Home » date » 2008 » Nov » 25 »

Toon Wouters

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 25 Nov 2008 04:18:32 -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/25/t1227611961exkn5vo19280oz1.htm/, Retrieved Tue, 25 Nov 2008 11:19:31 +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/25/t1227611961exkn5vo19280oz1.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 «

124 0 113 0 109 0 109 0 106 0 101 0 98 0 93 0 91 0 122 0 139 0 140 0 132 0 117 0 114 0 113 0 110 0 107 0 103 0 98 0 98 0 137 0 148 0 147 0 139 0 130 0 128 0 127 0 123 0 118 0 114 0 108 0 111 0 151 0 159 0 158 0 148 0 138 0 137 0 136 0 133 0 126 0 120 0 114 0 116 0 153 0 162 0 161 0 149 0 139 0 135 0 130 0 127 0 122 0 117 0 112 0 113 0 149 0 157 0 157 0 147 0 137 0 132 0 125 0 123 0 117 0 114 0 111 0 112 0 144 0 150 0 149 0 134 0 123 0 116 0 117 1 111 1 105 1 102 1 95 1 93 1 124 1 130 1 124 1 115 1 106 1 105 1 105 1 101 1 95 1 93 1 84 1 87 1 116 1 120 1 117 1 109 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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation

Y[t] = + 126.933333333333 -19.9333333333333X[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)	126.933333333333	1.96069	64.7391	0	0
X	-19.9333333333333	4.117024	-4.8417	5e-06	2e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.444880967754571
R-squared	0.197919075470244
Adjusted R-squared	0.189476118369931
F-TEST (value)	23.4419141443821
F-TEST (DF numerator)	1
F-TEST (DF denominator)	95
p-value	4.98278536154029e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	16.9800708778716
Sum Squared Residuals	27390.6666666667

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	124	126.933333333334	-2.93333333333362
2	113	126.933333333333	-13.9333333333333
3	109	126.933333333333	-17.9333333333333
4	109	126.933333333333	-17.9333333333333
5	106	126.933333333333	-20.9333333333333
6	101	126.933333333333	-25.9333333333333
7	98	126.933333333333	-28.9333333333333
8	93	126.933333333333	-33.9333333333333
9	91	126.933333333333	-35.9333333333333
10	122	126.933333333333	-4.93333333333333
11	139	126.933333333333	12.0666666666667
12	140	126.933333333333	13.0666666666667
13	132	126.933333333333	5.06666666666667
14	117	126.933333333333	-9.93333333333333
15	114	126.933333333333	-12.9333333333333
16	113	126.933333333333	-13.9333333333333
17	110	126.933333333333	-16.9333333333333
18	107	126.933333333333	-19.9333333333333
19	103	126.933333333333	-23.9333333333333
20	98	126.933333333333	-28.9333333333333
21	98	126.933333333333	-28.9333333333333
22	137	126.933333333333	10.0666666666667
23	148	126.933333333333	21.0666666666667
24	147	126.933333333333	20.0666666666667
25	139	126.933333333333	12.0666666666667
26	130	126.933333333333	3.06666666666667
27	128	126.933333333333	1.06666666666667
28	127	126.933333333333	0.0666666666666713
29	123	126.933333333333	-3.93333333333333
30	118	126.933333333333	-8.93333333333333
31	114	126.933333333333	-12.9333333333333
32	108	126.933333333333	-18.9333333333333
33	111	126.933333333333	-15.9333333333333
34	151	126.933333333333	24.0666666666667
35	159	126.933333333333	32.0666666666667
36	158	126.933333333333	31.0666666666667
37	148	126.933333333333	21.0666666666667
38	138	126.933333333333	11.0666666666667
39	137	126.933333333333	10.0666666666667
40	136	126.933333333333	9.06666666666667
41	133	126.933333333333	6.06666666666667
42	126	126.933333333333	-0.933333333333329
43	120	126.933333333333	-6.93333333333333
44	114	126.933333333333	-12.9333333333333
45	116	126.933333333333	-10.9333333333333
46	153	126.933333333333	26.0666666666667
47	162	126.933333333333	35.0666666666667
48	161	126.933333333333	34.0666666666667
49	149	126.933333333333	22.0666666666667
50	139	126.933333333333	12.0666666666667
51	135	126.933333333333	8.06666666666667
52	130	126.933333333333	3.06666666666667
53	127	126.933333333333	0.0666666666666713
54	122	126.933333333333	-4.93333333333333
55	117	126.933333333333	-9.93333333333333
56	112	126.933333333333	-14.9333333333333
57	113	126.933333333333	-13.9333333333333
58	149	126.933333333333	22.0666666666667
59	157	126.933333333333	30.0666666666667
60	157	126.933333333333	30.0666666666667
61	147	126.933333333333	20.0666666666667
62	137	126.933333333333	10.0666666666667
63	132	126.933333333333	5.06666666666667
64	125	126.933333333333	-1.93333333333333
65	123	126.933333333333	-3.93333333333333
66	117	126.933333333333	-9.93333333333333
67	114	126.933333333333	-12.9333333333333
68	111	126.933333333333	-15.9333333333333
69	112	126.933333333333	-14.9333333333333
70	144	126.933333333333	17.0666666666667
71	150	126.933333333333	23.0666666666667
72	149	126.933333333333	22.0666666666667
73	134	126.933333333333	7.06666666666667
74	123	126.933333333333	-3.93333333333333
75	116	126.933333333333	-10.9333333333333
76	117	107	10
77	111	107	4
78	105	107	-2
79	102	107	-5
80	95	107	-12
81	93	107	-14
82	124	107	17
83	130	107	23
84	124	107	17
85	115	107	8
86	106	107	-1
87	105	107	-2
88	105	107	-2
89	101	107	-6
90	95	107	-12
91	93	107	-14
92	84	107	-23
93	87	107	-20
94	116	107	9
95	120	107	13
96	117	107	10
97	109	107	2

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.124108646525355	0.248217293050711	0.875891353474645
6	0.101463078611682	0.202926157223364	0.898536921388318
7	0.0963010990437653	0.192602198087531	0.903698900956235
8	0.126955164720051	0.253910329440103	0.873044835279949
9	0.161835051405217	0.323670102810433	0.838164948594783
10	0.189850898622015	0.379701797244031	0.810149101377985
11	0.476756389799557	0.953512779599114	0.523243610200443
12	0.657859076638609	0.684281846722782	0.342140923361391
13	0.66785891851949	0.66428216296102	0.33214108148051
14	0.592311490523949	0.815377018952102	0.407688509476051
15	0.51689707754156	0.96620584491688	0.48310292245844
16	0.445414532487252	0.890829064974504	0.554585467512748
17	0.387202417932827	0.774404835865653	0.612797582067173
18	0.348062441435509	0.696124882871019	0.651937558564491
19	0.339859530681827	0.679719061363653	0.660140469318173
20	0.383253013120876	0.766506026241752	0.616746986879124
21	0.433125742441903	0.866251484883806	0.566874257558097
22	0.526040160459732	0.947919679080536	0.473959839540268
23	0.728993995760226	0.542012008479548	0.271006004239774
24	0.835898170099232	0.328203659801536	0.164101829900768
25	0.853372902416349	0.293254195167302	0.146627097583651
26	0.831660373008084	0.336679253983832	0.168339626991916
27	0.80205072047582	0.395898559048359	0.197949279524179
28	0.766950165818355	0.46609966836329	0.233049834181645
29	0.724584520454241	0.550830959091518	0.275415479545759
30	0.684481737494568	0.631036525010864	0.315518262505432
31	0.656544635329044	0.686910729341912	0.343455364670956
32	0.664720386977325	0.67055922604535	0.335279613022675
33	0.658402253654663	0.683195492690675	0.341597746345337
34	0.77343692274035	0.453126154519300	0.226563077259650
35	0.904030045033048	0.191939909933904	0.095969954966952
36	0.959645769133092	0.0807084617338158	0.0403542308669079
37	0.968639664763095	0.0627206704738096	0.0313603352369048
38	0.963424450498807	0.073151099002387	0.0365755495011935
39	0.956153439440923	0.0876931211181538	0.0438465605590769
40	0.94639119640394	0.107217607192119	0.0536088035960593
41	0.93186404930466	0.136271901390681	0.0681359506953407
42	0.912263025274875	0.175473949450250	0.0877369747251249
43	0.894777674202694	0.210444651594613	0.105222325797306
44	0.88997133955358	0.22005732089284	0.11002866044642
45	0.881034422236336	0.237931155527329	0.118965577763664
46	0.914300243178363	0.171399513643274	0.0856997568216372
47	0.965767762963393	0.0684644740732147	0.0342322370366073
48	0.987467230693813	0.0250655386123749	0.0125327693061875
49	0.989852582585292	0.0202948348294153	0.0101474174147077
50	0.987251458358111	0.0254970832837772	0.0127485416418886
51	0.982543426411769	0.0349131471764623	0.0174565735882311
52	0.975146344189221	0.0497073116215572	0.0248536558107786
53	0.965268842091692	0.0694623158166151	0.0347311579083075
54	0.95467312653682	0.0906537469263593	0.0453268734631797
55	0.947885258108348	0.104229483783304	0.0521147418916518
56	0.950783974165969	0.0984320516680628	0.0492160258340314
57	0.953528788212352	0.0929424235752963	0.0464712117876481
58	0.958284523848474	0.083430952303052	0.041715476151526
59	0.978075877644721	0.0438482447105573	0.0219241223552786
60	0.990542548368134	0.018914903263732	0.009457451631866
61	0.99228919963494	0.0154216007301191	0.00771080036505957
62	0.9899356274672	0.0201287450655978	0.0100643725327989
63	0.985346807234938	0.0293063855301241	0.0146531927650620
64	0.978175979239018	0.0436480415219637	0.0218240207609818
65	0.96871650138066	0.0625669972386779	0.0312834986193390
66	0.961380973620471	0.077238052759057	0.0386190263795285
67	0.95901343671173	0.0819731265765414	0.0409865632882707
68	0.966096872017531	0.0678062559649372	0.0339031279824686
69	0.975749775111073	0.0485004497778543	0.0242502248889272
70	0.970069931431256	0.0598601371374879	0.0299300685687439
71	0.97503007978574	0.0499398404285226	0.0249699202142613
72	0.983960981541167	0.0320780369176659	0.0160390184588329
73	0.980453052730677	0.0390938945386459	0.0195469472693230
74	0.970824752908283	0.0583504941834351	0.0291752470917176
75	0.956620010720332	0.0867599785593363	0.0433799892796682
76	0.944512103318397	0.110975793363206	0.0554878966816028
77	0.921020443362327	0.157959113275345	0.0789795566376725
78	0.888005706991774	0.223988586016452	0.111994293008226
79	0.848549576900108	0.302900846199784	0.151450423099892
80	0.825945656753286	0.348108686493429	0.174054343246714
81	0.815629952765128	0.368740094469745	0.184370047234872
82	0.82061498564779	0.358770028704421	0.179385014352211
83	0.888879279761475	0.22224144047705	0.111120720238525
84	0.914345807127103	0.171308385745794	0.085654192872897
85	0.895140025566614	0.209719948866772	0.104859974433386
86	0.839893802921704	0.320212394156593	0.160106197078296
87	0.763727823746609	0.472544352506783	0.236272176253392
88	0.666519913867127	0.666960172265745	0.333480086132873
89	0.549938327608513	0.900123344782973	0.450061672391487
90	0.450202201452269	0.900404402904539	0.54979779854773
91	0.370244309585607	0.740488619171215	0.629755690414393
92	0.49409936640891	0.98819873281782	0.50590063359109

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	15	0.170454545454545	NOK
10% type I error level	32	0.363636363636364	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/10mph91227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/10mph91227611906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/1h99z1227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/1h99z1227611906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/2rb711227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/2rb711227611906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/396v91227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/396v91227611906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/4fh8w1227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/4fh8w1227611906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/545lp1227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/545lp1227611906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/6wwtl1227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/6wwtl1227611906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/7kjhj1227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/7kjhj1227611906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/8ihoi1227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/8ihoi1227611906.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/9zgfr1227611906.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227611961exkn5vo19280oz1/9zgfr1227611906.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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