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*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 12 Dec 2010 18:41:38 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn.htm/, Retrieved Sun, 12 Dec 2010 19:39:47 +0100

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

31514 -9 8.3 1.2 27071 -13 8.2 1.7 29462 -18 8 1.8 26105 -11 7.9 1.5 22397 -9 7.6 1 23843 -10 7.6 1.6 21705 -13 8.3 1.5 18089 -11 8.4 1.8 20764 -5 8.4 1.8 25316 -15 8.4 1.6 17704 -6 8.4 1.9 15548 -6 8.6 1.7 28029 -3 8.9 1.6 29383 -1 8.8 1.3 36438 -3 8.3 1.1 32034 -4 7.5 1.9 22679 -6 7.2 2.6 24319 0 7.4 2.3 18004 -4 8.8 2.4 17537 -2 9.3 2.2 20366 -2 9.3 2 22782 -6 8.7 2.9 19169 -7 8.2 2.6 13807 -6 8.3 2.3 29743 -6 8.5 2.3 25591 -3 8.6 2.6 29096 -2 8.5 3.1 26482 -5 8.2 2.8 22405 -11 8.1 2.5 27044 -11 7.9 2.9 17970 -11 8.6 3.1 18730 -10 8.7 3.1 19684 -14 8.7 3.2 19785 -8 8.5 2.5 18479 -9 8.4 2.6 10698 -5 8.5 2.9 31956 -1 8.7 2.6 29506 -2 8.7 2.4 34506 -5 8.6 1.7 27165 -4 8.5 2 26736 -6 8.3 2.2 23691 -2 8 1.9 18157 -2 8.2 1.6 17328 -2 8.1 1.6 18205 -2 8.1 1.2 20995 2 8 1.2 17382 1 7.9 1.5 9367 -8 7.9 1.6 31124 -1 8 1.7 26551 1 8 1.8 30651 -1 7.9 1.8 25859 2 8 1.8 25100 2 7.7 1.3 25778 1 7.2 1.3 20418 -1 7.5 1.4 18688 -2 7.3 1.1 20424 -2 7 1.5 24776 -1 7 2.2 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation

Inschrijvingen[t] = + 26741.6979848119 + 107.4534255309Consumentenvertrouwen[t] -324.908031393456Totaal_Werkloosheid[t] + 96.7043019426652`Algemene_index `[t] -3.42800599544188t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	26741.6979848119	8862.900884	3.0173	0.003326	0.001663
Consumentenvertrouwen	107.4534255309	97.942198	1.0971	0.275553	0.137776
Totaal_Werkloosheid	-324.908031393456	1006.381801	-0.3228	0.747568	0.373784
`Algemene_index `	96.7043019426652	450.246462	0.2148	0.83043	0.415215
t	-3.42800599544188	23.614732	-0.1452	0.88491	0.442455

Multiple Linear Regression - Regression Statistics
Multiple R	0.132556392237079
R-squared	0.0175711971229105
Adjusted R-squared	-0.0265829063771836
F-TEST (value)	0.397951622387104
F-TEST (DF numerator)	4
F-TEST (DF denominator)	89
p-value	0.809629151055746
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5774.00203014016
Sum Squared Residuals	2967179850.52158

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	31514	23190.4976508039	8323.5023491961
2	27071	22838.0988967955	4232.90110320447
3	29462	22372.0557996185	7089.94420038145
4	26105	23124.281284896	2980.71871510405
5	22397	23384.880388409	-987.880388409016
6	23843	23332.0215380483	510.978461951727
7	21705	22769.1272032904	-1064.12720329044
8	18089	22977.1265358003	-4888.12653580026
9	20764	23618.4190829902	-2854.41908299022
10	25316	22521.1159612972	2794.88403870276
11	17704	23513.7800756627	-5809.7800756627
12	15548	23426.029603	-7878.02960300003
13	28029	23637.819033985	4391.18096601501
14	29383	23852.7773916079	5530.22260839211
15	36438	23777.5556898588	12660.4443101412
16	32034	24003.9641250014	8030.0358749986
17	22679	23950.7946887221	-1271.79468872206
18	24319	24498.0943390505	-179.094339050527
19	18004	23619.6518171749	-5615.65181717491
20	17537	23649.335786156	-6112.33578615601
21	20366	23626.566919772	-3260.56691977203
22	22782	23475.3039022375	-693.303902237466
23	19169	23497.8651958251	-4328.86519582505
24	13807	23540.3885216384	-9733.38852163837
25	29743	23471.9789093642	6271.02109063577
26	25591	23787.4316674049	1803.56833259506
27	29096	23972.3000410511	5123.69995894892
28	26482	23714.9728772982	2767.02712270182
29	22405	23070.3038306739	-665.30383067388
30	27044	23170.5391517342	3873.4608482658
31	17970	22959.0163841519	-4989.01638415187
32	18730	23030.551000548	-4300.55100054798
33	19684	22606.9797226232	-2922.9797226232
34	19785	23245.560864732	-3460.56086473199
35	18479	23176.8406665393	-4697.84066653926
36	10698	23599.7468501109	-12901.7468501109
37	31956	23932.1396493775	8023.86035062246
38	29506	23801.9173574627	5704.08264253734
39	34506	23440.926866654	11065.073133346
40	27165	23606.4543799116	3558.5456200884
41	26736	23472.4419895216	3263.55801047841
42	23691	23967.288804485	-276.288804484982
43	18157	23869.8679016281	-5712.86790162805
44	17328	23898.930698772	-6570.93069877195
45	18205	23856.8209719994	-5651.82097199945
46	20995	24315.6974712669	-3320.69747126695
47	17382	24266.3181334628	-6884.31813346275
48	9367	23305.4797278835	-13938.4797278835
49	31124	24031.4053276593	7092.59467234075
50	26551	24252.5546029199	2298.44539708012
51	30651	24066.710549002	6584.28945099802
52	25859	24353.1520164599	1505.8479835401
53	25100	24398.8442689112	701.155731088841
54	25778	24450.4168530815	1327.58314691845
55	20418	24144.2800168005	-3726.28001680053
56	18688	24069.3689009701	-5381.36890097008
57	20424	24202.0950251697	-3778.09502516974
58	24776	24373.8134560651	402.186543934932
59	19814	23685.9044827132	-3871.90448271319
60	12738	24066.6494329512	-11328.6494329512
61	31566	23854.5054935317	7711.49450646833
62	30111	24248.0898006019	5862.91019939811
63	30019	24419.4976455786	5599.50235442139
64	31934	24096.8782896284	7837.12171037158
65	25826	24072.7201439319	1753.27985606812
66	26835	23782.4446554829	3052.55534451714
67	20205	23183.8905909477	-2978.89059094773
68	17789	23282.0356787641	-5493.03567876407
69	20520	23633.1481665821	-3113.14816658207
70	22518	22887.6741336214	-369.674133621375
71	15572	22289.7245262812	-6717.7245262812
72	11509	21818.1116833037	-10309.1116833037
73	25447	22063.0130892652	3383.98691073481
74	24090	21493.617250951	2596.38274904896
75	27786	21364.4736524301	6421.52634756986
76	26195	21673.4249069145	4521.57509308547
77	20516	22025.6160881265	-1509.61608812651
78	22759	22029.4576931628	729.542306837247
79	19028	21848.0249095572	-2820.02490955719
80	16971	22511.3697222169	-5540.36972221686
81	20036	22534.241601723	-2498.24160172304
82	22485	22507.6826368639	-22.6826368639232
83	18730	22806.1953536787	-4076.19535367868
84	14538	22174.2187282484	-7636.21872824842
85	27561	22102.3296034177	5458.67039658226
86	25985	22076.0812244772	3908.91877552278
87	34670	22449.2459777649	12220.7540222351
88	32066	23057.737135897	9008.26286410305
89	27186	22662.8665626364	4523.13343736362
90	29586	23076.1023160137	6509.89768398627
91	21359	23037.3251661596	-1678.32516615958
92	21553	23327.246146174	-1774.24614617404
93	19573	23446.8223276229	-3873.82232762289
94	24256	23797.9348154409	458.065184559101

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0555167972575683	0.111033594515137	0.944483202742432
9	0.0343704260317717	0.0687408520635434	0.965629573968228
10	0.0821256928139573	0.164251385627915	0.917874307186043
11	0.0380347712309534	0.0760695424619068	0.961965228769047
12	0.0192546360127387	0.0385092720254775	0.980745363987261
13	0.146099397081257	0.292198794162514	0.853900602918743
14	0.183319261876257	0.366638523752514	0.816680738123743
15	0.44219496574301	0.88438993148602	0.55780503425699
16	0.648400409265318	0.703199181469365	0.351599590734682
17	0.563262181419292	0.873475637161416	0.436737818580708
18	0.476841492991933	0.953682985983866	0.523158507008067
19	0.400453802076505	0.80090760415301	0.599546197923495
20	0.334729472025495	0.66945894405099	0.665270527974505
21	0.265111967490319	0.530223934980637	0.734888032509681
22	0.271611354882469	0.543222709764937	0.728388645117531
23	0.222793750958237	0.445587501916475	0.777206249041763
24	0.302528083268357	0.605056166536715	0.697471916731643
25	0.380002738539444	0.760005477078887	0.619997261460556
26	0.355900774938106	0.711801549876212	0.644099225061894
27	0.461085135961313	0.922170271922627	0.538914864038687
28	0.41770393553448	0.83540787106896	0.58229606446552
29	0.361292248230555	0.722584496461109	0.638707751769445
30	0.333545971776259	0.667091943552517	0.666454028223741
31	0.290845854449015	0.58169170889803	0.709154145550985
32	0.242728177165218	0.485456354330436	0.757271822834782
33	0.194171711758094	0.388343423516189	0.805828288241906
34	0.169130490169977	0.338260980339954	0.830869509830023
35	0.151785076221939	0.303570152443879	0.84821492377806
36	0.310943353625602	0.621886707251204	0.689056646374398
37	0.39572806029677	0.79145612059354	0.60427193970323
38	0.38485584440251	0.769711688805021	0.61514415559749
39	0.466836225473012	0.933672450946024	0.533163774526988
40	0.436328233533868	0.872656467067736	0.563671766466132
41	0.411811338964744	0.823622677929488	0.588188661035256
42	0.411093760462697	0.822187520925395	0.588906239537303
43	0.487879313596159	0.975758627192318	0.512120686403841
44	0.531755308323797	0.936489383352406	0.468244691676203
45	0.534769373593159	0.930461252813681	0.465230626406841
46	0.491059424307389	0.982118848614777	0.508940575692611
47	0.495198678585379	0.990397357170757	0.504801321414621
48	0.714131996181227	0.571736007637546	0.285868003818773
49	0.758167099120049	0.483665801759902	0.241832900879951
50	0.72092544476551	0.55814911046898	0.27907455523449
51	0.757542274407059	0.484915451185881	0.242457725592941
52	0.718103404599358	0.563793190801284	0.281896595400642
53	0.67201742504667	0.655965149906659	0.32798257495333
54	0.629766985678972	0.740466028642056	0.370233014321028
55	0.576889706280517	0.846220587438965	0.423110293719483
56	0.539698859095758	0.920602281808484	0.460301140904242
57	0.487428884927853	0.974857769855706	0.512571115072147
58	0.428573498487585	0.85714699697517	0.571426501512415
59	0.378847948999415	0.757695897998829	0.621152051000585
60	0.559065344187882	0.881869311624236	0.440934655812118
61	0.624746766819549	0.750506466360902	0.375253233180451
62	0.624870092208722	0.750259815582556	0.375129907791278
63	0.625683819029836	0.748632361940327	0.374316180970164
64	0.738724286636656	0.522551426726688	0.261275713363344
65	0.741295933548022	0.517408132903956	0.258704066451978
66	0.808978402746693	0.382043194506614	0.191021597253307
67	0.758051690683282	0.483896618633436	0.241948309316718
68	0.710408497015404	0.579183005969193	0.289591502984596
69	0.657844301340508	0.684311397318985	0.342155698659492
70	0.660489799156241	0.679020401687519	0.339510200843759
71	0.596370599727126	0.807258800545748	0.403629400272874
72	0.861884231194401	0.276231537611197	0.138115768805599
73	0.84574378156824	0.30851243686352	0.15425621843176
74	0.831417417076337	0.337165165847326	0.168582582923663
75	0.800111012818697	0.399777974362606	0.199888987181303
76	0.741122817324284	0.517754365351433	0.258877182675716
77	0.727584279596247	0.544831440807506	0.272415720403753
78	0.658423082761963	0.683153834476074	0.341576917238037
79	0.568955981769731	0.862088036460537	0.431044018230269
80	0.526815102634292	0.946369794731416	0.473184897365708
81	0.424687527504117	0.849375055008234	0.575312472495883
82	0.421355965186267	0.842711930372533	0.578644034813733
83	0.328296205916724	0.656592411833447	0.671703794083276
84	0.945563607908818	0.108872784182364	0.0544363920911822
85	0.914175489853506	0.171649020292988	0.085824510146494
86	0.85470971859295	0.290580562814098	0.145290281407049

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0126582278481013	OK
10% type I error level	3	0.0379746835443038	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/106a5o1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/106a5o1292179287.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/1h9qc1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/1h9qc1292179287.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/2h9qc1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/2h9qc1292179287.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/3907f1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/3907f1292179287.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/4907f1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/4907f1292179287.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/5907f1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/5907f1292179287.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/6kspi1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/6kspi1292179287.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/7vjol1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/7vjol1292179287.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/8vjol1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/8vjol1292179287.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/9vjol1292179287.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t129217917874iy2gx2d6b5uzn/9vjol1292179287.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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('http://www.xycoon.com/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<br />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<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />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')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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