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R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 24 Nov 2008 15:49:19 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2.htm/, Retrieved Mon, 24 Nov 2008 22:50:57 +0000

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/2008/Nov/24/t1227567057ur4pshtj007itw2.htm/},
    year = {2008},
}
@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 = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

106.7 0 110.2 0 125.9 0 100.1 0 106.4 0 114.8 0 81.3 0 87 0 104.2 0 108 0 105 0 94.5 0 92 0 95.9 0 108.8 0 103.4 0 102.1 0 110.1 0 83.2 0 82.7 0 106.8 0 113.7 0 102.5 0 96.6 0 92.1 0 95.6 0 102.3 0 98.6 0 98.2 0 104.5 0 84 0 73.8 0 103.9 0 106 0 97.2 0 102.6 0 89 0 93.8 0 116.7 1 106.8 1 98.5 1 118.7 1 90 1 91.9 1 113.3 1 113.1 1 104.1 1 108.7 1 96.7 1 101 1 116.9 1 105.8 1 99 1 129.4 1 83 1 88.9 1 115.9 1 104.2 1 113.4 1 112.2 1 100.8 1 107.3 1 126.6 1 102.9 1 117.9 1 128.8 1 87.5 1 93.8 1 122.7 1 126.2 1 124.6 1 116.7 1 115.2 1 111.1 1 129.9 1 113.3 1 118.5 1 133.5 1 102.1 1 102.4 1

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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

y[t] = + 99.5657894736842 + 10.1961152882206x[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)	99.5657894736842	1.900597	52.3866	0	0
x	10.1961152882206	2.623073	3.8871	0.000212	0.000106

Multiple Linear Regression - Regression Statistics
Multiple R	0.402835258883971
R-squared	0.162276245800116
Adjusted R-squared	0.151536197669348
F-TEST (value)	15.1094523808727
F-TEST (DF numerator)	1
F-TEST (DF denominator)	78
p-value	0.000211700505996615
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	11.7160664254836
Sum Squared Residuals	10706.7645739348

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	106.7	99.5657894736844	7.13421052631564
2	110.2	99.5657894736842	10.6342105263158
3	125.9	99.5657894736842	26.3342105263158
4	100.1	99.5657894736842	0.534210526315787
5	106.4	99.5657894736842	6.8342105263158
6	114.8	99.5657894736842	15.2342105263158
7	81.3	99.5657894736842	-18.2657894736842
8	87	99.5657894736842	-12.5657894736842
9	104.2	99.5657894736842	4.6342105263158
10	108	99.5657894736842	8.4342105263158
11	105	99.5657894736842	5.43421052631579
12	94.5	99.5657894736842	-5.06578947368421
13	92	99.5657894736842	-7.5657894736842
14	95.9	99.5657894736842	-3.6657894736842
15	108.8	99.5657894736842	9.2342105263158
16	103.4	99.5657894736842	3.8342105263158
17	102.1	99.5657894736842	2.53421052631579
18	110.1	99.5657894736842	10.5342105263158
19	83.2	99.5657894736842	-16.3657894736842
20	82.7	99.5657894736842	-16.8657894736842
21	106.8	99.5657894736842	7.23421052631579
22	113.7	99.5657894736842	14.1342105263158
23	102.5	99.5657894736842	2.93421052631579
24	96.6	99.5657894736842	-2.96578947368421
25	92.1	99.5657894736842	-7.46578947368421
26	95.6	99.5657894736842	-3.96578947368421
27	102.3	99.5657894736842	2.73421052631579
28	98.6	99.5657894736842	-0.965789473684213
29	98.2	99.5657894736842	-1.36578947368420
30	104.5	99.5657894736842	4.93421052631579
31	84	99.5657894736842	-15.5657894736842
32	73.8	99.5657894736842	-25.7657894736842
33	103.9	99.5657894736842	4.3342105263158
34	106	99.5657894736842	6.43421052631579
35	97.2	99.5657894736842	-2.36578947368420
36	102.6	99.5657894736842	3.03421052631579
37	89	99.5657894736842	-10.5657894736842
38	93.8	99.5657894736842	-5.76578947368421
39	116.7	109.761904761905	6.93809523809524
40	106.8	109.761904761905	-2.96190476190477
41	98.5	109.761904761905	-11.2619047619048
42	118.7	109.761904761905	8.93809523809524
43	90	109.761904761905	-19.7619047619048
44	91.9	109.761904761905	-17.8619047619048
45	113.3	109.761904761905	3.53809523809523
46	113.1	109.761904761905	3.33809523809523
47	104.1	109.761904761905	-5.66190476190477
48	108.7	109.761904761905	-1.06190476190476
49	96.7	109.761904761905	-13.0619047619048
50	101	109.761904761905	-8.76190476190476
51	116.9	109.761904761905	7.13809523809524
52	105.8	109.761904761905	-3.96190476190477
53	99	109.761904761905	-10.7619047619048
54	129.4	109.761904761905	19.6380952380952
55	83	109.761904761905	-26.7619047619048
56	88.9	109.761904761905	-20.8619047619048
57	115.9	109.761904761905	6.13809523809524
58	104.2	109.761904761905	-5.56190476190476
59	113.4	109.761904761905	3.63809523809524
60	112.2	109.761904761905	2.43809523809524
61	100.8	109.761904761905	-8.96190476190477
62	107.3	109.761904761905	-2.46190476190477
63	126.6	109.761904761905	16.8380952380952
64	102.9	109.761904761905	-6.86190476190476
65	117.9	109.761904761905	8.13809523809524
66	128.8	109.761904761905	19.0380952380953
67	87.5	109.761904761905	-22.2619047619048
68	93.8	109.761904761905	-15.9619047619048
69	122.7	109.761904761905	12.9380952380952
70	126.2	109.761904761905	16.4380952380952
71	124.6	109.761904761905	14.8380952380952
72	116.7	109.761904761905	6.93809523809524
73	115.2	109.761904761905	5.43809523809524
74	111.1	109.761904761905	1.33809523809523
75	129.9	109.761904761905	20.1380952380952
76	113.3	109.761904761905	3.53809523809523
77	118.5	109.761904761905	8.73809523809524
78	133.5	109.761904761905	23.7380952380952
79	102.1	109.761904761905	-7.66190476190477
80	102.4	109.761904761905	-7.36190476190476

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.57299127177411	0.85401745645178	0.42700872822589
6	0.441223278446981	0.882446556893963	0.558776721553019
7	0.867593387519366	0.264813224961268	0.132406612480634
8	0.90613335141914	0.187733297161721	0.0938666485808604
9	0.851906102886994	0.296187794226013	0.148093897113006
10	0.793606876075039	0.412786247849922	0.206393123924961
11	0.71743462296234	0.56513075407532	0.28256537703766
12	0.676478767520883	0.647042464958235	0.323521232479117
13	0.654136914050785	0.691726171898429	0.345863085949215
14	0.589009255798222	0.821981488403555	0.410990744201778
15	0.533741780130421	0.932516439739159	0.466258219869579
16	0.451034623071719	0.902069246143437	0.548965376928281
17	0.370938455711285	0.74187691142257	0.629061544288715
18	0.336858386679553	0.673716773359106	0.663141613320447
19	0.455245771181407	0.910491542362814	0.544754228818593
20	0.557014008093525	0.88597198381295	0.442985991906475
21	0.504217629612033	0.991564740775933	0.495782370387967
22	0.52544688427133	0.94910623145734	0.47455311572867
23	0.457281180979884	0.914562361959769	0.542718819020116
24	0.396002762797628	0.792005525595257	0.603997237202372
25	0.362002811838375	0.72400562367675	0.637997188161625
26	0.307748072857206	0.615496145714411	0.692251927142794
27	0.253272944150555	0.506545888301109	0.746727055849445
28	0.202915418963982	0.405830837927964	0.797084581036018
29	0.159729377483924	0.319458754967847	0.840270622516076
30	0.131362557589017	0.262725115178033	0.868637442410984
31	0.161980175125449	0.323960350250898	0.838019824874551
32	0.358097775551755	0.71619555110351	0.641902224448245
33	0.306748754274985	0.613497508549971	0.693251245725015
34	0.271952088593059	0.543904177186119	0.72804791140694
35	0.221670519846155	0.443341039692311	0.778329480153845
36	0.186709137852051	0.373418275704101	0.81329086214795
37	0.167891687673863	0.335783375347727	0.832108312326137
38	0.135044705274297	0.270089410548595	0.864955294725703
39	0.106516382657533	0.213032765315067	0.893483617342467
40	0.0852444847412828	0.170488969482566	0.914755515258717
41	0.0820500551883784	0.164100110376757	0.917949944811622
42	0.0734837914887088	0.146967582977418	0.926516208511291
43	0.115619603163596	0.231239206327193	0.884380396836404
44	0.142406668496116	0.284813336992232	0.857593331503884
45	0.118620593483786	0.237241186967573	0.881379406516214
46	0.095227710711085	0.19045542142217	0.904772289288915
47	0.0738121916026232	0.147624383205246	0.926187808397377
48	0.0540686413517106	0.108137282703421	0.94593135864829
49	0.054093531718486	0.108187063436972	0.945906468281514
50	0.0446696166830652	0.0893392333661304	0.955330383316935
51	0.0381451823354213	0.0762903646708427	0.961854817664579
52	0.0273612796999327	0.0547225593998655	0.972638720300067
53	0.0247264169055313	0.0494528338110625	0.975273583094469
54	0.0493733889963513	0.0987467779927027	0.950626611003649
55	0.165631479076867	0.331262958153734	0.834368520923133
56	0.290562824078128	0.581125648156256	0.709437175921872
57	0.249548568843701	0.499097137687403	0.750451431156299
58	0.218585046130589	0.437170092261177	0.781414953869412
59	0.175070095321209	0.350140190642419	0.82492990467879
60	0.135508092651590	0.271016185303180	0.86449190734841
61	0.132952620773826	0.265905241547653	0.867047379226174
62	0.105795894410359	0.211591788820719	0.89420410558964
63	0.123441917282003	0.246883834564006	0.876558082717997
64	0.112256732163738	0.224513464327475	0.887743267836262
65	0.0855338110449208	0.171067622089842	0.91446618895508
66	0.110306908773689	0.220613817547379	0.88969309122631
67	0.331409503056667	0.662819006113334	0.668590496943333
68	0.583649940528558	0.832700118942884	0.416350059471442
69	0.522174188564959	0.955651622870083	0.477825811435041
70	0.504415466774962	0.991169066450076	0.495584533225038
71	0.464177484849645	0.92835496969929	0.535822515150355
72	0.353237067601509	0.706474135203018	0.646762932398491
73	0.24586725066692	0.49173450133384	0.75413274933308
74	0.162569197133989	0.325138394267978	0.837430802866011
75	0.183234635538057	0.366469271076114	0.816765364461943

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	1	0.0140845070422535	OK
10% type I error level	5	0.0704225352112676	OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/10cvfo1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/10cvfo1227566948.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/1ignl1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/1ignl1227566948.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/2kr9n1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/2kr9n1227566948.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/3is7f1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/3is7f1227566948.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/4hbfv1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/4hbfv1227566948.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/5y3se1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/5y3se1227566948.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/6xlpk1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/6xlpk1227566948.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/7kkug1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/7kkug1227566948.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/89s7s1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/89s7s1227566948.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/92i4b1227566948.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227567057ur4pshtj007itw2/92i4b1227566948.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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