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Author's title

Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 05 Aug 2017 00:37:06 +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/05/t1501886231k0mlfvtibwiee82.htm/, Retrieved Thu, 09 May 2024 22:05:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306936, Retrieved Thu, 09 May 2024 22:05:07 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

126

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

-       [Multiple Regression] [All death] [2017-08-04 22:37:06] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Boxplots

2498	1999	1	0	0	0	0	0	0	0	0	0	0	0	0
2296	1999	0	1	0	0	0	0	0	0	0	0	0	0	0
2465	1999	0	0	1	0	0	0	0	0	0	0	0	0	0
2475	1999	0	0	0	1	0	0	0	0	0	0	0	0	0
2542	1999	0	0	0	0	1	0	0	0	0	0	0	0	0
2434	1999	0	0	0	0	0	1	0	0	0	0	0	0	0
2550	1999	0	0	0	0	0	0	1	0	0	0	0	0	0
2589	1999	0	0	0	0	0	0	0	1	0	0	0	0	0
2251	1999	0	0	0	0	0	0	0	0	1	0	0	0	0
2439	1999	0	0	0	0	0	0	0	0	0	1	0	0	0
2424	1999	0	0	0	0	0	0	0	0	0	0	1	0	0
2236	1999	0	0	0	0	0	0	0	0	0	0	0	0	0
2592	2000	1	0	0	0	0	0	0	0	0	0	0	0	0
2338	2000	0	1	0	0	0	0	0	0	0	0	0	0	0
2520	2000	0	0	1	0	0	0	0	0	0	0	0	0	0
2483	2000	0	0	0	1	0	0	0	0	0	0	0	0	0
2622	2000	0	0	0	0	1	0	0	0	0	0	0	0	0
2445	2000	0	0	0	0	0	1	0	0	0	0	0	0	0
2482	2000	0	0	0	0	0	0	1	0	0	0	0	0	0
2556	2000	0	0	0	0	0	0	0	1	0	0	0	0	0
2336	2000	0	0	0	0	0	0	0	0	1	0	0	0	0
2480	2000	0	0	0	0	0	0	0	0	0	1	0	0	0
2273	2000	0	0	0	0	0	0	0	0	0	0	1	0	0
2223	2000	0	0	0	0	0	0	0	0	0	0	0	0	0
2510	2001	1	0	0	0	0	0	0	0	0	0	0	0	0
2328	2001	0	1	0	0	0	0	0	0	0	0	0	0	0
2546	2001	0	0	1	0	0	0	0	0	0	0	0	0	0
2599	2001	0	0	0	1	0	0	0	0	0	0	0	0	0
2747	2001	0	0	0	0	1	0	0	0	0	0	0	0	0
2560	2001	0	0	0	0	0	1	0	0	0	0	0	0	0
2746	2001	0	0	0	0	0	0	1	0	0	0	0	0	0
2674	2001	0	0	0	0	0	0	0	1	0	0	0	0	0
2407	2001	0	0	0	0	0	0	0	0	1	0	0	0	0
2589	2001	0	0	0	0	0	0	0	0	0	1	0	0	0
2513	2001	0	0	0	0	0	0	0	0	0	0	1	0	0
2403	2001	0	0	0	0	0	0	0	0	0	0	0	0	0
2760	2002	1	0	0	0	0	0	0	0	0	0	0	0	0
2418	2002	0	1	0	0	0	0	0	0	0	0	0	0	0
2611	2002	0	0	1	0	0	0	0	0	0	0	0	0	0
2754	2002	0	0	0	1	0	0	0	0	0	0	0	0	0
2775	2002	0	0	0	0	1	0	0	0	0	0	0	0	0
2588	2002	0	0	0	0	0	1	0	0	0	0	0	0	0
2813	2002	0	0	0	0	0	0	1	0	0	0	0	0	0
2791	2002	0	0	0	0	0	0	0	1	0	0	0	0	0
2648	2002	0	0	0	0	0	0	0	0	1	0	0	0	0
2589	2002	0	0	0	0	0	0	0	0	0	1	0	0	0
2481	2002	0	0	0	0	0	0	0	0	0	0	1	0	0
2427	2002	0	0	0	0	0	0	0	0	0	0	0	0	0
2692	2003	1	0	0	0	0	0	0	0	0	0	0	0	0
2302	2003	0	1	0	0	0	0	0	0	0	0	0	0	0
2773	2003	0	0	1	0	0	0	0	0	0	0	0	0	0
2637	2003	0	0	0	1	0	0	0	0	0	0	0	0	0
2785	2003	0	0	0	0	1	0	0	0	0	0	0	0	0
2803	2003	0	0	0	0	0	1	0	0	0	0	0	0	0
2767	2003	0	0	0	0	0	0	1	0	0	0	0	0	0
2693	2003	0	0	0	0	0	0	0	1	0	0	0	0	0
2559	2003	0	0	0	0	0	0	0	0	1	0	0	0	0
2564	2003	0	0	0	0	0	0	0	0	0	1	0	0	0
2499	2003	0	0	0	0	0	0	0	0	0	0	1	0	0
2410	2003	0	0	0	0	0	0	0	0	0	0	0	0	0
2624	2004	1	0	0	0	0	0	0	0	0	0	0	0	0
2509	2004	0	1	0	0	0	0	0	0	0	0	0	0	0
2845	2004	0	0	1	0	0	0	0	0	0	0	0	0	0
2718	2004	0	0	0	1	0	0	0	0	0	0	0	0	0
2771	2004	0	0	0	0	1	0	0	0	0	0	0	0	0
2722	2004	0	0	0	0	0	1	0	0	0	0	0	0	0
2911	2004	0	0	0	0	0	0	1	0	0	0	0	0	0
2743	2004	0	0	0	0	0	0	0	1	0	0	0	0	0
2715	2004	0	0	0	0	0	0	0	0	1	0	0	0	0
2772	2004	0	0	0	0	0	0	0	0	0	1	0	0	0
2642	2004	0	0	0	0	0	0	0	0	0	0	1	0	0
2467	2004	0	0	0	0	0	0	0	0	0	0	0	0	0
2703	2005	1	0	0	0	0	0	0	0	0	0	0	0	0
2454	2005	0	1	0	0	0	0	0	0	0	0	0	0	0
2826	2005	0	0	1	0	0	0	0	0	0	0	0	0	0
2804	2005	0	0	0	1	0	0	0	0	0	0	0	0	0
2896	2005	0	0	0	0	1	0	0	0	0	0	0	0	0
2763	2005	0	0	0	0	0	1	0	0	0	0	0	0	0
2833	2005	0	0	0	0	0	0	1	0	0	0	0	0	0
2752	2005	0	0	0	0	0	0	0	1	0	0	0	0	0
2770	2005	0	0	0	0	0	0	0	0	1	0	0	0	0
2718	2005	0	0	0	0	0	0	0	0	0	1	0	0	0
2572	2005	0	0	0	0	0	0	0	0	0	0	1	0	0
2546	2005	0	0	0	0	0	0	0	0	0	0	0	0	0
2730	2006	1	0	0	0	0	0	0	0	0	0	0	0	0
2424	2006	0	1	0	0	0	0	0	0	0	0	0	0	0
2763	2006	0	0	1	0	0	0	0	0	0	0	0	0	0
2844	2006	0	0	0	1	0	0	0	0	0	0	0	0	0
2952	2006	0	0	0	0	1	0	0	0	0	0	0	0	0
2875	2006	0	0	0	0	0	1	0	0	0	0	0	0	0
2984	2006	0	0	0	0	0	0	1	0	0	0	0	0	0
2810	2006	0	0	0	0	0	0	0	1	0	0	0	0	0
2724	2006	0	0	0	0	0	0	0	0	1	0	0	0	0
2866	2006	0	0	0	0	0	0	0	0	0	1	0	0	0
2697	2006	0	0	0	0	0	0	0	0	0	0	1	0	0
2631	2006	0	0	0	0	0	0	0	0	0	0	0	0	0
2841	2007	1	0	0	0	0	0	0	0	0	0	0	0	0
2473	2007	0	1	0	0	0	0	0	0	0	0	0	0	0
2954	2007	0	0	1	0	0	0	0	0	0	0	0	0	0
2792	2007	0	0	0	1	0	0	0	0	0	0	0	0	0
3089	2007	0	0	0	0	1	0	0	0	0	0	0	0	0
2915	2007	0	0	0	0	0	1	0	0	0	0	0	0	0
3088	2007	0	0	0	0	0	0	1	0	0	0	0	0	0
2998	2007	0	0	0	0	0	0	0	1	0	0	0	0	0
2951	2007	0	0	0	0	0	0	0	0	1	0	0	0	0
2991	2007	0	0	0	0	0	0	0	0	0	1	0	0	0
2794	2007	0	0	0	0	0	0	0	0	0	0	1	0	0
2712	2007	0	0	0	0	0	0	0	0	0	0	0	0	0
3036	2008	1	0	0	0	0	0	0	0	0	0	0	0	0
2785	2008	0	1	0	0	0	0	0	0	0	0	0	0	0
3044	2008	0	0	1	0	0	0	0	0	0	0	0	0	0
3058	2008	0	0	0	1	0	0	0	0	0	0	0	0	0
3166	2008	0	0	0	0	1	0	0	0	0	0	0	0	0
3096	2008	0	0	0	0	0	1	0	0	0	0	0	0	0
3128	2008	0	0	0	0	0	0	1	0	0	0	0	0	0
3024	2008	0	0	0	0	0	0	0	1	0	0	0	0	0
3115	2008	0	0	0	0	0	0	0	0	1	0	0	0	0
3011	2008	0	0	0	0	0	0	0	0	0	1	0	0	0
2812	2008	0	0	0	0	0	0	0	0	0	0	1	0	0
2760	2008	0	0	0	0	0	0	0	0	0	0	0	0	0
2976	2009	1	0	0	0	0	0	0	0	0	0	0	0	0
2720	2009	0	1	0	0	0	0	0	0	0	0	0	0	0
3044	2009	0	0	1	0	0	0	0	0	0	0	0	0	0
3027	2009	0	0	0	1	0	0	0	0	0	0	0	0	0
3266	2009	0	0	0	0	1	0	0	0	0	0	0	0	0
3254	2009	0	0	0	0	0	1	0	0	0	0	0	0	1
3246	2009	0	0	0	0	0	0	1	0	0	0	0	0	1
3305	2009	0	0	0	0	0	0	0	1	0	0	0	0	0
3265	2009	0	0	0	0	0	0	0	0	1	0	0	0	0
3126	2009	0	0	0	0	0	0	0	0	0	1	0	0	0
2833	2009	0	0	0	0	0	0	0	0	0	0	1	0	0
2847	2009	0	0	0	0	0	0	0	0	0	0	0	0	0
3066	2010	1	0	0	0	0	0	0	0	0	0	0	0	0
2785	2010	0	1	0	0	0	0	0	0	0	0	0	0	0
3251	2010	0	0	1	0	0	0	0	0	0	0	0	0	0
3238	2010	0	0	0	1	0	0	0	0	0	0	0	0	0
3419	2010	0	0	0	0	1	0	0	0	0	0	0	0	0
3286	2010	0	0	0	0	0	1	0	0	0	0	0	0	0
3450	2010	0	0	0	0	0	0	1	0	0	0	0	0	0
3447	2010	0	0	0	0	0	0	0	1	0	0	0	0	0
3153	2010	0	0	0	0	0	0	0	0	1	0	0	0	0
3233	2010	0	0	0	0	0	0	0	0	0	1	0	0	0
2992	2010	0	0	0	0	0	0	0	0	0	0	1	0	0
3044	2010	0	0	0	0	0	0	0	0	0	0	0	0	0
3155	2011	1	0	0	0	0	0	0	0	0	0	0	0	0
2778	2011	0	1	0	0	0	0	0	0	0	0	0	0	0
3392	2011	0	0	1	0	0	0	0	0	0	0	0	0	0
3446	2011	0	0	0	1	0	0	0	0	0	0	0	0	0
3466	2011	0	0	0	0	1	0	0	0	0	0	0	0	0
3355	2011	0	0	0	0	0	1	0	0	0	0	0	0	0
3573	2011	0	0	0	0	0	0	1	0	0	0	0	0	0
3498	2011	0	0	0	0	0	0	0	1	0	0	0	0	0
3332	2011	0	0	0	0	0	0	0	0	1	0	0	0	0
3306	2011	0	0	0	0	0	0	0	0	0	1	0	0	0
3136	2011	0	0	0	0	0	0	0	0	0	0	1	0	0
3081	2011	0	0	0	0	0	0	0	0	0	0	0	0	0
3308	2012	1	0	0	0	0	0	0	0	0	0	0	0	0
3094	2012	0	1	0	0	0	0	0	0	0	0	0	0	0
3397	2012	0	0	1	0	0	0	0	0	0	0	0	0	0
3424	2012	0	0	0	1	0	0	0	0	0	0	0	0	0
3704	2012	0	0	0	0	1	0	0	0	0	0	0	0	0
3468	2012	0	0	0	0	0	1	0	0	0	0	0	0	0
3636	2012	0	0	0	0	0	0	1	0	0	0	0	0	0
3544	2012	0	0	0	0	0	0	0	1	0	0	0	0	0
3387	2012	0	0	0	0	0	0	0	0	1	0	0	0	0
3330	2012	0	0	0	0	0	0	0	0	0	1	0	0	0
3137	2012	0	0	0	0	0	0	0	0	0	0	1	0	0
3171	2012	0	0	0	0	0	0	0	0	0	0	0	0	0
3529	2013	1	0	0	0	0	0	0	0	0	0	0	0	0
3016	2013	0	1	0	0	0	0	0	0	0	0	0	0	0
3576	2013	0	0	1	0	0	0	0	0	0	0	0	0	0
3526	2013	0	0	0	1	0	0	0	0	0	0	0	0	0
3538	2013	0	0	0	0	1	0	0	0	0	0	0	0	0
3479	2013	0	0	0	0	0	1	0	0	0	0	0	0	0
3640	2013	0	0	0	0	0	0	1	0	0	0	0	0	0
3580	2013	0	0	0	0	0	0	0	1	0	0	0	0	0
3450	2013	0	0	0	0	0	0	0	0	1	0	0	0	0
3467	2013	0	0	0	0	0	0	0	0	0	1	0	0	0
3172	2013	0	0	0	0	0	0	0	0	0	0	1	0	0
3176	2013	0	0	0	0	0	0	0	0	0	0	0	0	0
3320	2014	1	0	0	0	0	0	0	0	0	0	0	0	0
3091	2014	0	1	0	0	0	0	0	0	0	0	0	0	0
3408	2014	0	0	1	0	0	0	0	0	0	0	0	0	0
3606	2014	0	0	0	1	0	0	0	0	0	0	0	0	0
3589	2014	0	0	0	0	1	0	0	0	0	0	0	0	0
3552	2014	0	0	0	0	0	1	0	0	0	0	0	0	0
3534	2014	0	0	0	0	0	0	1	0	0	0	0	0	0
4027	2014	0	0	0	0	0	0	0	1	0	0	0	1	0
4034	2014	0	0	0	0	0	0	0	0	1	0	0	1	0
3791	2014	0	0	0	0	0	0	0	0	0	1	0	0	0
3480	2014	0	0	0	0	0	0	0	0	0	0	1	0	0
3394	2014	0	0	0	0	0	0	0	0	0	0	0	0	0
3618	2015	1	0	0	0	0	0	0	0	0	0	0	0	0
3215	2015	0	1	0	0	0	0	0	0	0	0	0	0	0
3935	2015	0	0	1	0	0	0	0	0	0	0	0	0	0
3726	2015	0	0	0	1	0	0	0	0	0	0	0	0	0
3966	2015	0	0	0	0	1	0	0	0	0	0	0	0	0
3785	2015	0	0	0	0	0	1	0	0	0	0	0	0	0
3944	2015	0	0	0	0	0	0	1	0	0	0	0	0	0
3912	2015	0	0	0	0	0	0	0	1	0	0	0	0	0
3578	2015	0	0	0	0	0	0	0	0	1	0	0	0	0
3688	2015	0	0	0	0	0	0	0	0	0	1	0	0	0
3357	2015	0	0	0	0	0	0	0	0	0	0	1	0	0
3469	2015	0	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	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306936&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306936&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306936&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	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
allsui[t] = -151647 + 76.9364year[t] + 174.176jan[t] -115.941feb[t] + 256.059mar[t] + 244.706apr[t] + 370.353may[t] + 256.788jun[t] + 371.2jul[t] + 321.992aug[t] + 188.581sep[t] + 233.118oct[t] + 48.0588nov[t] + 472.129RW[t] + 17.6038MJ[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
allsui[t] =  -151647 +  76.9364year[t] +  174.176jan[t] -115.941feb[t] +  256.059mar[t] +  244.706apr[t] +  370.353may[t] +  256.788jun[t] +  371.2jul[t] +  321.992aug[t] +  188.581sep[t] +  233.118oct[t] +  48.0588nov[t] +  472.129RW[t] +  17.6038MJ[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306936&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]allsui[t] =  -151647 +  76.9364year[t] +  174.176jan[t] -115.941feb[t] +  256.059mar[t] +  244.706apr[t] +  370.353may[t] +  256.788jun[t] +  371.2jul[t] +  321.992aug[t] +  188.581sep[t] +  233.118oct[t] +  48.0588nov[t] +  472.129RW[t] +  17.6038MJ[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306936&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306936&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
allsui[t] = -151647 + 76.9364year[t] + 174.176jan[t] -115.941feb[t] + 256.059mar[t] + 244.706apr[t] + 370.353may[t] + 256.788jun[t] + 371.2jul[t] + 321.992aug[t] + 188.581sep[t] + 233.118oct[t] + 48.0588nov[t] + 472.129RW[t] + 17.6038MJ[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)	-1.516e+05	2944	-5.1500e+01	3.487e-113	1.744e-113
year	+76.94	1.467	+5.2440e+01	1.424e-114	7.118e-115
jan	+174.2	34.8	+5.0050e+00	1.275e-06	6.374e-07
feb	-115.9	34.8	-3.3320e+00	0.001039	0.0005194
mar	+256.1	34.8	+7.3580e+00	5.554e-12	2.777e-12
apr	+244.7	34.8	+7.0310e+00	3.626e-11	1.813e-11
may	+370.4	34.8	+1.0640e+01	5.053e-21	2.527e-21
jun	+256.8	35.07	+7.3220e+00	6.852e-12	3.426e-12
jul	+371.2	35.07	+1.0580e+01	7.465e-21	3.733e-21
aug	+322	35.08	+9.1790e+00	7.637e-17	3.818e-17
sep	+188.6	35.08	+5.3760e+00	2.229e-07	1.114e-07
oct	+233.1	34.8	+6.6990e+00	2.345e-10	1.172e-10
nov	+48.06	34.8	+1.3810e+00	0.1689	0.08446
RW	+472.1	74.75	+6.3160e+00	1.876e-09	9.382e-10
MJ	+17.6	74.02	+2.3780e-01	0.8123	0.4061

\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) & -1.516e+05 &  2944 & -5.1500e+01 &  3.487e-113 &  1.744e-113 \tabularnewline
year & +76.94 &  1.467 & +5.2440e+01 &  1.424e-114 &  7.118e-115 \tabularnewline
jan & +174.2 &  34.8 & +5.0050e+00 &  1.275e-06 &  6.374e-07 \tabularnewline
feb & -115.9 &  34.8 & -3.3320e+00 &  0.001039 &  0.0005194 \tabularnewline
mar & +256.1 &  34.8 & +7.3580e+00 &  5.554e-12 &  2.777e-12 \tabularnewline
apr & +244.7 &  34.8 & +7.0310e+00 &  3.626e-11 &  1.813e-11 \tabularnewline
may & +370.4 &  34.8 & +1.0640e+01 &  5.053e-21 &  2.527e-21 \tabularnewline
jun & +256.8 &  35.07 & +7.3220e+00 &  6.852e-12 &  3.426e-12 \tabularnewline
jul & +371.2 &  35.07 & +1.0580e+01 &  7.465e-21 &  3.733e-21 \tabularnewline
aug & +322 &  35.08 & +9.1790e+00 &  7.637e-17 &  3.818e-17 \tabularnewline
sep & +188.6 &  35.08 & +5.3760e+00 &  2.229e-07 &  1.114e-07 \tabularnewline
oct & +233.1 &  34.8 & +6.6990e+00 &  2.345e-10 &  1.172e-10 \tabularnewline
nov & +48.06 &  34.8 & +1.3810e+00 &  0.1689 &  0.08446 \tabularnewline
RW & +472.1 &  74.75 & +6.3160e+00 &  1.876e-09 &  9.382e-10 \tabularnewline
MJ & +17.6 &  74.02 & +2.3780e-01 &  0.8123 &  0.4061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306936&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]-1.516e+05[/C][C] 2944[/C][C]-5.1500e+01[/C][C] 3.487e-113[/C][C] 1.744e-113[/C][/ROW]
[ROW][C]year[/C][C]+76.94[/C][C] 1.467[/C][C]+5.2440e+01[/C][C] 1.424e-114[/C][C] 7.118e-115[/C][/ROW]
[ROW][C]jan[/C][C]+174.2[/C][C] 34.8[/C][C]+5.0050e+00[/C][C] 1.275e-06[/C][C] 6.374e-07[/C][/ROW]
[ROW][C]feb[/C][C]-115.9[/C][C] 34.8[/C][C]-3.3320e+00[/C][C] 0.001039[/C][C] 0.0005194[/C][/ROW]
[ROW][C]mar[/C][C]+256.1[/C][C] 34.8[/C][C]+7.3580e+00[/C][C] 5.554e-12[/C][C] 2.777e-12[/C][/ROW]
[ROW][C]apr[/C][C]+244.7[/C][C] 34.8[/C][C]+7.0310e+00[/C][C] 3.626e-11[/C][C] 1.813e-11[/C][/ROW]
[ROW][C]may[/C][C]+370.4[/C][C] 34.8[/C][C]+1.0640e+01[/C][C] 5.053e-21[/C][C] 2.527e-21[/C][/ROW]
[ROW][C]jun[/C][C]+256.8[/C][C] 35.07[/C][C]+7.3220e+00[/C][C] 6.852e-12[/C][C] 3.426e-12[/C][/ROW]
[ROW][C]jul[/C][C]+371.2[/C][C] 35.07[/C][C]+1.0580e+01[/C][C] 7.465e-21[/C][C] 3.733e-21[/C][/ROW]
[ROW][C]aug[/C][C]+322[/C][C] 35.08[/C][C]+9.1790e+00[/C][C] 7.637e-17[/C][C] 3.818e-17[/C][/ROW]
[ROW][C]sep[/C][C]+188.6[/C][C] 35.08[/C][C]+5.3760e+00[/C][C] 2.229e-07[/C][C] 1.114e-07[/C][/ROW]
[ROW][C]oct[/C][C]+233.1[/C][C] 34.8[/C][C]+6.6990e+00[/C][C] 2.345e-10[/C][C] 1.172e-10[/C][/ROW]
[ROW][C]nov[/C][C]+48.06[/C][C] 34.8[/C][C]+1.3810e+00[/C][C] 0.1689[/C][C] 0.08446[/C][/ROW]
[ROW][C]RW[/C][C]+472.1[/C][C] 74.75[/C][C]+6.3160e+00[/C][C] 1.876e-09[/C][C] 9.382e-10[/C][/ROW]
[ROW][C]MJ[/C][C]+17.6[/C][C] 74.02[/C][C]+2.3780e-01[/C][C] 0.8123[/C][C] 0.4061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306936&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306936&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)	-1.516e+05	2944	-5.1500e+01	3.487e-113	1.744e-113
year	+76.94	1.467	+5.2440e+01	1.424e-114	7.118e-115
jan	+174.2	34.8	+5.0050e+00	1.275e-06	6.374e-07
feb	-115.9	34.8	-3.3320e+00	0.001039	0.0005194
mar	+256.1	34.8	+7.3580e+00	5.554e-12	2.777e-12
apr	+244.7	34.8	+7.0310e+00	3.626e-11	1.813e-11
may	+370.4	34.8	+1.0640e+01	5.053e-21	2.527e-21
jun	+256.8	35.07	+7.3220e+00	6.852e-12	3.426e-12
jul	+371.2	35.07	+1.0580e+01	7.465e-21	3.733e-21
aug	+322	35.08	+9.1790e+00	7.637e-17	3.818e-17
sep	+188.6	35.08	+5.3760e+00	2.229e-07	1.114e-07
oct	+233.1	34.8	+6.6990e+00	2.345e-10	1.172e-10
nov	+48.06	34.8	+1.3810e+00	0.1689	0.08446
RW	+472.1	74.75	+6.3160e+00	1.876e-09	9.382e-10
MJ	+17.6	74.02	+2.3780e-01	0.8123	0.4061

Multiple Linear Regression - Regression Statistics
Multiple R	0.9731
R-squared	0.947
Adjusted R-squared	0.9431
F-TEST (value)	241.1
F-TEST (DF numerator)	14
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	101.5
Sum Squared Residuals	1.946e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9731 \tabularnewline
R-squared &  0.947 \tabularnewline
Adjusted R-squared &  0.9431 \tabularnewline
F-TEST (value) &  241.1 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 189 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  101.5 \tabularnewline
Sum Squared Residuals &  1.946e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306936&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9731[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.947[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9431[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 241.1[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]189[/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] 101.5[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.946e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306936&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306936&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	0.9731
R-squared	0.947
Adjusted R-squared	0.9431
F-TEST (value)	241.1
F-TEST (DF numerator)	14
F-TEST (DF denominator)	189
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	101.5
Sum Squared Residuals	1.946e+06

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=306936&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=306936&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306936&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 = 46.095, df1 = 2, df2 = 187, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7859, df1 = 28, df2 = 161, p-value = 0.01393
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 29.039, df1 = 2, df2 = 187, p-value = 1.04e-11

\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 = 46.095, df1 = 2, df2 = 187, p-value < 2.2e-16
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7859, df1 = 28, df2 = 161, p-value = 0.01393
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 29.039, df1 = 2, df2 = 187, p-value = 1.04e-11
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306936&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 = 46.095, df1 = 2, df2 = 187, p-value < 2.2e-16
[/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 = 1.7859, df1 = 28, df2 = 161, p-value = 0.01393
[/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 = 29.039, df1 = 2, df2 = 187, p-value = 1.04e-11
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306936&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306936&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 = 46.095, df1 = 2, df2 = 187, p-value < 2.2e-16
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7859, df1 = 28, df2 = 161, p-value = 0.01393
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 29.039, df1 = 2, df2 = 187, p-value = 1.04e-11

Variance Inflation Factors (Multicollinearity)
> vif year jan feb mar apr may jun jul 1.023545 1.833333 1.833333 1.833333 1.833333 1.833333 1.862030 1.862030 aug sep oct nov RW MJ 1.862603 1.862603 1.833333 1.833333 1.074985 1.053953

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    year      jan      feb      mar      apr      may      jun      jul 
1.023545 1.833333 1.833333 1.833333 1.833333 1.833333 1.862030 1.862030 
     aug      sep      oct      nov       RW       MJ 
1.862603 1.862603 1.833333 1.833333 1.074985 1.053953 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306936&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    year      jan      feb      mar      apr      may      jun      jul 
1.023545 1.833333 1.833333 1.833333 1.833333 1.833333 1.862030 1.862030 
     aug      sep      oct      nov       RW       MJ 
1.862603 1.862603 1.833333 1.833333 1.074985 1.053953 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306936&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306936&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 1.023545 1.833333 1.833333 1.833333 1.833333 1.833333 1.862030 1.862030 aug sep oct nov RW MJ 1.862603 1.862603 1.833333 1.833333 1.074985 1.053953

Figure 1

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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