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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 21 Nov 2011 17:18:29 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321914002iizi0c4jyq8utg5.htm/, Retrieved Thu, 25 Apr 2024 03:58:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146010, Retrieved Thu, 25 Apr 2024 03:58:08 +0000

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [] [2011-11-21 22:18:29] [2be7aedefc35278abdba659ba29c8de8] [Current]

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1962	9,5	5,569	1,933	0,226
1963	9,6	5,634	1,947	0,231
1964	9,4	5,433	1,936	0,225
1965	9,4	5,425	1,956	0,229
1966	9,5	5,412	1,965	0,236
1967	9,4	5,247	1,973	0,234
1968	9,7	5,31	1,988	0,253
1969	9,5	5,168	1,985	0,251
1970	9,5	4,927	1,986	0,243
1971	9,3	4,929	1,993	0,239
1972	9,4	4,902	2,003	0,237
1973	9,3	4,82	2	0,23
1974	9,1	4,588	2,015	0,221
1975	8,8	4,312	2,001	0,203
1976	8,8	4,269	2,025	0,195
1977	8,6	4,137	2,035	0,182
1978	8,7	4,099	2,049	0,183
1979	8,5	4,016	2,04	0,175
1980	8,7	4,121	2,079	0,181
1981	8,6	3,97	2,064	0,176
1982	8,5	3,89	2,083	0,172
1983	8,6	3,889	2,091	0,176
1984	8,6	3,788	2,108	0,172
1985	8,7	3,75	2,113	0,174
1986	8,7	3,651	2,115	0,172
1987	8,7	3,559	2,117	0,174
1988	8,8	3,525	2,125	0,18
1989	8,7	3,32	2,142	0,205
1990	8,6	3,218	2,16	0,207
1991	8,5	3,138	2,158	0,207
1992	8,5	3,061	2,143	0,208
1993	8,8	3,099	2,146	0,22
1994	8,8	2,997	2,131	0,227
1995	8,8	2,963	2,117	0,234
1996	8,8	2,883	2,087	0,24
1997	8,6	2,804	2,057	0,24
1998	8,6	2,724	2,024	0,242
1999	8,8	2,678	2,027	0,252
2000	8,7	2,576	1,996	0,25
2001	8,5	2,478	1,96	0,253

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 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=146010&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 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=146010&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146010&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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
Cancer[t] = + 9.85008549642589 -0.00433295121643938Year[t] + 0.220653055238072Rate[t] -0.165991233759688Heart_disease[t] -2.42906802854214`Diabetes `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cancer[t] =  +  9.85008549642589 -0.00433295121643938Year[t] +  0.220653055238072Rate[t] -0.165991233759688Heart_disease[t] -2.42906802854214`Diabetes
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146010&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cancer[t] =  +  9.85008549642589 -0.00433295121643938Year[t] +  0.220653055238072Rate[t] -0.165991233759688Heart_disease[t] -2.42906802854214`Diabetes
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146010&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146010&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
Cancer[t] = + 9.85008549642589 -0.00433295121643938Year[t] + 0.220653055238072Rate[t] -0.165991233759688Heart_disease[t] -2.42906802854214`Diabetes `[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	9.85008549642589	14.35166	0.6863	0.497022	0.248511
Year	-0.00433295121643938	0.00722	-0.6001	0.552284	0.276142
Rate	0.220653055238072	0.070746	3.1189	0.003622	0.001811
Heart_disease	-0.165991233759688	0.102635	-1.6173	0.114794	0.057397
`Diabetes `	-2.42906802854214	0.421367	-5.7647	2e-06	1e-06

\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) & 9.85008549642589 & 14.35166 & 0.6863 & 0.497022 & 0.248511 \tabularnewline
Year & -0.00433295121643938 & 0.00722 & -0.6001 & 0.552284 & 0.276142 \tabularnewline
Rate & 0.220653055238072 & 0.070746 & 3.1189 & 0.003622 & 0.001811 \tabularnewline
Heart_disease & -0.165991233759688 & 0.102635 & -1.6173 & 0.114794 & 0.057397 \tabularnewline
`Diabetes
` & -2.42906802854214 & 0.421367 & -5.7647 & 2e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146010&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]9.85008549642589[/C][C]14.35166[/C][C]0.6863[/C][C]0.497022[/C][C]0.248511[/C][/ROW]
[ROW][C]Year[/C][C]-0.00433295121643938[/C][C]0.00722[/C][C]-0.6001[/C][C]0.552284[/C][C]0.276142[/C][/ROW]
[ROW][C]Rate[/C][C]0.220653055238072[/C][C]0.070746[/C][C]3.1189[/C][C]0.003622[/C][C]0.001811[/C][/ROW]
[ROW][C]Heart_disease[/C][C]-0.165991233759688[/C][C]0.102635[/C][C]-1.6173[/C][C]0.114794[/C][C]0.057397[/C][/ROW]
[ROW][C]`Diabetes
`[/C][C]-2.42906802854214[/C][C]0.421367[/C][C]-5.7647[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146010&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146010&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	9.85008549642589	14.35166	0.6863	0.497022	0.248511
Year	-0.00433295121643938	0.00722	-0.6001	0.552284	0.276142
Rate	0.220653055238072	0.070746	3.1189	0.003622	0.001811
Heart_disease	-0.165991233759688	0.102635	-1.6173	0.114794	0.057397
`Diabetes `	-2.42906802854214	0.421367	-5.7647	2e-06	1e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.867336184417206
R-squared	0.752272056799398
Adjusted R-squared	0.723960291862186
F-TEST (value)	26.5710053212065
F-TEST (DF numerator)	4
F-TEST (DF denominator)	35
p-value	3.51375706308943e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.036072444883317
Sum Squared Residuals	0.0455427447950979

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.867336184417206 \tabularnewline
R-squared & 0.752272056799398 \tabularnewline
Adjusted R-squared & 0.723960291862186 \tabularnewline
F-TEST (value) & 26.5710053212065 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 35 \tabularnewline
p-value & 3.51375706308943e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.036072444883317 \tabularnewline
Sum Squared Residuals & 0.0455427447950979 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146010&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.867336184417206[/C][/ROW]
[ROW][C]R-squared[/C][C]0.752272056799398[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.723960291862186[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]26.5710053212065[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]35[/C][/ROW]
[ROW][C]p-value[/C][C]3.51375706308943e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.036072444883317[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0455427447950979[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146010&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146010&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 R	0.867336184417206
R-squared	0.752272056799398
Adjusted R-squared	0.723960291862186
F-TEST (value)	26.5710053212065
F-TEST (DF numerator)	4
F-TEST (DF denominator)	35
p-value	3.51375706308943e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.036072444883317
Sum Squared Residuals	0.0455427447950979

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.933	1.97166467927529	-0.0386646792752893
2	1.947	1.96646226324557	-0.0194622632455667
3	1.936	1.96593734713846	-0.0299373471384635
4	1.956	1.95321605367793	0.00278394632206691
5	1.965	1.95610281782438	0.00889718217561827
6	1.973	1.96195125071157	0.011048749288432
7	1.988	1.96720447579739	0.0207955242026108
8	1.985	1.9471698047843	0.0378301952157046
9	1.986	2.00227328513228	-0.0162732851322782
10	1.993	1.96319401251487	0.0298059874851265
11	2.003	1.99026626619084	0.0127337338091629
12	2	1.99448276681868	0.00551723318131994
13	2.015	2.00639078304375	0.00860921695624715
14	2.001	2.02539872028732	-0.0243987202873247
15	2.025	2.04763593635089	-0.022635936350889
16	2.035	2.05266110131416	-0.0176611013141615
17	2.049	2.07427205447586	-0.0252720544758554
18	2.04	2.05901830884219	-0.0190183088421929
19	2.079	2.06681248095735	0.0121875190426526
20	2.064	2.07762424065752	-0.0136242406575247
21	2.083	2.07422155473222	0.00877844526777828
22	2.091	2.08240362815918	0.00859637184081935
23	2.108	2.10455206366664	0.00344793633336154
24	2.113	2.12373394879979	-0.0107339487997902
25	2.115	2.14069226578264	-0.025692265782644
26	2.117	2.14677237201501	-0.0297723720150118
27	2.125	2.15557402009896	-0.0305740200989566
28	2.142	2.10247726556589	0.0395227344341075
29	2.16	2.08815197861205	0.0718480213879503
30	2.158	2.07503302057258	0.0829669794274215
31	2.143	2.08105232632709	0.0619476736729069
32	2.146	2.1074588084567	0.0385411915432985
33	2.131	2.10305348688396	0.0279465131160444
34	2.117	2.08736076141555	0.0296392385844496
35	2.087	2.08173270072863	0.00526729927136689
36	2.057	2.04638244593159	0.0106175540684053
37	2.024	2.05047065735885	-0.026470657358846
38	2.027	2.07361323365755	-0.0466132336575455
39	1.996	2.06900421881787	-0.0730042188178712
40	1.96	2.02952059337664	-0.0695205933766406

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.933 & 1.97166467927529 & -0.0386646792752893 \tabularnewline
2 & 1.947 & 1.96646226324557 & -0.0194622632455667 \tabularnewline
3 & 1.936 & 1.96593734713846 & -0.0299373471384635 \tabularnewline
4 & 1.956 & 1.95321605367793 & 0.00278394632206691 \tabularnewline
5 & 1.965 & 1.95610281782438 & 0.00889718217561827 \tabularnewline
6 & 1.973 & 1.96195125071157 & 0.011048749288432 \tabularnewline
7 & 1.988 & 1.96720447579739 & 0.0207955242026108 \tabularnewline
8 & 1.985 & 1.9471698047843 & 0.0378301952157046 \tabularnewline
9 & 1.986 & 2.00227328513228 & -0.0162732851322782 \tabularnewline
10 & 1.993 & 1.96319401251487 & 0.0298059874851265 \tabularnewline
11 & 2.003 & 1.99026626619084 & 0.0127337338091629 \tabularnewline
12 & 2 & 1.99448276681868 & 0.00551723318131994 \tabularnewline
13 & 2.015 & 2.00639078304375 & 0.00860921695624715 \tabularnewline
14 & 2.001 & 2.02539872028732 & -0.0243987202873247 \tabularnewline
15 & 2.025 & 2.04763593635089 & -0.022635936350889 \tabularnewline
16 & 2.035 & 2.05266110131416 & -0.0176611013141615 \tabularnewline
17 & 2.049 & 2.07427205447586 & -0.0252720544758554 \tabularnewline
18 & 2.04 & 2.05901830884219 & -0.0190183088421929 \tabularnewline
19 & 2.079 & 2.06681248095735 & 0.0121875190426526 \tabularnewline
20 & 2.064 & 2.07762424065752 & -0.0136242406575247 \tabularnewline
21 & 2.083 & 2.07422155473222 & 0.00877844526777828 \tabularnewline
22 & 2.091 & 2.08240362815918 & 0.00859637184081935 \tabularnewline
23 & 2.108 & 2.10455206366664 & 0.00344793633336154 \tabularnewline
24 & 2.113 & 2.12373394879979 & -0.0107339487997902 \tabularnewline
25 & 2.115 & 2.14069226578264 & -0.025692265782644 \tabularnewline
26 & 2.117 & 2.14677237201501 & -0.0297723720150118 \tabularnewline
27 & 2.125 & 2.15557402009896 & -0.0305740200989566 \tabularnewline
28 & 2.142 & 2.10247726556589 & 0.0395227344341075 \tabularnewline
29 & 2.16 & 2.08815197861205 & 0.0718480213879503 \tabularnewline
30 & 2.158 & 2.07503302057258 & 0.0829669794274215 \tabularnewline
31 & 2.143 & 2.08105232632709 & 0.0619476736729069 \tabularnewline
32 & 2.146 & 2.1074588084567 & 0.0385411915432985 \tabularnewline
33 & 2.131 & 2.10305348688396 & 0.0279465131160444 \tabularnewline
34 & 2.117 & 2.08736076141555 & 0.0296392385844496 \tabularnewline
35 & 2.087 & 2.08173270072863 & 0.00526729927136689 \tabularnewline
36 & 2.057 & 2.04638244593159 & 0.0106175540684053 \tabularnewline
37 & 2.024 & 2.05047065735885 & -0.026470657358846 \tabularnewline
38 & 2.027 & 2.07361323365755 & -0.0466132336575455 \tabularnewline
39 & 1.996 & 2.06900421881787 & -0.0730042188178712 \tabularnewline
40 & 1.96 & 2.02952059337664 & -0.0695205933766406 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146010&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1.933[/C][C]1.97166467927529[/C][C]-0.0386646792752893[/C][/ROW]
[ROW][C]2[/C][C]1.947[/C][C]1.96646226324557[/C][C]-0.0194622632455667[/C][/ROW]
[ROW][C]3[/C][C]1.936[/C][C]1.96593734713846[/C][C]-0.0299373471384635[/C][/ROW]
[ROW][C]4[/C][C]1.956[/C][C]1.95321605367793[/C][C]0.00278394632206691[/C][/ROW]
[ROW][C]5[/C][C]1.965[/C][C]1.95610281782438[/C][C]0.00889718217561827[/C][/ROW]
[ROW][C]6[/C][C]1.973[/C][C]1.96195125071157[/C][C]0.011048749288432[/C][/ROW]
[ROW][C]7[/C][C]1.988[/C][C]1.96720447579739[/C][C]0.0207955242026108[/C][/ROW]
[ROW][C]8[/C][C]1.985[/C][C]1.9471698047843[/C][C]0.0378301952157046[/C][/ROW]
[ROW][C]9[/C][C]1.986[/C][C]2.00227328513228[/C][C]-0.0162732851322782[/C][/ROW]
[ROW][C]10[/C][C]1.993[/C][C]1.96319401251487[/C][C]0.0298059874851265[/C][/ROW]
[ROW][C]11[/C][C]2.003[/C][C]1.99026626619084[/C][C]0.0127337338091629[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.99448276681868[/C][C]0.00551723318131994[/C][/ROW]
[ROW][C]13[/C][C]2.015[/C][C]2.00639078304375[/C][C]0.00860921695624715[/C][/ROW]
[ROW][C]14[/C][C]2.001[/C][C]2.02539872028732[/C][C]-0.0243987202873247[/C][/ROW]
[ROW][C]15[/C][C]2.025[/C][C]2.04763593635089[/C][C]-0.022635936350889[/C][/ROW]
[ROW][C]16[/C][C]2.035[/C][C]2.05266110131416[/C][C]-0.0176611013141615[/C][/ROW]
[ROW][C]17[/C][C]2.049[/C][C]2.07427205447586[/C][C]-0.0252720544758554[/C][/ROW]
[ROW][C]18[/C][C]2.04[/C][C]2.05901830884219[/C][C]-0.0190183088421929[/C][/ROW]
[ROW][C]19[/C][C]2.079[/C][C]2.06681248095735[/C][C]0.0121875190426526[/C][/ROW]
[ROW][C]20[/C][C]2.064[/C][C]2.07762424065752[/C][C]-0.0136242406575247[/C][/ROW]
[ROW][C]21[/C][C]2.083[/C][C]2.07422155473222[/C][C]0.00877844526777828[/C][/ROW]
[ROW][C]22[/C][C]2.091[/C][C]2.08240362815918[/C][C]0.00859637184081935[/C][/ROW]
[ROW][C]23[/C][C]2.108[/C][C]2.10455206366664[/C][C]0.00344793633336154[/C][/ROW]
[ROW][C]24[/C][C]2.113[/C][C]2.12373394879979[/C][C]-0.0107339487997902[/C][/ROW]
[ROW][C]25[/C][C]2.115[/C][C]2.14069226578264[/C][C]-0.025692265782644[/C][/ROW]
[ROW][C]26[/C][C]2.117[/C][C]2.14677237201501[/C][C]-0.0297723720150118[/C][/ROW]
[ROW][C]27[/C][C]2.125[/C][C]2.15557402009896[/C][C]-0.0305740200989566[/C][/ROW]
[ROW][C]28[/C][C]2.142[/C][C]2.10247726556589[/C][C]0.0395227344341075[/C][/ROW]
[ROW][C]29[/C][C]2.16[/C][C]2.08815197861205[/C][C]0.0718480213879503[/C][/ROW]
[ROW][C]30[/C][C]2.158[/C][C]2.07503302057258[/C][C]0.0829669794274215[/C][/ROW]
[ROW][C]31[/C][C]2.143[/C][C]2.08105232632709[/C][C]0.0619476736729069[/C][/ROW]
[ROW][C]32[/C][C]2.146[/C][C]2.1074588084567[/C][C]0.0385411915432985[/C][/ROW]
[ROW][C]33[/C][C]2.131[/C][C]2.10305348688396[/C][C]0.0279465131160444[/C][/ROW]
[ROW][C]34[/C][C]2.117[/C][C]2.08736076141555[/C][C]0.0296392385844496[/C][/ROW]
[ROW][C]35[/C][C]2.087[/C][C]2.08173270072863[/C][C]0.00526729927136689[/C][/ROW]
[ROW][C]36[/C][C]2.057[/C][C]2.04638244593159[/C][C]0.0106175540684053[/C][/ROW]
[ROW][C]37[/C][C]2.024[/C][C]2.05047065735885[/C][C]-0.026470657358846[/C][/ROW]
[ROW][C]38[/C][C]2.027[/C][C]2.07361323365755[/C][C]-0.0466132336575455[/C][/ROW]
[ROW][C]39[/C][C]1.996[/C][C]2.06900421881787[/C][C]-0.0730042188178712[/C][/ROW]
[ROW][C]40[/C][C]1.96[/C][C]2.02952059337664[/C][C]-0.0695205933766406[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146010&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146010&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.933	1.97166467927529	-0.0386646792752893
2	1.947	1.96646226324557	-0.0194622632455667
3	1.936	1.96593734713846	-0.0299373471384635
4	1.956	1.95321605367793	0.00278394632206691
5	1.965	1.95610281782438	0.00889718217561827
6	1.973	1.96195125071157	0.011048749288432
7	1.988	1.96720447579739	0.0207955242026108
8	1.985	1.9471698047843	0.0378301952157046
9	1.986	2.00227328513228	-0.0162732851322782
10	1.993	1.96319401251487	0.0298059874851265
11	2.003	1.99026626619084	0.0127337338091629
12	2	1.99448276681868	0.00551723318131994
13	2.015	2.00639078304375	0.00860921695624715
14	2.001	2.02539872028732	-0.0243987202873247
15	2.025	2.04763593635089	-0.022635936350889
16	2.035	2.05266110131416	-0.0176611013141615
17	2.049	2.07427205447586	-0.0252720544758554
18	2.04	2.05901830884219	-0.0190183088421929
19	2.079	2.06681248095735	0.0121875190426526
20	2.064	2.07762424065752	-0.0136242406575247
21	2.083	2.07422155473222	0.00877844526777828
22	2.091	2.08240362815918	0.00859637184081935
23	2.108	2.10455206366664	0.00344793633336154
24	2.113	2.12373394879979	-0.0107339487997902
25	2.115	2.14069226578264	-0.025692265782644
26	2.117	2.14677237201501	-0.0297723720150118
27	2.125	2.15557402009896	-0.0305740200989566
28	2.142	2.10247726556589	0.0395227344341075
29	2.16	2.08815197861205	0.0718480213879503
30	2.158	2.07503302057258	0.0829669794274215
31	2.143	2.08105232632709	0.0619476736729069
32	2.146	2.1074588084567	0.0385411915432985
33	2.131	2.10305348688396	0.0279465131160444
34	2.117	2.08736076141555	0.0296392385844496
35	2.087	2.08173270072863	0.00526729927136689
36	2.057	2.04638244593159	0.0106175540684053
37	2.024	2.05047065735885	-0.026470657358846
38	2.027	2.07361323365755	-0.0466132336575455
39	1.996	2.06900421881787	-0.0730042188178712
40	1.96	2.02952059337664	-0.0695205933766406

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.00496726734122971	0.00993453468245942	0.99503273265877
9	0.00066053559529042	0.00132107119058084	0.99933946440471
10	7.74934960952028e-05	0.000154986992190406	0.999922506503905
11	8.64199139028549e-06	1.7283982780571e-05	0.99999135800861
12	1.89556011672931e-06	3.79112023345861e-06	0.999998104439883
13	4.63894129019207e-06	9.27788258038414e-06	0.99999536105871
14	6.41816373439772e-07	1.28363274687954e-06	0.999999358183627
15	5.85674965702096e-07	1.17134993140419e-06	0.999999414325034
16	4.87535089475572e-07	9.75070178951145e-07	0.99999951246491
17	4.72563893074171e-07	9.45127786148342e-07	0.999999527436107
18	2.62230615389973e-06	5.24461230779945e-06	0.999997377693846
19	1.43242571917353e-06	2.86485143834705e-06	0.999998567574281
20	7.73120970047312e-06	1.54624194009462e-05	0.9999922687903
21	1.11922374919484e-05	2.23844749838967e-05	0.999988807762508
22	4.01678828638499e-06	8.03357657276999e-06	0.999995983211714
23	2.06745942926022e-06	4.13491885852044e-06	0.999997932540571
24	9.45188711829552e-07	1.8903774236591e-06	0.999999054811288
25	7.81883800976076e-07	1.56376760195215e-06	0.999999218116199
26	7.5239529748978e-07	1.50479059497956e-06	0.999999247604702
27	1.47963762821483e-05	2.95927525642966e-05	0.999985203623718
28	0.179546021707549	0.359092043415098	0.820453978292451
29	0.91001678124518	0.17996643750964	0.0899832187548201
30	0.902462082116753	0.195075835766494	0.097537917883247
31	0.83783888526249	0.32432222947502	0.16216111473751
32	0.780429270436688	0.439141459126624	0.219570729563312

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.00496726734122971 & 0.00993453468245942 & 0.99503273265877 \tabularnewline
9 & 0.00066053559529042 & 0.00132107119058084 & 0.99933946440471 \tabularnewline
10 & 7.74934960952028e-05 & 0.000154986992190406 & 0.999922506503905 \tabularnewline
11 & 8.64199139028549e-06 & 1.7283982780571e-05 & 0.99999135800861 \tabularnewline
12 & 1.89556011672931e-06 & 3.79112023345861e-06 & 0.999998104439883 \tabularnewline
13 & 4.63894129019207e-06 & 9.27788258038414e-06 & 0.99999536105871 \tabularnewline
14 & 6.41816373439772e-07 & 1.28363274687954e-06 & 0.999999358183627 \tabularnewline
15 & 5.85674965702096e-07 & 1.17134993140419e-06 & 0.999999414325034 \tabularnewline
16 & 4.87535089475572e-07 & 9.75070178951145e-07 & 0.99999951246491 \tabularnewline
17 & 4.72563893074171e-07 & 9.45127786148342e-07 & 0.999999527436107 \tabularnewline
18 & 2.62230615389973e-06 & 5.24461230779945e-06 & 0.999997377693846 \tabularnewline
19 & 1.43242571917353e-06 & 2.86485143834705e-06 & 0.999998567574281 \tabularnewline
20 & 7.73120970047312e-06 & 1.54624194009462e-05 & 0.9999922687903 \tabularnewline
21 & 1.11922374919484e-05 & 2.23844749838967e-05 & 0.999988807762508 \tabularnewline
22 & 4.01678828638499e-06 & 8.03357657276999e-06 & 0.999995983211714 \tabularnewline
23 & 2.06745942926022e-06 & 4.13491885852044e-06 & 0.999997932540571 \tabularnewline
24 & 9.45188711829552e-07 & 1.8903774236591e-06 & 0.999999054811288 \tabularnewline
25 & 7.81883800976076e-07 & 1.56376760195215e-06 & 0.999999218116199 \tabularnewline
26 & 7.5239529748978e-07 & 1.50479059497956e-06 & 0.999999247604702 \tabularnewline
27 & 1.47963762821483e-05 & 2.95927525642966e-05 & 0.999985203623718 \tabularnewline
28 & 0.179546021707549 & 0.359092043415098 & 0.820453978292451 \tabularnewline
29 & 0.91001678124518 & 0.17996643750964 & 0.0899832187548201 \tabularnewline
30 & 0.902462082116753 & 0.195075835766494 & 0.097537917883247 \tabularnewline
31 & 0.83783888526249 & 0.32432222947502 & 0.16216111473751 \tabularnewline
32 & 0.780429270436688 & 0.439141459126624 & 0.219570729563312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146010&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]8[/C][C]0.00496726734122971[/C][C]0.00993453468245942[/C][C]0.99503273265877[/C][/ROW]
[ROW][C]9[/C][C]0.00066053559529042[/C][C]0.00132107119058084[/C][C]0.99933946440471[/C][/ROW]
[ROW][C]10[/C][C]7.74934960952028e-05[/C][C]0.000154986992190406[/C][C]0.999922506503905[/C][/ROW]
[ROW][C]11[/C][C]8.64199139028549e-06[/C][C]1.7283982780571e-05[/C][C]0.99999135800861[/C][/ROW]
[ROW][C]12[/C][C]1.89556011672931e-06[/C][C]3.79112023345861e-06[/C][C]0.999998104439883[/C][/ROW]
[ROW][C]13[/C][C]4.63894129019207e-06[/C][C]9.27788258038414e-06[/C][C]0.99999536105871[/C][/ROW]
[ROW][C]14[/C][C]6.41816373439772e-07[/C][C]1.28363274687954e-06[/C][C]0.999999358183627[/C][/ROW]
[ROW][C]15[/C][C]5.85674965702096e-07[/C][C]1.17134993140419e-06[/C][C]0.999999414325034[/C][/ROW]
[ROW][C]16[/C][C]4.87535089475572e-07[/C][C]9.75070178951145e-07[/C][C]0.99999951246491[/C][/ROW]
[ROW][C]17[/C][C]4.72563893074171e-07[/C][C]9.45127786148342e-07[/C][C]0.999999527436107[/C][/ROW]
[ROW][C]18[/C][C]2.62230615389973e-06[/C][C]5.24461230779945e-06[/C][C]0.999997377693846[/C][/ROW]
[ROW][C]19[/C][C]1.43242571917353e-06[/C][C]2.86485143834705e-06[/C][C]0.999998567574281[/C][/ROW]
[ROW][C]20[/C][C]7.73120970047312e-06[/C][C]1.54624194009462e-05[/C][C]0.9999922687903[/C][/ROW]
[ROW][C]21[/C][C]1.11922374919484e-05[/C][C]2.23844749838967e-05[/C][C]0.999988807762508[/C][/ROW]
[ROW][C]22[/C][C]4.01678828638499e-06[/C][C]8.03357657276999e-06[/C][C]0.999995983211714[/C][/ROW]
[ROW][C]23[/C][C]2.06745942926022e-06[/C][C]4.13491885852044e-06[/C][C]0.999997932540571[/C][/ROW]
[ROW][C]24[/C][C]9.45188711829552e-07[/C][C]1.8903774236591e-06[/C][C]0.999999054811288[/C][/ROW]
[ROW][C]25[/C][C]7.81883800976076e-07[/C][C]1.56376760195215e-06[/C][C]0.999999218116199[/C][/ROW]
[ROW][C]26[/C][C]7.5239529748978e-07[/C][C]1.50479059497956e-06[/C][C]0.999999247604702[/C][/ROW]
[ROW][C]27[/C][C]1.47963762821483e-05[/C][C]2.95927525642966e-05[/C][C]0.999985203623718[/C][/ROW]
[ROW][C]28[/C][C]0.179546021707549[/C][C]0.359092043415098[/C][C]0.820453978292451[/C][/ROW]
[ROW][C]29[/C][C]0.91001678124518[/C][C]0.17996643750964[/C][C]0.0899832187548201[/C][/ROW]
[ROW][C]30[/C][C]0.902462082116753[/C][C]0.195075835766494[/C][C]0.097537917883247[/C][/ROW]
[ROW][C]31[/C][C]0.83783888526249[/C][C]0.32432222947502[/C][C]0.16216111473751[/C][/ROW]
[ROW][C]32[/C][C]0.780429270436688[/C][C]0.439141459126624[/C][C]0.219570729563312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146010&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146010&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.00496726734122971	0.00993453468245942	0.99503273265877
9	0.00066053559529042	0.00132107119058084	0.99933946440471
10	7.74934960952028e-05	0.000154986992190406	0.999922506503905
11	8.64199139028549e-06	1.7283982780571e-05	0.99999135800861
12	1.89556011672931e-06	3.79112023345861e-06	0.999998104439883
13	4.63894129019207e-06	9.27788258038414e-06	0.99999536105871
14	6.41816373439772e-07	1.28363274687954e-06	0.999999358183627
15	5.85674965702096e-07	1.17134993140419e-06	0.999999414325034
16	4.87535089475572e-07	9.75070178951145e-07	0.99999951246491
17	4.72563893074171e-07	9.45127786148342e-07	0.999999527436107
18	2.62230615389973e-06	5.24461230779945e-06	0.999997377693846
19	1.43242571917353e-06	2.86485143834705e-06	0.999998567574281
20	7.73120970047312e-06	1.54624194009462e-05	0.9999922687903
21	1.11922374919484e-05	2.23844749838967e-05	0.999988807762508
22	4.01678828638499e-06	8.03357657276999e-06	0.999995983211714
23	2.06745942926022e-06	4.13491885852044e-06	0.999997932540571
24	9.45188711829552e-07	1.8903774236591e-06	0.999999054811288
25	7.81883800976076e-07	1.56376760195215e-06	0.999999218116199
26	7.5239529748978e-07	1.50479059497956e-06	0.999999247604702
27	1.47963762821483e-05	2.95927525642966e-05	0.999985203623718
28	0.179546021707549	0.359092043415098	0.820453978292451
29	0.91001678124518	0.17996643750964	0.0899832187548201
30	0.902462082116753	0.195075835766494	0.097537917883247
31	0.83783888526249	0.32432222947502	0.16216111473751
32	0.780429270436688	0.439141459126624	0.219570729563312

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	20	0.8	NOK
5% type I error level	20	0.8	NOK
10% type I error level	20	0.8	NOK

\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 & 20 & 0.8 & NOK \tabularnewline
5% type I error level & 20 & 0.8 & NOK \tabularnewline
10% type I error level & 20 & 0.8 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146010&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]20[/C][C]0.8[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.8[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.8[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146010&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146010&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 tests	OK/NOK
1% type I error level	20	0.8	NOK
5% type I error level	20	0.8	NOK
10% type I error level	20	0.8	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code