met seizonaliteit en lineaire 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, 14 Dec 2008 06:53:47 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx.htm/, Retrieved Sun, 14 Dec 2008 14:55:07 +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/2008/Dec/14/t1229262907g0diief4hrox2zx.htm/}, year = {2008}, } @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 = {2008}, 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 «

11554.5 180144 13182.1 173666 14800.1 165688 12150.7 161570 14478.2 156145 13253.9 153730 12036.8 182698 12653.2 200765 14035.4 176512 14571.4 166618 15400.9 158644 14283.2 159585 14485.3 163095 14196.3 159044 15559.1 155511 13767.4 153745 14634 150569 14381.1 150605 12509.9 179612 12122.3 194690 13122.3 189917 13908.7 184128 13456.5 175335 12441.6 179566 12953 181140 13057.2 177876 14350.1 175041 13830.2 169292 13755.5 166070 13574.4 166972 12802.6 206348 11737.3 215706 13850.2 202108 15081.8 195411 13653.3 193111 14019.1 195198 13962 198770 13768.7 194163 14747.1 190420 13858.1 189733 13188 186029 13693.1 191531 12970 232571 11392.8 243477 13985.2 227247 14994.7 217859 13584.7 208679 14257.8 213188 13553.4 216234 14007.3 213586 16535.8 209465 14721.4 204045 13664.6 200237 16805.9 203666 13829.4 241476 13735.6 260307 15870.5 243324 15962.4 244460 15744.1 233575 16083.7 237217 14863.9 235243 15533.1 230354 17473.1 227184 15925.5 221678 15573.7 217142 17495 219452 14155.8 256446 14913.9 265845 17250.4 248624 15879.8 241114 17647.8 229245 17749.9 231805 17111.8 219277 16934.8 219313 20280 212610 16238.2 214771 17896.1 211142 18089.3 211457 15660 240048 16162.4 240636 17850.1 230580 18520.4 208795 18524.7 197922 16843.7 194596

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation invoer[t] = + 16619.7122862875 -0.0264169980332389werkloosheid[t] -221.506514856914M1[t] -84.8207196974203M2[t] + 1581.42080662757M3[t] -470.77564642321M4[t] -268.267105528740M5[t] + 276.630630950171M6[t] -794.156866386965M7[t] -741.211476464895M8[t] + 682.797794531672M9[t] + 799.57433610845M10[t] + 357.246035517984M11[t] + 79.224681117806t + 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) 16619.7122862875 1340.356589 12.3995 0 0 werkloosheid -0.0264169980332389 0.008124 -3.2519 0.001765 0.000883 M1 -221.506514856914 507.288913 -0.4366 0.663711 0.331855 M2 -84.8207196974203 503.640533 -0.1684 0.866742 0.433371 M3 1581.42080662757 503.047295 3.1437 0.002447 0.001224 M4 -470.77564642321 504.99859 -0.9322 0.354421 0.177211 M5 -268.267105528740 510.268679 -0.5257 0.600733 0.300366 M6 276.630630950171 509.309734 0.5431 0.588753 0.294377 M7 -794.156866386965 535.107158 -1.4841 0.142269 0.071135 M8 -741.211476464895 571.525998 -1.2969 0.198925 0.099462 M9 682.797794531672 522.452761 1.3069 0.195521 0.097761 M10 799.57433610845 506.11843 1.5798 0.118657 0.059329 M11 357.246035517984 501.307963 0.7126 0.478445 0.239222 t 79.224681117806 8.650671 9.1582 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.881982891197928 R-squared 0.777893820365856 Adjusted R-squared 0.736645529862372 F-TEST (value) 18.8588135622288 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 937.660150865143 Sum Squared Residuals 61544459.096431 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11554.5 11718.5667588486 -164.066758848610 2 13182.1 12105.6065483852 1076.49345161479 3 14800.1 14061.8275661372 738.272433862806 4 12150.7 12197.6409921051 -46.9409921050941 5 14478.2 12622.6864284477 1855.51357155231 6 13253.9 13310.6058962947 -56.7058962946804 7 12036.8 11553.7954810485 483.004518951513 8 12653.2 11208.6896486218 1444.51035137816 9 14035.4 13352.6150540364 682.784945963649 10 14571.4 13809.9860552718 761.413944728197 11 15400.9 13657.5315781162 1743.36842188381 12 14283.2 13354.6518285667 928.548171433269 13 14485.3 13119.6463317310 1365.65366826904 14 14196.3 13442.5720670409 753.727932959093 15 15559.1 15281.3695285351 277.730471464862 16 13767.4 13355.0501751289 412.349824871138 17 14634 13720.6837828947 913.316217105297 18 14381.1 14343.8551885622 37.2448114377769 19 12509.9 12586.0145103927 -76.1145103927342 20 12122.3 12319.8690850874 -197.569085087435 21 13122.3 13949.1913688145 -826.891368814457 22 13908.7 14298.1205931235 -389.42059312346 23 13456.5 14167.3016373571 -710.80163735707 24 12441.6 13777.5099642783 -1335.90996427826 25 12953 13593.6477756348 -640.647775634832 26 13057.2 13895.7833334926 -838.583333492622 27 14350.1 15716.1417303597 -1366.04173035965 28 13830.2 13895.0412801198 -64.8412801197673 29 13755.5 14261.8900697951 -506.390069795139 30 13574.4 14862.1843551659 -1287.78435516587 31 12802.6 12830.4258243897 -27.8258243897306 32 11737.3 12715.3856278346 -978.085627834558 33 13850.2 14577.8379192049 -727.637919204911 34 15081.8 14950.7537777281 131.046222271901 35 13653.3 14648.4092537319 -995.109253731887 36 14019.1 14315.2556244363 -296.155624436339 37 13962 14078.6122737225 -116.612273722502 38 13768.7 14416.2258599389 -647.525859938932 39 14747.1 16260.5708910201 -1513.47089102015 40 13858.1 14305.747596736 -447.647596736004 41 13188 14685.3293794634 -1497.32937946340 42 13693.1 15164.1054738812 -1471.00547388123 43 12970 13088.3890583778 -118.389058377778 44 11392.8 12932.4553488672 -1539.65534886715 45 13985.2 14864.437179061 -879.237179060991 46 14994.7 15308.4411792916 -313.741179291622 47 13584.7 15187.8456017641 -1603.14560176409 48 14257.8 14790.7100032320 -532.910003232044 49 13553.4 14567.9619934837 -1014.56199348369 50 14007.3 14853.824680553 -846.524680553007 51 16535.8 16708.1553368908 -172.355336890783 52 14721.4 14878.3636942980 -156.963694297961 53 13664.6 15260.6928448208 -1596.09284482081 54 16805.9 15794.2313761615 1011.66862383845 55 13829.4 13803.8418643055 25.5581356945413 56 13735.6 13438.5534453814 297.046554618587 57 15870.5 15390.4272750943 480.072724905718 58 15962.4 15556.4187880231 405.981211976892 59 15744.1 15480.8641921423 263.235807857749 60 16083.7 15106.6321309050 977.067869094983 61 14863.9 15016.4974512835 -152.597451283524 62 15533.1 15361.5606309453 171.539369054673 63 17473.1 17190.7687221535 282.331277846504 64 15925.5 15363.2489413915 562.251058608469 65 15573.7 15764.8096664826 -191.109666482577 66 17495 16327.9088186225 1167.09118137749 67 14155.8 14359.0755771615 -203.275577161544 68 14913.9 14242.952283687 670.947716312992 69 17250.4 16201.1133589318 1049.28664106821 70 15879.8 16595.506236856 -715.706236855997 71 17647.8 16545.9459670398 1101.85403296015 72 17749.9 16200.2970976746 1549.60290232542 73 17111.8 16388.9674152959 722.832584704113 74 16934.8 16603.926879644 330.873120356009 75 20280 18526.4662249036 1753.53377509641 76 16238.2 16496.4073202208 -258.207320220782 77 17896.1 16874.0078280957 1022.09217190432 78 18089.3 17489.8088913119 599.491108688071 79 15660 15742.9576843243 -82.9576843242664 80 16162.4 15859.5945605206 302.805439479402 81 17850.1 17628.4778448572 221.622155142777 82 18520.4 18399.9733697059 120.426630294088 83 18524.7 18324.1017698487 200.598230151340 84 16843.7 18133.9433509070 -1290.24335090703 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.414095533925833 0.828191067851667 0.585904466074166 18 0.366700694856541 0.733401389713083 0.633299305143459 19 0.244685696951938 0.489371393903875 0.755314303048062 20 0.360453594999341 0.720907189998683 0.639546405000659 21 0.27633935655194 0.55267871310388 0.72366064344806 22 0.23195158481509 0.46390316963018 0.76804841518491 23 0.202894352182091 0.405788704364182 0.797105647817909 24 0.136969454326807 0.273938908653613 0.863030545673193 25 0.105709390377801 0.211418780755603 0.894290609622199 26 0.0674523153232014 0.134904630646403 0.932547684676799 27 0.0414665986725763 0.0829331973451526 0.958533401327424 28 0.120778909066360 0.241557818132719 0.87922109093364 29 0.103167169172924 0.206334338345847 0.896832830827076 30 0.0789095556351524 0.157819111270305 0.921090444364848 31 0.286154640419449 0.572309280838899 0.71384535958055 32 0.224743563913177 0.449487127826354 0.775256436086823 33 0.222387015356116 0.444774030712232 0.777612984643884 34 0.399280068552765 0.79856013710553 0.600719931447235 35 0.329800142911146 0.659600285822292 0.670199857088854 36 0.424219679865072 0.848439359730145 0.575780320134928 37 0.547998034732662 0.904003930534677 0.452001965267338 38 0.539361672847941 0.921276654304118 0.460638327152059 39 0.480126219872341 0.960252439744683 0.519873780127659 40 0.513296389945351 0.973407220109297 0.486703610054649 41 0.488598508561186 0.977197017122372 0.511401491438814 42 0.487320917927271 0.974641835854543 0.512679082072729 43 0.596843260301655 0.80631347939669 0.403156739698345 44 0.568510637608574 0.862978724782852 0.431489362391426 45 0.531076440345713 0.937847119308574 0.468923559654287 46 0.550128740546577 0.899742518906847 0.449871259453423 47 0.560150300009577 0.879699399980846 0.439849699990423 48 0.524787128700112 0.950425742599775 0.475212871299888 49 0.467567003462573 0.935134006925147 0.532432996537427 50 0.408310302679122 0.816620605358245 0.591689697320878 51 0.471971690252034 0.943943380504068 0.528028309747966 52 0.459940506060545 0.91988101212109 0.540059493939455 53 0.533198155745601 0.933603688508798 0.466801844254399 54 0.73569498797658 0.528610024046841 0.264305012023421 55 0.730061457655276 0.539877084689448 0.269938542344724 56 0.706005173684553 0.587989652630895 0.293994826315447 57 0.699104149700017 0.601791700599967 0.300895850299984 58 0.671683146917943 0.656633706164114 0.328316853082057 59 0.615575975916552 0.768848048166897 0.384424024083448 60 0.613872435918127 0.772255128163746 0.386127564081873 61 0.570592691248117 0.858814617503766 0.429407308751883 62 0.47590599151843 0.95181198303686 0.52409400848157 63 0.556631345908911 0.886737308182179 0.443368654091089 64 0.539902748717019 0.920194502565962 0.460097251282981 65 0.477630816698489 0.955261633396977 0.522369183301511 66 0.423157922861205 0.84631584572241 0.576842077138795 67 0.272633639398973 0.545267278797947 0.727366360601027 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 0.0196078431372549 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/10emq21229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/10emq21229262814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/1s2rn1229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/1s2rn1229262814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/24p731229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/24p731229262814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/34mmg1229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/34mmg1229262814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/4cviv1229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/4cviv1229262814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/5j6e21229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/5j6e21229262814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/6nfno1229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/6nfno1229262814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/7l8el1229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/7l8el1229262814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/8hsar1229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/8hsar1229262814.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/92sgi1229262814.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229262907g0diief4hrox2zx/92sgi1229262814.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

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

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

Copyright

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

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by