Q2

*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: Sat, 22 Nov 2008 06:16:56 -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/Nov/22/t1227359960hie3xz7foddm5d5.htm/, Retrieved Sat, 22 Nov 2008 13:19:20 +0000

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/Nov/22/t1227359960hie3xz7foddm5d5.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

91,2 0 99,2 0 108,2 0 101,5 0 106,9 0 104,4 0 77,9 0 60 0 99,5 0 95 0 105,6 0 102,5 0 93,3 0 97,3 0 127 0 111,7 0 96,4 0 133 0 72,2 0 95,8 0 124,1 0 127,6 0 110,7 0 104,6 0 112,7 1 115,3 1 139,4 1 119 1 97,4 1 154 1 81,5 1 88,8 1 127,7 1 105,1 1 114,9 1 106,4 1 104,5 1 121,6 1 141,4 1 99 1 126,7 1 134,1 1 81,3 1 88,6 1 132,7 1 132,9 1 134,4 1 103,7 1 119,7 1 115 1 132,9 1 108,5 1 113,9 1 142 1 97,7 1 92,2 1 128,8 1 134,9 1 128,2 1 114,8 1 117,9 1 119,1 1 120,7 1 129,1 1 117,6 1 129,2 1 100 1 87,3 1

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation X[t] = + 93.7593867924528 + 6.60165094339623Y[t] + 0.915393081760996M1[t] + 5.37429245283019M2[t] + 22.1498584905660M3[t] + 5.10875786163522M4[t] + 3.21765723270441M5[t] + 25.9432232704403M6[t] -21.9812106918239M7[t] -21.8723113207547M8[t] + 16.8833018867925M9[t] + 13.1822012578616M10[t] + 12.6011006289308M11[t] + 0.241100628930818t + 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) 93.7593867924528 4.861627 19.2856 0 0 Y 6.60165094339623 4.446279 1.4848 0.143422 0.071711 M1 0.915393081760996 5.899242 0.1552 0.877265 0.438632 M2 5.37429245283019 5.885677 0.9131 0.365242 0.182621 M3 22.1498584905660 5.874089 3.7708 0.000405 0.000203 M4 5.10875786163522 5.86449 0.8711 0.387537 0.193769 M5 3.21765723270441 5.85689 0.5494 0.58501 0.292505 M6 25.9432232704403 5.851296 4.4338 4.6e-05 2.3e-05 M7 -21.9812106918239 5.847715 -3.7589 0.000421 0.00021 M8 -21.8723113207547 5.84615 -3.7413 0.000445 0.000222 M9 16.8833018867925 6.112324 2.7622 0.007832 0.003916 M10 13.1822012578616 6.107497 2.1584 0.035363 0.017682 M11 12.6011006289308 6.104599 2.0642 0.043817 0.021908 t 0.241100628930818 0.108612 2.2198 0.030649 0.015325 Multiple Linear Regression - Regression Statistics Multiple R 0.883925035920019 R-squared 0.781323469126207 Adjusted R-squared 0.728679119101034 F-TEST (value) 14.8415446055011 F-TEST (DF numerator) 13 F-TEST (DF denominator) 54 p-value 1.9551027463649e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9.65069079591412 Sum Squared Residuals 5029.33497327044 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 91.2 94.9158805031447 -3.71588050314473 2 99.2 99.6158805031446 -0.415880503144636 3 108.2 116.632547169811 -8.43254716981132 4 101.5 99.8325471698113 1.66745283018868 5 106.9 98.1825471698113 8.71745283018868 6 104.4 121.149213836478 -16.7492138364780 7 77.9 73.4658805031447 4.43411949685534 8 60 73.8158805031447 -13.8158805031447 9 99.5 112.812594339623 -13.3125943396226 10 95 109.352594339623 -14.3525943396226 11 105.6 109.012594339623 -3.41259433962264 12 102.5 96.6525943396226 5.84740566037736 13 93.3 97.8090880503144 -4.50908805031445 14 97.3 102.509088050314 -5.20908805031447 15 127 119.525754716981 7.47424528301887 16 111.7 102.725754716981 8.97424528301887 17 96.4 101.075754716981 -4.67575471698113 18 133 124.042421383648 8.95757861635222 19 72.2 76.3590880503145 -4.15908805031446 20 95.8 76.7090880503145 19.0909119496855 21 124.1 115.705801886792 8.39419811320755 22 127.6 112.245801886792 15.3541981132075 23 110.7 111.905801886792 -1.20580188679245 24 104.6 99.5458018867924 5.05419811320755 25 112.7 107.303946540880 5.39605345911951 26 115.3 112.003946540881 3.29605345911949 27 139.4 129.020613207547 10.3793867924528 28 119 112.220613207547 6.77938679245283 29 97.4 110.570613207547 -13.1706132075472 30 154 133.537279874214 20.4627201257862 31 81.5 85.8539465408805 -4.3539465408805 32 88.8 86.2039465408805 2.5960534591195 33 127.7 125.200660377358 2.49933962264151 34 105.1 121.740660377358 -16.6406603773585 35 114.9 121.400660377358 -6.50066037735849 36 106.4 109.040660377358 -2.64066037735849 37 104.5 110.197154088050 -5.6971540880503 38 121.6 114.897154088050 6.70284591194968 39 141.4 131.913820754717 9.48617924528302 40 99 115.113820754717 -16.1138207547170 41 126.7 113.463820754717 13.2361792452830 42 134.1 136.430487421384 -2.33048742138365 43 81.3 88.7471540880503 -7.44715408805032 44 88.6 89.0971540880503 -0.497154088050317 45 132.7 128.093867924528 4.60613207547169 46 132.9 124.633867924528 8.2661320754717 47 134.4 124.293867924528 10.1061320754717 48 103.7 111.933867924528 -8.2338679245283 49 119.7 113.090361635220 6.60963836477989 50 115 117.790361635220 -2.79036163522013 51 132.9 134.807028301887 -1.90702830188679 52 108.5 118.007028301887 -9.50702830188679 53 113.9 116.357028301887 -2.45702830188679 54 142 139.323694968553 2.67630503144654 55 97.7 91.6403616352201 6.05963836477987 56 92.2 91.9903616352201 0.209638364779881 57 128.8 130.987075471698 -2.18707547169810 58 134.9 127.527075471698 7.3729245283019 59 128.2 127.187075471698 1.01292452830187 60 114.8 114.827075471698 -0.0270754716981177 61 117.9 115.98356918239 1.91643081761008 62 119.1 120.68356918239 -1.58356918238994 63 120.7 137.700235849057 -17.0002358490566 64 129.1 120.900235849057 8.19976415094339 65 117.6 119.250235849057 -1.65023584905661 66 129.2 142.216902515723 -13.0169025157233 67 100 94.53356918239 5.46643081761006 68 87.3 94.88356918239 -7.58356918238994 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.596483402855782 0.807033194288437 0.403516597144218 18 0.769432428684305 0.46113514263139 0.230567571315695 19 0.74627304941063 0.50745390117874 0.25372695058937 20 0.89569235244875 0.208615295102501 0.104307647551251 21 0.872460485937667 0.255079028124666 0.127539514062333 22 0.893077584227843 0.213844831544314 0.106922415772157 23 0.854793123064367 0.290413753871266 0.145206876935633 24 0.80871426710019 0.38257146579962 0.19128573289981 25 0.732643498189329 0.534713003621342 0.267356501810671 26 0.646722943268163 0.706554113463674 0.353277056731837 27 0.586303729678698 0.827392540642604 0.413696270321302 28 0.540836784267255 0.91832643146549 0.459163215732745 29 0.666326731857345 0.667346536285311 0.333673268142655 30 0.834812275924596 0.330375448150808 0.165187724075404 31 0.80041477179891 0.39917045640218 0.19958522820109 32 0.743737440756889 0.512525118486222 0.256262559243111 33 0.666165357427127 0.667669285145746 0.333834642572873 34 0.849734510615884 0.300530978768232 0.150265489384116 35 0.848063208973611 0.303873582052777 0.151936791026389 36 0.80213176142141 0.395736477157179 0.197868238578589 37 0.819898226330307 0.360203547339386 0.180101773669693 38 0.764161407277102 0.471677185445796 0.235838592722898 39 0.804182748194765 0.391634503610471 0.195817251805235 40 0.931057081964336 0.137885836071328 0.0689429180356642 41 0.94349655986821 0.113006880263579 0.0565034401317897 42 0.916652143443565 0.166695713112871 0.0833478565564354 43 0.950043075539231 0.0999138489215371 0.0499569244607686 44 0.916102814696006 0.167794370607989 0.0838971853039945 45 0.868515406301393 0.262969187397213 0.131484593698607 46 0.8037064992045 0.392587001591 0.1962935007955 47 0.741614014535935 0.51677197092813 0.258385985464065 48 0.71610025748408 0.56779948503184 0.28389974251592 49 0.586541385250757 0.826917229498486 0.413458614749243 50 0.458558702036097 0.917117404072194 0.541441297963903 51 0.414647906379662 0.829295812759323 0.585352093620338 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 0.0285714285714286 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/10n7kh1227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/10n7kh1227359805.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/1dwuq1227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/1dwuq1227359805.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/2lnm11227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/2lnm11227359805.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/3xwdm1227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/3xwdm1227359805.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/4yjvo1227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/4yjvo1227359805.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/5oom11227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/5oom11227359805.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/6ea8z1227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/6ea8z1227359805.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/7w85l1227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/7w85l1227359805.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/8e6781227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/8e6781227359805.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/953zg1227359805.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/22/t1227359960hie3xz7foddm5d5/953zg1227359805.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