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interactie effecten

*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: Fri, 03 Dec 2010 08:29:31 +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/03/t1291364853i6994gqeqncejqd.htm/, Retrieved Fri, 03 Dec 2010 09:27:33 +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/03/t1291364853i6994gqeqncejqd.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 162556 162556 1081 1081 213118 213118 230380558 6282929 1 29790 29790 309 309 81767 81767 25266003 4324047 1 87550 87550 458 458 153198 153198 70164684 4108272 0 84738 0 588 0 -26007 0 -15292116 -1212617 1 54660 54660 299 299 126942 126942 37955658 1485329 1 42634 42634 156 156 157214 157214 24525384 1779876 0 40949 0 481 0 129352 0 62218312 1367203 1 42312 42312 323 323 234817 234817 75845891 2519076 1 37704 37704 452 452 60448 60448 27322496 912684 1 16275 16275 109 109 47818 47818 5212162 1443586 0 25830 0 115 0 245546 0 28237790 1220017 0 12679 0 110 0 48020 0 5282200 984885 1 18014 18014 239 239 -1710 -1710 -408690 1457425 0 43556 0 247 0 32648 0 8064056 -572920 1 24524 24524 497 497 95350 95350 47388950 929144 0 6532 0 103 0 151352 0 15589256 1151176 0 7123 0 109 0 288170 0 31410530 790090 1 20813 20813 502 502 114337 114337 57397174 774497 1 37597 37597 248 248 37884 37884 9395232 990576 0 17821 0 373 0 122844 0 45820812 454195 1 12988 12988 119 119 82340 82340 9798460 876607 1 2 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
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation

Wealth [t] = + 247163.774713791 + 182388.039739411Group[t] -3.39043253775906Costs[t] + 36.6057608257932GrCosts[t] -950.85757753505Trades[t] -64.810941789383GrTrades[t] + 2.25602153340068Dividends[t] -2.69385322781055GrDiv[t] + 0.00881423353788577TrDiv[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)	247163.774713791	137801.618337	1.7936	0.076195	0.038098
Group	182388.039739411	221247.337963	0.8244	0.411889	0.205945
Costs	-3.39043253775906	5.733373	-0.5914	0.555752	0.277876
GrCosts	36.6057608257932	8.426913	4.3439	3.6e-05	1.8e-05
Trades	-950.85757753505	678.173243	-1.4021	0.164291	0.082146
GrTrades	-64.810941789383	760.588311	-0.0852	0.93228	0.46614
Dividends	2.25602153340068	1.192254	1.8922	0.061639	0.03082
GrDiv	-2.69385322781055	1.791254	-1.5039	0.136072	0.068036
TrDiv	0.00881423353788577	0.005171	1.7046	0.091683	0.045841

Multiple Linear Regression - Regression Statistics
Multiple R	0.883583221714003
R-squared	0.780719309694496
Adjusted R-squared	0.761441886370936
F-TEST (value)	40.4991526404001
F-TEST (DF numerator)	8
F-TEST (DF denominator)	91
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	427459.059792451
Sum Squared Residuals	16627633549676.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6282929	6668283.27600436	-385354.276004363
2	4324047	1292095.1385366	3031951.8614634
3	4108272	3423750.59518775	684521.404812246
4	-1212617	-792699.586993038	-419917.413006962
5	1485329	2220387.57414349	-735058.574143487
6	1779876	1834549.02184801	-54673.0218480117
7	1367203	491194.087620221	876008.91237978
8	2519076	2072610.92441150	446465.075588504
9	912684	1437181.19180886	-524497.19180886
10	1443586	884428.390876597	559157.609123403
11	1220017	853091.81994112	366925.180058879
12	984885	254474.845466408	730410.154533592
13	1457425	782294.365208152	675130.634791848
14	-572920	9361.33731705335	-582281.337317053
15	929144	1115287.29163792	-186143.291637919
16	1151176	605939.853080184	545236.146919816
17	790090	1046347.72004485	-256257.720044852
18	774497	1066848.57901812	-292351.579018115
19	990576	1492687.67238556	-502111.672385556
20	454195	513087.047150444	-58892.0471504442
21	876607	790402.797492505	86204.2025074948
22	711969	1110078.84310292	-398109.843102923
23	702380	627311.481458904	75068.5185410956
24	264449	476959.435172096	-212510.435172097
25	450033	549507.931961064	-99474.9319610641
26	541063	312690.302521140	228372.697478860
27	588864	1168549.79141259	-579685.791412595
28	-37216	191559.340355319	-228775.340355319
29	783310	265995.360832435	517314.639167565
30	467359	126148.077530972	341210.922469028
31	688779	496364.896061125	192414.103938875
32	608419	768833.225647468	-160414.225647468
33	696348	600718.936281598	95629.0637184024
34	597793	529278.066414091	68514.9335859086
35	821730	957789.940045404	-136059.940045404
36	377934	407448.627350862	-29514.6273508621
37	651939	396501.548836458	255437.451163542
38	697458	555576.942837637	141881.057162363
39	700368	477441.4764121	222926.5235879
40	225986	377527.906948903	-151541.906948903
41	348695	657301.41763294	-308606.41763294
42	373683	412097.300756584	-38414.3007565844
43	501709	415440.977134869	86268.0228651315
44	413743	497926.614740086	-84183.6147400862
45	379825	366338.058139618	13486.9418603824
46	336260	503476.843435158	-167216.843435158
47	636765	401224.362724376	235540.637275624
48	481231	680094.699529944	-198863.699529944
49	469107	515998.407332378	-46891.4073323785
50	211928	402631.858160142	-190703.858160142
51	563925	480391.774665587	83533.2253344132
52	511939	534212.982242691	-22273.9822426913
53	521016	663477.649756072	-142461.649756072
54	543856	447444.376869473	96411.6231305271
55	329304	573325.200339855	-244021.200339855
56	423262	337701.560664913	85560.4393350871
57	509665	343483.569498871	166181.430501129
58	455881	415125.551698341	40755.4483016589
59	367772	459344.997451247	-91572.9974512465
60	406339	676317.531696804	-269978.531696804
61	493408	564435.435698779	-71027.435698779
62	232942	362153.004065139	-129211.004065139
63	416002	485638.891828707	-69636.8918287074
64	337430	612211.047248021	-274781.047248021
65	361517	337215.221922451	24301.7780775493
66	360962	264837.279280385	96124.7207196148
67	235561	408779.163938133	-173218.163938133
68	408247	446857.123709099	-38610.123709099
69	450296	557390.288053113	-107094.288053113
70	418799	511399.862643932	-92600.8626439321
71	247405	473789.568911471	-226384.568911471
72	378519	288149.798187404	90369.201812596
73	326638	483149.487671292	-156511.487671292
74	328233	421381.910577223	-93148.9105772232
75	386225	512060.322552804	-125835.322552804
76	283662	432645.735436413	-148983.735436413
77	370225	505506.252056535	-135281.252056535
78	269236	398562.658617034	-129326.658617034
79	365732	359925.066038517	5806.93396148327
80	420383	404043.278982135	16339.7210178654
81	345811	369314.635275403	-23503.6352754030
82	431809	526995.841704156	-95186.841704156
83	418876	573885.175344989	-155009.175344989
84	297476	322970.131564323	-25494.1315643225
85	416776	476489.672847631	-59713.672847631
86	357257	558127.122933435	-200870.122933435
87	458343	425095.853284381	33247.1467156188
88	388386	479385.521730625	-90999.5217306248
89	358934	479849.515661453	-120915.515661453
90	407560	477950.488095989	-70390.4880959893
91	392558	450294.897808363	-57736.8978083634
92	373177	479523.629843297	-106346.629843297
93	428370	344349.239187901	84020.760812099
94	369419	598703.273244021	-229284.273244021
95	358649	297517.105133744	61131.8948662557
96	376641	483807.188451235	-107166.188451235
97	467427	501521.584158756	-34094.5841587563
98	364885	367173.539177131	-2288.53917713132
99	436230	536386.337095619	-100156.337095619
100	329118	439357.042199252	-110239.042199252

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	1	1.03365768149100e-23	5.16828840745502e-24
13	1	4.3972153837629e-26	2.19860769188145e-26
14	1	1.14351310777588e-29	5.71756553887939e-30
15	1	7.55585737875157e-30	3.77792868937579e-30
16	1	6.45088662103196e-33	3.22544331051598e-33
17	1	1.01687698584084e-32	5.08438492920419e-33
18	1	2.8910190717638e-32	1.4455095358819e-32
19	1	9.96091331591423e-33	4.98045665795712e-33
20	1	2.21708387372196e-32	1.10854193686098e-32
21	1	2.95603756716165e-33	1.47801878358082e-33
22	1	3.07178350391206e-33	1.53589175195603e-33
23	1	1.23962362898117e-32	6.19811814490587e-33
24	1	1.47432999889922e-32	7.37164999449612e-33
25	1	9.69560999776474e-32	4.84780499888237e-32
26	1	5.56186515366276e-31	2.78093257683138e-31
27	1	1.08992633539166e-30	5.4496316769583e-31
28	1	1.64417159640369e-32	8.22085798201845e-33
29	1	5.49411826016316e-35	2.74705913008158e-35
30	1	3.9207006321905e-34	1.96035031609525e-34
31	1	1.04433453769427e-34	5.22167268847135e-35
32	1	4.47171203669753e-34	2.23585601834877e-34
33	1	3.6829001116371e-35	1.84145005581855e-35
34	1	5.39627209937767e-35	2.69813604968883e-35
35	1	1.52261367231594e-34	7.61306836157972e-35
36	1	3.57016930648555e-34	1.78508465324277e-34
37	1	1.5098303432711e-34	7.5491517163555e-35
38	1	5.16597444015038e-36	2.58298722007519e-36
39	1	1.10887279251252e-35	5.5443639625626e-36
40	1	9.07230047384484e-36	4.53615023692242e-36
41	1	5.308462535336e-35	2.654231267668e-35
42	1	5.99577336897223e-35	2.99788668448612e-35
43	1	1.31534214308983e-34	6.57671071544916e-35
44	1	7.24078225651604e-34	3.62039112825802e-34
45	1	5.8620444786847e-33	2.93102223934235e-33
46	1	2.81251957565651e-32	1.40625978782826e-32
47	1	2.63554178373817e-31	1.31777089186909e-31
48	1	1.52315364653016e-30	7.61576823265078e-31
49	1	1.41383771932465e-29	7.06918859662327e-30
50	1	4.12353287172088e-30	2.06176643586044e-30
51	1	3.23042533210758e-30	1.61521266605379e-30
52	1	1.45070112321546e-29	7.25350561607732e-30
53	1	1.34994596238924e-28	6.74972981194619e-29
54	1	7.7554826760849e-29	3.87774133804245e-29
55	1	2.83482196046456e-28	1.41741098023228e-28
56	1	1.50735635390603e-27	7.53678176953016e-28
57	1	9.2898983945558e-28	4.6449491972779e-28
58	1	1.02446898464703e-26	5.12234492323514e-27
59	1	1.07064862165869e-25	5.35324310829343e-26
60	1	9.61111456941666e-25	4.80555728470833e-25
61	1	1.42894806349323e-24	7.14474031746614e-25
62	1	1.79838275935356e-24	8.9919137967678e-25
63	1	1.59668195237578e-23	7.98340976187892e-24
64	1	1.3209383008275e-22	6.6046915041375e-23
65	1	1.47852263776774e-21	7.3926131888387e-22
66	1	1.29321775300035e-20	6.46608876500176e-21
67	1	4.55722500861092e-21	2.27861250430546e-21
68	1	4.83998474363256e-20	2.41999237181628e-20
69	1	3.41717761150813e-19	1.70858880575407e-19
70	1	3.24169005790796e-18	1.62084502895398e-18
71	1	1.93373648727021e-17	9.66868243635106e-18
72	1	2.13871301685578e-16	1.06935650842789e-16
73	1	7.4817489406072e-16	3.7408744703036e-16
74	0.999999999999996	7.17712292687307e-15	3.58856146343654e-15
75	0.999999999999972	5.53706665309456e-14	2.76853332654728e-14
76	0.9999999999999	1.98098966807513e-13	9.90494834037564e-14
77	0.99999999999913	1.73864928834232e-12	8.6932464417116e-13
78	0.999999999999154	1.69161443940564e-12	8.4580721970282e-13
79	0.99999999999146	1.70789230599243e-11	8.53946152996215e-12
80	0.999999999990516	1.89670228256507e-11	9.48351141282535e-12
81	0.999999999859499	2.81003098029131e-10	1.40501549014565e-10
82	0.999999999602411	7.95178013079966e-10	3.97589006539983e-10
83	0.999999994485662	1.10286763175754e-08	5.5143381587877e-09
84	0.999999917470357	1.65059286855986e-07	8.2529643427993e-08
85	0.999998898708214	2.20258357117191e-06	1.10129178558596e-06
86	0.999991969543236	1.60609135289339e-05	8.03045676446694e-06
87	0.999991121089543	1.77578209139569e-05	8.87891045697844e-06
88	0.999934806623405	0.000130386753189749	6.51933765948747e-05

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	77	1	NOK
5% type I error level	77	1	NOK
10% type I error level	77	1	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/10fn2h1291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/10fn2h1291364961.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/1845n1291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/1845n1291364961.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/21dn81291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/21dn81291364961.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/31dn81291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/31dn81291364961.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/41dn81291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/41dn81291364961.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/51dn81291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/51dn81291364961.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/6u44t1291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/6u44t1291364961.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/74w3w1291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/74w3w1291364961.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/84w3w1291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/84w3w1291364961.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/94w3w1291364961.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/03/t1291364853i6994gqeqncejqd/94w3w1291364961.ps (open in new window)

Parameters (Session):

par1 = 9 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 9 ; 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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