Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 18 Apr 2018 23:05:56 +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/2018/Apr/18/t1524085782375xn8hb95bm93u.htm/, Retrieved Tue, 07 May 2024 03:31:50 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Tue, 07 May 2024 03:31:50 +0200

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

Dataseries X:

Download CSV

Histogram

Boxplots

2498	1	1	0	0	0	0	0	0	0	0	0	0	0
2296	2	0	0	0	0	0	0	0	0	0	0	0	0
2465	3	0	1	0	0	0	0	0	0	0	0	0	0
2475	4	0	0	1	0	0	0	0	0	0	0	0	0
2542	5	0	0	0	1	0	0	0	0	0	0	0	0
2434	6	0	0	0	0	1	0	0	0	0	0	0	0
2550	7	0	0	0	0	0	1	0	0	0	0	0	0
2589	8	0	0	0	0	0	0	1	0	0	0	0	0
2251	9	0	0	0	0	0	0	0	1	0	0	0	0
2439	10	0	0	0	0	0	0	0	0	1	0	0	0
2424	11	0	0	0	0	0	0	0	0	0	1	0	0
2236	12	0	0	0	0	0	0	0	0	0	0	1	0
2592	13	1	0	0	0	0	0	0	0	0	0	0	0
2338	14	0	0	0	0	0	0	0	0	0	0	0	0
2520	15	0	1	0	0	0	0	0	0	0	0	0	0
2483	16	0	0	1	0	0	0	0	0	0	0	0	0
2622	17	0	0	0	1	0	0	0	0	0	0	0	0
2445	18	0	0	0	0	1	0	0	0	0	0	0	0
2482	19	0	0	0	0	0	1	0	0	0	0	0	0
2556	20	0	0	0	0	0	0	1	0	0	0	0	0
2336	21	0	0	0	0	0	0	0	1	0	0	0	0
2480	22	0	0	0	0	0	0	0	0	1	0	0	0
2273	23	0	0	0	0	0	0	0	0	0	1	0	0
2223	24	0	0	0	0	0	0	0	0	0	0	1	0
2510	25	1	0	0	0	0	0	0	0	0	0	0	0
2328	26	0	0	0	0	0	0	0	0	0	0	0	0
2546	27	0	1	0	0	0	0	0	0	0	0	0	0
2599	28	0	0	1	0	0	0	0	0	0	0	0	0
2747	29	0	0	0	1	0	0	0	0	0	0	0	0
2560	30	0	0	0	0	1	0	0	0	0	0	0	0
2746	31	0	0	0	0	0	1	0	0	0	0	0	0
2674	32	0	0	0	0	0	0	1	0	0	0	0	0
2407	33	0	0	0	0	0	0	0	1	0	0	0	0
2589	34	0	0	0	0	0	0	0	0	1	0	0	0
2513	35	0	0	0	0	0	0	0	0	0	1	0	0
2403	36	0	0	0	0	0	0	0	0	0	0	1	0
2760	37	1	0	0	0	0	0	0	0	0	0	0	0
2418	38	0	0	0	0	0	0	0	0	0	0	0	0
2611	39	0	1	0	0	0	0	0	0	0	0	0	0
2754	40	0	0	1	0	0	0	0	0	0	0	0	0
2775	41	0	0	0	1	0	0	0	0	0	0	0	0
2588	42	0	0	0	0	1	0	0	0	0	0	0	0
2813	43	0	0	0	0	0	1	0	0	0	0	0	0
2791	44	0	0	0	0	0	0	1	0	0	0	0	0
2648	45	0	0	0	0	0	0	0	1	0	0	0	0
2589	46	0	0	0	0	0	0	0	0	1	0	0	0
2481	47	0	0	0	0	0	0	0	0	0	1	0	0
2427	48	0	0	0	0	0	0	0	0	0	0	1	0
2692	49	1	0	0	0	0	0	0	0	0	0	0	0
2302	50	0	0	0	0	0	0	0	0	0	0	0	0
2773	51	0	1	0	0	0	0	0	0	0	0	0	0
2637	52	0	0	1	0	0	0	0	0	0	0	0	0
2785	53	0	0	0	1	0	0	0	0	0	0	0	0
2803	54	0	0	0	0	1	0	0	0	0	0	0	0
2767	55	0	0	0	0	0	1	0	0	0	0	0	0
2693	56	0	0	0	0	0	0	1	0	0	0	0	0
2559	57	0	0	0	0	0	0	0	1	0	0	0	0
2564	58	0	0	0	0	0	0	0	0	1	0	0	0
2499	59	0	0	0	0	0	0	0	0	0	1	0	0
2410	60	0	0	0	0	0	0	0	0	0	0	1	0
2624	61	1	0	0	0	0	0	0	0	0	0	0	0
2509	62	0	0	0	0	0	0	0	0	0	0	0	0
2845	63	0	1	0	0	0	0	0	0	0	0	0	0
2718	64	0	0	1	0	0	0	0	0	0	0	0	0
2771	65	0	0	0	1	0	0	0	0	0	0	0	0
2722	66	0	0	0	0	1	0	0	0	0	0	0	0
2911	67	0	0	0	0	0	1	0	0	0	0	0	0
2743	68	0	0	0	0	0	0	1	0	0	0	0	0
2715	69	0	0	0	0	0	0	0	1	0	0	0	0
2772	70	0	0	0	0	0	0	0	0	1	0	0	0
2642	71	0	0	0	0	0	0	0	0	0	1	0	0
2467	72	0	0	0	0	0	0	0	0	0	0	1	0
2703	73	1	0	0	0	0	0	0	0	0	0	0	0
2454	74	0	0	0	0	0	0	0	0	0	0	0	0
2826	75	0	1	0	0	0	0	0	0	0	0	0	0
2804	76	0	0	1	0	0	0	0	0	0	0	0	0
2896	77	0	0	0	1	0	0	0	0	0	0	0	0
2763	78	0	0	0	0	1	0	0	0	0	0	0	0
2833	79	0	0	0	0	0	1	0	0	0	0	0	0
2752	80	0	0	0	0	0	0	1	0	0	0	0	0
2770	81	0	0	0	0	0	0	0	1	0	0	0	0
2718	82	0	0	0	0	0	0	0	0	1	0	0	0
2572	83	0	0	0	0	0	0	0	0	0	1	0	0
2546	84	0	0	0	0	0	0	0	0	0	0	1	0
2730	85	1	0	0	0	0	0	0	0	0	0	0	0
2424	86	0	0	0	0	0	0	0	0	0	0	0	0
2763	87	0	1	0	0	0	0	0	0	0	0	0	0
2844	88	0	0	1	0	0	0	0	0	0	0	0	0
2952	89	0	0	0	1	0	0	0	0	0	0	0	0
2875	90	0	0	0	0	1	0	0	0	0	0	0	0
2984	91	0	0	0	0	0	1	0	0	0	0	0	0
2810	92	0	0	0	0	0	0	1	0	0	0	0	0
2724	93	0	0	0	0	0	0	0	1	0	0	0	0
2866	94	0	0	0	0	0	0	0	0	1	0	0	0
2697	95	0	0	0	0	0	0	0	0	0	1	0	0
2631	96	0	0	0	0	0	0	0	0	0	0	1	0
2841	97	1	0	0	0	0	0	0	0	0	0	0	0
2473	98	0	0	0	0	0	0	0	0	0	0	0	0
2954	99	0	1	0	0	0	0	0	0	0	0	0	0
2792	100	0	0	1	0	0	0	0	0	0	0	0	0
3089	101	0	0	0	1	0	0	0	0	0	0	0	0
2915	102	0	0	0	0	1	0	0	0	0	0	0	0
3088	103	0	0	0	0	0	1	0	0	0	0	0	0
2998	104	0	0	0	0	0	0	1	0	0	0	0	0
2951	105	0	0	0	0	0	0	0	1	0	0	0	0
2991	106	0	0	0	0	0	0	0	0	1	0	0	0
2794	107	0	0	0	0	0	0	0	0	0	1	0	0
2712	108	0	0	0	0	0	0	0	0	0	0	1	0
3036	109	1	0	0	0	0	0	0	0	0	0	0	0
2785	110	0	0	0	0	0	0	0	0	0	0	0	0
3044	111	0	1	0	0	0	0	0	0	0	0	0	0
3058	112	0	0	1	0	0	0	0	0	0	0	0	0
3166	113	0	0	0	1	0	0	0	0	0	0	0	0
3096	114	0	0	0	0	1	0	0	0	0	0	0	0
3128	115	0	0	0	0	0	1	0	0	0	0	0	0
3024	116	0	0	0	0	0	0	1	0	0	0	0	0
3115	117	0	0	0	0	0	0	0	1	0	0	0	0
3011	118	0	0	0	0	0	0	0	0	1	0	0	0
2812	119	0	0	0	0	0	0	0	0	0	1	0	0
2760	120	0	0	0	0	0	0	0	0	0	0	1	0
2976	121	1	0	0	0	0	0	0	0	0	0	0	0
2720	122	0	0	0	0	0	0	0	0	0	0	0	0
3044	123	0	1	0	0	0	0	0	0	0	0	0	0
3027	124	0	0	1	0	0	0	0	0	0	0	0	0
3266	125	0	0	0	1	0	0	0	0	0	0	0	0
3254	126	0	0	0	0	1	0	0	0	0	0	0	0
3246	127	0	0	0	0	0	1	0	0	0	0	0	0
3305	128	0	0	0	0	0	0	1	0	0	0	0	0
3265	129	0	0	0	0	0	0	0	1	0	0	0	0
3126	130	0	0	0	0	0	0	0	0	1	0	0	0
2833	131	0	0	0	0	0	0	0	0	0	1	0	0
2847	132	0	0	0	0	0	0	0	0	0	0	1	0
3066	133	1	0	0	0	0	0	0	0	0	0	0	0
2785	134	0	0	0	0	0	0	0	0	0	0	0	0
3251	135	0	1	0	0	0	0	0	0	0	0	0	0
3238	136	0	0	1	0	0	0	0	0	0	0	0	0
3419	137	0	0	0	1	0	0	0	0	0	0	0	0
3286	138	0	0	0	0	1	0	0	0	0	0	0	0
3450	139	0	0	0	0	0	1	0	0	0	0	0	0
3447	140	0	0	0	0	0	0	1	0	0	0	0	0
3153	141	0	0	0	0	0	0	0	1	0	0	0	0
3233	142	0	0	0	0	0	0	0	0	1	0	0	0
2992	143	0	0	0	0	0	0	0	0	0	1	0	0
3044	144	0	0	0	0	0	0	0	0	0	0	1	0
3155	145	1	0	0	0	0	0	0	0	0	0	0	0
2778	146	0	0	0	0	0	0	0	0	0	0	0	0
3392	147	0	1	0	0	0	0	0	0	0	0	0	0
3446	148	0	0	1	0	0	0	0	0	0	0	0	0
3466	149	0	0	0	1	0	0	0	0	0	0	0	0
3355	150	0	0	0	0	1	0	0	0	0	0	0	0
3573	151	0	0	0	0	0	1	0	0	0	0	0	0
3498	152	0	0	0	0	0	0	1	0	0	0	0	0
3332	153	0	0	0	0	0	0	0	1	0	0	0	0
3306	154	0	0	0	0	0	0	0	0	1	0	0	0
3136	155	0	0	0	0	0	0	0	0	0	1	0	0
3081	156	0	0	0	0	0	0	0	0	0	0	1	0
3308	157	1	0	0	0	0	0	0	0	0	0	0	0
3094	158	0	0	0	0	0	0	0	0	0	0	0	0
3397	159	0	1	0	0	0	0	0	0	0	0	0	0
3424	160	0	0	1	0	0	0	0	0	0	0	0	0
3704	161	0	0	0	1	0	0	0	0	0	0	0	0
3468	162	0	0	0	0	1	0	0	0	0	0	0	0
3636	163	0	0	0	0	0	1	0	0	0	0	0	0
3544	164	0	0	0	0	0	0	1	0	0	0	0	0
3387	165	0	0	0	0	0	0	0	1	0	0	0	0
3330	166	0	0	0	0	0	0	0	0	1	0	0	0
3137	167	0	0	0	0	0	0	0	0	0	1	0	0
3171	168	0	0	0	0	0	0	0	0	0	0	1	0
3529	169	1	0	0	0	0	0	0	0	0	0	0	0
3016	170	0	0	0	0	0	0	0	0	0	0	0	0
3576	171	0	1	0	0	0	0	0	0	0	0	0	0
3526	172	0	0	1	0	0	0	0	0	0	0	0	0
3538	173	0	0	0	1	0	0	0	0	0	0	0	0
3479	174	0	0	0	0	1	0	0	0	0	0	0	0
3640	175	0	0	0	0	0	1	0	0	0	0	0	0
3580	176	0	0	0	0	0	0	1	0	0	0	0	0
3450	177	0	0	0	0	0	0	0	1	0	0	0	0
3467	178	0	0	0	0	0	0	0	0	1	0	0	0
3172	179	0	0	0	0	0	0	0	0	0	1	0	0
3176	180	0	0	0	0	0	0	0	0	0	0	1	0
3320	181	1	0	0	0	0	0	0	0	0	0	0	0
3091	182	0	0	0	0	0	0	0	0	0	0	0	0
3408	183	0	1	0	0	0	0	0	0	0	0	0	0
3606	184	0	0	1	0	0	0	0	0	0	0	0	0
3589	185	0	0	0	1	0	0	0	0	0	0	0	0
3552	186	0	0	0	0	1	0	0	0	0	0	0	0
3534	187	0	0	0	0	0	1	0	0	0	0	0	0
4027	188	0	0	0	0	0	0	1	0	0	0	0	1
4034	189	0	0	0	0	0	0	0	1	0	0	0	1
3791	190	0	0	0	0	0	0	0	0	1	0	0	0
3480	191	0	0	0	0	0	0	0	0	0	1	0	0
3394	192	0	0	0	0	0	0	0	0	0	0	1	0
3618	193	1	0	0	0	0	0	0	0	0	0	0	0
3215	194	0	0	0	0	0	0	0	0	0	0	0	0
3935	195	0	1	0	0	0	0	0	0	0	0	0	0
3726	196	0	0	1	0	0	0	0	0	0	0	0	0
3966	197	0	0	0	1	0	0	0	0	0	0	0	0
3785	198	0	0	0	0	1	0	0	0	0	0	0	0
3944	199	0	0	0	0	0	1	0	0	0	0	0	0
3912	200	0	0	0	0	0	0	1	0	0	0	0	0
3578	201	0	0	0	0	0	0	0	1	0	0	0	0
3688	202	0	0	0	0	0	0	0	0	1	0	0	0
3357	203	0	0	0	0	0	0	0	0	0	1	0	0
3469	204	0	0	0	0	0	0	0	0	0	0	1	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 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 time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

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

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
AllSuicides[t] = + 745.869 + 2.74418totalmo[t] + 367.372`1`[t] + 419.134`3`[t] + 348.255`4`[t] + 387.289`5`[t] + 232.414`6`[t] + 352.327`7`[t] + 288.718`8`[t] + 133.153`9`[t] + 223.784`10`[t] + 61.7883`11`[t] + 69.3017`12`[t] + 449.197rw[t] + 0.336478`AllSuicides(t-1)`[t] + 0.235723`AllSuicides(t-2)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
AllSuicides[t] =  +  745.869 +  2.74418totalmo[t] +  367.372`1`[t] +  419.134`3`[t] +  348.255`4`[t] +  387.289`5`[t] +  232.414`6`[t] +  352.327`7`[t] +  288.718`8`[t] +  133.153`9`[t] +  223.784`10`[t] +  61.7883`11`[t] +  69.3017`12`[t] +  449.197rw[t] +  0.336478`AllSuicides(t-1)`[t] +  0.235723`AllSuicides(t-2)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]AllSuicides[t] =  +  745.869 +  2.74418totalmo[t] +  367.372`1`[t] +  419.134`3`[t] +  348.255`4`[t] +  387.289`5`[t] +  232.414`6`[t] +  352.327`7`[t] +  288.718`8`[t] +  133.153`9`[t] +  223.784`10`[t] +  61.7883`11`[t] +  69.3017`12`[t] +  449.197rw[t] +  0.336478`AllSuicides(t-1)`[t] +  0.235723`AllSuicides(t-2)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
AllSuicides[t] = + 745.869 + 2.74418totalmo[t] + 367.372`1`[t] + 419.134`3`[t] + 348.255`4`[t] + 387.289`5`[t] + 232.414`6`[t] + 352.327`7`[t] + 288.718`8`[t] + 133.153`9`[t] + 223.784`10`[t] + 61.7883`11`[t] + 69.3017`12`[t] + 449.197rw[t] + 0.336478`AllSuicides(t-1)`[t] + 0.235723`AllSuicides(t-2)`[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)	+745.9	136.1	+5.4810e+00	1.361e-07	6.804e-08
totalmo	+2.744	0.4143	+6.6230e+00	3.676e-10	1.838e-10
`1`	+367.4	34.11	+1.0770e+01	2.474e-21	1.237e-21
`3`	+419.1	41.54	+1.0090e+01	2.273e-19	1.137e-19
`4`	+348.2	29.46	+1.1820e+01	2.083e-24	1.042e-24
`5`	+387.3	34.07	+1.1370e+01	4.437e-23	2.219e-23
`6`	+232.4	32.89	+7.0670e+00	3.088e-11	1.544e-11
`7`	+352.3	37.91	+9.2930e+00	4.055e-17	2.028e-17
`8`	+288.7	33.2	+8.6970e+00	1.774e-15	8.87e-16
`9`	+133.2	36.86	+3.6130e+00	0.0003898	0.0001949
`10`	+223.8	37.47	+5.9730e+00	1.161e-08	5.807e-09
`11`	+61.79	32.26	+1.9160e+00	0.05695	0.02848
`12`	+69.3	37.5	+1.8480e+00	0.06615	0.03308
rw	+449.2	61.57	+7.2960e+00	8.305e-12	4.153e-12
`AllSuicides(t-1)`	+0.3365	0.06433	+5.2300e+00	4.531e-07	2.265e-07
`AllSuicides(t-2)`	+0.2357	0.0638	+3.6950e+00	0.0002895	0.0001447

\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) & +745.9 &  136.1 & +5.4810e+00 &  1.361e-07 &  6.804e-08 \tabularnewline
totalmo & +2.744 &  0.4143 & +6.6230e+00 &  3.676e-10 &  1.838e-10 \tabularnewline
`1` & +367.4 &  34.11 & +1.0770e+01 &  2.474e-21 &  1.237e-21 \tabularnewline
`3` & +419.1 &  41.54 & +1.0090e+01 &  2.273e-19 &  1.137e-19 \tabularnewline
`4` & +348.2 &  29.46 & +1.1820e+01 &  2.083e-24 &  1.042e-24 \tabularnewline
`5` & +387.3 &  34.07 & +1.1370e+01 &  4.437e-23 &  2.219e-23 \tabularnewline
`6` & +232.4 &  32.89 & +7.0670e+00 &  3.088e-11 &  1.544e-11 \tabularnewline
`7` & +352.3 &  37.91 & +9.2930e+00 &  4.055e-17 &  2.028e-17 \tabularnewline
`8` & +288.7 &  33.2 & +8.6970e+00 &  1.774e-15 &  8.87e-16 \tabularnewline
`9` & +133.2 &  36.86 & +3.6130e+00 &  0.0003898 &  0.0001949 \tabularnewline
`10` & +223.8 &  37.47 & +5.9730e+00 &  1.161e-08 &  5.807e-09 \tabularnewline
`11` & +61.79 &  32.26 & +1.9160e+00 &  0.05695 &  0.02848 \tabularnewline
`12` & +69.3 &  37.5 & +1.8480e+00 &  0.06615 &  0.03308 \tabularnewline
rw & +449.2 &  61.57 & +7.2960e+00 &  8.305e-12 &  4.153e-12 \tabularnewline
`AllSuicides(t-1)` & +0.3365 &  0.06433 & +5.2300e+00 &  4.531e-07 &  2.265e-07 \tabularnewline
`AllSuicides(t-2)` & +0.2357 &  0.0638 & +3.6950e+00 &  0.0002895 &  0.0001447 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]+745.9[/C][C] 136.1[/C][C]+5.4810e+00[/C][C] 1.361e-07[/C][C] 6.804e-08[/C][/ROW]
[ROW][C]totalmo[/C][C]+2.744[/C][C] 0.4143[/C][C]+6.6230e+00[/C][C] 3.676e-10[/C][C] 1.838e-10[/C][/ROW]
[ROW][C]`1`[/C][C]+367.4[/C][C] 34.11[/C][C]+1.0770e+01[/C][C] 2.474e-21[/C][C] 1.237e-21[/C][/ROW]
[ROW][C]`3`[/C][C]+419.1[/C][C] 41.54[/C][C]+1.0090e+01[/C][C] 2.273e-19[/C][C] 1.137e-19[/C][/ROW]
[ROW][C]`4`[/C][C]+348.2[/C][C] 29.46[/C][C]+1.1820e+01[/C][C] 2.083e-24[/C][C] 1.042e-24[/C][/ROW]
[ROW][C]`5`[/C][C]+387.3[/C][C] 34.07[/C][C]+1.1370e+01[/C][C] 4.437e-23[/C][C] 2.219e-23[/C][/ROW]
[ROW][C]`6`[/C][C]+232.4[/C][C] 32.89[/C][C]+7.0670e+00[/C][C] 3.088e-11[/C][C] 1.544e-11[/C][/ROW]
[ROW][C]`7`[/C][C]+352.3[/C][C] 37.91[/C][C]+9.2930e+00[/C][C] 4.055e-17[/C][C] 2.028e-17[/C][/ROW]
[ROW][C]`8`[/C][C]+288.7[/C][C] 33.2[/C][C]+8.6970e+00[/C][C] 1.774e-15[/C][C] 8.87e-16[/C][/ROW]
[ROW][C]`9`[/C][C]+133.2[/C][C] 36.86[/C][C]+3.6130e+00[/C][C] 0.0003898[/C][C] 0.0001949[/C][/ROW]
[ROW][C]`10`[/C][C]+223.8[/C][C] 37.47[/C][C]+5.9730e+00[/C][C] 1.161e-08[/C][C] 5.807e-09[/C][/ROW]
[ROW][C]`11`[/C][C]+61.79[/C][C] 32.26[/C][C]+1.9160e+00[/C][C] 0.05695[/C][C] 0.02848[/C][/ROW]
[ROW][C]`12`[/C][C]+69.3[/C][C] 37.5[/C][C]+1.8480e+00[/C][C] 0.06615[/C][C] 0.03308[/C][/ROW]
[ROW][C]rw[/C][C]+449.2[/C][C] 61.57[/C][C]+7.2960e+00[/C][C] 8.305e-12[/C][C] 4.153e-12[/C][/ROW]
[ROW][C]`AllSuicides(t-1)`[/C][C]+0.3365[/C][C] 0.06433[/C][C]+5.2300e+00[/C][C] 4.531e-07[/C][C] 2.265e-07[/C][/ROW]
[ROW][C]`AllSuicides(t-2)`[/C][C]+0.2357[/C][C] 0.0638[/C][C]+3.6950e+00[/C][C] 0.0002895[/C][C] 0.0001447[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	+745.9	136.1	+5.4810e+00	1.361e-07	6.804e-08
totalmo	+2.744	0.4143	+6.6230e+00	3.676e-10	1.838e-10
`1`	+367.4	34.11	+1.0770e+01	2.474e-21	1.237e-21
`3`	+419.1	41.54	+1.0090e+01	2.273e-19	1.137e-19
`4`	+348.2	29.46	+1.1820e+01	2.083e-24	1.042e-24
`5`	+387.3	34.07	+1.1370e+01	4.437e-23	2.219e-23
`6`	+232.4	32.89	+7.0670e+00	3.088e-11	1.544e-11
`7`	+352.3	37.91	+9.2930e+00	4.055e-17	2.028e-17
`8`	+288.7	33.2	+8.6970e+00	1.774e-15	8.87e-16
`9`	+133.2	36.86	+3.6130e+00	0.0003898	0.0001949
`10`	+223.8	37.47	+5.9730e+00	1.161e-08	5.807e-09
`11`	+61.79	32.26	+1.9160e+00	0.05695	0.02848
`12`	+69.3	37.5	+1.8480e+00	0.06615	0.03308
rw	+449.2	61.57	+7.2960e+00	8.305e-12	4.153e-12
`AllSuicides(t-1)`	+0.3365	0.06433	+5.2300e+00	4.531e-07	2.265e-07
`AllSuicides(t-2)`	+0.2357	0.0638	+3.6950e+00	0.0002895	0.0001447

Multiple Linear Regression - Regression Statistics
Multiple R	0.9825
R-squared	0.9653
Adjusted R-squared	0.9625
F-TEST (value)	344.5
F-TEST (DF numerator)	15
F-TEST (DF denominator)	186
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	82.03
Sum Squared Residuals	1.252e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9825 \tabularnewline
R-squared &  0.9653 \tabularnewline
Adjusted R-squared &  0.9625 \tabularnewline
F-TEST (value) &  344.5 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 186 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  82.03 \tabularnewline
Sum Squared Residuals &  1.252e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9825[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9653[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9625[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 344.5[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]186[/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] 82.03[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.252e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.9825
R-squared	0.9653
Adjusted R-squared	0.9625
F-TEST (value)	344.5
F-TEST (DF numerator)	15
F-TEST (DF denominator)	186
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	82.03
Sum Squared Residuals	1.252e+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=&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=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 = 17.446, df1 = 2, df2 = 184, p-value = 1.154e-07
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.63812, df1 = 30, df2 = 156, p-value = 0.9257
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 9.0826, df1 = 2, df2 = 184, p-value = 0.0001731

\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 = 17.446, df1 = 2, df2 = 184, p-value = 1.154e-07
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.63812, df1 = 30, df2 = 156, p-value = 0.9257
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 9.0826, df1 = 2, df2 = 184, p-value = 0.0001731
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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 = 17.446, df1 = 2, df2 = 184, p-value = 1.154e-07
[/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 = 0.63812, df1 = 30, df2 = 156, p-value = 0.9257
[/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 = 9.0826, df1 = 2, df2 = 184, p-value = 0.0001731
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 = 17.446, df1 = 2, df2 = 184, p-value = 1.154e-07
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.63812, df1 = 30, df2 = 156, p-value = 0.9257
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 9.0826, df1 = 2, df2 = 184, p-value = 0.0001731

Variance Inflation Factors (Multicollinearity)

> vif
           totalmo                `1`                `3`                `4` 
         17.523818           2.546944           3.992335           2.008438 
               `5`                `6`                `7`                `8` 
          2.684985           2.502452           3.325742           2.549725 
               `9`               `10`               `11`               `12` 
          3.142910           3.247754           2.407114           3.252767 
                rw `AllSuicides(t-1)` `AllSuicides(t-2)` 
          1.115366          22.281816          21.952108

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
           totalmo                `1`                `3`                `4` 
         17.523818           2.546944           3.992335           2.008438 
               `5`                `6`                `7`                `8` 
          2.684985           2.502452           3.325742           2.549725 
               `9`               `10`               `11`               `12` 
          3.142910           3.247754           2.407114           3.252767 
                rw `AllSuicides(t-1)` `AllSuicides(t-2)` 
          1.115366          22.281816          21.952108 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=6

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
           totalmo                `1`                `3`                `4` 
         17.523818           2.546944           3.992335           2.008438 
               `5`                `6`                `7`                `8` 
          2.684985           2.502452           3.325742           2.549725 
               `9`               `10`               `11`               `12` 
          3.142910           3.247754           2.407114           3.252767 
                rw `AllSuicides(t-1)` `AllSuicides(t-2)` 
          1.115366          22.281816          21.952108 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
           totalmo                `1`                `3`                `4` 
         17.523818           2.546944           3.992335           2.008438 
               `5`                `6`                `7`                `8` 
          2.684985           2.502452           3.325742           2.549725 
               `9`               `10`               `11`               `12` 
          3.142910           3.247754           2.407114           3.252767 
                rw `AllSuicides(t-1)` `AllSuicides(t-2)` 
          1.115366          22.281816          21.952108

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 2 ; par5 = 0 ; par6 = 12 ;

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

par6 <- '12'
par5 <- '1'
par4 <- '2'
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