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Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 04 Aug 2017 23:54:20 +0200

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Aug/04/t15018837755l5e5us34histoa.htm/, Retrieved Sat, 11 May 2024 17:43:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306933, Retrieved Sat, 11 May 2024 17:43:22 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

111

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

-       [Multiple Regression] [RW significant pr...] [2017-08-04 21:54:20] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Boxplots

436	1999	1	0	0	0	0	0	0	0	0	0	0	0
391	1999	0	1	0	0	0	0	0	0	0	0	0	0
473	1999	0	0	1	0	0	0	0	0	0	0	0	0
459	1999	0	0	0	1	0	0	0	0	0	0	0	0
482	1999	0	0	0	0	1	0	0	0	0	0	0	0
461	1999	0	0	0	0	0	1	0	0	0	0	0	0
492	1999	0	0	0	0	0	0	1	0	0	0	0	0
524	1999	0	0	0	0	0	0	0	1	0	0	0	0
414	1999	0	0	0	0	0	0	0	0	1	0	0	0
426	1999	0	0	0	0	0	0	0	0	0	1	0	0
468	1999	0	0	0	0	0	0	0	0	0	0	1	0
401	1999	0	0	0	0	0	0	0	0	0	0	0	0
504	2000	1	0	0	0	0	0	0	0	0	0	0	0
426	2000	0	1	0	0	0	0	0	0	0	0	0	0
456	2000	0	0	1	0	0	0	0	0	0	0	0	0
464	2000	0	0	0	1	0	0	0	0	0	0	0	0
544	2000	0	0	0	0	1	0	0	0	0	0	0	0
466	2000	0	0	0	0	0	1	0	0	0	0	0	0
489	2000	0	0	0	0	0	0	1	0	0	0	0	0
509	2000	0	0	0	0	0	0	0	1	0	0	0	0
472	2000	0	0	0	0	0	0	0	0	1	0	0	0
498	2000	0	0	0	0	0	0	0	0	0	1	0	0
456	2000	0	0	0	0	0	0	0	0	0	0	1	0
404	2000	0	0	0	0	0	0	0	0	0	0	0	0
465	2001	1	0	0	0	0	0	0	0	0	0	0	0
455	2001	0	1	0	0	0	0	0	0	0	0	0	0
500	2001	0	0	1	0	0	0	0	0	0	0	0	0
516	2001	0	0	0	1	0	0	0	0	0	0	0	0
490	2001	0	0	0	0	1	0	0	0	0	0	0	0
568	2001	0	0	0	0	0	1	0	0	0	0	0	0
601	2001	0	0	0	0	0	0	1	0	0	0	0	0
578	2001	0	0	0	0	0	0	0	1	0	0	0	0
506	2001	0	0	0	0	0	0	0	0	1	0	0	0
517	2001	0	0	0	0	0	0	0	0	0	1	0	0
538	2001	0	0	0	0	0	0	0	0	0	0	1	0
464	2001	0	0	0	0	0	0	0	0	0	0	0	0
535	2002	1	0	0	0	0	0	0	0	0	0	0	0
476	2002	0	1	0	0	0	0	0	0	0	0	0	0
508	2002	0	0	1	0	0	0	0	0	0	0	0	0
543	2002	0	0	0	1	0	0	0	0	0	0	0	0
604	2002	0	0	0	0	1	0	0	0	0	0	0	0
555	2002	0	0	0	0	0	1	0	0	0	0	0	0
610	2002	0	0	0	0	0	0	1	0	0	0	0	0
601	2002	0	0	0	0	0	0	0	1	0	0	0	0
556	2002	0	0	0	0	0	0	0	0	1	0	0	0
532	2002	0	0	0	0	0	0	0	0	0	1	0	0
473	2002	0	0	0	0	0	0	0	0	0	0	1	0
469	2002	0	0	0	0	0	0	0	0	0	0	0	0
503	2003	1	0	0	0	0	0	0	0	0	0	0	0
470	2003	0	1	0	0	0	0	0	0	0	0	0	0
572	2003	0	0	1	0	0	0	0	0	0	0	0	0
520	2003	0	0	0	1	0	0	0	0	0	0	0	0
603	2003	0	0	0	0	1	0	0	0	0	0	0	0
573	2003	0	0	0	0	0	1	0	0	0	0	0	0
606	2003	0	0	0	0	0	0	1	0	0	0	0	0
573	2003	0	0	0	0	0	0	0	1	0	0	0	0
565	2003	0	0	0	0	0	0	0	0	1	0	0	0
553	2003	0	0	0	0	0	0	0	0	0	1	0	0
559	2003	0	0	0	0	0	0	0	0	0	0	1	0
538	2003	0	0	0	0	0	0	0	0	0	0	0	0
556	2004	1	0	0	0	0	0	0	0	0	0	0	0
519	2004	0	1	0	0	0	0	0	0	0	0	0	0
649	2004	0	0	1	0	0	0	0	0	0	0	0	0
682	2004	0	0	0	1	0	0	0	0	0	0	0	0
595	2004	0	0	0	0	1	0	0	0	0	0	0	0
630	2004	0	0	0	0	0	1	0	0	0	0	0	0
665	2004	0	0	0	0	0	0	1	0	0	0	0	0
587	2004	0	0	0	0	0	0	0	1	0	0	0	0
663	2004	0	0	0	0	0	0	0	0	1	0	0	0
657	2004	0	0	0	0	0	0	0	0	0	1	0	0
567	2004	0	0	0	0	0	0	0	0	0	0	1	0
566	2004	0	0	0	0	0	0	0	0	0	0	0	0
592	2005	1	0	0	0	0	0	0	0	0	0	0	0
498	2005	0	1	0	0	0	0	0	0	0	0	0	0
606	2005	0	0	1	0	0	0	0	0	0	0	0	0
629	2005	0	0	0	1	0	0	0	0	0	0	0	0
653	2005	0	0	0	0	1	0	0	0	0	0	0	0
674	2005	0	0	0	0	0	1	0	0	0	0	0	0
633	2005	0	0	0	0	0	0	1	0	0	0	0	0
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633	2005	0	0	0	0	0	0	0	0	1	0	0	0
575	2005	0	0	0	0	0	0	0	0	0	1	0	0
560	2005	0	0	0	0	0	0	0	0	0	0	1	0
539	2005	0	0	0	0	0	0	0	0	0	0	0	0
638	2006	1	0	0	0	0	0	0	0	0	0	0	0
534	2006	0	1	0	0	0	0	0	0	0	0	0	0
611	2006	0	0	1	0	0	0	0	0	0	0	0	0
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633	2006	0	0	0	0	0	0	0	0	0	1	0	0
607	2006	0	0	0	0	0	0	0	0	0	0	1	0
581	2006	0	0	0	0	0	0	0	0	0	0	0	0
612	2007	1	0	0	0	0	0	0	0	0	0	0	0
542	2007	0	1	0	0	0	0	0	0	0	0	0	0
669	2007	0	0	1	0	0	0	0	0	0	0	0	0
626	2007	0	0	0	1	0	0	0	0	0	0	0	0
746	2007	0	0	0	0	1	0	0	0	0	0	0	0
744	2007	0	0	0	0	0	1	0	0	0	0	0	0
800	2007	0	0	0	0	0	0	1	0	0	0	0	0
772	2007	0	0	0	0	0	0	0	1	0	0	0	0
700	2007	0	0	0	0	0	0	0	0	1	0	0	0
662	2007	0	0	0	0	0	0	0	0	0	1	0	0
642	2007	0	0	0	0	0	0	0	0	0	0	1	0
646	2007	0	0	0	0	0	0	0	0	0	0	0	0
656	2008	1	0	0	0	0	0	0	0	0	0	0	0
607	2008	0	1	0	0	0	0	0	0	0	0	0	0
674	2008	0	0	1	0	0	0	0	0	0	0	0	0
717	2008	0	0	0	1	0	0	0	0	0	0	0	0
825	2008	0	0	0	0	1	0	0	0	0	0	0	0
805	2008	0	0	0	0	0	1	0	0	0	0	0	0
790	2008	0	0	0	0	0	0	1	0	0	0	0	0
725	2008	0	0	0	0	0	0	0	1	0	0	0	0
772	2008	0	0	0	0	0	0	0	0	1	0	0	0
732	2008	0	0	0	0	0	0	0	0	0	1	0	0
645	2008	0	0	0	0	0	0	0	0	0	0	1	0
630	2008	0	0	0	0	0	0	0	0	0	0	0	0
691	2009	1	0	0	0	0	0	0	0	0	0	0	0
627	2009	0	1	0	0	0	0	0	0	0	0	0	0
724	2009	0	0	1	0	0	0	0	0	0	0	0	0
691	2009	0	0	0	1	0	0	0	0	0	0	0	0
828	2009	0	0	0	0	1	0	0	0	0	0	0	0
849	2009	0	0	0	0	0	1	0	0	0	0	0	0
788	2009	0	0	0	0	0	0	1	0	0	0	0	0
824	2009	0	0	0	0	0	0	0	1	0	0	0	0
832	2009	0	0	0	0	0	0	0	0	1	0	0	0
752	2009	0	0	0	0	0	0	0	0	0	1	0	0
702	2009	0	0	0	0	0	0	0	0	0	0	1	0
692	2009	0	0	0	0	0	0	0	0	0	0	0	0
699	2010	1	0	0	0	0	0	0	0	0	0	0	0
651	2010	0	1	0	0	0	0	0	0	0	0	0	0
787	2010	0	0	1	0	0	0	0	0	0	0	0	0
818	2010	0	0	0	1	0	0	0	0	0	0	0	0
874	2010	0	0	0	0	1	0	0	0	0	0	0	0
842	2010	0	0	0	0	0	1	0	0	0	0	0	0
854	2010	0	0	0	0	0	0	1	0	0	0	0	0
860	2010	0	0	0	0	0	0	0	1	0	0	0	0
833	2010	0	0	0	0	0	0	0	0	1	0	0	0
807	2010	0	0	0	0	0	0	0	0	0	1	0	0
702	2010	0	0	0	0	0	0	0	0	0	0	1	0
766	2010	0	0	0	0	0	0	0	0	0	0	0	0
760	2011	1	0	0	0	0	0	0	0	0	0	0	0
680	2011	0	1	0	0	0	0	0	0	0	0	0	0
770	2011	0	0	1	0	0	0	0	0	0	0	0	0
850	2011	0	0	0	1	0	0	0	0	0	0	0	0
922	2011	0	0	0	0	1	0	0	0	0	0	0	0
862	2011	0	0	0	0	0	1	0	0	0	0	0	0
894	2011	0	0	0	0	0	0	1	0	0	0	0	0
884	2011	0	0	0	0	0	0	0	1	0	0	0	0
882	2011	0	0	0	0	0	0	0	0	1	0	0	0
857	2011	0	0	0	0	0	0	0	0	0	1	0	0
790	2011	0	0	0	0	0	0	0	0	0	0	1	0
762	2011	0	0	0	0	0	0	0	0	0	0	0	0
801	2012	1	0	0	0	0	0	0	0	0	0	0	0
758	2012	0	1	0	0	0	0	0	0	0	0	0	0
831	2012	0	0	1	0	0	0	0	0	0	0	0	0
853	2012	0	0	0	1	0	0	0	0	0	0	0	0
886	2012	0	0	0	0	1	0	0	0	0	0	0	0
883	2012	0	0	0	0	0	1	0	0	0	0	0	0
1001	2012	0	0	0	0	0	0	1	0	0	0	0	0
912	2012	0	0	0	0	0	0	0	1	0	0	0	0
820	2012	0	0	0	0	0	0	0	0	1	0	0	0
824	2012	0	0	0	0	0	0	0	0	0	1	0	0
715	2012	0	0	0	0	0	0	0	0	0	0	1	0
804	2012	0	0	0	0	0	0	0	0	0	0	0	0
849	2013	1	0	0	0	0	0	0	0	0	0	0	0
704	2013	0	1	0	0	0	0	0	0	0	0	0	0
853	2013	0	0	1	0	0	0	0	0	0	0	0	0
847	2013	0	0	0	1	0	0	0	0	0	0	0	0
856	2013	0	0	0	0	1	0	0	0	0	0	0	0
827	2013	0	0	0	0	0	1	0	0	0	0	0	0
865	2013	0	0	0	0	0	0	1	0	0	0	0	0
918	2013	0	0	0	0	0	0	0	1	0	0	0	0
922	2013	0	0	0	0	0	0	0	0	1	0	0	0
926	2013	0	0	0	0	0	0	0	0	0	1	0	0
733	2013	0	0	0	0	0	0	0	0	0	0	1	0
762	2013	0	0	0	0	0	0	0	0	0	0	0	0
776	2014	1	0	0	0	0	0	0	0	0	0	0	0
754	2014	0	1	0	0	0	0	0	0	0	0	0	0
803	2014	0	0	1	0	0	0	0	0	0	0	0	0
883	2014	0	0	0	1	0	0	0	0	0	0	0	0
924	2014	0	0	0	0	1	0	0	0	0	0	0	0
863	2014	0	0	0	0	0	1	0	0	0	0	0	0
857	2014	0	0	0	0	0	0	1	0	0	0	0	0
1237	2014	0	0	0	0	0	0	0	1	0	0	0	1
1266	2014	0	0	0	0	0	0	0	0	1	0	0	1
1144	2014	0	0	0	0	0	0	0	0	0	1	0	0
929	2014	0	0	0	0	0	0	0	0	0	0	1	0
971	2014	0	0	0	0	0	0	0	0	0	0	0	0
946	2015	1	0	0	0	0	0	0	0	0	0	0	0
823	2015	0	1	0	0	0	0	0	0	0	0	0	0
1050	2015	0	0	1	0	0	0	0	0	0	0	0	0
921	2015	0	0	0	1	0	0	0	0	0	0	0	0
1065	2015	0	0	0	0	1	0	0	0	0	0	0	0
1052	2015	0	0	0	0	0	1	0	0	0	0	0	0
1061	2015	0	0	0	0	0	0	1	0	0	0	0	0
1117	2015	0	0	0	0	0	0	0	1	0	0	0	0
990	2015	0	0	0	0	0	0	0	0	1	0	0	0
1026	2015	0	0	0	0	0	0	0	0	0	1	0	0
926	2015	0	0	0	0	0	0	0	0	0	0	1	0
878	2015	0	0	0	0	0	0	0	0	0	0	0	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306933&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306933&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306933&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	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
amt[t] = -61947.7 + 31.1845year[t] + 8.58824jan[t] -56.3529feb[t] + 39mar[t] + 46.8824apr[t] + 96.9412may[t] + 83.9412jun[t] + 107.235jul[t] + 104.018aug[t] + 73.8418sep[t] + 73.4118oct[t] + 8.17647nov[t] + 304.69RW[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
amt[t] =  -61947.7 +  31.1845year[t] +  8.58824jan[t] -56.3529feb[t] +  39mar[t] +  46.8824apr[t] +  96.9412may[t] +  83.9412jun[t] +  107.235jul[t] +  104.018aug[t] +  73.8418sep[t] +  73.4118oct[t] +  8.17647nov[t] +  304.69RW[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306933&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]amt[t] =  -61947.7 +  31.1845year[t] +  8.58824jan[t] -56.3529feb[t] +  39mar[t] +  46.8824apr[t] +  96.9412may[t] +  83.9412jun[t] +  107.235jul[t] +  104.018aug[t] +  73.8418sep[t] +  73.4118oct[t] +  8.17647nov[t] +  304.69RW[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306933&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306933&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
amt[t] = -61947.7 + 31.1845year[t] + 8.58824jan[t] -56.3529feb[t] + 39mar[t] + 46.8824apr[t] + 96.9412may[t] + 83.9412jun[t] + 107.235jul[t] + 104.018aug[t] + 73.8418sep[t] + 73.4118oct[t] + 8.17647nov[t] + 304.69RW[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)	-6.195e+04	1338	-4.6290e+01	2.033e-105	1.017e-105
year	+31.18	0.6667	+4.6770e+01	3.357e-106	1.679e-106
jan	+8.588	15.83	+5.4250e-01	0.5881	0.294
feb	-56.35	15.83	-3.5600e+00	0.0004688	0.0002344
mar	+39	15.83	+2.4640e+00	0.01464	0.007321
apr	+46.88	15.83	+2.9620e+00	0.003451	0.001726
may	+96.94	15.83	+6.1240e+00	5.138e-09	2.569e-09
jun	+83.94	15.83	+5.3030e+00	3.153e-07	1.576e-07
jul	+107.2	15.83	+6.7740e+00	1.524e-10	7.62e-11
aug	+104	15.96	+6.5190e+00	6.211e-10	3.106e-10
sep	+73.84	15.96	+4.6280e+00	6.829e-06	3.414e-06
oct	+73.41	15.83	+4.6370e+00	6.549e-06	3.275e-06
nov	+8.177	15.83	+5.1650e-01	0.6061	0.303
RW	+304.7	34	+8.9610e+00	3.001e-16	1.5e-16

\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) & -6.195e+04 &  1338 & -4.6290e+01 &  2.033e-105 &  1.017e-105 \tabularnewline
year & +31.18 &  0.6667 & +4.6770e+01 &  3.357e-106 &  1.679e-106 \tabularnewline
jan & +8.588 &  15.83 & +5.4250e-01 &  0.5881 &  0.294 \tabularnewline
feb & -56.35 &  15.83 & -3.5600e+00 &  0.0004688 &  0.0002344 \tabularnewline
mar & +39 &  15.83 & +2.4640e+00 &  0.01464 &  0.007321 \tabularnewline
apr & +46.88 &  15.83 & +2.9620e+00 &  0.003451 &  0.001726 \tabularnewline
may & +96.94 &  15.83 & +6.1240e+00 &  5.138e-09 &  2.569e-09 \tabularnewline
jun & +83.94 &  15.83 & +5.3030e+00 &  3.153e-07 &  1.576e-07 \tabularnewline
jul & +107.2 &  15.83 & +6.7740e+00 &  1.524e-10 &  7.62e-11 \tabularnewline
aug & +104 &  15.96 & +6.5190e+00 &  6.211e-10 &  3.106e-10 \tabularnewline
sep & +73.84 &  15.96 & +4.6280e+00 &  6.829e-06 &  3.414e-06 \tabularnewline
oct & +73.41 &  15.83 & +4.6370e+00 &  6.549e-06 &  3.275e-06 \tabularnewline
nov & +8.177 &  15.83 & +5.1650e-01 &  0.6061 &  0.303 \tabularnewline
RW & +304.7 &  34 & +8.9610e+00 &  3.001e-16 &  1.5e-16 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306933&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]-6.195e+04[/C][C] 1338[/C][C]-4.6290e+01[/C][C] 2.033e-105[/C][C] 1.017e-105[/C][/ROW]
[ROW][C]year[/C][C]+31.18[/C][C] 0.6667[/C][C]+4.6770e+01[/C][C] 3.357e-106[/C][C] 1.679e-106[/C][/ROW]
[ROW][C]jan[/C][C]+8.588[/C][C] 15.83[/C][C]+5.4250e-01[/C][C] 0.5881[/C][C] 0.294[/C][/ROW]
[ROW][C]feb[/C][C]-56.35[/C][C] 15.83[/C][C]-3.5600e+00[/C][C] 0.0004688[/C][C] 0.0002344[/C][/ROW]
[ROW][C]mar[/C][C]+39[/C][C] 15.83[/C][C]+2.4640e+00[/C][C] 0.01464[/C][C] 0.007321[/C][/ROW]
[ROW][C]apr[/C][C]+46.88[/C][C] 15.83[/C][C]+2.9620e+00[/C][C] 0.003451[/C][C] 0.001726[/C][/ROW]
[ROW][C]may[/C][C]+96.94[/C][C] 15.83[/C][C]+6.1240e+00[/C][C] 5.138e-09[/C][C] 2.569e-09[/C][/ROW]
[ROW][C]jun[/C][C]+83.94[/C][C] 15.83[/C][C]+5.3030e+00[/C][C] 3.153e-07[/C][C] 1.576e-07[/C][/ROW]
[ROW][C]jul[/C][C]+107.2[/C][C] 15.83[/C][C]+6.7740e+00[/C][C] 1.524e-10[/C][C] 7.62e-11[/C][/ROW]
[ROW][C]aug[/C][C]+104[/C][C] 15.96[/C][C]+6.5190e+00[/C][C] 6.211e-10[/C][C] 3.106e-10[/C][/ROW]
[ROW][C]sep[/C][C]+73.84[/C][C] 15.96[/C][C]+4.6280e+00[/C][C] 6.829e-06[/C][C] 3.414e-06[/C][/ROW]
[ROW][C]oct[/C][C]+73.41[/C][C] 15.83[/C][C]+4.6370e+00[/C][C] 6.549e-06[/C][C] 3.275e-06[/C][/ROW]
[ROW][C]nov[/C][C]+8.177[/C][C] 15.83[/C][C]+5.1650e-01[/C][C] 0.6061[/C][C] 0.303[/C][/ROW]
[ROW][C]RW[/C][C]+304.7[/C][C] 34[/C][C]+8.9610e+00[/C][C] 3.001e-16[/C][C] 1.5e-16[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306933&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306933&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)	-6.195e+04	1338	-4.6290e+01	2.033e-105	1.017e-105
year	+31.18	0.6667	+4.6770e+01	3.357e-106	1.679e-106
jan	+8.588	15.83	+5.4250e-01	0.5881	0.294
feb	-56.35	15.83	-3.5600e+00	0.0004688	0.0002344
mar	+39	15.83	+2.4640e+00	0.01464	0.007321
apr	+46.88	15.83	+2.9620e+00	0.003451	0.001726
may	+96.94	15.83	+6.1240e+00	5.138e-09	2.569e-09
jun	+83.94	15.83	+5.3030e+00	3.153e-07	1.576e-07
jul	+107.2	15.83	+6.7740e+00	1.524e-10	7.62e-11
aug	+104	15.96	+6.5190e+00	6.211e-10	3.106e-10
sep	+73.84	15.96	+4.6280e+00	6.829e-06	3.414e-06
oct	+73.41	15.83	+4.6370e+00	6.549e-06	3.275e-06
nov	+8.177	15.83	+5.1650e-01	0.6061	0.303
RW	+304.7	34	+8.9610e+00	3.001e-16	1.5e-16

Multiple Linear Regression - Regression Statistics
Multiple R	0.9665
R-squared	0.9341
Adjusted R-squared	0.9296
F-TEST (value)	207.2
F-TEST (DF numerator)	13
F-TEST (DF denominator)	190
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	46.15
Sum Squared Residuals	4.047e+05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9665 \tabularnewline
R-squared &  0.9341 \tabularnewline
Adjusted R-squared &  0.9296 \tabularnewline
F-TEST (value) &  207.2 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 190 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  46.15 \tabularnewline
Sum Squared Residuals &  4.047e+05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306933&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9665[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9341[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9296[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 207.2[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]190[/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] 46.15[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 4.047e+05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306933&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306933&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	0.9665
R-squared	0.9341
Adjusted R-squared	0.9296
F-TEST (value)	207.2
F-TEST (DF numerator)	13
F-TEST (DF denominator)	190
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	46.15
Sum Squared Residuals	4.047e+05

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306933&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306933&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306933&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 11.784, df1 = 2, df2 = 188, p-value = 1.508e-05
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.28223, df1 = 26, df2 = 164, p-value = 0.9998
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.206, df1 = 2, df2 = 188, p-value = 0.01633

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 11.784, df1 = 2, df2 = 188, p-value = 1.508e-05
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.28223, df1 = 26, df2 = 164, p-value = 0.9998
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.206, df1 = 2, df2 = 188, p-value = 0.01633
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306933&T=5

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 11.784, df1 = 2, df2 = 188, p-value = 1.508e-05
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.28223, df1 = 26, df2 = 164, p-value = 0.9998
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.206, df1 = 2, df2 = 188, p-value = 0.01633
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306933&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306933&T=5

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 11.784, df1 = 2, df2 = 188, p-value = 1.508e-05
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.28223, df1 = 26, df2 = 164, p-value = 0.9998
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.206, df1 = 2, df2 = 188, p-value = 0.01633

Variance Inflation Factors (Multicollinearity)
> vif year jan feb mar apr may jun jul 1.021729 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 aug sep oct nov RW 1.862602 1.862602 1.833333 1.833333 1.074945

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    year      jan      feb      mar      apr      may      jun      jul 
1.021729 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 
     aug      sep      oct      nov       RW 
1.862602 1.862602 1.833333 1.833333 1.074945 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306933&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    year      jan      feb      mar      apr      may      jun      jul 
1.021729 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 
     aug      sep      oct      nov       RW 
1.862602 1.862602 1.833333 1.833333 1.074945 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306933&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306933&T=6

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif year jan feb mar apr may jun jul 1.021729 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 1.833333 aug sep oct nov RW 1.862602 1.862602 1.833333 1.833333 1.074945

Figure 1

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 1 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;

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

par6 <- '12'
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
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.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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,'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')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code