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Paper - Multiple Regression

*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: Tue, 21 Dec 2010 19:58:27 +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/21/t1292961421a54q0lv1ymsb8jt.htm/, Retrieved Tue, 21 Dec 2010 20:57:12 +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/21/t1292961421a54q0lv1ymsb8jt.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 «

631923,00 -12 -10,8 654294,00 -13 -12,2 671833,00 -16 -14,1 586840,00 -10 -15,2 600969,00 -4 -15,8 625568,00 -9 -15,8 558110,00 -8 -14,9 630577,00 -9 -12,6 628654,00 -3 -9,9 603184,00 -13 -7,8 656255,00 -3 -6 600730,00 -1 -5 670326,00 -2 -4,5 678423,00 0 -3,9 641502,00 0 -2,9 625311,00 -3 -1,5 628177,00 0 -0,5 589767,00 5 0 582471,00 3 0,5 636248,00 4 0,9 599885,00 3 0,8 621694,00 1 0,1 637406,00 -1 -1 595994,00 0 -2 696308,00 -2 -3 674201,00 -1 -3,7 648861,00 2 -4,7 649605,00 0 -6,4 672392,00 -6 -7,5 598396,00 -7 -7,8 613177,00 -6 -7,7 638104,00 -4 -6,6 615632,00 -9 -4,2 634465,00 -2 -2 638686,00 -3 -0,7 604243,00 2 0,1 706669,00 3 0,9 677185,00 1 2,1 644328,00 0 3,5 644825,00 1 4,9 605707,00 1 5,7 600136,00 3 6,2 612166,00 5 6,5 599659,00 5 6,5 634210,00 4 6,3 618234,00 11 6,2 613576,00 8 6,4 627200,00 -1 6,3 668973,00 4 5,8 651479,00 4 5,1 619661,00 4 5,1 644260,00 6 5,8 579936,00 6 6,7 601752,00 6 7,1 595376,00 6 6,7 588902,00 4 5,5 634341,00 1 4,2 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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Werkloosheid[t] = + 625608.204689032 + 2019.82640144426Consumentenvertrouwen[t] -3338.05320518869Producentenvertrouwen[t] + 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)	625608.204689032	4315.649339	144.9627	0	0
Consumentenvertrouwen	2019.82640144426	1181.229105	1.7099	0.091106	0.045553
Producentenvertrouwen	-3338.05320518869	820.757932	-4.067	0.00011	5.5e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.53918479258151
R-squared	0.290720240551166
Adjusted R-squared	0.273207160070948
F-TEST (value)	16.6001772720424
F-TEST (DF numerator)	2
F-TEST (DF denominator)	81
p-value	9.08053527703956e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	35379.160424248
Sum Squared Residuals	101386484378.299

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	631923	637421.262487738	-5498.26248773766
2	654294	640074.710573559	14219.2894264414
3	671833	640357.532459084	31475.4675409156
4	586840	656148.349393458	-69308.3493934575
5	600969	670270.139725236	-69301.1397252363
6	625568	660171.007718015	-34603.007718015
7	558110	659186.58623479	-101076.586234789
8	630577	649489.237461411	-18912.2374614112
9	628654	652595.452216067	-23941.4522160673
10	603184	625387.276470728	-22203.2764707284
11	656255	639577.044715831	16677.9552841686
12	600730	640278.644313531	-39548.6443135312
13	670326	636589.791309493	33736.2086905074
14	678423	638626.612189268	39796.3878107321
15	641502	635288.558984079	6213.44101592075
16	625311	624555.805292482	755.194707517704
17	628177	627277.231291626	899.768708373607
18	589767	635707.336696253	-45940.3366962534
19	582471	629998.65729077	-47527.6572907705
20	636248	630683.262410139	5564.73758986073
21	599885	628997.241329214	-29112.2413292139
22	621694	627294.225769957	-5600.22576995744
23	637406	626926.431492776	10479.5685072235
24	595994	632284.31109941	-36290.3110994094
25	696308	631582.71150171	64725.2884982904
26	674201	635939.175146786	38261.8248532141
27	648861	645336.707556307	3524.29244369257
28	649605	646971.74520224	2633.25479776033
29	672392	638524.645319282	33867.3546807184
30	598396	637506.234879394	-39110.234879394
31	613177	639192.25596032	-26015.2559603194
32	638104	639560.0502375	-1456.05023750036
33	615632	621449.590537826	-5817.59053782618
34	634465	628244.658296521	6220.34170347909
35	638686	621885.362728331	16800.6372716687
36	604243	629314.052171402	-25071.0521714017
37	706669	628663.436008695	78005.563991305
38	677185	620618.11935958	56566.8806404199
39	644328	613925.018470872	30402.9815291284
40	644825	611271.570385052	33553.4296149483
41	605707	608601.127820901	-2894.12782090077
42	600136	610971.754021195	-10835.7540211950
43	612166	614009.990862527	-1843.99086252687
44	599659	614009.990862527	-14350.9908625269
45	634210	612657.77510212	21552.2248978797
46	618234	627130.365232749	-8896.36523274906
47	613576	620403.275387379	-6827.27538737853
48	627200	602558.643094899	24641.3569051010
49	668973	614326.801704715	54646.1982952853
50	651479	616663.438948347	34815.5610516532
51	619661	616663.438948347	2997.56105165323
52	644260	618366.454507603	25893.5454923968
53	579936	615362.206622933	-35426.2066229334
54	601752	614026.985340858	-12274.9853408579
55	595376	615362.206622933	-19986.2066229334
56	588902	615328.217666271	-26426.2176662713
57	634341	613608.207628684	20732.7923713162
58	594305	627713.003482132	-33408.0034821316
59	606200	618264.487637617	-12064.4876376169
60	610926	622971.751081543	-12045.7510815432
61	633685	615560.056116804	18124.9438831961
62	639696	619599.708919692	20096.2910803076
63	659451	622620.951282693	36830.0487173067
64	593248	618213.504202624	-24965.5042026238
65	606677	622552.973369369	-15875.9733693691
66	599434	621183.763130632	-21749.7631306315
67	569578	620148.358212413	-50570.3582124128
68	629873	626156.853981752	3716.14601824755
69	613438	643214.914284877	-29776.9142848769
70	604172	647418.427624974	-43246.4276249738
71	658328	664391.515536443	-6063.51553644297
72	612633	673670.086597647	-61037.0865976471
73	707372	686722.483054545	20649.5169454549
74	739770	686003.888978514	53766.1110214858
75	777535	688357.520700477	89177.4792995226
76	685030	691763.55181899	-6733.55181899024
77	730234	687791.876929426	42442.1230705741
78	714154	682818.786078305	31335.2139216950
79	630872	678513.305868222	-47641.3058682218
80	719492	683973.152344841	35518.847655159
81	677023	674275.803571463	2747.19642853725
82	679272	663594.033314859	15677.9666851411
83	718317	656635.105018956	61681.8949810442
84	645672	635820.213798469	9851.7862015314

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.342128815088764	0.684257630177528	0.657871184911236
7	0.594804611422986	0.810390777154028	0.405195388577014
8	0.486340257171061	0.972680514342122	0.513659742828939
9	0.421250505837079	0.842501011674157	0.578749494162921
10	0.581717657021501	0.836564685956999	0.418282342978499
11	0.566096378778207	0.867807242443587	0.433903621221794
12	0.535901382252246	0.928197235495508	0.464098617747754
13	0.564075909405051	0.871848181189897	0.435924090594949
14	0.578659276808589	0.842681446382823	0.421340723191411
15	0.493808926292891	0.987617852585783	0.506191073707109
16	0.478753657954584	0.957507315909168	0.521246342045416
17	0.422841599338275	0.84568319867655	0.577158400661725
18	0.496481705792915	0.99296341158583	0.503518294207085
19	0.585272783583792	0.829454432832415	0.414727216416208
20	0.515087801385494	0.969824397229012	0.484912198614506
21	0.495235438344448	0.990470876688896	0.504764561655552
22	0.422789616138741	0.845579232277483	0.577210383861259
23	0.353604351531798	0.707208703063597	0.646395648468202
24	0.354853085199562	0.709706170399124	0.645146914800438
25	0.576533259735899	0.846933480528201	0.423466740264101
26	0.626225723930568	0.747548552138864	0.373774276069432
27	0.60151781242828	0.79696437514344	0.39848218757172
28	0.569557789747309	0.860884420505382	0.430442210252691
29	0.58248800050025	0.8350239989995	0.41751199949975
30	0.601135309408278	0.797729381183445	0.398864690591722
31	0.567680893476847	0.864638213046307	0.432319106523153
32	0.505161926817066	0.989676146365867	0.494838073182934
33	0.466083932150947	0.932167864301893	0.533916067849054
34	0.400833289481448	0.801666578962896	0.599166710518552
35	0.343833524574471	0.687667049148941	0.65616647542553
36	0.330455635543168	0.660911271086336	0.669544364456832
37	0.570386736313772	0.859226527372455	0.429613263686228
38	0.625671960152578	0.748656079694844	0.374328039847422
39	0.594734778722474	0.810530442555052	0.405265221277526
40	0.575188555554712	0.849622888890576	0.424811444445288
41	0.564872255383615	0.870255489232771	0.435127744616385
42	0.550944838782248	0.898110322435504	0.449055161217752
43	0.50320384883576	0.99359230232848	0.49679615116424
44	0.475974871942322	0.951949743884645	0.524025128057678
45	0.429908469476895	0.85981693895379	0.570091530523105
46	0.37964187848375	0.7592837569675	0.62035812151625
47	0.329245947766874	0.658491895533747	0.670754052233126
48	0.317138662169937	0.634277324339873	0.682861337830063
49	0.400833430876165	0.80166686175233	0.599166569123835
50	0.404402604576856	0.808805209153711	0.595597395423144
51	0.349775188963143	0.699550377926286	0.650224811036857
52	0.328240856481260	0.656481712962519	0.67175914351874
53	0.351805514929369	0.703611029858738	0.648194485070631
54	0.307919091644738	0.615838183289477	0.692080908355262
55	0.276979071399293	0.553958142798586	0.723020928600707
56	0.261406697062385	0.52281339412477	0.738593302937615
57	0.239295188645729	0.478590377291458	0.760704811354271
58	0.249524466038651	0.499048932077302	0.750475533961349
59	0.205532778790601	0.411065557581201	0.7944672212094
60	0.166195071872458	0.332390143744915	0.833804928127542
61	0.147367104173833	0.294734208347665	0.852632895826168
62	0.130035167944694	0.260070335889388	0.869964832055306
63	0.161395128295100	0.322790256590200	0.8386048717049
64	0.134122052337421	0.268244104674842	0.865877947662579
65	0.102382689580229	0.204765379160458	0.897617310419771
66	0.0786969268377078	0.157393853675416	0.921303073162292
67	0.079708673366326	0.159417346732652	0.920291326633674
68	0.0656550970987989	0.131310194197598	0.934344902901201
69	0.0485773233189715	0.097154646637943	0.951422676681029
70	0.0449775501001531	0.0899551002003063	0.955022449899847
71	0.0313839392096003	0.0627678784192007	0.9686160607904
72	0.143172291182057	0.286344582364114	0.856827708817943
73	0.131135631738814	0.262271263477629	0.868864368261186
74	0.129756090183526	0.259512180367052	0.870243909816474
75	0.444737479381190	0.889474958762380	0.55526252061881
76	0.328886186950221	0.657772373900442	0.671113813049779
77	0.407670664473118	0.815341328946236	0.592329335526882
78	0.929993969284254	0.140012061431492	0.0700060307157458

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	3	0.0410958904109589	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/10japp1292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/10japp1292961499.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/1vrad1292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/1vrad1292961499.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/2vrad1292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/2vrad1292961499.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/35iag1292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/35iag1292961499.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/45iag1292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/45iag1292961499.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/55iag1292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/55iag1292961499.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/6ysr11292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/6ysr11292961499.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/79jqm1292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/79jqm1292961499.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/89jqm1292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/89jqm1292961499.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/99jqm1292961499.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292961421a54q0lv1ymsb8jt/99jqm1292961499.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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('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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