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paper met seiz zonder 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, 12 Dec 2010 10:38:23 +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/12/t1292150187id7ki0x8x431mz5.htm/, Retrieved Sun, 12 Dec 2010 11:36:30 +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/12/t1292150187id7ki0x8x431mz5.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	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation

geboortes[t] = + 9408.25091717042 + 0.137954676030061huwelijken[t] + 298.227157357765M1[t] -632.241511011687M2[t] + 245.786550111648M3[t] -308.745601500791M4[t] -150.993507661975M5[t] -429.437894990934M6[t] + 142.379368334902M7[t] + 52.8309915392159M8[t] -237.832881744925M9[t] + 271.340701244311M10[t] -320.011421321119M11[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)	9408.25091717042	307.51014	30.5949	0	0
huwelijken	0.137954676030061	0.117087	1.1782	0.242076	0.121038
M1	298.227157357765	223.972436	1.3315	0.186658	0.093329
M2	-632.241511011687	201.303379	-3.1407	0.002335	0.001168
M3	245.786550111648	200.544663	1.2256	0.223817	0.111909
M4	-308.745601500791	226.703764	-1.3619	0.176918	0.088459
M5	-150.993507661975	333.510398	-0.4527	0.651917	0.325959
M6	-429.437894990934	406.871973	-1.0555	0.294277	0.147138
M7	142.379368334902	414.231468	0.3437	0.731927	0.365963
M8	52.8309915392159	456.358991	0.1158	0.908117	0.454059
M9	-237.832881744925	418.761669	-0.5679	0.571607	0.285803
M10	271.340701244311	217.327822	1.2485	0.215347	0.107673
M11	-320.011421321119	206.426701	-1.5502	0.124888	0.062444

Multiple Linear Regression - Regression Statistics
Multiple R	0.683579024069009
R-squared	0.467280282147138
Adjusted R-squared	0.390260563903351
F-TEST (value)	6.06702144336696
F-TEST (DF numerator)	12
F-TEST (DF denominator)	83
p-value	1.65786151251623e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	400.473517651365
Sum Squared Residuals	13311460.1822248

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	9769	9924.30850797962	-155.308507979622
2	9321	9072.06014091924	248.93985908076
3	9939	9993.68187966807	-54.6818796680747
4	9336	9609.2478436007	-273.2478436007
5	10195	9923.71644940967	271.283550590334
6	9464	9687.07232891781	-223.072328917815
7	10010	10412.8470106932	-402.847010693199
8	10213	10255.7008426428	-42.7008426427831
9	9563	10032.4968059373	-469.496805937342
10	9890	10082.0054083944	-192.005408394415
11	9305	9328.41858681763	-23.418586817634
12	9391	9733.68599792533	-342.685997925331
13	9928	9914.9275900096	13.0724099903958
14	8686	9060.05808410462	-374.058084104625
15	9843	10017.5480386213	-174.548038621275
16	9627	9495.02137184781	131.97862815219
17	10074	9864.6718480688	209.3281519312
18	9503	9767.91376907143	-264.913769071431
19	10119	10171.7022369927	-52.7022369926525
20	10000	10254.1833412065	-254.183341206452
21	9313	9946.68899744664	-633.688997446645
22	9866	10072.2106263963	-206.210626396281
23	9172	9331.31563501427	-159.315635014265
24	9241	9743.34282524744	-502.342825247435
25	9659	9867.74709080732	-208.747090807324
26	8904	9073.16377832748	-169.163778327481
27	9755	9964.29753367367	-209.297533673672
28	9080	9470.1895301624	-390.189530162399
29	9435	9858.60184232348	-423.601842323477
30	8971	9721.28508857327	-750.28508857327
31	10063	10165.7701859234	-102.77018592336
32	9793	10343.71592595	-550.715925949962
33	9454	9804.45772645965	-350.457726459652
34	9759	10096.7665587296	-337.766558729632
35	8820	9348.55996951802	-528.559969518023
36	9403	9693.81709655264	-290.817096552643
37	9676	9892.8548418448	-216.854841844795
38	8642	9081.9928775934	-439.992877593405
39	9402	9993.54392499205	-591.543924992045
40	9610	9517.23207468865	92.7679253113503
41	9294	9917.23257963625	-623.232579636253
42	9448	9665.13753542904	-217.137535429035
43	10319	10186.1874779758	132.812522024191
44	9548	10323.1606792215	-775.160679221483
45	9801	9833.84207245405	-32.8420724540547
46	9596	10115.1145306416	-519.11453064163
47	8923	9351.73292706671	-428.732927066714
48	9746	9715.6139353654	30.3860646346075
49	9829	9926.3778281201	-97.3778281200994
50	9125	9116.34359192489	8.65640807510965
51	9782	9960.57275742086	-178.57275742086
52	9441	9597.10783211005	-156.107832110055
53	9162	9903.57506670928	-741.575066709277
54	9915	9663.34412464065	251.655875359355
55	10444	10346.6287661988	97.3712338012304
56	10209	10217.0735333544	-8.07353335436605
57	9985	9860.1914155758	124.808584424204
58	9842	10124.9093126398	-282.909312639764
59	9429	9363.18316517721	65.8168348227907
60	10132	9723.8912159272	408.108784072804
61	9849	9924.44646265568	-75.4464626556785
62	9172	9067.23172725819	104.768272741811
63	10313	9956.43411713996	356.565882860042
64	9819	9596.28010405387	222.719895946126
65	9955	9900.81597318868	54.1840268113241
66	10048	9651.48002250206	396.51997749794
67	10082	10352.5608172681	-270.560817268062
68	10541	10232.1105930416	308.889406958358
69	10208	9905.85441334175	302.145586658254
70	10233	10103.8022472072	129.197752792835
71	9439	9364.42475726148	74.5752427385203
72	9963	9726.37440009574	236.625599904263
73	10158	9914.37577130548	243.624228694516
74	9225	9050.81512081061	174.184879189389
75	10474	9997.13074656883	476.869253431174
76	9757	9580.96713501454	176.032864985462
77	10490	9898.33278902013	591.667210979865
78	10281	9750.53147989164	530.468520108357
79	10444	10324.69397271	119.30602729001
80	10640	10259.5635735716	380.436426428375
81	10695	10026.288845516	668.71115448401
82	10786	10095.2490572933	690.750942706699
83	9832	9354.62997526335	477.370024736655
84	9747	9759.89738637104	-12.8973863710421
85	10411	9913.9619072774	497.038092722606
86	9511	9064.33467906156	446.665320938443
87	10402	10026.7910019153	375.208998084711
88	9701	9504.95410852197	196.045891478026
89	10540	9878.05345164372	661.946548356284
90	10112	9835.2356509741	276.7643490259
91	10915	10435.6095322382	479.390467761841
92	11183	10241.4915110117	941.508488988313
93	10384	9993.17972326877	390.820276731225
94	10834	10115.9422586978	718.05774130219
95	9886	9363.73498388133	522.265016118671
96	10216	9742.37714251522	473.622857484775

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.263712923166974	0.527425846333948	0.736287076833026
17	0.15601395413573	0.312027908271461	0.84398604586427
18	0.080759800132706	0.161519600265412	0.919240199867294
19	0.0366445898552199	0.0732891797104398	0.96335541014478
20	0.0199034936383619	0.0398069872767237	0.980096506361638
21	0.0150799650554628	0.0301599301109256	0.984920034944537
22	0.00648889930864635	0.0129777986172927	0.993511100691354
23	0.00292606782732871	0.00585213565465743	0.997073932172671
24	0.00152879573205383	0.00305759146410767	0.998471204267946
25	0.000862793754344284	0.00172558750868857	0.999137206245656
26	0.000365051789212431	0.000730103578424861	0.999634948210788
27	0.000168794348928304	0.000337588697856609	0.999831205651072
28	0.000277127168780342	0.000554254337560685	0.99972287283122
29	0.00241577084767281	0.00483154169534562	0.997584229152327
30	0.00609810040738427	0.0121962008147685	0.993901899592616
31	0.00330556820075728	0.00661113640151456	0.996694431799243
32	0.00401711744353077	0.00803423488706154	0.99598288255647
33	0.00259954667486133	0.00519909334972266	0.997400453325139
34	0.0016711501220786	0.0033423002441572	0.998328849877921
35	0.00249236936417633	0.00498473872835265	0.997507630635824
36	0.00165964401758915	0.0033192880351783	0.99834035598241
37	0.00100884629511926	0.00201769259023851	0.998991153704881
38	0.00120677578847555	0.0024135515769511	0.998793224211524
39	0.0028206149546975	0.005641229909395	0.997179385045302
40	0.00206769469789813	0.00413538939579626	0.997932305302102
41	0.0073721991377829	0.0147443982755658	0.992627800862217
42	0.00629803374182574	0.0125960674836515	0.993701966258174
43	0.00450603344542895	0.0090120668908579	0.99549396655457
44	0.0235838817555356	0.0471677635110711	0.976416118244464
45	0.0214141738326222	0.0428283476652445	0.978585826167378
46	0.035269264747704	0.070538529495408	0.964730735252296
47	0.0464468474730128	0.0928936949460256	0.953553152526987
48	0.0478840687555949	0.0957681375111898	0.952115931244405
49	0.0383685805589685	0.076737161117937	0.961631419441031
50	0.0341552253385284	0.0683104506770568	0.965844774661472
51	0.0350373990887474	0.0700747981774948	0.964962600911253
52	0.0297454987979141	0.0594909975958281	0.970254501202086
53	0.259544847679493	0.519089695358986	0.740455152320507
54	0.286912491681658	0.573824983363317	0.713087508318342
55	0.271038909502387	0.542077819004774	0.728961090497613
56	0.323626809387498	0.647253618774997	0.676373190612502
57	0.333687742989713	0.667375485979427	0.666312257010287
58	0.556865563491841	0.886268873016319	0.443134436508159
59	0.57277123249057	0.85445753501886	0.42722876750943
60	0.637678867498414	0.724642265003173	0.362321132501586
61	0.664429746975718	0.671140506048564	0.335570253024282
62	0.628014004830468	0.743971990339064	0.371985995169532
63	0.621563591334625	0.756872817330751	0.378436408665375
64	0.587762645965311	0.824474708069378	0.412237354034689
65	0.704880186451565	0.590239627096871	0.295119813548435
66	0.697519445019715	0.604961109960569	0.302480554980285
67	0.774205684047622	0.451588631904755	0.225794315952378
68	0.80318774851525	0.393624502969502	0.196812251484751
69	0.79271105383271	0.414577892334581	0.20728894616729
70	0.901426662620949	0.197146674758101	0.0985733373790507
71	0.93323539371591	0.133529212568181	0.0667646062840904
72	0.901184594887503	0.197630810224995	0.0988154051124974
73	0.881874675904575	0.23625064819085	0.118125324095425
74	0.856047501275043	0.287904997449913	0.143952498724957
75	0.81561956545079	0.368760869098419	0.18438043454921
76	0.731819238621745	0.53636152275651	0.268180761378255
77	0.670951072764672	0.658097854470656	0.329048927235328
78	0.666512549180456	0.666974901639087	0.333487450819544
79	0.59474267762053	0.81051464475894	0.40525732237947
80	0.698291015417387	0.603417969165226	0.301708984582613

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	18	0.276923076923077	NOK
5% type I error level	26	0.4	NOK
10% type I error level	34	0.523076923076923	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/1015ev1292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/1015ev1292150293.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/1u4h11292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/1u4h11292150293.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/2nvy41292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/2nvy41292150293.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/3nvy41292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/3nvy41292150293.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/4nvy41292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/4nvy41292150293.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/5nvy41292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/5nvy41292150293.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/6gmxp1292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/6gmxp1292150293.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/7rvfs1292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/7rvfs1292150293.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/8rvfs1292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/8rvfs1292150293.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/9rvfs1292150293.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292150187id7ki0x8x431mz5/9rvfs1292150293.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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