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paper 2

*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, 28 Nov 2010 12:51:19 +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/Nov/28/t1290948649nt4uc3gbmczi3oc.htm/, Retrieved Sun, 28 Nov 2010 13:50:49 +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/Nov/28/t1290948649nt4uc3gbmczi3oc.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 «

2.462 9.939 9.321 9.769 3.695 9.336 9.939 9.321 4.831 10.195 9.336 9.939 5.134 9.464 10.195 9.336 6.250 10.010 9.464 10.195 5.760 10.213 10.010 9.464 6.249 9.563 10.213 10.010 2.917 9.890 9.563 10.213 1.741 9.305 9.890 9.563 2.359 9.391 9.305 9.890 1.511 9.928 9.391 9.305 2.059 8.686 9.928 9.391 2.635 9.843 8.686 9.928 2.867 9.627 9.843 8.686 4.403 10.074 9.627 9.843 5.720 9.503 10.074 9.627 4.502 10.119 9.503 10.074 5.749 10.000 10.119 9.503 5.627 9.313 10.000 10.119 2.846 9.866 9.313 10.000 1.762 9.172 9.866 9.313 2.429 9.241 9.172 9.866 1.169 9.659 9.241 9.172 2.154 8.904 9.659 9.241 2.249 9.755 8.904 9.659 2.687 9.080 9.755 8.904 4.359 9.435 9.080 9.755 5.382 8.971 9.435 9.080 4.459 10.063 8.971 9.435 6.398 9.793 10.063 8.971 4.596 9.454 9.793 10.063 3.024 9.759 9.454 9.793 1.887 8.820 9.759 9.454 2.070 9.403 8.820 9.759 1.351 9.676 9.403 8.820 2.218 8.642 9.676 9.403 2.461 9.402 8.642 9.676 3.028 9.610 9.402 8.642 4.784 9.294 9.610 9.402 4.975 9.448 9.294 9.610 4.6 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

geboortes[t] = + 3.63649072146089 -0.112892682481441huwelijk[t] + 0.263683500627482`geboortes-1`[t] + 0.288035361724047`geboortes-2`[t] + 1.16087808712201M1[t] + 0.804780496030499M2[t] + 1.15406440433495M3[t] + 1.09394205740714M4[t] + 1.62510788116370M5[t] + 1.52928339577008M6[t] + 0.984223018622452M7[t] + 0.980829801148078M8[t] + 0.147756081412070M9[t] + 0.717424338136779M10[t] + 1.00090518467358M11[t] + 0.00507968162684446t + 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)	3.63649072146089	1.159936	3.1351	0.002422	0.001211
huwelijk	-0.112892682481441	0.086241	-1.309	0.194366	0.097183
`geboortes-1`	0.263683500627482	0.113501	2.3232	0.022777	0.011388
`geboortes-2`	0.288035361724047	0.103306	2.7882	0.006656	0.003328
M1	1.16087808712201	0.179015	6.4848	0	0
M2	0.804780496030499	0.173408	4.641	1.4e-05	7e-06
M3	1.15406440433495	0.273383	4.2214	6.5e-05	3.3e-05
M4	1.09394205740714	0.308809	3.5425	0.000673	0.000336
M5	1.62510788116370	0.32464	5.0059	3e-06	2e-06
M6	1.52928339577008	0.331643	4.6112	1.5e-05	8e-06
M7	0.984223018622452	0.310683	3.1679	0.002192	0.001096
M8	0.980829801148078	0.169087	5.8007	0	0
M9	0.147756081412070	0.141153	1.0468	0.298435	0.149218
M10	0.717424338136779	0.167258	4.2893	5.1e-05	2.5e-05
M11	1.00090518467358	0.153908	6.5033	0	0
t	0.00507968162684446	0.001519	3.3441	0.001271	0.000635

Multiple Linear Regression - Regression Statistics
Multiple R	0.884336823751576
R-squared	0.782051617843026
Adjusted R-squared	0.740138467428223
F-TEST (value)	18.6588602885558
F-TEST (DF numerator)	15
F-TEST (DF denominator)	78
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.26290500426931
Sum Squared Residuals	5.39128521904797

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9.939	9.7961180639714	0.142881936028592
2	9.336	9.33982003834253	-0.00382003834253139
3	10.195	9.584942243642	0.610057756358005
4	9.464	9.54851189946857	-0.0845118994685673
5	10.01	10.0134389079649	-0.00343890796494194
6	10.213	9.9114288605364	0.301571139463593
7	9.563	9.5270387014109	0.0359612985890964
8	9.89	9.79196048661366	0.0980395133863446
9	9.305	8.99572976268722	0.309270237312777
10	9.391	9.44064273868193	-0.0496427386819326
11	9.928	9.67911235603523	0.248887643964767
12	8.686	8.7877907439339	-0.101790743933897
13	9.843	9.71590240903992	0.127097590960079
14	9.627	9.28603528820429	0.340964711795710
15	10.074	9.74329699522328	0.330703004776725
16	9.503	9.59522555374235	-0.0922255537423493
17	10.119	10.2471628742205	-0.128162874220497
18	10	10.0136017402415	-0.0136017402414687
19	9.313	9.63344539823076	-0.320445398230761
20	9.866	9.73365963938788	0.132340360612120
21	9.172	8.97597795143117	0.196022048568826
22	9.241	9.45171367616553	-0.210713676165533
23	9.659	9.7008166047626	-0.0418166047625992
24	8.904	8.7238859526929	0.180114047307106
25	9.755	9.80043665483291	-0.0454366548329126
26	9.08	9.4068997113737	-0.326899711373707
27	9.435	9.63963846609964	-0.204638466099642
28	8.971	9.3682903601792	-0.397290360179195
29	10.063	9.98863922061385	0.0743607793861539
30	9.793	9.83328948036081	-0.0402894803608154
31	9.454	9.74008146850483	-0.286081468504825
32	9.759	9.75207697513991	0.00692302486008691
33	8.82	9.03522139707908	-0.215221397079078
34	9.403	9.42956195277316	-0.0265619527731571
35	9.676	9.6825545958479	-0.0065545958478985
36	8.642	8.82876134864618	-0.186761348646178
37	9.402	9.77327110965389	-0.371271109653890
38	9.61	9.26081394567647	0.349186054323532
39	9.294	9.69069102821114	-0.396691028211140
40	9.448	9.59067322959655	-0.142673229596545
41	10.319	10.1180513269249	0.200948673075056
42	9.548	10.1159625132757	-0.567962513275689
43	9.801	9.78612610160604	0.0148738983939647
44	9.596	9.81894793898736	-0.22294793898736
45	8.923	9.1505489048201	-0.227548904820103
46	9.746	9.45289072506683	0.293109274933167
47	9.829	9.83618893649984	-0.00718893649984425
48	9.125	9.00074695489779	0.124253045102215
49	9.782	10.0326371814359	-0.250637181435941
50	9.441	9.495726071993	-0.0547260719930037
51	9.162	9.82771450914803	-0.665714509148029
52	9.915	9.56961281577675	0.345387184223253
53	10.444	10.1328328437666	0.311167156233379
54	10.209	10.4312061171296	-0.222206117129616
55	9.985	10.0358189929045	-0.0508189929044853
56	9.842	10.1107978762684	-0.268797876268362
57	9.429	9.31999963940786	0.109000360592138
58	10.132	9.7113538939417	0.420646106058297
59	9.849	10.1462533378513	-0.297253337851294
60	9.172	9.21834824902134	-0.0463482490213426
61	10.313	10.1151339731965	0.197866026803511
62	9.819	9.71091320844425	0.108086791555751
63	9.955	10.1435476826324	-0.188547682632450
64	10.048	9.95825614872157	0.0897438512784332
65	10.082	10.4524165853727	-0.370416585372677
66	10.541	10.4227122701434	0.118287729856578
67	10.208	10.0426818157895	0.165318184210520
68	10.233	10.3434557969425	-0.110455796942469
69	9.439	9.54727191919747	-0.108271919197471
70	9.963	9.88553666661955	0.0774633333804518
71	10.158	10.1737685252058	-0.0157685252057693
72	9.225	9.3255388833213	-0.100538883321296
73	10.474	10.2457649636926	0.228235036307415
74	9.757	9.84211939349422	-0.0851193934942226
75	10.49	10.2365612466379	0.253438753362083
76	10.281	10.0613678626308	0.219632137369164
77	10.444	10.7517142609246	-0.30771426092463
78	10.64	10.6237684723606	0.0162315276393998
79	10.695	10.1354561510115	0.559543848988459
80	10.786	10.5683406883947	0.217659311605283
81	9.832	9.9023336761824	-0.0703336761823944
82	9.747	10.1819710950787	-0.434971095078689
83	10.411	10.2913056437974	0.119694356202638
84	9.511	9.3799278674866	0.131072132513392
85	10.402	10.4307356441769	-0.0287356441768531
86	9.701	10.0286723424715	-0.327672342471528
87	10.54	10.2786078284056	0.261392171594448
88	10.112	10.0500621298842	0.0619378701158074
89	10.915	10.6917439802118	0.223256019788156
90	11.183	10.7750305459520	0.407969454048018
91	10.384	10.5023513705420	-0.118351370541969
92	10.834	10.6867605982656	0.147239401734355
93	9.886	9.8789167491947	0.0070832508053054
94	10.216	10.2853292516726	-0.0693292516726039

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.201182633859419	0.402365267718838	0.798817366140581
20	0.104773992835337	0.209547985670674	0.895226007164663
21	0.0706728628117465	0.141345725623493	0.929327137188253
22	0.0340786981996978	0.0681573963993956	0.965921301800302
23	0.0369507619329122	0.0739015238658245	0.963049238067088
24	0.0463920786586959	0.0927841573173918	0.953607921341304
25	0.0296777603368791	0.0593555206737582	0.97032223966312
26	0.0452378030011019	0.0904756060022037	0.954762196998898
27	0.251133967892405	0.502267935784811	0.748866032107595
28	0.253202265363621	0.506404530727243	0.746797734636379
29	0.205827469367374	0.411654938734749	0.794172530632626
30	0.161554530874448	0.323109061748896	0.838445469125552
31	0.138294937491487	0.276589874982974	0.861705062508513
32	0.103264711407728	0.206529422815456	0.896735288592272
33	0.0755784135863155	0.151156827172631	0.924421586413684
34	0.094130427734539	0.188260855469078	0.905869572265461
35	0.0669925425152414	0.133985085030483	0.933007457484759
36	0.0481968602958175	0.096393720591635	0.951803139704183
37	0.0379180784555803	0.0758361569111606	0.96208192154442
38	0.143321067205704	0.286642134411407	0.856678932794297
39	0.154041480919744	0.308082961839488	0.845958519080256
40	0.168888597567549	0.337777195135097	0.831111402432451
41	0.244096986301647	0.488193972603295	0.755903013698353
42	0.264810288311703	0.529620576623407	0.735189711688297
43	0.329939281986396	0.659878563972791	0.670060718013604
44	0.333593505747289	0.667187011494578	0.666406494252711
45	0.306111148654745	0.61222229730949	0.693888851345255
46	0.434353035691275	0.86870607138255	0.565646964308725
47	0.418365676716487	0.836731353432974	0.581634323283513
48	0.514201346488835	0.97159730702233	0.485798653511165
49	0.460465929288416	0.920931858576831	0.539534070711584
50	0.407831311308258	0.815662622616516	0.592168688691742
51	0.7917325306124	0.416534938775199	0.208267469387600
52	0.843270301189233	0.313459397621534	0.156729698810767
53	0.876863273479987	0.246273453040027	0.123136726520013
54	0.85261189308446	0.294776213831082	0.147388106915541
55	0.829543458370346	0.340913083259309	0.170456541629654
56	0.886576326456197	0.226847347087606	0.113423673543803
57	0.860332345954134	0.279335308091733	0.139667654045867
58	0.933998230229001	0.132003539541997	0.0660017697709987
59	0.90925219809031	0.181495603819378	0.0907478019096891
60	0.876331446088796	0.247337107822408	0.123668553911204
61	0.864236806830292	0.271526386339416	0.135763193169708
62	0.898391347483003	0.203217305033993	0.101608652516997
63	0.870320215729552	0.259359568540896	0.129679784270448
64	0.852241302104236	0.295517395791529	0.147758697895764
65	0.879465696102535	0.241068607794930	0.120534303897465
66	0.836107602374452	0.327784795251096	0.163892397625548
67	0.79984813041903	0.400303739161942	0.200151869580971
68	0.838727087474007	0.322545825051987	0.161272912525993
69	0.848150847229068	0.303698305541865	0.151849152770932
70	0.773806674706728	0.452386650586544	0.226193325293272
71	0.712434134760899	0.575131730478203	0.287565865239101
72	0.597733776226377	0.804532447547246	0.402266223773623
73	0.487497449406802	0.974994898813604	0.512502550593198
74	0.356287802738769	0.712575605477538	0.643712197261231
75	0.277259191942271	0.554518383884542	0.722740808057729

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	7	0.122807017543860	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/100pnx1290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/100pnx1290948670.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/1b6ql1290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/1b6ql1290948670.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/2b6ql1290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/2b6ql1290948670.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/3mgpo1290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/3mgpo1290948670.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/4mgpo1290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/4mgpo1290948670.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/5mgpo1290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/5mgpo1290948670.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/6ep691290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/6ep691290948670.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/7pgoc1290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/7pgoc1290948670.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/8pgoc1290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/8pgoc1290948670.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/9pgoc1290948670.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290948649nt4uc3gbmczi3oc/9pgoc1290948670.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = 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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