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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: Tue, 21 Dec 2010 14:25:29 +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/Dec/21/t12929414636gobet05zad330d.htm/, Retrieved Tue, 21 Dec 2010 15:24:34 +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/Dec/21/t12929414636gobet05zad330d.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 «

13.193 15.234 14.718 16.961 13.945 15.876 16.226 18.316 16.748 17.904 17.209 18.950 17.225 18.710 17.236 18.687 17.580 19.568 17.381 19.580 17.260 18.661 15.658 18.674 15.908 17.475 17.725 19.562 16.368 19.555 17.743 19.867 15.703 19.324 18.162 19.074 15.323 19.704 18.375 18.352 13.927 17.795 16.761 18.902 16.239 19.158 18.279 15.698 16.239 18.431 18.414 19.801 14.995 18.706 18.232 19.409 16.263 19.017 20.298 19.891 15.203 17.845 17.502 18.532 15.737 17.770 17.224 17.601 14.940 18.507 17.635 19.392 15.699 17.661 18.243 19.643 15.770 17.344 17.229 17.322 16.152 17.919 16.918 18.114 16.308 17.759 16.021 17.952 15.954 17.762 16.610 17.751 15.458 18.106 15.990 15.349 13.185 15.409 16.007 16.633 14.800 15.974 15.693

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	16 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

aantal[t] = + 18.40052 -2.78044307692307Q1[t] -0.432289230769232Q2[t] -1.18940461538462Q3[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)	18.40052	0.240043	76.6551	0	0
Q1	-2.78044307692307	0.336192	-8.2704	0	0
Q2	-0.432289230769232	0.336192	-1.2858	0.201499	0.100749
Q3	-1.18940461538462	0.336192	-3.5379	0.000616	0.000308

Multiple Linear Regression - Regression Statistics
Multiple R	0.668872960553794
R-squared	0.447391037359998
Adjusted R-squared	0.430645311219392
F-TEST (value)	26.7167296063164
F-TEST (DF numerator)	3
F-TEST (DF denominator)	99
p-value	9.62230295442623e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.20021560923038
Sum Squared Residuals	142.611233355385

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	13.193	15.6200769230769	-2.42707692307693
2	15.234	17.9682307692308	-2.73423076923079
3	14.718	17.2111153846154	-2.49311538461538
4	16.961	18.40052	-1.439520
5	13.945	15.6200769230769	-1.67507692307692
6	15.876	17.9682307692308	-2.09223076923077
7	16.226	17.2111153846154	-0.985115384615385
8	18.316	18.40052	-0.0845200000000007
9	16.748	15.6200769230769	1.12792307692308
10	17.904	17.9682307692308	-0.0642307692307695
11	17.209	17.2111153846154	-0.00211538461538446
12	18.95	18.40052	0.54948
13	17.225	15.6200769230769	1.60492307692308
14	18.71	17.9682307692308	0.741769230769231
15	17.236	17.2111153846154	0.0248846153846166
16	18.687	18.40052	0.286480000000002
17	17.58	15.6200769230769	1.95992307692307
18	19.568	17.9682307692308	1.59976923076923
19	17.381	17.2111153846154	0.169884615384616
20	19.58	18.40052	1.17948
21	17.26	15.6200769230769	1.63992307692308
22	18.661	17.9682307692308	0.692769230769232
23	15.658	17.2111153846154	-1.55311538461538
24	18.674	18.40052	0.27348
25	15.908	15.6200769230769	0.287923076923076
26	17.475	17.9682307692308	-0.493230769230768
27	17.725	17.2111153846154	0.513884615384617
28	19.562	18.40052	1.16148000000000
29	16.368	15.6200769230769	0.747923076923075
30	19.555	17.9682307692308	1.58676923076923
31	17.743	17.2111153846154	0.531884615384615
32	19.867	18.40052	1.46648
33	15.703	15.6200769230769	0.0829230769230759
34	19.324	17.9682307692308	1.35576923076923
35	18.162	17.2111153846154	0.950884615384615
36	19.074	18.40052	0.673480000000002
37	15.323	15.6200769230769	-0.297076923076923
38	19.704	17.9682307692308	1.73576923076923
39	18.375	17.2111153846154	1.16388461538462
40	18.352	18.40052	-0.0485199999999993
41	13.927	15.6200769230769	-1.69307692307692
42	17.795	17.9682307692308	-0.173230769230768
43	16.761	17.2111153846154	-0.450115384615385
44	18.902	18.40052	0.501480000000001
45	16.239	15.6200769230769	0.618923076923077
46	19.158	17.9682307692308	1.18976923076923
47	18.279	17.2111153846154	1.06788461538462
48	15.698	18.40052	-2.70252
49	16.239	15.6200769230769	0.618923076923077
50	18.431	17.9682307692308	0.462769230769231
51	18.414	17.2111153846154	1.20288461538462
52	19.801	18.40052	1.40048
53	14.995	15.6200769230769	-0.625076923076924
54	18.706	17.9682307692308	0.73776923076923
55	18.232	17.2111153846154	1.02088461538462
56	19.409	18.40052	1.00848
57	16.263	15.6200769230769	0.642923076923078
58	19.017	17.9682307692308	1.04876923076923
59	20.298	17.2111153846154	3.08688461538461
60	19.891	18.40052	1.49048
61	15.203	15.6200769230769	-0.417076923076924
62	17.845	17.9682307692308	-0.123230769230771
63	17.502	17.2111153846154	0.290884615384615
64	18.532	18.40052	0.131480000000000
65	15.737	15.6200769230769	0.116923076923077
66	17.77	17.9682307692308	-0.19823076923077
67	17.224	17.2111153846154	0.0128846153846161
68	17.601	18.40052	-0.79952
69	14.94	15.6200769230769	-0.680076923076924
70	18.507	17.9682307692308	0.538769230769232
71	17.635	17.2111153846154	0.423884615384618
72	19.392	18.40052	0.99148
73	15.699	15.6200769230769	0.0789230769230763
74	17.661	17.9682307692308	-0.307230769230768
75	18.243	17.2111153846154	1.03188461538461
76	19.643	18.40052	1.24248
77	15.77	15.6200769230769	0.149923076923076
78	17.344	17.9682307692308	-0.624230769230768
79	17.229	17.2111153846154	0.0178846153846151
80	17.322	18.40052	-1.07852
81	16.152	15.6200769230769	0.531923076923077
82	17.919	17.9682307692308	-0.049230769230769
83	16.918	17.2111153846154	-0.293115384615385
84	18.114	18.40052	-0.286519999999999
85	16.308	15.6200769230769	0.687923076923076
86	17.759	17.9682307692308	-0.209230769230769
87	16.021	17.2111153846154	-1.19011538461538
88	17.952	18.40052	-0.448519999999998
89	15.954	15.6200769230769	0.333923076923077
90	17.762	17.9682307692308	-0.206230769230769
91	16.61	17.2111153846154	-0.601115384615385
92	17.751	18.40052	-0.649519999999998
93	15.458	15.6200769230769	-0.162076923076923
94	18.106	17.9682307692308	0.137769230769232
95	15.99	17.2111153846154	-1.22111538461538
96	15.349	18.40052	-3.05152
97	13.185	15.6200769230769	-2.43507692307692
98	15.409	17.9682307692308	-2.55923076923077
99	16.007	17.2111153846154	-1.20411538461538
100	16.633	18.40052	-1.76752
101	14.8	15.6200769230769	-0.820076923076923
102	15.974	17.9682307692308	-1.99423076923077
103	15.693	17.2111153846154	-1.51811538461538

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.282838267329252	0.565676534658504	0.717161732670748
8	0.281423668449825	0.56284733689965	0.718576331550175
9	0.809990265081931	0.380019469836138	0.190009734918069
10	0.882576920042244	0.234846159915511	0.117423079957756
11	0.885301800898327	0.229396398203345	0.114698199101673
12	0.864943646145316	0.270112707709367	0.135056353854684
13	0.936209830400236	0.127580339199528	0.063790169599764
14	0.959521117988409	0.0809577640231825	0.0404788820115913
15	0.948497677211785	0.103004645576430	0.0515023227882148
16	0.926113900290914	0.147772199418173	0.0738860997090865
17	0.958167434711495	0.0836651305770098	0.0418325652885049
18	0.979848541890544	0.0403029162189118	0.0201514581094559
19	0.972995672023515	0.0540086559529703	0.0270043279764852
20	0.970746394300762	0.0585072113984753	0.0292536056992377
21	0.973873512181744	0.0522529756365127	0.0261264878182564
22	0.968594082128568	0.0628118357428643	0.0314059178714321
23	0.966477987904603	0.0670440241907939	0.0335220120953969
24	0.95188229495383	0.0962354100923382	0.0481177050461691
25	0.932995553689144	0.134008892621713	0.0670044463108563
26	0.91046275718676	0.179074485626479	0.0895372428132396
27	0.900793501151265	0.198412997697471	0.0992064988487355
28	0.892076658901215	0.21584668219757	0.107923341098785
29	0.866962386721404	0.266075226557191	0.133037613278596
30	0.894739682340661	0.210520635318678	0.105260317659339
31	0.879427904719212	0.241144190561576	0.120572095280788
32	0.883543116051864	0.232913767896272	0.116456883948136
33	0.85198076093315	0.2960384781337	0.14801923906685
34	0.859733578629645	0.280532842740710	0.140266421370355
35	0.855181358381237	0.289637283237525	0.144818641618763
36	0.826814425568609	0.346371148862783	0.173185574431391
37	0.792161636161149	0.415676727677703	0.207838363838851
38	0.829106514339176	0.341786971321649	0.170893485660824
39	0.831180422312722	0.337639155374556	0.168819577687278
40	0.794916066096117	0.410167867807766	0.205083933903883
41	0.836761928304719	0.326476143390562	0.163238071695281
42	0.799731438681692	0.400537122636615	0.200268561318307
43	0.761542428164083	0.476915143671834	0.238457571835917
44	0.723411365013487	0.553177269973026	0.276588634986513
45	0.685270037326422	0.629459925347155	0.314729962673578
46	0.681976585270746	0.636046829458509	0.318023414729254
47	0.672458996311148	0.655082007377704	0.327541003688852
48	0.851053097539103	0.297893804921794	0.148946902460897
49	0.82546234831212	0.34907530337576	0.17453765168788
50	0.793873199409707	0.412253601180586	0.206126800590293
51	0.793152038139804	0.413695923720391	0.206847961860196
52	0.8103018466282	0.379396306743601	0.189698153371801
53	0.779632322860154	0.440735354279693	0.220367677139846
54	0.757695367798993	0.484609264402014	0.242304632201007
55	0.745530931191234	0.508938137617532	0.254469068808766
56	0.740041346935622	0.519917306128755	0.259958653064378
57	0.708666933351115	0.582666133297770	0.291333066648885
58	0.713094108488643	0.573811783022715	0.286905891511357
59	0.937064174080135	0.125871651839730	0.0629358259198649
60	0.959665944939806	0.0806681101203885	0.0403340550601943
61	0.946200083286053	0.107599833427893	0.0537999167139467
62	0.930332191510819	0.139335616978362	0.0696678084891809
63	0.914400161858607	0.171199676282787	0.0855998381413935
64	0.896885708431843	0.206228583136314	0.103114291568157
65	0.868590601089424	0.262818797821153	0.131409398910577
66	0.837657127879741	0.324685744240517	0.162342872120259
67	0.802745297007456	0.394509405985087	0.197254702992543
68	0.769906319339727	0.460187361320545	0.230093680660273
69	0.731647207807106	0.536705584385787	0.268352792192894
70	0.721060187922436	0.557879624155128	0.278939812077564
71	0.696312058901768	0.607375882196464	0.303687941098232
72	0.74343837273662	0.513123254526761	0.256561627263380
73	0.69036596791844	0.61926806416312	0.30963403208156
74	0.640912743963941	0.718174512072118	0.359087256036059
75	0.7031306147379	0.593738770524199	0.296869385262099
76	0.839974181400314	0.320051637199373	0.160025818599686
77	0.799391042498645	0.401217915002711	0.200608957501355
78	0.753286328990126	0.493427342019747	0.246713671009874
79	0.732482566599532	0.535034866800935	0.267517433400468
80	0.688344261596323	0.623311476807355	0.311655738403677
81	0.662274257112248	0.675451485775505	0.337725742887752
82	0.626232002375496	0.747535995249008	0.373767997624504
83	0.585641742055464	0.828716515889072	0.414358257944536
84	0.572849280574678	0.854301438850643	0.427150719425322
85	0.593979357589818	0.812041284820364	0.406020642410182
86	0.555464584064395	0.889070831871211	0.444535415935605
87	0.48567549401042	0.97135098802084	0.51432450598958
88	0.490102565655771	0.980205131311541	0.509897434344229
89	0.507950014045658	0.984099971908685	0.492049985954342
90	0.498926092902857	0.997852185805714	0.501073907097143
91	0.428404108721016	0.856808217442033	0.571595891278984
92	0.49459154968783	0.98918309937566	0.50540845031217
93	0.511060491158903	0.977879017682194	0.488939508841097
94	0.845118386623084	0.309763226753831	0.154881613376916
95	0.736860469354456	0.526279061291089	0.263139530645544
96	0.75542541080768	0.489149178384641	0.244574589192320

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0111111111111111	OK
10% type I error level	10	0.111111111111111	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/10fjmn1292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/10fjmn1292941512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/18z7c1292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/18z7c1292941512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/21rof1292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/21rof1292941512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/31rof1292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/31rof1292941512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/41rof1292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/41rof1292941512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/5ci601292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/5ci601292941512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/6ci601292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/6ci601292941512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/7m95k1292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/7m95k1292941512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/8m95k1292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/8m95k1292941512.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/9fjmn1292941512.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929414636gobet05zad330d/9fjmn1292941512.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Include Quarterly 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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