Vacatures, ondernemersvertrouwen, CPI

*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: Tue, 30 Nov 2010 17:16:59 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz.htm/, Retrieved Tue, 30 Nov 2010 18:15:22 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

27951 6,4 91,18 29781 7,7 91,53 32914 9,2 91,88 33488 8,6 92,05 35652 7,4 92,31 36488 8,6 92,66 35387 6,2 92,85 35676 6 92,82 34844 6,6 93,45 32447 5,1 93,23 31068 4,7 93,53 29010 5 93,29 29812 3,6 93,19 30951 1,9 93,59 32974 -0,1 93,8 32936 -5,7 94,62 34012 -5,6 95,21 32946 -6,4 95,38 31948 -7,7 95,31 30599 -8 95,3 27691 -11,9 95,57 25073 -15,4 95,42 23406 -15,5 95,52 22248 -13,4 95,32 22896 -10,9 95,9 25317 -10,8 96,06 26558 -7,3 96,31 26471 -6,5 96,33 27543 -5,1 96,48 26198 -5,3 96,21 24725 -6,8 96,53 25005 -8,4 96,5 23462 -8,4 96,77 20780 -9,7 96,66 19815 -8,8 96,58 19761 -9,6 96,63 21454 -11,5 97,06 23899 -11 97,73 24939 -14,9 98 23580 -16,2 97,76 24562 -14,4 97,48 24696 -17,3 97,77 23785 -15,7 97,96 23812 -12,6 98,22 21917 -9,4 98,51 19713 -8,1 98,19 19282 -5,4 98,37 18788 -4,6 98,31 21453 -4,9 98,6 24482 -4 98,96 27474 -3,1 99,11 27264 -1,3 99,64 27349 0 100,02 30632 -0,4 99,98 29429 3 100,32 30084 0,4 100,44 26290 1,2 100,51 24379 0,6 101 23335 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 8 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Vacatures[t] = -334812.325375605 + 363.52729951788Ondernemersvertrouwen[t] + 3918.00539786377CPI[t] + 1371.87950407921M1[t] + 2535.85091872678M2[t] + 4180.64883367968M3[t] + 4183.76149508151M4[t] + 4812.49208059306M5[t] + 5890.78995606329M6[t] + 4340.18992860402M7[t] + 4958.41592071836M8[t] + 3155.01221160367M9[t] + 2162.43607970123M10[t] + 884.882858929163M11[t] -550.550353635371t + 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) -334812.325375605 33278.128642 -10.061 0 0 Ondernemersvertrouwen 363.52729951788 34.831317 10.4368 0 0 CPI 3918.00539786377 367.409366 10.6639 0 0 M1 1371.87950407921 1556.741892 0.8813 0.380036 0.190018 M2 2535.85091872678 1563.36397 1.622 0.107556 0.053778 M3 4180.64883367968 1565.133096 2.6711 0.008666 0.004333 M4 4183.76149508151 1570.602 2.6638 0.008846 0.004423 M5 4812.49208059306 1575.717689 3.0542 0.00281 0.001405 M6 5890.78995606329 1571.677049 3.7481 0.000281 0.000141 M7 4340.18992860402 1576.744312 2.7526 0.006881 0.00344 M8 4958.41592071836 1568.294584 3.1617 0.00201 0.001005 M9 3155.01221160367 1567.450648 2.0128 0.046489 0.023245 M10 2162.43607970123 1595.186848 1.3556 0.177906 0.088953 M11 884.882858929163 1593.857961 0.5552 0.579857 0.289929 t -550.550353635371 66.380213 -8.2939 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.905972198675616 R-squared 0.82078562477313 Adjusted R-squared 0.798776841850532 F-TEST (value) 37.2935490190313 F-TEST (DF numerator) 14 F-TEST (DF denominator) 114 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3558.82473961745 Sum Squared Residuals 1443836622.11371 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 27951 25579.3106689693 2371.68933103072 2 29781 28036.6191086094 1744.38089139064 3 32914 31047.4595084560 1866.54049154404 4 33488 30947.9663541486 2540.03364585141 5 35652 31608.5952300479 4043.40476995208 6 36488 33943.8774005565 2544.12259944347 7 35387 31714.6825262131 3672.31747378692 8 35676 31592.1125428525 4083.88745714745 9 34844 31924.6182604675 2919.38173953255 10 32447 28974.2396381228 3472.76036187722 11 31068 28176.1267632673 2891.87323673267 12 29010 25909.4304450709 3100.56955492914 13 29812 25830.0208364033 3981.97916359675 14 30951 27392.6476473806 3558.3523526194 15 32974 28582.6217432138 4391.37825678624 16 32936 29212.1955999284 3723.80440007162 17 34012 31638.3517464959 2373.64825350406 18 32946 32541.3383463533 404.661653646666 19 31948 29693.342098035 2254.65790196499 20 30599 29612.77949268 986.22050732004 21 27691 26898.9304192334 792.069580766621 22 25073 23495.7575757035 1577.24242429655 23 23406 22023.1018111306 1382.89818886942 24 22248 20567.4748479808 1680.52515201917 25 22896 24570.0653779804 -1674.06537798040 26 25317 25846.7200326026 -529.720032602583 27 26558 29192.8144916986 -2634.81449169864 28 26471 29014.5587470367 -2543.55874703665 29 27543 30189.3780079175 -2646.37800791746 30 26198 29586.5586124255 -3388.55861242547 31 24725 28193.8790093704 -3468.87900937045 32 25005 27562.3708066849 -2557.37080668489 33 23462 26266.2782013580 -2804.27820135804 34 20780 23819.5856326820 -3039.58563268197 35 19815 22005.2161960115 -2190.21619601153 36 19761 20474.8614137259 -713.861413725869 37 21454 22290.2310161672 -836.23101616718 38 23899 25710.4793435071 -1811.47934350706 39 24939 26444.8318941281 -1505.83189412806 40 23580 24484.487417034 -904.48741703399 41 24562 24119.9752766405 442.024723359507 42 24696 24729.7151952540 -33.7151952539551 43 23785 23954.6295189820 -169.629518982032 44 23812 26167.9211894110 -2355.92118941103 45 21917 26113.4760504987 -4196.47605049871 46 19713 23789.1733270177 -4076.17332701770 47 19282 23647.8344329240 -4365.83443292405 48 18788 22268.142736102 -3480.14273610198 49 21453 24116.6352620709 -2663.63526207092 50 24482 26467.7128358802 -1985.71283588017 51 27474 28476.8357764434 -1002.83577644338 52 27264 30660.2900842098 -3396.29008420982 53 27349 32699.8978566475 -5350.89785664746 54 30632 32925.5142427606 -2293.51424276064 55 29429 33392.4785153004 -3963.47851530043 56 30084 32985.1438227766 -2901.14382277659 57 26290 31196.2719774913 -4906.27197749133 58 24379 31354.851757196 -6975.85175719601 59 23335 28365.8856659609 -5030.88566596094 60 21346 24946.8088030174 -3600.80880301739 61 21106 27334.9376302095 -6228.93763020946 62 24514 29958.2532226369 -5444.25322263687 63 28353 33381.0879128552 -5028.08791285515 64 30805 32792.4478074106 -1987.44780741062 65 31348 33058.4488194967 -1710.44881949672 66 34556 34982.5416644282 -426.541664428237 67 33855 35056.0855203466 -1201.08552034664 68 34787 35694.4980243441 -907.49802434409 69 32529 35267.641131436 -2738.64113143604 70 29998 33667.9681653612 -3669.96816536121 71 29257 32125.0317825144 -2868.0317825144 72 28155 30938.4130315587 -2783.41303155869 73 30466 32022.6932637456 -1556.69326374560 74 35704 34980.8604330251 723.139566974875 75 39327 36318.2678078977 3008.73219210226 76 39351 38496.0674676105 854.932532389493 77 42234 40174.9752645571 2059.02473544286 78 43630 40915.184518049 2714.81548195102 79 43722 40733.4542995103 2988.54570048968 80 43121 40874.2378802902 2246.76211970983 81 37985 36981.3548986631 1003.64510133695 82 37135 35124.3854988989 2010.6145011011 83 34646 33878.7305685146 767.269431485386 84 33026 33428.0508710725 -402.05087107246 85 35087 34154.4584517952 932.541548204829 86 38846 36528.5571002165 2317.44289978354 87 42013 37262.104686043 4750.89531395702 88 43908 39358.7169137716 4549.2830862284 89 42868 39291.0837434433 3576.91625655666 90 44423 40397.64762048 4025.35237952001 91 44167 39587.0141790508 4579.98582094924 92 43636 38345.5890568681 5290.41094313188 93 44382 36274.3773440491 8107.62265595089 94 42142 36052.8683624720 6089.13163752795 95 43452 38179.1229158802 5272.87708411983 96 36912 37082.9785337837 -170.978533783692 97 42413 40282.5791984198 2130.42080158023 98 45344 43879.3390098621 1464.66099013785 99 44873 48217.049079326 -3344.04907932604 100 47510 47231.3562684387 278.643731561285 101 49554 52060.8273630429 -2506.82736304293 102 47369 54363.3890924211 -6994.38909242114 103 45998 53846.8072970304 -7848.80729703039 104 48140 51392.8228607423 -3252.8228607423 105 48441 47853.7678121147 587.232187885282 106 44928 43382.2291798486 1545.77082015135 107 40454 36020.8435927294 4433.15640727056 108 38661 31136.7507180042 7524.24928199576 109 37246 32825.2932216346 4420.70677836545 110 36843 33546.152595667 3296.84740433297 111 36424 31987.4657825446 4436.53421745541 112 37594 33213.6348506626 4380.36514933736 113 38144 33632.7237898419 4511.27621015809 114 38737 34791.7993761969 3945.20062380308 115 34560 32707.2104568661 1852.78954313392 116 36080 35779.233508401 300.766491599007 117 33508 32434.4688000776 1073.53119992243 118 35462 32395.9408626973 3066.05913730270 119 33374 33667.1062710669 -293.106271066941 120 32110 33264.088599684 -1154.088599684 121 35533 36410.7750726044 -877.775072604417 122 35532 38865.6586706126 -3333.65867061258 123 37903 42841.4613173937 -4938.4613173937 124 36763 44258.2784897485 -7495.27848974849 125 40399 45190.7429018687 -4791.74290186867 126 44164 44661.4339310748 -497.433931074808 127 44496 43192.4165792948 1303.58342070519 128 43110 44043.2908149493 -933.290814949295 129 43880 43717.8151046106 162.184895389403

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/1cwpo1291137410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/1cwpo1291137410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/2cwpo1291137410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/2cwpo1291137410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/35no91291137410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/35no91291137410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/45no91291137410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/45no91291137410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/55no91291137410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/55no91291137410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/6gfnu1291137410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/6gfnu1291137410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/7qo5f1291137410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/7qo5f1291137410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/8qo5f1291137410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/8qo5f1291137410.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/9qo5f1291137410.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291137313gxnnkcxvpby02rz/9qo5f1291137410.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) 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)) 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$resid, main='Residual Normal Q-Q Plot') grid() dev.off() (myerror <- as.ts(mysum$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() 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')

Copyright

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

Software written by Ed van Stee & Patrick Wessa

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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.

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