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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 18:36:06 +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/t1292178925e3y2k9up3vl05c2.htm/, Retrieved Sun, 12 Dec 2010 19:35:35 +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/t1292178925e3y2k9up3vl05c2.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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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[t] = + 26301.6797583303 + 110.787113940842Consumentenvertrouwen[t] -288.509324225093Totaal_Werkloosheid[t] + 102.551249237694`Algemene_index `[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)26301.67975833038283.0238853.17540.002050.001025
Consumentenvertrouwen110.78711394084294.6925561.170.2451030.122552
Totaal_Werkloosheid-288.509324225093969.329818-0.29760.7666650.383333
`Algemene_index `102.551249237694445.9957230.22990.8186620.409331


Multiple Linear Regression - Regression Statistics
Multiple R0.131676069312389
R-squared0.0173385872295612
Adjusted R-squared-0.0154167931961202
F-TEST (value)0.529335547450005
F-TEST (DF numerator)3
F-TEST (DF denominator)90
p-value0.663284278349626
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5742.51434514383
Sum Squared Residuals2967882390.37644


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
13151423033.02984087968480.97015912037
22707122670.00794215774400.99205784235
32946222184.02936222227277.97063777776
42610522957.62471745933147.37528254067
52239723214.4761179897-817.476117989695
62384323165.2197535915677.78024640853
72170522620.6467598876-915.646759887609
81808922844.1354301181-4755.13543011809
92076423508.8581137631-2744.85811376314
102531622380.47672450722935.52327549281
111770423408.3261247461-5704.32612474607
121554823330.1140100535-7782.11401005351
132802923565.66742968474463.33257031526
142938323785.32721521765597.67278478237
153643823687.497399601012750.5026003990
163203423889.55874443038144.44125556966
172267923826.3231882826-1147.32318828257
182431924402.5786323113-83.5786323112936
191800423565.7722475566-5561.77224755657
201753723622.5815634782-6085.58156347817
212036623602.0713136306-3236.07131363062
222278223424.3245767162-642.324576716239
231916923427.0267501166-4258.02675011663
241380723478.1975568637-9671.19755686366
252974323420.49569201866322.50430798136
262559123754.77147619001836.22852381003
272909623945.68514717225150.31485282784
282648223669.11122784592812.88877215414
292240523002.474101852-597.474101852007
302704423101.19646639213942.8035336079
311797022919.7501892821-4949.75018928208
321873023001.6863708004-4271.68637080041
331968422568.7930399608-2884.79303996081
341978523219.4317139845-3434.43171398450
351847923147.7506573899-4668.75065738993
361069823592.8135555021-12894.8135555021
373195623947.49477164918008.50522835086
382950623816.19740786085689.80259213924
393450623440.901123994411065.0988760056
402716523611.3045451293553.69545487098
412673623467.94243193993268.05756806011
422369123966.8783101995-275.878310199477
431815723878.4110705832-5721.41107058315
441732823907.2620030057-6579.26200300566
451820523866.2415033106-5661.24150331058
462099524338.2408914965-3343.24089149646
471738224287.0700847494-6905.07008474943
48936723300.2411842056-13933.2411842056
493112424057.15517429287066.84482570722
502655124288.98452709822262.01547290177
513065124096.26123163916554.73876836094
522585924399.77164103911459.22835896093
532510024435.0488136878664.951186312244
542577824468.51636185951309.48363814054
552041824170.644461634-3752.64446163402
561868824086.7938377669-5398.79383776689
572042424214.3671347295-3790.36713472949
582477624396.9401231367379.059876863281
591981423693.2162000172-3879.21620001721
601273824099.1730407831-11361.1730407831
613156623889.7683801747676.23161982601
623011124290.08671176535820.9132882347
633001924458.6805084235560.31949157702
643193424110.42553250217823.57446749789
652582624077.95535112571748.04464887435
662683523802.50814309653032.49185690345
672020523244.9778510685-3039.97785106852
681778923357.5745894863-5568.57458948634
692052023704.8076719818-3184.80767198183
702251822909.5753556-391.575355599994
711557222278.110584383-6706.110584383
721150921807.9208206741-10298.9208206741
732544722100.28355622693346.71644377307
742409021529.56180350102560.43819649905
752778621396.24517949206389.75482050805
762619521704.37220464124490.62779535884
772051622049.5860269160-1533.58602691603
782275922059.7363339680699.263666032022
791902821907.0361714086-2879.03617140864
801697122606.3531145226-5635.3531145226
812003622623.0344796725-2587.03447967254
822248522590.4594804243-105.459480424252
831873022904.3298326199-4174.32983261986
841453822276.0110329207-7738.0110329207
852756122220.22361042455340.77638957553
862598522201.62780292573783.37219707427
873467022583.455144890112086.5448551099
883206623205.34770436318860.65229563687
892718622789.24055654534396.75944345471
902958623224.04832973376361.95167026631
912135923225.0702231591-1866.07022315907
922155323526.6661902103-1973.66619021029
931957323645.8988045979-4072.89880459792
942425623993.1318870934262.868112906587


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.4007797953427720.8015595906855440.599220204657228
80.4620941045137320.9241882090274640.537905895486268
90.3326797039512870.6653594079025750.667320296048713
100.2249721415107910.4499442830215820.775027858489209
110.1499512975678700.2999025951357390.85004870243213
120.1320601729719330.2641203459438660.867939827028067
130.2380885916782490.4761771833564980.761911408321751
140.2216579808869240.4433159617738480.778342019113076
150.3596772435349010.7193544870698010.6403227564651
160.5809735939938550.8380528120122890.419026406006145
170.4977420674880880.9954841349761760.502257932511912
180.4122351725319450.824470345063890.587764827468055
190.3430592410251560.6861184820503120.656940758974844
200.2849570378576590.5699140757153170.715042962142341
210.2246318179088530.4492636358177050.775368182091147
220.2422523262632020.4845046525264030.757747673736798
230.192476511472950.38495302294590.80752348852705
240.2560819548750810.5121639097501620.743918045124919
250.3454891982779960.6909783965559920.654510801722004
260.3314404610336040.6628809220672090.668559538966396
270.417521049792330.835042099584660.58247895020767
280.3830633677505170.7661267355010340.616936632249483
290.3191297469635320.6382594939270630.680870253036468
300.2984047298163460.5968094596326910.701595270183654
310.2606889708981210.5213779417962430.739311029101879
320.2203068562359340.4406137124718680.779693143764066
330.1806268395473510.3612536790947010.81937316045265
340.1509618207377770.3019236414755550.849038179262223
350.1341637811353560.2683275622707130.865836218864644
360.2944716698075630.5889433396151250.705528330192437
370.3827679024104110.7655358048208230.617232097589589
380.3853816542921760.7707633085843530.614618345707824
390.5137667935880840.9724664128238310.486233206411916
400.4668113200236280.9336226400472550.533188679976372
410.4213779582571250.8427559165142510.578622041742875
420.3726556981351090.7453113962702170.627344301864891
430.4164017176790690.8328034353581380.583598282320931
440.4721920817286040.9443841634572080.527807918271396
450.5052700874631810.9894598250736390.494729912536819
460.4790480783986880.9580961567973750.520951921601312
470.5150191890295860.9699616219408280.484980810970414
480.80044363517360.3991127296528000.199556364826400
490.8142857732435310.3714284535129370.185714226756469
500.775355212739170.4492895745216590.224644787260829
510.7833626707302490.4332746585395020.216637329269751
520.7373826686396760.5252346627206480.262617331360324
530.685866401704290.6282671965914210.314133598295710
540.6352689019317980.7294621961364040.364731098068202
550.6006663769476150.798667246104770.399333623052385
560.5930141164741330.8139717670517330.406985883525867
570.5550533544455550.889893291108890.444946645554445
580.4936553364980990.9873106729961980.506344663501901
590.4536726133980510.9073452267961020.546327386601949
600.642055881588350.71588823682330.35794411841165
610.6950740556480480.6098518887039050.304925944351952
620.6903656982934060.6192686034131880.309634301706594
630.6809579582933440.6380840834133120.319042041706656
640.7543219829187350.4913560341625310.245678017081265
650.747154896488790.505690207022420.25284510351121
660.7932616645913770.4134766708172460.206738335408623
670.7529745109160730.4940509781678540.247025489083927
680.758050750394550.4838984992109020.241949249605451
690.7059948039041990.5880103921916030.294005196095802
700.6481538916483450.703692216703310.351846108351655
710.6194301964825370.7611396070349250.380569803517463
720.8834677506887240.2330644986225530.116532249311276
730.859721395334320.2805572093313590.140278604665680
740.8723836931922070.2552326136155870.127616306807793
750.8359398769585420.3281202460829170.164060123041458
760.7898063578940320.4203872842119350.210193642105968
770.7840690952206650.431861809558670.215930904779335
780.719496834966410.561006330067180.28050316503359
790.6444401160852120.7111197678295770.355559883914788
800.5708975402371640.8582049195256720.429102459762836
810.4800464111131840.9600928222263680.519953588886816
820.4175343779980160.8350687559960320.582465622001984
830.3178955782369290.6357911564738590.682104421763071
840.8308790792495570.3382418415008870.169120920750444
850.7688993213580950.4622013572838110.231100678641905
860.9185192694670260.1629614610659480.081480730532974
870.9471477509810480.1057044980379040.0528522490189518


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/105uzy1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/105uzy1292178957.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/1rk1p1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/1rk1p1292178957.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/2rk1p1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/2rk1p1292178957.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/3rk1p1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/3rk1p1292178957.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/4jt0a1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/4jt0a1292178957.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/5jt0a1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/5jt0a1292178957.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/6jt0a1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/6jt0a1292178957.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/7u2iv1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/7u2iv1292178957.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/85uzy1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/85uzy1292178957.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/95uzy1292178957.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/12/t1292178925e3y2k9up3vl05c2/95uzy1292178957.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')
}
 





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Software written by Ed van Stee & Patrick Wessa


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