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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 03 Dec 2010 16:58:31 +0000

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/2010/Dec/03/t129139549631ynoo86vyhsm0f.htm/, Retrieved Tue, 07 May 2024 06:03:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104923, Retrieved Tue, 07 May 2024 06:03:34 +0000

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Original text written by user:

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No (this computation is public)

User-defined keywords

Estimated Impact

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

-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [interactie effecten] [2010-12-03 16:58:31] [87bb5e10c18d96bd329dff2d857096c8] [Current]

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Dataseries X:

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1	350	1595	17	60720	319369	143	7022	75	0
0	61	5565	47	94355	493408	38	6243	53	39
0	287	601	6	60720	319210	144	19868	198	0
1	268	188	11	77655	381180	67	16471	964	0
1	25	7146	105	134028	297978	367	933	14	305
0	418	1135	17	62285	290476	384	5322	80	-9
1	392	450	8	59325	292136	380	11517	205	-1
1	131	34	8	60630	314353	309	14294	3340	0
1	316	133	14	65990	339445	109	9960	1052	0
1	347	119	6	59118	303677	354	17280	872	0
0	68	2053	53	100423	397144	60	3720	96	33
0	70	4036	63	100269	424898	53	3570	56	32
1	147	0	0	60720	315380	203	 NA	 NA	0
1	169	4655	393	60720	341570	105	360	30	0
1	248	131	11	61808	308989	338	9908	834	0
1	152	1766	73	60438	305959	349	1451	60	0
1	351	312	14	58598	318690	147	8478	381	0
1	372	448	40	59781	323361	132	3084	275	0
1	137	115	13	60945	318903	145	9146	1037	0
1	352	60	10	61124	314049	314	11405	1916	0
1	338	0	0	60720	315380	180	 NA	 NA	0
1	258	364	35	47705	298466	365	2813	271	0
1	343	0	0	60720	315380	183	 NA	 NA	0
0	398	1442	118	121173	438493	49	2021	165	-3
0	115	1389	9	57530	378049	71	19783	128	6
1	188	149	5	62041	313332	318	22666	761	0
1	379	2212	14	60720	319864	142	8562	54	0
0	48	7489	322	136996	430866	50	717	31	70
1	366	0	0	60720	315380	287	 NA	 NA	0
1	117	402	26	84990	332743	115	5106	330	5
0	412	7419	29	63255	286963	387	2999	12	-5
1	157	0	0	60720	315380	219	 NA	 NA	0
1	264	307	15	58856	331955	116	8797	430	0
0	54	1134	162	146216	527021	34	2019	288	58
0	64	7561	87	82425	364304	86	1889	22	37
1	113	5131	71	86111	292154	379	1298	18	9
1	148	9	6	60835	314987	300	19165	12458	0
1	425	2264	24	55174	272713	394	3030	32	-31
1	7	40949	481	129352	1398893	9	2493	29	2865
0	197	4980	91	113521	409642	55	2304	42	0
1	97	272	8	112995	429112	51	28639	844	14
1	168	757	19	45689	382712	66	9616	242	0
1	69	1424	94	131116	374943	77	1861	123	32
1	127	0	0	60720	315380	191	 NA	 NA	0
0	184	378	52	71873	297413	369	1873	258	0
1	401	1061	5	70415	304252	353	20850	98	-4
1	371	0	0	60720	315380	290	 NA	 NA	0
1	174	203	9	61938	309596	334	12177	541	0
0	46	3689	25	101792	377305	74	7092	48	74
1	90	567	11	103345	386212	65	16928	329	20
0	27	27664	282	74996	657954	24	1624	17	247
1	105	1686	40	68696	368186	81	4205	100	11
0	413	1164	17	57320	269753	395	4103	60	-6
1	26	15824	64	63838	585715	30	6027	24	284
1	16	6575	159	151352	1071292	11	5480	133	819
0	19	37597	248	37884	992426	12	3195	21	652
1	336	314	5	60720	315371	294	23074	367	0
0	110	1932	36	72954	249898	403	1386	26	10
0	86	5692	72	64239	357312	91	2185	28	22
1	294	0	0	60720	315380	257	 NA	 NA	0
1	322	0	0	60720	315380	177	 NA	 NA	0
0	94	6533	75	126910	364839	85	2198	25	16
0	400	2126	138	21001	230621	413	222	14	-3
1	150	14	1	61016	315877	161	115877	8041	0
0	107	3192	60	59831	125390	427	-1243	-23	10
1	166	47	3	60720	314882	305	38294	2432	0
0	47	4891	217	210568	370837	79	787	35	73
0	182	531	10	71641	330068	120	13007	245	0
1	245	2332	4	72680	324385	131	31096	53	0
1	330	0	0	60720	315380	277	 NA	 NA	0
0	10	16275	109	47818	1443586	7	11409	76	1974
1	429	9252	48	79510	253537	401	1115	6	-129
0	2	29790	309	81767	4321023	2	13337	138	28328
1	235	0	0	60720	315380	204	 NA	 NA	0
0	187	2300	29	64873	352108	97	5245	66	0
1	213	267	32	57640	330059	121	4064	487	0
0	296	382	21	59365	340968	107	6713	369	0
1	218	397	28	60646	317736	152	4205	297	0
1	28	11920	182	31080	209458	419	52	1	238
0	5	54660	302	126942	1491348	6	4276	24	3040
1	138	5	2	60720	314887	304	57443	24814	0
1	163	0	0	60720	315380	218	 NA	 NA	0
1	219	0	0	60720	315380	199	 NA	 NA	0
0	125	2647	37	62920	353058	95	4137	58	1
1	216	0	0	60720	315380	201	 NA	 NA	0
0	196	0	0	60720	315380	263	 NA	 NA	0
1	128	94	7	60793	314533	308	16362	1212	0
1	223	3	2	60698	315354	296	57677	36505	0
1	165	454	51	63261	302187	357	2004	225	0
1	373	227	9	76644	336639	110	15182	603	0
0	324	0	0	60720	315380	245	 NA	 NA	0
1	251	206	9	53110	296702	372	10745	469	0
1	11	25830	115	245546	1073089	10	7592	34	1910
1	62	1528	38	60326	146494	426	-1408	-35	38
1	149	271	2	60720	325249	129	62625	463	0
1	270	138	6	69817	331420	117	21903	954	0
1	171	0	0	60720	315380	211	 NA	 NA	0
1	226	278	26	61404	314922	302	4420	413	0
1	276	282	30	62452	320016	141	4001	425	0
1	383	571	80	41477	280398	390	1005	141	0
1	93	2253	99	63593	452469	43	2550	112	17
1	302	290	2	58790	301164	360	50582	348	0
1	385	78	16	62700	317330	154	7333	1511	0
1	225	20	2	60805	315576	169	57788	5924	0
1	275	0	0	60720	315380	284	 NA	 NA	0
1	431	18073	180	27284	-7170	429	-1151	-11	-366
1	220	1866	29	56225	322331	135	4218	66	0
0	6	42634	156	157214	1629616	5	9164	34	2964
1	290	249	62	54323	292754	378	1496	373	0
1	129	422	28	57935	318056	150	4216	280	0
1	65	2675	79	59017	355178	93	1964	58	36
1	416	965	3	73490	204325	421	1442	4	-8
1	299	0	0	60720	315380	224	 NA	 NA	0
1	407	621	16	68005	317046	156	7315	188	-5
0	252	0	0	60720	315380	240	 NA	 NA	0
1	257	365	8	54820	309560	335	13695	300	0
0	63	3122	34	94670	414462	54	6308	69	38
0	21	12988	119	82340	857217	16	5523	51	601
0	38	5336	81	112477	697458	22	6141	93	132
0	51	3160	108	108094	530670	33	3062	105	66
1	386	2489	40	67804	238125	408	953	15	0
1	325	0	0	60720	315380	273	 NA	 NA	0
0	33	6984	45	80570	741409	19	12031	78	195
1	91	2001	52	95551	393343	62	3718	97	19
1	430	15236	90	77440	372631	78	1918	11	-158
0	259	0	0	60720	315380	239	 NA	 NA	0
1	382	530	60	73433	317291	155	1955	221	0
1	369	0	0	60720	315380	292	 NA	 NA	0
1	428	35624	223	157278	306275	347	477	3	-70
0	100	1383	11	73221	317892	151	10717	85	13
1	122	875	14	67000	334280	112	9591	153	3
0	198	0	0	60720	315380	259	 NA	 NA	0
0	289	0	0	60720	315380	254	 NA	 NA	0
0	307	0	0	60720	315380	222	 NA	 NA	0
1	389	72	3	60398	314210	312	38070	1591	0
0	210	265	31	60720	306948	346	3450	404	
1	194	0	0	60720	315380	186	 NA	 NA	0
1	277	335	33	64175	320398	140	3648	359	0
0	298	0	0	60720	315380	230	 NA	 NA	0
1	356	0	0	60720	315380	269	 NA	 NA	0
1	43	4525	226	93811	501749	37	1335	67	101
0	71	3045	58	27330	202055	422	35	1	31
1	133	0	0	60720	315380	196	 NA	 NA	0
1	212	0	0	60720	315380	206	 NA	 NA	0
0	186	638	14	60370	333210	114	9515	209	0
0	262	0	0	60720	315380	283	 NA	 NA	0
1	378	607	11	60436	322340	134	11122	201	0
1	132	1558	63	55637	369448	80	2690	109	0
0	279	1324	117	67440	291841	381	785	69	0
1	376	0	0	60720	315380	280	 NA	 NA	0
0	267	0	0	60720	315380	282	 NA	 NA	0
1	403	611	8	59190	296919	371	12115	159	-4
1	303	0	0	60720	315380	225	 NA	 NA	0
1	388	923	8	58620	309038	337	13630	118	0
1	420	661	3	65920	246541	405	15514	70	-12
0	114	2397	14	74020	289513	385	6394	37	6
1	305	366	53	61808	344425	101	2725	394	0
1	346	0	0	60720	315380	181	 NA	 NA	0
1	354	135	2	62065	314210	311	57105	847	0
0	124	1659	29	107577	480382	40	9668	169	2
1	136	316	9	60505	315009	299	12779	364	0
1	130	0	0	60720	315380	197	 NA	 NA	0
1	146	309	11	56535	312878	322	10262	365	0
0	314	49	8	64107	322031	137	15254	2500	0
1	241	0	0	60720	315380	200	 	 	0
0	34	4519	49	102129	597793	29	8118	88	163
1	384	0	0	60720	315380	267	 	 	0
1	120	837	69	61262	315688	165	1677	138	4
1	72	5119	49	39039	378525	69	3643	35	31
1	79	1280	117	69465	312378	326	960	88	26
1	99	2564	22	130140	403560	59	9253	79	13
0	108	2045	20	97890	510834	36	15542	152	10
0	119	2234	16	77200	214215	418	888	6	5
1	102	975	23	90534	235133	410	1528	36	13
0	209	1136	32	48522	343613	103	4488	126	0
1	293	453	21	81125	365959	84	7903	366	0
1	211	0	0	60720	315380	210	 NA	 NA	0
1	227	61	14	60720	314551	307	8182	1874	0
1	234	368	17	61686	303230	355	6072	281	0
0	247	0	0	60720	315380	233	 NA	 NA	0
1	49	4901	84	121920	469107	41	3204	55	67
1	88	540	14	103960	354228	94	11016	286	22
1	329	0	0	60720	315380	253	 NA	 NA	0
1	160	0	0	60720	315380	213	 NA	 NA	0
0	271	0	0	60720	315380	286	 NA	 NA	0
1	306	2	9	60735	315394	174	12822	63057	0
1	172	36	4	61564	312412	325	28103	3146	0
1	402	776	9	64230	333505	113	14834	172	-4
1	4	84738	588	-26007	223193	415	39	0	4145
1	156	0	0	60720	315380	217	 NA	 NA	0
1	315	3	4	60761	315656	167	28914	37920	0
1	399	529	9	63870	296261	374	10696	182	-3
1	381	0	0	60720	315380	264	 NA	 NA	0
1	159	405	7	60845	336425	111	19489	337	0
1	181	972	34	71642	359335	87	4686	164	0
1	278	0	0	60720	315380	232	 NA	 NA	0
1	89	2099	64	106611	308636	339	1697	52	20
0	112	3437	47	48022	158492	425	-883	-12	9
0	201	0	0	60720	315380	243	 NA	 NA	0
1	344	0	0	60720	315380	182	 NA	 NA	0
0	22	22330	84	79801	711969	20	6095	23	446
0	291	0	0	60720	315380	258	 NA	 NA	0
0	269	0	0	60720	315380	221	 NA	 NA	0
1	164	483	27	60830	306268	348	3936	220	0
1	358	0	0	60720	315380	279	 NA	 NA	0
1	118	2239	21	88590	442882	47	11566	108	5
0	92	2949	41	82903	378509	70	4354	61	18
1	300	0	0	60720	315380	223	 NA	 NA	0
1	312	365	83	87192	346611	99	1766	402	0
1	183	2461	57	55792	314289	310	2005	46	0
0	18	21950	519	114337	856956	17	1266	30	745
0	126	3294	23	75832	217193	416	748	5	1
1	193	141	6	61630	315366	295	19228	819	0
1	390	572	16	58580	307930	342	6746	189	-1
1	23	13326	102	165548	702380	21	4925	38	436
1	50	2284	33	76403	194493	423	-167	-2	66
0	206	10	2	61656	316155	160	58077	11639	0
1	162	0	0	60720	315380	207	 NA	 NA	0
0	98	1414	198	70184	330546	119	659	92	13
1	75	1975	35	118881	394510	61	5557	98	30
1	134	43	3	60887	312846	323	37615	2613	0
1	180	0	0	60720	315380	190	 NA	 NA	0
1	337	844	78	60925	296139	375	1233	114	0
1	341	304	15	62969	295580	376	6372	314	0
1	154	458	11	58625	297765	368	8888	213	0
1	36	18562	155	102313	377934	72	1148	10	143
1	192	0	0	60720	315380	179	 NA	 NA	0
1	17	7123	109	288170	638830	28	4026	62	769
1	254	622	73	73007	304376	352	1430	168	0
1	374	174	1	64820	307424	344	107424	618	0
1	35	2220	22	301670	644190	26	20190	200	154
1	377	121	24	56178	295370	377	3974	788	0
0	32	11819	85	106113	574339	31	4404	32	199
1	260	0	0	60720	315380	235	 NA	 NA	0
1	274	125	12	60798	310201	332	9183	883	0
1	406	1182	13	70694	327007	124	9770	107	-5
1	116	1503	39	56364	343466	104	3679	95	6
1	320	0	0	60720	315380	274	 NA	 NA	0
1	367	0	0	60720	315380	248	 NA	 NA	0
1	288	30	4	62045	318098	149	29525	3912	0
0	414	3310	33	75230	448243	44	7523	75	-6
1	74	554	11	79285	325738	125	11431	227	30
0	250	0	0	60720	315380	234	 NA	 NA	0
1	224	468	21	60720	312161	328	5341	239	0
1	397	4917	42	52811	243650	406	1039	9	-3
1	417	3256	66	35250	407159	57	3139	64	-8
1	323	0	0	60720	315380	185	 NA	 NA	0
1	340	125	13	59734	317698	153	9054	944	0
1	239	0	0	60720	315380	195	 NA	 NA	0
1	326	22	2	60722	312502	324	56251	5154	0
0	261	0	0	60720	315380	262	 NA	 NA	0
1	357	514	52	78780	322378	133	2353	238	0
0	313	0	0	60720	315380	226	 NA	 NA	0
1	360	0	0	60720	315380	270	 NA	 NA	0
0	272	0	0	60720	315380	285	 NA	 NA	0
1	333	0	0	60720	315380	276	 NA	 NA	0
1	355	0	0	60720	315380	272	 NA	 NA	0
1	362	0	0	60720	315380	268	 NA	 NA	0
1	37	10327	91	88577	640273	27	4838	43	135
1	42	13718	61	96448	345783	100	2390	11	104
1	57	3748	9	50350	652925	25	50325	121	53
1	30	14416	361	49857	439798	48	664	17	220
0	404	1526	25	69351	278990	391	3160	52	-4
0	80	666	171	117869	339836	108	818	210	25
1	422	2844	67	72683	240897	407	610	14	-15
1	297	0	0	60720	315380	229	 NA	 NA	0
1	318	368	17	61167	297141	370	5714	264	0
1	175	0	0	60720	315380	205	 NA	 NA	0
1	285	333	13	70811	331323	118	10102	394	0
1	339	26	1	60896	313880	315	113880	4328	0
1	243	0	0	60720	315380	237	 NA	 NA	0
1	167	0	0	60720	315380	193	 NA	 NA	0
1	411	1303	61	69863	309422	336	1794	84	-5
1	155	20	3	60938	315245	297	38415	5674	0
1	56	2384	97	61348	405972	58	2123	86	54
0	204	203	27	50804	300962	361	3739	498	0
0	178	71	1	60745	316176	159	116176	1646	0
1	295	53	14	59506	302409	356	7315	1932	0
1	396	562	13	58456	283587	389	6430	149	-3
1	393	622	7	60950	263276	398	9039	102	-2
1	135	645	5	60720	312075	329	22415	174	0
0	364	1763	24	61600	308336	340	4514	61	0
1	179	0	0	60720	315380	209	 NA	 NA	0
1	231	317	9	63915	298700	363	10967	311	0
1	319	1	6	60719	315372	293	19229	84213	0
1	191	275	4	59500	318745	146	29686	431	0
1	321	0	0	60720	315380	246	 NA	 NA	0
1	111	936	30	67939	408881	56	6963	223	9
1	29	8568	37	32168	786690	18	15856	68	223
0	423	11528	70	-14545	-83265	431	-4047	-25	-25
0	202	0	0	60720	315380	250	 NA	 NA	0
1	232	738	10	60720	321376	139	12138	164	0
1	266	0	0	60720	315380	281	 NA	 NA	0
1	405	592	7	64270	276898	392	10985	130	-4
1	176	126	10	60951	315547	171	11555	921	0
1	236	2	1	60743	315487	172	115487	52257	0
1	142	0	0	60720	315380	220	 NA	 NA	0
0	13	18014	239	-1710	1405225	8	5043	67	943
0	281	0	0	60720	315380	256	 NA	 NA	0
0	328	0	0	60720	315380	249	 NA	 NA	0
0	9	37704	452	60448	983660	14	1734	21	2167
0	207	63	4	65688	318574	148	29643	1891	0
1	73	1431	34	106885	310768	330	3258	77	30
1	242	94	3	61360	312887	321	37629	1200	0
1	229	192	4	65276	312339	327	28085	586	0
1	317	32	1	59988	314964	301	114964	3603	0
1	44	6869	105	117520	379983	68	1714	26	90
1	349	0	0	60720	315380	176	 NA	 NA	0
1	140	0	0	60720	315380	188	 NA	 NA	0
1	361	1	4	60722	315398	173	28849	172235	0
0	203	0	0	60720	315380	247	 NA	 NA	0
1	255	2328	94	82732	253588	400	570	23	0
0	308	209	13	64016	316647	157	8973	558	0
1	195	28	2	60890	315688	166	57844	4148	0
1	280	176	28	68136	310670	331	3952	629	0
1	415	1920	15	79420	165404	424	-2306	-18	-6
0	3	87550	458	153198	4111912	3	8541	45	17936
1	151	520	10	58650	291650	383	9165	176	0
0	304	0	0	60720	315380	231	 NA	 NA	0
1	421	1013	2	69770	253468	402	26734	53	-14
1	217	15	8	60831	315688	164	14461	7687	0
1	353	587	12	59595	325699	126	10475	214	0
1	58	5371	66	87720	446211	45	3730	46	49
1	359	0	0	60720	315380	251	 NA	 NA	0
1	77	1012	47	114768	368078	82	3576	166	26
1	228	0	0	60720	315380	215	 NA	 NA	0
1	83	876	59	138971	352850	96	2591	174	24
0	1	162556	1081	213118	6282154	1	5626	37	56289
1	14	43556	247	32648	227132	414	110	1	929
0	395	3425	12	83620	283910	388	6993	25	-3
1	78	810	43	74015	236761	409	855	45	26
1	190	0	0	60720	315380	184	 NA	 NA	0
0	311	0	0	60720	315380	244	 NA	 NA	0
0	39	2365	79	191778	550608	32	4438	148	120
0	101	1261	33	76114	307528	343	3258	85	13
1	256	0	0	60720	315380	238	 NA	 NA	0
1	123	1585	107	93099	355864	92	1457	98	2
1	24	16189	295	116384	358589	89	538	10	379
1	284	0	0	60720	315380	242	 NA	 NA	0
0	273	0	0	60720	315380	265	 NA	 NA	0
1	121	10579	92	110309	375195	76	1904	17	4
1	214	474	20	61977	288985	386	4449	188	0
1	348	0	0	60720	315380	178	 NA	 NA	0
1	87	3642	86	90262	458343	42	3004	71	22
1	170	0	0	60720	315380	192	 NA	 NA	0
0	200	472	20	80045	269587	397	3479	148	0
1	158	98	2	61490	315236	298	57618	1179	0
1	427	3999	30	51252	42754	428	-5242	-39	-58
1	233	0	0	60720	315380	216	 NA	 NA	0
1	345	621	48	60720	308256	341	2255	174	0
1	375	0	0	60720	315380	266	 NA	 NA	0
1	173	0	0	60720	315380	189	 NA	 NA	0
0	301	30	4	60798	313164	320	28291	3808	0
1	368	0	0	60720	315380	291	 NA	 NA	0
1	106	746	23	71561	269661	396	3029	93	10
0	208	0	0	60720	315380	228	 NA	 NA	0
1	282	0	0	60720	315380	255	 NA	 NA	0
0	249	0	0	60720	315380	260	 NA	 NA	0
1	240	0	0	60720	315380	194	 NA	 NA	0
0	52	4150	150	134759	518365	35	2122	77	66
0	55	4658	158	156608	233773	411	214	7	58
1	84	814	18	39625	301881	358	5660	125	23
1	143	1002	15	56750	298568	364	6571	98	0
0	205	496	17	87390	325479	128	7381	253	0
1	141	389	14	58990	325506	127	8965	322	0
1	12	12679	110	48020	984885	13	7135	62	1871
1	177	400	152	60720	313267	319	745	283	0
1	238	53	6	60349	315793	163	19299	2197	0
1	331	0	0	60720	315380	278	 NA	 NA	0
0	426	5109	97	67038	215362	417	158	3	-31
1	104	576	18	113761	314073	313	6337	198	12
1	409	438	11	58320	298096	366	8918	224	-5
1	292	165	24	62841	325176	130	5216	759	0
1	40	4069	145	79804	207393	420	51	2	119
0	185	23	5	62555	314806	306	22961	4974	0
1	59	1285	107	99489	341340	106	1321	110	49
0	82	4677	75	90131	426280	52	3017	48	24
0	15	24811	505	95350	929118	15	1444	29	822
1	253	167	2	64245	307322	345	53661	642	0
1	342	0	0	60720	315380	187	 NA	 NA	0
1	45	4628	62	69159	387475	64	3024	41	85
1	189	226	5	65745	291787	382	18357	406	0
0	76	1765	20	77623	247060	404	2353	27	28
1	145	460	15	63346	329784	122	8652	282	0
1	144	36	3	60894	315834	162	38611	3215	0
1	408	989	11	58930	304555	350	9505	106	-5
0	96	3055	43	60247	376641	75	4108	58	14
0	265	0	0	60720	315380	288	 NA	 NA	0
1	161	0	0	60720	315380	212	 NA	 NA	0
0	246	0	0	60720	315380	236	 NA	 NA	0
1	237	0	0	60720	315380	202	 NA	 NA	0
1	230	0	0	60720	315380	214	 NA	 NA	0
1	66	13253	947	90829	357760	90	167	12	35
1	244	24	8	59818	315637	168	14455	4772	0
1	153	0	0	60720	315380	208	 NA	 NA	0
0	199	0	0	60720	315380	261	 NA	 NA	0
1	222	0	0	60720	315380	198	 NA	 NA	0
1	363	0	0	60720	315380	289	 NA	 NA	0
1	387	131	6	59661	327071	123	21179	969	0
1	85	514	15	102725	377516	73	11834	346	23
1	103	3366	80	108479	299243	362	1241	29	12
0	60	9327	101	87419	387699	63	1858	20	44
1	309	384	22	54683	309836	333	4993	286	0
1	20	17821	373	122844	444477	46	655	14	643
1	391	397	10	62710	322327	136	12233	308	-1
1	139	897	5	60720	314913	303	22983	128	0
1	370	218	21	60720	315553	170	5503	531	0
0	31	3369	28	87161	688779	23	17456	145	217
1	109	5702	61	101481	321896	138	1998	21	10
1	41	8636	855	128294	301607	359	119	12	108
1	215	534	7	62620	304485	351	14926	196	0
1	332	0	0	60720	315380	275	 NA	 NA	0
0	310	0	0	60720	315380	227	 NA	 NA	0
1	410	726	3	69980	231861	412	10620	44	-5
1	81	1380	26	60982	347385	98	5669	107	25
1	365	180	11	59635	316386	158	10581	647	0
0	53	7285	115	188873	491303	39	2533	40	59
1	67	880	74	80791	261216	399	827	70	34
1	335	1	2	60727	315388	175	57694	117743	0
1	380	0	0	60720	315380	271	 NA	 NA	0
1	334	96	5	60379	313729	316	22746	1188	0
1	95	1889	45	37527	358649	88	3526	84	14
0	8	45187	353	234817	1926517	4	4891	38	2854
1	221	288	4	60510	296656	373	24164	335	0
1	419	1270	26	69206	275311	393	2897	59	-10
1	424	6526	26	55830	-42143	430	-9313	-37	-28
0	327	0	0	60720	315380	252	 NA	 NA	0
1	263	226	12	72835	343929	102	11994	636	0
1	394	694	8	68060	367655	83	20957	241	-2
1	286	0	0	60720	315380	241	 NA	 NA	0
1	283	249	72	56726	313491	317	1576	455	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.
R Engine error message	Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 + : missing value where TRUE/FALSE needed Execution halted

\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 & 13 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
R Engine error message & Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 +  : 
  missing value where TRUE/FALSE needed
Execution halted
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104923&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[ROW][C]R Engine error message[/C][C]Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 +  : 
  missing value where TRUE/FALSE needed
Execution halted
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104923&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104923&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	13 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.
R Engine error message	Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 + : missing value where TRUE/FALSE needed Execution halted

Parameters (Session):

par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 10 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code