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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 15:16:58 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321993063pcq5qb650xdq69v.htm/, Retrieved Fri, 29 Mar 2024 15:16:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146402, Retrieved Fri, 29 Mar 2024 15:16:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS VII-Multiple R...] [2011-11-18 14:15:52] [7c680a04865e75aa8ab422cdbfd97ac3]
- R PD    [Multiple Regression] [WS VII-Multiple R...] [2011-11-18 14:42:56] [7c680a04865e75aa8ab422cdbfd97ac3]
-   PD        [Multiple Regression] [WS VII-Multiple R...] [2011-11-22 20:16:58] [3e388c05c22237d436c48535c44f60bb] [Current]
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Dataseries X:
1972	33907	71433	152	74272	99	765
1973	35981	53655	99	78867	128	1371
1974	36588	70556	92	80176	57	1880
1975	16967	74702	138	36541	95	232
1976	25333	61201	106	55107	205	230
1977	21027	686	95	45527	51	828
1978	21114	87586	145	46001	59	1833
1979	28777	6615	181	62854	194	906
1980	35612	89725	190	78112	27	1781
1981	24183	40420	150	52653	9	1264
1982	22262	49569	186	48467	24	1123
1983	20637	13963	174	44873	189	1461
1984	29948	62508	151	65605	37	820
1985	22093	90901	112	48016	81	107
1986	36997	89418	143	81110	72	1349
1987	31089	83237	120	68019	81	870
1988	19477	22183	169	42198	90	1471
1989	31301	24346	135	68531	216	731
1990	18497	74341	161	40071	216	1945
1991	30142	24188	98	65849	13	521
1992	21326	11781	142	46362	153	1920
1993	16779	23072	190	36313	185	1924
1994	38068	49119	169	83521	131	100
1995	29707	67776	130	64932	136	34
1996	35016	86910	160	76730	182	325
1997	26131	69358	176	56982	139	1677
1998	29251	16144	111	63793	42	1779
1999	22855	77863	165	49740	213	477
2000	31806	89070	117	69447	184	1007
2001	34124	34790	122	74708	44	1527




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=146402&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=146402&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146402&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Y[t] = -480.616464654015 + 0.533659564985219t -0.0001356487609356X1[t] -0.647651480072149X2[t] + 0.450211575231433X3[t] + 0.0196229415021703X4[t] -0.00826729887996951`X5 `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -480.616464654015 +  0.533659564985219t -0.0001356487609356X1[t] -0.647651480072149X2[t] +  0.450211575231433X3[t] +  0.0196229415021703X4[t] -0.00826729887996951`X5
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146402&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -480.616464654015 +  0.533659564985219t -0.0001356487609356X1[t] -0.647651480072149X2[t] +  0.450211575231433X3[t] +  0.0196229415021703X4[t] -0.00826729887996951`X5
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146402&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146402&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = -480.616464654015 + 0.533659564985219t -0.0001356487609356X1[t] -0.647651480072149X2[t] + 0.450211575231433X3[t] + 0.0196229415021703X4[t] -0.00826729887996951`X5 `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-480.6164646540151248.06171-0.38510.7037090.351854
t0.5336595649852190.6319240.84450.4070890.203544
X1-0.00013564876093560.000192-0.70650.4869920.243496
X2-0.6476514800721490.191151-3.38820.002530.001265
X30.4502115752314330.0003951139.747200
X40.01962294150217030.0844960.23220.8184090.409205
`X5 `-0.008267298879969510.008993-0.91930.3674740.183737

\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) & -480.616464654015 & 1248.06171 & -0.3851 & 0.703709 & 0.351854 \tabularnewline
t & 0.533659564985219 & 0.631924 & 0.8445 & 0.407089 & 0.203544 \tabularnewline
X1 & -0.0001356487609356 & 0.000192 & -0.7065 & 0.486992 & 0.243496 \tabularnewline
X2 & -0.647651480072149 & 0.191151 & -3.3882 & 0.00253 & 0.001265 \tabularnewline
X3 & 0.450211575231433 & 0.000395 & 1139.7472 & 0 & 0 \tabularnewline
X4 & 0.0196229415021703 & 0.084496 & 0.2322 & 0.818409 & 0.409205 \tabularnewline
`X5
` & -0.00826729887996951 & 0.008993 & -0.9193 & 0.367474 & 0.183737 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146402&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]-480.616464654015[/C][C]1248.06171[/C][C]-0.3851[/C][C]0.703709[/C][C]0.351854[/C][/ROW]
[ROW][C]t[/C][C]0.533659564985219[/C][C]0.631924[/C][C]0.8445[/C][C]0.407089[/C][C]0.203544[/C][/ROW]
[ROW][C]X1[/C][C]-0.0001356487609356[/C][C]0.000192[/C][C]-0.7065[/C][C]0.486992[/C][C]0.243496[/C][/ROW]
[ROW][C]X2[/C][C]-0.647651480072149[/C][C]0.191151[/C][C]-3.3882[/C][C]0.00253[/C][C]0.001265[/C][/ROW]
[ROW][C]X3[/C][C]0.450211575231433[/C][C]0.000395[/C][C]1139.7472[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X4[/C][C]0.0196229415021703[/C][C]0.084496[/C][C]0.2322[/C][C]0.818409[/C][C]0.409205[/C][/ROW]
[ROW][C]`X5
`[/C][C]-0.00826729887996951[/C][C]0.008993[/C][C]-0.9193[/C][C]0.367474[/C][C]0.183737[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146402&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146402&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-480.6164646540151248.06171-0.38510.7037090.351854
t0.5336595649852190.6319240.84450.4070890.203544
X1-0.00013564876093560.000192-0.70650.4869920.243496
X2-0.6476514800721490.191151-3.38820.002530.001265
X30.4502115752314330.0003951139.747200
X40.01962294150217030.0844960.23220.8184090.409205
`X5 `-0.008267298879969510.008993-0.91930.3674740.183737







Multiple Linear Regression - Regression Statistics
Multiple R0.999992562355233
R-squared0.999985124765785
Adjusted R-squared0.999981244269903
F-TEST (value)257695.190292214
F-TEST (DF numerator)6
F-TEST (DF denominator)23
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation28.6248261545832
Sum Squared Residuals18845.7554647426

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999992562355233 \tabularnewline
R-squared & 0.999985124765785 \tabularnewline
Adjusted R-squared & 0.999981244269903 \tabularnewline
F-TEST (value) & 257695.190292214 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 23 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 28.6248261545832 \tabularnewline
Sum Squared Residuals & 18845.7554647426 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146402&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999992562355233[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999985124765785[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999981244269903[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]257695.190292214[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]23[/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]28.6248261545832[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]18845.7554647426[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146402&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146402&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.999992562355233
R-squared0.999985124765785
Adjusted R-squared0.999981244269903
F-TEST (value)257695.190292214
F-TEST (DF numerator)6
F-TEST (DF denominator)23
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation28.6248261545832
Sum Squared Residuals18845.7554647426







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13390733897.35967774059.64032225952268
23598135998.911699792-17.9116997919542
33658836585.41198801032.58801198973816
41696716924.979374836842.0206251632287
52533325308.872439595224.1275604047555
62102721003.746381770423.2536182296456
72111421165.3582248238-51.358224823775
82877728751.288607467825.7113925322463
93561235593.536932321118.4630676788965
102418324168.649300003714.3506999962515
112226222261.50083511820.49916488183501
122063720657.0192591705-20.0192591705458
132994830001.9668628494-53.9668628493769
142209322111.8940516498-18.8940516497624
153699736981.408961471615.5910385284063
163108931108.0939613519-19.093961351911
171947719455.469473639321.5305263606554
183130131341.7415776267-40.741577626661
191849718495.59660697991.4033930200797
203014230157.0786648927-15.0786648926774
212132621348.7069476537-22.7069476537017
221677916793.0404714474-14.0404714473809
233806838071.2495266592-3.24952665923964
242970729726.1715794707-19.1715794707361
253501635014.77322715731.22677284270564
262613126104.526007852426.4739921475894
272925129218.019775876332.9802241236634
282285522862.5044290525-7.50442905250096
293180631759.973923373146.0260766269477
303412434126.1492303459-2.14923034594463

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 33907 & 33897.3596777405 & 9.64032225952268 \tabularnewline
2 & 35981 & 35998.911699792 & -17.9116997919542 \tabularnewline
3 & 36588 & 36585.4119880103 & 2.58801198973816 \tabularnewline
4 & 16967 & 16924.9793748368 & 42.0206251632287 \tabularnewline
5 & 25333 & 25308.8724395952 & 24.1275604047555 \tabularnewline
6 & 21027 & 21003.7463817704 & 23.2536182296456 \tabularnewline
7 & 21114 & 21165.3582248238 & -51.358224823775 \tabularnewline
8 & 28777 & 28751.2886074678 & 25.7113925322463 \tabularnewline
9 & 35612 & 35593.5369323211 & 18.4630676788965 \tabularnewline
10 & 24183 & 24168.6493000037 & 14.3506999962515 \tabularnewline
11 & 22262 & 22261.5008351182 & 0.49916488183501 \tabularnewline
12 & 20637 & 20657.0192591705 & -20.0192591705458 \tabularnewline
13 & 29948 & 30001.9668628494 & -53.9668628493769 \tabularnewline
14 & 22093 & 22111.8940516498 & -18.8940516497624 \tabularnewline
15 & 36997 & 36981.4089614716 & 15.5910385284063 \tabularnewline
16 & 31089 & 31108.0939613519 & -19.093961351911 \tabularnewline
17 & 19477 & 19455.4694736393 & 21.5305263606554 \tabularnewline
18 & 31301 & 31341.7415776267 & -40.741577626661 \tabularnewline
19 & 18497 & 18495.5966069799 & 1.4033930200797 \tabularnewline
20 & 30142 & 30157.0786648927 & -15.0786648926774 \tabularnewline
21 & 21326 & 21348.7069476537 & -22.7069476537017 \tabularnewline
22 & 16779 & 16793.0404714474 & -14.0404714473809 \tabularnewline
23 & 38068 & 38071.2495266592 & -3.24952665923964 \tabularnewline
24 & 29707 & 29726.1715794707 & -19.1715794707361 \tabularnewline
25 & 35016 & 35014.7732271573 & 1.22677284270564 \tabularnewline
26 & 26131 & 26104.5260078524 & 26.4739921475894 \tabularnewline
27 & 29251 & 29218.0197758763 & 32.9802241236634 \tabularnewline
28 & 22855 & 22862.5044290525 & -7.50442905250096 \tabularnewline
29 & 31806 & 31759.9739233731 & 46.0260766269477 \tabularnewline
30 & 34124 & 34126.1492303459 & -2.14923034594463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146402&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]33907[/C][C]33897.3596777405[/C][C]9.64032225952268[/C][/ROW]
[ROW][C]2[/C][C]35981[/C][C]35998.911699792[/C][C]-17.9116997919542[/C][/ROW]
[ROW][C]3[/C][C]36588[/C][C]36585.4119880103[/C][C]2.58801198973816[/C][/ROW]
[ROW][C]4[/C][C]16967[/C][C]16924.9793748368[/C][C]42.0206251632287[/C][/ROW]
[ROW][C]5[/C][C]25333[/C][C]25308.8724395952[/C][C]24.1275604047555[/C][/ROW]
[ROW][C]6[/C][C]21027[/C][C]21003.7463817704[/C][C]23.2536182296456[/C][/ROW]
[ROW][C]7[/C][C]21114[/C][C]21165.3582248238[/C][C]-51.358224823775[/C][/ROW]
[ROW][C]8[/C][C]28777[/C][C]28751.2886074678[/C][C]25.7113925322463[/C][/ROW]
[ROW][C]9[/C][C]35612[/C][C]35593.5369323211[/C][C]18.4630676788965[/C][/ROW]
[ROW][C]10[/C][C]24183[/C][C]24168.6493000037[/C][C]14.3506999962515[/C][/ROW]
[ROW][C]11[/C][C]22262[/C][C]22261.5008351182[/C][C]0.49916488183501[/C][/ROW]
[ROW][C]12[/C][C]20637[/C][C]20657.0192591705[/C][C]-20.0192591705458[/C][/ROW]
[ROW][C]13[/C][C]29948[/C][C]30001.9668628494[/C][C]-53.9668628493769[/C][/ROW]
[ROW][C]14[/C][C]22093[/C][C]22111.8940516498[/C][C]-18.8940516497624[/C][/ROW]
[ROW][C]15[/C][C]36997[/C][C]36981.4089614716[/C][C]15.5910385284063[/C][/ROW]
[ROW][C]16[/C][C]31089[/C][C]31108.0939613519[/C][C]-19.093961351911[/C][/ROW]
[ROW][C]17[/C][C]19477[/C][C]19455.4694736393[/C][C]21.5305263606554[/C][/ROW]
[ROW][C]18[/C][C]31301[/C][C]31341.7415776267[/C][C]-40.741577626661[/C][/ROW]
[ROW][C]19[/C][C]18497[/C][C]18495.5966069799[/C][C]1.4033930200797[/C][/ROW]
[ROW][C]20[/C][C]30142[/C][C]30157.0786648927[/C][C]-15.0786648926774[/C][/ROW]
[ROW][C]21[/C][C]21326[/C][C]21348.7069476537[/C][C]-22.7069476537017[/C][/ROW]
[ROW][C]22[/C][C]16779[/C][C]16793.0404714474[/C][C]-14.0404714473809[/C][/ROW]
[ROW][C]23[/C][C]38068[/C][C]38071.2495266592[/C][C]-3.24952665923964[/C][/ROW]
[ROW][C]24[/C][C]29707[/C][C]29726.1715794707[/C][C]-19.1715794707361[/C][/ROW]
[ROW][C]25[/C][C]35016[/C][C]35014.7732271573[/C][C]1.22677284270564[/C][/ROW]
[ROW][C]26[/C][C]26131[/C][C]26104.5260078524[/C][C]26.4739921475894[/C][/ROW]
[ROW][C]27[/C][C]29251[/C][C]29218.0197758763[/C][C]32.9802241236634[/C][/ROW]
[ROW][C]28[/C][C]22855[/C][C]22862.5044290525[/C][C]-7.50442905250096[/C][/ROW]
[ROW][C]29[/C][C]31806[/C][C]31759.9739233731[/C][C]46.0260766269477[/C][/ROW]
[ROW][C]30[/C][C]34124[/C][C]34126.1492303459[/C][C]-2.14923034594463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146402&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146402&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13390733897.35967774059.64032225952268
23598135998.911699792-17.9116997919542
33658836585.41198801032.58801198973816
41696716924.979374836842.0206251632287
52533325308.872439595224.1275604047555
62102721003.746381770423.2536182296456
72111421165.3582248238-51.358224823775
82877728751.288607467825.7113925322463
93561235593.536932321118.4630676788965
102418324168.649300003714.3506999962515
112226222261.50083511820.49916488183501
122063720657.0192591705-20.0192591705458
132994830001.9668628494-53.9668628493769
142209322111.8940516498-18.8940516497624
153699736981.408961471615.5910385284063
163108931108.0939613519-19.093961351911
171947719455.469473639321.5305263606554
183130131341.7415776267-40.741577626661
191849718495.59660697991.4033930200797
203014230157.0786648927-15.0786648926774
212132621348.7069476537-22.7069476537017
221677916793.0404714474-14.0404714473809
233806838071.2495266592-3.24952665923964
242970729726.1715794707-19.1715794707361
253501635014.77322715731.22677284270564
262613126104.526007852426.4739921475894
272925129218.019775876332.9802241236634
282285522862.5044290525-7.50442905250096
293180631759.973923373146.0260766269477
303412434126.1492303459-2.14923034594463







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5917992218672640.8164015562654720.408200778132736
110.5666831067621890.8666337864756220.433316893237811
120.4249090059508540.8498180119017080.575090994049146
130.8903655892688170.2192688214623670.109634410731183
140.8149309667784290.3701380664431430.185069033221571
150.8395965853273790.3208068293452420.160403414672621
160.8194142233144120.3611715533711770.180585776685588
170.8995580683551350.2008838632897310.100441931644865
180.843244828374220.313510343251560.15675517162578
190.76469896262920.47060207474160.2353010373708
200.5929372335953950.814125532809210.407062766404605

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.591799221867264 & 0.816401556265472 & 0.408200778132736 \tabularnewline
11 & 0.566683106762189 & 0.866633786475622 & 0.433316893237811 \tabularnewline
12 & 0.424909005950854 & 0.849818011901708 & 0.575090994049146 \tabularnewline
13 & 0.890365589268817 & 0.219268821462367 & 0.109634410731183 \tabularnewline
14 & 0.814930966778429 & 0.370138066443143 & 0.185069033221571 \tabularnewline
15 & 0.839596585327379 & 0.320806829345242 & 0.160403414672621 \tabularnewline
16 & 0.819414223314412 & 0.361171553371177 & 0.180585776685588 \tabularnewline
17 & 0.899558068355135 & 0.200883863289731 & 0.100441931644865 \tabularnewline
18 & 0.84324482837422 & 0.31351034325156 & 0.15675517162578 \tabularnewline
19 & 0.7646989626292 & 0.4706020747416 & 0.2353010373708 \tabularnewline
20 & 0.592937233595395 & 0.81412553280921 & 0.407062766404605 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146402&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.591799221867264[/C][C]0.816401556265472[/C][C]0.408200778132736[/C][/ROW]
[ROW][C]11[/C][C]0.566683106762189[/C][C]0.866633786475622[/C][C]0.433316893237811[/C][/ROW]
[ROW][C]12[/C][C]0.424909005950854[/C][C]0.849818011901708[/C][C]0.575090994049146[/C][/ROW]
[ROW][C]13[/C][C]0.890365589268817[/C][C]0.219268821462367[/C][C]0.109634410731183[/C][/ROW]
[ROW][C]14[/C][C]0.814930966778429[/C][C]0.370138066443143[/C][C]0.185069033221571[/C][/ROW]
[ROW][C]15[/C][C]0.839596585327379[/C][C]0.320806829345242[/C][C]0.160403414672621[/C][/ROW]
[ROW][C]16[/C][C]0.819414223314412[/C][C]0.361171553371177[/C][C]0.180585776685588[/C][/ROW]
[ROW][C]17[/C][C]0.899558068355135[/C][C]0.200883863289731[/C][C]0.100441931644865[/C][/ROW]
[ROW][C]18[/C][C]0.84324482837422[/C][C]0.31351034325156[/C][C]0.15675517162578[/C][/ROW]
[ROW][C]19[/C][C]0.7646989626292[/C][C]0.4706020747416[/C][C]0.2353010373708[/C][/ROW]
[ROW][C]20[/C][C]0.592937233595395[/C][C]0.81412553280921[/C][C]0.407062766404605[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146402&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146402&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5917992218672640.8164015562654720.408200778132736
110.5666831067621890.8666337864756220.433316893237811
120.4249090059508540.8498180119017080.575090994049146
130.8903655892688170.2192688214623670.109634410731183
140.8149309667784290.3701380664431430.185069033221571
150.8395965853273790.3208068293452420.160403414672621
160.8194142233144120.3611715533711770.180585776685588
170.8995580683551350.2008838632897310.100441931644865
180.843244828374220.313510343251560.15675517162578
190.76469896262920.47060207474160.2353010373708
200.5929372335953950.814125532809210.407062766404605







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

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146402&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146402&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146402&T=6

As an alternative you can also use a QR Code:  

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

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



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')
}