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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 19:24:22 +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/t1292181747w09ygzm9tk4943g.htm/, Retrieved Sun, 12 Dec 2010 20:22:37 +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/t1292181747w09ygzm9tk4943g.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 «

26105 29462 27071 31514 22397 26105 29462 27071 23843 22397 26105 29462 21705 23843 22397 26105 18089 21705 23843 22397 20764 18089 21705 23843 25316 20764 18089 21705 17704 25316 20764 18089 15548 17704 25316 20764 28029 15548 17704 25316 29383 28029 15548 17704 36438 29383 28029 15548 32034 36438 29383 28029 22679 32034 36438 29383 24319 22679 32034 36438 18004 24319 22679 32034 17537 18004 24319 22679 20366 17537 18004 24319 22782 20366 17537 18004 19169 22782 20366 17537 13807 19169 22782 20366 29743 13807 19169 22782 25591 29743 13807 19169 29096 25591 29743 13807 26482 29096 25591 29743 22405 26482 29096 25591 27044 22405 26482 29096 17970 27044 22405 26482 18730 17970 27044 22405 19684 18730 17970 27044 19785 19684 18730 17970 18479 19785 19684 18730 10698 18479 19785 19684 31956 10698 18479 19785 29506 31956 10698 18479 34506 29506 31956 10698 27165 34506 29506 31956 26736 27165 34506 29506 23691 26736 27165 34506 18157 23691 26736 27165 17328 18157 23691 26736 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

X[t] = + 15528.2166014861 + 0.283174803792777Y_1[t] + 0.298954678015929Y_2[t] -0.0123849708445849Y_3[t] -3780.91761340949M1[t] -8590.0139987651M2[t] -4995.57765924133M3[t] -9992.05694547716M4[t] -10063.8242839980M5[t] -6319.54201284951M6[t] -3551.14205129836M7[t] -9582.68297879728M8[t] -14587.2298411741M9[t] + 5113.37309057484M10[t] -64.3210395853369M11[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)	15528.2166014861	3489.29975	4.4502	2.9e-05	1.4e-05
Y_1	0.283174803792777	0.113263	2.5002	0.014569	0.007284
Y_2	0.298954678015929	0.114488	2.6112	0.010865	0.005432
Y_3	-0.0123849708445849	0.114838	-0.1078	0.914401	0.4572
M1	-3780.91761340949	2225.513895	-1.6989	0.093429	0.046715
M2	-8590.0139987651	1879.994129	-4.5692	1.9e-05	9e-06
M3	-4995.57765924133	2534.664761	-1.9709	0.052376	0.026188
M4	-9992.05694547716	2348.815557	-4.2541	5.9e-05	3e-05
M5	-10063.8242839980	2108.647425	-4.7726	9e-06	4e-06
M6	-6319.54201284951	2569.088047	-2.4598	0.016172	0.008086
M7	-3551.14205129836	2071.511517	-1.7143	0.090554	0.045277
M8	-9582.68297879728	1758.231334	-5.4502	1e-06	0
M9	-14587.2298411741	1964.628892	-7.4249	0	0
M10	5113.37309057484	2739.961795	1.8662	0.065868	0.032934
M11	-64.3210395853369	2471.396257	-0.026	0.979305	0.489652

Multiple Linear Regression - Regression Statistics
Multiple R	0.949610283391202
R-squared	0.901759690322319
Adjusted R-squared	0.883662791171167
F-TEST (value)	49.8295140394219
F-TEST (DF numerator)	14
F-TEST (DF denominator)	76
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1935.56350504491
Sum Squared Residuals	284726862.236691

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	26105	27792.8971747924	-1687.89717479238
2	22397	22803.0100337030	-406.010033703028
3	23843	24314.2308813743	-471.2308813743
4	21705	18660.2747624650	3044.72523753496
5	18089	18461.2916297380	-372.291629737961
6	20764	20524.5400409325	239.459959067528
7	25316	22995.8915545894	2320.10844541058
8	17704	19097.8501522218	-1393.85015222184
9	15548	13265.4885806936	2282.51141930637
10	28029	30023.5472391236	-1994.54723912357
11	29383	27829.8859473677	1553.11405263233
12	36438	32035.5810047462	4402.41899525382
13	32034	30502.6694450170	1531.33055498297
14	22679	26538.8272266368	-3859.82722663684
15	24319	26080.1909053885	-1761.19090538848
16	18004	18805.9406961333	-801.940696133347
17	17537	17552.0715458583	-15.0715458583007
18	20366	19255.9010397799	1110.09896022013
19	22782	22764.0017775109	17.9982224890889
20	19169	18268.1377414668	900.86225853318
21	13807	12927.7177325538	879.282267446175
22	29743	29999.8920251339	-256.892025133856
23	25591	27776.6234843554	-2185.62348435545
24	29096	31495.7527011237	-2399.75270112368
25	26482	27268.7360565064	-786.73605650644
26	22405	22818.6792794291	-413.679279429058
27	27044	24433.7350927458	2610.26490725424
28	17970	19564.4398128214	-1594.43981282143
29	18730	18360.4885821342	369.511417865829
30	19684	19549.8150761006	134.184923899377
31	19785	22927.9505812060	-3142.95058120596
32	18479	17200.8004938754	1278.19950612459
33	10698	11844.8064980391	-1146.80649803908
34	31956	28950.3405899323	3005.65941006766
35	29506	27482.3848610801	2023.61513891990
36	34506	33304.4736347775	1201.52636522253
37	27165	29943.7113689787	-2778.71136897866
38	26736	24580.9453176292	2155.05468237085
39	23691	25797.3485207880	-2106.34852078796
40	18157	19901.2684711044	-1744.26847110439
41	17328	17357.4079263281	-29.4079263281101
42	18205	19250.2353332140	-1045.23533321402
43	20995	22087.6845982702	-1092.68459827017
44	17382	17118.6517668032	263.348233196776
45	9367	11914.2162705568	-2547.21627055681
46	31124	28230.4958295787	2893.50417042127
47	26551	26862.4610609018	-311.461060901819
48	30651	32235.4461936547	-1584.4461936547
49	25859	27978.9657225631	-2119.96572256313
50	25100	23095.2463289701	2004.75367102987
51	25778	24991.3837949000	786.616205099955
52	20418	20019.3392053089	398.66079469112
53	18688	18641.8463830246	46.1536169754334
54	20424	20285.4421592136	138.557840786435
55	24776	23094.6254309084	1681.3745690916
56	19814	18835.8725701124	978.127429887575
57	12738	13705.7627806549	-967.762780654945
58	31566	29865.3082953355	1700.69170466445
59	30111	27965.2802946759	2145.71970532411
60	30019	33333.9367261229	-3314.93672612293
61	31934	28858.8037431895	3075.19625681051
62	25826	24582.5034092985	1243.49659070154
63	26835	27020.9456729742	-185.945672974149
64	20205	20460.4573712766	-255.457371276559
65	17789	18888.5337556464	-1099.53375564637
66	20520	19954.0997500037	565.90024999626
67	22518	22855.6879553261	-337.687955326084
68	15572	18236.2976010271	-2664.29760102714
69	11509	11828.3066428049	-319.306642804933
70	25447	28277.0859814977	-2830.08598149773
71	24090	25917.6554173090	-1827.65541730904
72	27786	29814.8586868751	-2028.85868687515
73	26195	26502.2519265843	-307.251926584328
74	20516	22364.3673237774	-1848.36732377739
75	22759	23829.2422075970	-1070.24220759704
76	19028	17789.8648784297	1238.13512157031
77	16971	17402.4619391741	-431.461939174095
78	20036	19421.0742456390	614.925754360975
79	22485	22488.6635343574	-3.66353435742568
80	18730	18092.3896744931	637.610325506859
81	14538	12718.7014946968	1819.29850530322
82	27561	30079.3300393982	-2518.33003939823
83	25985	27382.7089343100	-1397.70893431002
84	34670	30945.9510526999	3724.04894730011
85	32066	28991.9645623685	3074.03543763146
86	27186	26061.4210805559	1124.57891944405
87	29586	27387.9229242323	2198.07707576774
88	21359	21644.4148024607	-285.414802460667
89	21553	20020.8982380964	1532.10176190358
90	19573	21330.8923551167	-1757.89235511668
91	24256	23698.4945678316	557.50543216837

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.441396375022166	0.882792750044332	0.558603624977834
19	0.600318068217923	0.799363863564154	0.399681931782077
20	0.537086816887232	0.925826366225535	0.462913183112768
21	0.448098021551798	0.896196043103596	0.551901978448202
22	0.332048430678689	0.664096861357379	0.66795156932131
23	0.343963323986153	0.687926647972306	0.656036676013847
24	0.840588087839614	0.318823824320772	0.159411912160386
25	0.779671048016554	0.440657903966891	0.220328951983446
26	0.711823983774338	0.576352032451324	0.288176016225662
27	0.702103713406819	0.595792573186362	0.297896286593181
28	0.762690795892442	0.474618408215116	0.237309204107558
29	0.702469702624331	0.595060594751338	0.297530297375669
30	0.625294990562958	0.749410018874084	0.374705009437042
31	0.761130745191144	0.477738509617712	0.238869254808856
32	0.722237771639335	0.555524456721329	0.277762228360665
33	0.743374407344538	0.513251185310924	0.256625592655462
34	0.775399573621534	0.449200852756931	0.224600426378466
35	0.770630355020125	0.45873928995975	0.229369644979875
36	0.731557534383313	0.536884931233374	0.268442465616687
37	0.76529354528691	0.469412909426179	0.234706454713090
38	0.82279419022098	0.354411619558042	0.177205809779021
39	0.821528088740679	0.356943822518642	0.178471911259321
40	0.806281562557162	0.387436874885676	0.193718437442838
41	0.754837022425662	0.490325955148675	0.245162977574338
42	0.719078766851162	0.561842466297676	0.280921233148838
43	0.674834009747382	0.650331980505236	0.325165990252618
44	0.61085354955047	0.778292900899059	0.389146450449529
45	0.665462908028117	0.669074183943766	0.334537091971883
46	0.752680056624289	0.494639886751421	0.247319943375711
47	0.696599106193974	0.606801787612051	0.303400893806025
48	0.688048764184126	0.623902471631748	0.311951235815874
49	0.796404689374823	0.407190621250355	0.203595310625177
50	0.789668153582554	0.420663692834891	0.210331846417445
51	0.736306166786221	0.527387666427557	0.263693833213779
52	0.694388200276487	0.611223599447026	0.305611799723513
53	0.626838288925003	0.746323422149993	0.373161711074997
54	0.556561797352774	0.886876405294453	0.443438202647226
55	0.53496144955604	0.93007710088792	0.46503855044396
56	0.482584874616921	0.965169749233843	0.517415125383079
57	0.430028626046065	0.86005725209213	0.569971373953935
58	0.520675232888065	0.95864953422387	0.479324767111935
59	0.536803826427483	0.926392347145033	0.463196173572517
60	0.749918166183122	0.500163667633756	0.250081833816878
61	0.76743751394201	0.465124972115979	0.232562486057989
62	0.732566275179782	0.534867449640436	0.267433724820218
63	0.708930713588155	0.582138572823689	0.291069286411845
64	0.63049459485146	0.739010810297081	0.369505405148540
65	0.565364287765549	0.869271424468902	0.434635712234451
66	0.50818798402215	0.9836240319557	0.49181201597785
67	0.409211889894579	0.818423779789158	0.590788110105421
68	0.455129500334603	0.910259000669205	0.544870499665397
69	0.366768469503080	0.733536939006159	0.633231530496920
70	0.318832163880075	0.63766432776015	0.681167836119925
71	0.232479720560809	0.464959441121619	0.76752027943919
72	0.54544978404701	0.90910043190598	0.45455021595299
73	0.503492250011608	0.993015499976783	0.496507749988392

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/Dec/12/t1292181747w09ygzm9tk4943g/10ad7f1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/10ad7f1292181853.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/1e29o1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/1e29o1292181853.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/2e29o1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/2e29o1292181853.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/3e29o1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/3e29o1292181853.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/47c8r1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/47c8r1292181853.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/57c8r1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/57c8r1292181853.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/67c8r1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/67c8r1292181853.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/7z37u1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/7z37u1292181853.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/8ad7f1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/8ad7f1292181853.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/9ad7f1292181853.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292181747w09ygzm9tk4943g/9ad7f1292181853.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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