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paper - multiple regression

*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, 19 Dec 2010 19:38:22 +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/19/t12927873724741ustsx2cpd95.htm/, Retrieved Sun, 19 Dec 2010 20:36:23 +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/19/t12927873724741ustsx2cpd95.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 «

99.2 96.7 101.0 99.0 98.1 100.1 631 923 -12 -10,8 654 294 -13 -12,2 671 833 -16 -14,1 586 840 -10 -15,2 600 969 -4 -15,8 625 568 -9 -15,8 558 110 -8 -14,9 630 577 -9 -12,6 628 654 -3 -9,9 603 184 -13 -7,8 656 255 -3 -6 600 730 -1 -5 670 326 -2 -4,5 678 423 0 -3,9 641 502 0 -2,9 625 311 -3 -1,5 628 177 0 -0,5 589 767 5 0 582 471 3 0,5 636 248 4 0,9 599 885 3 0,8 621 694 1 0,1 637 406 -1 -1 595 994 0 -2 696 308 -2 -3 674 201 -1 -3,7 648 861 2 -4,7 649 605 0 -6,4 672 392 -6 -7,5 598 396 -7 -7,8 613 177 -6 -7,7 638 104 -4 -6,6 615 632 -9 -4,2 634 465 -2 -2 638 686 -3 -0,7 604 243 2 0,1 706 669 3 0,9 677 185 1 2,1 644 328 0 3,5 644 825 1 4,9 605 707 1 5,7 600 136 3 6,2 612 166 5 6,5 599 659 5 6,5 634 210 4 6,3 618 234 11 6,2 613 576 8 6,4 627 200 -1 6,3 668 973 4 5,8 651 479 4 5,1 619 661 4 5,1 644 260 6 5,8 579 936 6 6,7 601 752 6 7,1 595 376 6 6,7 588 902 4 5,5 634 341 1 4,2 594 305 6 3 606 200 0 2,2 610 926 2 2 633 685 -2 1,8 639 696 0 1,8 659 451 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	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation

Werkloosheid[t] = + 548.485695515317 -0.0824479631015943Consumenten[t] -0.85218979218082Ondernemers[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)	548.485695515317	33.924089	16.168	0	0
Consumenten	-0.0824479631015943	0.068467	-1.2042	0.231936	0.115968
Ondernemers	-0.85218979218082	0.069102	-12.3323	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.805482329234559
R-squared	0.648801782709131
Adjusted R-squared	0.640339175063568
F-TEST (value)	76.6668868370961
F-TEST (DF numerator)	2
F-TEST (DF denominator)	83
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	196.783926478273
Sum Squared Residuals	3214084.83877714

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	99.2	454.441808473131	-355.241808473131
2	99	455.09335213775	-356.09335213775
3	631	482.612503078714	148.387496921286
4	-10.8	244.020928745713	-254.820928745714
5	-13	-22.3277898881733	9.3277898881733
6	833	561.820738994692	271.179261005308
7	586	487.751304431786	98.248695568214
8	-15.2	-326.754990968853	311.554990968853
9	-4	17.1697532193101	-21.1697532193101
10	568	562.692325899688	5.30767410031193
11	558	546.233937911588	11.766062088412
12	-14.9	4.82996867297985	-19.7299686729799
13	-9	14.3493503608425	-23.3493503608425
14	654	557.169718347212	96.8302816527883
15	603	544.393737602974	58.6062623970259
16	-7.8	277.091434714562	-284.891434714562
17	-3	37.6665079854349	-40.6665079854349
18	730	552.829092439322	177.170907560678
19	670	523.312039128559	146.687960871441
20	-4.5	132.109694439950	-136.609694439950
21	0	2.55358578350801	-2.55358578350801
22	502	550.957045912641	-48.9570459126411
23	625	525.400948367263	99.5990516327366
24	-1.5	345.870781471511	-347.370781471511
25	0	46.5871319023652	-46.5871319023652
26	767	548.073455699809	218.926544300191
27	582	507.096135517924	74.9038644820765
28	0.5	284.70572252186	-284.20572252186
29	4	37.9498068322147	-33.9498068322147
30	885	547.556599792267	337.443400207733
31	621	490.41461933063	130.585380669370
32	0.1	149.977287394189	-149.877287394189
33	-1	41.5152171308311	-42.5152171308311
34	994	550.190075099678	443.809924900322
35	696	524.796102464387	171.203897535613
36	-3	321.625620156498	-324.625620156498
37	-1	-3.42823235437804	2.42823235437804
38	861	552.326091612363	308.673908387637
39	649	498.604677838852	150.395322161148
40	-6.4	159.022265776164	-165.422265776164
41	-6	39.4945595144490	-45.4945595144490
42	396	555.709911636038	-159.709911636038
43	613	539.00554479942	73.9944552005804
44	-7.7	407.256156669695	-414.956156669695
45	-4	24.9331298805837	-28.9331298805837
46	632	552.80692431039	79.1930756896095
47	634	511.851772257437	122.148227742563
48	-2	-88.7183023795421	86.7183023795421
49	-3	33.8207746122733	-36.8207746122733
50	243	548.235580609896	-305.235580609896
51	706	490.771438823808	215.228561176192
52	0.9	335.013312942086	-334.113312942086
53	1	-0.497671371643975	1.49767137164397
54	328	545.503031242684	-217.503031242684
55	644	479.613936164321	164.386063835679
56	4.9	-103.893505232987	108.793505232987
57	1	36.7018668171463	-35.7018668171463
58	136	542.954774914491	-406.954774914491
59	612	530.538384679548	81.4616153204519
60	6.5	-62.4937074296978	68.9937074296978
61	5	7.66145551251715	-2.66145551251715
62	210	542.787107972171	-332.787107972171
63	618	519.818784435555	98.1812155644452
64	6.2	7.08377383788783	-0.88377383788783
65	8	13.6350288540931	-5.63502885409308
66	200	543.199347787679	-343.199347787679
67	668	464.855068248742	203.144931751258
68	5.8	86.6131610815667	-80.8131610815667
69	4	20.5597295435717	-16.5597295435717
70	661	543.809735722788	117.190264277212
71	644	521.936086355817	122.063913644183
72	5.8	-296.901320601753	302.701320601753
73	6	35.7672290618639	-29.7672290618639
74	752	541.940460212223	210.059539787777
75	595	512.372122636032	82.6278773639675
76	6.7	-268.668899335519	275.368899335519
77	4	7.74390347561879	-3.74390347561879
78	341	544.824050425056	-203.824050425056
79	594	518.225928016246	75.7740719837543
80	3	328.084271439587	-325.084271439587
81	0	28.4685367661936	-28.4685367661936
82	926	546.616420004752	379.383579995248
83	633	493.713220375086	139.286779624914
84	1.8	-97.3226482644518	99.1226482644518
85	0	-13.2557838654258	13.2557838654258
86	451	547.124962863944	-96.124962863944

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.974895948710734	0.0502081025785309	0.0251040512892654
7	0.952470971119204	0.0950580577615925	0.0475290288807962
8	0.956278220445396	0.0874435591092074	0.0437217795546037
9	0.921636355013697	0.156727289972606	0.0783636449863031
10	0.891258716564234	0.217482566871533	0.108741283435766
11	0.84477768837687	0.310444623246260	0.155222311623130
12	0.789170217878913	0.421659564242175	0.210829782121088
13	0.712593942634603	0.574812114730795	0.287406057365397
14	0.679027544150097	0.641944911699806	0.320972455849903
15	0.609533468615242	0.780933062769515	0.390466531384758
16	0.693855217705688	0.612289564588623	0.306144782294311
17	0.618020870627604	0.763958258744793	0.381979129372396
18	0.617171202405785	0.765657595188429	0.382828797594215
19	0.583120392703175	0.83375921459365	0.416879607296825
20	0.539173699120434	0.921652601759133	0.460826300879566
21	0.461511566458311	0.923023132916621	0.538488433541689
22	0.389878121333107	0.779756242666215	0.610121878666893
23	0.33853721284993	0.67707442569986	0.66146278715007
24	0.466671631619181	0.933343263238361	0.533328368380819
25	0.398432557066549	0.796865114133097	0.601567442933451
26	0.411334801458298	0.822669602916596	0.588665198541702
27	0.356018304773955	0.71203660954791	0.643981695226045
28	0.404590100766483	0.809180201532967	0.595409899233517
29	0.34062843620626	0.68125687241252	0.65937156379374
30	0.448238230277454	0.896476460554908	0.551761769722546
31	0.418709806628181	0.837419613256363	0.581290193371819
32	0.381320259566168	0.762640519132335	0.618679740433832
33	0.321640500340694	0.643281000681387	0.678359499659306
34	0.549682882374815	0.900634235250371	0.450317117625185
35	0.528675209380689	0.942649581238622	0.471324790619311
36	0.625838438828547	0.748323122342907	0.374161561171453
37	0.563410610847956	0.873178778304089	0.436589389152044
38	0.648521142860925	0.70295771427815	0.351478857139075
39	0.626515811739616	0.746968376520767	0.373484188260384
40	0.611075576972828	0.777848846054344	0.388924423027172
41	0.551055832488958	0.897888335022084	0.448944167511042
42	0.551532073377321	0.896935853245358	0.448467926622679
43	0.501578260565647	0.996843478868707	0.498421739434353
44	0.732555533014999	0.534888933970003	0.267444466985001
45	0.677799702718527	0.644400594562946	0.322200297281473
46	0.642714749569131	0.714570500861737	0.357285250430869
47	0.606708794498795	0.78658241100241	0.393291205501205
48	0.579749694483057	0.840500611033885	0.420250305516943
49	0.517098963137105	0.96580207372579	0.482901036862895
50	0.606197468735866	0.787605062528268	0.393802531264134
51	0.614781338682747	0.770437322634505	0.385218661317253
52	0.768827859596245	0.46234428080751	0.231172140403755
53	0.715588117035514	0.568823765928972	0.284411882964486
54	0.722066130740815	0.555867738518369	0.277933869259185
55	0.692139505089823	0.615720989820355	0.307860494910177
56	0.650588134529006	0.698823730941989	0.349411865470994
57	0.586766834792617	0.826466330414766	0.413233165207383
58	0.771185481632477	0.457629036735046	0.228814518367523
59	0.725802410802098	0.548395178395804	0.274197589197902
60	0.672226211972633	0.655547576054733	0.327773788027367
61	0.60510006424293	0.78979987151414	0.39489993575707
62	0.720836487653834	0.558327024692332	0.279163512346166
63	0.667485296055424	0.665029407889153	0.332514703944576
64	0.611880319964956	0.776239360070087	0.388119680035044
65	0.538208217347626	0.923583565304748	0.461791782652374
66	0.704891956583804	0.590216086832392	0.295108043416196
67	0.668804094718007	0.662391810563987	0.331195905281993
68	0.644143461831467	0.711713076337066	0.355856538168533
69	0.57359774967263	0.85280450065474	0.42640225032737
70	0.50841347808743	0.98317304382514	0.49158652191257
71	0.439154148937873	0.878308297875747	0.560845851062127
72	0.4574011454802	0.9148022909604	0.5425988545198
73	0.376010297464307	0.752020594928614	0.623989702535693
74	0.368444395959386	0.736888791918772	0.631555604040614
75	0.286314063966588	0.572628127933175	0.713685936033412
76	0.306588704216288	0.613177408432577	0.693411295783712
77	0.213022670160512	0.426045340321024	0.786977329839488
78	0.242836673272886	0.485673346545771	0.757163326727114
79	0.150680174636220	0.301360349272441	0.84931982536378
80	0.391795423653151	0.783590847306302	0.608204576346849

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	3	0.04	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/10elkh1292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/10elkh1292787493.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/1pkm51292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/1pkm51292787493.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/2pkm51292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/2pkm51292787493.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/30bm81292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/30bm81292787493.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/40bm81292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/40bm81292787493.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/50bm81292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/50bm81292787493.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/6t23t1292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/6t23t1292787493.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/73bkw1292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/73bkw1292787493.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/83bkw1292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/83bkw1292787493.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/93bkw1292787493.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927873724741ustsx2cpd95/93bkw1292787493.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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