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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: Wed, 18 Nov 2009 08:49:51 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj.htm/, Retrieved Wed, 18 Nov 2009 17:01:43 +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/2009/Nov/18/t12585600915cablmxvvwwgozj.htm/},
    year = {2009},
}
@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 = {2009},
    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 «

274412 244752 272433 244576 268361 241572 268586 240541 264768 236089 269974 236997 304744 264579 309365 270349 308347 269645 298427 267037 289231 258113 291975 262813 294912 267413 293488 267366 290555 264777 284736 258863 281818 254844 287854 254868 316263 277267 325412 285351 326011 286602 328282 283042 317480 276687 317539 277915 313737 277128 312276 277103 309391 275037 302950 270150 300316 267140 304035 264993 333476 287259 337698 291186 335932 292300 323931 288186 313927 281477 314485 282656 313218 280190 309664 280408 302963 276836 298989 275216 298423 274352 301631 271311 329765 289802 335083 290726 327616 292300 309119 278506 295916 269826 291413 265861 291542 269034 284678 264176 276475 255198 272566 253353 264981 246057 263290 235372 296806 258556 303598 260993 286994 254663 276427 250643 266424 243422 267153 247105 268381 248541 262522 245039 255542 237080 253158 237085 243803 225554 250741 226839 280445 247934 285257 248333 2709 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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = -27354.1547199785 + 1.20506140098866X[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)	-27354.1547199785	22876.346434	-1.1957	0.235468	0.117734
X	1.20506140098866	0.086488	13.9333	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.84617392808301
R-squared	0.71601031656743
Adjusted R-squared	0.712322138860514
F-TEST (value)	194.136609856051
F-TEST (DF numerator)	1
F-TEST (DF denominator)	77
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	13019.2691582835
Sum Squared Residuals	13051605445.0191

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	274412	267587.033294799	6824.96670520105
2	272433	267374.942488225	5058.05751177492
3	268361	263754.938039655	4606.06196034492
4	268586	262512.519735236	6073.48026476424
5	264768	257147.586378034	7620.41362196577
6	269974	258241.782130132	11732.2178698681
7	304744	291479.785692201	13264.2143077987
8	309365	298432.989975906	10932.0100240941
9	308347	297584.62674961	10762.3732503902
10	298427	294441.826615831	3985.17338416859
11	289231	283687.858673409	5543.14132659143
12	291975	289351.647258055	2623.35274194471
13	294912	294894.929702603	17.0702973968564
14	293488	294838.291816757	-1350.29181675668
15	290555	291718.387849597	-1163.38784959703
16	284736	284591.65472415	144.345275849934
17	281818	279748.512953577	2069.48704642337
18	287854	279777.434427200	8076.56557279965
19	316263	306769.604747945	9493.39525205456
20	325412	316511.321113538	8900.6788864622
21	326011	318018.852926175	7992.14707382539
22	328282	313728.834338655	14553.1656613450
23	317480	306070.669135372	11409.3308646280
24	317539	307550.484535786	9988.5154642139
25	313737	306602.101213208	7134.89878679199
26	312276	306571.974678183	5704.0253218167
27	309391	304082.317823741	5308.68217625929
28	302950	298193.182757109	4756.81724289088
29	300316	294565.947940133	5750.05205986676
30	304035	291978.681112211	12056.3188877894
31	333476	318810.578266624	14665.4217333758
32	337698	323542.854388307	14155.1456116933
33	335932	324885.292789008	11046.7072109920
34	323931	319927.670185341	4003.32981465934
35	313927	311842.913246108	2084.08675389229
36	314485	313263.680637873	1221.31936212665
37	313218	310291.999223035	2926.0007769647
38	309664	310554.702608451	-890.702608450832
39	302963	306250.223284119	-3287.22328411932
40	298989	304298.023814518	-5309.02381451769
41	298423	303256.850764063	-4833.85076406348
42	301631	299592.259043657	2038.74095634305
43	329765	321875.049409338	7889.95059066166
44	335083	322988.526143852	12094.4738561481
45	327616	324885.292789008	2730.70721099198
46	309119	308262.675823770	856.32417622961
47	295916	297802.742863189	-1886.74286318879
48	291413	293024.674408269	-1611.67440826874
49	291542	296848.334233606	-5306.33423360577
50	284678	290994.145947603	-6316.14594760284
51	276475	280175.104689527	-3700.10468952661
52	272566	277951.766404703	-5385.76640470253
53	264981	269159.638423089	-4178.63842308924
54	263290	256283.557353525	7006.44264647464
55	296806	284221.700874047	12584.2991259535
56	303598	287158.435508256	16439.5644917441
57	286994	279530.396839998	7463.60316000232
58	276427	274686.050008023	1740.94999197675
59	266424	265984.301631484	439.698368515892
60	267153	270422.542771325	-3269.54277132535
61	268381	272153.010943145	-3772.01094314508
62	262522	267932.885916883	-5410.88591688278
63	255542	258341.802226414	-2799.802226414
64	253158	258347.827533419	-5189.82753341894
65	243803	244452.264518619	-649.264518618657
66	250741	246000.768418889	4740.23158111091
67	280445	271421.538672745	9023.46132725504
68	285257	271902.358171739	13354.6418282606
69	270976	270258.654420791	717.345579209103
70	261076	268003.984539541	-6927.98453954111
71	255603	269407.881071693	-13804.8810716929
72	260376	280858.374503887	-20482.3745038872
73	263903	291166.469727944	-27263.4697279442
74	264291	296020.457051127	-31729.4570511266
75	263276	301683.040574372	-38407.0405743723
76	262572	302788.081879079	-40216.0818790789
77	256167	294915.41574642	-38748.4157464200
78	264221	300414.110919131	-36193.1109191312
79	293860	325379.367963413	-31519.3679634134

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.00105906134169988	0.00211812268339975	0.9989409386583
6	0.00144982991885538	0.00289965983771077	0.998550170081145
7	0.00208661006154178	0.00417322012308355	0.997913389938458
8	0.000465554891980519	0.000931109783961038	0.99953444510802
9	9.46716697733628e-05	0.000189343339546726	0.999905328330227
10	0.000136857737457364	0.000273715474914728	0.999863142262543
11	4.55203051187467e-05	9.10406102374934e-05	0.999954479694881
12	3.76115313461762e-05	7.52230626923524e-05	0.999962388468654
13	5.46012289859357e-05	0.000109202457971871	0.999945398771014
14	6.58218532680194e-05	0.000131643706536039	0.999934178146732
15	5.198584794153e-05	0.00010397169588306	0.999948014152058
16	2.85845329174931e-05	5.71690658349862e-05	0.999971415467082
17	1.13517983215940e-05	2.27035966431881e-05	0.999988648201678
18	4.31662527544772e-06	8.63325055089544e-06	0.999995683374725
19	2.55175455045572e-06	5.10350910091144e-06	0.99999744824545
20	1.13542681204548e-06	2.27085362409096e-06	0.999998864573188
21	3.98805227287052e-07	7.97610454574105e-07	0.999999601194773
22	5.51324855726217e-07	1.10264971145243e-06	0.999999448675144
23	2.85046639457800e-07	5.70093278915601e-07	0.99999971495336
24	1.13238686614869e-07	2.26477373229739e-07	0.999999886761313
25	3.75236678560666e-08	7.50473357121332e-08	0.999999962476332
26	1.27998523221189e-08	2.55997046442377e-08	0.999999987200148
27	4.34118860975102e-09	8.68237721950205e-09	0.999999995658811
28	1.46468574481044e-09	2.92937148962088e-09	0.999999998535314
29	4.53960121638516e-10	9.07920243277033e-10	0.99999999954604
30	3.51422017058101e-10	7.02844034116203e-10	0.999999999648578
31	4.22026447693047e-10	8.44052895386093e-10	0.999999999577974
32	3.69801225255684e-10	7.39602450511368e-10	0.999999999630199
33	1.87075230914667e-10	3.74150461829333e-10	0.999999999812925
34	1.31345182032653e-10	2.62690364065306e-10	0.999999999868655
35	1.08911688628708e-10	2.17823377257417e-10	0.999999999891088
36	1.05167890966174e-10	2.10335781932348e-10	0.999999999894832
37	6.4199760897787e-11	1.28399521795574e-10	0.9999999999358
38	8.81589047201603e-11	1.76317809440321e-10	0.99999999991184
39	1.9336629854856e-10	3.8673259709712e-10	0.999999999806634
40	5.98224393771533e-10	1.19644878754307e-09	0.999999999401776
41	1.14467245512077e-09	2.28934491024154e-09	0.999999998855328
42	5.99989790152716e-10	1.19997958030543e-09	0.99999999940001
43	6.56476178096608e-10	1.31295235619322e-09	0.999999999343524
44	3.57944488802962e-09	7.15888977605925e-09	0.999999996420555
45	1.31233091873551e-08	2.62466183747101e-08	0.99999998687669
46	3.16469937741856e-08	6.32939875483713e-08	0.999999968353006
47	5.65872290768067e-08	1.13174458153613e-07	0.99999994341277
48	8.16467113889987e-08	1.63293422777997e-07	0.999999918353289
49	2.28391682311620e-07	4.56783364623241e-07	0.999999771608318
50	4.87887605569166e-07	9.7577521113833e-07	0.999999512112394
51	4.33036076479883e-07	8.66072152959767e-07	0.999999566963923
52	4.07270896677826e-07	8.14541793355652e-07	0.999999592729103
53	2.61618020628772e-07	5.23236041257544e-07	0.99999973838198
54	1.3640683204389e-07	2.7281366408778e-07	0.999999863593168
55	1.58048251817032e-06	3.16096503634065e-06	0.999998419517482
56	0.000269004994957507	0.000538009989915015	0.999730995005042
57	0.00120389322642633	0.00240778645285265	0.998796106773574
58	0.00152770039013659	0.00305540078027317	0.998472299609863
59	0.00105069246524893	0.00210138493049787	0.998949307534751
60	0.000833459311101238	0.00166691862220248	0.999166540688899
61	0.0007115468530885	0.001423093706177	0.999288453146912
62	0.000519210441636871	0.00103842088327374	0.999480789558363
63	0.000291910796091049	0.000583821592182098	0.99970808920391
64	0.000195660012246604	0.000391320024493208	0.999804339987753
65	0.000221067722603859	0.000442135445207718	0.999778932277396
66	0.000192828543592768	0.000385657087185536	0.999807171456407
67	0.000701943039238475	0.00140388607847695	0.999298056960761
68	0.0451358637691549	0.0902717275383097	0.954864136230845
69	0.154535005178777	0.309070010357554	0.845464994821223
70	0.254454460444143	0.508908920888285	0.745545539555857
71	0.384175458335627	0.768350916671254	0.615824541664373
72	0.714535530960774	0.570928938078451	0.285464469039226
73	0.922720925709229	0.154558148581541	0.0772790742907707
74	0.983931999730423	0.0321360005391539	0.0160680002695769

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	63	0.9	NOK
5% type I error level	64	0.914285714285714	NOK
10% type I error level	65	0.928571428571429	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/10untn1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/10untn1258559386.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/1yn8v1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/1yn8v1258559386.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/207pj1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/207pj1258559386.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/3hbsx1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/3hbsx1258559386.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/4j9va1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/4j9va1258559386.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/5bjay1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/5bjay1258559386.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/6rn0o1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/6rn0o1258559386.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/7q1hh1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/7q1hh1258559386.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/8jwmq1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/8jwmq1258559386.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/9pijy1258559386.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585600915cablmxvvwwgozj/9pijy1258559386.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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