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Author

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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 29 Nov 2015 22:22:42 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Nov/29/t1448835789hihudhvb57gmllz.htm/, Retrieved Wed, 15 May 2024 08:17:27 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284550, Retrieved Wed, 15 May 2024 08:17:27 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

110

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

-     [Multiple Regression] [] [2015-11-25 14:21:14] [32b17a345b130fdf5cc88718ed94a974]
- R PD    [Multiple Regression] [MLR X4] [2015-11-29 22:22:42] [83aba8bbc702c7812e095afce40a5d1d] [Current]
- RMPD      [Partial Correlation Matrix] [Partial Correlati...] [2015-12-01 13:02:39] [f59657f1a0fa4fc4e0fb7285a3a88ff5] 
- RMPD      [Partial Correlation Matrix] [Partial Correlati...] [2015-12-01 13:03:37] [f59657f1a0fa4fc4e0fb7285a3a88ff5] 

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Dataseries X:

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Histogram

Boxplots

6,8	225	0,672
6,3	180	0,797
6,4	190	0,761
6,2	180	0,651
6,9	205	0,9
6,4	225	0,78
6,3	185	0,771
6,8	235	0,75
6,9	235	0,818
6,7	210	0,825
6,9	245	0,632
6,9	245	0,757
6,3	185	0,709
6,1	185	0,782
6,2	180	0,775
6,8	220	0,88
6,5	194	0,833
7,6	225	0,571
6,3	210	0,816
7,1	240	0,714
6,8	225	0,765
7,3	263	0,655
6,4	210	0,244
6,8	235	0,728
7,2	230	0,721
6,4	190	0,757
6,6	220	0,747
6,8	210	0,739
6,1	180	0,713
6,5	235	0,742
6,4	185	0,861
6	175	0,721
6	192	0,785
7,3	263	0,655
6,1	180	0,821
6,7	240	0,728
6,4	210	0,846
5,8	160	0,813
6,9	230	0,595
7	245	0,573
7,3	228	0,726
5,9	155	0,707
6,2	200	0,804
6,8	235	0,784
7	235	0,744
5,9	105	0,839
6,1	180	0,79
5,7	185	0,701
7,1	245	0,778
5,8	180	0,872
7,4	240	0,713
6,8	225	0,701
6,8	215	0,734
7	230	0,764

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.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=284550&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.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=284550&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284550&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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.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
X4 [t] = + 0.991907 -0.0123405X1[t] -0.000804012X2[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X4
[t] =  +  0.991907 -0.0123405X1[t] -0.000804012X2[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284550&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X4
[t] =  +  0.991907 -0.0123405X1[t] -0.000804012X2[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284550&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284550&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
X4 [t] = + 0.991907 -0.0123405X1[t] -0.000804012X2[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)	+0.9919	0.2287	+4.3380e+00	6.802e-05	3.401e-05
X1	-0.01234	0.05302	-2.3280e-01	0.8169	0.4084
X2	-0.000804	0.0008039	-1.0000e+00	0.322	0.161

\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) & +0.9919 &  0.2287 & +4.3380e+00 &  6.802e-05 &  3.401e-05 \tabularnewline
X1 & -0.01234 &  0.05302 & -2.3280e-01 &  0.8169 &  0.4084 \tabularnewline
X2 & -0.000804 &  0.0008039 & -1.0000e+00 &  0.322 &  0.161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284550&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]+0.9919[/C][C] 0.2287[/C][C]+4.3380e+00[/C][C] 6.802e-05[/C][C] 3.401e-05[/C][/ROW]
[ROW][C]X1[/C][C]-0.01234[/C][C] 0.05302[/C][C]-2.3280e-01[/C][C] 0.8169[/C][C] 0.4084[/C][/ROW]
[ROW][C]X2[/C][C]-0.000804[/C][C] 0.0008039[/C][C]-1.0000e+00[/C][C] 0.322[/C][C] 0.161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284550&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284550&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)	+0.9919	0.2287	+4.3380e+00	6.802e-05	3.401e-05
X1	-0.01234	0.05302	-2.3280e-01	0.8169	0.4084
X2	-0.000804	0.0008039	-1.0000e+00	0.322	0.161

Multiple Linear Regression - Regression Statistics
Multiple R	0.2918
R-squared	0.08516
Adjusted R-squared	0.04929
F-TEST (value)	2.374
F-TEST (DF numerator)	2
F-TEST (DF denominator)	51
p-value	0.1033
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.09765
Sum Squared Residuals	0.4863

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2918 \tabularnewline
R-squared &  0.08516 \tabularnewline
Adjusted R-squared &  0.04929 \tabularnewline
F-TEST (value) &  2.374 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value &  0.1033 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.09765 \tabularnewline
Sum Squared Residuals &  0.4863 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284550&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2918[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.08516[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04929[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.374[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1033[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.09765[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.4863[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284550&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284550&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	0.2918
R-squared	0.08516
Adjusted R-squared	0.04929
F-TEST (value)	2.374
F-TEST (DF numerator)	2
F-TEST (DF denominator)	51
p-value	0.1033
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.09765
Sum Squared Residuals	0.4863

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.672	0.7271	-0.05509
2	0.797	0.7694	0.02756
3	0.761	0.7602	0.0008342
4	0.651	0.7707	-0.1197
5	0.9	0.7419	0.1581
6	0.78	0.732	0.04797
7	0.771	0.7654	0.00558
8	0.75	0.719	0.03095
9	0.818	0.7178	0.1002
10	0.825	0.7404	0.08462
11	0.632	0.7098	-0.07777
12	0.757	0.7098	0.04723
13	0.709	0.7654	-0.05642
14	0.782	0.7679	0.01411
15	0.775	0.7707	0.004326
16	0.88	0.7311	0.1489
17	0.833	0.7557	0.07728
18	0.571	0.7172	-0.1462
19	0.816	0.7453	0.07068
20	0.714	0.7113	0.002673
21	0.765	0.7271	0.03791
22	0.655	0.6904	-0.03537
23	0.244	0.7441	-0.5001
24	0.728	0.719	0.008951
25	0.721	0.7181	0.002867
26	0.757	0.7602	-0.003166
27	0.747	0.7336	0.01342
28	0.739	0.7391	-0.0001493
29	0.713	0.7719	-0.05891
30	0.742	0.7228	0.01925
31	0.861	0.7642	0.09681
32	0.721	0.7772	-0.05616
33	0.785	0.7635	0.02151
34	0.655	0.6904	-0.03537
35	0.821	0.7719	0.04909
36	0.728	0.7163	0.01174
37	0.846	0.7441	0.1019
38	0.813	0.7917	0.02131
39	0.595	0.7218	-0.1268
40	0.573	0.7085	-0.1355
41	0.726	0.7185	0.007493
42	0.707	0.7945	-0.08748
43	0.804	0.7546	0.04941
44	0.784	0.719	0.06495
45	0.744	0.7166	0.02742
46	0.839	0.8347	0.004323
47	0.79	0.7719	0.01809
48	0.701	0.7728	-0.07182
49	0.778	0.7073	0.07069
50	0.872	0.7756	0.09639
51	0.713	0.7076	0.005375
52	0.701	0.7271	-0.02609
53	0.734	0.7351	-0.001129
54	0.764	0.7206	0.0434

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.672 &  0.7271 & -0.05509 \tabularnewline
2 &  0.797 &  0.7694 &  0.02756 \tabularnewline
3 &  0.761 &  0.7602 &  0.0008342 \tabularnewline
4 &  0.651 &  0.7707 & -0.1197 \tabularnewline
5 &  0.9 &  0.7419 &  0.1581 \tabularnewline
6 &  0.78 &  0.732 &  0.04797 \tabularnewline
7 &  0.771 &  0.7654 &  0.00558 \tabularnewline
8 &  0.75 &  0.719 &  0.03095 \tabularnewline
9 &  0.818 &  0.7178 &  0.1002 \tabularnewline
10 &  0.825 &  0.7404 &  0.08462 \tabularnewline
11 &  0.632 &  0.7098 & -0.07777 \tabularnewline
12 &  0.757 &  0.7098 &  0.04723 \tabularnewline
13 &  0.709 &  0.7654 & -0.05642 \tabularnewline
14 &  0.782 &  0.7679 &  0.01411 \tabularnewline
15 &  0.775 &  0.7707 &  0.004326 \tabularnewline
16 &  0.88 &  0.7311 &  0.1489 \tabularnewline
17 &  0.833 &  0.7557 &  0.07728 \tabularnewline
18 &  0.571 &  0.7172 & -0.1462 \tabularnewline
19 &  0.816 &  0.7453 &  0.07068 \tabularnewline
20 &  0.714 &  0.7113 &  0.002673 \tabularnewline
21 &  0.765 &  0.7271 &  0.03791 \tabularnewline
22 &  0.655 &  0.6904 & -0.03537 \tabularnewline
23 &  0.244 &  0.7441 & -0.5001 \tabularnewline
24 &  0.728 &  0.719 &  0.008951 \tabularnewline
25 &  0.721 &  0.7181 &  0.002867 \tabularnewline
26 &  0.757 &  0.7602 & -0.003166 \tabularnewline
27 &  0.747 &  0.7336 &  0.01342 \tabularnewline
28 &  0.739 &  0.7391 & -0.0001493 \tabularnewline
29 &  0.713 &  0.7719 & -0.05891 \tabularnewline
30 &  0.742 &  0.7228 &  0.01925 \tabularnewline
31 &  0.861 &  0.7642 &  0.09681 \tabularnewline
32 &  0.721 &  0.7772 & -0.05616 \tabularnewline
33 &  0.785 &  0.7635 &  0.02151 \tabularnewline
34 &  0.655 &  0.6904 & -0.03537 \tabularnewline
35 &  0.821 &  0.7719 &  0.04909 \tabularnewline
36 &  0.728 &  0.7163 &  0.01174 \tabularnewline
37 &  0.846 &  0.7441 &  0.1019 \tabularnewline
38 &  0.813 &  0.7917 &  0.02131 \tabularnewline
39 &  0.595 &  0.7218 & -0.1268 \tabularnewline
40 &  0.573 &  0.7085 & -0.1355 \tabularnewline
41 &  0.726 &  0.7185 &  0.007493 \tabularnewline
42 &  0.707 &  0.7945 & -0.08748 \tabularnewline
43 &  0.804 &  0.7546 &  0.04941 \tabularnewline
44 &  0.784 &  0.719 &  0.06495 \tabularnewline
45 &  0.744 &  0.7166 &  0.02742 \tabularnewline
46 &  0.839 &  0.8347 &  0.004323 \tabularnewline
47 &  0.79 &  0.7719 &  0.01809 \tabularnewline
48 &  0.701 &  0.7728 & -0.07182 \tabularnewline
49 &  0.778 &  0.7073 &  0.07069 \tabularnewline
50 &  0.872 &  0.7756 &  0.09639 \tabularnewline
51 &  0.713 &  0.7076 &  0.005375 \tabularnewline
52 &  0.701 &  0.7271 & -0.02609 \tabularnewline
53 &  0.734 &  0.7351 & -0.001129 \tabularnewline
54 &  0.764 &  0.7206 &  0.0434 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284550&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] 0.672[/C][C] 0.7271[/C][C]-0.05509[/C][/ROW]
[ROW][C]2[/C][C] 0.797[/C][C] 0.7694[/C][C] 0.02756[/C][/ROW]
[ROW][C]3[/C][C] 0.761[/C][C] 0.7602[/C][C] 0.0008342[/C][/ROW]
[ROW][C]4[/C][C] 0.651[/C][C] 0.7707[/C][C]-0.1197[/C][/ROW]
[ROW][C]5[/C][C] 0.9[/C][C] 0.7419[/C][C] 0.1581[/C][/ROW]
[ROW][C]6[/C][C] 0.78[/C][C] 0.732[/C][C] 0.04797[/C][/ROW]
[ROW][C]7[/C][C] 0.771[/C][C] 0.7654[/C][C] 0.00558[/C][/ROW]
[ROW][C]8[/C][C] 0.75[/C][C] 0.719[/C][C] 0.03095[/C][/ROW]
[ROW][C]9[/C][C] 0.818[/C][C] 0.7178[/C][C] 0.1002[/C][/ROW]
[ROW][C]10[/C][C] 0.825[/C][C] 0.7404[/C][C] 0.08462[/C][/ROW]
[ROW][C]11[/C][C] 0.632[/C][C] 0.7098[/C][C]-0.07777[/C][/ROW]
[ROW][C]12[/C][C] 0.757[/C][C] 0.7098[/C][C] 0.04723[/C][/ROW]
[ROW][C]13[/C][C] 0.709[/C][C] 0.7654[/C][C]-0.05642[/C][/ROW]
[ROW][C]14[/C][C] 0.782[/C][C] 0.7679[/C][C] 0.01411[/C][/ROW]
[ROW][C]15[/C][C] 0.775[/C][C] 0.7707[/C][C] 0.004326[/C][/ROW]
[ROW][C]16[/C][C] 0.88[/C][C] 0.7311[/C][C] 0.1489[/C][/ROW]
[ROW][C]17[/C][C] 0.833[/C][C] 0.7557[/C][C] 0.07728[/C][/ROW]
[ROW][C]18[/C][C] 0.571[/C][C] 0.7172[/C][C]-0.1462[/C][/ROW]
[ROW][C]19[/C][C] 0.816[/C][C] 0.7453[/C][C] 0.07068[/C][/ROW]
[ROW][C]20[/C][C] 0.714[/C][C] 0.7113[/C][C] 0.002673[/C][/ROW]
[ROW][C]21[/C][C] 0.765[/C][C] 0.7271[/C][C] 0.03791[/C][/ROW]
[ROW][C]22[/C][C] 0.655[/C][C] 0.6904[/C][C]-0.03537[/C][/ROW]
[ROW][C]23[/C][C] 0.244[/C][C] 0.7441[/C][C]-0.5001[/C][/ROW]
[ROW][C]24[/C][C] 0.728[/C][C] 0.719[/C][C] 0.008951[/C][/ROW]
[ROW][C]25[/C][C] 0.721[/C][C] 0.7181[/C][C] 0.002867[/C][/ROW]
[ROW][C]26[/C][C] 0.757[/C][C] 0.7602[/C][C]-0.003166[/C][/ROW]
[ROW][C]27[/C][C] 0.747[/C][C] 0.7336[/C][C] 0.01342[/C][/ROW]
[ROW][C]28[/C][C] 0.739[/C][C] 0.7391[/C][C]-0.0001493[/C][/ROW]
[ROW][C]29[/C][C] 0.713[/C][C] 0.7719[/C][C]-0.05891[/C][/ROW]
[ROW][C]30[/C][C] 0.742[/C][C] 0.7228[/C][C] 0.01925[/C][/ROW]
[ROW][C]31[/C][C] 0.861[/C][C] 0.7642[/C][C] 0.09681[/C][/ROW]
[ROW][C]32[/C][C] 0.721[/C][C] 0.7772[/C][C]-0.05616[/C][/ROW]
[ROW][C]33[/C][C] 0.785[/C][C] 0.7635[/C][C] 0.02151[/C][/ROW]
[ROW][C]34[/C][C] 0.655[/C][C] 0.6904[/C][C]-0.03537[/C][/ROW]
[ROW][C]35[/C][C] 0.821[/C][C] 0.7719[/C][C] 0.04909[/C][/ROW]
[ROW][C]36[/C][C] 0.728[/C][C] 0.7163[/C][C] 0.01174[/C][/ROW]
[ROW][C]37[/C][C] 0.846[/C][C] 0.7441[/C][C] 0.1019[/C][/ROW]
[ROW][C]38[/C][C] 0.813[/C][C] 0.7917[/C][C] 0.02131[/C][/ROW]
[ROW][C]39[/C][C] 0.595[/C][C] 0.7218[/C][C]-0.1268[/C][/ROW]
[ROW][C]40[/C][C] 0.573[/C][C] 0.7085[/C][C]-0.1355[/C][/ROW]
[ROW][C]41[/C][C] 0.726[/C][C] 0.7185[/C][C] 0.007493[/C][/ROW]
[ROW][C]42[/C][C] 0.707[/C][C] 0.7945[/C][C]-0.08748[/C][/ROW]
[ROW][C]43[/C][C] 0.804[/C][C] 0.7546[/C][C] 0.04941[/C][/ROW]
[ROW][C]44[/C][C] 0.784[/C][C] 0.719[/C][C] 0.06495[/C][/ROW]
[ROW][C]45[/C][C] 0.744[/C][C] 0.7166[/C][C] 0.02742[/C][/ROW]
[ROW][C]46[/C][C] 0.839[/C][C] 0.8347[/C][C] 0.004323[/C][/ROW]
[ROW][C]47[/C][C] 0.79[/C][C] 0.7719[/C][C] 0.01809[/C][/ROW]
[ROW][C]48[/C][C] 0.701[/C][C] 0.7728[/C][C]-0.07182[/C][/ROW]
[ROW][C]49[/C][C] 0.778[/C][C] 0.7073[/C][C] 0.07069[/C][/ROW]
[ROW][C]50[/C][C] 0.872[/C][C] 0.7756[/C][C] 0.09639[/C][/ROW]
[ROW][C]51[/C][C] 0.713[/C][C] 0.7076[/C][C] 0.005375[/C][/ROW]
[ROW][C]52[/C][C] 0.701[/C][C] 0.7271[/C][C]-0.02609[/C][/ROW]
[ROW][C]53[/C][C] 0.734[/C][C] 0.7351[/C][C]-0.001129[/C][/ROW]
[ROW][C]54[/C][C] 0.764[/C][C] 0.7206[/C][C] 0.0434[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284550&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284550&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	0.672	0.7271	-0.05509
2	0.797	0.7694	0.02756
3	0.761	0.7602	0.0008342
4	0.651	0.7707	-0.1197
5	0.9	0.7419	0.1581
6	0.78	0.732	0.04797
7	0.771	0.7654	0.00558
8	0.75	0.719	0.03095
9	0.818	0.7178	0.1002
10	0.825	0.7404	0.08462
11	0.632	0.7098	-0.07777
12	0.757	0.7098	0.04723
13	0.709	0.7654	-0.05642
14	0.782	0.7679	0.01411
15	0.775	0.7707	0.004326
16	0.88	0.7311	0.1489
17	0.833	0.7557	0.07728
18	0.571	0.7172	-0.1462
19	0.816	0.7453	0.07068
20	0.714	0.7113	0.002673
21	0.765	0.7271	0.03791
22	0.655	0.6904	-0.03537
23	0.244	0.7441	-0.5001
24	0.728	0.719	0.008951
25	0.721	0.7181	0.002867
26	0.757	0.7602	-0.003166
27	0.747	0.7336	0.01342
28	0.739	0.7391	-0.0001493
29	0.713	0.7719	-0.05891
30	0.742	0.7228	0.01925
31	0.861	0.7642	0.09681
32	0.721	0.7772	-0.05616
33	0.785	0.7635	0.02151
34	0.655	0.6904	-0.03537
35	0.821	0.7719	0.04909
36	0.728	0.7163	0.01174
37	0.846	0.7441	0.1019
38	0.813	0.7917	0.02131
39	0.595	0.7218	-0.1268
40	0.573	0.7085	-0.1355
41	0.726	0.7185	0.007493
42	0.707	0.7945	-0.08748
43	0.804	0.7546	0.04941
44	0.784	0.719	0.06495
45	0.744	0.7166	0.02742
46	0.839	0.8347	0.004323
47	0.79	0.7719	0.01809
48	0.701	0.7728	-0.07182
49	0.778	0.7073	0.07069
50	0.872	0.7756	0.09639
51	0.713	0.7076	0.005375
52	0.701	0.7271	-0.02609
53	0.734	0.7351	-0.001129
54	0.764	0.7206	0.0434

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.6264	0.7471	0.3735
7	0.4654	0.9307	0.5346
8	0.3201	0.6402	0.6799
9	0.2253	0.4507	0.7747
10	0.1531	0.3063	0.8469
11	0.2248	0.4495	0.7752
12	0.1517	0.3034	0.8483
13	0.1083	0.2167	0.8917
14	0.08592	0.1718	0.9141
15	0.05256	0.1051	0.9474
16	0.06897	0.1379	0.931
17	0.05027	0.1005	0.9497
18	0.2421	0.4843	0.7579
19	0.194	0.388	0.806
20	0.1397	0.2793	0.8603
21	0.09939	0.1988	0.9006
22	0.07553	0.1511	0.9245
23	1	8.729e-05	4.365e-05
24	0.9999	0.0002052	0.0001026
25	0.9998	0.0004652	0.0002326
26	0.9995	0.001009	0.0005047
27	0.999	0.002086	0.001043
28	0.9979	0.004202	0.002101
29	0.9971	0.005867	0.002934
30	0.9945	0.01097	0.005483
31	0.995	0.00995	0.004975
32	0.9931	0.01372	0.006862
33	0.9876	0.0247	0.01235
34	0.98	0.03998	0.01999
35	0.9698	0.06041	0.03021
36	0.9498	0.1004	0.05021
37	0.9555	0.08896	0.04448
38	0.9291	0.1417	0.07086
39	0.9606	0.07887	0.03944
40	0.9936	0.01279	0.006393
41	0.9866	0.02688	0.01344
42	0.9909	0.01825	0.009124
43	0.9819	0.0362	0.0181
44	0.9691	0.06179	0.0309
45	0.9342	0.1316	0.0658
46	0.8695	0.261	0.1305
47	0.7599	0.4802	0.2401
48	0.8929	0.2143	0.1071

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.6264 &  0.7471 &  0.3735 \tabularnewline
7 &  0.4654 &  0.9307 &  0.5346 \tabularnewline
8 &  0.3201 &  0.6402 &  0.6799 \tabularnewline
9 &  0.2253 &  0.4507 &  0.7747 \tabularnewline
10 &  0.1531 &  0.3063 &  0.8469 \tabularnewline
11 &  0.2248 &  0.4495 &  0.7752 \tabularnewline
12 &  0.1517 &  0.3034 &  0.8483 \tabularnewline
13 &  0.1083 &  0.2167 &  0.8917 \tabularnewline
14 &  0.08592 &  0.1718 &  0.9141 \tabularnewline
15 &  0.05256 &  0.1051 &  0.9474 \tabularnewline
16 &  0.06897 &  0.1379 &  0.931 \tabularnewline
17 &  0.05027 &  0.1005 &  0.9497 \tabularnewline
18 &  0.2421 &  0.4843 &  0.7579 \tabularnewline
19 &  0.194 &  0.388 &  0.806 \tabularnewline
20 &  0.1397 &  0.2793 &  0.8603 \tabularnewline
21 &  0.09939 &  0.1988 &  0.9006 \tabularnewline
22 &  0.07553 &  0.1511 &  0.9245 \tabularnewline
23 &  1 &  8.729e-05 &  4.365e-05 \tabularnewline
24 &  0.9999 &  0.0002052 &  0.0001026 \tabularnewline
25 &  0.9998 &  0.0004652 &  0.0002326 \tabularnewline
26 &  0.9995 &  0.001009 &  0.0005047 \tabularnewline
27 &  0.999 &  0.002086 &  0.001043 \tabularnewline
28 &  0.9979 &  0.004202 &  0.002101 \tabularnewline
29 &  0.9971 &  0.005867 &  0.002934 \tabularnewline
30 &  0.9945 &  0.01097 &  0.005483 \tabularnewline
31 &  0.995 &  0.00995 &  0.004975 \tabularnewline
32 &  0.9931 &  0.01372 &  0.006862 \tabularnewline
33 &  0.9876 &  0.0247 &  0.01235 \tabularnewline
34 &  0.98 &  0.03998 &  0.01999 \tabularnewline
35 &  0.9698 &  0.06041 &  0.03021 \tabularnewline
36 &  0.9498 &  0.1004 &  0.05021 \tabularnewline
37 &  0.9555 &  0.08896 &  0.04448 \tabularnewline
38 &  0.9291 &  0.1417 &  0.07086 \tabularnewline
39 &  0.9606 &  0.07887 &  0.03944 \tabularnewline
40 &  0.9936 &  0.01279 &  0.006393 \tabularnewline
41 &  0.9866 &  0.02688 &  0.01344 \tabularnewline
42 &  0.9909 &  0.01825 &  0.009124 \tabularnewline
43 &  0.9819 &  0.0362 &  0.0181 \tabularnewline
44 &  0.9691 &  0.06179 &  0.0309 \tabularnewline
45 &  0.9342 &  0.1316 &  0.0658 \tabularnewline
46 &  0.8695 &  0.261 &  0.1305 \tabularnewline
47 &  0.7599 &  0.4802 &  0.2401 \tabularnewline
48 &  0.8929 &  0.2143 &  0.1071 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284550&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]6[/C][C] 0.6264[/C][C] 0.7471[/C][C] 0.3735[/C][/ROW]
[ROW][C]7[/C][C] 0.4654[/C][C] 0.9307[/C][C] 0.5346[/C][/ROW]
[ROW][C]8[/C][C] 0.3201[/C][C] 0.6402[/C][C] 0.6799[/C][/ROW]
[ROW][C]9[/C][C] 0.2253[/C][C] 0.4507[/C][C] 0.7747[/C][/ROW]
[ROW][C]10[/C][C] 0.1531[/C][C] 0.3063[/C][C] 0.8469[/C][/ROW]
[ROW][C]11[/C][C] 0.2248[/C][C] 0.4495[/C][C] 0.7752[/C][/ROW]
[ROW][C]12[/C][C] 0.1517[/C][C] 0.3034[/C][C] 0.8483[/C][/ROW]
[ROW][C]13[/C][C] 0.1083[/C][C] 0.2167[/C][C] 0.8917[/C][/ROW]
[ROW][C]14[/C][C] 0.08592[/C][C] 0.1718[/C][C] 0.9141[/C][/ROW]
[ROW][C]15[/C][C] 0.05256[/C][C] 0.1051[/C][C] 0.9474[/C][/ROW]
[ROW][C]16[/C][C] 0.06897[/C][C] 0.1379[/C][C] 0.931[/C][/ROW]
[ROW][C]17[/C][C] 0.05027[/C][C] 0.1005[/C][C] 0.9497[/C][/ROW]
[ROW][C]18[/C][C] 0.2421[/C][C] 0.4843[/C][C] 0.7579[/C][/ROW]
[ROW][C]19[/C][C] 0.194[/C][C] 0.388[/C][C] 0.806[/C][/ROW]
[ROW][C]20[/C][C] 0.1397[/C][C] 0.2793[/C][C] 0.8603[/C][/ROW]
[ROW][C]21[/C][C] 0.09939[/C][C] 0.1988[/C][C] 0.9006[/C][/ROW]
[ROW][C]22[/C][C] 0.07553[/C][C] 0.1511[/C][C] 0.9245[/C][/ROW]
[ROW][C]23[/C][C] 1[/C][C] 8.729e-05[/C][C] 4.365e-05[/C][/ROW]
[ROW][C]24[/C][C] 0.9999[/C][C] 0.0002052[/C][C] 0.0001026[/C][/ROW]
[ROW][C]25[/C][C] 0.9998[/C][C] 0.0004652[/C][C] 0.0002326[/C][/ROW]
[ROW][C]26[/C][C] 0.9995[/C][C] 0.001009[/C][C] 0.0005047[/C][/ROW]
[ROW][C]27[/C][C] 0.999[/C][C] 0.002086[/C][C] 0.001043[/C][/ROW]
[ROW][C]28[/C][C] 0.9979[/C][C] 0.004202[/C][C] 0.002101[/C][/ROW]
[ROW][C]29[/C][C] 0.9971[/C][C] 0.005867[/C][C] 0.002934[/C][/ROW]
[ROW][C]30[/C][C] 0.9945[/C][C] 0.01097[/C][C] 0.005483[/C][/ROW]
[ROW][C]31[/C][C] 0.995[/C][C] 0.00995[/C][C] 0.004975[/C][/ROW]
[ROW][C]32[/C][C] 0.9931[/C][C] 0.01372[/C][C] 0.006862[/C][/ROW]
[ROW][C]33[/C][C] 0.9876[/C][C] 0.0247[/C][C] 0.01235[/C][/ROW]
[ROW][C]34[/C][C] 0.98[/C][C] 0.03998[/C][C] 0.01999[/C][/ROW]
[ROW][C]35[/C][C] 0.9698[/C][C] 0.06041[/C][C] 0.03021[/C][/ROW]
[ROW][C]36[/C][C] 0.9498[/C][C] 0.1004[/C][C] 0.05021[/C][/ROW]
[ROW][C]37[/C][C] 0.9555[/C][C] 0.08896[/C][C] 0.04448[/C][/ROW]
[ROW][C]38[/C][C] 0.9291[/C][C] 0.1417[/C][C] 0.07086[/C][/ROW]
[ROW][C]39[/C][C] 0.9606[/C][C] 0.07887[/C][C] 0.03944[/C][/ROW]
[ROW][C]40[/C][C] 0.9936[/C][C] 0.01279[/C][C] 0.006393[/C][/ROW]
[ROW][C]41[/C][C] 0.9866[/C][C] 0.02688[/C][C] 0.01344[/C][/ROW]
[ROW][C]42[/C][C] 0.9909[/C][C] 0.01825[/C][C] 0.009124[/C][/ROW]
[ROW][C]43[/C][C] 0.9819[/C][C] 0.0362[/C][C] 0.0181[/C][/ROW]
[ROW][C]44[/C][C] 0.9691[/C][C] 0.06179[/C][C] 0.0309[/C][/ROW]
[ROW][C]45[/C][C] 0.9342[/C][C] 0.1316[/C][C] 0.0658[/C][/ROW]
[ROW][C]46[/C][C] 0.8695[/C][C] 0.261[/C][C] 0.1305[/C][/ROW]
[ROW][C]47[/C][C] 0.7599[/C][C] 0.4802[/C][C] 0.2401[/C][/ROW]
[ROW][C]48[/C][C] 0.8929[/C][C] 0.2143[/C][C] 0.1071[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284550&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284550&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
6	0.6264	0.7471	0.3735
7	0.4654	0.9307	0.5346
8	0.3201	0.6402	0.6799
9	0.2253	0.4507	0.7747
10	0.1531	0.3063	0.8469
11	0.2248	0.4495	0.7752
12	0.1517	0.3034	0.8483
13	0.1083	0.2167	0.8917
14	0.08592	0.1718	0.9141
15	0.05256	0.1051	0.9474
16	0.06897	0.1379	0.931
17	0.05027	0.1005	0.9497
18	0.2421	0.4843	0.7579
19	0.194	0.388	0.806
20	0.1397	0.2793	0.8603
21	0.09939	0.1988	0.9006
22	0.07553	0.1511	0.9245
23	1	8.729e-05	4.365e-05
24	0.9999	0.0002052	0.0001026
25	0.9998	0.0004652	0.0002326
26	0.9995	0.001009	0.0005047
27	0.999	0.002086	0.001043
28	0.9979	0.004202	0.002101
29	0.9971	0.005867	0.002934
30	0.9945	0.01097	0.005483
31	0.995	0.00995	0.004975
32	0.9931	0.01372	0.006862
33	0.9876	0.0247	0.01235
34	0.98	0.03998	0.01999
35	0.9698	0.06041	0.03021
36	0.9498	0.1004	0.05021
37	0.9555	0.08896	0.04448
38	0.9291	0.1417	0.07086
39	0.9606	0.07887	0.03944
40	0.9936	0.01279	0.006393
41	0.9866	0.02688	0.01344
42	0.9909	0.01825	0.009124
43	0.9819	0.0362	0.0181
44	0.9691	0.06179	0.0309
45	0.9342	0.1316	0.0658
46	0.8695	0.261	0.1305
47	0.7599	0.4802	0.2401
48	0.8929	0.2143	0.1071

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.186	NOK
5% type I error level	16	0.372093	NOK
10% type I error level	20	0.465116	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 & 8 &  0.186 & NOK \tabularnewline
5% type I error level & 16 & 0.372093 & NOK \tabularnewline
10% type I error level & 20 & 0.465116 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284550&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]8[/C][C] 0.186[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.372093[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.465116[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284550&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284550&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	8	0.186	NOK
5% type I error level	16	0.372093	NOK
10% type I error level	20	0.465116	NOK

Figure 1

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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 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '3'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
x <- na.omit(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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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