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multiple lin regr met seiz en trend

*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 11:43:39 +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/t1290944515srr2gbch9s3lcx8.htm/, Retrieved Sun, 28 Nov 2010 12:42:05 +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/t1290944515srr2gbch9s3lcx8.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 «

1.579 9.769 2.146 9.321 2.462 9.939 3.695 9.336 4.831 10.195 5.134 9.464 6.250 10.010 5.760 10.213 6.249 9.563 2.917 9.890 1.741 9.305 2.359 9.391 1.511 9.928 2.059 8.686 2.635 9.843 2.867 9.627 4.403 10.074 5.720 9.503 4.502 10.119 5.749 10.000 5.627 9.313 2.846 9.866 1.762 9.172 2.429 9.241 1.169 9.659 2.154 8.904 2.249 9.755 2.687 9.080 4.359 9.435 5.382 8.971 4.459 10.063 6.398 9.793 4.596 9.454 3.024 9.759 1.887 8.820 2.070 9.403 1.351 9.676 2.218 8.642 2.461 9.402 3.028 9.610 4.784 9.294 4.975 9.448 4.607 10.319 6.249 9.548 4.809 9.801 3.157 9.596 1.910 8.923 2.228 9.746 1.594 9.829 2.467 9.125 2.222 9.782 3.607 9.441 4.685 9.162 4.962 9.915 5.770 10.444 5.480 10.209 5.000 9.985 3.228 9.842 1.993 9.429 2.288 10.132 1.580 9.849 2.111 9.172 2.192 10.313 3.601 9.819 4.665 9.955 4.876 10.048 5.813 10.082 5.589 10.541 5.331 10.208 3.075 10.233 2.002 9.439 2.306 9.963 1.507 10.158 1.992 9.225 2.487 10.474 3.490 9.757 4.647 10.490 5.5 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

geboortes[t] = + 9.19818672888627 + 0.0132098392350795huwelijken[t] + 0.293354221184415M1[t] -0.56153253458797M2[t] + 0.341103580197079M3[t] -0.121286781733764M4[t] + 0.198089975881454M5[t] + 0.00356981211022246M6[t] + 0.575092941052574M7[t] + 0.52683723983993M8[t] + 0.18183353163088M9[t] + 0.379895626257337M10[t] -0.364188821255045M11[t] + 0.00927576263272296t + 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)	9.19818672888627	0.229712	40.0422	0	0
huwelijken	0.0132098392350795	0.088226	0.1497	0.881347	0.440674
M1	0.293354221184415	0.166287	1.7641	0.081432	0.040716
M2	-0.56153253458797	0.149699	-3.7511	0.000327	0.000164
M3	0.341103580197079	0.149336	2.2841	0.024949	0.012475
M4	-0.121286781733764	0.169829	-0.7142	0.47715	0.238575
M5	0.198089975881454	0.251174	0.7887	0.432587	0.216293
M6	0.00356981211022246	0.30657	0.0116	0.990738	0.495369
M7	0.575092941052574	0.311949	1.8435	0.068862	0.034431
M8	0.52683723983993	0.34362	1.5332	0.129076	0.064538
M9	0.18183353163088	0.315008	0.5772	0.565363	0.282681
M10	0.379895626257337	0.161884	2.3467	0.021353	0.010676
M11	-0.364188821255045	0.153352	-2.3749	0.019891	0.009945
t	0.00927576263272296	0.00112	8.2811	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.842552367519587
R-squared	0.709894492012861
Adjusted R-squared	0.663902155380753
F-TEST (value)	15.4350603599748
F-TEST (DF numerator)	13
F-TEST (DF denominator)	82
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.297327119757428
Sum Squared Residuals	7.24908012374631

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9.769	9.52167504885562	0.247324951144384
2	9.321	8.68355403456223	0.637445965437768
3	9.939	9.5996402211783	0.33935977882171
4	9.336	9.16281335365702	0.173186646342977
5	10.195	9.50647225127601	0.688527748723986
6	9.464	9.32523043142573	0.138769568574266
7	10.01	9.92077150358716	0.0892284964128417
8	10.213	9.87531874378205	0.337681256217951
9	9.563	9.54605040959168	0.0169495904083260
10	9.89	9.70937308251957	0.180626917480431
11	9.305	8.95902962669946	0.345970373300543
12	9.391	9.3406578912345	0.0503421087654952
13	9.928	9.6320859313803	0.295914068619706
14	8.686	8.79371393014146	-0.107713930141456
15	9.843	9.71323467495863	0.129765325041366
16	9.627	9.26318475836305	0.363815241636948
17	10.074	9.61212759167608	0.461872408323924
18	9.503	9.44428054881017	0.0587194511898332
19	10.119	10.0089898561969	0.110010143803086
20	10	9.98648258714314	0.0135174128568620
21	9.313	9.64914304118013	-0.336143041180130
22	9.866	9.81974433552655	0.0462556644734458
23	9.172	9.07061618491607	0.101383815083931
24	9.241	9.45289173157363	-0.211891731573636
25	9.659	9.73887731795457	-0.079877317954572
26	8.904	8.90627801646147	-0.00227801646146475
27	9.755	9.81944482860657	-0.0644448286065678
28	9.08	9.37211613889341	-0.292116138893414
29	9.435	9.7228555103424	-0.287855510342408
30	8.971	9.55112477474139	-0.580124774741386
31	10.063	10.1197309847025	-0.0567309847024809
32	9.793	10.1063649243994	-0.313364924399381
33	9.454	9.74683284852144	-0.292832848521439
34	9.759	9.93340483850307	-0.174404838503073
35	8.82	9.18357656641313	-0.36357656641313
36	9.403	9.55945855088092	-0.156458550880917
37	9.676	9.85259066028803	-0.176590660288033
38	8.642	9.01843259776519	-0.376432597765186
39	9.402	9.93355446611708	-0.531554466117082
40	9.61	9.48792984566525	0.122070154334747
41	9.294	9.83977884361	-0.545778843609992
42	9.448	9.65705752176538	-0.209057521765383
43	10.319	10.2329951925019	0.086004807498052
44	9.548	10.2157058099460	-0.667705809946029
45	9.801	9.86095569587119	-0.059955695871187
46	9.596	10.0464708987140	-0.450470898714015
47	8.923	9.29518954430821	-0.372189544308212
48	9.746	9.67285485707274	0.0731451429272648
49	9.829	9.96710980281483	-0.138109802814832
50	9.125	9.1330309993274	-0.0080309993273959
51	9.782	10.0417064661326	-0.259706466132573
52	9.441	9.60688749417504	-0.165887494175038
53	9.162	9.9497802211184	-0.787780221118394
54	9.915	9.768194945448	0.146805054551996
55	10.444	10.3596673871250	0.0843326128749786
56	10.209	10.3168565951669	-0.107856595166929
57	9.985	9.97478792675776	0.0102120732422363
58	9.842	10.1587179488924	-0.316717948892381
59	9.429	9.4075951125574	0.0214048874426007
60	10.132	9.78495659901952	0.347043400980484
61	9.849	10.0782340166582	-0.229234016658217
62	9.172	9.23963744815238	-0.0676374481523826
63	10.313	10.1526193225482	0.160380677451805
64	9.819	9.7181173867323	0.100882613267697
65	9.955	10.0608251759264	-0.105825175926369
66	10.048	9.87836805086646	0.169631949133538
67	10.082	10.4715445618048	-0.389544561804806
68	10.541	10.4296056192362	0.111394380763773
69	10.208	10.0904695351373	0.117530464862750
70	10.233	10.2680059950821	-0.0350059950820889
71	9.439	9.5190231527032	-0.0800231527031908
72	9.963	9.89650352771842	0.066496472281576
73	10.158	10.1885788499867	-0.0305788499867327
74	9.225	9.34937462887608	-0.124374628876085
75	10.474	10.2678253767152	0.206174623284780
76	9.757	9.82796024616989	-0.0709602461698861
77	10.49	10.1718965504128	0.318103449587187
78	10.281	9.99916186702992	0.281838132970076
79	10.444	10.580185325872	-0.136185325871995
80	10.64	10.5435435288367	0.0964564711633165
81	10.695	10.2133108763822	0.48168912361785
82	10.786	10.3784961366422	0.407503863357809
83	9.832	9.62939440571018	0.202605594289825
84	9.747	10.0110226702452	-0.264022670245223
85	10.411	10.2998483720617	0.111151627938297
86	9.511	9.4619783447138	0.0490216552862017
87	10.402	10.3819746437434	0.0200253562565613
88	9.701	9.93199077634403	-0.230990776344032
89	10.54	10.2812638556379	0.258736144362067
90	10.112	10.1185818599129	-0.00658185991293939
91	10.915	10.7021151882097	0.212884811790324
92	11.183	10.6531221914896	0.529877808510436
93	10.384	10.3214496665584	0.0625503334415937
94	10.834	10.4917867641201	0.342213235879872
95	9.886	9.74157540669237	0.144424593307632
96	10.216	10.1206541722550	0.0953458277449558

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.67417403727776	0.651651925444481	0.325825962722241
18	0.651590928487866	0.696818143024268	0.348409071512134
19	0.520960517994039	0.958078964011921	0.479039482005961
20	0.424097459808015	0.84819491961603	0.575902540191985
21	0.356810078223399	0.713620156446799	0.6431899217766
22	0.276617967954898	0.553235935909795	0.723382032045102
23	0.220717439565316	0.441434879130632	0.779282560434684
24	0.151236161811382	0.302472323622764	0.848763838188618
25	0.105956298318973	0.211912596637947	0.894043701681027
26	0.0833373687296885	0.166674737459377	0.916662631270311
27	0.0580849650312214	0.116169930062443	0.941915034968779
28	0.0676380404900322	0.135276080980064	0.932361959509968
29	0.192322137976191	0.384644275952383	0.807677862023809
30	0.198538631024161	0.397077262048322	0.801461368975839
31	0.165481880991179	0.330963761982357	0.834518119008821
32	0.123484952393755	0.246969904787509	0.876515047606245
33	0.0992712622336255	0.198542524467251	0.900728737766375
34	0.0817239316394561	0.163447863278912	0.918276068360544
35	0.0617861045773184	0.123572209154637	0.938213895422682
36	0.0667930557001041	0.133586111400208	0.933206944299896
37	0.0587819320347	0.1175638640694	0.9412180679653
38	0.0397042929891868	0.0794085859783735	0.960295707010813
39	0.0330084890676300	0.0660169781352601	0.96699151093237
40	0.130179808809243	0.260359617618487	0.869820191190757
41	0.134275196776096	0.268550393552193	0.865724803223904
42	0.154793918578909	0.309587837157819	0.845206081421091
43	0.211633585387060	0.423267170774119	0.78836641461294
44	0.252986157988446	0.505972315976893	0.747013842011554
45	0.313302597781031	0.626605195562062	0.686697402218969
46	0.296132371792156	0.592264743584311	0.703867628207844
47	0.265144965966306	0.530289931932613	0.734855034033694
48	0.405525933802797	0.811051867605595	0.594474066197203
49	0.400224484319562	0.800448968639125	0.599775515680438
50	0.472250566448615	0.94450113289723	0.527749433551385
51	0.441636475963339	0.883272951926677	0.558363524036661
52	0.406476279127109	0.812952558254219	0.59352372087289
53	0.745264711985653	0.509470576028694	0.254735288014347
54	0.823957582423416	0.352084835153169	0.176042417576584
55	0.877676412920504	0.244647174158992	0.122323587079496
56	0.875687411584721	0.248625176830558	0.124312588415279
57	0.875639370221727	0.248721259556545	0.124360629778273
58	0.91148267689142	0.177034646217158	0.0885173231085792
59	0.900537542840177	0.198924914319646	0.099462457159823
60	0.970880936442703	0.0582381271145935	0.0291190635572968
61	0.960611289437656	0.0787774211246878	0.0393887105623439
62	0.944310123244036	0.111379753511929	0.0556898767559643
63	0.94308410897194	0.113831782056119	0.0569158910280597
64	0.960750219679636	0.0784995606407281	0.0392497803203640
65	0.956480692632868	0.0870386147342645	0.0435193073671323
66	0.951536797886915	0.09692640422617	0.048463202113085
67	0.951486440750347	0.0970271184993054	0.0485135592496527
68	0.937172138808479	0.125655722383042	0.062827861191521
69	0.91589809402169	0.168203811956618	0.0841019059783092
70	0.929872279168651	0.140255441662697	0.0701277208313486
71	0.919076917512761	0.161846164974477	0.0809230824872386
72	0.890277767729447	0.219444464541107	0.109722232270553
73	0.841789632586994	0.316420734826013	0.158210367413006
74	0.781389726934178	0.437220546131644	0.218610273065822
75	0.731688743055345	0.536622513889309	0.268311256944655
76	0.628838222438627	0.742323555122745	0.371161777561373
77	0.532288096581564	0.935423806836872	0.467711903418436
78	0.572506247794692	0.854987504410617	0.427493752205308
79	0.412155475515325	0.82431095103065	0.587844524484675

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	8	0.126984126984127	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/10io3d1290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/10io3d1290944611.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/1u5o21290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/1u5o21290944611.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/2u5o21290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/2u5o21290944611.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/3menn1290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/3menn1290944611.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/4menn1290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/4menn1290944611.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/5menn1290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/5menn1290944611.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/6x54p1290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/6x54p1290944611.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/7x54p1290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/7x54p1290944611.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/8pw3a1290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/8pw3a1290944611.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/9pw3a1290944611.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Nov/28/t1290944515srr2gbch9s3lcx8/9pw3a1290944611.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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