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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 20 Nov 2012 12:41:48 -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/2012/Nov/20/t1353433342jkpokpnmlqtd4qa.htm/, Retrieved Sat, 27 Apr 2024 23:55:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191209, Retrieved Sat, 27 Apr 2024 23:55:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [workshop 7] [2012-11-20 17:36:49] [7f9ff716c6b21ddb03ac377f3f036840]
- R       [Multiple Regression] [workshop 7] [2012-11-20 17:41:48] [468d5fc6f63a6d6dca168804c24af07d] [Current]
-   P       [Multiple Regression] [workshop 7 tijd] [2012-11-20 18:10:29] [7f9ff716c6b21ddb03ac377f3f036840]
-    D      [Multiple Regression] [workshop 7 kleine...] [2012-11-20 18:29:12] [7f9ff716c6b21ddb03ac377f3f036840]
- RMPD      [Skewness and Kurtosis Test] [paper multiple re...] [2012-12-20 16:40:44] [7f9ff716c6b21ddb03ac377f3f036840]
-   P       [Multiple Regression] [paper multiple re...] [2012-12-20 17:33:25] [7f9ff716c6b21ddb03ac377f3f036840]
- RMPD      [Skewness and Kurtosis Test] [paper multiple re...] [2012-12-20 18:01:55] [7f9ff716c6b21ddb03ac377f3f036840]
- RMPD      [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [paper deel 5] [2012-12-20 18:39:26] [7f9ff716c6b21ddb03ac377f3f036840]
- R P         [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2012-12-21 15:32:35] [74be16979710d4c4e7c6647856088456]
- RMPD      [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [paper deel 5] [2012-12-20 18:41:24] [7f9ff716c6b21ddb03ac377f3f036840]
- R P         [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2012-12-21 15:33:53] [74be16979710d4c4e7c6647856088456]
- RMPD      [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [paper deel 5] [2012-12-20 18:47:42] [7f9ff716c6b21ddb03ac377f3f036840]
- R P         [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2012-12-21 15:38:46] [74be16979710d4c4e7c6647856088456]
- R PD      [Multiple Regression] [paper deel 5] [2012-12-20 18:56:24] [7f9ff716c6b21ddb03ac377f3f036840]
- R P         [Multiple Regression] [] [2012-12-21 15:48:12] [74be16979710d4c4e7c6647856088456]
- R PD      [Multiple Regression] [paper deel 5] [2012-12-20 19:00:42] [7f9ff716c6b21ddb03ac377f3f036840]
- RMP         [Multiple Regression] [] [2012-12-21 15:49:03] [74be16979710d4c4e7c6647856088456]
- R P         [Multiple Regression] [paper deel 5] [2012-12-21 17:59:14] [74be16979710d4c4e7c6647856088456]
- RMPD      [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [paper deel 5] [2012-12-20 19:04:28] [7f9ff716c6b21ddb03ac377f3f036840]
- R P         [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2012-12-21 15:37:55] [74be16979710d4c4e7c6647856088456]
-    D          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2012-12-21 15:41:30] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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93120	58521	24375	18179	26903	40994	112999	84371	27313	12957	31941
91063	56734	23899	18342	25504	40001	113754	85368	26470	12833	31951
90930	55327	23065	17765	24488	38675	110388	81981	25747	12147	30525
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92505	59681	26099	18171	29528	44467	108831	81806	27294	13126	32082
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102495	73335	29009	18629	34412	50367	107841	81061	33253	13493	31198
104552	71257	28645	19245	33735	48695	111377	84209	31988	13885	32524
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103950	68045	27078	17937	30483	46454	105055	78790	30136	13090	30513
102858	66338	26260	17421	29281	44895	102265	76645	28948	12529	29594
106952	67339	27078	17708	29589	45313	102323	76614	29244	12690	29836
110901	75744	31018	19608	35155	52826	110832	83558	34396	14137	32816
107706	76098	31546	20209	35198	52560	112899	85307	34125	14887	33843
111267	71483	29293	19983	32032	48224	110949	84348	30836	14661	33035
107643	69240	28528	19256	30642	46029	106594	81247	29116	13827	31546
105387	66421	27151	18582	30011	44262	104743	79685	27925	13530	30907
105718	67840	27241	18430	30464	45453	103932	79365	28836	13383	30512
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106203	68564	27106	18023	30010	44620	103163	78666	28180	13324	30111
105558	67149	26457	17821	28403	43467	102364	78790	27208	13166	29941
105230	65656	25897	17482	26988	42542	100650	77396	26744	12777	29215
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108173	71369	29824	19738	31486	47924	110051	84929	30848	14431	32529
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107847	66073	27117	18854	27972	42923	104773	80655	27483	13303	30612
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107733	70281	27876	19102	30786	45173	105190	81507	29467	13652	30882
107573	70149	27531	19070	30055	45079	104718	81284	29106	13568	30552
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106382	67404	26335	17990	27189	43087	100434	78122	27380	12804	29225
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109728	76518	31450	20370	33356	50385	110188	86563	32616	14767	32773
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109609	73417	28905	19441	30973	46726	104495	82590	30485	13854	30982

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ yule.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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191209&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191209&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191209&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 time8 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Werkloosheid_BRUSSELS_HOOFDSTEDELIJK_GEWEST[t] = + 62563.6644381663 + 1.57460189433131Werkloosheid_ANTWERPEN[t] + 0.390143895024618`Werkloosheid_VLAAMS-BRABANT`[t] + 0.190514181494486`Werkloosheid_WAALS-BRABANT`[t] -0.532879882911811`Werkloosheid_WEST-VLAANDEREN`[t] -1.06054429055137`Werkloosheid_OOST-VLAANDEREN`[t] -0.178659030213334Werkloosheid_HENEGOUWEN[t] -0.856280147459766Werkloosheid_LUIK[t] -0.326671455987136Werkloosheid_LIMBURG[t] + 0.455374238448602Werkloosheid_LUXEMBURG[t] + 2.48013083591232Werkloosheid_NAMEN[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkloosheid_BRUSSELS_HOOFDSTEDELIJK_GEWEST[t] =  +  62563.6644381663 +  1.57460189433131Werkloosheid_ANTWERPEN[t] +  0.390143895024618`Werkloosheid_VLAAMS-BRABANT`[t] +  0.190514181494486`Werkloosheid_WAALS-BRABANT`[t] -0.532879882911811`Werkloosheid_WEST-VLAANDEREN`[t] -1.06054429055137`Werkloosheid_OOST-VLAANDEREN`[t] -0.178659030213334Werkloosheid_HENEGOUWEN[t] -0.856280147459766Werkloosheid_LUIK[t] -0.326671455987136Werkloosheid_LIMBURG[t] +  0.455374238448602Werkloosheid_LUXEMBURG[t] +  2.48013083591232Werkloosheid_NAMEN[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191209&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkloosheid_BRUSSELS_HOOFDSTEDELIJK_GEWEST[t] =  +  62563.6644381663 +  1.57460189433131Werkloosheid_ANTWERPEN[t] +  0.390143895024618`Werkloosheid_VLAAMS-BRABANT`[t] +  0.190514181494486`Werkloosheid_WAALS-BRABANT`[t] -0.532879882911811`Werkloosheid_WEST-VLAANDEREN`[t] -1.06054429055137`Werkloosheid_OOST-VLAANDEREN`[t] -0.178659030213334Werkloosheid_HENEGOUWEN[t] -0.856280147459766Werkloosheid_LUIK[t] -0.326671455987136Werkloosheid_LIMBURG[t] +  0.455374238448602Werkloosheid_LUXEMBURG[t] +  2.48013083591232Werkloosheid_NAMEN[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191209&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191209&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
Werkloosheid_BRUSSELS_HOOFDSTEDELIJK_GEWEST[t] = + 62563.6644381663 + 1.57460189433131Werkloosheid_ANTWERPEN[t] + 0.390143895024618`Werkloosheid_VLAAMS-BRABANT`[t] + 0.190514181494486`Werkloosheid_WAALS-BRABANT`[t] -0.532879882911811`Werkloosheid_WEST-VLAANDEREN`[t] -1.06054429055137`Werkloosheid_OOST-VLAANDEREN`[t] -0.178659030213334Werkloosheid_HENEGOUWEN[t] -0.856280147459766Werkloosheid_LUIK[t] -0.326671455987136Werkloosheid_LIMBURG[t] + 0.455374238448602Werkloosheid_LUXEMBURG[t] + 2.48013083591232Werkloosheid_NAMEN[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)62563.66443816638977.5010446.968900
Werkloosheid_ANTWERPEN1.574601894331310.1964738.014300
`Werkloosheid_VLAAMS-BRABANT`0.3901438950246180.5234710.74530.458550.229275
`Werkloosheid_WAALS-BRABANT`0.1905141814944860.7932140.24020.8108830.405441
`Werkloosheid_WEST-VLAANDEREN`-0.5328798829118110.296361-1.79810.0764160.038208
`Werkloosheid_OOST-VLAANDEREN`-1.060544290551370.383501-2.76540.0072390.00362
Werkloosheid_HENEGOUWEN-0.1786590302133340.189271-0.94390.3484060.174203
Werkloosheid_LUIK-0.8562801474597660.191969-4.46053e-051.5e-05
Werkloosheid_LIMBURG-0.3266714559871360.497867-0.65610.5138530.256927
Werkloosheid_LUXEMBURG0.4553742384486020.8895220.51190.6102880.305144
Werkloosheid_NAMEN2.480130835912320.6067574.08750.0001135.7e-05

\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) & 62563.6644381663 & 8977.501044 & 6.9689 & 0 & 0 \tabularnewline
Werkloosheid_ANTWERPEN & 1.57460189433131 & 0.196473 & 8.0143 & 0 & 0 \tabularnewline
`Werkloosheid_VLAAMS-BRABANT` & 0.390143895024618 & 0.523471 & 0.7453 & 0.45855 & 0.229275 \tabularnewline
`Werkloosheid_WAALS-BRABANT` & 0.190514181494486 & 0.793214 & 0.2402 & 0.810883 & 0.405441 \tabularnewline
`Werkloosheid_WEST-VLAANDEREN` & -0.532879882911811 & 0.296361 & -1.7981 & 0.076416 & 0.038208 \tabularnewline
`Werkloosheid_OOST-VLAANDEREN` & -1.06054429055137 & 0.383501 & -2.7654 & 0.007239 & 0.00362 \tabularnewline
Werkloosheid_HENEGOUWEN & -0.178659030213334 & 0.189271 & -0.9439 & 0.348406 & 0.174203 \tabularnewline
Werkloosheid_LUIK & -0.856280147459766 & 0.191969 & -4.4605 & 3e-05 & 1.5e-05 \tabularnewline
Werkloosheid_LIMBURG & -0.326671455987136 & 0.497867 & -0.6561 & 0.513853 & 0.256927 \tabularnewline
Werkloosheid_LUXEMBURG & 0.455374238448602 & 0.889522 & 0.5119 & 0.610288 & 0.305144 \tabularnewline
Werkloosheid_NAMEN & 2.48013083591232 & 0.606757 & 4.0875 & 0.000113 & 5.7e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191209&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]62563.6644381663[/C][C]8977.501044[/C][C]6.9689[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Werkloosheid_ANTWERPEN[/C][C]1.57460189433131[/C][C]0.196473[/C][C]8.0143[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Werkloosheid_VLAAMS-BRABANT`[/C][C]0.390143895024618[/C][C]0.523471[/C][C]0.7453[/C][C]0.45855[/C][C]0.229275[/C][/ROW]
[ROW][C]`Werkloosheid_WAALS-BRABANT`[/C][C]0.190514181494486[/C][C]0.793214[/C][C]0.2402[/C][C]0.810883[/C][C]0.405441[/C][/ROW]
[ROW][C]`Werkloosheid_WEST-VLAANDEREN`[/C][C]-0.532879882911811[/C][C]0.296361[/C][C]-1.7981[/C][C]0.076416[/C][C]0.038208[/C][/ROW]
[ROW][C]`Werkloosheid_OOST-VLAANDEREN`[/C][C]-1.06054429055137[/C][C]0.383501[/C][C]-2.7654[/C][C]0.007239[/C][C]0.00362[/C][/ROW]
[ROW][C]Werkloosheid_HENEGOUWEN[/C][C]-0.178659030213334[/C][C]0.189271[/C][C]-0.9439[/C][C]0.348406[/C][C]0.174203[/C][/ROW]
[ROW][C]Werkloosheid_LUIK[/C][C]-0.856280147459766[/C][C]0.191969[/C][C]-4.4605[/C][C]3e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]Werkloosheid_LIMBURG[/C][C]-0.326671455987136[/C][C]0.497867[/C][C]-0.6561[/C][C]0.513853[/C][C]0.256927[/C][/ROW]
[ROW][C]Werkloosheid_LUXEMBURG[/C][C]0.455374238448602[/C][C]0.889522[/C][C]0.5119[/C][C]0.610288[/C][C]0.305144[/C][/ROW]
[ROW][C]Werkloosheid_NAMEN[/C][C]2.48013083591232[/C][C]0.606757[/C][C]4.0875[/C][C]0.000113[/C][C]5.7e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191209&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191209&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)62563.66443816638977.5010446.968900
Werkloosheid_ANTWERPEN1.574601894331310.1964738.014300
`Werkloosheid_VLAAMS-BRABANT`0.3901438950246180.5234710.74530.458550.229275
`Werkloosheid_WAALS-BRABANT`0.1905141814944860.7932140.24020.8108830.405441
`Werkloosheid_WEST-VLAANDEREN`-0.5328798829118110.296361-1.79810.0764160.038208
`Werkloosheid_OOST-VLAANDEREN`-1.060544290551370.383501-2.76540.0072390.00362
Werkloosheid_HENEGOUWEN-0.1786590302133340.189271-0.94390.3484060.174203
Werkloosheid_LUIK-0.8562801474597660.191969-4.46053e-051.5e-05
Werkloosheid_LIMBURG-0.3266714559871360.497867-0.65610.5138530.256927
Werkloosheid_LUXEMBURG0.4553742384486020.8895220.51190.6102880.305144
Werkloosheid_NAMEN2.480130835912320.6067574.08750.0001135.7e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.982628358041529
R-squared0.965558490027392
Adjusted R-squared0.960707573129841
F-TEST (value)199.046594781898
F-TEST (DF numerator)10
F-TEST (DF denominator)71
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1252.93930041798
Sum Squared Residuals111459839.227765

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.982628358041529 \tabularnewline
R-squared & 0.965558490027392 \tabularnewline
Adjusted R-squared & 0.960707573129841 \tabularnewline
F-TEST (value) & 199.046594781898 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 71 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1252.93930041798 \tabularnewline
Sum Squared Residuals & 111459839.227765 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191209&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.982628358041529[/C][/ROW]
[ROW][C]R-squared[/C][C]0.965558490027392[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.960707573129841[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]199.046594781898[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]71[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1252.93930041798[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]111459839.227765[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191209&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191209&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 R0.982628358041529
R-squared0.965558490027392
Adjusted R-squared0.960707573129841
F-TEST (value)199.046594781898
F-TEST (DF numerator)10
F-TEST (DF denominator)71
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1252.93930041798
Sum Squared Residuals111459839.227765






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19768797711.1931456625-24.1931456624716
29851297458.41475778741053.58524221262
39867398213.710029071459.289970929
49602897048.5929473528-1020.59294735274
59801496435.70254389971578.29745610033
69558094428.68625456251151.31374543752
79783896785.35205657371052.64794342626
89776098213.8617539905-453.861753990453
99991398776.51175709981136.48824290025
109758896027.05062441511560.94937558487
119394295670.4478970048-1728.44789700481
129365694662.2712811815-1006.27128118146
139336594490.4037950793-1125.40379507927
149288193974.3905991304-1093.39059913042
159312093634.2979897758-514.297989775837
169106391719.569224229-656.569224229004
179093090905.202676277824.797323722187
189194692520.2936145572-574.293614557225
199462495797.7796807931-1173.77968079309
209548496934.2822180759-1450.28221807591
219586295391.473572796470.526427204036
229553094660.1323584436869.86764155639
239457494234.2983398955339.701660104546
249467793363.85677127231313.14322872766
259384593454.6896117537390.310388246351
269153393006.3987209122-1473.39872091217
279121491827.791116087-613.791116086981
289092291668.3350147213-746.335014721258
298956390058.1295444415-495.129544441531
308994589945.7219207439-0.721920743880667
319185092209.0518741837-359.051874183713
329250594423.7130030309-1918.71300303091
339243792124.1296546039312.87034539612
349387692713.0281557541162.97184424601
359356193099.2934464043461.706553595722
369411993105.88535369651013.11464630346
379526495368.7590030497-104.759003049663
389608996182.6523457532-93.6523457532225
399716096897.4173889187262.582611081273
409864496295.01030316422348.98969683576
419626697183.9505858925-917.950585892512
429793897956.6030082684-18.6030082684264
4399757101342.063480864-1585.06348086382
44101550102845.795431364-1295.79543136444
45102449102130.820880366318.179119634334
46102416101753.83948073662.160519270286
47102491103001.735372156-510.735372156271
48102495105128.957465588-2633.95746558787
49104552104519.3601683932.6398316101341
50104798104487.918946935310.081053065016
51104947104130.814137261816.18586273932
52103950103735.890399249214.109600750809
53102858103113.07222441-255.072224409963
54106952105048.6241039951903.37589600496
55110901108148.9042746492752.09572535112
56107706110396.230109748-2690.23010974763
57111267108630.1296157952636.87038420497
58107643107652.494220787-9.49422078734734
59105387105095.509152783291.490847216667
60105718104906.233929847811.766070152672
61106039107004.665621749-965.665621749387
62106203106970.223411685-767.223411684553
63105558106390.143127813-832.143127812863
64105230105164.9622072165.0377927902213
65104864104759.282428857104.717571143062
66104374104042.951736921331.048263078653
67107450105771.5453140681678.4546859321
68108173107519.565845039653.434154960768
69108629106874.6467647591754.35323524147
70107847105566.1422656792280.85773432086
71107394105234.0373503982159.96264960201
72106278105784.466182765493.533817234868
73107733108026.078391107-293.07839110692
74107573107703.272520059-130.272520059061
75107500107216.853514535283.146485464665
76106382106746.189384165-364.189384164825
77104412106519.527099757-2107.52709975687
78105871106611.158928104-740.158928104065
79108767110189.890747939-1422.89074793866
80109728111532.449540681-1804.44954068127
81109769110035.556031718-266.556031718201
82109609110887.662249768-1278.66224976837

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 97687 & 97711.1931456625 & -24.1931456624716 \tabularnewline
2 & 98512 & 97458.4147577874 & 1053.58524221262 \tabularnewline
3 & 98673 & 98213.710029071 & 459.289970929 \tabularnewline
4 & 96028 & 97048.5929473528 & -1020.59294735274 \tabularnewline
5 & 98014 & 96435.7025438997 & 1578.29745610033 \tabularnewline
6 & 95580 & 94428.6862545625 & 1151.31374543752 \tabularnewline
7 & 97838 & 96785.3520565737 & 1052.64794342626 \tabularnewline
8 & 97760 & 98213.8617539905 & -453.861753990453 \tabularnewline
9 & 99913 & 98776.5117570998 & 1136.48824290025 \tabularnewline
10 & 97588 & 96027.0506244151 & 1560.94937558487 \tabularnewline
11 & 93942 & 95670.4478970048 & -1728.44789700481 \tabularnewline
12 & 93656 & 94662.2712811815 & -1006.27128118146 \tabularnewline
13 & 93365 & 94490.4037950793 & -1125.40379507927 \tabularnewline
14 & 92881 & 93974.3905991304 & -1093.39059913042 \tabularnewline
15 & 93120 & 93634.2979897758 & -514.297989775837 \tabularnewline
16 & 91063 & 91719.569224229 & -656.569224229004 \tabularnewline
17 & 90930 & 90905.2026762778 & 24.797323722187 \tabularnewline
18 & 91946 & 92520.2936145572 & -574.293614557225 \tabularnewline
19 & 94624 & 95797.7796807931 & -1173.77968079309 \tabularnewline
20 & 95484 & 96934.2822180759 & -1450.28221807591 \tabularnewline
21 & 95862 & 95391.473572796 & 470.526427204036 \tabularnewline
22 & 95530 & 94660.1323584436 & 869.86764155639 \tabularnewline
23 & 94574 & 94234.2983398955 & 339.701660104546 \tabularnewline
24 & 94677 & 93363.8567712723 & 1313.14322872766 \tabularnewline
25 & 93845 & 93454.6896117537 & 390.310388246351 \tabularnewline
26 & 91533 & 93006.3987209122 & -1473.39872091217 \tabularnewline
27 & 91214 & 91827.791116087 & -613.791116086981 \tabularnewline
28 & 90922 & 91668.3350147213 & -746.335014721258 \tabularnewline
29 & 89563 & 90058.1295444415 & -495.129544441531 \tabularnewline
30 & 89945 & 89945.7219207439 & -0.721920743880667 \tabularnewline
31 & 91850 & 92209.0518741837 & -359.051874183713 \tabularnewline
32 & 92505 & 94423.7130030309 & -1918.71300303091 \tabularnewline
33 & 92437 & 92124.1296546039 & 312.87034539612 \tabularnewline
34 & 93876 & 92713.028155754 & 1162.97184424601 \tabularnewline
35 & 93561 & 93099.2934464043 & 461.706553595722 \tabularnewline
36 & 94119 & 93105.8853536965 & 1013.11464630346 \tabularnewline
37 & 95264 & 95368.7590030497 & -104.759003049663 \tabularnewline
38 & 96089 & 96182.6523457532 & -93.6523457532225 \tabularnewline
39 & 97160 & 96897.4173889187 & 262.582611081273 \tabularnewline
40 & 98644 & 96295.0103031642 & 2348.98969683576 \tabularnewline
41 & 96266 & 97183.9505858925 & -917.950585892512 \tabularnewline
42 & 97938 & 97956.6030082684 & -18.6030082684264 \tabularnewline
43 & 99757 & 101342.063480864 & -1585.06348086382 \tabularnewline
44 & 101550 & 102845.795431364 & -1295.79543136444 \tabularnewline
45 & 102449 & 102130.820880366 & 318.179119634334 \tabularnewline
46 & 102416 & 101753.83948073 & 662.160519270286 \tabularnewline
47 & 102491 & 103001.735372156 & -510.735372156271 \tabularnewline
48 & 102495 & 105128.957465588 & -2633.95746558787 \tabularnewline
49 & 104552 & 104519.36016839 & 32.6398316101341 \tabularnewline
50 & 104798 & 104487.918946935 & 310.081053065016 \tabularnewline
51 & 104947 & 104130.814137261 & 816.18586273932 \tabularnewline
52 & 103950 & 103735.890399249 & 214.109600750809 \tabularnewline
53 & 102858 & 103113.07222441 & -255.072224409963 \tabularnewline
54 & 106952 & 105048.624103995 & 1903.37589600496 \tabularnewline
55 & 110901 & 108148.904274649 & 2752.09572535112 \tabularnewline
56 & 107706 & 110396.230109748 & -2690.23010974763 \tabularnewline
57 & 111267 & 108630.129615795 & 2636.87038420497 \tabularnewline
58 & 107643 & 107652.494220787 & -9.49422078734734 \tabularnewline
59 & 105387 & 105095.509152783 & 291.490847216667 \tabularnewline
60 & 105718 & 104906.233929847 & 811.766070152672 \tabularnewline
61 & 106039 & 107004.665621749 & -965.665621749387 \tabularnewline
62 & 106203 & 106970.223411685 & -767.223411684553 \tabularnewline
63 & 105558 & 106390.143127813 & -832.143127812863 \tabularnewline
64 & 105230 & 105164.96220721 & 65.0377927902213 \tabularnewline
65 & 104864 & 104759.282428857 & 104.717571143062 \tabularnewline
66 & 104374 & 104042.951736921 & 331.048263078653 \tabularnewline
67 & 107450 & 105771.545314068 & 1678.4546859321 \tabularnewline
68 & 108173 & 107519.565845039 & 653.434154960768 \tabularnewline
69 & 108629 & 106874.646764759 & 1754.35323524147 \tabularnewline
70 & 107847 & 105566.142265679 & 2280.85773432086 \tabularnewline
71 & 107394 & 105234.037350398 & 2159.96264960201 \tabularnewline
72 & 106278 & 105784.466182765 & 493.533817234868 \tabularnewline
73 & 107733 & 108026.078391107 & -293.07839110692 \tabularnewline
74 & 107573 & 107703.272520059 & -130.272520059061 \tabularnewline
75 & 107500 & 107216.853514535 & 283.146485464665 \tabularnewline
76 & 106382 & 106746.189384165 & -364.189384164825 \tabularnewline
77 & 104412 & 106519.527099757 & -2107.52709975687 \tabularnewline
78 & 105871 & 106611.158928104 & -740.158928104065 \tabularnewline
79 & 108767 & 110189.890747939 & -1422.89074793866 \tabularnewline
80 & 109728 & 111532.449540681 & -1804.44954068127 \tabularnewline
81 & 109769 & 110035.556031718 & -266.556031718201 \tabularnewline
82 & 109609 & 110887.662249768 & -1278.66224976837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191209&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]97687[/C][C]97711.1931456625[/C][C]-24.1931456624716[/C][/ROW]
[ROW][C]2[/C][C]98512[/C][C]97458.4147577874[/C][C]1053.58524221262[/C][/ROW]
[ROW][C]3[/C][C]98673[/C][C]98213.710029071[/C][C]459.289970929[/C][/ROW]
[ROW][C]4[/C][C]96028[/C][C]97048.5929473528[/C][C]-1020.59294735274[/C][/ROW]
[ROW][C]5[/C][C]98014[/C][C]96435.7025438997[/C][C]1578.29745610033[/C][/ROW]
[ROW][C]6[/C][C]95580[/C][C]94428.6862545625[/C][C]1151.31374543752[/C][/ROW]
[ROW][C]7[/C][C]97838[/C][C]96785.3520565737[/C][C]1052.64794342626[/C][/ROW]
[ROW][C]8[/C][C]97760[/C][C]98213.8617539905[/C][C]-453.861753990453[/C][/ROW]
[ROW][C]9[/C][C]99913[/C][C]98776.5117570998[/C][C]1136.48824290025[/C][/ROW]
[ROW][C]10[/C][C]97588[/C][C]96027.0506244151[/C][C]1560.94937558487[/C][/ROW]
[ROW][C]11[/C][C]93942[/C][C]95670.4478970048[/C][C]-1728.44789700481[/C][/ROW]
[ROW][C]12[/C][C]93656[/C][C]94662.2712811815[/C][C]-1006.27128118146[/C][/ROW]
[ROW][C]13[/C][C]93365[/C][C]94490.4037950793[/C][C]-1125.40379507927[/C][/ROW]
[ROW][C]14[/C][C]92881[/C][C]93974.3905991304[/C][C]-1093.39059913042[/C][/ROW]
[ROW][C]15[/C][C]93120[/C][C]93634.2979897758[/C][C]-514.297989775837[/C][/ROW]
[ROW][C]16[/C][C]91063[/C][C]91719.569224229[/C][C]-656.569224229004[/C][/ROW]
[ROW][C]17[/C][C]90930[/C][C]90905.2026762778[/C][C]24.797323722187[/C][/ROW]
[ROW][C]18[/C][C]91946[/C][C]92520.2936145572[/C][C]-574.293614557225[/C][/ROW]
[ROW][C]19[/C][C]94624[/C][C]95797.7796807931[/C][C]-1173.77968079309[/C][/ROW]
[ROW][C]20[/C][C]95484[/C][C]96934.2822180759[/C][C]-1450.28221807591[/C][/ROW]
[ROW][C]21[/C][C]95862[/C][C]95391.473572796[/C][C]470.526427204036[/C][/ROW]
[ROW][C]22[/C][C]95530[/C][C]94660.1323584436[/C][C]869.86764155639[/C][/ROW]
[ROW][C]23[/C][C]94574[/C][C]94234.2983398955[/C][C]339.701660104546[/C][/ROW]
[ROW][C]24[/C][C]94677[/C][C]93363.8567712723[/C][C]1313.14322872766[/C][/ROW]
[ROW][C]25[/C][C]93845[/C][C]93454.6896117537[/C][C]390.310388246351[/C][/ROW]
[ROW][C]26[/C][C]91533[/C][C]93006.3987209122[/C][C]-1473.39872091217[/C][/ROW]
[ROW][C]27[/C][C]91214[/C][C]91827.791116087[/C][C]-613.791116086981[/C][/ROW]
[ROW][C]28[/C][C]90922[/C][C]91668.3350147213[/C][C]-746.335014721258[/C][/ROW]
[ROW][C]29[/C][C]89563[/C][C]90058.1295444415[/C][C]-495.129544441531[/C][/ROW]
[ROW][C]30[/C][C]89945[/C][C]89945.7219207439[/C][C]-0.721920743880667[/C][/ROW]
[ROW][C]31[/C][C]91850[/C][C]92209.0518741837[/C][C]-359.051874183713[/C][/ROW]
[ROW][C]32[/C][C]92505[/C][C]94423.7130030309[/C][C]-1918.71300303091[/C][/ROW]
[ROW][C]33[/C][C]92437[/C][C]92124.1296546039[/C][C]312.87034539612[/C][/ROW]
[ROW][C]34[/C][C]93876[/C][C]92713.028155754[/C][C]1162.97184424601[/C][/ROW]
[ROW][C]35[/C][C]93561[/C][C]93099.2934464043[/C][C]461.706553595722[/C][/ROW]
[ROW][C]36[/C][C]94119[/C][C]93105.8853536965[/C][C]1013.11464630346[/C][/ROW]
[ROW][C]37[/C][C]95264[/C][C]95368.7590030497[/C][C]-104.759003049663[/C][/ROW]
[ROW][C]38[/C][C]96089[/C][C]96182.6523457532[/C][C]-93.6523457532225[/C][/ROW]
[ROW][C]39[/C][C]97160[/C][C]96897.4173889187[/C][C]262.582611081273[/C][/ROW]
[ROW][C]40[/C][C]98644[/C][C]96295.0103031642[/C][C]2348.98969683576[/C][/ROW]
[ROW][C]41[/C][C]96266[/C][C]97183.9505858925[/C][C]-917.950585892512[/C][/ROW]
[ROW][C]42[/C][C]97938[/C][C]97956.6030082684[/C][C]-18.6030082684264[/C][/ROW]
[ROW][C]43[/C][C]99757[/C][C]101342.063480864[/C][C]-1585.06348086382[/C][/ROW]
[ROW][C]44[/C][C]101550[/C][C]102845.795431364[/C][C]-1295.79543136444[/C][/ROW]
[ROW][C]45[/C][C]102449[/C][C]102130.820880366[/C][C]318.179119634334[/C][/ROW]
[ROW][C]46[/C][C]102416[/C][C]101753.83948073[/C][C]662.160519270286[/C][/ROW]
[ROW][C]47[/C][C]102491[/C][C]103001.735372156[/C][C]-510.735372156271[/C][/ROW]
[ROW][C]48[/C][C]102495[/C][C]105128.957465588[/C][C]-2633.95746558787[/C][/ROW]
[ROW][C]49[/C][C]104552[/C][C]104519.36016839[/C][C]32.6398316101341[/C][/ROW]
[ROW][C]50[/C][C]104798[/C][C]104487.918946935[/C][C]310.081053065016[/C][/ROW]
[ROW][C]51[/C][C]104947[/C][C]104130.814137261[/C][C]816.18586273932[/C][/ROW]
[ROW][C]52[/C][C]103950[/C][C]103735.890399249[/C][C]214.109600750809[/C][/ROW]
[ROW][C]53[/C][C]102858[/C][C]103113.07222441[/C][C]-255.072224409963[/C][/ROW]
[ROW][C]54[/C][C]106952[/C][C]105048.624103995[/C][C]1903.37589600496[/C][/ROW]
[ROW][C]55[/C][C]110901[/C][C]108148.904274649[/C][C]2752.09572535112[/C][/ROW]
[ROW][C]56[/C][C]107706[/C][C]110396.230109748[/C][C]-2690.23010974763[/C][/ROW]
[ROW][C]57[/C][C]111267[/C][C]108630.129615795[/C][C]2636.87038420497[/C][/ROW]
[ROW][C]58[/C][C]107643[/C][C]107652.494220787[/C][C]-9.49422078734734[/C][/ROW]
[ROW][C]59[/C][C]105387[/C][C]105095.509152783[/C][C]291.490847216667[/C][/ROW]
[ROW][C]60[/C][C]105718[/C][C]104906.233929847[/C][C]811.766070152672[/C][/ROW]
[ROW][C]61[/C][C]106039[/C][C]107004.665621749[/C][C]-965.665621749387[/C][/ROW]
[ROW][C]62[/C][C]106203[/C][C]106970.223411685[/C][C]-767.223411684553[/C][/ROW]
[ROW][C]63[/C][C]105558[/C][C]106390.143127813[/C][C]-832.143127812863[/C][/ROW]
[ROW][C]64[/C][C]105230[/C][C]105164.96220721[/C][C]65.0377927902213[/C][/ROW]
[ROW][C]65[/C][C]104864[/C][C]104759.282428857[/C][C]104.717571143062[/C][/ROW]
[ROW][C]66[/C][C]104374[/C][C]104042.951736921[/C][C]331.048263078653[/C][/ROW]
[ROW][C]67[/C][C]107450[/C][C]105771.545314068[/C][C]1678.4546859321[/C][/ROW]
[ROW][C]68[/C][C]108173[/C][C]107519.565845039[/C][C]653.434154960768[/C][/ROW]
[ROW][C]69[/C][C]108629[/C][C]106874.646764759[/C][C]1754.35323524147[/C][/ROW]
[ROW][C]70[/C][C]107847[/C][C]105566.142265679[/C][C]2280.85773432086[/C][/ROW]
[ROW][C]71[/C][C]107394[/C][C]105234.037350398[/C][C]2159.96264960201[/C][/ROW]
[ROW][C]72[/C][C]106278[/C][C]105784.466182765[/C][C]493.533817234868[/C][/ROW]
[ROW][C]73[/C][C]107733[/C][C]108026.078391107[/C][C]-293.07839110692[/C][/ROW]
[ROW][C]74[/C][C]107573[/C][C]107703.272520059[/C][C]-130.272520059061[/C][/ROW]
[ROW][C]75[/C][C]107500[/C][C]107216.853514535[/C][C]283.146485464665[/C][/ROW]
[ROW][C]76[/C][C]106382[/C][C]106746.189384165[/C][C]-364.189384164825[/C][/ROW]
[ROW][C]77[/C][C]104412[/C][C]106519.527099757[/C][C]-2107.52709975687[/C][/ROW]
[ROW][C]78[/C][C]105871[/C][C]106611.158928104[/C][C]-740.158928104065[/C][/ROW]
[ROW][C]79[/C][C]108767[/C][C]110189.890747939[/C][C]-1422.89074793866[/C][/ROW]
[ROW][C]80[/C][C]109728[/C][C]111532.449540681[/C][C]-1804.44954068127[/C][/ROW]
[ROW][C]81[/C][C]109769[/C][C]110035.556031718[/C][C]-266.556031718201[/C][/ROW]
[ROW][C]82[/C][C]109609[/C][C]110887.662249768[/C][C]-1278.66224976837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191209&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191209&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
19768797711.1931456625-24.1931456624716
29851297458.41475778741053.58524221262
39867398213.710029071459.289970929
49602897048.5929473528-1020.59294735274
59801496435.70254389971578.29745610033
69558094428.68625456251151.31374543752
79783896785.35205657371052.64794342626
89776098213.8617539905-453.861753990453
99991398776.51175709981136.48824290025
109758896027.05062441511560.94937558487
119394295670.4478970048-1728.44789700481
129365694662.2712811815-1006.27128118146
139336594490.4037950793-1125.40379507927
149288193974.3905991304-1093.39059913042
159312093634.2979897758-514.297989775837
169106391719.569224229-656.569224229004
179093090905.202676277824.797323722187
189194692520.2936145572-574.293614557225
199462495797.7796807931-1173.77968079309
209548496934.2822180759-1450.28221807591
219586295391.473572796470.526427204036
229553094660.1323584436869.86764155639
239457494234.2983398955339.701660104546
249467793363.85677127231313.14322872766
259384593454.6896117537390.310388246351
269153393006.3987209122-1473.39872091217
279121491827.791116087-613.791116086981
289092291668.3350147213-746.335014721258
298956390058.1295444415-495.129544441531
308994589945.7219207439-0.721920743880667
319185092209.0518741837-359.051874183713
329250594423.7130030309-1918.71300303091
339243792124.1296546039312.87034539612
349387692713.0281557541162.97184424601
359356193099.2934464043461.706553595722
369411993105.88535369651013.11464630346
379526495368.7590030497-104.759003049663
389608996182.6523457532-93.6523457532225
399716096897.4173889187262.582611081273
409864496295.01030316422348.98969683576
419626697183.9505858925-917.950585892512
429793897956.6030082684-18.6030082684264
4399757101342.063480864-1585.06348086382
44101550102845.795431364-1295.79543136444
45102449102130.820880366318.179119634334
46102416101753.83948073662.160519270286
47102491103001.735372156-510.735372156271
48102495105128.957465588-2633.95746558787
49104552104519.3601683932.6398316101341
50104798104487.918946935310.081053065016
51104947104130.814137261816.18586273932
52103950103735.890399249214.109600750809
53102858103113.07222441-255.072224409963
54106952105048.6241039951903.37589600496
55110901108148.9042746492752.09572535112
56107706110396.230109748-2690.23010974763
57111267108630.1296157952636.87038420497
58107643107652.494220787-9.49422078734734
59105387105095.509152783291.490847216667
60105718104906.233929847811.766070152672
61106039107004.665621749-965.665621749387
62106203106970.223411685-767.223411684553
63105558106390.143127813-832.143127812863
64105230105164.9622072165.0377927902213
65104864104759.282428857104.717571143062
66104374104042.951736921331.048263078653
67107450105771.5453140681678.4546859321
68108173107519.565845039653.434154960768
69108629106874.6467647591754.35323524147
70107847105566.1422656792280.85773432086
71107394105234.0373503982159.96264960201
72106278105784.466182765493.533817234868
73107733108026.078391107-293.07839110692
74107573107703.272520059-130.272520059061
75107500107216.853514535283.146485464665
76106382106746.189384165-364.189384164825
77104412106519.527099757-2107.52709975687
78105871106611.158928104-740.158928104065
79108767110189.890747939-1422.89074793866
80109728111532.449540681-1804.44954068127
81109769110035.556031718-266.556031718201
82109609110887.662249768-1278.66224976837






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.5615388812458840.8769222375082320.438461118754116
150.4812971541629410.9625943083258820.518702845837059
160.3330280306012970.6660560612025930.666971969398703
170.2626311164457170.5252622328914340.737368883554283
180.175104440916580.3502088818331610.82489555908342
190.1868949120258090.3737898240516170.813105087974191
200.1357819666758180.2715639333516370.864218033324182
210.08644464485832970.1728892897166590.91355535514167
220.06055572201591740.1211114440318350.939444277984083
230.04941590669926160.09883181339852310.950584093300738
240.03395891945612010.06791783891224020.96604108054388
250.02533163062336230.05066326124672460.974668369376638
260.02870515395248960.05741030790497910.97129484604751
270.1008297669973540.2016595339947090.899170233002646
280.1360428557543090.2720857115086170.863957144245691
290.1291182617244960.2582365234489930.870881738275504
300.1412093282321640.2824186564643280.858790671767836
310.1201625251938820.2403250503877640.879837474806118
320.2035555870310210.4071111740620410.796444412968979
330.1783528165966330.3567056331932660.821647183403367
340.1427934314682490.2855868629364990.85720656853175
350.1429536019815530.2859072039631060.857046398018447
360.1150989013995350.230197802799070.884901098600465
370.142990811706680.285981623413360.85700918829332
380.1770162811456820.3540325622913630.822983718854318
390.1423610904045830.2847221808091670.857638909595417
400.1479601569126850.2959203138253710.852039843087315
410.1872065188048590.3744130376097170.812793481195141
420.1452893213676810.2905786427353610.854710678632319
430.1927351226160230.3854702452320470.807264877383977
440.179458382608450.35891676521690.82054161739155
450.176558829877460.3531176597549210.82344117012254
460.1815491252557850.3630982505115710.818450874744215
470.2160997434085980.4321994868171970.783900256591402
480.3134328682089230.6268657364178460.686567131791077
490.3503285402337730.7006570804675460.649671459766227
500.3618445842339190.7236891684678370.638155415766081
510.3709943850164960.7419887700329920.629005614983504
520.3495741834878210.6991483669756420.650425816512179
530.5821921709648810.8356156580702380.417807829035119
540.7386877724352240.5226244551295520.261312227564776
550.9330185658227320.1339628683545360.0669814341772682
560.9792058103782090.04158837924358280.0207941896217914
570.997738788101460.004522423797079090.00226121189853954
580.9959373835356040.008125232928792630.00406261646439631
590.9946832759748250.01063344805035010.00531672402517505
600.9889931586933630.02201368261327460.0110068413066373
610.9783954464209740.04320910715805170.0216045535790259
620.9628933832430430.07421323351391320.0371066167569566
630.9472659292412390.1054681415175230.0527340707587614
640.9040065972563650.1919868054872710.0959934027436353
650.8509143313940330.2981713372119340.149085668605967
660.7834930587538480.4330138824923030.216506941246152
670.8539118937020820.2921762125958370.146088106297918
680.7306070026545270.5387859946909450.269392997345473

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.561538881245884 & 0.876922237508232 & 0.438461118754116 \tabularnewline
15 & 0.481297154162941 & 0.962594308325882 & 0.518702845837059 \tabularnewline
16 & 0.333028030601297 & 0.666056061202593 & 0.666971969398703 \tabularnewline
17 & 0.262631116445717 & 0.525262232891434 & 0.737368883554283 \tabularnewline
18 & 0.17510444091658 & 0.350208881833161 & 0.82489555908342 \tabularnewline
19 & 0.186894912025809 & 0.373789824051617 & 0.813105087974191 \tabularnewline
20 & 0.135781966675818 & 0.271563933351637 & 0.864218033324182 \tabularnewline
21 & 0.0864446448583297 & 0.172889289716659 & 0.91355535514167 \tabularnewline
22 & 0.0605557220159174 & 0.121111444031835 & 0.939444277984083 \tabularnewline
23 & 0.0494159066992616 & 0.0988318133985231 & 0.950584093300738 \tabularnewline
24 & 0.0339589194561201 & 0.0679178389122402 & 0.96604108054388 \tabularnewline
25 & 0.0253316306233623 & 0.0506632612467246 & 0.974668369376638 \tabularnewline
26 & 0.0287051539524896 & 0.0574103079049791 & 0.97129484604751 \tabularnewline
27 & 0.100829766997354 & 0.201659533994709 & 0.899170233002646 \tabularnewline
28 & 0.136042855754309 & 0.272085711508617 & 0.863957144245691 \tabularnewline
29 & 0.129118261724496 & 0.258236523448993 & 0.870881738275504 \tabularnewline
30 & 0.141209328232164 & 0.282418656464328 & 0.858790671767836 \tabularnewline
31 & 0.120162525193882 & 0.240325050387764 & 0.879837474806118 \tabularnewline
32 & 0.203555587031021 & 0.407111174062041 & 0.796444412968979 \tabularnewline
33 & 0.178352816596633 & 0.356705633193266 & 0.821647183403367 \tabularnewline
34 & 0.142793431468249 & 0.285586862936499 & 0.85720656853175 \tabularnewline
35 & 0.142953601981553 & 0.285907203963106 & 0.857046398018447 \tabularnewline
36 & 0.115098901399535 & 0.23019780279907 & 0.884901098600465 \tabularnewline
37 & 0.14299081170668 & 0.28598162341336 & 0.85700918829332 \tabularnewline
38 & 0.177016281145682 & 0.354032562291363 & 0.822983718854318 \tabularnewline
39 & 0.142361090404583 & 0.284722180809167 & 0.857638909595417 \tabularnewline
40 & 0.147960156912685 & 0.295920313825371 & 0.852039843087315 \tabularnewline
41 & 0.187206518804859 & 0.374413037609717 & 0.812793481195141 \tabularnewline
42 & 0.145289321367681 & 0.290578642735361 & 0.854710678632319 \tabularnewline
43 & 0.192735122616023 & 0.385470245232047 & 0.807264877383977 \tabularnewline
44 & 0.17945838260845 & 0.3589167652169 & 0.82054161739155 \tabularnewline
45 & 0.17655882987746 & 0.353117659754921 & 0.82344117012254 \tabularnewline
46 & 0.181549125255785 & 0.363098250511571 & 0.818450874744215 \tabularnewline
47 & 0.216099743408598 & 0.432199486817197 & 0.783900256591402 \tabularnewline
48 & 0.313432868208923 & 0.626865736417846 & 0.686567131791077 \tabularnewline
49 & 0.350328540233773 & 0.700657080467546 & 0.649671459766227 \tabularnewline
50 & 0.361844584233919 & 0.723689168467837 & 0.638155415766081 \tabularnewline
51 & 0.370994385016496 & 0.741988770032992 & 0.629005614983504 \tabularnewline
52 & 0.349574183487821 & 0.699148366975642 & 0.650425816512179 \tabularnewline
53 & 0.582192170964881 & 0.835615658070238 & 0.417807829035119 \tabularnewline
54 & 0.738687772435224 & 0.522624455129552 & 0.261312227564776 \tabularnewline
55 & 0.933018565822732 & 0.133962868354536 & 0.0669814341772682 \tabularnewline
56 & 0.979205810378209 & 0.0415883792435828 & 0.0207941896217914 \tabularnewline
57 & 0.99773878810146 & 0.00452242379707909 & 0.00226121189853954 \tabularnewline
58 & 0.995937383535604 & 0.00812523292879263 & 0.00406261646439631 \tabularnewline
59 & 0.994683275974825 & 0.0106334480503501 & 0.00531672402517505 \tabularnewline
60 & 0.988993158693363 & 0.0220136826132746 & 0.0110068413066373 \tabularnewline
61 & 0.978395446420974 & 0.0432091071580517 & 0.0216045535790259 \tabularnewline
62 & 0.962893383243043 & 0.0742132335139132 & 0.0371066167569566 \tabularnewline
63 & 0.947265929241239 & 0.105468141517523 & 0.0527340707587614 \tabularnewline
64 & 0.904006597256365 & 0.191986805487271 & 0.0959934027436353 \tabularnewline
65 & 0.850914331394033 & 0.298171337211934 & 0.149085668605967 \tabularnewline
66 & 0.783493058753848 & 0.433013882492303 & 0.216506941246152 \tabularnewline
67 & 0.853911893702082 & 0.292176212595837 & 0.146088106297918 \tabularnewline
68 & 0.730607002654527 & 0.538785994690945 & 0.269392997345473 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191209&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]14[/C][C]0.561538881245884[/C][C]0.876922237508232[/C][C]0.438461118754116[/C][/ROW]
[ROW][C]15[/C][C]0.481297154162941[/C][C]0.962594308325882[/C][C]0.518702845837059[/C][/ROW]
[ROW][C]16[/C][C]0.333028030601297[/C][C]0.666056061202593[/C][C]0.666971969398703[/C][/ROW]
[ROW][C]17[/C][C]0.262631116445717[/C][C]0.525262232891434[/C][C]0.737368883554283[/C][/ROW]
[ROW][C]18[/C][C]0.17510444091658[/C][C]0.350208881833161[/C][C]0.82489555908342[/C][/ROW]
[ROW][C]19[/C][C]0.186894912025809[/C][C]0.373789824051617[/C][C]0.813105087974191[/C][/ROW]
[ROW][C]20[/C][C]0.135781966675818[/C][C]0.271563933351637[/C][C]0.864218033324182[/C][/ROW]
[ROW][C]21[/C][C]0.0864446448583297[/C][C]0.172889289716659[/C][C]0.91355535514167[/C][/ROW]
[ROW][C]22[/C][C]0.0605557220159174[/C][C]0.121111444031835[/C][C]0.939444277984083[/C][/ROW]
[ROW][C]23[/C][C]0.0494159066992616[/C][C]0.0988318133985231[/C][C]0.950584093300738[/C][/ROW]
[ROW][C]24[/C][C]0.0339589194561201[/C][C]0.0679178389122402[/C][C]0.96604108054388[/C][/ROW]
[ROW][C]25[/C][C]0.0253316306233623[/C][C]0.0506632612467246[/C][C]0.974668369376638[/C][/ROW]
[ROW][C]26[/C][C]0.0287051539524896[/C][C]0.0574103079049791[/C][C]0.97129484604751[/C][/ROW]
[ROW][C]27[/C][C]0.100829766997354[/C][C]0.201659533994709[/C][C]0.899170233002646[/C][/ROW]
[ROW][C]28[/C][C]0.136042855754309[/C][C]0.272085711508617[/C][C]0.863957144245691[/C][/ROW]
[ROW][C]29[/C][C]0.129118261724496[/C][C]0.258236523448993[/C][C]0.870881738275504[/C][/ROW]
[ROW][C]30[/C][C]0.141209328232164[/C][C]0.282418656464328[/C][C]0.858790671767836[/C][/ROW]
[ROW][C]31[/C][C]0.120162525193882[/C][C]0.240325050387764[/C][C]0.879837474806118[/C][/ROW]
[ROW][C]32[/C][C]0.203555587031021[/C][C]0.407111174062041[/C][C]0.796444412968979[/C][/ROW]
[ROW][C]33[/C][C]0.178352816596633[/C][C]0.356705633193266[/C][C]0.821647183403367[/C][/ROW]
[ROW][C]34[/C][C]0.142793431468249[/C][C]0.285586862936499[/C][C]0.85720656853175[/C][/ROW]
[ROW][C]35[/C][C]0.142953601981553[/C][C]0.285907203963106[/C][C]0.857046398018447[/C][/ROW]
[ROW][C]36[/C][C]0.115098901399535[/C][C]0.23019780279907[/C][C]0.884901098600465[/C][/ROW]
[ROW][C]37[/C][C]0.14299081170668[/C][C]0.28598162341336[/C][C]0.85700918829332[/C][/ROW]
[ROW][C]38[/C][C]0.177016281145682[/C][C]0.354032562291363[/C][C]0.822983718854318[/C][/ROW]
[ROW][C]39[/C][C]0.142361090404583[/C][C]0.284722180809167[/C][C]0.857638909595417[/C][/ROW]
[ROW][C]40[/C][C]0.147960156912685[/C][C]0.295920313825371[/C][C]0.852039843087315[/C][/ROW]
[ROW][C]41[/C][C]0.187206518804859[/C][C]0.374413037609717[/C][C]0.812793481195141[/C][/ROW]
[ROW][C]42[/C][C]0.145289321367681[/C][C]0.290578642735361[/C][C]0.854710678632319[/C][/ROW]
[ROW][C]43[/C][C]0.192735122616023[/C][C]0.385470245232047[/C][C]0.807264877383977[/C][/ROW]
[ROW][C]44[/C][C]0.17945838260845[/C][C]0.3589167652169[/C][C]0.82054161739155[/C][/ROW]
[ROW][C]45[/C][C]0.17655882987746[/C][C]0.353117659754921[/C][C]0.82344117012254[/C][/ROW]
[ROW][C]46[/C][C]0.181549125255785[/C][C]0.363098250511571[/C][C]0.818450874744215[/C][/ROW]
[ROW][C]47[/C][C]0.216099743408598[/C][C]0.432199486817197[/C][C]0.783900256591402[/C][/ROW]
[ROW][C]48[/C][C]0.313432868208923[/C][C]0.626865736417846[/C][C]0.686567131791077[/C][/ROW]
[ROW][C]49[/C][C]0.350328540233773[/C][C]0.700657080467546[/C][C]0.649671459766227[/C][/ROW]
[ROW][C]50[/C][C]0.361844584233919[/C][C]0.723689168467837[/C][C]0.638155415766081[/C][/ROW]
[ROW][C]51[/C][C]0.370994385016496[/C][C]0.741988770032992[/C][C]0.629005614983504[/C][/ROW]
[ROW][C]52[/C][C]0.349574183487821[/C][C]0.699148366975642[/C][C]0.650425816512179[/C][/ROW]
[ROW][C]53[/C][C]0.582192170964881[/C][C]0.835615658070238[/C][C]0.417807829035119[/C][/ROW]
[ROW][C]54[/C][C]0.738687772435224[/C][C]0.522624455129552[/C][C]0.261312227564776[/C][/ROW]
[ROW][C]55[/C][C]0.933018565822732[/C][C]0.133962868354536[/C][C]0.0669814341772682[/C][/ROW]
[ROW][C]56[/C][C]0.979205810378209[/C][C]0.0415883792435828[/C][C]0.0207941896217914[/C][/ROW]
[ROW][C]57[/C][C]0.99773878810146[/C][C]0.00452242379707909[/C][C]0.00226121189853954[/C][/ROW]
[ROW][C]58[/C][C]0.995937383535604[/C][C]0.00812523292879263[/C][C]0.00406261646439631[/C][/ROW]
[ROW][C]59[/C][C]0.994683275974825[/C][C]0.0106334480503501[/C][C]0.00531672402517505[/C][/ROW]
[ROW][C]60[/C][C]0.988993158693363[/C][C]0.0220136826132746[/C][C]0.0110068413066373[/C][/ROW]
[ROW][C]61[/C][C]0.978395446420974[/C][C]0.0432091071580517[/C][C]0.0216045535790259[/C][/ROW]
[ROW][C]62[/C][C]0.962893383243043[/C][C]0.0742132335139132[/C][C]0.0371066167569566[/C][/ROW]
[ROW][C]63[/C][C]0.947265929241239[/C][C]0.105468141517523[/C][C]0.0527340707587614[/C][/ROW]
[ROW][C]64[/C][C]0.904006597256365[/C][C]0.191986805487271[/C][C]0.0959934027436353[/C][/ROW]
[ROW][C]65[/C][C]0.850914331394033[/C][C]0.298171337211934[/C][C]0.149085668605967[/C][/ROW]
[ROW][C]66[/C][C]0.783493058753848[/C][C]0.433013882492303[/C][C]0.216506941246152[/C][/ROW]
[ROW][C]67[/C][C]0.853911893702082[/C][C]0.292176212595837[/C][C]0.146088106297918[/C][/ROW]
[ROW][C]68[/C][C]0.730607002654527[/C][C]0.538785994690945[/C][C]0.269392997345473[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191209&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191209&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
140.5615388812458840.8769222375082320.438461118754116
150.4812971541629410.9625943083258820.518702845837059
160.3330280306012970.6660560612025930.666971969398703
170.2626311164457170.5252622328914340.737368883554283
180.175104440916580.3502088818331610.82489555908342
190.1868949120258090.3737898240516170.813105087974191
200.1357819666758180.2715639333516370.864218033324182
210.08644464485832970.1728892897166590.91355535514167
220.06055572201591740.1211114440318350.939444277984083
230.04941590669926160.09883181339852310.950584093300738
240.03395891945612010.06791783891224020.96604108054388
250.02533163062336230.05066326124672460.974668369376638
260.02870515395248960.05741030790497910.97129484604751
270.1008297669973540.2016595339947090.899170233002646
280.1360428557543090.2720857115086170.863957144245691
290.1291182617244960.2582365234489930.870881738275504
300.1412093282321640.2824186564643280.858790671767836
310.1201625251938820.2403250503877640.879837474806118
320.2035555870310210.4071111740620410.796444412968979
330.1783528165966330.3567056331932660.821647183403367
340.1427934314682490.2855868629364990.85720656853175
350.1429536019815530.2859072039631060.857046398018447
360.1150989013995350.230197802799070.884901098600465
370.142990811706680.285981623413360.85700918829332
380.1770162811456820.3540325622913630.822983718854318
390.1423610904045830.2847221808091670.857638909595417
400.1479601569126850.2959203138253710.852039843087315
410.1872065188048590.3744130376097170.812793481195141
420.1452893213676810.2905786427353610.854710678632319
430.1927351226160230.3854702452320470.807264877383977
440.179458382608450.35891676521690.82054161739155
450.176558829877460.3531176597549210.82344117012254
460.1815491252557850.3630982505115710.818450874744215
470.2160997434085980.4321994868171970.783900256591402
480.3134328682089230.6268657364178460.686567131791077
490.3503285402337730.7006570804675460.649671459766227
500.3618445842339190.7236891684678370.638155415766081
510.3709943850164960.7419887700329920.629005614983504
520.3495741834878210.6991483669756420.650425816512179
530.5821921709648810.8356156580702380.417807829035119
540.7386877724352240.5226244551295520.261312227564776
550.9330185658227320.1339628683545360.0669814341772682
560.9792058103782090.04158837924358280.0207941896217914
570.997738788101460.004522423797079090.00226121189853954
580.9959373835356040.008125232928792630.00406261646439631
590.9946832759748250.01063344805035010.00531672402517505
600.9889931586933630.02201368261327460.0110068413066373
610.9783954464209740.04320910715805170.0216045535790259
620.9628933832430430.07421323351391320.0371066167569566
630.9472659292412390.1054681415175230.0527340707587614
640.9040065972563650.1919868054872710.0959934027436353
650.8509143313940330.2981713372119340.149085668605967
660.7834930587538480.4330138824923030.216506941246152
670.8539118937020820.2921762125958370.146088106297918
680.7306070026545270.5387859946909450.269392997345473






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0363636363636364NOK
5% type I error level60.109090909090909NOK
10% type I error level110.2NOK

\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 & 2 & 0.0363636363636364 & NOK \tabularnewline
5% type I error level & 6 & 0.109090909090909 & NOK \tabularnewline
10% type I error level & 11 & 0.2 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191209&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]2[/C][C]0.0363636363636364[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.109090909090909[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.2[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191209&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191209&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 level20.0363636363636364NOK
5% type I error level60.109090909090909NOK
10% type I error level110.2NOK


Figures (Output of Computation)

Figure 1
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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 = 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')
}