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multiple lineair regression zonder trens/seizoenaliteit

*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, 05 Dec 2010 10:28:13 +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/05/t12915448288d5kj6k04e4dprq.htm/, Retrieved Sun, 05 Dec 2010 11:27:08 +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/05/t12915448288d5kj6k04e4dprq.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 «

9769 1579 9321 2146 9939 2462 9336 3695 10195 4831 9464 5134 10010 6250 10213 5760 9563 6249 9890 2917 9305 1741 9391 2359 9928 1511 8686 2059 9843 2635 9627 2867 10074 4403 9503 5720 10119 4502 10000 5749 9313 5627 9866 2846 9172 1762 9241 2429 9659 1169 8904 2154 9755 2249 9080 2687 9435 4359 8971 5382 10063 4459 9793 6398 9454 4596 9759 3024 8820 1887 9403 2070 9676 1351 8642 2218 9402 2461 9610 3028 9294 4784 9448 4975 10319 4607 9548 6249 9801 4809 9596 3157 8923 1910 9746 2228 9829 1594 9125 2467 9782 2222 9441 3607 9162 4685 9915 4962 10444 5770 10209 5480 9985 5000 9842 3228 9429 1993 10132 2288 9849 1580 9172 2111 10313 2192 9819 3601 9955 4665 10048 4876 10082 5813 10541 5589 10208 5331 10233 3075 9439 2002 9963 2306 10158 1507 9225 1992 10474 2487 9757 3490 10490 4647 10281 5594 10444 5611 10640 5788 10695 6204 10786 3013 9832 1931 9747 2549 10411 1504 9511 2090 10402 2702 9701 2939 10540 4500 10112 6208 10915 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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation

geboortes[t] = + 9374.9968309701 + 0.122482864440576huwelijken[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)	9374.9968309701	120.632919	77.7151	0	0
huwelijken	0.122482864440576	0.030606	4.0019	0.000125	6.3e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.381537512253696
R-squared	0.145570873256740
Adjusted R-squared	0.136481201695641
F-TEST (value)	16.0149761493854
F-TEST (DF numerator)	1
F-TEST (DF denominator)	94
p-value	0.000125324935773441
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	476.581915316111
Sum Squared Residuals	21350250.2685991

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9769	9568.39727392173	200.602726078269
2	9321	9637.84505805956	-316.845058059564
3	9939	9676.54964322279	262.450356777214
4	9336	9827.57101507802	-491.571015078016
5	10195	9966.7115490825	228.288450917490
6	9464	10003.823857008	-539.823857008004
7	10010	10140.5147337237	-130.514733723686
8	10213	10080.4981301478	132.501869852196
9	9563	10140.3922508592	-577.392250859246
10	9890	9732.27934654325	157.720653456752
11	9305	9588.23949796113	-283.239497961131
12	9391	9663.9339081854	-272.933908185407
13	9928	9560.0684391398	367.931560860202
14	8686	9627.18904885323	-941.189048853234
15	9843	9697.739178771	145.260821228995
16	9627	9726.15520332122	-99.155203321219
17	10074	9914.28888310194	159.711116898057
18	9503	10075.5988155702	-572.598815570181
19	10119	9926.41468668156	192.58531331844
20	10000	10079.1508186390	-79.150818638958
21	9313	10064.2079091772	-751.207909177208
22	9866	9723.58306316797	142.416936832033
23	9172	9590.81163811438	-418.811638114383
24	9241	9672.50770869625	-431.507708696247
25	9659	9518.17929950112	140.820700498878
26	8904	9638.82492097509	-734.824920975089
27	9755	9650.46079309694	104.539206903057
28	9080	9704.10828772192	-624.108287721915
29	9435	9908.89963706656	-473.899637066558
30	8971	10034.1996073893	-1063.19960738927
31	10063	9921.14792351062	141.852076489385
32	9793	10158.6421976609	-365.642197660892
33	9454	9937.92807593897	-483.928075938974
34	9759	9745.38501303839	13.6149869616106
35	8820	9606.12199616946	-786.121996169455
36	9403	9628.53636036208	-225.53636036208
37	9676	9540.4711808293	135.528819170694
38	8642	9646.66382429928	-1004.66382429929
39	9402	9676.42716035835	-274.427160358345
40	9610	9745.87494449615	-135.874944496152
41	9294	9960.9548544538	-666.954854453803
42	9448	9984.34908156195	-536.349081561952
43	10319	9939.27538744782	379.724612552179
44	9548	10140.3922508592	-592.392250859246
45	9801	9964.01692606482	-163.016926064817
46	9596	9761.67523400899	-165.675234008986
47	8923	9608.93910205159	-685.939102051588
48	9746	9647.8886529437	98.1113470563089
49	9829	9570.23451688837	258.765483111634
50	9125	9677.16205754499	-552.162057544989
51	9782	9647.15375575705	134.846244242952
52	9441	9816.79252300725	-375.792523007245
53	9162	9948.82905087418	-786.829050874186
54	9915	9982.75680432422	-67.756804324225
55	10444	10081.7229587922	362.27704120779
56	10209	10046.2029281044	162.797071895557
57	9985	9987.41115317297	-2.41115317296683
58	9842	9770.37151738427	71.6284826157332
59	9429	9619.10517980016	-190.105179800156
60	10132	9655.23762481013	476.762375189874
61	9849	9568.5197567862	280.480243213802
62	9172	9633.55815780414	-461.558157804144
63	10313	9643.47926982383	669.52073017617
64	9819	9816.0576258206	2.94237417939849
65	9955	9946.37939358537	8.62060641462601
66	10048	9972.22327798234	75.7767220176646
67	10082	10086.9897219632	-4.98972196315485
68	10541	10059.5535603285	481.446439671534
69	10208	10027.9529813028	180.047018697203
70	10233	9751.63163912486	481.368360875141
71	9439	9620.20752558012	-181.207525580121
72	9963	9657.44231637006	305.557683629944
73	10158	9559.57850768204	598.421492317964
74	9225	9618.98269693571	-393.982696935715
75	10474	9679.6117148338	794.3882851662
76	9757	9802.4620278677	-45.4620278676976
77	10490	9944.17470202544	545.825297974556
78	10281	10060.1659746507	220.834025349331
79	10444	10062.2481833462	381.751816653842
80	10640	10083.9276503521	556.07234964786
81	10695	10134.8805219594	560.11947804058
82	10786	9744.03770152954	1041.96229847046
83	9832	9611.51124220484	220.48875779516
84	9747	9687.20565242912	59.7943475708841
85	10411	9559.21105908872	851.788940911286
86	9511	9630.9860176509	-119.986017650892
87	10402	9705.94553068852	696.054469311476
88	9701	9734.97396956094	-33.9739695609404
89	10540	9926.16972095268	613.830279047321
90	10112	10135.3704534172	-23.3704534171822
91	10915	10160.7244063564	754.275593643619
92	11183	10067.8823951104	1115.11760488957
93	10384	10105.4846344937	278.515365506318
94	10834	9762.41013119563	1071.58986880437
95	9886	9619.59511125792	266.404888742082
96	10216	9671.65032864516	544.349671354837

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.451898136511276	0.903796273022552	0.548101863488724
6	0.385084640685739	0.770169281371478	0.614915359314261
7	0.268701338333755	0.53740267666751	0.731298661666245
8	0.214903419607008	0.429806839214016	0.785096580392992
9	0.196272926202853	0.392545852405705	0.803727073797147
10	0.136644272221847	0.273288544443695	0.863355727778153
11	0.109144520236776	0.218289040473552	0.890855479763224
12	0.076010471241874	0.152020942483748	0.923989528758126
13	0.0706269731358913	0.141253946271783	0.929373026864109
14	0.250422421169435	0.500844842338869	0.749577578830566
15	0.203622830929254	0.407245661858508	0.796377169070746
16	0.146496942203767	0.292993884407534	0.853503057796233
17	0.121999561939501	0.243999123879001	0.8780004380605
18	0.114736083388783	0.229472166777567	0.885263916611217
19	0.100790418931515	0.201580837863031	0.899209581068485
20	0.0715579274386718	0.143115854877344	0.928442072561328
21	0.0954489171712365	0.190897834342473	0.904551082828764
22	0.073647161087001	0.147294322174002	0.926352838912999
23	0.0677541171889389	0.135508234377878	0.932245882811061
24	0.0596107762403864	0.119221552480773	0.940389223759614
25	0.0440222543268648	0.0880445086537296	0.955977745673135
26	0.0699821949163954	0.139964389832791	0.930017805083605
27	0.0536095770322717	0.107219154064543	0.946390422967728
28	0.0625335402586693	0.125067080517339	0.93746645974133
29	0.0545140509220755	0.109028101844151	0.945485949077924
30	0.138221508453200	0.276443016906400	0.8617784915468
31	0.125815384559526	0.251630769119052	0.874184615440474
32	0.105462509052964	0.210925018105928	0.894537490947036
33	0.0970307605551455	0.194061521110291	0.902969239444855
34	0.0759557796731503	0.151911559346301	0.92404422032685
35	0.123103032809666	0.246206065619332	0.876896967190334
36	0.098479515447507	0.196959030895014	0.901520484552493
37	0.080268168305085	0.16053633661017	0.919731831694915
38	0.201820113042306	0.403640226084611	0.798179886957694
39	0.174641052117326	0.349282104234653	0.825358947882674
40	0.14473850280576	0.28947700561152	0.85526149719424
41	0.178044277647826	0.356088555295652	0.821955722352174
42	0.191128135450993	0.382256270901987	0.808871864549007
43	0.218923188941862	0.437846377883723	0.781076811058138
44	0.258722308023831	0.517444616047662	0.741277691976169
45	0.2341692818799	0.4683385637598	0.7658307181201
46	0.205390464852295	0.41078092970459	0.794609535147705
47	0.289225523380681	0.578451046761361	0.71077447661932
48	0.25648471802958	0.51296943605916	0.74351528197042
49	0.237201740713678	0.474403481427356	0.762798259286322
50	0.292373506194207	0.584747012388413	0.707626493805793
51	0.260515902010827	0.521031804021655	0.739484097989173
52	0.27621741116187	0.55243482232374	0.72378258883813
53	0.486914740527857	0.973829481055714	0.513085259472143
54	0.474815317165548	0.949630634331097	0.525184682834452
55	0.496489917321255	0.99297983464251	0.503510082678745
56	0.478872641041418	0.957745282082836	0.521127358958582
57	0.459922852127489	0.919845704254978	0.540077147872511
58	0.426173520683216	0.852347041366433	0.573826479316784
59	0.42064309017329	0.84128618034658	0.57935690982671
60	0.430162814544883	0.860325629089766	0.569837185455117
61	0.394592785470542	0.789185570941085	0.605407214529458
62	0.500981869962598	0.998036260074804	0.499018130037402
63	0.55642263513291	0.88715472973418	0.44357736486709
64	0.533029572421492	0.933940855157015	0.466970427578508
65	0.517445071023088	0.965109857953824	0.482554928976912
66	0.496980440876919	0.993960881753838	0.503019559123081
67	0.50318696645359	0.99362606709282	0.49681303354641
68	0.496993667536268	0.993987335072536	0.503006332463732
69	0.472667763239422	0.945335526478844	0.527332236760578
70	0.451038684896732	0.902077369793463	0.548961315103268
71	0.472680358451616	0.945360716903233	0.527319641548384
72	0.425236211680509	0.850472423361018	0.574763788319491
73	0.418940383874565	0.83788076774913	0.581059616125435
74	0.568580289144805	0.86283942171039	0.431419710855195
75	0.610295697030626	0.779408605938748	0.389704302969374
76	0.630015753686022	0.739968492627955	0.369984246313978
77	0.594944940411019	0.810110119177962	0.405055059588981
78	0.562422555817974	0.875154888364053	0.437577444182027
79	0.513212144200536	0.973575711598928	0.486787855799464
80	0.464556146423788	0.929112292847576	0.535443853576212
81	0.410989607100148	0.821979214200295	0.589010392899852
82	0.534646076100786	0.93070784779843	0.465353923899215
83	0.46063662627359	0.92127325254718	0.53936337372641
84	0.431670884293312	0.863341768586623	0.568329115706688
85	0.456797904639893	0.913595809279787	0.543202095360107
86	0.497998356445395	0.99599671289079	0.502001643554605
87	0.429830639464229	0.859661278928459	0.570169360535771
88	0.482762483723425	0.96552496744685	0.517237516276575
89	0.372001580136170	0.744003160272341	0.62799841986383
90	0.514393189669482	0.971213620661035	0.485606810330518
91	0.367156646250097	0.734313292500195	0.632843353749903

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	1	0.0114942528735632	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/1098bm1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/1098bm1291544884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/126es1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/126es1291544884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/2dgdv1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/2dgdv1291544884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/3dgdv1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/3dgdv1291544884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/4dgdv1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/4dgdv1291544884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/567cy1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/567cy1291544884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/667cy1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/667cy1291544884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/7gguj1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/7gguj1291544884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/8gguj1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/8gguj1291544884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/998bm1291544884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/05/t12915448288d5kj6k04e4dprq/998bm1291544884.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)

library(lmtest)

n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test

par1 <- as.numeric(par1)

x <- t(y)

k <- length(x[1,])

n <- length(x[,1])

x1 <- cbind(x[,par1], x[,1:k!=par1])

mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])

colnames(x1) <- mycolnames #colnames(x)[par1]

x <- x1

if (par3 == 'First Differences'){

x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))

for (i in 1:n-1) {

for (j in 1:k) {

x2[i,j] <- x[i+1,j] - x[i,j]

}

}

x <- x2

}

if (par2 == 'Include Monthly Dummies'){

x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))

for (i in 1:11){

x2[seq(i,n,12),i] <- 1

}

x <- cbind(x, x2)

}

if (par2 == 'Include Quarterly Dummies'){

x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))

for (i in 1:3){

x2[seq(i,n,4),i] <- 1

}

x <- cbind(x, x2)

}

k <- length(x[1,])

if (par3 == 'Linear Trend'){

x <- cbind(x, c(1:n))

colnames(x)[k+1] <- 't'

}

x

k <- length(x[1,])

df <- as.data.frame(x)

(mylm <- lm(df))

(mysum <- summary(mylm))

if (n > n25) {

kp3 <- k + 3

nmkm3 <- n - k - 3

gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))

numgqtests <- 0

numsignificant1 <- 0

numsignificant5 <- 0

numsignificant10 <- 0

for (mypoint in kp3:nmkm3) {

j <- 0

numgqtests <- numgqtests + 1

for (myalt in c('greater', 'two.sided', 'less')) {

j <- j + 1

gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value

}

if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1

if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1

if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1

}

gqarr

}

bitmap(file='test0.png')

plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')

points(x[,1]-mysum$resid)

grid()

dev.off()

bitmap(file='test1.png')

plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')

grid()

dev.off()

bitmap(file='test2.png')

hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')

grid()

dev.off()

bitmap(file='test3.png')

densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')

dev.off()

bitmap(file='test4.png')

qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')

qqline(mysum$resid)

grid()

dev.off()

(myerror <- as.ts(mysum$resid))

bitmap(file='test5.png')

dum <- cbind(lag(myerror,k=1),myerror)

dum

dum1 <- dum[2:length(myerror),]

dum1

z <- as.data.frame(dum1)

z

plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')

lines(lowess(z))

abline(lm(z))

grid()

dev.off()

bitmap(file='test6.png')

acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')

grid()

dev.off()

bitmap(file='test7.png')

pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')

grid()

dev.off()

bitmap(file='test8.png')

opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))

plot(mylm, las = 1, sub='Residual Diagnostics')

par(opar)

dev.off()

if (n > n25) {

bitmap(file='test9.png')

plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')

grid()

dev.off()

}

load(file='createtable')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)

a<-table.row.end(a)

myeq <- colnames(x)[1]

myeq <- paste(myeq, '[t] = ', sep='')

for (i in 1:k){

if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')

myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')

if (rownames(mysum$coefficients)[i] != '(Intercept)') {

myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')

if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')

}

}

myeq <- paste(myeq, ' + e[t]')

a<-table.row.start(a)

a<-table.element(a, myeq)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable1.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Variable',header=TRUE)

a<-table.element(a,'Parameter',header=TRUE)

a<-table.element(a,'S.D.',header=TRUE)

a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)

a<-table.element(a,'2-tail p-value',header=TRUE)

a<-table.element(a,'1-tail p-value',header=TRUE)

a<-table.row.end(a)

for (i in 1:k){

a<-table.row.start(a)

a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)

a<-table.element(a,mysum$coefficients[i,1])

a<-table.element(a, round(mysum$coefficients[i,2],6))

a<-table.element(a, round(mysum$coefficients[i,3],4))

a<-table.element(a, round(mysum$coefficients[i,4],6))

a<-table.element(a, round(mysum$coefficients[i,4]/2,6))

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable2.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple R',1,TRUE)

a<-table.element(a, sqrt(mysum$r.squared))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'R-squared',1,TRUE)

a<-table.element(a, mysum$r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Adjusted R-squared',1,TRUE)

a<-table.element(a, mysum$adj.r.squared)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (value)',1,TRUE)

a<-table.element(a, mysum$fstatistic[1])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[2])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)

a<-table.element(a, mysum$fstatistic[3])

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'p-value',1,TRUE)

a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Residual Standard Deviation',1,TRUE)

a<-table.element(a, mysum$sigma)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Sum Squared Residuals',1,TRUE)

a<-table.element(a, sum(myerror*myerror))

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable3.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a, 'Time or Index', 1, TRUE)

a<-table.element(a, 'Actuals', 1, TRUE)

a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)

a<-table.row.end(a)

for (i in 1:n) {

a<-table.row.start(a)

a<-table.element(a,i, 1, TRUE)

a<-table.element(a,x[i])

a<-table.element(a,x[i]-mysum$resid[i])

a<-table.element(a,mysum$resid[i])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable4.tab')

if (n > n25) {

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'p-values',header=TRUE)

a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'breakpoint index',header=TRUE)

a<-table.element(a,'greater',header=TRUE)

a<-table.element(a,'2-sided',header=TRUE)

a<-table.element(a,'less',header=TRUE)

a<-table.row.end(a)

for (mypoint in kp3:nmkm3) {

a<-table.row.start(a)

a<-table.element(a,mypoint,header=TRUE)

a<-table.element(a,gqarr[mypoint-kp3+1,1])

a<-table.element(a,gqarr[mypoint-kp3+1,2])

a<-table.element(a,gqarr[mypoint-kp3+1,3])

a<-table.row.end(a)

}

a<-table.end(a)

table.save(a,file='mytable5.tab')

a<-table.start()

a<-table.row.start(a)

a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'Description',header=TRUE)

a<-table.element(a,'# significant tests',header=TRUE)

a<-table.element(a,'% significant tests',header=TRUE)

a<-table.element(a,'OK/NOK',header=TRUE)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'1% type I error level',header=TRUE)

a<-table.element(a,numsignificant1)

a<-table.element(a,numsignificant1/numgqtests)

if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'5% type I error level',header=TRUE)

a<-table.element(a,numsignificant5)

a<-table.element(a,numsignificant5/numgqtests)

if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.row.start(a)

a<-table.element(a,'10% type I error level',header=TRUE)

a<-table.element(a,numsignificant10)

a<-table.element(a,numsignificant10/numgqtests)

if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'

a<-table.element(a,dum)

a<-table.row.end(a)

a<-table.end(a)

table.save(a,file='mytable6.tab')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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