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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 13:05:30 +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/t1292677576r29pkb5d4yd6v05.htm/, Retrieved Sat, 18 Dec 2010 14:06:19 +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/t1292677576r29pkb5d4yd6v05.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 0 8.3 1.2 27071 -13 4 8.2 1.7 29462 -18 5 8 1.8 26105 -11 -7 7.9 1.5 22397 -9 -2 7.6 1 23843 -10 1 7.6 1.6 21705 -13 3 8.3 1.5 18089 -11 -2 8.4 1.8 20764 -5 -6 8.4 1.8 25316 -15 10 8.4 1.6 17704 -6 -9 8.4 1.9 15548 -6 0 8.6 1.7 28029 -3 -3 8.9 1.6 29383 -1 -2 8.8 1.3 36438 -3 2 8.3 1.1 32034 -4 1 7.5 1.9 22679 -6 2 7.2 2.6 24319 0 -6 7.4 2.3 18004 -4 4 8.8 2.4 17537 -2 -2 9.3 2.2 20366 -2 0 9.3 2 22782 -6 4 8.7 2.9 19169 -7 1 8.2 2.6 13807 -6 -1 8.3 2.3 29743 -6 0 8.5 2.3 25591 -3 -3 8.6 2.6 29096 -2 -1 8.5 3.1 26482 -5 3 8.2 2.8 22405 -11 6 8.1 2.5 27044 -11 0 7.9 2.9 17970 -11 0 8.6 3.1 18730 -10 -1 8.7 3.1 19684 -14 4 8.7 3.2 19785 -8 -6 8.5 2.5 18479 -9 1 8.4 2.6 10698 -5 -4 8.5 2.9 31956 -1 -4 8.7 2.6 29506 -2 1 8.7 2.4 34506 -5 3 8.6 1.7 27165 -4 -1 8.5 2 26736 -6 2 8.3 2.2 23691 -2 -4 8 1.9 18157 -2 0 8.2 1.6 17328 -2 0 8.1 1.6 18205 -2 0 8.1 1.2 20995 2 -4 8 1.2 17382 1 1 7.9 1.5 9367 -8 9 7.9 1.6 31124 -1 -7 8 1.7 26551 1 -2 8 1.8 30651 -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	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

Inschrijvingen[t] = + 15316.8874709035 + 114.501933947428Consumentenvertrouwen[t] + 165.694377258374Evolutie_consumentenvertrouwen[t] -244.267372838732Totaal_Werkloosheid[t] + 80.5212962136631Algemene_index[t] + 17577.3863681635M1[t] + 14741.720068206M2[t] + 18266.4426680063M3[t] + 15324.4906635976M4[t] + 10791.1023094009M5[t] + 12224.0696155823M6[t] + 6632.16474600372M7[t] + 5502.95942190991M8[t] + 7345.2172974919M9[t] + 9767.73251289017M10[t] + 5439.17349325196M11[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)	15316.8874709035	3262.83232	4.6944	1.3e-05	7e-06
Consumentenvertrouwen	114.501933947428	33.607292	3.4071	0.001107	0.000553
Evolutie_consumentenvertrouwen	165.694377258374	64.826469	2.556	0.012829	0.006414
Totaal_Werkloosheid	-244.267372838732	375.124661	-0.6512	0.517136	0.258568
Algemene_index	80.5212962136631	152.771644	0.5271	0.599861	0.29993
M1	17577.3863681635	1032.414246	17.0255	0	0
M2	14741.720068206	1014.779998	14.527	0	0
M3	18266.4426680063	1007.970509	18.122	0	0
M4	15324.4906635976	1018.304037	15.049	0	0
M5	10791.1023094009	1024.900151	10.5289	0	0
M6	12224.0696155823	1039.112564	11.764	0	0
M7	6632.16474600372	1008.910848	6.5736	0	0
M8	5502.95942190991	1025.569936	5.3658	1e-06	1e-06
M9	7345.2172974919	1014.88181	7.2375	0	0
M10	9767.73251289017	1008.579525	9.6846	0	0
M11	5439.17349325196	1009.301574	5.389	1e-06	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.954492285384403
R-squared	0.91105552285834
Adjusted R-squared	0.891435417606504
F-TEST (value)	46.4347928395061
F-TEST (DF numerator)	15
F-TEST (DF denominator)	68
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1880.51879878566
Sum Squared Residuals	240471864.775865

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	31514	29932.9627944351	1581.03720556486
2	27071	27366.7536531121	-295.753653112101
3	29462	30541.5665646227	-1079.56656462271
4	26105	26413.0659191653	-308.065919165289
5	22397	22970.1728829001	-573.172882900146
6	23843	24834.0341646374	-991.034164637404
7	21705	19050.9729571248	2654.02704287516
8	18089	17322.0292662142	766.970733785756
9	20764	19188.5212364473	1575.4787635527
10	25316	23101.0228892625	2214.97711073745
11	17704	16678.9444961062	1025.05550389382
12	15548	12666.0626643691	2881.93733563089
13	28029	30008.5393611268	-1979.53936112682
14	29383	27567.8416547423	1815.15834525771
15	36438	31632.3673228578	4805.63267714219
16	32034	28670.0499424852	3363.95005751476
17	22679	24202.9972168533	-1523.99721685328
18	24319	24924.4112452204	-605.411245220372
19	18004	20197.520220083	-2193.52022008299
20	17537	18164.9145546717	-627.9145546717
21	20366	20322.4569255277	43.5430744723003
22	22782	23168.7715044653	-386.771504465299
23	19169	18326.6047166598	842.395283340197
24	13807	12621.9612766905	1185.03872330945
25	29743	30316.1885475447	-573.188547544722
26	25591	27326.6745692346	-1735.67456923457
27	29096	31361.9752428897	-2265.9752428897
28	26482	28788.4187686597	-2306.41876865975
29	22405	24065.3722909734	-1660.37229097341
30	27044	24585.2353266577	2458.76467334226
31	17970	18838.4475553348	-868.447555334813
32	18730	17633.6230506462	1096.37694935382
33	19684	19854.3972063517	-170.397206351698
34	19785	21299.468820069	-1514.46882006898
35	18479	18048.7473741972	430.252625802798
36	10698	12238.8393820233	-1540.83938202331
37	31956	30201.2236225447	1754.77637745528
38	29506	28063.4230156889	1442.57698431111
39	34506	31544.0903980979	2961.90960190209
40	27165	28102.4459447511	-937.445944751128
41	26736	23902.0945882452	2833.90541175479
42	23691	24848.0271896536	-1157.02718965356
43	18157	19845.8899656767	-1688.88996567666
44	17328	18741.1113788667	-1413.11137886673
45	18205	20551.1607359632	-2346.16073596325
46	20995	22793.3329154016	-1798.33291540162
47	17382	19227.3269742558	-1845.32697425582
48	9367	14091.2432231654	-4724.24322316537
49	31124	29802.6584851644	1321.34151483559
50	26551	28032.520069015	-1481.52006901496
51	30651	32015.4430472377	-1364.44304723773
52	25859	28564.0982210956	-2705.09822109558
53	25100	24560.8125624188	539.18743758117
54	25778	26167.1059983305	-389.105998330503
55	20418	20446.6635558852	-28.6635558852189
56	18688	19061.9590062893	-373.959006289257
57	20424	20844.01123495	-420.011234949954
58	24776	23271.6989143868	1504.30108561315
59	19814	19523.5462825332	290.453717466803
60	12738	12686.9931599038	51.0068400961811
61	31566	31037.3237049243	528.676295075656
62	30111	27773.5969247063	2337.40307529368
63	30019	31933.0999051043	-1914.09990510426
64	31934	29277.5203638321	2656.47963616789
65	25826	24337.5408952891	1488.45910471087
66	26835	25492.1101621683	1342.88983783166
67	20205	19386.1327069098	818.86729309018
68	17789	17734.0396190028	54.9603809972058
69	20520	19911.0604412365	608.939558763507
70	22518	23156.6473443714	-638.6473443714
71	15572	17702.9416611307	-2130.94166113068
72	11509	11475.3327515612	33.6672484387603
73	25447	28080.1034842598	-2633.10348425984
74	24090	26172.1901135009	-2082.19011350087
75	27786	28929.4575191899	-1143.45751918987
76	26195	25958.4008400109	236.599159989092
77	20516	21620.00956332	-1104.00956331999
78	22759	23418.0759133321	-659.075913332084
79	19028	17721.3730389857	1306.62696101435
80	16971	16474.3231243091	496.676875690907
81	20036	19327.3922195236	708.607780476394
82	22485	21866.0576120433	618.942387956692
83	18730	17341.8884951171	1388.11150488288
84	14538	12424.5675422866	2113.43245771338

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.163851060548097	0.327702121096194	0.836148939451903
20	0.193343765089943	0.386687530179886	0.806656234910057
21	0.128648286352451	0.257296572704902	0.871351713647549
22	0.0927533831058624	0.185506766211725	0.907246616894138
23	0.0475050012418946	0.0950100024837893	0.952494998758105
24	0.0273630030701381	0.0547260061402762	0.972636996929862
25	0.0334323135115983	0.0668646270231966	0.966567686488402
26	0.0194869873377169	0.0389739746754338	0.980513012662283
27	0.0102841701520601	0.0205683403041203	0.98971582984794
28	0.00535764501932559	0.0107152900386512	0.994642354980674
29	0.052912347454351	0.105824694908702	0.947087652545649
30	0.596251449639815	0.80749710072037	0.403748550360185
31	0.547805838566839	0.904388322866322	0.452194161433161
32	0.550665286213616	0.898669427572767	0.449334713786384
33	0.467721879044913	0.935443758089825	0.532278120955087
34	0.404841253662933	0.809682507325865	0.595158746337067
35	0.331727806346835	0.663455612693669	0.668272193653165
36	0.294705279255508	0.589410558511016	0.705294720744492
37	0.381562246220356	0.763124492440712	0.618437753779644
38	0.372721943391621	0.745443886783241	0.62727805660838
39	0.529229668140195	0.94154066371961	0.470770331859805
40	0.453218426700106	0.906436853400212	0.546781573299894
41	0.706007247609381	0.587985504781238	0.293992752390619
42	0.659064341901076	0.681871316197847	0.340935658098924
43	0.67203206380206	0.65593587239588	0.32796793619794
44	0.663915198956189	0.672169602087622	0.336084801043811
45	0.701101443785435	0.59779711242913	0.298898556214565
46	0.692621376413256	0.614757247173488	0.307378623586744
47	0.676133636745158	0.647732726509684	0.323866363254842
48	0.911294017886901	0.177411964226197	0.0887059821130987
49	0.909190602308552	0.181618795382897	0.0908093976914485
50	0.891246093040001	0.217507813919998	0.108753906959999
51	0.849401052969133	0.301197894061733	0.150598947030866
52	0.966983985490326	0.0660320290193476	0.0330160145096738
53	0.958121368699836	0.083757262600328	0.041878631300164
54	0.96353451619312	0.0729309676137607	0.0364654838068803
55	0.971091914149801	0.0578161717003971	0.0289080858501985
56	0.960747779845796	0.0785044403084071	0.0392522201542036
57	0.956014050896146	0.0879718982077088	0.0439859491038544
58	0.936313954084326	0.127372091831349	0.0636860459156743
59	0.9041414296089	0.191717140782199	0.0958585703910997
60	0.937536002285286	0.124927995429428	0.0624639977147142
61	0.901226127391088	0.197547745217824	0.098773872608912
62	0.897539462701968	0.204921074596064	0.102460537298032
63	0.995806151286524	0.0083876974269511	0.00419384871347555
64	0.997221925275208	0.00555614944958336	0.00277807472479168
65	0.989120005094856	0.0217599898102877	0.0108799949051438

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0425531914893617	NOK
5% type I error level	6	0.127659574468085	NOK
10% type I error level	15	0.319148936170213	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/10j3tf1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/10j3tf1292677520.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/1u2wl1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/1u2wl1292677520.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/2ntdo1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/2ntdo1292677520.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/3ntdo1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/3ntdo1292677520.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/4ntdo1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/4ntdo1292677520.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/5glur1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/5glur1292677520.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/6glur1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/6glur1292677520.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/7qcuc1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/7qcuc1292677520.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/8qcuc1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/8qcuc1292677520.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/9j3tf1292677520.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292677576r29pkb5d4yd6v05/9j3tf1292677520.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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