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WS 7

*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, 20 Nov 2010 16:26:00 +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/Nov/20/t129027032029ozstnaigfj9a6.htm/, Retrieved Sat, 20 Nov 2010 17:25:20 +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/Nov/20/t129027032029ozstnaigfj9a6.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 «

9 15 6 25 68 14 10 8 23 48 8 10 7 17 44 8 12 9 19 67 14 9 8 29 46 15 18 11 23 54 9 14 9 23 61 11 11 11 21 52 14 11 12 26 46 14 9 6 24 55 6 17 8 25 52 10 21 12 26 76 9 16 9 23 49 11 21 7 29 30 14 14 8 24 75 8 24 20 20 51 11 7 8 23 50 10 9 6 29 38 16 18 16 24 47 8 14 6 22 52 11 13 6 22 66 11 13 6 22 66 7 18 11 17 33 13 14 12 24 48 10 12 8 21 57 9 12 8 24 64 9 9 7 23 58 15 11 9 21 59 13 8 9 24 42 16 5 4 24 39 11 9 6 19 59 6 11 8 26 37 14 11 8 24 49 4 15 4 28 80 12 16 14 22 62 10 12 8 23 44 14 14 10 24 53 9 13 6 23 58 10 10 8 23 69 14 18 10 30 63 14 17 11 20 36 10 12 8 23 38 9 13 8 21 46 14 13 10 27 56 8 11 8 12 37 9 13 10 15 51 8 12 7 22 44 10 12 8 27 58 9 12 8 21 37 9 12 7 21 65 9 13 6 21 48 9 17 9 21 53 11 18 5 18 51 15 7 5 24 39 8 17 7 24 64 12 14 7 28 47 8 12 7 25 47 14 9 9 14 64 11 9 5 30 59 10 13 8 19 54 12 10 8 29 55 9 12 9 25 72 13 10 6 25 58 14 11 8 25 59 15 13 8 16 36 8 6 6 25 62 7 7 4 28 63 10 13 6 24 50 10 11 5 24 70 11 9 6 22 59 8 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

Intrinsic[t] = + 54.2386642084418 -0.587744609266962Doubts[t] + 0.0524751067601769PerantalExpectations[t] -0.423549311998046ParentalCriticism[t] + 0.371892413952631Organization[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)	54.2386642084418	9.998049	5.4249	1e-06	0
Doubts	-0.587744609266962	0.452204	-1.2997	0.197379	0.09869
PerantalExpectations	0.0524751067601769	0.395455	0.1327	0.894763	0.447382
ParentalCriticism	-0.423549311998046	0.546946	-0.7744	0.440955	0.220477
Organization	0.371892413952631	0.32555	1.1423	0.256674	0.128337

Multiple Linear Regression - Regression Statistics
Multiple R	0.219898124366846
R-squared	0.0483551851000568
Adjusted R-squared	0.00136037942598555
F-TEST (value)	1.02894744230714
F-TEST (DF numerator)	4
F-TEST (DF denominator)	81
p-value	0.39745463009183
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	11.0129215191381
Sum Squared Residuals	9824.03967132238

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	68	56.4921038032693	11.5078961967307
2	48	51.7001217712323	-3.70012177123229
3	44	53.4187842551163	-9.41878425511632
4	67	53.4204206725458	13.5795793274542
5	46	53.8790011481879	-7.8790011481879
6	54	50.2615300800526	3.73846991994739
7	61	54.4251959326098	6.57480406739024
8	52	51.501397941894	0.498602058106046
9	46	51.1740768718582	-5.17407687185818
10	55	52.8666377024208	2.13336229757916
11	52	57.5131892205945	-5.51318922059449
12	76	54.0498063765278	21.9501936234722
13	49	54.5301461461301	-5.53014614613011
14	30	56.695485569109	-26.6954855691090
15	75	52.2819146122256	22.7180853877744
16	51	49.7629719356421	1.2370280643579
17	50	53.3059302787526	-3.30593027875264
18	38	57.0770782092518	-19.0770782092518
19	47	47.9279313247481	-0.927931324748048
20	52	55.9116960639182	-3.91169606391823
21	66	54.0959871293572	11.9040128706428
22	66	54.0959871293572	11.9040128706428
23	33	52.7321324704725	-19.7321324704725
24	48	51.1754619735004	-3.17546197350041
25	57	53.4122655939152	3.58773440608477
26	64	55.1156874450401	8.88431255495992
27	58	55.009919022805	2.99008097719503
28	59	49.9975181288222	9.0024818711778
29	42	52.1312592689335	-10.1312592689335
30	39	52.3283466808423	-13.3283466808423
31	59	52.7704094604586	6.22959053954143
32	37	57.570230993986	-20.5702309939861
33	49	52.1244892919451	-3.1244892919451
34	80	61.3936027154581	18.6063972845419
35	62	50.2772733443864	11.7227266556136
36	44	54.1560504218205	-10.1560504218205
37	53	51.4348159882295	1.56518401177046
38	58	55.6433687618437	2.35663123815628
39	69	54.0511002083001	14.9488997916999
40	63	53.876070898986	9.12392910101397
41	36	49.6811223407015	-13.6811223407015
42	38	54.1560504218205	-16.1560504218205
43	46	54.0524853099424	-8.05248530994237
44	56	52.4980181233273	3.50198187667275
45	37	51.1882479801153	-14.1882479801153
46	51	50.9740322022305	0.0259677977695099
47	44	55.3831965383998	-11.3831965383998
48	58	55.643620077631	2.35637992236898
49	37	54.0000102031822	-17.0000102031822
50	65	54.4235595151802	10.5764404848198
51	48	54.8995839339385	-6.89958393393846
52	53	53.838836424985	-0.838836424985029
53	51	53.2943423193456	-2.29434231934557
54	39	52.5974921916316	-13.5974921916316
55	64	56.389356900106	7.61064309989403
56	47	55.3685227985681	-8.36852279856812
57	47	56.4988737802577	-9.49887378025772
58	64	47.8770656269004	16.1229343730996
59	59	57.2847753259356	1.71522467406445
60	54	52.7209558727701	1.27904412722986
61	55	55.106965473482	-0.106965473482001
62	72	55.0640305469947	16.9359694530053
63	58	53.8787498324006	4.12125016759940
64	59	52.4963817058977	6.50361829410227
65	36	48.6665555845774	-12.6665555845774
66	62	56.6075724516947	5.39242754830529
67	63	59.2105680335758	3.78943196642417
68	50	55.4275165665294	-5.42751656652939
69	70	55.7461156650071	14.2538843349929
70	59	53.8860867023165	5.11391329768354
71	73	52.7877891422219	20.2122108577781
72	62	55.3317909561417	6.66820904385829
73	41	52.7225922901997	-11.7225922901997
74	56	54.6315079476512	1.36849205234875
75	52	54.9531285652007	-2.95312856520069
76	54	51.0547684956463	2.94523150435371
77	73	51.3336784939787	21.6663215060213
78	40	50.6812396642641	-10.6812396642641
79	41	55.0264804958535	-14.0264804958535
80	54	54.2596155336986	-0.259615533698615
81	42	49.9004716781452	-7.90047167814517
82	70	53.6718709244317	16.3281290755683
83	51	51.7472120026463	-0.747212002646311
84	60	56.2243433941223	3.77565660587770
85	49	54.788115059217	-5.78811505921701
86	52	55.4373080625895	-3.43730806258953

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.286237989499656	0.572475978999312	0.713762010500344
9	0.168125989608239	0.336251979216478	0.83187401039176
10	0.169105116511431	0.338210233022863	0.830894883488569
11	0.276396613868645	0.55279322773729	0.723603386131355
12	0.325449884273283	0.650899768546565	0.674550115726717
13	0.341447103244330	0.682894206488661	0.65855289675567
14	0.779269721870058	0.441460556259885	0.220730278129942
15	0.917829560226778	0.164340879546444	0.0821704397732218
16	0.907251874633806	0.185496250732389	0.0927481253661944
17	0.867027004570496	0.265945990859009	0.132972995429504
18	0.869675246106047	0.260649507787906	0.130324753893953
19	0.835386353697692	0.329227292604616	0.164613646302308
20	0.789053836279615	0.421892327440769	0.210946163720385
21	0.768523925605712	0.462952148788575	0.231476074394288
22	0.742755747781793	0.514488504436414	0.257244252218207
23	0.898405771382532	0.203188457234936	0.101594228617468
24	0.864529786629819	0.270940426740363	0.135470213370181
25	0.823744565260079	0.352510869479843	0.176255434739921
26	0.819997977456535	0.36000404508693	0.180002022543465
27	0.777312445008661	0.445375109982677	0.222687554991339
28	0.739490396907365	0.521019206185271	0.260509603092635
29	0.722930881718046	0.554138236563909	0.277069118281954
30	0.764212332372556	0.471575335254888	0.235787667627444
31	0.716525056154643	0.566949887690714	0.283474943845357
32	0.779456248320404	0.441087503359193	0.220543751679596
33	0.732227210613075	0.535545578773851	0.267772789386925
34	0.862275996236708	0.275448007526584	0.137724003763292
35	0.871764638652674	0.256470722694651	0.128235361347326
36	0.864000997163627	0.271998005672746	0.135999002836373
37	0.825838435420207	0.348323129159586	0.174161564579793
38	0.781912357383288	0.436175285233425	0.218087642616712
39	0.823185121008296	0.353629757983407	0.176814878991704
40	0.82456684393758	0.350866312124839	0.175433156062420
41	0.848242268151872	0.303515463696255	0.151757731848128
42	0.883888156617435	0.232223686765129	0.116111843382565
43	0.866513182954505	0.266973634090989	0.133486817045495
44	0.834325690336979	0.331348619326043	0.165674309663021
45	0.867622920727188	0.264754158545624	0.132377079272812
46	0.829867788876663	0.340264422246675	0.170132211123337
47	0.838185520215485	0.323628959569030	0.161814479784515
48	0.797669260851261	0.404661478297478	0.202330739148739
49	0.86856454037833	0.262870919243338	0.131435459621669
50	0.860028496782855	0.279943006434291	0.139971503217145
51	0.845367849244923	0.309264301510155	0.154632150755077
52	0.801422900487789	0.397154199024422	0.198577099512211
53	0.754851442779486	0.490297114441028	0.245148557220514
54	0.816543573448037	0.366912853103926	0.183456426551963
55	0.813690621779944	0.372618756440112	0.186309378220056
56	0.784595861551249	0.430808276897502	0.215404138448751
57	0.780258030809199	0.439483938381602	0.219741969190801
58	0.815081457405477	0.369837085189047	0.184918542594523
59	0.774370875756136	0.451258248487728	0.225629124243864
60	0.715543923908292	0.568912152183416	0.284456076091708
61	0.654635688610498	0.690728622779004	0.345364311389502
62	0.756340513416382	0.487318973167236	0.243659486583618
63	0.694345734570089	0.611308530859822	0.305654265429911
64	0.643471300594002	0.713057398811997	0.356528699405998
65	0.667335068736981	0.665329862526038	0.332664931263019
66	0.602683285658674	0.794633428682652	0.397316714341326
67	0.567837885510869	0.864324228978261	0.432162114489131
68	0.539990513491816	0.920018973016367	0.460009486508184
69	0.49298514144767	0.98597028289534	0.50701485855233
70	0.41515235847481	0.83030471694962	0.58484764152519
71	0.763543111868891	0.472913776262218	0.236456888131109
72	0.767914049675977	0.464171900648047	0.232085950324023
73	0.685484474732587	0.629031050534826	0.314515525267413
74	0.575842342385135	0.84831531522973	0.424157657614865
75	0.459246177626421	0.918492355252842	0.540753822373579
76	0.439661830523934	0.879323661047868	0.560338169476066
77	0.462435076050967	0.924870152101934	0.537564923949033
78	0.53817141831744	0.92365716336512	0.46182858168256

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	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/106hlu1290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/106hlu1290270350.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/1hy6i1290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/1hy6i1290270350.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/2a7n31290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/2a7n31290270350.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/3a7n31290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/3a7n31290270350.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/4a7n31290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/4a7n31290270350.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/5lh4o1290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/5lh4o1290270350.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/6lh4o1290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/6lh4o1290270350.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/7v84r1290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/7v84r1290270350.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/8v84r1290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/8v84r1290270350.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/96hlu1290270350.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/20/t129027032029ozstnaigfj9a6/96hlu1290270350.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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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