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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 18:44:50 +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/t12921793715d7fgb11lyac8tx.htm/, Retrieved Sun, 12 Dec 2010 19:43:01 +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/t12921793715d7fgb11lyac8tx.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 «

50556 -9 0 8,3 1,2 43901 -13 4 8,2 1,7 48572 -18 5 8 1,8 43899 -11 -7 7,9 1,5 37532 -9 -2 7,6 1 40357 -10 1 7,6 1,6 35489 -13 3 8,3 1,5 29027 -11 -2 8,4 1,8 34485 -5 -6 8,4 1,8 42598 -15 10 8,4 1,6 30306 -6 -9 8,4 1,9 26451 -6 0 8,6 1,7 47460 -3 -3 8,9 1,6 50104 -1 -2 8,8 1,3 61465 -3 2 8,3 1,1 53726 -4 1 7,5 1,9 39477 -6 2 7,2 2,6 43895 0 -6 7,4 2,3 31481 -4 4 8,8 2,4 29896 -2 -2 9,3 2,2 33842 -2 0 9,3 2 39120 -6 4 8,7 2,9 33702 -7 1 8,2 2,6 25094 -6 -1 8,3 2,3 51442 -6 0 8,5 2,3 45594 -3 -3 8,6 2,6 52518 -2 -1 8,5 3,1 48564 -5 3 8,2 2,8 41745 -11 6 8,1 2,5 49585 -11 0 7,9 2,9 32747 -11 0 8,6 3,1 33379 -10 -1 8,7 3,1 35645 -14 4 8,7 3,2 37034 -8 -6 8,5 2,5 35681 -9 1 8,4 2,6 20972 -5 -4 8,5 2,9 58552 -1 -4 8,7 2,6 54955 -2 1 8,7 2,4 65540 -5 3 8,6 1,7 51570 -4 -1 8,5 2 51145 -6 2 8,3 2,2 46641 -2 -4 8 1,9 35704 -2 0 8,2 1,6 33253 -2 0 8,1 1,6 35193 -2 0 8,1 1,2 41668 2 -4 8 1,2 34865 1 1 7,9 1,5 21210 -8 9 7,9 1,6 56126 -1 -7 8 1,7 49231 1 -2 8 1,8 59723 -1 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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132
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_met_transit[t] = + 45154.1899984168 + 331.286415273221Consumentenvertrouwen[t] + 33.1752327449542Evolutie_consumentenvertrouwen[t] -524.465533822917Totaal_Werkloosheid[t] + 549.593645272708`Algemene_index `[t] + 68.6401600734777t + 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)	45154.1899984168	17817.622236	2.5342	0.013148	0.006574
Consumentenvertrouwen	331.286415273221	189.565632	1.7476	0.08423	0.042115
Evolutie_consumentenvertrouwen	33.1752327449542	329.567953	0.1007	0.920061	0.46003
Totaal_Werkloosheid	-524.465533822917	2011.101885	-0.2608	0.794904	0.397452
`Algemene_index `	549.593645272708	844.681717	0.6507	0.517068	0.258534
t	68.6401600734777	50.611496	1.3562	0.178708	0.089354

Multiple Linear Regression - Regression Statistics
Multiple R	0.242329991256108
R-squared	0.0587238246621852
Adjusted R-squared	0.00202044060569018
F-TEST (value)	1.03563174648760
F-TEST (DF numerator)	5
F-TEST (DF denominator)	83
p-value	0.402276300608291
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10405.2880891738
Sum Squared Residuals	8986411678.15228

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	50556	38547.7008646282	12008.2991353718
2	43901	37751.1396706074	6149.86032939265
3	48572	36356.3754583515	12215.6245416485
4	43899	38233.4861921986	5665.51380780141
5	37532	39013.1181840538	-1481.11818405380
6	40357	39179.7538142526	1177.24618574745
7	35489	37898.7999557930	-2409.79995579296
8	29027	38576.5683228876	-9549.56832288763
9	34485	40500.2260436206	-6015.22604362062
10	42598	37676.8870458266	4921.1129541734
11	30306	40261.6536147868	-9955.65361478676
12	26451	40414.0590337457	-13963.0590337457
13	47460	41164.7337167298	6295.26628327017
14	50104	41816.6903998952	8287.30960010482
15	61465	41507.7726982590	19957.2273017410
16	53726	42071.1985535908	11654.8014464092
17	39477	42052.4963277005	-2575.49632770051
18	43895	43573.6819171073	321.318082892708
19	31481	41969.6363607126	-10488.6363607126
20	29896	42129.6464588968	-12233.6464588968
21	33842	42154.7183554057	-8312.71835540565
22	39120	41840.2273864052	-2720.22738640525
23	33702	41575.4101063003	-7873.41010630029
24	25094	41691.661569193	-16597.6615691930
25	51442	41688.5838552468	9753.41614475318
26	45594	42763.9891031046	2830.01089689538
27	52518	43557.5095199599	8960.49048004012
28	48564	42757.4529317586	5806.54706824143
29	41745	40825.4687582281	919.531241771939
30	49585	41019.7880867055	8565.21191329452
31	32747	40831.2211021575	-8084.22110215746
32	33379	41145.5258913769	-7766.52589137691
33	35645	40109.8559186095	-4464.85591860955
34	37034	41554.6397979465	-4520.6397979465
35	35681	41631.626089871	-5950.626089871
36	20972	42971.9672875121	-21999.9672875121
37	58552	44095.9819083321	14456.0180916679
38	54955	43889.2930878026	11065.7069121974
39	65540	42698.1554692377	22841.8445307623
40	51570	43182.7057605687	8387.29423943134
41	51145	42903.1106241497	8241.88937585031
42	46641	44090.3066154114	2550.69338458861
43	35704	44021.8765061183	-8317.87650611829
44	33253	44142.9632195741	-10889.9632195741
45	35193	43991.7659215384	-8798.76592153845
46	41668	45305.2973651073	-3637.29736510729
47	34865	45425.8519205964	-10560.8519205964
48	21210	42833.2755696978	-21623.2755696978
49	56126	44692.6297239095	11433.3702760905
50	49231	45644.6782427815	3586.32175721849
51	59723	45235.8930566707	14487.1069433293
52	48103	46080.0697454567	2022.93025454327
53	47472	46130.7784412756	1341.22155872441
54	50497	46163.5401857323	4333.45981426774
55	40059	45500.4024523846	-5441.40245238465
56	34149	45144.5959776227	-10995.5959776227
57	36860	45557.2380232072	-8697.2380232072
58	46356	46308.7049174998	47.2950825001558
59	36577	44708.4575843113	-8131.4575843113
60	23872	45742.3414675731	-21870.3414675731
61	57276	45514.8510982967	11761.1489017033
62	56389	46571.3268117569	9817.6731882431
63	57657	47336.5072464303	10320.4927535697
64	62300	46312.5701608653	15987.4298391347
65	48929	46359.220330322	2569.77966967797
66	51168	45885.2576334835	5282.74236651648
67	39636	44716.9256869503	-5080.92568695033
68	33213	44935.7478171895	-11722.7478171895
69	38127	45931.7063858659	-7804.70638586585
70	43291	43556.8119552787	-265.811955278683
71	30600	41147.4841684617	-10547.4841684617
72	21956	39723.7781344424	-17767.7781344424
73	48033	40598.9979882822	7434.00201171778
74	46148	39263.8924637114	6884.10753628856
75	50736	38485.3599539506	12250.6400460494
76	48114	39307.5621392275	8806.43786077254
77	38390	39997.0788806321	-1607.07888063211
78	44112	40026.1938163955	4085.80618360447
79	36287	39729.2383309026	-3442.23833090257
80	30333	42009.4619928715	-11676.4619928715
81	35908	42162.2091980702	-6254.20919807015
82	40005	42147.5500114345	-2142.55001143449
83	35263	43273.871584565	-8010.87158456498
84	26591	41928.3575420670	-15337.3575420670
85	49709	41838.6599718506	7870.3400281494
86	47840	41909.812943069	5930.18705693094
87	64781	43229.1622202364	21551.8377797636
88	57802	45014.5612297329	12787.4387702671
89	48154	44190.6581236731	3963.34187632693

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0883528228581743	0.176705645716349	0.911647177141826
10	0.126870470571807	0.253740941143614	0.873129529428193
11	0.0631586590176654	0.126317318035331	0.936841340982335
12	0.0336322203094731	0.0672644406189461	0.966367779690527
13	0.173446378051787	0.346892756103573	0.826553621948213
14	0.188235173713861	0.376470347427722	0.811764826286139
15	0.331500943126140	0.663001886252279	0.66849905687386
16	0.34124843374635	0.6824968674927	0.65875156625365
17	0.261107823944934	0.522215647889869	0.738892176055066
18	0.194454269657015	0.38890853931403	0.805545730342985
19	0.161090224567489	0.322180449134977	0.838909775432511
20	0.119788440612103	0.239576881224206	0.880211559387897
21	0.0841688221085218	0.168337644217044	0.915831177891478
22	0.0818203203763046	0.163640640752609	0.918179679623695
23	0.0553142614290712	0.110628522858142	0.944685738570929
24	0.052115260421661	0.104230520843322	0.947884739578339
25	0.132738379033928	0.265476758067856	0.867261620966072
26	0.147574225911884	0.295148451823768	0.852425774088116
27	0.233633010792323	0.467266021584645	0.766366989207677
28	0.202721148618748	0.405442297237496	0.797278851381252
29	0.159388331268978	0.318776662537957	0.840611668731022
30	0.168772243609676	0.337544487219353	0.831227756390324
31	0.132319809779152	0.264639619558303	0.867680190220848
32	0.101831920145884	0.203663840291767	0.898168079854116
33	0.0742774713772344	0.148554942754469	0.925722528622766
34	0.0542174548736976	0.108434909747395	0.945782545126302
35	0.0434367395486617	0.0868734790973233	0.956563260451338
36	0.117651252261838	0.235302504523677	0.882348747738162
37	0.180873479038772	0.361746958077545	0.819126520961228
38	0.162922111132946	0.325844222265892	0.837077888867054
39	0.244136359032047	0.488272718064094	0.755863640967953
40	0.217777666089857	0.435555332179715	0.782222333910143
41	0.214904071627146	0.429808143254293	0.785095928372854
42	0.200200902135880	0.400401804271761	0.79979909786412
43	0.295158056134025	0.590316112268051	0.704841943865975
44	0.368723812449119	0.737447624898237	0.631276187550881
45	0.383223794286124	0.766447588572248	0.616776205713876
46	0.334830142667566	0.669660285335133	0.665169857332434
47	0.346681266776945	0.693362533553889	0.653318733223055
48	0.515868337116247	0.968263325767507	0.484131662883753
49	0.541934035618185	0.91613192876363	0.458065964381815
50	0.484826751408401	0.969653502816803	0.515173248591599
51	0.562001242109586	0.875997515780828	0.437998757890414
52	0.505398350361021	0.989203299277958	0.494601649638979
53	0.453898219069415	0.90779643813883	0.546101780930585
54	0.429187443599057	0.858374887198114	0.570812556400943
55	0.379066244405608	0.758132488811217	0.620933755594392
56	0.348161286083063	0.696322572166126	0.651838713916937
57	0.302897918572641	0.605795837145281	0.69710208142736
58	0.249938215436511	0.499876430873023	0.750061784563489
59	0.204094744542478	0.408189489084956	0.795905255457522
60	0.369640572226273	0.739281144452546	0.630359427773727
61	0.411902022742457	0.823804045484914	0.588097977257543
62	0.405947311820355	0.811894623640711	0.594052688179645
63	0.416102228090255	0.83220445618051	0.583897771909745
64	0.701429035085776	0.597141929828448	0.298570964914224
65	0.725477175435113	0.549045649129775	0.274522824564887
66	0.833845573368704	0.332308853262593	0.166154426631296
67	0.783966393257545	0.432067213484909	0.216033606742455
68	0.760216929232771	0.479566141534458	0.239783070767229
69	0.710033368540921	0.579933262918158	0.289966631459079
70	0.814651262052627	0.370697475894746	0.185348737947373
71	0.782809668018957	0.434380663962086	0.217190331981043
72	0.911584042808687	0.176831914382626	0.0884159571913132
73	0.896752090210572	0.206495819578855	0.103247909789428
74	0.864520659217974	0.270958681564053	0.135479340782026
75	0.872020869281963	0.255958261436073	0.127979130718037
76	0.925502123785646	0.148995752428709	0.0744978762143544
77	0.865473330438512	0.269053339122977	0.134526669561488
78	0.801083422933728	0.397833154132544	0.198916577066272
79	0.675008030935137	0.649983938129726	0.324991969064863
80	0.582398004244976	0.835203991510049	0.417601995755024

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	0	0	OK
10% type I error level	2	0.0277777777777778	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/10njlj1292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/10njlj1292179482.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/1z0681292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/1z0681292179482.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/2r95b1292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/2r95b1292179482.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/3r95b1292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/3r95b1292179482.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/4r95b1292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/4r95b1292179482.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/5r95b1292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/5r95b1292179482.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/6k14e1292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/6k14e1292179482.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/7dsly1292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/7dsly1292179482.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/8dsly1292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/8dsly1292179482.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/9njlj1292179482.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921793715d7fgb11lyac8tx/9njlj1292179482.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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