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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 23 Dec 2011 05:56:39 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/23/t1324637831yvbbiq459nwlwil.htm/, Retrieved Thu, 06 Nov 2025 01:41:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160268, Retrieved Thu, 06 Nov 2025 01:41:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact408

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
-  M D  [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 10:32:18] [d946de7cca328fbcf207448a112523ab]
-         [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 20:25:47] [3635fb7041b1998c5a1332cf9de22bce]
-    D      [Multiple Regression] [Workshop 8, Multi...] [2010-11-29 20:46:24] [3635fb7041b1998c5a1332cf9de22bce]
- R PD        [Multiple Regression] [Paper, MR poging 2] [2010-12-19 20:57:32] [3635fb7041b1998c5a1332cf9de22bce]
- R  D            [Multiple Regression] [] [2011-12-23 10:56:39] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   P               [Multiple Regression] [] [2011-12-23 11:07:47] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
631.923	9.911	58608
654.294	8.915	46865
671.833	9.452	51378
586.840	9.112	46235
600.969	8.472	47206
625.568	8.230	45382
558.110	8.384	41227
630.577	8.625	33795
628.654	8.221	31295
603.184	8.649	42625
656.255	8.625	33625
600.730	10.443	21538
670.326	10.357	56421
678.423	8.586	53152
641.502	8.892	53536
625.311	8.329	52408
628.177	8.101	41454
589.767	7.922	38271
582.471	8.120	35306
636.248	7.838	26414
599.885	7.735	31917
621.694	8.406	38030
637.406	8.209	27534
595.994	9.451	18387
696.308	10.041	50556
674.201	9.411	43901
648.861	10.405	48572
649.605	8.467	43899
672.392	8.464	37532
598.396	8.102	40357
613.177	7.627	35489
638.104	7.513	29027
615.632	7.510	34485
634.465	8.291	42598
638.686	8.064	30306
604.243	9.383	26451
706.669	9.706	47460
677.185	8.579	50104
644.328	9.474	61465
664.825	8.318	53726
605.707	8.213	39477
600.136	8.059	43895
612.166	9.111	31481
599.659	7.708	29896
634.210	7.680	33842
618.234	8.014	39120
613.576	8.007	33702
627.200	8.718	25094
668.973	9.486	51442
651.479	9.113	45594
619.661	9.025	52518
644.260	8.476	48564
579.936	7.952	41745
601.752	7.759	49585
595.376	7.835	32747
588.902	7.600	33379
634.341	7.651	35645
594.305	8.319	37034
606.200	8.812	35681
610.926	8.630	20972

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160268&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160268&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160268&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
OVERLIJDENS[t] = + 2.86996118153291 + 0.00815743505189026WERKLOZEN[t] + 1.46321307875556e-05INSCHRIJVINGEN[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
OVERLIJDENS[t] =  +  2.86996118153291 +  0.00815743505189026WERKLOZEN[t] +  1.46321307875556e-05INSCHRIJVINGEN[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160268&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]OVERLIJDENS[t] =  +  2.86996118153291 +  0.00815743505189026WERKLOZEN[t] +  1.46321307875556e-05INSCHRIJVINGEN[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160268&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160268&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
OVERLIJDENS[t] = + 2.86996118153291 + 0.00815743505189026WERKLOZEN[t] + 1.46321307875556e-05INSCHRIJVINGEN[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.869961181532911.8620381.54130.1287770.064389
WERKLOZEN0.008157435051890260.0031852.56120.0131020.006551
INSCHRIJVINGEN1.46321307875556e-051e-051.46620.1480910.074046

\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) & 2.86996118153291 & 1.862038 & 1.5413 & 0.128777 & 0.064389 \tabularnewline
WERKLOZEN & 0.00815743505189026 & 0.003185 & 2.5612 & 0.013102 & 0.006551 \tabularnewline
INSCHRIJVINGEN & 1.46321307875556e-05 & 1e-05 & 1.4662 & 0.148091 & 0.074046 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160268&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]2.86996118153291[/C][C]1.862038[/C][C]1.5413[/C][C]0.128777[/C][C]0.064389[/C][/ROW]
[ROW][C]WERKLOZEN[/C][C]0.00815743505189026[/C][C]0.003185[/C][C]2.5612[/C][C]0.013102[/C][C]0.006551[/C][/ROW]
[ROW][C]INSCHRIJVINGEN[/C][C]1.46321307875556e-05[/C][C]1e-05[/C][C]1.4662[/C][C]0.148091[/C][C]0.074046[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160268&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160268&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)2.869961181532911.8620381.54130.1287770.064389
WERKLOZEN0.008157435051890260.0031852.56120.0131020.006551
INSCHRIJVINGEN1.46321307875556e-051e-051.46620.1480910.074046






Multiple Linear Regression - Regression Statistics
Multiple R0.453052915620016
R-squared0.205256944351798
Adjusted R-squared0.177371223100983
F-TEST (value)7.36064678068189
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0.00143371272974002
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.684799630737921
Sum Squared Residuals26.7301804527512

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.453052915620016 \tabularnewline
R-squared & 0.205256944351798 \tabularnewline
Adjusted R-squared & 0.177371223100983 \tabularnewline
F-TEST (value) & 7.36064678068189 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0.00143371272974002 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.684799630737921 \tabularnewline
Sum Squared Residuals & 26.7301804527512 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160268&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.453052915620016[/C][/ROW]
[ROW][C]R-squared[/C][C]0.205256944351798[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.177371223100983[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.36064678068189[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]0.00143371272974002[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.684799630737921[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]26.7301804527512[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160268&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160268&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 R0.453052915620016
R-squared0.205256944351798
Adjusted R-squared0.177371223100983
F-TEST (value)7.36064678068189
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0.00143371272974002
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.684799630737921
Sum Squared Residuals26.7301804527512






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.9118.882391933025621.02860806697438
28.9158.893056800733190.0219431992668083
39.4529.102164860352530.349835139647467
49.1128.333586934346830.778413065653174
58.4728.46305113318970.00894886681029972
68.238.63702687147465-0.407026871474646
78.3848.025946114321940.358053885678061
88.6258.508344964214160.116655035785842
98.2218.45607788964048-0.235077889640484
108.6498.414090060691840.234909939308155
118.6258.71532411924271-0.0903241192427117
1210.4438.085523973157322.35747602684268
1310.3579.163661441290981.19333855870902
148.5869.18187975736161-0.595879757361613
158.8928.88631783603320.00568216396680518
168.3298.73773576157968-0.408735761579676
178.1018.60083460979151-0.499834609791509
187.9228.24093345715162-0.318933457151616
198.128.13803254322792-0.0180325432279223
207.8388.44660602105048-0.60860602105048
217.7358.23049782598251-0.495497825982512
228.4068.49784954253351-0.0918495425335139
238.2098.47244031732263-0.263440317322631
249.4518.000784516639981.45021548336002
2510.0419.289790471740180.751209528259824
269.4119.012077224656860.398922775343143
2710.4058.873714503350631.53128549664937
288.4678.811407687859-0.344407687858988
298.4648.90412838366204-0.440128383662045
308.1028.34184658903722-0.239846589037217
317.6278.39119242386539-0.764192423865387
327.5138.49997997825467-0.986979978254671
337.518.39652826760707-0.886528267607071
348.2918.66886771901876-0.377867719018759
358.0648.52344210073215-0.459442100732155
369.3838.186068701053871.19693129894613
379.7069.329008579394540.37699142060546
388.5799.1271821181269-0.548182118126903
399.4749.025388912504360.448611087495635
408.3189.07935379859807-0.761353798598068
418.2138.38860932160854-0.175609321608539
428.0598.40780900475388-0.348809004753879
439.1118.32429967683140.786700323168598
447.7088.19908270933914-0.491082709339135
457.688.5386686359047-0.858668635904691
468.0148.48557383981241-0.471573839812411
478.0078.36829962273373-0.36129962273373
488.7188.35348313606140.364516863938597
499.4869.079771052474530.40622894752547
509.1138.851496182831140.261503817168862
519.0258.693255787923130.331744212076873
528.4768.83606508763058-0.36006508763058
537.9528.21156973551245-0.259569735512451
547.7598.50424824397892-0.745248243978924
557.8358.2058606198872-0.37086061988721
567.68.16229689201901-0.562296892019009
577.6518.56611899170645-0.915118991706451
588.3198.259851951632890.0591480483671143
598.8128.337087368619560.47491263138044
608.638.160415394920640.469584605079364

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.911 & 8.88239193302562 & 1.02860806697438 \tabularnewline
2 & 8.915 & 8.89305680073319 & 0.0219431992668083 \tabularnewline
3 & 9.452 & 9.10216486035253 & 0.349835139647467 \tabularnewline
4 & 9.112 & 8.33358693434683 & 0.778413065653174 \tabularnewline
5 & 8.472 & 8.4630511331897 & 0.00894886681029972 \tabularnewline
6 & 8.23 & 8.63702687147465 & -0.407026871474646 \tabularnewline
7 & 8.384 & 8.02594611432194 & 0.358053885678061 \tabularnewline
8 & 8.625 & 8.50834496421416 & 0.116655035785842 \tabularnewline
9 & 8.221 & 8.45607788964048 & -0.235077889640484 \tabularnewline
10 & 8.649 & 8.41409006069184 & 0.234909939308155 \tabularnewline
11 & 8.625 & 8.71532411924271 & -0.0903241192427117 \tabularnewline
12 & 10.443 & 8.08552397315732 & 2.35747602684268 \tabularnewline
13 & 10.357 & 9.16366144129098 & 1.19333855870902 \tabularnewline
14 & 8.586 & 9.18187975736161 & -0.595879757361613 \tabularnewline
15 & 8.892 & 8.8863178360332 & 0.00568216396680518 \tabularnewline
16 & 8.329 & 8.73773576157968 & -0.408735761579676 \tabularnewline
17 & 8.101 & 8.60083460979151 & -0.499834609791509 \tabularnewline
18 & 7.922 & 8.24093345715162 & -0.318933457151616 \tabularnewline
19 & 8.12 & 8.13803254322792 & -0.0180325432279223 \tabularnewline
20 & 7.838 & 8.44660602105048 & -0.60860602105048 \tabularnewline
21 & 7.735 & 8.23049782598251 & -0.495497825982512 \tabularnewline
22 & 8.406 & 8.49784954253351 & -0.0918495425335139 \tabularnewline
23 & 8.209 & 8.47244031732263 & -0.263440317322631 \tabularnewline
24 & 9.451 & 8.00078451663998 & 1.45021548336002 \tabularnewline
25 & 10.041 & 9.28979047174018 & 0.751209528259824 \tabularnewline
26 & 9.411 & 9.01207722465686 & 0.398922775343143 \tabularnewline
27 & 10.405 & 8.87371450335063 & 1.53128549664937 \tabularnewline
28 & 8.467 & 8.811407687859 & -0.344407687858988 \tabularnewline
29 & 8.464 & 8.90412838366204 & -0.440128383662045 \tabularnewline
30 & 8.102 & 8.34184658903722 & -0.239846589037217 \tabularnewline
31 & 7.627 & 8.39119242386539 & -0.764192423865387 \tabularnewline
32 & 7.513 & 8.49997997825467 & -0.986979978254671 \tabularnewline
33 & 7.51 & 8.39652826760707 & -0.886528267607071 \tabularnewline
34 & 8.291 & 8.66886771901876 & -0.377867719018759 \tabularnewline
35 & 8.064 & 8.52344210073215 & -0.459442100732155 \tabularnewline
36 & 9.383 & 8.18606870105387 & 1.19693129894613 \tabularnewline
37 & 9.706 & 9.32900857939454 & 0.37699142060546 \tabularnewline
38 & 8.579 & 9.1271821181269 & -0.548182118126903 \tabularnewline
39 & 9.474 & 9.02538891250436 & 0.448611087495635 \tabularnewline
40 & 8.318 & 9.07935379859807 & -0.761353798598068 \tabularnewline
41 & 8.213 & 8.38860932160854 & -0.175609321608539 \tabularnewline
42 & 8.059 & 8.40780900475388 & -0.348809004753879 \tabularnewline
43 & 9.111 & 8.3242996768314 & 0.786700323168598 \tabularnewline
44 & 7.708 & 8.19908270933914 & -0.491082709339135 \tabularnewline
45 & 7.68 & 8.5386686359047 & -0.858668635904691 \tabularnewline
46 & 8.014 & 8.48557383981241 & -0.471573839812411 \tabularnewline
47 & 8.007 & 8.36829962273373 & -0.36129962273373 \tabularnewline
48 & 8.718 & 8.3534831360614 & 0.364516863938597 \tabularnewline
49 & 9.486 & 9.07977105247453 & 0.40622894752547 \tabularnewline
50 & 9.113 & 8.85149618283114 & 0.261503817168862 \tabularnewline
51 & 9.025 & 8.69325578792313 & 0.331744212076873 \tabularnewline
52 & 8.476 & 8.83606508763058 & -0.36006508763058 \tabularnewline
53 & 7.952 & 8.21156973551245 & -0.259569735512451 \tabularnewline
54 & 7.759 & 8.50424824397892 & -0.745248243978924 \tabularnewline
55 & 7.835 & 8.2058606198872 & -0.37086061988721 \tabularnewline
56 & 7.6 & 8.16229689201901 & -0.562296892019009 \tabularnewline
57 & 7.651 & 8.56611899170645 & -0.915118991706451 \tabularnewline
58 & 8.319 & 8.25985195163289 & 0.0591480483671143 \tabularnewline
59 & 8.812 & 8.33708736861956 & 0.47491263138044 \tabularnewline
60 & 8.63 & 8.16041539492064 & 0.469584605079364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160268&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]9.911[/C][C]8.88239193302562[/C][C]1.02860806697438[/C][/ROW]
[ROW][C]2[/C][C]8.915[/C][C]8.89305680073319[/C][C]0.0219431992668083[/C][/ROW]
[ROW][C]3[/C][C]9.452[/C][C]9.10216486035253[/C][C]0.349835139647467[/C][/ROW]
[ROW][C]4[/C][C]9.112[/C][C]8.33358693434683[/C][C]0.778413065653174[/C][/ROW]
[ROW][C]5[/C][C]8.472[/C][C]8.4630511331897[/C][C]0.00894886681029972[/C][/ROW]
[ROW][C]6[/C][C]8.23[/C][C]8.63702687147465[/C][C]-0.407026871474646[/C][/ROW]
[ROW][C]7[/C][C]8.384[/C][C]8.02594611432194[/C][C]0.358053885678061[/C][/ROW]
[ROW][C]8[/C][C]8.625[/C][C]8.50834496421416[/C][C]0.116655035785842[/C][/ROW]
[ROW][C]9[/C][C]8.221[/C][C]8.45607788964048[/C][C]-0.235077889640484[/C][/ROW]
[ROW][C]10[/C][C]8.649[/C][C]8.41409006069184[/C][C]0.234909939308155[/C][/ROW]
[ROW][C]11[/C][C]8.625[/C][C]8.71532411924271[/C][C]-0.0903241192427117[/C][/ROW]
[ROW][C]12[/C][C]10.443[/C][C]8.08552397315732[/C][C]2.35747602684268[/C][/ROW]
[ROW][C]13[/C][C]10.357[/C][C]9.16366144129098[/C][C]1.19333855870902[/C][/ROW]
[ROW][C]14[/C][C]8.586[/C][C]9.18187975736161[/C][C]-0.595879757361613[/C][/ROW]
[ROW][C]15[/C][C]8.892[/C][C]8.8863178360332[/C][C]0.00568216396680518[/C][/ROW]
[ROW][C]16[/C][C]8.329[/C][C]8.73773576157968[/C][C]-0.408735761579676[/C][/ROW]
[ROW][C]17[/C][C]8.101[/C][C]8.60083460979151[/C][C]-0.499834609791509[/C][/ROW]
[ROW][C]18[/C][C]7.922[/C][C]8.24093345715162[/C][C]-0.318933457151616[/C][/ROW]
[ROW][C]19[/C][C]8.12[/C][C]8.13803254322792[/C][C]-0.0180325432279223[/C][/ROW]
[ROW][C]20[/C][C]7.838[/C][C]8.44660602105048[/C][C]-0.60860602105048[/C][/ROW]
[ROW][C]21[/C][C]7.735[/C][C]8.23049782598251[/C][C]-0.495497825982512[/C][/ROW]
[ROW][C]22[/C][C]8.406[/C][C]8.49784954253351[/C][C]-0.0918495425335139[/C][/ROW]
[ROW][C]23[/C][C]8.209[/C][C]8.47244031732263[/C][C]-0.263440317322631[/C][/ROW]
[ROW][C]24[/C][C]9.451[/C][C]8.00078451663998[/C][C]1.45021548336002[/C][/ROW]
[ROW][C]25[/C][C]10.041[/C][C]9.28979047174018[/C][C]0.751209528259824[/C][/ROW]
[ROW][C]26[/C][C]9.411[/C][C]9.01207722465686[/C][C]0.398922775343143[/C][/ROW]
[ROW][C]27[/C][C]10.405[/C][C]8.87371450335063[/C][C]1.53128549664937[/C][/ROW]
[ROW][C]28[/C][C]8.467[/C][C]8.811407687859[/C][C]-0.344407687858988[/C][/ROW]
[ROW][C]29[/C][C]8.464[/C][C]8.90412838366204[/C][C]-0.440128383662045[/C][/ROW]
[ROW][C]30[/C][C]8.102[/C][C]8.34184658903722[/C][C]-0.239846589037217[/C][/ROW]
[ROW][C]31[/C][C]7.627[/C][C]8.39119242386539[/C][C]-0.764192423865387[/C][/ROW]
[ROW][C]32[/C][C]7.513[/C][C]8.49997997825467[/C][C]-0.986979978254671[/C][/ROW]
[ROW][C]33[/C][C]7.51[/C][C]8.39652826760707[/C][C]-0.886528267607071[/C][/ROW]
[ROW][C]34[/C][C]8.291[/C][C]8.66886771901876[/C][C]-0.377867719018759[/C][/ROW]
[ROW][C]35[/C][C]8.064[/C][C]8.52344210073215[/C][C]-0.459442100732155[/C][/ROW]
[ROW][C]36[/C][C]9.383[/C][C]8.18606870105387[/C][C]1.19693129894613[/C][/ROW]
[ROW][C]37[/C][C]9.706[/C][C]9.32900857939454[/C][C]0.37699142060546[/C][/ROW]
[ROW][C]38[/C][C]8.579[/C][C]9.1271821181269[/C][C]-0.548182118126903[/C][/ROW]
[ROW][C]39[/C][C]9.474[/C][C]9.02538891250436[/C][C]0.448611087495635[/C][/ROW]
[ROW][C]40[/C][C]8.318[/C][C]9.07935379859807[/C][C]-0.761353798598068[/C][/ROW]
[ROW][C]41[/C][C]8.213[/C][C]8.38860932160854[/C][C]-0.175609321608539[/C][/ROW]
[ROW][C]42[/C][C]8.059[/C][C]8.40780900475388[/C][C]-0.348809004753879[/C][/ROW]
[ROW][C]43[/C][C]9.111[/C][C]8.3242996768314[/C][C]0.786700323168598[/C][/ROW]
[ROW][C]44[/C][C]7.708[/C][C]8.19908270933914[/C][C]-0.491082709339135[/C][/ROW]
[ROW][C]45[/C][C]7.68[/C][C]8.5386686359047[/C][C]-0.858668635904691[/C][/ROW]
[ROW][C]46[/C][C]8.014[/C][C]8.48557383981241[/C][C]-0.471573839812411[/C][/ROW]
[ROW][C]47[/C][C]8.007[/C][C]8.36829962273373[/C][C]-0.36129962273373[/C][/ROW]
[ROW][C]48[/C][C]8.718[/C][C]8.3534831360614[/C][C]0.364516863938597[/C][/ROW]
[ROW][C]49[/C][C]9.486[/C][C]9.07977105247453[/C][C]0.40622894752547[/C][/ROW]
[ROW][C]50[/C][C]9.113[/C][C]8.85149618283114[/C][C]0.261503817168862[/C][/ROW]
[ROW][C]51[/C][C]9.025[/C][C]8.69325578792313[/C][C]0.331744212076873[/C][/ROW]
[ROW][C]52[/C][C]8.476[/C][C]8.83606508763058[/C][C]-0.36006508763058[/C][/ROW]
[ROW][C]53[/C][C]7.952[/C][C]8.21156973551245[/C][C]-0.259569735512451[/C][/ROW]
[ROW][C]54[/C][C]7.759[/C][C]8.50424824397892[/C][C]-0.745248243978924[/C][/ROW]
[ROW][C]55[/C][C]7.835[/C][C]8.2058606198872[/C][C]-0.37086061988721[/C][/ROW]
[ROW][C]56[/C][C]7.6[/C][C]8.16229689201901[/C][C]-0.562296892019009[/C][/ROW]
[ROW][C]57[/C][C]7.651[/C][C]8.56611899170645[/C][C]-0.915118991706451[/C][/ROW]
[ROW][C]58[/C][C]8.319[/C][C]8.25985195163289[/C][C]0.0591480483671143[/C][/ROW]
[ROW][C]59[/C][C]8.812[/C][C]8.33708736861956[/C][C]0.47491263138044[/C][/ROW]
[ROW][C]60[/C][C]8.63[/C][C]8.16041539492064[/C][C]0.469584605079364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160268&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160268&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
19.9118.882391933025621.02860806697438
28.9158.893056800733190.0219431992668083
39.4529.102164860352530.349835139647467
49.1128.333586934346830.778413065653174
58.4728.46305113318970.00894886681029972
68.238.63702687147465-0.407026871474646
78.3848.025946114321940.358053885678061
88.6258.508344964214160.116655035785842
98.2218.45607788964048-0.235077889640484
108.6498.414090060691840.234909939308155
118.6258.71532411924271-0.0903241192427117
1210.4438.085523973157322.35747602684268
1310.3579.163661441290981.19333855870902
148.5869.18187975736161-0.595879757361613
158.8928.88631783603320.00568216396680518
168.3298.73773576157968-0.408735761579676
178.1018.60083460979151-0.499834609791509
187.9228.24093345715162-0.318933457151616
198.128.13803254322792-0.0180325432279223
207.8388.44660602105048-0.60860602105048
217.7358.23049782598251-0.495497825982512
228.4068.49784954253351-0.0918495425335139
238.2098.47244031732263-0.263440317322631
249.4518.000784516639981.45021548336002
2510.0419.289790471740180.751209528259824
269.4119.012077224656860.398922775343143
2710.4058.873714503350631.53128549664937
288.4678.811407687859-0.344407687858988
298.4648.90412838366204-0.440128383662045
308.1028.34184658903722-0.239846589037217
317.6278.39119242386539-0.764192423865387
327.5138.49997997825467-0.986979978254671
337.518.39652826760707-0.886528267607071
348.2918.66886771901876-0.377867719018759
358.0648.52344210073215-0.459442100732155
369.3838.186068701053871.19693129894613
379.7069.329008579394540.37699142060546
388.5799.1271821181269-0.548182118126903
399.4749.025388912504360.448611087495635
408.3189.07935379859807-0.761353798598068
418.2138.38860932160854-0.175609321608539
428.0598.40780900475388-0.348809004753879
439.1118.32429967683140.786700323168598
447.7088.19908270933914-0.491082709339135
457.688.5386686359047-0.858668635904691
468.0148.48557383981241-0.471573839812411
478.0078.36829962273373-0.36129962273373
488.7188.35348313606140.364516863938597
499.4869.079771052474530.40622894752547
509.1138.851496182831140.261503817168862
519.0258.693255787923130.331744212076873
528.4768.83606508763058-0.36006508763058
537.9528.21156973551245-0.259569735512451
547.7598.50424824397892-0.745248243978924
557.8358.2058606198872-0.37086061988721
567.68.16229689201901-0.562296892019009
577.6518.56611899170645-0.915118991706451
588.3198.259851951632890.0591480483671143
598.8128.337087368619560.47491263138044
608.638.160415394920640.469584605079364






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.2041461914498570.4082923828997130.795853808550143
70.1054706890291740.2109413780583480.894529310970826
80.1397743022534950.279548604506990.860225697746505
90.07276224544423750.1455244908884750.927237754555763
100.03521987511609080.07043975023218150.96478012488391
110.01768368007074180.03536736014148360.982316319929258
120.8004043309284360.3991913381431280.199595669071564
130.8944696751809410.2110606496381190.105530324819059
140.8914421320156590.2171157359686820.108557867984341
150.8457926638402150.3084146723195690.154207336159785
160.824206335476570.351587329046860.17579366452343
170.8245256897318980.3509486205362040.175474310268102
180.810082661325220.379834677349560.18991733867478
190.7635330778904050.4729338442191910.236466922109595
200.7749103727280610.4501792545438770.225089627271939
210.7571144645068530.4857710709862940.242885535493147
220.6928879862949790.6142240274100420.307112013705021
230.6312110692303390.7375778615393220.368788930769661
240.8213768927374020.3572462145251960.178623107262598
250.8292444730225470.3415110539549060.170755526977453
260.7921583116931870.4156833766136270.207841688306813
270.9497830375608040.1004339248783920.0502169624391959
280.934252777510110.1314944449797810.0657472224898903
290.9190121583785630.1619756832428740.0809878416214372
300.8926437718829750.214712456234050.107356228117025
310.9007370725399320.1985258549201360.099262927460068
320.9341924459722610.1316151080554780.0658075540277389
330.9491501093606870.1016997812786250.0508498906393126
340.9314758012247530.1370483975504930.0685241987752466
350.9189023697609070.1621952604781870.0810976302390933
360.9716352496296920.05672950074061690.0283647503703084
370.9618768951910480.07624620961790370.0381231048089518
380.9542187298068280.09156254038634420.0457812701931721
390.9560256625990170.08794867480196510.0439743374009825
400.9586460917406280.08270781651874460.0413539082593723
410.9356473937404330.1287052125191330.0643526062595665
420.905832471438170.1883350571236580.0941675285618292
430.939635155705980.1207296885880390.0603648442940196
440.916737279511450.1665254409770990.0832627204885494
450.9488124372724110.1023751254551770.0511875627275885
460.9311571134650370.1376857730699260.0688428865349629
470.902211336484970.195577327030060.0977886635150298
480.8539094487221080.2921811025557850.146090551277892
490.8060183934916440.3879632130167130.193981606508357
500.7541820838607960.4916358322784080.245817916139204
510.8224197810047560.3551604379904870.177580218995244
520.7805899385717480.4388201228565050.219410061428252
530.6473070892366630.7053858215266730.352692910763337
540.4857819747787380.9715639495574760.514218025221262

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.204146191449857 & 0.408292382899713 & 0.795853808550143 \tabularnewline
7 & 0.105470689029174 & 0.210941378058348 & 0.894529310970826 \tabularnewline
8 & 0.139774302253495 & 0.27954860450699 & 0.860225697746505 \tabularnewline
9 & 0.0727622454442375 & 0.145524490888475 & 0.927237754555763 \tabularnewline
10 & 0.0352198751160908 & 0.0704397502321815 & 0.96478012488391 \tabularnewline
11 & 0.0176836800707418 & 0.0353673601414836 & 0.982316319929258 \tabularnewline
12 & 0.800404330928436 & 0.399191338143128 & 0.199595669071564 \tabularnewline
13 & 0.894469675180941 & 0.211060649638119 & 0.105530324819059 \tabularnewline
14 & 0.891442132015659 & 0.217115735968682 & 0.108557867984341 \tabularnewline
15 & 0.845792663840215 & 0.308414672319569 & 0.154207336159785 \tabularnewline
16 & 0.82420633547657 & 0.35158732904686 & 0.17579366452343 \tabularnewline
17 & 0.824525689731898 & 0.350948620536204 & 0.175474310268102 \tabularnewline
18 & 0.81008266132522 & 0.37983467734956 & 0.18991733867478 \tabularnewline
19 & 0.763533077890405 & 0.472933844219191 & 0.236466922109595 \tabularnewline
20 & 0.774910372728061 & 0.450179254543877 & 0.225089627271939 \tabularnewline
21 & 0.757114464506853 & 0.485771070986294 & 0.242885535493147 \tabularnewline
22 & 0.692887986294979 & 0.614224027410042 & 0.307112013705021 \tabularnewline
23 & 0.631211069230339 & 0.737577861539322 & 0.368788930769661 \tabularnewline
24 & 0.821376892737402 & 0.357246214525196 & 0.178623107262598 \tabularnewline
25 & 0.829244473022547 & 0.341511053954906 & 0.170755526977453 \tabularnewline
26 & 0.792158311693187 & 0.415683376613627 & 0.207841688306813 \tabularnewline
27 & 0.949783037560804 & 0.100433924878392 & 0.0502169624391959 \tabularnewline
28 & 0.93425277751011 & 0.131494444979781 & 0.0657472224898903 \tabularnewline
29 & 0.919012158378563 & 0.161975683242874 & 0.0809878416214372 \tabularnewline
30 & 0.892643771882975 & 0.21471245623405 & 0.107356228117025 \tabularnewline
31 & 0.900737072539932 & 0.198525854920136 & 0.099262927460068 \tabularnewline
32 & 0.934192445972261 & 0.131615108055478 & 0.0658075540277389 \tabularnewline
33 & 0.949150109360687 & 0.101699781278625 & 0.0508498906393126 \tabularnewline
34 & 0.931475801224753 & 0.137048397550493 & 0.0685241987752466 \tabularnewline
35 & 0.918902369760907 & 0.162195260478187 & 0.0810976302390933 \tabularnewline
36 & 0.971635249629692 & 0.0567295007406169 & 0.0283647503703084 \tabularnewline
37 & 0.961876895191048 & 0.0762462096179037 & 0.0381231048089518 \tabularnewline
38 & 0.954218729806828 & 0.0915625403863442 & 0.0457812701931721 \tabularnewline
39 & 0.956025662599017 & 0.0879486748019651 & 0.0439743374009825 \tabularnewline
40 & 0.958646091740628 & 0.0827078165187446 & 0.0413539082593723 \tabularnewline
41 & 0.935647393740433 & 0.128705212519133 & 0.0643526062595665 \tabularnewline
42 & 0.90583247143817 & 0.188335057123658 & 0.0941675285618292 \tabularnewline
43 & 0.93963515570598 & 0.120729688588039 & 0.0603648442940196 \tabularnewline
44 & 0.91673727951145 & 0.166525440977099 & 0.0832627204885494 \tabularnewline
45 & 0.948812437272411 & 0.102375125455177 & 0.0511875627275885 \tabularnewline
46 & 0.931157113465037 & 0.137685773069926 & 0.0688428865349629 \tabularnewline
47 & 0.90221133648497 & 0.19557732703006 & 0.0977886635150298 \tabularnewline
48 & 0.853909448722108 & 0.292181102555785 & 0.146090551277892 \tabularnewline
49 & 0.806018393491644 & 0.387963213016713 & 0.193981606508357 \tabularnewline
50 & 0.754182083860796 & 0.491635832278408 & 0.245817916139204 \tabularnewline
51 & 0.822419781004756 & 0.355160437990487 & 0.177580218995244 \tabularnewline
52 & 0.780589938571748 & 0.438820122856505 & 0.219410061428252 \tabularnewline
53 & 0.647307089236663 & 0.705385821526673 & 0.352692910763337 \tabularnewline
54 & 0.485781974778738 & 0.971563949557476 & 0.514218025221262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160268&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]6[/C][C]0.204146191449857[/C][C]0.408292382899713[/C][C]0.795853808550143[/C][/ROW]
[ROW][C]7[/C][C]0.105470689029174[/C][C]0.210941378058348[/C][C]0.894529310970826[/C][/ROW]
[ROW][C]8[/C][C]0.139774302253495[/C][C]0.27954860450699[/C][C]0.860225697746505[/C][/ROW]
[ROW][C]9[/C][C]0.0727622454442375[/C][C]0.145524490888475[/C][C]0.927237754555763[/C][/ROW]
[ROW][C]10[/C][C]0.0352198751160908[/C][C]0.0704397502321815[/C][C]0.96478012488391[/C][/ROW]
[ROW][C]11[/C][C]0.0176836800707418[/C][C]0.0353673601414836[/C][C]0.982316319929258[/C][/ROW]
[ROW][C]12[/C][C]0.800404330928436[/C][C]0.399191338143128[/C][C]0.199595669071564[/C][/ROW]
[ROW][C]13[/C][C]0.894469675180941[/C][C]0.211060649638119[/C][C]0.105530324819059[/C][/ROW]
[ROW][C]14[/C][C]0.891442132015659[/C][C]0.217115735968682[/C][C]0.108557867984341[/C][/ROW]
[ROW][C]15[/C][C]0.845792663840215[/C][C]0.308414672319569[/C][C]0.154207336159785[/C][/ROW]
[ROW][C]16[/C][C]0.82420633547657[/C][C]0.35158732904686[/C][C]0.17579366452343[/C][/ROW]
[ROW][C]17[/C][C]0.824525689731898[/C][C]0.350948620536204[/C][C]0.175474310268102[/C][/ROW]
[ROW][C]18[/C][C]0.81008266132522[/C][C]0.37983467734956[/C][C]0.18991733867478[/C][/ROW]
[ROW][C]19[/C][C]0.763533077890405[/C][C]0.472933844219191[/C][C]0.236466922109595[/C][/ROW]
[ROW][C]20[/C][C]0.774910372728061[/C][C]0.450179254543877[/C][C]0.225089627271939[/C][/ROW]
[ROW][C]21[/C][C]0.757114464506853[/C][C]0.485771070986294[/C][C]0.242885535493147[/C][/ROW]
[ROW][C]22[/C][C]0.692887986294979[/C][C]0.614224027410042[/C][C]0.307112013705021[/C][/ROW]
[ROW][C]23[/C][C]0.631211069230339[/C][C]0.737577861539322[/C][C]0.368788930769661[/C][/ROW]
[ROW][C]24[/C][C]0.821376892737402[/C][C]0.357246214525196[/C][C]0.178623107262598[/C][/ROW]
[ROW][C]25[/C][C]0.829244473022547[/C][C]0.341511053954906[/C][C]0.170755526977453[/C][/ROW]
[ROW][C]26[/C][C]0.792158311693187[/C][C]0.415683376613627[/C][C]0.207841688306813[/C][/ROW]
[ROW][C]27[/C][C]0.949783037560804[/C][C]0.100433924878392[/C][C]0.0502169624391959[/C][/ROW]
[ROW][C]28[/C][C]0.93425277751011[/C][C]0.131494444979781[/C][C]0.0657472224898903[/C][/ROW]
[ROW][C]29[/C][C]0.919012158378563[/C][C]0.161975683242874[/C][C]0.0809878416214372[/C][/ROW]
[ROW][C]30[/C][C]0.892643771882975[/C][C]0.21471245623405[/C][C]0.107356228117025[/C][/ROW]
[ROW][C]31[/C][C]0.900737072539932[/C][C]0.198525854920136[/C][C]0.099262927460068[/C][/ROW]
[ROW][C]32[/C][C]0.934192445972261[/C][C]0.131615108055478[/C][C]0.0658075540277389[/C][/ROW]
[ROW][C]33[/C][C]0.949150109360687[/C][C]0.101699781278625[/C][C]0.0508498906393126[/C][/ROW]
[ROW][C]34[/C][C]0.931475801224753[/C][C]0.137048397550493[/C][C]0.0685241987752466[/C][/ROW]
[ROW][C]35[/C][C]0.918902369760907[/C][C]0.162195260478187[/C][C]0.0810976302390933[/C][/ROW]
[ROW][C]36[/C][C]0.971635249629692[/C][C]0.0567295007406169[/C][C]0.0283647503703084[/C][/ROW]
[ROW][C]37[/C][C]0.961876895191048[/C][C]0.0762462096179037[/C][C]0.0381231048089518[/C][/ROW]
[ROW][C]38[/C][C]0.954218729806828[/C][C]0.0915625403863442[/C][C]0.0457812701931721[/C][/ROW]
[ROW][C]39[/C][C]0.956025662599017[/C][C]0.0879486748019651[/C][C]0.0439743374009825[/C][/ROW]
[ROW][C]40[/C][C]0.958646091740628[/C][C]0.0827078165187446[/C][C]0.0413539082593723[/C][/ROW]
[ROW][C]41[/C][C]0.935647393740433[/C][C]0.128705212519133[/C][C]0.0643526062595665[/C][/ROW]
[ROW][C]42[/C][C]0.90583247143817[/C][C]0.188335057123658[/C][C]0.0941675285618292[/C][/ROW]
[ROW][C]43[/C][C]0.93963515570598[/C][C]0.120729688588039[/C][C]0.0603648442940196[/C][/ROW]
[ROW][C]44[/C][C]0.91673727951145[/C][C]0.166525440977099[/C][C]0.0832627204885494[/C][/ROW]
[ROW][C]45[/C][C]0.948812437272411[/C][C]0.102375125455177[/C][C]0.0511875627275885[/C][/ROW]
[ROW][C]46[/C][C]0.931157113465037[/C][C]0.137685773069926[/C][C]0.0688428865349629[/C][/ROW]
[ROW][C]47[/C][C]0.90221133648497[/C][C]0.19557732703006[/C][C]0.0977886635150298[/C][/ROW]
[ROW][C]48[/C][C]0.853909448722108[/C][C]0.292181102555785[/C][C]0.146090551277892[/C][/ROW]
[ROW][C]49[/C][C]0.806018393491644[/C][C]0.387963213016713[/C][C]0.193981606508357[/C][/ROW]
[ROW][C]50[/C][C]0.754182083860796[/C][C]0.491635832278408[/C][C]0.245817916139204[/C][/ROW]
[ROW][C]51[/C][C]0.822419781004756[/C][C]0.355160437990487[/C][C]0.177580218995244[/C][/ROW]
[ROW][C]52[/C][C]0.780589938571748[/C][C]0.438820122856505[/C][C]0.219410061428252[/C][/ROW]
[ROW][C]53[/C][C]0.647307089236663[/C][C]0.705385821526673[/C][C]0.352692910763337[/C][/ROW]
[ROW][C]54[/C][C]0.485781974778738[/C][C]0.971563949557476[/C][C]0.514218025221262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160268&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160268&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
60.2041461914498570.4082923828997130.795853808550143
70.1054706890291740.2109413780583480.894529310970826
80.1397743022534950.279548604506990.860225697746505
90.07276224544423750.1455244908884750.927237754555763
100.03521987511609080.07043975023218150.96478012488391
110.01768368007074180.03536736014148360.982316319929258
120.8004043309284360.3991913381431280.199595669071564
130.8944696751809410.2110606496381190.105530324819059
140.8914421320156590.2171157359686820.108557867984341
150.8457926638402150.3084146723195690.154207336159785
160.824206335476570.351587329046860.17579366452343
170.8245256897318980.3509486205362040.175474310268102
180.810082661325220.379834677349560.18991733867478
190.7635330778904050.4729338442191910.236466922109595
200.7749103727280610.4501792545438770.225089627271939
210.7571144645068530.4857710709862940.242885535493147
220.6928879862949790.6142240274100420.307112013705021
230.6312110692303390.7375778615393220.368788930769661
240.8213768927374020.3572462145251960.178623107262598
250.8292444730225470.3415110539549060.170755526977453
260.7921583116931870.4156833766136270.207841688306813
270.9497830375608040.1004339248783920.0502169624391959
280.934252777510110.1314944449797810.0657472224898903
290.9190121583785630.1619756832428740.0809878416214372
300.8926437718829750.214712456234050.107356228117025
310.9007370725399320.1985258549201360.099262927460068
320.9341924459722610.1316151080554780.0658075540277389
330.9491501093606870.1016997812786250.0508498906393126
340.9314758012247530.1370483975504930.0685241987752466
350.9189023697609070.1621952604781870.0810976302390933
360.9716352496296920.05672950074061690.0283647503703084
370.9618768951910480.07624620961790370.0381231048089518
380.9542187298068280.09156254038634420.0457812701931721
390.9560256625990170.08794867480196510.0439743374009825
400.9586460917406280.08270781651874460.0413539082593723
410.9356473937404330.1287052125191330.0643526062595665
420.905832471438170.1883350571236580.0941675285618292
430.939635155705980.1207296885880390.0603648442940196
440.916737279511450.1665254409770990.0832627204885494
450.9488124372724110.1023751254551770.0511875627275885
460.9311571134650370.1376857730699260.0688428865349629
470.902211336484970.195577327030060.0977886635150298
480.8539094487221080.2921811025557850.146090551277892
490.8060183934916440.3879632130167130.193981606508357
500.7541820838607960.4916358322784080.245817916139204
510.8224197810047560.3551604379904870.177580218995244
520.7805899385717480.4388201228565050.219410061428252
530.6473070892366630.7053858215266730.352692910763337
540.4857819747787380.9715639495574760.514218025221262






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0204081632653061OK
10% type I error level70.142857142857143NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0204081632653061 & OK \tabularnewline
10% type I error level & 7 & 0.142857142857143 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160268&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0204081632653061[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.142857142857143[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160268&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160268&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0204081632653061OK
10% type I error level70.142857142857143NOK


Figures (Output of Computation)

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

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