FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 01 Dec 2010 12:59:59 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/01/t1291208383eat0ek0bp7ht4p2.htm/, Retrieved Sun, 05 May 2024 02:56:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103952, Retrieved Sun, 05 May 2024 02:56:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-12-01 12:59:59] [a3cd012a7211edfe9ed4466e21aef6a6] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	162556	1081	807	213118	6282154
0	29790	309	444	81767	4321023
0	87550	458	412	153198	4111912
1	84738	588	428	-26007	223193
0	54660	302	315	126942	1491348
0	42634	156	168	157214	1629616
1	40949	481	263	129352	1398893
0	45187	353	267	234817	1926517
0	37704	452	228	60448	983660
0	16275	109	129	47818	1443586
1	25830	115	104	245546	1073089
1	12679	110	122	48020	984885
0	18014	239	393	-1710	1405225
1	43556	247	190	32648	227132
0	24811	505	280	95350	929118
1	6575	159	63	151352	1071292
1	7123	109	102	288170	638830
0	21950	519	265	114337	856956
0	37597	248	234	37884	992426
1	17821	373	277	122844	444477
0	12988	119	73	82340	857217
0	22330	84	67	79801	711969
1	13326	102	103	165548	702380
1	16189	295	290	116384	358589
1	7146	105	83	134028	297978
1	15824	64	56	63838	585715
0	27664	282	236	74996	657954
1	11920	182	73	31080	209458
1	8568	37	34	32168	786690
1	14416	361	139	49857	439798
0	3369	28	26	87161	688779
0	11819	85	70	106113	574339
0	6984	45	40	80570	741409
0	4519	49	42	102129	597793
1	2220	22	12	301670	644190
1	18562	155	211	102313	377934
1	10327	91	74	88577	640273
0	5336	81	80	112477	697458
0	2365	79	83	191778	550608
1	4069	145	131	79804	207393
1	8636	855	203	128294	301607
1	13718	61	56	96448	345783
1	4525	226	89	93811	501749
1	6869	105	88	117520	379983
1	4628	62	39	69159	387475
0	3689	25	25	101792	377305
0	4891	217	49	210568	370837
0	7489	322	149	136996	430866
1	4901	84	58	121920	469107
1	2284	33	41	76403	194493
0	3160	108	90	108094	530670
0	4150	150	136	134759	518365
0	7285	115	97	188873	491303
0	1134	162	63	146216	527021
0	4658	158	114	156608	233773
1	2384	97	77	61348	405972
1	3748	9	6	50350	652925
1	5371	66	47	87720	446211
1	1285	107	51	99489	341340
0	9327	101	85	87419	387699
0	5565	47	43	94355	493408
1	1528	38	32	60326	146494
0	3122	34	25	94670	414462
0	7561	87	77	82425	364304
1	2675	79	54	59017	355178
1	13253	947	251	90829	357760
1	880	74	15	80791	261216
0	2053	53	44	100423	397144
1	1424	94	73	131116	374943
0	4036	63	85	100269	424898
0	3045	58	49	27330	202055
1	5119	49	38	39039	378525
1	1431	34	35	106885	310768
1	554	11	9	79285	325738
1	1975	35	34	118881	394510
0	1765	20	20	77623	247060
1	1012	47	29	114768	368078
1	810	43	11	74015	236761
1	1280	117	52	69465	312378
0	666	171	13	117869	339836
1	1380	26	29	60982	347385
0	4677	75	66	90131	426280
1	876	59	33	138971	352850
1	814	18	15	39625	301881
1	514	15	15	102725	377516
0	5692	72	68	64239	357312
1	3642	86	100	90262	458343
1	540	14	13	103960	354228
1	2099	64	45	106611	308636
1	567	11	14	103345	386212
1	2001	52	36	95551	393343
0	2949	41	40	82903	378509
1	2253	99	68	63593	452469
0	6533	75	29	126910	364839
1	1889	45	43	37527	358649
0	3055	43	30	60247	376641
1	272	8	9	112995	429112
0	1414	198	22	70184	330546
1	2564	22	19	130140	403560
0	1383	11	9	73221	317892
0	1261	33	31	76114	307528
1	975	23	19	90534	235133
1	3366	80	55	108479	299243
1	576	18	8	113761	314073
1	1686	40	28	68696	368186
1	746	23	29	71561	269661
0	3192	60	48	59831	125390
0	2045	20	16	97890	510834
1	5702	61	47	101481	321896
0	1932	36	20	72954	249898
1	936	30	22	67939	408881
0	3437	47	33	48022	158492
1	5131	71	44	86111	292154
0	2397	14	13	74020	289513
0	1389	9	6	57530	378049
1	1503	39	35	56364	343466
1	402	26	8	84990	332743
1	2239	21	17	88590	442882
0	2234	16	11	77200	214215
1	837	69	21	61262	315688
1	10579	92	92	110309	375195
1	875	14	12	67000	334280
1	1585	107	112	93099	355864
0	1659	29	25	107577	480382
0	2647	37	17	62920	353058
0	3294	23	23	75832	217193
1	0	0	0	60720	315380
1	94	7	10	60793	314533
1	422	28	23	57935	318056
1	0	0	0	60720	315380
1	34	8	7	60630	314353
1	1558	63	25	55637	369448
1	0	0	0	60720	315380
1	43	3	20	60887	312846
1	645	5	4	60720	312075
1	316	9	4	60505	315009
1	115	13	10	60945	318903
1	5	2	1	60720	314887
1	897	5	4	60720	314913
1	0	0	0	60720	315380
1	389	14	8	58990	325506
1	0	0	0	60720	315380
1	1002	15	11	56750	298568
1	36	3	4	60894	315834
1	460	15	15	63346	329784
1	309	11	9	56535	312878
1	0	0	0	60720	315380
1	9	6	7	60835	314987
1	271	2	2	60720	325249
1	14	1	0	61016	315877
1	520	10	7	58650	291650
1	1766	73	46	60438	305959
1	0	0	5	60720	315380
1	458	11	7	58625	297765
1	20	3	2	60938	315245
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	98	2	2	61490	315236
1	405	7	5	60845	336425
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	483	27	7	60830	306268
1	454	51	24	63261	302187
1	47	3	1	60720	314882
1	0	0	0	60720	315380
1	757	19	18	45689	382712
1	4655	393	55	60720	341570
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	36	4	3	61564	312412
1	0	0	0	60720	315380
1	203	9	9	61938	309596
1	0	0	0	60720	315380
1	126	10	8	60951	315547
1	400	152	113	60720	313267
0	71	1	0	60745	316176
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	972	34	19	71642	359335
0	531	10	11	71641	330068
1	2461	57	25	55792	314289
0	378	52	16	71873	297413
0	23	5	5	62555	314806
0	638	14	11	60370	333210
0	2300	29	23	64873	352108
1	149	5	6	62041	313332
1	226	5	5	65745	291787
1	0	0	0	60720	315380
1	275	4	7	59500	318745
1	0	0	0	60720	315380
1	141	6	7	61630	315366
1	0	0	0	60720	315380
1	28	2	3	60890	315688
0	0	0	0	60720	315380
0	4980	91	89	113521	409642
0	0	0	0	60720	315380
0	0	0	0	60720	315380
0	472	20	19	80045	269587
0	0	0	0	60720	315380
0	0	0	0	60720	315380
0	0	0	0	60720	315380
0	203	27	12	50804	300962
0	496	17	12	87390	325479
0	10	2	5	61656	316155
0	63	4	2	65688	318574
0	0	0	0	60720	315380
0	1136	32	26	48522	343613
0	265	31	3	60720	306948
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	267	32	11	57640	330059
1	474	20	10	61977	288985
1	534	7	5	62620	304485
1	0	0	2	60720	315380
1	15	8	6	60831	315688
1	397	28	7	60646	317736
1	0	0	2	60720	315380
1	1866	29	28	56225	322331
1	288	4	3	60510	296656
1	0	0	0	60720	315380
1	3	2	1	60698	315354
1	468	21	20	60720	312161
1	20	2	1	60805	315576
1	278	26	22	61404	314922
1	61	14	9	60720	314551
1	0	0	0	60720	315380
1	192	4	2	65276	312339
1	0	0	0	60720	315380
1	317	9	7	63915	298700
1	738	10	9	60720	321376
1	0	0	0	60720	315380
1	368	17	13	61686	303230
1	0	0	0	60720	315380
1	2	1	0	60743	315487
1	0	0	0	60720	315380
1	53	6	6	60349	315793
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	94	3	3	61360	312887
1	0	0	0	60720	315380
1	24	8	7	59818	315637
1	2332	4	2	72680	324385
0	0	0	0	60720	315380
0	0	0	0	60720	315380
1	131	11	15	61808	308989
0	0	0	0	60720	315380
0	0	0	0	60720	315380
1	206	9	9	53110	296702
0	0	0	0	60720	315380
1	167	2	1	64245	307322
1	622	73	38	73007	304376
1	2328	94	57	82732	253588
1	0	0	0	60720	315380
1	365	8	7	54820	309560
1	364	35	26	47705	298466
0	0	0	0	60720	315380
1	0	0	0	60720	315380
0	0	0	0	60720	315380
0	0	0	0	60720	315380
1	226	12	13	72835	343929
1	307	15	10	58856	331955
0	0	0	0	60720	315380
1	0	0	0	60720	315380
0	0	0	0	60720	315380
1	188	11	9	77655	381180
0	0	0	0	60720	315380
1	138	6	26	69817	331420
0	0	0	0	60720	315380
0	0	0	0	60720	315380
0	0	0	0	60720	315380
1	125	12	19	60798	310201
1	0	0	0	60720	315380
1	282	30	12	62452	320016
1	335	33	23	64175	320398
1	0	0	0	60720	315380
0	1324	117	29	67440	291841
1	176	28	8	68136	310670
0	0	0	0	60720	315380
1	0	0	0	60720	315380
1	249	72	26	56726	313491
1	0	0	0	60720	315380
1	333	13	9	70811	331323
1	0	0	0	60720	315380
0	601	6	5	60720	319210
1	30	4	3	62045	318098
0	0	0	0	60720	315380
1	249	62	13	54323	292754
0	0	0	0	60720	315380
1	165	24	12	62841	325176
1	453	21	19	81125	365959
1	0	0	0	60720	315380
1	53	14	10	59506	302409
0	382	21	9	59365	340968
1	0	0	0	60720	315380
0	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	9	60720	315380
0	30	4	4	60798	313164
1	290	2	1	58790	301164
1	0	0	1	60720	315380
0	0	0	0	60720	315380
1	366	53	14	61808	344425
1	2	9	12	60735	315394
0	0	0	0	60720	315380
0	209	13	19	64016	316647
1	384	22	17	54683	309836
0	0	0	0	60720	315380
0	0	0	0	60720	315380
1	365	83	32	87192	346611
0	0	0	0	60720	315380
0	49	8	14	64107	322031
1	3	4	8	60761	315656
1	133	14	4	65990	339445
1	32	1	0	59988	314964
1	368	17	20	61167	297141
1	1	6	5	60719	315372
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
0	0	0	0	60720	315380
1	0	0	0	60720	315380
1	22	2	1	60722	312502
0	0	0	0	60720	315380
0	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	96	5	4	60379	313729
1	1	2	1	60727	315388
1	314	5	4	60720	315371
1	844	78	20	60925	296139
1	0	0	0	60720	315380
1	26	1	1	60896	313880
1	125	13	10	59734	317698
1	304	15	12	62969	295580
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	621	48	13	60720	308256
1	0	0	0	60720	315380
1	119	6	3	59118	303677
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	1595	17	10	60720	319369
1	312	14	3	58598	318690
1	60	10	7	61124	314049
1	587	12	10	59595	325699
1	135	2	1	62065	314210
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	514	52	15	78780	322378
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	1	4	4	60722	315398
1	0	0	0	60720	315380
1	0	0	0	60720	315380
0	1763	24	28	61600	308336
1	180	11	9	59635	316386
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	218	21	7	60720	315553
1	0	0	0	60720	315380
1	448	40	7	59781	323361
1	227	9	7	76644	336639
1	174	1	3	64820	307424
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	121	24	11	56178	295370
1	607	11	7	60436	322340
1	2212	14	10	60720	319864
1	0	0	0	60720	315380
1	0	0	0	60720	315380
1	530	60	18	73433	317291
1	571	80	14	41477	280398
1	0	0	0	60720	315380
1	78	16	12	62700	317330
1	2489	40	29	67804	238125
1	131	6	3	59661	327071
1	923	8	6	58620	309038
1	72	3	3	60398	314210
1	572	16	8	58580	307930
1	397	10	10	62710	322327
1	450	8	6	59325	292136
1	622	7	8	60950	263276
1	694	8	6	68060	367655
0	3425	12	9	83620	283910
1	562	13	8	58456	283587
1	4917	42	26	52811	243650
0	1442	118	239	121173	438493
1	529	9	7	63870	296261
0	2126	138	41	21001	230621
1	1061	5	3	70415	304252
1	776	9	8	64230	333505
1	611	8	6	59190	296919
0	1526	25	21	69351	278990
1	592	7	7	64270	276898
1	1182	13	11	70694	327007
1	621	16	11	68005	317046
1	989	11	12	58930	304555
1	438	11	9	58320	298096
1	726	3	3	69980	231861
1	1303	61	57	69863	309422
0	7419	29	21	63255	286963
0	1164	17	15	57320	269753
0	3310	33	32	75230	448243
1	1920	15	11	79420	165404
1	965	3	2	73490	204325
1	3256	66	23	35250	407159
0	1135	17	20	62285	290476
1	1270	26	24	69206	275311
1	661	3	1	65920	246541
1	1013	2	1	69770	253468
1	2844	67	74	72683	240897
0	11528	70	68	-14545	-83265
1	6526	26	20	55830	-42143
1	2264	24	20	55174	272713
0	5109	97	82	67038	215362
1	3999	30	21	51252	42754
1	35624	223	244	157278	306275
1	9252	48	32	79510	253537
1	15236	90	86	77440	372631
1	18073	180	69	27284	-7170

Tables (Output of Computation)





\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Engine error message & 
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases
Calls: lm -> lm.fit
In addition: Warning messages:
1: In model.matrix.default(mt, mf, contrasts) :
  the response appeared on the right-hand side and was dropped
2: In model.matrix.default(mt, mf, contrasts) :
  problem with term 1 in model.matrix: no columns are assigned
Execution halted

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103952&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Engine error message[/C][C]
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases
Calls: lm -> lm.fit
In addition: Warning messages:
1: In model.matrix.default(mt, mf, contrasts) :
  the response appeared on the right-hand side and was dropped
2: In model.matrix.default(mt, mf, contrasts) :
  problem with term 1 in model.matrix: no columns are assigned
Execution halted

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103952&T=0


Figures (Output of Computation)

Input Parameters & R Code

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