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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: Thu, 17 Dec 2009 03:36:26 -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/Dec/17/t1261046248o8bru9axf8rn2xa.htm/, Retrieved Thu, 17 Dec 2009 11:37:40 +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/Dec/17/t1261046248o8bru9axf8rn2xa.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 «

1322,4 0 1,0622 1089,2 0 1,0773 1147,3 0 1,0807 1196,4 0 1,0848 1190,2 0 1,1582 1146 0 1,1663 1139,8 0 1,1372 1045,6 0 1,1139 1050,9 0 1,1222 1117,3 0 1,1692 1120 0 1,1702 1052,1 0 1,2286 1065,8 0 1,2613 1092,5 0 1,2646 1422 0 1,2262 1367,5 0 1,1985 1136,3 0 1,2007 1293,7 0 1,2138 1154,8 0 1,2266 1206,7 0 1,2176 1199 0 1,2218 1265 0 1,249 1247,1 0 1,2991 1116,5 0 1,3408 1153,9 0 1,3119 1077,4 0 1,3014 1132,5 0 1,3201 1058,8 0 1,2938 1195,1 0 1,2694 1263,4 0 1,2165 1023,1 0 1,2037 1141 0 1,2292 1116,3 0 1,2256 1135,6 0 1,2015 1210,5 0 1,1786 1230 0 1,1856 1136,5 0 1,2103 1068,7 0 1,1938 1372,5 0 1,202 1049,9 0 1,2271 1302,2 0 1,277 1305,9 0 1,265 1173,5 0 1,2684 1277,4 0 1,2811 1238,6 0 1,2727 1508,6 0 1,2611 1423,4 0 1,2881 1375,1 0 1,3213 1344,1 0 1,2999 1287,5 0 1,3074 1446,9 0 1,3242 1451 0 1,3516 1604,4 0 1,3511 1501,5 0 1,3419 1522,8 0 1,3716 1328 0 1,3622 1420,5 0 1,3896 1648 0 1,4227 1631,1 0 1,4684 1396,6 0 1,457 1663,4 0 1,4718 1283 0 1,474 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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Import_Uit_USA[t] = -490.773338089126 + 165.712720727400Dummy_Crisis[t] + 1402.66628529179`Wisselkoers_EUR/DOLLAR`[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)	-490.773338089126	198.758094	-2.4692	0.015727	0.007864
Dummy_Crisis	165.712720727400	51.908844	3.1924	0.002035	0.001017
`Wisselkoers_EUR/DOLLAR`	1402.66628529179	153.967198	9.1102	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.764136460746423
R-squared	0.58390453064207
Adjusted R-squared	0.573235416043149
F-TEST (value)	54.7284899068481
F-TEST (DF numerator)	2
F-TEST (DF denominator)	78
p-value	1.33226762955019e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	162.524460615078
Sum Squared Residuals	2060307.62326133

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	1322.4	999.138790147819	323.261209852181
2	1089.2	1020.31905105572	68.8809489442824
3	1147.3	1025.08811642571	122.21188357429
4	1196.4	1030.83904819541	165.560951804594
5	1190.2	1133.79475353582	56.4052464641771
6	1146	1145.15635044669	0.843649553313565
7	1139.8	1104.33876154470	35.4612384553044
8	1045.6	1071.65663709740	-26.0566370973968
9	1050.9	1083.29876726532	-32.3987672653188
10	1117.3	1149.22408267403	-31.9240826740328
11	1120	1150.62674895932	-30.6267489593244
12	1052.1	1232.54246002036	-180.442460020365
13	1065.8	1278.40964754941	-212.609647549407
14	1092.5	1283.03844629087	-190.538446290869
15	1422	1229.17606093566	192.823939064335
16	1367.5	1190.32220483308	177.177795166918
17	1136.3	1193.40807066072	-57.1080706607243
18	1293.7	1211.78299899805	81.9170010019536
19	1154.8	1229.73712744978	-74.9371274497813
20	1206.7	1217.11313088216	-10.4131308821553
21	1199	1223.00432928038	-24.0043292803808
22	1265	1261.15685224032	3.84314775968244
23	1247.1	1331.43043313344	-84.330433133436
24	1116.5	1389.92161723010	-273.421617230103
25	1153.9	1349.38456158517	-195.484561585171
26	1077.4	1334.65656558961	-257.256565589607
27	1132.5	1360.88642512456	-228.386425124564
28	1058.8	1323.99630182139	-265.196301821390
29	1195.1	1289.77124446027	-94.671244460270
30	1263.4	1215.57019796833	47.8298020316659
31	1023.1	1197.6160695166	-174.516069516599
32	1141	1233.38405979154	-92.3840597915401
33	1116.3	1228.33446116449	-112.034461164490
34	1135.6	1194.53020368896	-58.9302036889576
35	1210.5	1162.40914575578	48.0908542442243
36	1230	1172.22780975282	57.7721902471819
37	1136.5	1206.87366699953	-70.3736669995251
38	1068.7	1183.72967329221	-115.029673292211
39	1372.5	1195.23153683160	177.268463168397
40	1049.9	1230.43846059243	-180.538460592427
41	1302.2	1300.43150822849	1.76849177151272
42	1305.9	1283.59951280499	22.3004871950142
43	1173.5	1288.36857817498	-114.868578174978
44	1277.4	1306.18243999818	-28.7824399981835
45	1238.6	1294.40004320173	-55.8000432017328
46	1508.6	1278.12911429235	230.470885707652
47	1423.4	1316.00110399523	107.398896004774
48	1375.1	1362.56962466691	12.5303753330864
49	1344.1	1332.55256616167	11.5474338383305
50	1287.5	1343.07256330136	-55.5725633013576
51	1446.9	1366.63735689426	80.2626431057402
52	1451	1405.07041311125	45.9295868887454
53	1604.4	1404.36907996861	200.030920031391
54	1501.5	1391.46455014392	110.035449856075
55	1522.8	1433.12373881709	89.6762611829095
56	1328	1419.93867573535	-91.9386757353478
57	1420.5	1458.37173195234	-37.8717319523426
58	1648	1504.7999859955	143.200014004499
59	1631.1	1568.90183523334	62.1981647666645
60	1396.6	1552.91143958101	-156.311439581009
61	1663.4	1573.67090060333	89.7290993966726
62	1283	1577.87889945920	-294.878899459203
63	1582.4	1687.14660308343	-104.746603083433
64	1785.2	1718.42606124544	66.77393875456
65	1853.6	1691.35460193931	162.245398060691
66	1994.1	1690.79353542519	303.306464574808
67	2042.8	1721.23139381602	321.568606183976
68	1586.1	1609.71942413533	-23.6194241353267
69	1942.4	1524.71784724664	417.682152753356
70	1763.6	1543.57140790399	220.028592096005
71	1819.9	1460.81409707178	359.085902928221
72	1836	1561.3852697272	274.6147302728
73	1449.9	1531.92927773607	-82.0292777360726
74	1513.3	1468.24822838383	45.0517716161746
75	1677.7	1505.41888494406	172.281115055942
76	1494.4	1525.05621293814	-30.6562129381426
77	1375.3	1589.57886206156	-214.278862061565
78	1577.7	1640.91644810324	-63.2164481032443
79	1537.7	1651.01564535735	-113.315645357345
80	1356.6	1676.26363849260	-319.663638492598
81	1469.6	1717.50202728018	-247.902027280176

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.226888410475168	0.453776820950337	0.773111589524832
7	0.111293083258899	0.222586166517797	0.888706916741101
8	0.106942875104555	0.213885750209110	0.893057124895445
9	0.0797472952759798	0.159494590551960	0.92025270472402
10	0.0391471668606039	0.0782943337212077	0.960852833139396
11	0.0180755066076529	0.0361510132153057	0.981924493392347
12	0.0084223645068664	0.0168447290137328	0.991577635493134
13	0.00390075838190680	0.00780151676381359	0.996099241618093
14	0.00187639880324374	0.00375279760648748	0.998123601196756
15	0.0716835567814406	0.143367113562881	0.928316443218559
16	0.129743719474307	0.259487438948613	0.870256280525693
17	0.0878139178860186	0.175627835772037	0.912186082113981
18	0.0816738411851229	0.163347682370246	0.918326158814877
19	0.0536876660873082	0.107375332174616	0.946312333912692
20	0.0349558106346497	0.0699116212692995	0.96504418936535
21	0.0217785882656016	0.0435571765312033	0.978221411734398
22	0.0154998346813430	0.0309996693626859	0.984500165318657
23	0.00978218379753586	0.0195643675950717	0.990217816202464
24	0.00911330629875317	0.0182266125975063	0.990886693701247
25	0.00629567771302493	0.0125913554260499	0.993704322286975
26	0.00634954613741874	0.0126990922748375	0.993650453862581
27	0.00496991156451186	0.00993982312902372	0.995030088435488
28	0.00571574735735957	0.0114314947147191	0.99428425264264
29	0.00364191015591969	0.00728382031183939	0.99635808984408
30	0.00276076520153223	0.00552153040306446	0.997239234798468
31	0.00339533549374770	0.00679067098749539	0.996604664506252
32	0.00211933069790273	0.00423866139580545	0.997880669302097
33	0.00142249813538362	0.00284499627076723	0.998577501864616
34	0.00084326281774518	0.00168652563549036	0.999156737182255
35	0.000490000856826863	0.000980001713653727	0.999509999143173
36	0.000300324223352914	0.000600648446705828	0.999699675776647
37	0.000172969055918705	0.000345938111837409	0.999827030944081
38	0.000147514551715005	0.000295029103430010	0.999852485448285
39	0.000350591499363237	0.000701182998726474	0.999649408500637
40	0.000422986014769809	0.000845972029539618	0.99957701398523
41	0.000406002894245015	0.00081200578849003	0.999593997105755
42	0.000368287852216776	0.000736575704433551	0.999631712147783
43	0.000276856260769325	0.00055371252153865	0.99972314373923
44	0.000223864367570100	0.000447728735140201	0.99977613563243
45	0.000169091707863288	0.000338183415726576	0.999830908292137
46	0.00116610087376914	0.00233220174753827	0.998833899126231
47	0.00168061692218567	0.00336123384437133	0.998319383077814
48	0.00156922863321551	0.00313845726643101	0.998430771366784
49	0.00127004502585350	0.00254009005170701	0.998729954974147
50	0.00104663352543740	0.00209326705087479	0.998953366474563
51	0.00120184367233174	0.00240368734466347	0.998798156327668
52	0.00121213962079585	0.00242427924159169	0.998787860379204
53	0.00274325055921559	0.00548650111843118	0.997256749440784
54	0.00260362443163169	0.00520724886326338	0.997396375568368
55	0.0022570758980927	0.0045141517961854	0.997742924101907
56	0.00237328616828697	0.00474657233657395	0.997626713831713
57	0.00241947273528128	0.00483894547056256	0.997580527264719
58	0.00257495532506465	0.0051499106501293	0.997425044674935
59	0.00196155861604862	0.00392311723209725	0.998038441383951
60	0.00406946911034285	0.0081389382206857	0.995930530889657
61	0.00344433905878981	0.00688867811757963	0.99655566094121
62	0.080689930680339	0.161379861360678	0.919310069319661
63	0.111799604586974	0.223599209173949	0.888200395413026
64	0.0935843376468763	0.187168675293753	0.906415662353124
65	0.0825537867415123	0.165107573483025	0.917446213258488
66	0.116499899347143	0.232999798694287	0.883500100652856
67	0.339591070881284	0.679182141762569	0.660408929118716
68	0.368236798802340	0.736473597604681	0.63176320119766
69	0.364922851264116	0.729845702528232	0.635077148735884
70	0.366710462969472	0.733420925938944	0.633289537030528
71	0.406325640919247	0.812651281838494	0.593674359080753
72	0.793143534987827	0.413712930024347	0.206856465012173
73	0.741684610790043	0.516630778419915	0.258315389209957
74	0.634025169236677	0.731949661526647	0.365974830763324
75	0.645310503912082	0.709378992175837	0.354689496087918

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	36	0.514285714285714	NOK
5% type I error level	45	0.642857142857143	NOK
10% type I error level	47	0.671428571428571	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/10xmqf1261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/10xmqf1261046180.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/18u4s1261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/18u4s1261046180.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/2q1v01261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/2q1v01261046180.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/3ne8h1261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/3ne8h1261046180.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/4pavg1261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/4pavg1261046180.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/505uv1261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/505uv1261046180.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/6jb8a1261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/6jb8a1261046180.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/717061261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/717061261046180.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/88ror1261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/88ror1261046180.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/9nqxi1261046180.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Dec/17/t1261046248o8bru9axf8rn2xa/9nqxi1261046180.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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