workshop10MR

*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, 21 Dec 2010 22:41:51 +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/Dec/21/t1292971246cvr9n3ifvbc8t8f.htm/, Retrieved Tue, 21 Dec 2010 23:40:59 +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/Dec/21/t1292971246cvr9n3ifvbc8t8f.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 «

1.3866 126.64 40.7819 1.3582 126.81 39.5915 1.3332 125.84 38.8859 1.3595 126.77 39.9068 1.3617 124.34 41.47 1.3684 124.4 41.5613 1.3394 120.48 41.6005 1.3262 118.54 41.4113 1.3173 117.66 41.84 1.3085 116.97 42.2892 1.327 120.11 43.1521 1.3182 119.16 43.5998 1.293 116.9 43.116 1.291 116.11 42.4185 1.2984 114.98 42.3687 1.2795 113.65 42.2975 1.299 115.82 42.8528 1.3174 117.59 43.535 1.326 118.57 44.7265 1.3111 118.07 45.7293 1.2816 114.98 45.7585 1.276 114.04 46.1685 1.2849 115.02 46.5075 1.2818 114.28 46.527 1.2829 115.04 46.601 1.2796 116.7 46.4607 1.3008 119.21 46.7135 1.2967 118.39 46.4113 1.2938 116.5 45.55 1.2833 115.46 44.6081 1.2823 117.59 44.4395 1.2765 117.33 44.9847 1.2634 116.2 45.7558 1.2596 116.83 45.3942 1.2705 118.99 45.697 1.2591 118.62 45.5664 1.2798 121.09 46.0205 1.2763 122.4 45.9195 1.2795 123.76 45.8005 1.2782 125.33 45.535 1.2644 123.23 45.4977 1.2596 122.52 45.5782 1.2615 123.64 45.7697 1.2555 124.67 45.2445 1.2555 124.71 45.0615 1.2658 122.53 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 40 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Roebel[t] = + 19.0193331868182 + 4.05376191997606Dollar[t] + 0.143051902745892Yen[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) 19.0193331868182 1.452777 13.0917 0 0 Dollar 4.05376191997606 1.591261 2.5475 0.011155 0.005577 Yen 0.143051902745892 0.012981 11.0204 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.661024443114109 R-squared 0.436953314394318 Adjusted R-squared 0.434645746010688 F-TEST (value) 189.356604768082 F-TEST (DF numerator) 2 F-TEST (DF denominator) 488 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.8030210851058 Sum Squared Residuals 1586.43189626801 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 40.7819 42.7563724287961 -1.97447242879612 2 39.5915 42.6655644137362 -3.07406441373617 3 38.8859 42.4254600200733 -3.53956002007327 4 39.9068 42.6651122281223 -2.75831222812232 5 41.47 42.3264143806737 -0.856414380673746 6 41.5613 42.3621576997023 -0.800857699702336 7 41.6005 41.6838351452591 -0.0833351452591394 8 41.4113 41.3528047965884 0.0584952034115744 9 41.84 41.1908406410843 0.649159358915754 10 42.2892 41.0564617232938 1.23273827670621 11 43.1521 41.5806392934355 1.57146070656454 12 43.5998 41.4090668809311 2.19073311906894 13 43.116 40.9836147803420 2.13238521965805 14 42.4185 40.8624962533327 1.55600374666725 15 42.3687 40.7308454414377 1.63785455856228 16 42.2975 40.4639703104981 1.83352968950187 17 42.8528 40.8534412968962 1.99935870310376 18 43.535 41.181232384084 2.35376761591596 19 44.7265 41.3562856012868 3.3702143987132 20 45.7293 41.2243585973062 4.50494140269379 21 45.7585 40.6627422411821 5.09575775881788 22 46.1685 40.5055723858491 5.66292761415089 23 46.5075 40.6818417316279 5.82565826837213 24 46.527 40.563416661644 5.96358333835602 25 46.601 40.6765952458428 5.92440475415716 26 46.4607 40.9006839900651 5.56001600993491 27 46.7135 41.3456840186608 5.36781598133923 28 46.4113 41.2117610345372 5.19953896546275 29 45.55 40.9296370287796 4.62036297122042 30 44.6081 40.7382985497641 3.8698014502359 31 44.4395 41.0389453406929 3.40055465930713 32 44.9847 40.9782400268431 4.00645997315691 33 45.7558 40.7634870955885 4.99231290441146 34 45.3942 40.8382054990225 4.55599450097746 35 45.697 41.1913836138814 4.5056163861186 36 45.5664 41.0922415239777 4.4741584760223 37 46.0205 41.5294925955036 4.49100740449644 38 45.9195 41.7027024213808 4.21679757861924 39 45.8005 41.9102250472591 3.89027495274090 40 45.535 42.1295466440742 3.40545335592582 41 45.4977 41.7731957338121 3.72450426618787 42 45.5782 41.6521708256467 3.92602917435334 43 45.7697 41.82009110437 3.94960889562998 44 45.2445 41.9431119926784 3.30138800732157 45 45.0615 41.9488340687883 3.11266593121174 46 45.2865 41.678734668578 3.60776533142202 47 44.791 41.908541740857 2.88245825914298 48 44.7625 42.1471182485957 2.61538175140432 49 44.7644 42.1340291868971 2.63037081310292 50 44.9973 41.9822031842459 3.01509681575410 51 44.7265 42.4225961503579 2.30390384964210 52 45.1465 42.6898495047255 2.45665049527451 53 44.7465 42.5906606053999 2.15583939460008 54 45.1795 42.787034927802 2.39246507219798 55 45.6515 43.1107713366772 2.54072866332278 56 45.492 43.0899258218027 2.40207417819731 57 45.2775 43.272389085963 2.00511091403697 58 45.2115 43.4748758684547 1.73662413154525 59 45.411 43.4066631407506 2.00433685924942 60 45.4005 43.6513127952631 1.74918720473690 61 44.7692 42.9926823451451 1.77651765485487 62 44.8913 42.6680912061245 2.22320879387549 63 45.032 43.1781976331009 1.85380236689907 64 44.879 43.1087182193458 1.77028178065415 65 44.833 43.5426991285717 1.29030087142827 66 44.8257 43.6419076845552 1.18379231544480 67 44.7815 43.9482100855140 0.833289914485949 68 44.479 43.3898872964009 1.08911270359914 69 44.6317 43.3186459496122 1.31305405038782 70 44.5043 43.4329470435433 1.07135295645671 71 44.3217 43.3654982588013 0.95620174119875 72 44.1005 43.0562374528907 1.04426254710932 73 44.047 43.0369507247574 1.01004927524255 74 43.6835 42.8622757309827 0.821224269017317 75 43.7864 42.5574740631841 1.22892593681591 76 44.1807 42.4035004079652 1.77719959203481 77 43.9595 42.4138726079275 1.54562739207246 78 43.937 42.6601905766299 1.27680942337008 79 43.991 42.7368300816284 1.2541699183716 80 43.865 42.4817380130207 1.38326198697927 81 43.671 42.1789126687072 1.4920873312928 82 43.93 42.7835148097879 1.14648519021213 83 43.863 43.0460871394859 0.816912860514085 84 43.7095 43.1938088227422 0.515691177257805 85 43.9435 43.4198420732399 0.523657926760135 86 43.736 43.1838728997889 0.552127100211107 87 43.6295 43.4222237729169 0.207276227083074 88 43.598 43.5389103145782 0.0590896854218483 89 43.8726 43.522063339703 0.350536660296989 90 43.8935 43.5776934968504 0.315806503149581 91 43.5957 43.2672671198388 0.328432880161219 92 43.7155 43.0297972132275 0.685702786772453 93 43.528 42.9056968765557 0.622303123444308 94 43.3415 42.9832295086227 0.358270491377259 95 43.3374 43.2642385007543 0.073161499245737 96 43.332 43.317315629142 0.0146843708580374 97 43.3869 43.3186796820897 0.0682203179103238 98 43.5016 43.4816998812463 0.0199001187536537 99 43.4875 43.7560281205123 -0.268528120512326 100 43.6023 43.5487395417433 0.0535604582566661 101 43.3886 43.6059837075526 -0.217383707552626 102 43.3105 43.8595403941299 -0.549040394129928 103 43.4455 44.0778050308999 -0.632305030899893 104 43.5185 44.0800537984175 -0.561553798417455 105 43.5755 44.2590128531685 -0.68351285316849 106 43.6217 44.2807786478756 -0.65907864787558 107 43.644 44.2339459953444 -0.589945995344385 108 43.5789 44.4331270502734 -0.854227050273424 109 43.5215 44.1711557469536 -0.649655746953567 110 43.5033 44.2474797465507 -0.744179746550736 111 43.632 44.4814023823838 -0.849402382383845 112 43.263 44.3359341310908 -1.07293413109079 113 43.3717 44.3730106022500 -1.00131060225005 114 43.2745 44.100296371646 -0.825796371645956 115 43.2647 43.9305439457562 -0.665843945756205 116 43.324 43.6956972820375 -0.371697282037486 117 43.4455 43.7696124291936 -0.324112429193597 118 43.4098 43.9561528599849 -0.546352859984858 119 43.41 43.6658005587795 -0.255800558779514 120 43.93 43.7816800961098 0.148319903890208 121 43.8104 43.8424088146705 -0.0320088146705186 122 43.54 43.8878699181479 -0.347869918147891 123 43.858 43.9739969085389 -0.115996908538862 124 43.8375 43.8913582189524 -0.0538582189523821 125 43.881 44.1338836256081 -0.252883625608117 126 43.887 44.264392271113 -0.377392271113019 127 43.8009 44.1623694864965 -0.361469486496515 128 43.7877 43.9129798373408 -0.125279837340805 129 43.811 43.5986401266748 0.212359873325202 130 44.0625 43.8154667483112 0.247033251688799 131 44.125 43.3971279295436 0.727872070456377 132 44.52 43.2944460649679 1.22555393503211 133 45.4005 42.9951520828277 2.40534791717233 134 45.89 43.1424524812871 2.74754751871288 135 45.189 43.3363364929562 1.8526635070438 136 44.9035 43.5520172001494 1.35148279985059 137 44.9351 43.720272640932 1.21482735906804 138 44.801 43.6225180086869 1.17848199131310 139 43.98 44.0144643136058 -0.0344643136058186 140 44.11 43.9682589238242 0.141741076175802 141 44.2661 43.7249733222594 0.541126677740611 142 44.361 43.9892879283331 0.371712071666848 143 44.099 44.0871829888438 0.0118170111561779 144 43.8435 44.2029146018024 -0.359414601802388 145 43.8914 44.0322034991569 -0.140803499156921 146 44.217 43.8856867003175 0.331313299682527 147 44.506 43.869304071608 0.636695928392034 148 44.54 44.1097557878819 0.430244212118149 149 44.4465 44.2524603680168 0.194039631983222 150 44.842 44.3367945245571 0.505205475442896 151 44.8946 44.5132745127343 0.381325487265722 152 44.951 44.4870458318622 0.463954168137841 153 45.445 44.4216941422129 1.02330585778709 154 45.0035 44.5190789635287 0.484421036471319 155 45.769 44.3199484660746 1.44905153392543 156 46.09 44.1856706632340 1.90432933676604 157 45.412 44.485872764655 0.926127235345017 158 45.12 44.2130490066023 0.90695099339767 159 45.48 43.7411069116473 1.73889308835267 160 45.105 43.9216640664554 1.18333593354459 161 45.056 43.7015317172563 1.35446828274366 162 45.22 43.9777969666916 1.24220303330839 163 45.39 44.0245088476151 1.36549115238493 164 45.041 44.2190828400604 0.821917159939578 165 44.9399 44.1451012268246 0.794798773175447 166 44.9315 44.0187830234522 0.912716976547819 167 45.1935 43.9164054201186 1.27709457988139 168 45.3466 44.1184007036807 1.22819929631931 169 45.4645 43.8450704544862 1.61942954551381 170 45.5685 43.8849845589894 1.68351544101057 171 45.3921 43.6137045954659 1.77839540453415 172 45.34 43.7804864558521 1.55951354414788 173 45.1308 43.7480329557814 1.38276704421862 174 45.1005 43.9014842112536 1.19901578874639 175 45.37 43.9937854633683 1.37621453663172 176 45.2 44.0937579623139 1.10624203768614 177 44.9614 44.0730407151532 0.888359284846833 178 44.8015 43.9069366749914 0.894563325008606 179 44.9152 43.8477826517768 1.06741734822321 180 45.095 44.0053812880127 1.08961871198728 181 44.9271 43.9352896037203 0.991810396279715 182 44.6026 44.2065424144799 0.396057585520125 183 44.5 44.1736638815593 0.326336118440745 184 44.54 44.3391481550776 0.200851844922428 185 44.5532 44.3356748464853 0.217525153514698 186 44.407 44.3182942277043 0.0887057722956708 187 44.259 44.1534259727764 0.105574027223581 188 44.1365 43.9334537085008 0.203046291499166 189 44.112 43.7193514447068 0.392648555293206 190 43.8814 43.644075992656 0.237324007343977 191 43.98 43.7050696591431 0.274930340856858 192 43.7294 43.6214254833791 0.107974516620917 193 43.9119 43.501881651716 0.410018348284009 194 43.955 43.7427621488928 0.212237851107190 195 43.9 43.7571216446954 0.142878355304621 196 43.7065 43.6699536028641 0.0365463971358765 197 43.6939 43.6664531415079 0.027446858492139 198 43.6587 43.7555975068242 -0.096897506824177 199 43.5885 44.0291852078390 -0.440685207838958 200 43.8885 44.0736090079291 -0.185109007929101 201 43.8216 44.133429607209 -0.311829607209041 202 43.751 44.2238135058123 -0.472813505812288 203 43.699 44.4447097979735 -0.745709797973491 204 43.7425 44.4731563455461 -0.730656345546126 205 43.639 44.476044536365 -0.83704453636503 206 43.589 44.4972607785615 -0.908260778561462 207 43.606 44.6837815526948 -1.07778155269483 208 43.5325 44.8449546124729 -1.31245461247289 209 43.385 44.8617154646104 -1.47671546461040 210 43.3745 44.6341346715306 -1.25963467153060 211 43.236 44.2904945995391 -1.05449459953914 212 43.1957 44.3446399321311 -1.14893993213111 213 43.01 44.2822700521445 -1.27227005214452 214 43.1401 44.0263007650731 -0.88620076507311 215 43.0487 43.8799515472633 -0.83125154726326 216 43.1972 44.2149616956681 -1.01776169566809 217 43.2461 44.2579315720198 -1.01183157201984 218 43.0866 44.3217085663230 -1.23510856632296 219 43.0865 44.378317056884 -1.29181705688396 220 43.0194 44.3281047146042 -1.30870471460424 221 43.08 44.4427175658836 -1.36271756588360 222 43.007 44.2830883006346 -1.27608830063462 223 42.9278 44.1453259450426 -1.21752594504258 224 42.9545 44.2576039060667 -1.30310390606675 225 42.7995 44.0408746513271 -1.24137465132714 226 42.9048 44.1642381978009 -1.25943819780085 227 42.9468 43.9344432860736 -0.987643286073586 228 43.08 43.8892358863634 -0.809235886363445 229 43.1274 44.1171986509243 -0.98979865092431 230 43.1625 44.0518001518532 -0.889300151853195 231 43.45 44.0307786434504 -0.580778643450352 232 43.831 43.8256414653021 0.00535853469786818 233 43.7769 43.5490410538077 0.227858946192335 234 43.98 43.6731451385326 0.306854861467424 235 43.92 43.8726342027568 0.0473657972431858 236 44.11 43.9549377302841 0.155062269715900 237 44.03 44.1988430984924 -0.168843098492369 238 44.1582 44.1648888652614 -0.00668886526136667 239 44.14 44.0738664597494 0.0661335402506184 240 45.07 43.7109668123887 1.35903318761132 241 44.8737 43.5898014759576 1.28389852404239 242 44.8505 43.5915634089912 1.25893659100879 243 44.373 43.7598656591956 0.613134340804377 244 44.075 43.4892522717374 0.585747728262626 245 43.9725 43.5421271702254 0.43037282977462 246 44.094 43.6056195599442 0.48838044005577 247 44.191 43.3259633766013 0.865036623398748 248 43.9685 43.3821823995751 0.586317600424914 249 43.79 43.4548308606803 0.335169139319716 250 43.6041 43.523180268597 0.080919731403003 251 43.1707 43.5391303683505 -0.368430368350514 252 42.71 43.700166747916 -0.990166747916013 253 42.755 43.7216011286170 -0.966601128616964 254 43.3316 43.8159217655855 -0.484321765585513 255 43.5 43.7645363560986 -0.264536356098611 256 43.154 43.9079739783786 -0.753973978378618 257 43.16 43.9668864583778 -0.806886458377777 258 43.1 43.8338753415881 -0.73387534158808 259 42.85 43.8180385173362 -0.968038517336185 260 42.6175 43.9152632537285 -1.29776325372847 261 42.5 43.8826693253921 -1.38266932539212 262 42.6285 44.1104954097404 -1.48199540974043 263 42.6974 43.831088265719 -1.13368826571902 264 43.04 43.9515907741378 -0.911590774137823 265 42.673 43.9003495409696 -1.22734954096958 266 42.5015 43.5516773736446 -1.05017737364456 267 42.538 43.4881381745038 -0.950138174503842 268 42.3735 43.3744062897413 -1.00090628974127 269 42.014 43.1517368203873 -1.13773682038730 270 41.8618 43.1942666716770 -1.33246667167695 271 42.1824 42.9669854753936 -0.784585475393637 272 42.605 43.0292617365365 -0.424261736536501 273 42.7345 42.797942686938 -0.0634426869380348 274 42.615 42.7411741114535 -0.126174111453540 275 42.465 42.7702329295635 -0.305232929563528 276 42.34 42.726814615651 -0.386814615650971 277 42.251 42.6266511309649 -0.375651130964861 278 42.0475 42.7222113012203 -0.674711301220338 279 41.86 42.8170814906996 -0.95708149069955 280 41.685 42.6113404545127 -0.926340454512688 281 41.735 42.0917661988017 -0.356766198801667 282 41.706 42.0251983805764 -0.319198380576427 283 41.764 42.2499143875483 -0.485914387548261 284 41.58 42.1860166216374 -0.606016621637417 285 41.373 42.1799593834684 -0.806959383468387 286 41.088 42.0206381275146 -0.932638127514609 287 41.137 42.0477009654817 -0.910700965481656 288 41.1587 42.1019202602595 -0.943220260259485 289 41.185 42.4206685515625 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40.1185767179718 -1.07267671797179 355 38.45 39.9986668234325 -1.54866682343246 356 38.505 39.6427212893624 -1.13772128936242 357 38.2885 39.9379381048717 -1.64943810487174 358 37.795 39.8359387249662 -2.04093872496617 359 37.92 40.212960072967 -2.29296007296703 360 38.034 40.118803268975 -2.08480326897503 361 38.029 39.775373839382 -1.74637383938198 362 38.063 40.0455312931733 -1.98253129317331 363 37.9828 40.2632117285625 -2.28041172856252 364 37.745 39.8913591561078 -2.14635915610778 365 37.969 39.5829091025812 -1.61390910258120 366 38.007 39.4987696798345 -1.49176967983446 367 38.0615 39.62218003573 -1.56068003573004 368 38.0912 39.6149104170381 -1.52371041703808 369 38.091 39.8326882193241 -1.74168821932408 370 38.431 40.0681115883174 -1.63711158831745 371 38.48 39.9773457182331 -1.49734571823314 372 38.35 40.0394075889245 -1.68940758892453 373 38.214 40.1801283494683 -1.96612834946829 374 38.384 40.0736267700819 -1.68962677008193 375 38.1375 40.2215197824209 -2.08401978242085 376 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0.152501676208144 440 40.55 40.351048261727 0.198951738273047 441 40.7955 40.3668345285039 0.428665471496074 442 41.456 40.6216728933444 0.834327106655641 443 41.3557 40.5120687326876 0.84363126731237 444 41.374 40.649714064725 0.724285935275002 445 41.2235 40.7218401260835 0.501659873916472 446 41.15 40.6720331599958 0.477966840004195 447 41.3725 40.8233676637174 0.549132336282649 448 41.6923 40.8140477593545 0.878252240645539 449 41.8 40.9286372059229 0.871362794077102 450 41.8045 40.8972360014516 0.907263998548397 451 41.64 41.0277755477735 0.612224452226455 452 41.36 41.0843334808596 0.275666519140374 453 41.5745 41.1477125950769 0.426787404923142 454 41.593 40.9814802872012 0.61151971279876 455 41.575 41.0109051681875 0.56409483181253 456 41.68 40.8418352269679 0.838164773032124 457 42.0055 41.018392925384 0.987107074616036 458 42.3188 41.1049721013888 1.21382789861120 459 42.565 41.0786498016729 1.48635019832706 460 42.3575 40.8173057611027 1.54019423889734 461 42.29 40.8251951464381 1.46480485356191 462 42.695 40.7444663142569 1.95053368574307 463 43.0028 40.9589346409272 2.04386535907279 464 42.4507 40.8584593988928 1.59224060110716 465 42.4705 40.902072446599 1.56842755340103 466 42.2875 40.8395274894767 1.44797251052333 467 42.3172 40.7481343566428 1.56906564335721 468 42.55 40.739983771434 1.81001622856598 469 42.7523 40.6398474395119 2.1124525604881 470 42.8993 40.6778320299519 2.22146797004805 471 43.1555 40.8925109990206 2.26298900097937 472 43.1885 40.9609849265981 2.22751507340187 473 43.43 41.2659382668215 2.1640617331785 474 43.31 41.0952196680699 2.21478033193008 475 42.815 40.8086524328051 2.00634756719489 476 42.7017 40.7570601289728 1.94463987102716 477 42.28 40.834893274229 1.44510672577101 478 41.922 40.706380608867 1.21561939113296 479 42.17 40.6836598854573 1.48634011454271 480 42.1962 40.7522002791145 1.44399972088546 481 42.3215 40.732219822152 1.58928017784802 482 42.3173 40.5775486900507 1.73975130994929 483 42.391 40.8193644592723 1.57163554072770 484 42.463 40.8832388204722 1.57976117952778 485 42.4125 40.8551274349588 1.55737256504123 486 42.304 40.5836293329307 1.72037066706933 487 41.813 40.3454619494371 1.46753805056294 488 41.651 40.3481788111733 1.30282118882669 489 41.539 40.2477503785608 1.29124962143919 490 41.1575 40.1885457978713 0.968954202128714 491 40.9545 39.8810703297052 1.07342967029476 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.00266660941359359 0.00533321882718719 0.997333390586406 7 0.00223585786696979 0.00447171573393957 0.99776414213303 8 0.000866796637940212 0.00173359327588042 0.99913320336206 9 0.000168966650558154 0.000337933301116309 0.999831033349442 10 0.000102458692269553 0.000204917384539106 0.99989754130773 11 0.00360082317315796 0.00720164634631591 0.996399176826842 12 0.0127634132105448 0.0255268264210897 0.987236586789455 13 0.00829730846368479 0.0165946169273696 0.991702691536315 14 0.0039323630259838 0.0078647260519676 0.996067636974016 15 0.00272913782498815 0.00545827564997629 0.997270862175012 16 0.00172948491875953 0.00345896983751905 0.99827051508124 17 0.000783017440421584 0.00156603488084317 0.999216982559578 18 0.000613924851956944 0.00122784970391389 0.999386075148043 19 0.00290205680796307 0.00580411361592614 0.997097943192037 20 0.0374410159624658 0.0748820319249317 0.962558984037534 21 0.0816696461004585 0.163339292200917 0.918330353899542 22 0.126766576235319 0.253533152470637 0.873233423764681 23 0.208255590506582 0.416511181013163 0.791744409493418 24 0.261376299805725 0.52275259961145 0.738623700194275 25 0.333548414382771 0.667096828765541 0.66645158561723 26 0.417276340638824 0.834552681277648 0.582723659361176 27 0.601483036802428 0.797033926395145 0.398516963197572 28 0.676315627745245 0.64736874450951 0.323684372254755 29 0.668136290117826 0.663727419764348 0.331863709882174 30 0.640218765029388 0.719562469941225 0.359781234970612 31 0.607766225075911 0.784467549848178 0.392233774924089 32 0.578405306816816 0.843189386366368 0.421594693183184 33 0.562988428596472 0.874023142807056 0.437011571403528 34 0.547522065401126 0.904955869197748 0.452477934598874 35 0.536087602507875 0.92782479498425 0.463912397492125 36 0.520845814053945 0.95830837189211 0.479154185946055 37 0.543786068998128 0.912427862003745 0.456213931001872 38 0.546062857831749 0.907874284336502 0.453937142168251 39 0.541491918321025 0.91701616335795 0.458508081678975 40 0.521606752488649 0.956786495022702 0.478393247511351 41 0.51038762370834 0.97922475258332 0.48961237629166 42 0.507018773973031 0.985962452053938 0.492981226026969 43 0.501380297411477 0.997239405177046 0.498619702588523 44 0.503617272643176 0.992765454713648 0.496382727356824 45 0.506447222138337 0.987105555723326 0.493552777861663 46 0.502582004660428 0.994835990679144 0.497417995339572 47 0.510009325834014 0.979981348331972 0.489990674165986 48 0.492537815902171 0.985075631804343 0.507462184097829 49 0.476871209194913 0.953742418389827 0.523128790805087 50 0.470381677149246 0.940763354298492 0.529618322850754 51 0.46218843484486 0.92437686968972 0.53781156515514 52 0.510565365776687 0.978869268446626 0.489434634223313 53 0.50407788402018 0.99184423195964 0.49592211597982 54 0.570421879111629 0.859156241776743 0.429578120888371 55 0.875343265355 0.249313469289999 0.124656734644999 56 0.946553126445113 0.106893747109774 0.0534468735548872 57 0.970614301814585 0.058771396370831 0.0293856981854155 58 0.978729138931238 0.0425417221375240 0.0212708610687620 59 0.98492547922951 0.0301490415409787 0.0150745207704893 60 0.989447156800634 0.021105686398731 0.0105528431993655 61 0.988902948250127 0.022194103499745 0.0110970517498725 62 0.988998602243312 0.0220027955133765 0.0110013977566882 63 0.988982799904253 0.0220344001914939 0.0110172000957469 64 0.988549100273706 0.0229017994525884 0.0114508997262942 65 0.98765203091949 0.0246959381610205 0.0123479690805102 66 0.986628442312971 0.0267431153740576 0.0133715576870288 67 0.985070284589528 0.0298594308209443 0.0149297154104722 68 0.98381712340236 0.0323657531952799 0.0161828765976400 69 0.98294624822293 0.0341075035541399 0.0170537517770700 70 0.981672597807137 0.0366548043857256 0.0183274021928628 71 0.98025429495255 0.0394914100948988 0.0197457050474494 72 0.97990774632165 0.0401845073566987 0.0200922536783493 73 0.97932627835867 0.0413474432826606 0.0206737216413303 74 0.981608854628254 0.0367822907434922 0.0183911453717461 75 0.984627095821681 0.0307458083566378 0.0153729041783189 76 0.9872541069521 0.0254917860958016 0.0127458930479008 77 0.989529823778669 0.0209403524426622 0.0104701762213311 78 0.990654761545712 0.0186904769085763 0.00934523845428813 79 0.990630777095344 0.0187384458093123 0.00936922290465616 80 0.991234359643001 0.0175312807139971 0.00876564035699856 81 0.992807755969201 0.0143844880615976 0.0071922440307988 82 0.992832846615401 0.0143343067691969 0.00716715338459844 83 0.992767989858216 0.0144640202835686 0.00723201014178429 84 0.993000052946178 0.013999894107643 0.0069999470538215 85 0.992623192984343 0.0147536140313134 0.00737680701565671 86 0.992440673461958 0.0151186530760832 0.00755932653804161 87 0.992054098862939 0.0158918022741223 0.00794590113706115 88 0.991385831560988 0.0172283368780229 0.00861416843901145 89 0.990875962710291 0.0182480745794176 0.00912403728970881 90 0.990616620137367 0.0187667597252651 0.00938337986263254 91 0.989762591667975 0.0204748166640493 0.0102374083320246 92 0.989358681155336 0.0212826376893285 0.0106413188446642 93 0.988638085937026 0.022723828125948 0.011361914062974 94 0.987599034254825 0.0248019314903508 0.0124009657451754 95 0.986026223479084 0.0279475530418328 0.0139737765209164 96 0.984234247834303 0.0315315043313941 0.0157657521656970 97 0.982690681785503 0.0346186364289944 0.0173093182144972 98 0.98290329949385 0.0341934010122989 0.0170967005061495 99 0.98235314365885 0.0352937126823007 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4.25184258196783e-30 2.12592129098391e-30 413 1 1.37812843698447e-29 6.89064218492237e-30 414 1 3.75012119241606e-29 1.87506059620803e-29 415 1 9.17331086455501e-29 4.58665543227751e-29 416 1 1.60332299472247e-28 8.01661497361233e-29 417 1 2.53320888385803e-28 1.26660444192901e-28 418 1 4.12637543661348e-28 2.06318771830674e-28 419 1 9.77719065624832e-28 4.88859532812416e-28 420 1 2.30693358356120e-27 1.15346679178060e-27 421 1 8.62735802438683e-27 4.31367901219342e-27 422 1 3.27291946292031e-26 1.63645973146016e-26 423 1 1.07462963657194e-25 5.37314818285971e-26 424 1 3.02976502220814e-25 1.51488251110407e-25 425 1 7.74848790045898e-25 3.87424395022949e-25 426 1 2.42536216905687e-24 1.21268108452844e-24 427 1 6.82637262027195e-24 3.41318631013598e-24 428 1 1.77151003868589e-23 8.85755019342945e-24 429 1 3.89431966093267e-23 1.94715983046634e-23 430 1 7.18601175604749e-23 3.59300587802375e-23 431 1 1.53943436375605e-22 7.69717181878024e-23 432 1 3.28268894296894e-22 1.64134447148447e-22 433 1 5.28618169535308e-22 2.64309084767654e-22 434 1 4.95924309152447e-22 2.47962154576223e-22 435 1 1.21639504367967e-22 6.08197521839836e-23 436 1 1.11615225789027e-24 5.58076128945135e-25 437 1 1.18177225567391e-24 5.90886127836956e-25 438 1 4.59580444523679e-24 2.29790222261840e-24 439 1 1.56970290888395e-23 7.84851454441976e-24 440 1 5.52923469870806e-23 2.76461734935403e-23 441 1 2.24851531357330e-22 1.12425765678665e-22 442 1 9.01491972000652e-22 4.50745986000326e-22 443 1 3.93094119088104e-21 1.96547059544052e-21 444 1 1.70958903306617e-20 8.54794516533087e-21 445 1 6.4086180107612e-20 3.2043090053806e-20 446 1 1.92192684174427e-19 9.60963420872134e-20 447 1 5.79155476332781e-19 2.89577738166391e-19 448 1 2.25646347902303e-18 1.12823173951152e-18 449 1 9.11224188563981e-18 4.55612094281991e-18 450 1 3.65960563861539e-17 1.82980281930770e-17 451 1 1.18102759080345e-16 5.90513795401723e-17 452 1 9.37647412085148e-17 4.68823706042574e-17 453 1 4.05407825925875e-17 2.02703912962938e-17 454 1 1.64114504137968e-17 8.2057252068984e-18 455 1 1.11428606942020e-18 5.57143034710102e-19 456 1 2.37384072752362e-19 1.18692036376181e-19 457 1 5.88243018189345e-20 2.94121509094672e-20 458 1 1.22581268361533e-20 6.12906341807663e-21 459 1 1.41360396008407e-20 7.06801980042033e-21 460 1 4.59582940233866e-20 2.29791470116933e-20 461 1 1.19543784345254e-19 5.9771892172627e-20 462 1 8.0488177590948e-19 4.0244088795474e-19 463 1 5.46486566981857e-18 2.73243283490929e-18 464 1 1.82428332818093e-17 9.12141664090466e-18 465 1 1.68131374624174e-17 8.40656873120869e-18 466 1 9.28196669946233e-18 4.64098334973117e-18 467 1 3.27710573771758e-17 1.63855286885879e-17 468 1 2.38379043309745e-16 1.19189521654873e-16 469 1 1.88436352048792e-15 9.4218176024396e-16 470 0.999999999999994 1.22561579551557e-14 6.12807897757783e-15 471 0.99999999999997 6.0900867070042e-14 3.0450433535021e-14 472 0.999999999999878 2.43846378186778e-13 1.21923189093389e-13 473 0.999999999998882 2.23633811204790e-12 1.11816905602395e-12 474 0.999999999995437 9.1249809426633e-12 4.56249047133165e-12 475 0.999999999978444 4.31121229439933e-11 2.15560614719967e-11 476 0.999999999961692 7.66161033989374e-11 3.83080516994687e-11 477 0.999999999645119 7.09762227388859e-10 3.54881113694429e-10 478 0.999999999319183 1.36163286813401e-09 6.80816434067003e-10 479 0.999999998446498 3.10700357910561e-09 1.55350178955281e-09 480 0.999999996611321 6.77735722265165e-09 3.38867861132582e-09 481 0.999999975984262 4.80314754945818e-08 2.40157377472909e-08 482 0.999999944126772 1.11746455673037e-07 5.58732278365187e-08 483 0.999999277169641 1.44566071793858e-06 7.22830358969292e-07 484 0.999987574311955 2.48513760907869e-05 1.24256880453934e-05 485 0.999820325428727 0.000359349142545301 0.000179674571272651 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 347 0.722916666666667 NOK 5% type I error level 410 0.854166666666667 NOK 10% type I error level 422 0.879166666666667 NOK

Charts produced by software:

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Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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

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• 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.

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