021550701310.00769010775350654
490.997306662073160.005386675853680740.00269333792684037
500.9976028971199080.004794205760183110.00239710288009156
510.9964706569280290.00705868614394260.0035293430719713
520.9945888366019420.01082232679611650.00541116339805827
530.9869331238122630.02613375237547410.0130668761877370
540.9815390776765030.03692184464699320.0184609223234966

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.000160260858545991 & 0.000320521717091981 & 0.999839739141454 \tabularnewline
6 & 0.000107590566214935 & 0.000215181132429869 & 0.999892409433785 \tabularnewline
7 & 1.09216463710682e-05 & 2.18432927421364e-05 & 0.999989078353629 \tabularnewline
8 & 1.75815778138549e-05 & 3.51631556277098e-05 & 0.999982418422186 \tabularnewline
9 & 2.93994683241157e-06 & 5.87989366482313e-06 & 0.999997060053168 \tabularnewline
10 & 3.24501830791238e-07 & 6.49003661582476e-07 & 0.99999967549817 \tabularnewline
11 & 7.20580726249537e-08 & 1.44116145249907e-07 & 0.999999927941927 \tabularnewline
12 & 2.41226928391766e-08 & 4.82453856783531e-08 & 0.999999975877307 \tabularnewline
13 & 6.8883202382099e-09 & 1.37766404764198e-08 & 0.99999999311168 \tabularnewline
14 & 8.60218118610691e-10 & 1.72043623722138e-09 & 0.999999999139782 \tabularnewline
15 & 1.22252677963667e-10 & 2.44505355927335e-10 & 0.999999999877747 \tabularnewline
16 & 2.46892871807879e-10 & 4.93785743615758e-10 & 0.999999999753107 \tabularnewline
17 & 9.94794726997828e-10 & 1.98958945399566e-09 & 0.999999999005205 \tabularnewline
18 & 1.00179869090714e-08 & 2.00359738181427e-08 & 0.999999989982013 \tabularnewline
19 & 7.00995827068686e-09 & 1.40199165413737e-08 & 0.999999992990042 \tabularnewline
20 & 1.46896592907789e-09 & 2.93793185815578e-09 & 0.999999998531034 \tabularnewline
21 & 9.77708595705104e-10 & 1.95541719141021e-09 & 0.999999999022291 \tabularnewline
22 & 1.99881184723772e-10 & 3.99762369447544e-10 & 0.999999999800119 \tabularnewline
23 & 7.3007192157926e-11 & 1.46014384315852e-10 & 0.999999999926993 \tabularnewline
24 & 6.10301213008156e-11 & 1.22060242601631e-10 & 0.99999999993897 \tabularnewline
25 & 2.46130638261067e-10 & 4.92261276522134e-10 & 0.99999999975387 \tabularnewline
26 & 5.82469712530865e-10 & 1.16493942506173e-09 & 0.99999999941753 \tabularnewline
27 & 2.02023073248592e-09 & 4.04046146497183e-09 & 0.99999999797977 \tabularnewline
28 & 1.12543661175767e-08 & 2.25087322351535e-08 & 0.999999988745634 \tabularnewline
29 & 3.20887868923206e-08 & 6.41775737846412e-08 & 0.999999967911213 \tabularnewline
30 & 2.62202467596579e-08 & 5.24404935193159e-08 & 0.999999973779753 \tabularnewline
31 & 4.12725668255237e-08 & 8.25451336510474e-08 & 0.999999958727433 \tabularnewline
32 & 7.99774216482774e-08 & 1.59954843296555e-07 & 0.999999920022578 \tabularnewline
33 & 2.46891102144227e-07 & 4.93782204288454e-07 & 0.999999753108898 \tabularnewline
34 & 1.11674851500439e-06 & 2.23349703000877e-06 & 0.999998883251485 \tabularnewline
35 & 1.57611303054016e-06 & 3.15222606108033e-06 & 0.99999842388697 \tabularnewline
36 & 3.68355018513957e-06 & 7.36710037027913e-06 & 0.999996316449815 \tabularnewline
37 & 4.53393797149537e-05 & 9.06787594299073e-05 & 0.999954660620285 \tabularnewline
38 & 6.76338247154399e-05 & 0.000135267649430880 & 0.999932366175285 \tabularnewline
39 & 0.000135449213342421 & 0.000270898426684842 & 0.999864550786658 \tabularnewline
40 & 0.000190091761492391 & 0.000380183522984783 & 0.999809908238508 \tabularnewline
41 & 0.000254862511913534 & 0.000509725023827068 & 0.999745137488086 \tabularnewline
42 & 0.000470221011065732 & 0.000940442022131465 & 0.999529778988934 \tabularnewline
43 & 0.00223110596542600 & 0.00446221193085201 & 0.997768894034574 \tabularnewline
44 & 0.0308317567773984 & 0.0616635135547968 & 0.969168243222602 \tabularnewline
45 & 0.215915040349879 & 0.431830080699757 & 0.784084959650121 \tabularnewline
46 & 0.378249474587941 & 0.756498949175883 & 0.621750525412059 \tabularnewline
47 & 0.682419882944925 & 0.63516023411015 & 0.317580117055075 \tabularnewline
48 & 0.992309892246493 & 0.0153802155070131 & 0.00769010775350654 \tabularnewline
49 & 0.99730666207316 & 0.00538667585368074 & 0.00269333792684037 \tabularnewline
50 & 0.997602897119908 & 0.00479420576018311 & 0.00239710288009156 \tabularnewline
51 & 0.996470656928029 & 0.0070586861439426 & 0.0035293430719713 \tabularnewline
52 & 0.994588836601942 & 0.0108223267961165 & 0.00541116339805827 \tabularnewline
53 & 0.986933123812263 & 0.0261337523754741 & 0.0130668761877370 \tabularnewline
54 & 0.981539077676503 & 0.0369218446469932 & 0.0184609223234966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58289&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.000160260858545991[/C][C]0.000320521717091981[/C][C]0.999839739141454[/C][/ROW]
[ROW][C]6[/C][C]0.000107590566214935[/C][C]0.000215181132429869[/C][C]0.999892409433785[/C][/ROW]
[ROW][C]7[/C][C]1.09216463710682e-05[/C][C]2.18432927421364e-05[/C][C]0.999989078353629[/C][/ROW]
[ROW][C]8[/C][C]1.75815778138549e-05[/C][C]3.51631556277098e-05[/C][C]0.999982418422186[/C][/ROW]
[ROW][C]9[/C][C]2.93994683241157e-06[/C][C]5.87989366482313e-06[/C][C]0.999997060053168[/C][/ROW]
[ROW][C]10[/C][C]3.24501830791238e-07[/C][C]6.49003661582476e-07[/C][C]0.99999967549817[/C][/ROW]
[ROW][C]11[/C][C]7.20580726249537e-08[/C][C]1.44116145249907e-07[/C][C]0.999999927941927[/C][/ROW]
[ROW][C]12[/C][C]2.41226928391766e-08[/C][C]4.82453856783531e-08[/C][C]0.999999975877307[/C][/ROW]
[ROW][C]13[/C][C]6.8883202382099e-09[/C][C]1.37766404764198e-08[/C][C]0.99999999311168[/C][/ROW]
[ROW][C]14[/C][C]8.60218118610691e-10[/C][C]1.72043623722138e-09[/C][C]0.999999999139782[/C][/ROW]
[ROW][C]15[/C][C]1.22252677963667e-10[/C][C]2.44505355927335e-10[/C][C]0.999999999877747[/C][/ROW]
[ROW][C]16[/C][C]2.46892871807879e-10[/C][C]4.93785743615758e-10[/C][C]0.999999999753107[/C][/ROW]
[ROW][C]17[/C][C]9.94794726997828e-10[/C][C]1.98958945399566e-09[/C][C]0.999999999005205[/C][/ROW]
[ROW][C]18[/C][C]1.00179869090714e-08[/C][C]2.00359738181427e-08[/C][C]0.999999989982013[/C][/ROW]
[ROW][C]19[/C][C]7.00995827068686e-09[/C][C]1.40199165413737e-08[/C][C]0.999999992990042[/C][/ROW]
[ROW][C]20[/C][C]1.46896592907789e-09[/C][C]2.93793185815578e-09[/C][C]0.999999998531034[/C][/ROW]
[ROW][C]21[/C][C]9.77708595705104e-10[/C][C]1.95541719141021e-09[/C][C]0.999999999022291[/C][/ROW]
[ROW][C]22[/C][C]1.99881184723772e-10[/C][C]3.99762369447544e-10[/C][C]0.999999999800119[/C][/ROW]
[ROW][C]23[/C][C]7.3007192157926e-11[/C][C]1.46014384315852e-10[/C][C]0.999999999926993[/C][/ROW]
[ROW][C]24[/C][C]6.10301213008156e-11[/C][C]1.22060242601631e-10[/C][C]0.99999999993897[/C][/ROW]
[ROW][C]25[/C][C]2.46130638261067e-10[/C][C]4.92261276522134e-10[/C][C]0.99999999975387[/C][/ROW]
[ROW][C]26[/C][C]5.82469712530865e-10[/C][C]1.16493942506173e-09[/C][C]0.99999999941753[/C][/ROW]
[ROW][C]27[/C][C]2.02023073248592e-09[/C][C]4.04046146497183e-09[/C][C]0.99999999797977[/C][/ROW]
[ROW][C]28[/C][C]1.12543661175767e-08[/C][C]2.25087322351535e-08[/C][C]0.999999988745634[/C][/ROW]
[ROW][C]29[/C][C]3.20887868923206e-08[/C][C]6.41775737846412e-08[/C][C]0.999999967911213[/C][/ROW]
[ROW][C]30[/C][C]2.62202467596579e-08[/C][C]5.24404935193159e-08[/C][C]0.999999973779753[/C][/ROW]
[ROW][C]31[/C][C]4.12725668255237e-08[/C][C]8.25451336510474e-08[/C][C]0.999999958727433[/C][/ROW]
[ROW][C]32[/C][C]7.99774216482774e-08[/C][C]1.59954843296555e-07[/C][C]0.999999920022578[/C][/ROW]
[ROW][C]33[/C][C]2.46891102144227e-07[/C][C]4.93782204288454e-07[/C][C]0.999999753108898[/C][/ROW]
[ROW][C]34[/C][C]1.11674851500439e-06[/C][C]2.23349703000877e-06[/C][C]0.999998883251485[/C][/ROW]
[ROW][C]35[/C][C]1.57611303054016e-06[/C][C]3.15222606108033e-06[/C][C]0.99999842388697[/C][/ROW]
[ROW][C]36[/C][C]3.68355018513957e-06[/C][C]7.36710037027913e-06[/C][C]0.999996316449815[/C][/ROW]
[ROW][C]37[/C][C]4.53393797149537e-05[/C][C]9.06787594299073e-05[/C][C]0.999954660620285[/C][/ROW]
[ROW][C]38[/C][C]6.76338247154399e-05[/C][C]0.000135267649430880[/C][C]0.999932366175285[/C][/ROW]
[ROW][C]39[/C][C]0.000135449213342421[/C][C]0.000270898426684842[/C][C]0.999864550786658[/C][/ROW]
[ROW][C]40[/C][C]0.000190091761492391[/C][C]0.000380183522984783[/C][C]0.999809908238508[/C][/ROW]
[ROW][C]41[/C][C]0.000254862511913534[/C][C]0.000509725023827068[/C][C]0.999745137488086[/C][/ROW]
[ROW][C]42[/C][C]0.000470221011065732[/C][C]0.000940442022131465[/C][C]0.999529778988934[/C][/ROW]
[ROW][C]43[/C][C]0.00223110596542600[/C][C]0.00446221193085201[/C][C]0.997768894034574[/C][/ROW]
[ROW][C]44[/C][C]0.0308317567773984[/C][C]0.0616635135547968[/C][C]0.969168243222602[/C][/ROW]
[ROW][C]45[/C][C]0.215915040349879[/C][C]0.431830080699757[/C][C]0.784084959650121[/C][/ROW]
[ROW][C]46[/C][C]0.378249474587941[/C][C]0.756498949175883[/C][C]0.621750525412059[/C][/ROW]
[ROW][C]47[/C][C]0.682419882944925[/C][C]0.63516023411015[/C][C]0.317580117055075[/C][/ROW]
[ROW][C]48[/C][C]0.992309892246493[/C][C]0.0153802155070131[/C][C]0.00769010775350654[/C][/ROW]
[ROW][C]49[/C][C]0.99730666207316[/C][C]0.00538667585368074[/C][C]0.00269333792684037[/C][/ROW]
[ROW][C]50[/C][C]0.997602897119908[/C][C]0.00479420576018311[/C][C]0.00239710288009156[/C][/ROW]
[ROW][C]51[/C][C]0.996470656928029[/C][C]0.0070586861439426[/C][C]0.0035293430719713[/C][/ROW]
[ROW][C]52[/C][C]0.994588836601942[/C][C]0.0108223267961165[/C][C]0.00541116339805827[/C][/ROW]
[ROW][C]53[/C][C]0.986933123812263[/C][C]0.0261337523754741[/C][C]0.0130668761877370[/C][/ROW]
[ROW][C]54[/C][C]0.981539077676503[/C][C]0.0369218446469932[/C][C]0.0184609223234966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58289&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58289&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.0001602608585459910.0003205217170919810.999839739141454
60.0001075905662149350.0002151811324298690.999892409433785
71.09216463710682e-052.18432927421364e-050.999989078353629
81.75815778138549e-053.51631556277098e-050.999982418422186
92.93994683241157e-065.87989366482313e-060.999997060053168
103.24501830791238e-076.49003661582476e-070.99999967549817
117.20580726249537e-081.44116145249907e-070.999999927941927
122.41226928391766e-084.82453856783531e-080.999999975877307
136.8883202382099e-091.37766404764198e-080.99999999311168
148.60218118610691e-101.72043623722138e-090.999999999139782
151.22252677963667e-102.44505355927335e-100.999999999877747
162.46892871807879e-104.93785743615758e-100.999999999753107
179.94794726997828e-101.98958945399566e-090.999999999005205
181.00179869090714e-082.00359738181427e-080.999999989982013
197.00995827068686e-091.40199165413737e-080.999999992990042
201.46896592907789e-092.93793185815578e-090.999999998531034
219.77708595705104e-101.95541719141021e-090.999999999022291
221.99881184723772e-103.99762369447544e-100.999999999800119
237.3007192157926e-111.46014384315852e-100.999999999926993
246.10301213008156e-111.22060242601631e-100.99999999993897
252.46130638261067e-104.92261276522134e-100.99999999975387
265.82469712530865e-101.16493942506173e-090.99999999941753
272.02023073248592e-094.04046146497183e-090.99999999797977
281.12543661175767e-082.25087322351535e-080.999999988745634
293.20887868923206e-086.41775737846412e-080.999999967911213
302.62202467596579e-085.24404935193159e-080.999999973779753
314.12725668255237e-088.25451336510474e-080.999999958727433
327.99774216482774e-081.59954843296555e-070.999999920022578
332.46891102144227e-074.93782204288454e-070.999999753108898
341.11674851500439e-062.23349703000877e-060.999998883251485
351.57611303054016e-063.15222606108033e-060.99999842388697
363.68355018513957e-067.36710037027913e-060.999996316449815
374.53393797149537e-059.06787594299073e-050.999954660620285
386.76338247154399e-050.0001352676494308800.999932366175285
390.0001354492133424210.0002708984266848420.999864550786658
400.0001900917614923910.0003801835229847830.999809908238508
410.0002548625119135340.0005097250238270680.999745137488086
420.0004702210110657320.0009404420221314650.999529778988934
430.002231105965426000.004462211930852010.997768894034574
440.03083175677739840.06166351355479680.969168243222602
450.2159150403498790.4318300806997570.784084959650121
460.3782494745879410.7564989491758830.621750525412059
470.6824198829449250.635160234110150.317580117055075
480.9923098922464930.01538021550701310.00769010775350654
490.997306662073160.005386675853680740.00269333792684037
500.9976028971199080.004794205760183110.00239710288009156
510.9964706569280290.00705868614394260.0035293430719713
520.9945888366019420.01082232679611650.00541116339805827
530.9869331238122630.02613375237547410.0130668761877370
540.9815390776765030.03692184464699320.0184609223234966







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level420.84NOK
5% type I error level460.92NOK
10% type I error level470.94NOK

\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 & 42 & 0.84 & NOK \tabularnewline
5% type I error level & 46 & 0.92 & NOK \tabularnewline
10% type I error level & 47 & 0.94 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58289&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]42[/C][C]0.84[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]46[/C][C]0.92[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.94[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58289&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58289&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 level420.84NOK
5% type I error level460.92NOK
10% type I error level470.94NOK



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')
}