Openstaande vacatures met ondernemersvertrouwen

*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 17:10:12 +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/t12911369532mah8cb7bezbgm5.htm/, Retrieved Tue, 30 Nov 2010 18:09:13 +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/t12911369532mah8cb7bezbgm5.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 29781 7,7 32914 9,2 33488 8,6 35652 7,4 36488 8,6 35387 6,2 35676 6 34844 6,6 32447 5,1 31068 4,7 29010 5 29812 3,6 30951 1,9 32974 -0,1 32936 -5,7 34012 -5,6 32946 -6,4 31948 -7,7 30599 -8 27691 -11,9 25073 -15,4 23406 -15,5 22248 -13,4 22896 -10,9 25317 -10,8 26558 -7,3 26471 -6,5 27543 -5,1 26198 -5,3 24725 -6,8 25005 -8,4 23462 -8,4 20780 -9,7 19815 -8,8 19761 -9,6 21454 -11,5 23899 -11 24939 -14,9 23580 -16,2 24562 -14,4 24696 -17,3 23785 -15,7 23812 -12,6 21917 -9,4 19713 -8,1 19282 -5,4 18788 -4,6 21453 -4,9 24482 -4 27474 -3,1 27264 -1,3 27349 0 30632 -0,4 29429 3 30084 0,4 26290 1,2 24379 0,6 23335 -1,3 21346 -3,2 21106 -1,8 24514 -3,6 28353 -4,2 30805 -6,9 31348 -8 34556 -7,5 33855 -8,2 34787 -7,6 32529 -3,7 29998 -1,7 29257 -0,7 28155 0,2 30466 0,6 35704 2,2 39327 3,3 39351 5,3 42234 5,5 43630 6,3 43722 7,7 43121 6,5 37985 5,5 37135 6,9 34646 5,7 33026 6,9 35087 6,1 38846 4,8 42013 3,7 43908 5,8 42868 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Vacatures[t] = + 19808.1911422930 + 323.353687092964Ondernemersvertrouwen[t] + 1875.45382952760M1[t] + 4198.64902031432M2[t] + 6017.04319488648M3[t] + 6515.51000274085M4[t] + 7483.68743024643M5[t] + 8277.93638082915M6[t] + 7080.48854674907M7[t] + 7047.33837698085M8[t] + 5151.9492986196M9[t] + 3273.65973157583M10[t] + 1774.12559149839M11[t] + 151.237193141381t + 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) 19808.1911422930 1768.272469 11.202 0 0 Ondernemersvertrouwen 323.353687092964 48.726429 6.6361 0 0 M1 1875.45382952760 2189.609004 0.8565 0.393489 0.196744 M2 4198.64902031432 2188.965894 1.9181 0.057578 0.028789 M3 6017.04319488648 2189.053194 2.7487 0.00695 0.003475 M4 6515.51000274085 2188.597547 2.977 0.00355 0.001775 M5 7483.68743024643 2189.123097 3.4186 0.000872 0.000436 M6 8277.93638082915 2189.085742 3.7815 0.000249 0.000124 M7 7080.48854674907 2189.099567 3.2344 0.001591 0.000796 M8 7047.33837698085 2189.59114 3.2186 0.001674 0.000837 M9 5151.9492986196 2189.888941 2.3526 0.020342 0.010171 M10 3273.65973157583 2239.92157 1.4615 0.146604 0.073302 M11 1774.12559149839 2239.774413 0.7921 0.429933 0.214967 t 151.237193141381 12.218785 12.3774 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.801258013461579 R-squared 0.642014404136396 Adjusted R-squared 0.601546467212684 F-TEST (value) 15.8647673427655 F-TEST (DF numerator) 13 F-TEST (DF denominator) 115 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5007.90937821103 Sum Squared Residuals 2884102979.14301 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 27951 23904.3457623569 4046.65423764311 2 29781 26799.1379395059 2981.86206049414 3 32914 29253.7998378588 3660.20016214115 4 33488 29709.4916265988 3778.50837340118 5 35652 30440.8818227342 5211.11817726577 6 36488 31774.3923909699 4713.60760903012 7 35387 29952.1329010081 5434.86709899192 8 35676 30005.5491869626 5670.45081303735 9 34844 28455.4095139986 6388.59048600145 10 32447 26243.3266094567 6203.67339054329 11 31068 24765.6881876835 6302.31181231653 12 29010 23239.8058954543 5770.19410454565 13 29812 24813.8017561932 4998.19824380682 14 30951 26738.5328720632 4212.46712793676 15 32974 28061.4568655909 4912.54313440915 16 32936 26900.380218866 6035.619781134 17 34012 28052.1302082223 5959.86979177774 18 32946 28738.933402272 4207.06659772801 19 31948 27272.3629681124 4675.63703188756 20 30599 27293.4438853577 3305.55611464229 21 27691 24288.2126204753 3402.78737952472 22 25073 21429.4223417475 3643.57765825248 23 23406 20048.7900261022 3357.20997389784 24 22248 19104.9443706404 3143.05562935963 25 22896 21940.0196110418 955.980388958232 26 25317 24446.7873636792 870.212636320842 27 26558 27548.1566362181 -990.15663621808 28 26471 28456.5435868882 -1985.54358688820 29 27543 30028.6533694653 -2485.65336946532 30 26198 30909.4687757708 -4711.46877577082 31 24725 29378.2276041927 -4653.22760419268 32 25005 28978.9487282171 -3973.9487282171 33 23462 27234.7968429972 -3772.79684299722 34 20780 25087.384675874 -4307.38467587398 35 19815 24030.1060473216 -4215.10604732159 36 19761 22148.5346992902 -2387.53469929021 37 21454 23560.8537164826 -2106.85371648256 38 23899 26196.9629439571 -2297.96294395714 39 24939 26905.5149320081 -1966.51493200812 40 23580 27134.859139783 -3554.85913978302 41 24562 28836.3103971973 -4274.31039719731 42 24696 28844.0708483518 -4148.07084835182 43 23785 28315.2261067619 -4530.22610676187 44 23812 29435.7095601232 -5623.70956012322 45 21917 28726.2894736008 -6809.28947360083 46 19713 27419.5968929193 -7706.59689291929 47 19282 26944.3549011342 -7662.35490113424 48 18788 25580.1494524516 -6792.1494524516 49 21453 27509.8343689927 -6056.8343689927 50 24482 30275.2850713045 -5793.28507130445 51 27474 32535.9347574017 -5061.93475740167 52 27264 33767.6753951648 -6503.67539516475 53 27349 35307.4498090326 -7958.44980903258 54 30632 36123.5944779195 -5491.59447791949 55 29429 36176.7863730969 -6747.78637309687 56 30084 35454.1538100283 -5370.15381002832 57 26290 33968.6848744828 -7678.68487448282 58 24379 32047.6202883247 -7668.62028832465 59 23335 30084.9513359120 -6749.95133591196 60 21346 27847.6909320783 -6501.69093207832 61 21106 30327.0771166775 -9221.07711667746 62 24514 32219.4728638382 -7705.47286383821 63 28353 33995.092019296 -5642.09201929598 64 30805 33771.7410651407 -2966.74106514073 65 31348 34535.4666299854 -3187.46662998543 66 34556 35642.629617256 -1086.62961725601 67 33855 34370.0713953522 -515.071395352239 68 34787 34682.1706309812 104.829369018820 69 32529 34199.0981254239 -1670.09812542387 70 29998 33118.7531257074 -3120.75312570740 71 29257 32093.8098658643 -2836.80986586431 72 28155 30761.939785891 -2606.93978589097 73 30466 32917.9722833971 -2451.97228339714 74 35704 35909.770566674 -205.770566673977 75 39327 38235.0909901898 1091.90900981022 76 39351 39531.5023653715 -180.502365371463 77 42234 40715.587723437 1518.41227656298 78 43630 41919.7568168355 1710.24318316451 79 43722 41326.2413378269 2395.75866217305 80 43121 41056.3039366885 2064.69606331145 81 37985 38988.7983643757 -1003.79836437571 82 37135 37714.4411524035 -579.44115240347 83 34646 35978.1197809559 -1332.11978095586 84 33026 34743.2558071104 -1717.25580711040 85 35087 36511.263880105 -1424.26388010501 86 38846 38565.3364708123 280.663529187747 87 42013 40179.2787827235 1833.72121727646 88 43908 41508.0255266145 2399.97447338548 89 42868 42950.7938343544 -82.7938343544389 90 44423 44445.9812461366 -22.9812461365819 91 44167 42979.410811977 1187.58918802297 92 43636 42386.1197237457 1249.88027625433 93 44382 40544.9617323979 3837.03826760209 94 42142 38041.8605094724 4100.1394905276 95 43452 36725.8989312456 6726.10106875436 96 36912 34359.2970525748 2552.70294742518 97 42413 36968.0247120111 5444.97528798887 98 45344 39377.7863585206 5966.21364147937 99 44873 41444.4238323621 3428.57616763793 100 47510 40833.0484536953 6676.95154630474 101 49554 42728.5119233653 6825.48807663466 102 47369 43091.9614303221 4277.0385696779 103 45998 41399.0434151975 4598.95658480253 104 48140 41678.8072821171 6461.19271788288 105 48441 38253.2162240138 10187.7837759862 106 44928 34618.3770962629 10309.6229037370 107 40454 30683.2506525832 9770.74934741682 108 38661 26829.2218132847 11831.7781867153 109 37246 29243.9372604653 8002.06273953474 110 36843 30489.6256334401 6353.37436655991 111 36424 32329.9155263164 4094.08447368355 112 37594 33755.6683763353 3838.33162366469 113 38144 35457.1196337496 2686.88036625039 114 38737 37696.0205258456 1040.97947415444 115 34560 36908.4928345812 -2348.49283458124 116 36080 38514.0068185820 -2434.00681858204 117 33508 36899.1964081994 -3391.19640819935 118 35462 36336.2173078316 -874.21730783163 119 33374 36734.0302711976 -3360.03027119758 120 32110 35402.1601912242 -3292.16019122424 121 35533 37719.8695322769 -2186.86953227689 122 35532 40194.301916205 -4662.30191620499 123 37903 43263.3358200346 -5360.33582003461 124 36763 44301.0642455419 -7538.06424554192 125 40399 44612.0946484565 -4213.09464845647 126 44164 44652.1904683203 -488.190468320264 127 44496 43994.0042518931 501.995748106872 128 43110 44564.7864371964 -1454.78643719644 129 43880 43370.3358200346 509.664179965391

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/1bree1291137004.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/1bree1291137004.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/2livz1291137004.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/2livz1291137004.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/3livz1291137004.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/3livz1291137004.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/4livz1291137004.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/4livz1291137004.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/5esc21291137004.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/5esc21291137004.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/6esc21291137004.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/6esc21291137004.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/771tm1291137004.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/771tm1291137004.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/871tm1291137004.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/871tm1291137004.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/9iabq1291137004.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t12911369532mah8cb7bezbgm5/9iabq1291137004.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 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.

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