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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 05:06:02 -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/t1258546123wn4pb1jj4kb9etl.htm/, Retrieved Wed, 18 Nov 2009 13:09:00 +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/t1258546123wn4pb1jj4kb9etl.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 «

149657 0 142773 0 133639 0 128332 0 120297 0 118632 0 155276 0 169316 0 167395 0 157939 0 149601 0 146310 0 141579 0 136473 0 129818 0 124226 0 116428 0 116440 0 147747 0 160069 0 163129 0 151108 0 141481 0 139174 0 134066 0 130104 0 123090 0 116598 0 109627 0 105428 0 137272 0 159836 0 155283 0 141514 0 131852 0 130691 0 128461 0 123066 0 117599 0 111599 0 105395 0 102334 0 131305 0 149033 0 144954 0 132404 0 122104 0 118755 0 116222 1 110924 1 103753 1 99983 1 93302 1 91496 1 119321 1 139261 1 133739 1 123913 1 113438 1 109416 1 109406 1 105645 1 101328 1 97686 1 93093 1 91382 1 122257 1 139183 1 139887 1 131822 1 116805 1 113706 1 113012 1 110452 1 107005 1 102841 1 98173 1 98181 1 137277 1 147579 1 146571 1 138920 1 130340 1 128140 1 127059 1 122860 1 117702 1 113537 1 108366 1 111078 1 150739 1 159129 1 157928 1 147768 1 137507 1 136919 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	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 135213.4375 -14649.1250000000X[t] -456.124999999875M1[t] -5101.74999999997M2[t] -11147.1250000000M3[t] -16038.625M4[t] -22303.75M5[t] -23517.5000000000M6[t] + 9760.37499999999M7[t] + 25036.875M8[t] + 23221.8750000000M9[t] + 12784.625M10[t] + 2502.12500000000M11[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)	135213.4375	3374.050343	40.0745	0	0
X	-14649.1250000000	1871.586388	-7.8271	0	0
M1	-456.124999999875	4584.431659	-0.0995	0.920986	0.460493
M2	-5101.74999999997	4584.431659	-1.1128	0.268989	0.134494
M3	-11147.1250000000	4584.431659	-2.4315	0.017188	0.008594
M4	-16038.625	4584.431659	-3.4985	0.000755	0.000377
M5	-22303.75	4584.431659	-4.8651	5e-06	3e-06
M6	-23517.5000000000	4584.431659	-5.1299	2e-06	1e-06
M7	9760.37499999999	4584.431659	2.129	0.036216	0.018108
M8	25036.875	4584.431659	5.4613	0	0
M9	23221.8750000000	4584.431659	5.0654	2e-06	1e-06
M10	12784.625	4584.431659	2.7887	0.006561	0.00328
M11	2502.12500000000	4584.431659	0.5458	0.586676	0.293338

Multiple Linear Regression - Regression Statistics
Multiple R	0.895492940373178
R-squared	0.8019076062582
Adjusted R-squared	0.773267742102758
F-TEST (value)	27.9997000651224
F-TEST (DF numerator)	12
F-TEST (DF denominator)	83
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9168.86331797323
Sum Squared Residuals	6977648527.12503

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	149657	134757.312499999	14899.6875000008
2	142773	130111.6875	12661.3125
3	133639	124066.3125	9572.68749999997
4	128332	119174.8125	9157.1874999999
5	120297	112909.6875	7387.31250000003
6	118632	111695.9375	6936.06249999996
7	155276	144973.8125	10302.1875
8	169316	160250.3125	9065.68750000006
9	167395	158435.3125	8959.68750000002
10	157939	147998.0625	9940.9375
11	149601	137715.5625	11885.4375000000
12	146310	135213.4375	11096.5625
13	141579	134757.3125	6821.68749999987
14	136473	130111.6875	6361.31249999998
15	129818	124066.3125	5751.68749999998
16	124226	119174.8125	5051.1875
17	116428	112909.6875	3518.31249999998
18	116440	111695.9375	4744.06249999999
19	147747	144973.8125	2773.18749999999
20	160069	160250.3125	-181.312500000025
21	163129	158435.3125	4693.68749999998
22	151108	147998.0625	3109.93749999999
23	141481	137715.5625	3765.43749999999
24	139174	135213.4375	3960.5625
25	134066	134757.3125	-691.312500000135
26	130104	130111.6875	-7.68750000001614
27	123090	124066.3125	-976.312500000013
28	116598	119174.8125	-2576.8125
29	109627	112909.6875	-3282.68750000002
30	105428	111695.9375	-6267.93750000001
31	137272	144973.8125	-7701.8125
32	159836	160250.3125	-414.312500000025
33	155283	158435.3125	-3152.31250000002
34	141514	147998.0625	-6484.06250000001
35	131852	137715.5625	-5863.56250000001
36	130691	135213.4375	-4522.4375
37	128461	134757.3125	-6296.31250000013
38	123066	130111.6875	-7045.68750000002
39	117599	124066.3125	-6467.31250000001
40	111599	119174.8125	-7575.8125
41	105395	112909.6875	-7514.68750000001
42	102334	111695.9375	-9361.9375
43	131305	144973.8125	-13668.8125
44	149033	160250.3125	-11217.3125000000
45	144954	158435.3125	-13481.3125000000
46	132404	147998.0625	-15594.0625
47	122104	137715.5625	-15611.5625
48	118755	135213.4375	-16458.4375
49	116222	120108.187500000	-3886.18750000011
50	110924	115462.5625	-4538.56249999999
51	103753	109417.1875	-5664.18749999999
52	99983	104525.6875	-4542.68749999997
53	93302	98260.5625	-4958.5625
54	91496	97046.8125	-5550.81249999999
55	119321	130324.6875	-11003.6875
56	139261	145601.1875	-6340.18750000001
57	133739	143786.1875	-10047.1875
58	123913	133348.9375	-9435.9375
59	113438	123066.4375	-9628.43749999999
60	109416	120564.3125	-11148.3125000000
61	109406	120108.187500000	-10702.1875000001
62	105645	115462.5625	-9817.56249999999
63	101328	109417.1875	-8089.18749999999
64	97686	104525.6875	-6839.68749999997
65	93093	98260.5625	-5167.5625
66	91382	97046.8125	-5664.81249999999
67	122257	130324.6875	-8067.68749999999
68	139183	145601.1875	-6418.18750000001
69	139887	143786.1875	-3899.1875
70	131822	133348.9375	-1526.93749999999
71	116805	123066.4375	-6261.43749999999
72	113706	120564.3125	-6858.31249999999
73	113012	120108.187500000	-7096.18750000011
74	110452	115462.5625	-5010.56249999999
75	107005	109417.1875	-2412.18749999999
76	102841	104525.6875	-1684.68749999997
77	98173	98260.5625	-87.5624999999964
78	98181	97046.8125	1134.18750000001
79	137277	130324.6875	6952.31250000001
80	147579	145601.1875	1977.81249999999
81	146571	143786.1875	2784.8125
82	138920	133348.9375	5571.06250000001
83	130340	123066.4375	7273.56250000001
84	128140	120564.3125	7575.68750000001
85	127059	120108.187500000	6950.81249999989
86	122860	115462.5625	7397.43750
87	117702	109417.1875	8284.81250000001
88	113537	104525.6875	9011.31250000003
89	108366	98260.5625	10105.4375
90	111078	97046.8125	14031.1875
91	150739	130324.6875	20414.3125
92	159129	145601.1875	13527.8125
93	157928	143786.1875	14141.8125
94	147768	133348.9375	14419.0625
95	137507	123066.4375	14440.5625
96	136919	120564.3125	16354.6875

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.187323749425993	0.374647498851985	0.812676250574007
17	0.099818338994644	0.199636677989288	0.900181661005356
18	0.0466066921167102	0.0932133842334204	0.95339330788329
19	0.0414690678713540	0.0829381357427081	0.958530932128646
20	0.0462999400546926	0.0925998801093853	0.953700059945307
21	0.0282239484153418	0.0564478968306836	0.971776051584658
22	0.0224074717207951	0.0448149434415903	0.977592528279205
23	0.0216585318813203	0.0433170637626407	0.97834146811868
24	0.0185506246771088	0.0371012493542176	0.981449375322891
25	0.0371690848031994	0.0743381696063988	0.9628309151968
26	0.0457071019226669	0.0914142038453338	0.954292898077333
27	0.0477599613755033	0.0955199227510066	0.952240038624497
28	0.0535329928471103	0.107065985694221	0.94646700715289
29	0.052576650370356	0.105153300740712	0.947423349629644
30	0.0693043527496294	0.138608705499259	0.930695647250371
31	0.103698401218470	0.207396802436940	0.89630159878153
32	0.0838157144435086	0.167631428887017	0.916184285556491
33	0.0859558371398517	0.171911674279703	0.914044162860148
34	0.104218736787950	0.208437473575899	0.89578126321205
35	0.127735163030926	0.255470326061852	0.872264836969074
36	0.138304156407395	0.27660831281479	0.861695843592605
37	0.169679844037286	0.339359688074571	0.830320155962714
38	0.199486224941496	0.398972449882992	0.800513775058504
39	0.20858867009898	0.41717734019796	0.79141132990102
40	0.214704679033129	0.429409358066259	0.78529532096687
41	0.208578090244988	0.417156180489975	0.791421909755012
42	0.205167379792625	0.410334759585249	0.794832620207375
43	0.231945212247523	0.463890424495046	0.768054787752477
44	0.245105745368563	0.490211490737126	0.754894254631437
45	0.280658225975045	0.561316451950089	0.719341774024955
46	0.317091952409945	0.63418390481989	0.682908047590055
47	0.358548019328817	0.717096038657634	0.641451980671183
48	0.403743975484524	0.807487950969049	0.596256024515476
49	0.339180167141078	0.678360334282155	0.660819832858922
50	0.279909911649528	0.559819823299056	0.720090088350472
51	0.230008639804645	0.46001727960929	0.769991360195355
52	0.185727549251918	0.371455098503837	0.814272450748082
53	0.15035727356723	0.30071454713446	0.84964272643277
54	0.124151159451281	0.248302318902562	0.87584884054872
55	0.132460424563585	0.264920849127169	0.867539575436415
56	0.110341309338156	0.220682618676311	0.889658690661844
57	0.10736409170867	0.21472818341734	0.89263590829133
58	0.110164145472449	0.220328290944897	0.889835854527551
59	0.111282522896227	0.222565045792455	0.888717477103773
60	0.126918436398266	0.253836872796532	0.873081563601734
61	0.116715694256567	0.233431388513134	0.883284305743433
62	0.105498956771448	0.210997913542896	0.894501043228552
63	0.0928038506775762	0.185607701355152	0.907196149322424
64	0.0794678454370682	0.158935690874136	0.920532154562932
65	0.0673504690084116	0.134700938016823	0.932649530991588
66	0.0654140325164357	0.130828065032871	0.934585967483564
67	0.116667528911250	0.233335057822501	0.88333247108875
68	0.125881301587028	0.251762603174055	0.874118698412972
69	0.127610751667425	0.255221503334850	0.872389248332575
70	0.130089487847321	0.260178975694641	0.86991051215268
71	0.184001655999505	0.36800331199901	0.815998344000495
72	0.309983030779993	0.619966061559986	0.690016969220007
73	0.335322209931192	0.670644419862384	0.664677790068808
74	0.339339778092081	0.678679556184161	0.66066022190792
75	0.321615108654824	0.643230217309648	0.678384891345176
76	0.304939305327843	0.609878610655686	0.695060694672157
77	0.282939321427437	0.565878642854873	0.717060678572563
78	0.312154314609704	0.624308629219409	0.687845685390295
79	0.38847775060058	0.77695550120116	0.61152224939942
80	0.398154014002545	0.79630802800509	0.601845985997455

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	3	0.0461538461538462	OK
10% type I error level	10	0.153846153846154	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/10yexh1258545955.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/10yexh1258545955.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/1m7291258545954.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/1m7291258545954.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/2s6a41258545954.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/2s6a41258545954.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/3gl0n1258545954.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/3gl0n1258545954.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/45rm91258545954.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/45rm91258545954.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/5rxzx1258545955.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/5rxzx1258545955.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/60vup1258545955.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/60vup1258545955.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/7sdk01258545955.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/7sdk01258545955.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/8eas51258545955.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/8eas51258545955.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/9gylc1258545955.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258546123wn4pb1jj4kb9etl/9gylc1258545955.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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