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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 11:35:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352133336hob6uvofgh56lkz.htm/, Retrieved Wed, 01 Feb 2023 16:59:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186153, Retrieved Wed, 01 Feb 2023 16:59:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 16:35:28] [1243027a8c443f5e4dc521447a752890] [Current]
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Dataseries X:
1418	210907	56	396	81	3	79	30
869	120982	56	297	55	4	58	28
1530	176508	54	559	50	12	60	38
2172	179321	89	967	125	2	108	30
901	123185	40	270	40	1	49	22
463	52746	25	143	37	3	0	26
3201	385534	92	1562	63	0	121	25
371	33170	18	109	44	0	1	18
1192	101645	63	371	88	0	20	11
1583	149061	44	656	66	5	43	26
1439	165446	33	511	57	0	69	25
1764	237213	84	655	74	0	78	38
1495	173326	88	465	49	7	86	44
1373	133131	55	525	52	7	44	30
2187	258873	60	885	88	3	104	40
1491	180083	66	497	36	9	63	34
4041	324799	154	1436	108	0	158	47
1706	230964	53	612	43	4	102	30
2152	236785	119	865	75	3	77	31
1036	135473	41	385	32	0	82	23
1882	202925	61	567	44	7	115	36
1929	215147	58	639	85	0	101	36
2242	344297	75	963	86	1	80	30
1220	153935	33	398	56	5	50	25
1289	132943	40	410	50	7	83	39
2515	174724	92	966	135	0	123	34
2147	174415	100	801	63	0	73	31
2352	225548	112	892	81	5	81	31
1638	223632	73	513	52	0	105	33
1222	124817	40	469	44	0	47	25
1812	221698	45	683	113	0	105	33
1677	210767	60	643	39	3	94	35
1579	170266	62	535	73	4	44	42
1731	260561	75	625	48	1	114	43
807	84853	31	264	33	4	38	30
2452	294424	77	992	59	2	107	33
829	101011	34	238	41	0	30	13
1940	215641	46	818	69	0	71	32
2662	325107	99	937	64	0	84	36
186	7176	17	70	1	0	0	0
1499	167542	66	507	59	2	59	28
865	106408	30	260	32	1	33	14
1793	96560	76	503	129	0	42	17
2527	265769	146	927	37	2	96	32
2747	269651	67	1269	31	10	106	30
1324	149112	56	537	65	6	56	35
2702	175824	107	910	107	0	57	20
1383	152871	58	532	74	5	59	28
1179	111665	34	345	54	4	39	28
2099	116408	61	918	76	1	34	39
4308	362301	119	1635	715	2	76	34
918	78800	42	330	57	2	20	26
1831	183167	66	557	66	0	91	39
3373	277965	89	1178	106	8	115	39
1713	150629	44	740	54	3	85	33
1438	168809	66	452	32	0	76	28
496	24188	24	218	20	0	8	4
2253	329267	259	764	71	8	79	39
744	65029	17	255	21	5	21	18
1161	101097	64	454	70	3	30	14
2352	218946	41	866	112	1	76	29
2144	244052	68	574	66	5	101	44
4691	341570	168	1276	190	1	94	21
1112	103597	43	379	66	1	27	16
2694	233328	132	825	165	5	92	28
1973	256462	105	798	56	0	123	35
1769	206161	71	663	61	12	75	28
3148	311473	112	1069	53	8	128	38
2474	235800	94	921	127	8	105	23
2084	177939	82	858	63	8	55	36
1954	207176	70	711	38	8	56	32
1226	196553	57	503	50	2	41	29
1389	174184	53	382	52	0	72	25
1496	143246	103	464	42	5	67	27
2269	187559	121	717	76	8	75	36
1833	187681	62	690	67	2	114	28
1268	119016	52	462	50	5	118	23
1943	182192	52	657	53	12	77	40
893	73566	32	385	39	6	22	23
1762	194979	62	577	50	7	66	40
1403	167488	45	619	77	2	69	28
1425	143756	46	479	57	0	105	34
1857	275541	63	817	73	4	116	33
1840	243199	75	752	34	3	88	28
1502	182999	88	430	39	6	73	34
1441	135649	46	451	46	2	99	30
1420	152299	53	537	63	0	62	33
1416	120221	37	519	35	1	53	22
2970	346485	90	1000	106	0	118	38
1317	145790	63	637	43	5	30	26
1644	193339	78	465	47	2	100	35
870	80953	25	437	31	0	49	8
1654	122774	45	711	162	0	24	24
1054	130585	46	299	57	5	67	29
937	112611	41	248	36	0	46	20
3004	286468	144	1162	263	1	57	29
2008	241066	82	714	78	0	75	45
2547	148446	91	905	63	1	135	37
1885	204713	71	649	54	1	68	33
1626	182079	63	512	63	2	124	33
1468	140344	53	472	77	6	33	25
2445	220516	62	905	79	1	98	32
1964	243060	63	786	110	4	58	29
1381	162765	32	489	56	2	68	28
1369	182613	39	479	56	3	81	28
1659	232138	62	617	43	0	131	31
2888	265318	117	925	111	10	110	52
1290	85574	34	351	71	0	37	21
2845	310839	92	1144	62	9	130	24
1982	225060	93	669	56	7	93	41
1904	232317	54	707	74	0	118	33
1391	144966	144	458	60	0	39	32
602	43287	14	214	43	4	13	19
1743	155754	61	599	68	4	74	20
1559	164709	109	572	53	0	81	31
2014	201940	38	897	87	0	109	31
2143	235454	73	819	46	0	151	32
2146	220801	75	720	105	1	51	18
874	99466	50	273	32	0	28	23
1590	92661	61	508	133	1	40	17
1590	133328	55	506	79	0	56	20
1210	61361	77	451	51	0	27	12
2072	125930	75	699	207	4	37	17
1281	100750	72	407	67	0	83	30
1401	224549	50	465	47	4	54	31
834	82316	32	245	34	4	27	10
1105	102010	53	370	66	3	28	13
1272	101523	42	316	76	0	59	22
1944	243511	71	603	65	0	133	42
391	22938	10	154	9	0	12	1
761	41566	35	229	42	5	0	9
1605	152474	65	577	45	0	106	32
530	61857	25	192	25	4	23	11
1988	99923	66	617	115	0	44	25
1386	132487	41	411	97	0	71	36
2395	317394	86	975	53	1	116	31
387	21054	16	146	2	0	4	0
1742	209641	42	705	52	5	62	24
620	22648	19	184	44	0	12	13
449	31414	19	200	22	0	18	8
800	46698	45	274	35	0	14	13
1684	131698	65	502	74	0	60	19
1050	91735	35	382	103	0	7	18
2699	244749	95	964	144	2	98	33
1606	184510	49	537	60	7	64	40
1502	79863	37	438	134	1	29	22
1204	128423	64	369	89	8	32	38
1138	97839	38	417	42	2	25	24
568	38214	34	276	52	0	16	8
1459	151101	32	514	98	2	48	35
2158	272458	65	822	99	0	100	43
1111	172494	52	389	52	0	46	43
1421	108043	62	466	29	1	45	14
2833	328107	65	1255	125	3	129	41
1955	250579	83	694	106	0	130	38
2922	351067	95	1024	95	3	136	45
1002	158015	29	400	40	0	59	31
1060	98866	18	397	140	0	25	13
956	85439	33	350	43	0	32	28
2186	229242	247	719	128	4	63	31
3604	351619	139	1277	142	4	95	40
1035	84207	29	356	73	11	14	30
1417	120445	118	457	72	0	36	16
3261	324598	110	1402	128	0	113	37
1587	131069	67	600	61	4	47	30
1424	204271	42	480	73	0	92	35
1701	165543	65	595	148	1	70	32
1249	141722	94	436	64	0	19	27
946	116048	64	230	45	0	50	20
1926	250047	81	651	58	0	41	18
3352	299775	95	1367	97	9	91	31
1641	195838	67	564	50	1	111	31
2035	173260	63	716	37	3	41	21
2312	254488	83	747	50	10	120	39
1369	104389	45	467	105	5	135	41
1577	136084	30	671	69	0	27	13
2201	199476	70	861	46	2	87	32
961	92499	32	319	57	0	25	18
1900	224330	83	612	52	1	131	39
1254	135781	31	433	98	2	45	14
1335	74408	67	434	61	4	29	7
1597	81240	66	503	89	0	58	17
207	14688	10	85	0	0	4	0
1645	181633	70	564	48	2	47	30
2429	271856	103	824	91	1	109	37
151	7199	5	74	0	0	7	0
474	46660	20	259	7	0	12	5
141	17547	5	69	3	0	0	1
1639	133368	36	535	54	1	37	16
872	95227	34	239	70	0	37	32
1318	152601	48	438	36	2	46	24
1018	98146	40	459	37	0	15	17
1383	79619	43	426	123	3	42	11
1314	59194	31	288	247	6	7	24
1335	139942	42	498	46	0	54	22
1403	118612	46	454	72	2	54	12
910	72880	33	376	41	0	14	19
616	65475	18	225	24	2	16	13
1407	99643	55	555	45	1	33	17
771	71965	35	252	33	1	32	15
766	77272	59	208	27	2	21	16
473	49289	19	130	36	1	15	24
1376	135131	66	481	87	0	38	15
1232	108446	60	389	90	1	22	17
1521	89746	36	565	114	3	28	18
572	44296	25	173	31	0	10	20
1059	77648	47	278	45	0	31	16
1544	181528	54	609	69	0	32	16
1230	134019	53	422	51	0	32	18
1206	124064	40	445	34	1	43	22
1205	92630	40	387	60	4	27	8
1255	121848	39	339	45	0	37	17
613	52915	14	181	54	0	20	18
721	81872	45	245	25	0	32	16
1109	58981	36	384	38	7	0	23
740	53515	28	212	52	2	5	22
1126	60812	44	399	67	0	26	13
728	56375	30	229	74	7	10	13
689	65490	22	224	38	3	27	16
592	80949	17	203	30	0	11	16
995	76302	31	333	26	0	29	20
1613	104011	55	384	67	6	25	22
2048	98104	54	636	132	2	55	17
705	67989	21	185	42	0	23	18
301	30989	14	93	35	0	5	17
1803	135458	81	581	118	3	43	12
799	73504	35	248	68	0	23	7
861	63123	43	304	43	1	34	17
1186	61254	46	344	76	1	36	14
1451	74914	30	407	64	0	35	23
628	31774	23	170	48	1	0	17
1161	81437	38	312	64	0	37	14
1463	87186	54	507	56	0	28	15
742	50090	20	224	71	0	16	17
979	65745	53	340	75	0	26	21
675	56653	45	168	39	0	38	18
1241	158399	39	443	42	0	23	18
676	46455	20	204	39	0	22	17
1049	73624	24	367	93	0	30	17
620	38395	31	210	38	0	16	16
1081	91899	35	335	60	0	18	15
1688	139526	151	364	71	0	28	21
736	52164	52	178	52	0	32	16
617	51567	30	206	27	2	21	14
812	70551	31	279	59	0	23	15
1051	84856	29	387	40	1	29	17
1656	102538	57	490	79	1	50	15
705	86678	40	238	44	0	12	15
945	85709	44	343	65	0	21	10
554	34662	25	232	10	0	18	6
1597	150580	77	530	124	0	27	22
982	99611	35	291	81	0	41	21
222	19349	11	67	15	0	13	1
1212	99373	63	397	92	1	12	18
1143	86230	44	467	42	0	21	17
435	30837	19	178	10	0	8	4
532	31706	13	175	24	0	26	10
882	89806	42	299	64	0	27	16
608	62088	38	154	45	1	13	16
459	40151	29	106	22	0	16	9
578	27634	20	189	56	0	2	16
826	76990	27	194	94	0	42	17
509	37460	20	135	19	0	5	7
717	54157	19	201	35	0	37	15
637	49862	37	207	32	0	17	14
857	84337	26	280	35	0	38	14
830	64175	42	260	48	0	37	18
652	59382	49	227	49	0	29	12
707	119308	30	239	48	0	32	16
954	76702	49	333	62	0	35	21
1461	103425	67	428	96	1	17	19
672	70344	28	230	45	0	20	16
778	43410	19	292	63	0	7	1
1141	104838	49	350	71	1	46	16
680	62215	27	186	26	0	24	10
1090	69304	30	326	48	6	40	19
616	53117	22	155	29	3	3	12
285	19764	12	75	19	1	10	2
1145	86680	31	361	45	2	37	14
733	84105	20	261	45	0	17	17
888	77945	20	299	67	0	28	19
849	89113	39	300	30	0	19	14
1182	91005	29	450	36	3	29	11
528	40248	16	183	34	1	8	4
642	64187	27	238	36	0	10	16
947	50857	21	165	34	0	15	20
819	56613	19	234	37	1	15	12
757	62792	35	176	46	0	28	15
894	72535	14	329	44	0	17	16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Engine error message
Error in array(list(1418, 210907, 56, 396, 81, 3, 79, 30, 869, 120982,  : 
  length of 'dimnames' [1] not equal to array extent
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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Engine error message & 
Error in array(list(1418, 210907, 56, 396, 81, 3, 79, 30, 869, 120982,  : 
  length of 'dimnames' [1] not equal to array extent
Execution halted
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186153&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Engine error message[/C][C]
Error in array(list(1418, 210907, 56, 396, 81, 3, 79, 30, 869, 120982,  : 
  length of 'dimnames' [1] not equal to array extent
Execution halted
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186153&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186153&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net
R Engine error message
Error in array(list(1418, 210907, 56, 396, 81, 3, 79, 30, 869, 120982,  : 
  length of 'dimnames' [1] not equal to array extent
Execution halted



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}