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Multiple Regression (1)

*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: Fri, 17 Dec 2010 14:44:29 +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/17/t1292597990qlb7z4gr97spczs.htm/, Retrieved Fri, 17 Dec 2010 16:00:07 +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/17/t1292597990qlb7z4gr97spczs.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 «

10102 8863 8366 8236 12008 8463 10102 8626 8253 9169 9114 8463 8863 7733 8788 8563 9114 10102 8366 8417 8872 8563 8463 8626 8247 8301 8872 9114 8863 8197 8301 8301 8563 10102 8236 8278 8301 8872 8463 8253 7736 8278 8301 9114 7733 7973 7736 8301 8563 8366 8268 7973 8278 8872 8626 9476 8268 7736 8301 8863 11100 9476 7973 8301 10102 8962 11100 8268 8278 8463 9173 8962 9476 7736 9114 8738 9173 11100 7973 8563 8459 8738 8962 8268 8872 8078 8459 9173 9476 8301 8411 8078 8738 11100 8301 8291 8411 8459 8962 8278 7810 8291 8078 9173 7736 8616 7810 8411 8738 7973 8312 8616 8291 8459 8268 9692 8312 7810 8078 9476 9911 9692 8616 8411 11100 8915 9911 8312 8291 8962 9452 8915 9692 7810 9173 9112 9452 9911 8616 8738 8472 9112 8915 8312 8459 8230 8472 9452 9692 8078 8384 8230 9112 9911 8411 8625 8384 8472 8915 8291 8221 8625 8230 9452 7810 8649 8221 8384 9112 8616 8625 8649 8625 8472 8312 10443 8625 8221 8230 9692 10357 10443 8649 8384 9911 8586 10357 8625 8625 8915 8892 8586 10443 8221 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	8 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation

Yt[t] = + 3000.29080206064 + 0.241703589564587`Yt-1`[t] -0.0637255373607908`Yt-3`[t] -0.105740638589231`Yt-6`[t] + 0.576198585815679`Yt-12 `[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)	3000.29080206064	1347.539397	2.2265	0.028829	0.014415
`Yt-1`	0.241703589564587	0.079842	3.0273	0.003331	0.001666
`Yt-3`	-0.0637255373607908	0.07025	-0.9071	0.367098	0.183549
`Yt-6`	-0.105740638589231	0.0802	-1.3185	0.191158	0.095579
`Yt-12 `	0.576198585815679	0.07139	8.0711	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.806187929180415
R-squared	0.649938977156206
Adjusted R-squared	0.632214368404622
F-TEST (value)	36.6687347667465
F-TEST (DF numerator)	4
F-TEST (DF denominator)	79
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	459.357740870306
Sum Squared Residuals	16669753.1937002

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	10102	10657.4945898650	-555.494589864962
2	8463	9302.77132163496	-839.77132163496
3	9114	8726.96965685472	387.030343145281
4	8563	8524.65925330665	38.3407466933546
5	8872	8370.48040556903	501.519594430971
6	8301	8349.81102928618	-48.8110292861799
7	8301	8138.37014436535	162.629855634649
8	8278	8301.78323592748	-23.7832359274822
9	7736	7964.15091485477	-228.150914854766
10	7973	8256.14436599475	-283.144365994751
11	8268	8432.03157906886	-164.03157906886
12	9476	8734.81034871273	741.189651287271
13	11100	9725.59538037787	1374.40461962213
14	8962	9157.36552884498	-195.36552884498
15	9173	8996.03951070543	176.960489294573
16	8738	8601.00274329954	136.997256700456
17	8459	8778.95875534954	-319.958755349541
18	8078	8241.33328156135	-163.333281561351
19	8411	8005.24202562028	405.757974379723
20	8291	8316.32966369896	-25.3296636989592
21	7810	7976.99375443124	-166.993754431245
22	8616	8022.06996653417	593.930033465834
23	8312	8424.01034518854	-112.010345188539
24	9692	9117.51951239928	574.480487600718
25	9911	10300.2425536001	-389.242553600063
26	8915	9153.32450322917	-238.324503229175
27	9452	8997.08563523348	454.914364766518
28	9112	8777.05123061491	334.948769385087
29	8472	8629.72839406285	-157.728394062852
30	8230	8075.36374072986	154.63625927014
31	8384	8207.25508398348	176.744916016522
32	8625	8321.43562642432	303.564373575677
33	8221	8061.17352885094	159.826471149059
34	8649	8454.07942320106	194.920576798938
35	8625	8434.6803436399	190.319656360104
36	10443	9275.36785754834	1167.63214245166
37	10357	9797.41388533723	559.58611466277
38	8586	9178.77950415891	-592.779504158913
39	8892	8987.00727869118	-95.00727869118
40	8329	8825.28446081745	-496.28446081745
41	8101	8435.83394696266	-334.833946962655
42	7922	8029.54897538691	-107.548975386912
43	8120	8119.98978752326	0.010212476735866
44	7838	8508.50705089842	-670.507050898419
45	7735	8186.61264575095	-451.612645750948
46	8406	8455.2444938832	-49.2444938832061
47	8209	8645.67830355556	-436.678303555555
48	9451	9671.08303007987	-220.083030079870
49	10041	9858.02932792918	182.970672070819
50	9411	9022.55954123496	388.440458765041
51	10405	8978.34721544146	1426.65278455855
52	8467	8785.65074411819	-318.650744118187
53	8464	8246.83390431542	217.166095684581
54	8102	7948.29618942127	153.703810578732
55	7627	8035.99992462796	-408.999924627958
56	7513	7825.51049730806	-312.510497308056
57	7510	7656.57028352559	-146.570283525589
58	8291	8277.66941167152	13.3305883284799
59	8064	8360.51072689067	-296.510726890672
60	9383	9059.75194342397	323.248056576032
61	9706	9718.97330234202	-12.9733023420156
62	8579	9460.55858248757	-881.558582487571
63	9474	9677.16326948595	-203.163269485951
64	8318	8673.64833552975	-355.648335529746
65	8213	8488.332195801	-275.332195801003
66	8059	8057.86317259434	1.13682740565717
67	9111	7786.4589864637	1324.54101353630
68	7708	8100.9054050156	-392.905405015606
69	7680	7675.24253431524	4.75746568475654
70	8014	8173.68284223508	-159.682842235079
71	8007	8224.12445813855	-217.124458138550
72	8718	9000.50684109132	-282.506841091322
73	9486	9225.94675521583	260.053244784167
74	9113	8910.99950048938	202.000499510617
75	9025	9294.1936767038	-269.193676703800
76	8476	8522.5796096373	-46.5796096373016
77	7952	8353.8932973614	-401.893297361396
78	7759	8068.93228746475	-309.932287464745
79	7835	8582.22091653142	-747.220916531419
80	7600	7865.01721320977	-265.017213209767
81	7651	7813.68751416573	-162.687514165735
82	8319	8071.67319464203	247.326805357967
83	8812	8299.48139827101	512.518601728989
84	8630	8845.47640328362	-215.476403283621

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.276335513087138	0.552671026174277	0.723664486912862
9	0.327824731929404	0.655649463858808	0.672175268070596
10	0.410114439690031	0.820228879380062	0.589885560309969
11	0.300240855430904	0.600481710861807	0.699759144569096
12	0.544338535020122	0.911322929959756	0.455661464979878
13	0.967327226150502	0.0653455476989957	0.0326727738494978
14	0.955549583104901	0.088900833790198	0.044450416895099
15	0.933338684807976	0.133322630384049	0.0666613151920245
16	0.91348538086579	0.173029238268420	0.0865146191342102
17	0.897675207971843	0.204649584056314	0.102324792028157
18	0.855673200921566	0.288653598156867	0.144326799078434
19	0.853134172614955	0.293731654770091	0.146865827385045
20	0.80441837535946	0.391163249281081	0.195581624640540
21	0.762190417231992	0.475619165536017	0.237809582768008
22	0.762781367427515	0.47443726514497	0.237218632572485
23	0.710339505640487	0.579320988719025	0.289660494359513
24	0.704072020545687	0.591855958908627	0.295927979454314
25	0.672111603663626	0.655776792672748	0.327888396336374
26	0.617654060969023	0.764691878061953	0.382345939030977
27	0.609995296492681	0.780009407014637	0.390004703507319
28	0.586428178493848	0.827143643012304	0.413571821506152
29	0.527420872361172	0.945158255277656	0.472579127638828
30	0.462992284561133	0.925984569122267	0.537007715438867
31	0.400873742412532	0.801747484825064	0.599126257587468
32	0.353237741798266	0.706475483596531	0.646762258201734
33	0.295493180734147	0.590986361468295	0.704506819265853
34	0.245027789741765	0.49005557948353	0.754972210258235
35	0.199830720825401	0.399661441650802	0.800169279174599
36	0.489360960141012	0.978721920282023	0.510639039858988
37	0.551207062158698	0.897585875682605	0.448792937841302
38	0.575532396160635	0.84893520767873	0.424467603839365
39	0.517989107587281	0.964021784825437	0.482010892412719
40	0.512231290320684	0.975537419358632	0.487768709679316
41	0.498689056715854	0.997378113431708	0.501310943284146
42	0.435765566543469	0.871531133086938	0.564234433456531
43	0.374538261824518	0.749076523649037	0.625461738175481
44	0.475929011209274	0.951858022418548	0.524070988790726
45	0.485909080066162	0.971818160132324	0.514090919933838
46	0.427228303521997	0.854456607043993	0.572771696478003
47	0.424259095354283	0.848518190708566	0.575740904645717
48	0.376366971712316	0.752733943424631	0.623633028287684
49	0.330207459792645	0.66041491958529	0.669792540207355
50	0.309900006146192	0.619800012292384	0.690099993853808
51	0.860586085640912	0.278827828718175	0.139413914359088
52	0.828084520574296	0.343830958851408	0.171915479425704
53	0.802568514194823	0.394862971610354	0.197431485805177
54	0.778024519827552	0.443950960344896	0.221975480172448
55	0.762620464611636	0.474759070776728	0.237379535388364
56	0.731935306961998	0.536129386076004	0.268064693038002
57	0.696610392529937	0.606779214940127	0.303389607470063
58	0.629490677232357	0.741018645535286	0.370509322767643
59	0.595939943080857	0.808120113838286	0.404060056919143
60	0.575501694764031	0.848996610471938	0.424498305235969
61	0.528399774455452	0.943200451089096	0.471600225544548
62	0.66392944546801	0.672141109063979	0.336070554531990
63	0.644110101074491	0.711779797851018	0.355889898925509
64	0.590798424199388	0.818403151601223	0.409201575800612
65	0.515463058455447	0.969073883089106	0.484536941544553
66	0.430011271054494	0.860022542108989	0.569988728945506
67	0.95436455300343	0.0912708939931406	0.0456354469965703
68	0.96655245698608	0.0668950860278417	0.0334475430139209
69	0.942307353768112	0.115385292463776	0.0576926462318878
70	0.929974782867829	0.140050434264342	0.0700252171321708
71	0.898066371823888	0.203867256352224	0.101933628176112
72	0.836828821240169	0.326342357519662	0.163171178759831
73	0.946067038558727	0.107865922882546	0.053932961441273
74	0.905821549292529	0.188356901414943	0.0941784507074714
75	0.81981539171538	0.360369216569239	0.180184608284619
76	0.885769109374186	0.228461781251627	0.114230890625814

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	4	0.0579710144927536	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/10ch501292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/10ch501292597060.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/1g78r1292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/1g78r1292597060.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/2g78r1292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/2g78r1292597060.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/3g78r1292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/3g78r1292597060.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/4qy7c1292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/4qy7c1292597060.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/5qy7c1292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/5qy7c1292597060.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/6qy7c1292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/6qy7c1292597060.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/7jqof1292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/7jqof1292597060.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/8ch501292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/8ch501292597060.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/9ch501292597060.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292597990qlb7z4gr97spczs/9ch501292597060.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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