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Author

*The author of this computation has been verified*

R Software Module

rwasp_Simple Regression Y ~ X.wasp

Title produced by software

Simple Linear Regression

Date of computation

Fri, 21 Dec 2012 12:27:36 -0500

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/2012/Dec/21/t1356110867zq797o6ac83qtb0.htm/, Retrieved Thu, 25 Apr 2024 23:12:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204003, Retrieved Thu, 25 Apr 2024 23:12:18 +0000

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Estimated Impact

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

-     [Two-Way ANOVA] [] [2010-11-02 14:42:14] [b98453cac15ba1066b407e146608df68]
- RMPD  [Multiple Regression] [] [2012-12-17 15:24:19] [887fa8b28255337447ca2249cc73e1d0]
- RMP       [Simple Linear Regression] [] [2012-12-21 17:27:36] [84239eaa0322a9ca7457d355f1a51cc2] [Current]

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Dataseries X:

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Histogram

Boxplots

1418	210907	56	396	81	3	79	30	115
869	120982	56	297	55	4	58	28	109
1530	176508	54	559	50	12	60	38	146
2172	179321	89	967	125	2	108	30	116
901	123185	40	270	40	1	49	22	68
463	52746	25	143	37	3	0	26	101
3201	385534	92	1562	63	0	121	25	96
371	33170	18	109	44	0	1	18	67
1192	101645	63	371	88	0	20	11	44
1583	149061	44	656	66	5	43	26	100
1439	165446	33	511	57	0	69	25	93
1764	237213	84	655	74	0	78	38	140
1495	173326	88	465	49	7	86	44	166
1373	133131	55	525	52	7	44	30	99
2187	258873	60	885	88	3	104	40	139
1491	180083	66	497	36	9	63	34	130
4041	324799	154	1436	108	0	158	47	181
1706	230964	53	612	43	4	102	30	116
2152	236785	119	865	75	3	77	31	116
1036	135473	41	385	32	0	82	23	88
1882	202925	61	567	44	7	115	36	139
1929	215147	58	639	85	0	101	36	135
2242	344297	75	963	86	1	80	30	108
1220	153935	33	398	56	5	50	25	89
1289	132943	40	410	50	7	83	39	156
2515	174724	92	966	135	0	123	34	129
2147	174415	100	801	63	0	73	31	118
2352	225548	112	892	81	5	81	31	118
1638	223632	73	513	52	0	105	33	125
1222	124817	40	469	44	0	47	25	95
1812	221698	45	683	113	0	105	33	126
1677	210767	60	643	39	3	94	35	135
1579	170266	62	535	73	4	44	42	154
1731	260561	75	625	48	1	114	43	165
807	84853	31	264	33	4	38	30	113
2452	294424	77	992	59	2	107	33	127
829	101011	34	238	41	0	30	13	52
1940	215641	46	818	69	0	71	32	121
2662	325107	99	937	64	0	84	36	136
186	7176	17	70	1	0	0	0	0
1499	167542	66	507	59	2	59	28	108
865	106408	30	260	32	1	33	14	46
1793	96560	76	503	129	0	42	17	54
2527	265769	146	927	37	2	96	32	124
2747	269651	67	1269	31	10	106	30	115
1324	149112	56	537	65	6	56	35	128
2702	175824	107	910	107	0	57	20	80
1383	152871	58	532	74	5	59	28	97
1179	111665	34	345	54	4	39	28	104
2099	116408	61	918	76	1	34	39	59
4308	362301	119	1635	715	2	76	34	125
918	78800	42	330	57	2	20	26	82
1831	183167	66	557	66	0	91	39	149
3373	277965	89	1178	106	8	115	39	149
1713	150629	44	740	54	3	85	33	122
1438	168809	66	452	32	0	76	28	118
496	24188	24	218	20	0	8	4	12
2253	329267	259	764	71	8	79	39	144
744	65029	17	255	21	5	21	18	67
1161	101097	64	454	70	3	30	14	52
2352	218946	41	866	112	1	76	29	108
2144	244052	68	574	66	5	101	44	166
4691	341570	168	1276	190	1	94	21	80
1112	103597	43	379	66	1	27	16	60
2694	233328	132	825	165	5	92	28	107
1973	256462	105	798	56	0	123	35	127
1769	206161	71	663	61	12	75	28	107
3148	311473	112	1069	53	8	128	38	146
2474	235800	94	921	127	8	105	23	84
2084	177939	82	858	63	8	55	36	141
1954	207176	70	711	38	8	56	32	123
1226	196553	57	503	50	2	41	29	111
1389	174184	53	382	52	0	72	25	98
1496	143246	103	464	42	5	67	27	105
2269	187559	121	717	76	8	75	36	135
1833	187681	62	690	67	2	114	28	107
1268	119016	52	462	50	5	118	23	85
1943	182192	52	657	53	12	77	40	155
893	73566	32	385	39	6	22	23	88
1762	194979	62	577	50	7	66	40	155
1403	167488	45	619	77	2	69	28	104
1425	143756	46	479	57	0	105	34	132
1857	275541	63	817	73	4	116	33	127
1840	243199	75	752	34	3	88	28	108
1502	182999	88	430	39	6	73	34	129
1441	135649	46	451	46	2	99	30	116
1420	152299	53	537	63	0	62	33	122
1416	120221	37	519	35	1	53	22	85
2970	346485	90	1000	106	0	118	38	147
1317	145790	63	637	43	5	30	26	99
1644	193339	78	465	47	2	100	35	87
870	80953	25	437	31	0	49	8	28
1654	122774	45	711	162	0	24	24	90
1054	130585	46	299	57	5	67	29	109
937	112611	41	248	36	0	46	20	78
3004	286468	144	1162	263	1	57	29	111
2008	241066	82	714	78	0	75	45	158
2547	148446	91	905	63	1	135	37	141
1885	204713	71	649	54	1	68	33	122
1626	182079	63	512	63	2	124	33	124
1468	140344	53	472	77	6	33	25	93
2445	220516	62	905	79	1	98	32	124
1964	243060	63	786	110	4	58	29	112
1381	162765	32	489	56	2	68	28	108
1369	182613	39	479	56	3	81	28	99
1659	232138	62	617	43	0	131	31	117
2888	265318	117	925	111	10	110	52	199
1290	85574	34	351	71	0	37	21	78
2845	310839	92	1144	62	9	130	24	91
1982	225060	93	669	56	7	93	41	158
1904	232317	54	707	74	0	118	33	126
1391	144966	144	458	60	0	39	32	122
602	43287	14	214	43	4	13	19	71
1743	155754	61	599	68	4	74	20	75
1559	164709	109	572	53	0	81	31	115
2014	201940	38	897	87	0	109	31	119
2143	235454	73	819	46	0	151	32	124
2146	220801	75	720	105	1	51	18	72
874	99466	50	273	32	0	28	23	91
1590	92661	61	508	133	1	40	17	45
1590	133328	55	506	79	0	56	20	78
1210	61361	77	451	51	0	27	12	39
2072	125930	75	699	207	4	37	17	68
1281	100750	72	407	67	0	83	30	119
1401	224549	50	465	47	4	54	31	117
834	82316	32	245	34	4	27	10	39
1105	102010	53	370	66	3	28	13	50
1272	101523	42	316	76	0	59	22	88
1944	243511	71	603	65	0	133	42	155
391	22938	10	154	9	0	12	1	0
761	41566	35	229	42	5	0	9	36
1605	152474	65	577	45	0	106	32	123
530	61857	25	192	25	4	23	11	32
1988	99923	66	617	115	0	44	25	99
1386	132487	41	411	97	0	71	36	136
2395	317394	86	975	53	1	116	31	117
387	21054	16	146	2	0	4	0	0
1742	209641	42	705	52	5	62	24	88
620	22648	19	184	44	0	12	13	39
449	31414	19	200	22	0	18	8	25
800	46698	45	274	35	0	14	13	52
1684	131698	65	502	74	0	60	19	75
1050	91735	35	382	103	0	7	18	71
2699	244749	95	964	144	2	98	33	124
1606	184510	49	537	60	7	64	40	151
1502	79863	37	438	134	1	29	22	71
1204	128423	64	369	89	8	32	38	145
1138	97839	38	417	42	2	25	24	87
568	38214	34	276	52	0	16	8	27
1459	151101	32	514	98	2	48	35	131
2158	272458	65	822	99	0	100	43	162
1111	172494	52	389	52	0	46	43	165
1421	108043	62	466	29	1	45	14	54
2833	328107	65	1255	125	3	129	41	159
1955	250579	83	694	106	0	130	38	147
2922	351067	95	1024	95	3	136	45	170
1002	158015	29	400	40	0	59	31	119
1060	98866	18	397	140	0	25	13	49
956	85439	33	350	43	0	32	28	104
2186	229242	247	719	128	4	63	31	120
3604	351619	139	1277	142	4	95	40	150
1035	84207	29	356	73	11	14	30	112
1417	120445	118	457	72	0	36	16	59
3261	324598	110	1402	128	0	113	37	136
1587	131069	67	600	61	4	47	30	107
1424	204271	42	480	73	0	92	35	130
1701	165543	65	595	148	1	70	32	115
1249	141722	94	436	64	0	19	27	107
946	116048	64	230	45	0	50	20	75
1926	250047	81	651	58	0	41	18	71
3352	299775	95	1367	97	9	91	31	120
1641	195838	67	564	50	1	111	31	116
2035	173260	63	716	37	3	41	21	79
2312	254488	83	747	50	10	120	39	150
1369	104389	45	467	105	5	135	41	156
1577	136084	30	671	69	0	27	13	51
2201	199476	70	861	46	2	87	32	118
961	92499	32	319	57	0	25	18	71
1900	224330	83	612	52	1	131	39	144
1254	135781	31	433	98	2	45	14	47
1335	74408	67	434	61	4	29	7	28
1597	81240	66	503	89	0	58	17	68
207	14688	10	85	0	0	4	0	0
1645	181633	70	564	48	2	47	30	110
2429	271856	103	824	91	1	109	37	147
151	7199	5	74	0	0	7	0	0
474	46660	20	259	7	0	12	5	15
141	17547	5	69	3	0	0	1	4
1639	133368	36	535	54	1	37	16	64
872	95227	34	239	70	0	37	32	111
1318	152601	48	438	36	2	46	24	85
1018	98146	40	459	37	0	15	17	68
1383	79619	43	426	123	3	42	11	40
1314	59194	31	288	247	6	7	24	80
1335	139942	42	498	46	0	54	22	88
1403	118612	46	454	72	2	54	12	48
910	72880	33	376	41	0	14	19	76
616	65475	18	225	24	2	16	13	51
1407	99643	55	555	45	1	33	17	67
771	71965	35	252	33	1	32	15	59
766	77272	59	208	27	2	21	16	61
473	49289	19	130	36	1	15	24	76
1376	135131	66	481	87	0	38	15	60
1232	108446	60	389	90	1	22	17	68
1521	89746	36	565	114	3	28	18	71
572	44296	25	173	31	0	10	20	76
1059	77648	47	278	45	0	31	16	62
1544	181528	54	609	69	0	32	16	61
1230	134019	53	422	51	0	32	18	67
1206	124064	40	445	34	1	43	22	88
1205	92630	40	387	60	4	27	8	30
1255	121848	39	339	45	0	37	17	64
613	52915	14	181	54	0	20	18	68
721	81872	45	245	25	0	32	16	64
1109	58981	36	384	38	7	0	23	91
740	53515	28	212	52	2	5	22	88
1126	60812	44	399	67	0	26	13	52
728	56375	30	229	74	7	10	13	49
689	65490	22	224	38	3	27	16	62
592	80949	17	203	30	0	11	16	61
995	76302	31	333	26	0	29	20	76
1613	104011	55	384	67	6	25	22	88
2048	98104	54	636	132	2	55	17	66
705	67989	21	185	42	0	23	18	71
301	30989	14	93	35	0	5	17	68
1803	135458	81	581	118	3	43	12	48
799	73504	35	248	68	0	23	7	25
861	63123	43	304	43	1	34	17	68
1186	61254	46	344	76	1	36	14	41
1451	74914	30	407	64	0	35	23	90
628	31774	23	170	48	1	0	17	66
1161	81437	38	312	64	0	37	14	54
1463	87186	54	507	56	0	28	15	59
742	50090	20	224	71	0	16	17	60
979	65745	53	340	75	0	26	21	77
675	56653	45	168	39	0	38	18	68
1241	158399	39	443	42	0	23	18	72
676	46455	20	204	39	0	22	17	67
1049	73624	24	367	93	0	30	17	64
620	38395	31	210	38	0	16	16	63
1081	91899	35	335	60	0	18	15	59
1688	139526	151	364	71	0	28	21	84
736	52164	52	178	52	0	32	16	64
617	51567	30	206	27	2	21	14	56
812	70551	31	279	59	0	23	15	54
1051	84856	29	387	40	1	29	17	67
1656	102538	57	490	79	1	50	15	58
705	86678	40	238	44	0	12	15	59
945	85709	44	343	65	0	21	10	40
554	34662	25	232	10	0	18	6	22
1597	150580	77	530	124	0	27	22	83
982	99611	35	291	81	0	41	21	81
222	19349	11	67	15	0	13	1	2
1212	99373	63	397	92	1	12	18	72
1143	86230	44	467	42	0	21	17	61
435	30837	19	178	10	0	8	4	15
532	31706	13	175	24	0	26	10	32
882	89806	42	299	64	0	27	16	62
608	62088	38	154	45	1	13	16	58
459	40151	29	106	22	0	16	9	36
578	27634	20	189	56	0	2	16	59
826	76990	27	194	94	0	42	17	68
509	37460	20	135	19	0	5	7	21
717	54157	19	201	35	0	37	15	55
637	49862	37	207	32	0	17	14	54
857	84337	26	280	35	0	38	14	55
830	64175	42	260	48	0	37	18	72
652	59382	49	227	49	0	29	12	41
707	119308	30	239	48	0	32	16	61
954	76702	49	333	62	0	35	21	67
1461	103425	67	428	96	1	17	19	76
672	70344	28	230	45	0	20	16	64
778	43410	19	292	63	0	7	1	3
1141	104838	49	350	71	1	46	16	63
680	62215	27	186	26	0	24	10	40
1090	69304	30	326	48	6	40	19	69
616	53117	22	155	29	3	3	12	48
285	19764	12	75	19	1	10	2	8
1145	86680	31	361	45	2	37	14	52
733	84105	20	261	45	0	17	17	66
888	77945	20	299	67	0	28	19	76
849	89113	39	300	30	0	19	14	43
1182	91005	29	450	36	3	29	11	39
528	40248	16	183	34	1	8	4	14
642	64187	27	238	36	0	10	16	61
947	50857	21	165	34	0	15	20	71
819	56613	19	234	37	1	15	12	44
757	62792	35	176	46	0	28	15	60
894	72535	14	329	44	0	17	16	64

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204003&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204003&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204003&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Linear Regression Model
Y ~ X
coefficients:
	Estimate	Std. Error	t value	Pr(>\|t\|)
(Intercept)	194.44	23.498	8.275	0
X	2.47	0.041	60.098	0
- - -
Residual Std. Err.	200.089 on 287 df
Multiple R-sq.	0.926
Adjusted R-sq.	0.926

\begin{tabular}{lllllllll}
\hline
Linear Regression Model \tabularnewline
Y ~ X \tabularnewline
coefficients: &   \tabularnewline
  & Estimate & Std. Error & t value & Pr(>|t|) \tabularnewline
(Intercept) & 194.44 & 23.498 & 8.275 & 0 \tabularnewline
X & 2.47 & 0.041 & 60.098 & 0 \tabularnewline
- - -  &   \tabularnewline
Residual Std. Err.  & 200.089  on  287 df \tabularnewline
Multiple R-sq.  & 0.926 \tabularnewline
Adjusted R-sq.  & 0.926 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204003&T=1

[TABLE]
[ROW][C]Linear Regression Model[/C][/ROW]
[ROW][C]Y ~ X[/C][/ROW]
[ROW][C]coefficients:[/C][C] [/C][/ROW]
[ROW][C] [/C][C]Estimate[/C][C]Std. Error[/C][C]t value[/C][C]Pr(>|t|)[/C][/ROW]
[C](Intercept)[/C][C]194.44[/C][C]23.498[/C][C]8.275[/C][C]0[/C][/ROW]
[C]X[/C][C]2.47[/C][C]0.041[/C][C]60.098[/C][C]0[/C][/ROW]
[ROW][C]- - - [/C][C] [/C][/ROW]
[ROW][C]Residual Std. Err. [/C][C]200.089  on  287 df[/C][/ROW]
[ROW][C]Multiple R-sq. [/C][C]0.926[/C][/ROW]
[ROW][C]Adjusted R-sq. [/C][C]0.926[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204003&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204003&T=1

As an alternative you can also use a QR Code:

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

Linear Regression Model
Y ~ X
coefficients:
	Estimate	Std. Error	t value	Pr(>\|t\|)
(Intercept)	194.44	23.498	8.275	0
X	2.47	0.041	60.098	0
- - -
Residual Std. Err.	200.089 on 287 df
Multiple R-sq.	0.926
Adjusted R-sq.	0.926

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
compendium_views_info	1	144601420.19	144601420.19	3611.823	0
Residuals	287	11490211.824	40035.581

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
compendium_views_info & 1 & 144601420.19 & 144601420.19 & 3611.823 & 0 \tabularnewline
Residuals & 287 & 11490211.824 & 40035.581 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204003&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]compendium_views_info[/C][C]1[/C][C]144601420.19[/C][C]144601420.19[/C][C]3611.823[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]287[/C][C]11490211.824[/C][C]40035.581[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204003&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204003&T=2

As an alternative you can also use a QR Code:

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

ANOVA Statistics
	Df	Sum Sq	Mean Sq	F value	Pr(>F)
compendium_views_info	1	144601420.19	144601420.19	3611.823	0
Residuals	287	11490211.824	40035.581

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

Parameters (Session):

par1 = 1 ; par2 = 4 ; par3 = TRUE ;

Parameters (R input):

par1 = 1 ; par2 = 4 ; par3 = TRUE ;

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

par3 <- 'TRUE'
par2 <- '8'
par1 <- '1'
cat1 <- as.numeric(par1)
cat2<- as.numeric(par2)
intercept<-as.logical(par3)
x <- t(x)
xdf<-data.frame(t(y))
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
xdf <- data.frame(xdf[[cat1]], xdf[[cat2]])
names(xdf)<-c('Y', 'X')
if(intercept == FALSE) (lmxdf<-lm(Y~ X - 1, data = xdf) ) else (lmxdf<-lm(Y~ X, data = xdf) )
sumlmxdf<-summary(lmxdf)
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
nc <- ncol(sumlmxdf$'coefficients')
nr <- nrow(sumlmxdf$'coefficients')
a<-table.row.start(a)
a<-table.element(a,'Linear Regression Model', nc+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],nc+1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'coefficients:',1,TRUE)
a<-table.element(a, ' ',nc,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
for(i in 1 : nc){
a<-table.element(a, dimnames(sumlmxdf$'coefficients')[[2]][i],1,TRUE)
}#end header
a<-table.row.end(a)
for(i in 1: nr){
a<-table.element(a,dimnames(sumlmxdf$'coefficients')[[1]][i] ,1,TRUE)
for(j in 1 : nc){
a<-table.element(a, round(sumlmxdf$coefficients[i, j], digits=3), 1 ,FALSE)
}
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, '- - - ',1,TRUE)
a<-table.element(a, ' ',nc,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Std. Err. ',1,TRUE)
a<-table.element(a, paste(round(sumlmxdf$'sigma', digits=3), ' on ', sumlmxdf$'df'[2], 'df') ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-sq. ',1,TRUE)
a<-table.element(a, round(sumlmxdf$'adj.r.squared', digits=3) ,nc, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',1,TRUE)
a<-table.element(a, 'Df',1,TRUE)
a<-table.element(a, 'Sum Sq',1,TRUE)
a<-table.element(a, 'Mean Sq',1,TRUE)
a<-table.element(a, 'F value',1,TRUE)
a<-table.element(a, 'Pr(>F)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, V2,1,TRUE)
a<-table.element(a, anova.xdf$Df[1])
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3))
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3))
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',1,TRUE)
a<-table.element(a, anova.xdf$Df[2])
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3))
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], digits=3))
a<-table.element(a, ' ')
a<-table.element(a, ' ')
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='regressionplot.png')
plot(Y~ X, data=xdf, xlab=V2, ylab=V1, main='Regression Solution')
if(intercept == TRUE) abline(coef(lmxdf), col='red')
if(intercept == FALSE) abline(0.0, coef(lmxdf), col='red')
dev.off()
library(car)
bitmap(file='residualsQQplot.png')
qq.plot(resid(lmxdf), main='QQplot of Residuals of Fit')
dev.off()
bitmap(file='residualsplot.png')
plot(xdf$X, resid(lmxdf), main='Scatterplot of Residuals of Model Fit')
dev.off()
bitmap(file='cooksDistanceLmplot.png')
plot.lm(lmxdf, which=4)
dev.off()

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code