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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: Sat, 18 Dec 2010 12:47:39 +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/18/t1292676323a9a31oojsumjne3.htm/, Retrieved Sat, 18 Dec 2010 13:45:34 +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/18/t1292676323a9a31oojsumjne3.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 «

31.514 -9 0 8,3 1,2 27.071 -13 4 8,2 1,7 29.462 -18 5 8 1,8 26.105 -11 -7 7,9 1,5 22.397 -9 -2 7,6 1 23.843 -10 1 7,6 1,6 21.705 -13 3 8,3 1,5 18.089 -11 -2 8,4 1,8 20.764 -5 -6 8,4 1,8 25.316 -15 10 8,4 1,6 17.704 -6 -9 8,4 1,9 15.548 -6 0 8,6 1,7 28.029 -3 -3 8,9 1,6 29.383 -1 -2 8,8 1,3 36.438 -3 2 8,3 1,1 32.034 -4 1 7,5 1,9 22.679 -6 2 7,2 2,6 24.319 0 -6 7,4 2,3 18.004 -4 4 8,8 2,4 17.537 -2 -2 9,3 2,2 20.366 -2 0 9,3 2 22.782 -6 4 8,7 2,9 19.169 -7 1 8,2 2,6 13.807 -6 -1 8,3 2,3 29.743 -6 0 8,5 2,3 25.591 -3 -3 8,6 2,6 29.096 -2 -1 8,5 3,1 26.482 -5 3 8,2 2,8 22.405 -11 6 8,1 2,5 27.044 -11 0 7,9 2,9 17.970 -11 0 8,6 3,1 18.730 -10 -1 8,7 3,1 19.684 -14 4 8,7 3,2 19.785 -8 -6 8,5 2,5 18.479 -9 1 8,4 2,6 10.698 -5 -4 8,5 2,9 31.956 -1 -4 8,7 2,6 29.506 -2 1 8,7 2,4 34.506 -5 3 8,6 1,7 27.165 -4 -1 8,5 2 26.736 -6 2 8,3 2,2 23.691 -2 -4 8 1,9 18.157 -2 0 8,2 1,6 17.328 -2 0 8,1 1,6 18.205 -2 0 8,1 1,2 20.995 2 -4 8 1,2 17.382 1 1 7,9 1,5 9.367 -8 9 7,9 1,6 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	15 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Inschrijvingen[t] = + 29.1923025763464 + 0.174389476389964Consumentenvertrouwen[t] + 0.0428784606729751Evolutie_consumentenvertrouwen[t] -0.649244649320326Totaal_Werkloosheid[t] + 0.135258379303979Algemene_index[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)	29.1923025763464	8.666019	3.3686	0.00117	0.000585
Consumentenvertrouwen	0.174389476389964	0.101418	1.7195	0.089436	0.044718
Evolutie_consumentenvertrouwen	0.0428784606729751	0.184355	0.2326	0.816685	0.408342
Totaal_Werkloosheid	-0.649244649320326	1.024485	-0.6337	0.528089	0.264044
Algemene_index	0.135258379303979	0.458341	0.2951	0.768688	0.384344

Multiple Linear Regression - Regression Statistics
Multiple R	0.208606962378333
R-squared	0.0435168647527153
Adjusted R-squared	-0.00491266108259025
F-TEST (value)	0.898560619831449
F-TEST (DF numerator)	4
F-TEST (DF denominator)	79
p-value	0.468975153690604
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.7213399735494
Sum Squared Residuals	2585.96475634181

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	31.514	22.3963767546426	9.11762324535736
2	27.071	22.0028863463588	5.06811365364116
3	29.462	21.3171921928765	8.14480780712354
4	26.105	22.0477239506714	4.05727604932864
5	22.397	22.7380394119603	-0.341039411960255
6	23.843	22.7734403451716	1.06955965482840
7	21.705	21.8680317448930	-0.163031744893034
8	18.089	21.9780714431672	-3.88907144316725
9	20.764	22.8528944588151	-2.08889445881513
10	25.316	21.7680033898223	3.54799661017770
11	17.704	22.5633954383366	-4.85939543833664
12	15.548	22.7924009786686	-7.24440097866855
13	28.029	22.9786347930930	5.05036520690698
14	29.383	23.3946391576868	5.98836084231324
15	36.438	23.5149446963981	12.9230553036019
16	32.034	23.9252791822346	8.10872081776539
17	22.679	23.9088329504365	-1.22983295043655
18	24.319	24.4417156797373	-0.122715679737267
19	18.004	23.2775257097891	-5.2735257097891
20	17.537	23.0173598980102	-5.48035989801022
21	20.366	23.0760651434954	-2.71006514349537
22	22.782	23.0613004115932	-0.279300411593194
23	19.169	23.0423203640533	-3.87332036405328
24	13.807	23.0254509403741	-9.21845094037406
25	29.743	22.9384804711830	6.80451952881703
26	25.591	23.3086665671931	2.2823334328069
27	29.096	23.7013666195130	5.39463338048696
28	26.482	23.5039079140399	2.97809208596005
29	22.405	22.6105533888599	-0.205553388859929
30	27.044	22.5372349064077	4.50676509359226
31	17.97	22.1098153277443	-4.1398153277443
32	18.73	22.1764018785293	-3.44640187852926
33	19.684	21.7067621142647	-2.02276211426468
34	19.785	22.359482430226	-2.57448243022599
35	18.479	22.5636924814093	-4.08469248140928
36	10.698	23.0225111324634	-12.3245111324634
37	31.956	23.549642594368	8.40635740563198
38	29.506	23.5625937454821	5.94340625451786
39	34.506	23.0954258370774	11.4105741629226
40	27.165	23.2038034494987	3.96119655050127
41	26.736	23.1405604844626	3.59543951553741
42	23.691	23.7350435069895	-0.0440435069895015
43	18.157	23.7361309060261	-5.57913090602614
44	17.328	23.8010553709582	-6.47305537095817
45	18.205	23.7469520192366	-5.54195201923658
46	20.995	24.3379205470366	-3.34292054703657
47	17.382	24.4834253527347	-7.10142535273471
48	9.367	23.2704735885392	-13.9034735885392
49	31.124	23.7537459254997	7.37025407450026
50	26.551	24.3304430195749	2.22055698042505
51	30.651	24.2181023744189	6.43289762558105
52	25.859	24.4619540352919	1.39704596470807
53	25.1	24.7177336224550	0.382266377545034
54	25.778	24.9108449313981	0.867155068601856
55	20.418	24.4236968824255	-4.00569688242549
56	18.688	24.2957003614354	-5.60770036143542
57	20.424	24.5016986472801	-4.07769864728014
58	24.776	24.7278905285099	0.0481094714900878
59	19.814	23.9448727446768	-4.13087274467675
60	12.738	24.0679703288306	-11.3299703288306
61	31.566	23.9656410268781	7.60035897312191
62	30.111	24.4177919204776	5.69320807952243
63	30.019	24.8494074007358	5.16959259926422
64	31.934	24.5560095217352	7.37799047826484
65	25.826	24.4515054217094	1.37449457829064
66	26.835	23.9241836367837	2.91081636321631
67	20.205	22.8098369427498	-2.60483694274976
68	17.789	22.7896239333217	-5.00062393332173
69	20.52	23.4116265837602	-2.89162658376019
70	22.518	22.7266354655192	-0.208635465519244
71	15.572	21.7044126894602	-6.13241268946023
72	11.509	20.8330847544963	-9.3240847544963
73	25.447	20.8020312178149	4.64496878218513
74	24.09	20.2415027117941	3.8484972882059
75	27.786	19.894152976007	7.89184702399298
76	26.195	20.3519484022371	5.8430515977629
77	20.516	20.9566778511582	-0.440677851158169
78	22.759	21.0572189184493	1.70178108155074
79	19.028	20.6959821127326	-1.66798211273261
80	16.971	21.5198102792170	-4.54881027921703
81	20.036	21.8528266213974	-1.81682662139736
82	22.485	21.8782162114052	0.606783788594769
83	18.73	22.2200923235398	-3.49009232353981
84	14.538	21.4426996582943	-6.90469965829428

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.626954474016643	0.746091051966714	0.373045525983357
9	0.476761926570292	0.953523853140584	0.523238073429708
10	0.347829655297524	0.695659310595048	0.652170344702476
11	0.243345717466518	0.486691434933036	0.756654282533482
12	0.207691324350086	0.415382648700171	0.792308675649914
13	0.336362342286513	0.672724684573026	0.663637657713487
14	0.311955923895476	0.623911847790952	0.688044076104524
15	0.43270493220583	0.86540986441166	0.56729506779417
16	0.469890621597059	0.939781243194118	0.530109378402941
17	0.385109662848931	0.770219325697861	0.614890337151069
18	0.302795889648185	0.60559177929637	0.697204110351815
19	0.264729141569838	0.529458283139676	0.735270858430162
20	0.207828996301788	0.415657992603576	0.792171003698212
21	0.153383747559006	0.306767495118013	0.846616252440994
22	0.151624334757333	0.303248669514666	0.848375665242667
23	0.111751741953843	0.223503483907687	0.888248258046157
24	0.152531250233663	0.305062500467327	0.847468749766337
25	0.254599264275675	0.50919852855135	0.745400735724325
26	0.273383150312409	0.546766300624818	0.726616849687591
27	0.375906597804288	0.751813195608576	0.624093402195712
28	0.331563223479364	0.663126446958727	0.668436776520636
29	0.273545197185710	0.547090394371421	0.72645480281429
30	0.286389009077302	0.572778018154603	0.713610990922698
31	0.235954084242841	0.471908168485681	0.764045915757159
32	0.191075961355059	0.382151922710118	0.808924038644941
33	0.148451556723700	0.296903113447401	0.8515484432763
34	0.117518755132130	0.235037510264261	0.88248124486787
35	0.0991478039383964	0.198295607876793	0.900852196061604
36	0.226528677187786	0.453057354375571	0.773471322812214
37	0.331077471438807	0.662154942877613	0.668922528561193
38	0.328195227558706	0.656390455117412	0.671804772441294
39	0.512021696649851	0.975956606700298	0.487978303350149
40	0.485529141133597	0.971058282267194	0.514470858866403
41	0.474775649925769	0.949551299851537	0.525224350074231
42	0.415791524883852	0.831583049767703	0.584208475116148
43	0.479235874995712	0.958471749991424	0.520764125004288
44	0.545502510524226	0.908994978951547	0.454497489475774
45	0.579657662896902	0.840684674206197	0.420342337103098
46	0.55257568572143	0.89484862855714	0.44742431427857
47	0.595231789855041	0.809536420289918	0.404768210144959
48	0.82784369377514	0.34431261244972	0.17215630622486
49	0.844666731152888	0.310666537694225	0.155333268847112
50	0.808704248284584	0.382591503430831	0.191295751715416
51	0.836603553790458	0.326792892419084	0.163396446209542
52	0.799486461144435	0.401027077711131	0.200513538855565
53	0.755039883715798	0.489920232568404	0.244960116284202
54	0.706809426158033	0.586381147683935	0.293190573841967
55	0.660157234560557	0.679685530878886	0.339842765439443
56	0.641003214764972	0.717993570470056	0.358996785235028
57	0.601189655259416	0.797620689481168	0.398810344740584
58	0.530297517311191	0.939404965377618	0.469702482688809
59	0.482552589172402	0.965105178344805	0.517447410827598
60	0.722592008417032	0.554815983165936	0.277407991582968
61	0.788886255286006	0.422227489427988	0.211113744713994
62	0.776364320339695	0.44727135932061	0.223635679660305
63	0.765822147427542	0.468355705144917	0.234177852572458
64	0.854157674685922	0.291684650628156	0.145842325314078
65	0.833761307905326	0.332477384189348	0.166238692094674
66	0.89681956004271	0.206360879914580	0.103180439957290
67	0.850757148219773	0.298485703560454	0.149242851780227
68	0.82132112498933	0.35735775002134	0.17867887501067
69	0.748773357031045	0.502453285937911	0.251226642968955
70	0.84395048761983	0.312099024760339	0.156049512380170
71	0.792002364812806	0.415995270374389	0.207997635187194
72	0.958848404318428	0.082303191363145	0.0411515956815725
73	0.921071674889003	0.157856650221995	0.0789283251109975
74	0.85224383980844	0.295512320383121	0.147756160191561
75	0.842726338195866	0.314547323608269	0.157273661804134
76	0.996925372529832	0.00614925494033615	0.00307462747016807

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.0144927536231884	NOK
5% type I error level	1	0.0144927536231884	OK
10% type I error level	2	0.0289855072463768	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/10vemj1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/10vemj1292676443.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/17d7p1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/17d7p1292676443.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/27d7p1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/27d7p1292676443.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/3hnoa1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/3hnoa1292676443.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/4hnoa1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/4hnoa1292676443.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/5hnoa1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/5hnoa1292676443.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/6aw6d1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/6aw6d1292676443.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/73nng1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/73nng1292676443.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/83nng1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/83nng1292676443.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/93nng1292676443.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292676323a9a31oojsumjne3/93nng1292676443.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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