Werkloosheid- Azië

*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: Wed, 17 Dec 2008 07:21:21 -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/17/t1229523867ws7rq4kl6dx4vyi.htm/, Retrieved Wed, 17 Dec 2008 15:24:32 +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/17/t1229523867ws7rq4kl6dx4vyi.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 «

180144 1235.8 173666 1147.1 165688 1376.9 161570 1157.7 156145 1506 153730 1271.3 182698 1240.2 200765 1408.3 176512 1334.6 166618 1601.2 158644 1566.4 159585 1297.5 163095 1487.6 159044 1320.9 155511 1514 153745 1290.9 150569 1392.5 150605 1288.2 179612 1304.4 194690 1297.8 189917 1211 184128 1454 175335 1405.7 179566 1160.8 181140 1492.1 177876 1263 175041 1376.3 169292 1368.6 166070 1427.6 166972 1339.8 206348 1248.3 215706 1309.8 202108 1424 195411 1590.5 193111 1423.1 195198 1355.3 198770 1515 194163 1385.6 190420 1430 189733 1494.2 186029 1580.9 191531 1369.8 232571 1407.5 243477 1388.3 227247 1478.5 217859 1630.4 208679 1413.5 213188 1493.8 216234 1641.3 213586 1465 209465 1725.1 204045 1628.4 200237 1679.8 203666 1876 241476 1669.4 260307 1712.4 243324 1768.8 244460 1820.5 233575 1776.2 237217 1693.7 235243 1799.1 230354 1917.5 227184 1887.2 221678 1787.8 217142 1803.8 219452 2196.4 256446 1759.5 265845 2002.6 248624 2056.8 241114 1851.1 229245 1984.3 231805 1725.3 219277 2096.6 219313 1792.2 212610 2029.9 214771 1785.3 211142 2026.5 211457 1930.8 240048 1845.5 240636 1943.1 230580 2066.8 208795 2354.4 197922 2190.7 194596 1929.6

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 11 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 94175.953920998 + 70.5633748642092`Azië`[t] -8628.7862470663M1[t] -2488.36245528899M2[t] -17637.0014831715M3[t] -12317.6258652760M4[t] -25360.9692298785M5[t] -22461.4582755446M6[t] + 20118.5833607707M7[t] + 25943.0143989531M8[t] + 8408.05284363553M9[t] -9846.33876628212M10[t] -13219.8442160858M11[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) 94175.953920998 13424.10176 7.0154 0 0 `Azië` 70.5633748642092 7.517131 9.387 0 0 M1 -8628.7862470663 9947.342272 -0.8674 0.38862 0.19431 M2 -2488.36245528899 9933.367001 -0.2505 0.80292 0.40146 M3 -17637.0014831715 9952.737607 -1.7721 0.080672 0.040336 M4 -12317.6258652760 9926.832882 -1.2408 0.218749 0.109374 M5 -25360.9692298785 9959.237892 -2.5465 0.013052 0.006526 M6 -22461.4582755446 9947.683883 -2.258 0.027024 0.013512 M7 20118.5833607707 9927.55055 2.0265 0.046467 0.023233 M8 25943.0143989531 9935.228611 2.6112 0.011 0.0055 M9 8408.05284363553 9952.82478 0.8448 0.401066 0.200533 M10 -9846.33876628212 10081.824454 -0.9766 0.332062 0.166031 M11 -13219.8442160858 9996.183847 -1.3225 0.19025 0.095125 Multiple Linear Regression - Regression Statistics Multiple R 0.812369983333462 R-squared 0.659944989821209 Adjusted R-squared 0.602470903593808 F-TEST (value) 11.4824790290720 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 2.21533902333704e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 18569.1783972673 Sum Squared Residuals 24481821430.8173 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 180144 172749.386331122 7394.6136688783 2 173666 172630.838772444 1035.16122755651 3 165688 173697.663288356 -8009.66328835625 4 161570 163549.547136017 -1979.54713601715 5 156145 175083.427236619 -18938.4272366186 6 153730 161421.714110323 -7691.71411032266 7 182698 201807.234788361 -19109.2347883610 8 200765 219493.369141217 -18728.3691412170 9 176512 196757.886858407 -20245.8868584073 10 166618 197315.690987288 -30697.6909872878 11 158644 191486.580092210 -32842.5800922096 12 159585 185731.932807310 -26146.9328073096 13 163095 190517.244121929 -27422.2441219294 14 159044 184894.753323843 -25850.7533238431 15 155511 183371.901982239 -27860.9019822393 16 153745 172948.588667930 -19203.5886679298 17 150569 167074.484189531 -16505.4841895309 18 150605 162614.235145528 -12009.2351455278 19 179612 206337.403454643 -26725.4034546433 20 194690 211696.116218722 -17006.1162187219 21 189917 188036.253725191 1880.74627480901 22 184128 186928.762207276 -2800.76220727617 23 175335 180147.045751531 -4812.04575153119 24 179566 176085.919463372 3480.08053662785 25 181140 190834.779308818 -9694.77930881835 26 177876 180809.133919205 -2933.13391920534 27 175041 173655.325263438 1385.67473656229 28 169292 178431.362894879 -9139.36289487886 29 166070 169551.258647265 -3481.25864726463 30 166972 166255.305288521 716.694711478997 31 206348 202378.798124761 3969.20187523888 32 215706 212542.876717092 3163.12328290756 33 202108 203066.252571268 -958.252571267552 34 195411 196560.662876241 -1149.66287624073 35 193111 181374.848474168 11736.1515258316 36 195198 189810.495874461 5387.50412553916 37 198770 192450.680593209 6319.31940679125 38 194163 189460.203677557 4702.79632244262 39 190420 177444.578493646 12975.4215063542 40 189733 187294.122777824 2438.87722217645 41 186029 180368.624013948 5660.37598605208 42 191531 168372.206534447 23158.7934655527 43 232571 213612.487403143 18958.5125968568 44 243477 218082.101643933 25394.8983560672 45 227247 206911.956501367 20335.0434986330 46 217859 199376.141533323 18482.8584666773 47 208679 180697.440075472 27981.559924528 48 213188 199583.523293154 13604.4767068462 49 216234 201362.834838558 14871.1651614416 50 213586 195062.935641776 18523.0643582244 51 209465 198267.830416074 11197.1695839261 52 204045 196763.727684600 7281.27231539957 53 200237 187347.341788018 12889.6582119818 54 203666 204091.38689071 -425.386890709981 55 241476 232093.035280080 9382.96471992038 56 260307 240951.691437423 19355.3085625769 57 243324 227396.504224447 15927.4957755531 58 244460 212790.239095009 31669.7609049911 59 233575 206290.776138721 27284.2238612793 60 237217 213689.141928509 23527.8580714908 61 235243 212497.735392131 22745.2646078694 62 230354 226992.862767830 3361.13723216973 63 227184 209706.153481562 17477.8465184378 64 221678 208011.529637955 13666.4703620446 65 217142 196097.20027118 21044.7997288198 66 219452 226699.892197203 -7247.89219720262 67 256446 238450.795355345 17995.2046446551 68 265845 261429.182823017 4415.81717698343 69 248624 247718.756185339 905.243814660847 70 241114 214949.478365854 26164.5216341464 71 229245 220975.014447963 8269.98555203736 72 231805 215918.944574218 15886.0554257818 73 219277 233490.339414233 -14213.3394142328 74 219313 218151.271897345 1161.72810265514 75 212610 219775.547074685 -7165.54707468486 76 214771 207835.121200795 6935.87879920516 77 211142 211811.663853440 -669.663853439544 78 211457 207958.259833269 3498.74016673135 79 240048 244519.245593667 -4471.24559366686 80 240636 257230.662018596 -16594.6620185961 81 230580 248424.389933981 -17844.3899339812 82 208795 250464.02493501 -41669.0249350101 83 197922 235539.295019935 -37617.2950199354 84 194596 230335.042058976 -35739.0420589762 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000517928383126769 0.00103585676625354 0.999482071616873 17 0.0121404969091800 0.0242809938183600 0.98785950309082 18 0.00326709409162192 0.00653418818324385 0.996732905908378 19 0.000975592980862331 0.00195118596172466 0.999024407019138 20 0.00183999497132298 0.00367998994264597 0.998160005028677 21 0.000853808597718712 0.00170761719543742 0.999146191402281 22 0.000552389588318887 0.00110477917663777 0.999447610411681 23 0.000264645581826012 0.000529291163652024 0.999735354418174 24 0.000213497640308283 0.000426995280616566 0.999786502359692 25 0.00145760577230636 0.00291521154461271 0.998542394227694 26 0.00194147247758076 0.00388294495516152 0.99805852752242 27 0.00166186086028172 0.00332372172056343 0.998338139139718 28 0.00550969555520806 0.0110193911104161 0.994490304444792 29 0.00588764221495836 0.0117752844299167 0.994112357785042 30 0.0103652605973788 0.0207305211947575 0.989634739402621 31 0.0299353557395004 0.0598707114790008 0.9700646442605 32 0.0384242144671498 0.0768484289342997 0.96157578553285 33 0.0880115780326595 0.176023156065319 0.91198842196734 34 0.141786493119090 0.283572986238179 0.85821350688091 35 0.193480406686881 0.386960813373761 0.80651959331312 36 0.298097783599884 0.596195567199768 0.701902216400116 37 0.390875604386789 0.781751208773578 0.609124395613211 38 0.48259437887044 0.96518875774088 0.51740562112956 39 0.564526904507937 0.870946190984126 0.435473095492063 40 0.648854308798019 0.702291382403962 0.351145691201981 41 0.719066822383302 0.561866355233396 0.280933177616698 42 0.794120741057168 0.411758517885663 0.205879258942832 43 0.856348738967047 0.287302522065906 0.143651261032953 44 0.896335483102108 0.207329033795785 0.103664516897892 45 0.908821552496954 0.182356895006092 0.0911784475030458 46 0.924169241107291 0.151661517785417 0.0758307588927085 47 0.94962747275539 0.100745054489219 0.0503725272446096 48 0.943482170097732 0.113035659804536 0.0565178299022682 49 0.942883815106601 0.114232369786798 0.0571161848933989 50 0.953261685502713 0.093476628994575 0.0467383144972875 51 0.95086812309778 0.0982637538044401 0.0491318769022201 52 0.951921726262394 0.0961565474752118 0.0480782737376059 53 0.96552838088647 0.0689432382270601 0.0344716191135300 54 0.973148137330671 0.0537037253386577 0.0268518626693288 55 0.967139402122233 0.0657211957555347 0.0328605978777674 56 0.952335468638005 0.0953290627239906 0.0476645313619953 57 0.942580774753626 0.114838450492748 0.057419225246374 58 0.92342881195171 0.153142376096581 0.0765711880482904 59 0.886202722528519 0.227594554942962 0.113797277471481 60 0.872894011209652 0.254211977580696 0.127105988790348 61 0.81361363412173 0.372772731756539 0.186386365878270 62 0.803659050883783 0.392681898232435 0.196340949116217 63 0.72122612143245 0.557547757135099 0.278773878567550 64 0.623755941532701 0.752488116934598 0.376244058467299 65 0.523542180974923 0.952915638050155 0.476457819025077 66 0.621647167447602 0.756705665104795 0.378352832552398 67 0.494808629547596 0.989617259095191 0.505191370452404 68 0.623172095516131 0.753655808967737 0.376827904483869 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.207547169811321 NOK 5% type I error level 15 0.283018867924528 NOK 10% type I error level 24 0.452830188679245 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/107j221229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/107j221229523647.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/1fc841229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/1fc841229523647.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/2da4v1229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/2da4v1229523647.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/33e3h1229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/33e3h1229523647.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/418c31229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/418c31229523647.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/5bp3j1229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/5bp3j1229523647.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/65r8z1229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/65r8z1229523647.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/7f8wm1229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/7f8wm1229523647.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/8aw6x1229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/8aw6x1229523647.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/9c7y01229523647.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/17/t1229523867ws7rq4kl6dx4vyi/9c7y01229523647.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') }

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