*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 27 Nov 2009 07:34:20 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu.htm/, Retrieved Fri, 27 Nov 2009 15:37:37 +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/2009/Nov/27/t1259332646iyk86y2zu3udnuu.htm/}, year = {2009}, } @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 = {2009}, 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 «

427.25 1113,89 1144,94 1131,13 1111,92 391.25 1107,3 1113,89 1144,94 1131,13 397.20 1120,68 1107,3 1113,89 1144,94 394.80 1140,84 1120,68 1107,3 1113,89 391.50 1101,72 1140,84 1120,68 1107,3 407.65 1104,24 1101,72 1140,84 1120,68 418.10 1114,58 1104,24 1101,72 1140,84 429.10 1130,2 1114,58 1104,24 1101,72 452.85 1173,78 1130,2 1114,58 1104,24 427.75 1211,92 1173,78 1130,2 1114,58 420.90 1181,27 1211,92 1173,78 1130,2 433.45 1203,6 1181,27 1211,92 1173,78 427.15 1180,59 1203,6 1181,27 1211,92 427.90 1156,85 1180,59 1203,6 1181,27 415.35 1191,5 1156,85 1180,59 1203,6 432.60 1191,33 1191,5 1156,85 1180,59 431.65 1234,18 1191,33 1191,5 1156,85 439.60 1220,33 1234,18 1191,33 1191,5 466.10 1228,81 1220,33 1234,18 1191,33 459.50 1207,01 1228,81 1220,33 1234,18 499.75 1249,48 1207,01 1228,81 1220,33 530.00 1248,29 1249,48 1207,01 1228,81 568.25 1280,08 1248,29 1249,48 1207,01 564.25 1280,66 1280,08 1248,29 1249,48 587.00 1302,88 1280,66 1280,08 1248,29 661.00 1310,61 1302,88 1280,66 1280,08 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation y(t)[t] = -101.373072224221 -0.177026843225496`x(t)`[t] + 1.3382306133261`y(t-1)`[t] -0.612915876284746`y(t-2)`[t] + 0.435208040597549`y(t-3) `[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) -101.373072224221 72.088641 -1.4062 0.165604 0.082802 `x(t)` -0.177026843225496 0.058496 -3.0263 0.003844 0.001922 `y(t-1)` 1.3382306133261 0.129047 10.3701 0 0 `y(t-2)` -0.612915876284746 0.223408 -2.7435 0.008322 0.004161 `y(t-3) ` 0.435208040597549 0.168438 2.5838 0.012622 0.006311 Multiple Linear Regression - Regression Statistics Multiple R 0.959219638099753 R-squared 0.920102314116222 Adjusted R-squared 0.913956338279008 F-TEST (value) 149.708091682533 F-TEST (DF numerator) 4 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 41.9409779524104 Sum Squared Residuals 91470.3728434381 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1113.89 1145.81495678853 -31.9249567885287 2 1107.3 1110.53184080926 -3.23184080926111 3 1120.68 1125.70085234954 -5.02085234954376 4 1140.84 1134.55714834375 6.28285165624898 5 1101.72 1151.05123067882 -49.3312306788211 6 1104.24 1089.30736508471 14.9326349152927 7 1114.58 1123.58083889729 -9.00083889728846 8 1130.2 1116.90096160719 13.299038392814 9 1173.78 1128.35891036226 45.421089637744 10 1211.92 1186.04867940818 25.8713205918221 11 1181.27 1218.38850458217 -37.1185045821748 12 1203.6 1170.73980428899 32.8601957110093 13 1180.59 1237.1224692734 -56.5324692734011 14 1156.85 1179.17147476660 -22.3214747665952 15 1191.5 1173.44495674857 18.0550432514313 16 1191.33 1221.29742034353 -29.9674203435287 17 1234.18 1189.66872264328 44.511277356725 18 1220.33 1260.78869532633 -40.4586953263295 19 1228.81 1211.22555932058 17.5844406794159 20 1207.01 1250.87968151303 -43.8696815130265 21 1249.48 1203.35576570952 46.1242342904794 22 1248.29 1271.88748813718 -23.5974881371836 23 1280.08 1228.00564440311 52.0743555968895 24 1280.66 1290.46875835061 -9.80875835060577 25 1302.88 1267.21507814755 35.6649218524481 26 1310.61 1297.33034837932 13.2796516206778 27 1270.05 1300.68126726895 -30.63126726895 28 1270.06 1251.69802155945 18.3619784405477 29 1278.53 1277.40394610339 1.12605389661077 30 1303.8 1273.94842697311 29.8515730268850 31 1335.83 1306.19866812410 29.6313318758990 32 1377.76 1334.87016019554 42.8898398044626 33 1400.63 1376.21420136304 24.4157986369630 34 1418.03 1396.65292792655 21.3770720734454 35 1437.9 1420.54882870609 17.3511712939099 36 1406.8 1444.62226883309 -37.8222688330914 37 1420.83 1400.54815434846 20.2818456515413 38 1482.37 1444.31543532904 38.0545646709585 39 1530.63 1505.79285805317 24.8371419468284 40 1504.66 1540.84305864321 -36.1830586432093 41 1455.18 1501.34529696852 -46.1652969685213 42 1473.96 1470.94379379734 3.01620620266076 43 1527.29 1502.62009701246 24.6699029875432 44 1545.79 1532.49024985173 13.2997501482741 45 1479.63 1533.79608057777 -54.1660805777681 46 1467.97 1446.06526659232 21.9047334076804 47 1378.6 1467.02553542766 -88.425535427657 48 1330.45 1312.72537097837 17.7246290216308 49 1326.41 1315.82678751189 10.5832124881132 50 1385.97 1307.18937549104 78.7806245089571 51 1399.62 1362.17510758246 37.4448924175349 52 1276.69 1333.45987334998 -56.7698733499768 53 1269.42 1190.93154432114 78.4884556788597 54 1287.83 1278.46561878920 9.36438121079896 55 1164.17 1243.83491817420 -79.6649181741952 56 968.67 1090.54411669818 -121.874116698181 57 888.61 904.139587185264 -15.5295871852643 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.0445986528287035 0.089197305657407 0.955401347171297 9 0.084580506012258 0.169161012024516 0.915419493987742 10 0.158414729384387 0.316829458768774 0.841585270615613 11 0.0967154129371502 0.193430825874300 0.90328458706285 12 0.0608730427603036 0.121746085520607 0.939126957239696 13 0.0543667989265295 0.108733597853059 0.94563320107347 14 0.0346827313214778 0.0693654626429556 0.965317268678522 15 0.0334570271776749 0.0669140543553498 0.966542972822325 16 0.0187628903601131 0.0375257807202263 0.981237109639887 17 0.0274519945333828 0.0549039890667657 0.972548005466617 18 0.0177709441763800 0.0355418883527600 0.98222905582362 19 0.0099978466771729 0.0199956933543458 0.990002153322827 20 0.0103860922827398 0.0207721845654795 0.98961390771726 21 0.00582195172063677 0.0116439034412735 0.994178048279363 22 0.00547316447677123 0.0109463289535425 0.994526835523229 23 0.00357104401816051 0.00714208803632101 0.99642895598184 24 0.00223410998223719 0.00446821996447439 0.997765890017763 25 0.00122771499366293 0.00245542998732587 0.998772285006337 26 0.000866805853335414 0.00173361170667083 0.999133194146665 27 0.00204936135074388 0.00409872270148776 0.997950638649256 28 0.00121102478707346 0.00242204957414691 0.998788975212927 29 0.000594516452072362 0.00118903290414472 0.999405483547928 30 0.000329683094161596 0.000659366188323191 0.999670316905838 31 0.000258127969070384 0.000516255938140768 0.99974187203093 32 0.000333716391374584 0.000667432782749167 0.999666283608625 33 0.000208041966988373 0.000416083933976746 0.999791958033012 34 0.000131614926840773 0.000263229853681546 0.99986838507316 35 7.7202454236211e-05 0.000154404908472422 0.999922797545764 36 6.02962903802461e-05 0.000120592580760492 0.99993970370962 37 3.60929004935715e-05 7.21858009871429e-05 0.999963907099506 38 9.20159590040587e-05 0.000184031918008117 0.999907984040996 39 0.000125260571239683 0.000250521142479367 0.99987473942876 40 7.3670977332184e-05 0.000147341954664368 0.999926329022668 41 6.25149243998892e-05 0.000125029848799778 0.9999374850756 42 2.85421327235316e-05 5.70842654470632e-05 0.999971457867276 43 1.90374975874115e-05 3.8074995174823e-05 0.999980962502413 44 1.29328582321173e-05 2.58657164642346e-05 0.999987067141768 45 2.25302528999468e-05 4.50605057998935e-05 0.9999774697471 46 3.05063263711419e-05 6.10126527422838e-05 0.999969493673629 47 0.00385735133915445 0.0077147026783089 0.996142648660846 48 0.00438775482962213 0.00877550965924427 0.995612245170378 49 0.0299819039945489 0.0599638079890977 0.97001809600545 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 26 0.619047619047619 NOK 5% type I error level 32 0.761904761904762 NOK 10% type I error level 37 0.880952380952381 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/10wfic1259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/10wfic1259332455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/1eups1259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/1eups1259332455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/2vqhm1259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/2vqhm1259332455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/3uivh1259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/3uivh1259332455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/4k8z21259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/4k8z21259332455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/5pzni1259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/5pzni1259332455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/6wgmz1259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/6wgmz1259332455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/7zjgc1259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/7zjgc1259332455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/8gfbp1259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/8gfbp1259332455.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/9rk371259332455.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332646iyk86y2zu3udnuu/9rk371259332455.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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') }

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