*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: Wed, 17 Nov 2010 09:28:57 +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/17/t1289986052iuu6wbiswsl22l0.htm/, Retrieved Wed, 17 Nov 2010 10:27:32 +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/17/t1289986052iuu6wbiswsl22l0.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 9 41 38 13 12 14 12 53 1 9 39 32 16 11 18 11 83 1 9 30 35 19 15 11 14 66 1 9 31 33 15 6 12 12 67 1 9 34 37 14 13 16 21 76 1 9 35 29 13 10 18 12 78 1 9 39 31 19 12 14 22 53 1 9 34 36 15 14 14 11 80 1 9 36 35 14 12 15 10 74 1 9 37 38 15 9 15 13 76 1 9 38 31 16 10 17 10 79 1 9 36 34 16 12 19 8 54 1 9 38 35 16 12 10 15 67 1 9 39 38 16 11 16 14 54 1 9 33 37 17 15 18 10 87 1 9 32 33 15 12 14 14 58 1 9 36 32 15 10 14 14 75 1 9 38 38 20 12 17 11 88 1 9 39 38 18 11 14 10 64 1 9 32 32 16 12 16 13 57 1 9 32 33 16 11 18 9.5 66 1 9 31 31 16 12 11 14 68 1 9 39 38 19 13 14 12 54 1 9 37 39 16 11 12 14 56 1 9 39 32 17 12 17 11 86 1 9 41 32 17 13 9 9 80 1 9 36 35 16 10 16 11 76 1 9 33 37 15 14 14 15 69 1 9 33 33 16 12 15 14 78 1 9 34 33 14 10 11 13 67 1 9 31 31 15 12 16 9 80 1 9 27 32 12 8 13 15 54 1 9 37 31 14 10 17 10 71 1 9 34 37 16 12 15 11 84 1 9 34 30 14 12 14 13 74 1 9 32 33 10 7 16 8 71 1 9 29 31 10 9 9 20 63 1 9 36 33 14 12 15 12 71 1 9 29 31 16 10 17 10 76 1 9 35 33 16 10 13 1 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 11 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Learning[t] = + 2.71036882048956 + 0.267704067574296Pop[t] + 0.298790977527051month[t] + 0.0331344672091642Connected[t] + 0.0409019846242882Separate[t] + 0.552243687717541Software[t] + 0.0669242791647773Happiness[t] -0.0340036411629706Depression[t] + 0.00642778731101736Belonging[t] -0.00640786153072009t + 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) 2.71036882048956 4.475042 0.6057 0.545279 0.27264 Pop 0.267704067574296 0.498621 0.5369 0.591814 0.295907 month 0.298790977527051 0.455163 0.6564 0.51213 0.256065 Connected 0.0331344672091642 0.034572 0.9584 0.338769 0.169384 Separate 0.0409019846242882 0.035311 1.1584 0.247809 0.123905 Software 0.552243687717541 0.054773 10.0824 0 0 Happiness 0.0669242791647773 0.058168 1.1505 0.251007 0.125503 Depression -0.0340036411629706 0.042131 -0.8071 0.420363 0.210181 Belonging 0.00642778731101736 0.011998 0.5358 0.592599 0.296299 t -0.00640786153072009 0.004338 -1.4772 0.140862 0.070431 Multiple Linear Regression - Regression Statistics Multiple R 0.66803004658921 R-squared 0.446264143145982 Adjusted R-squared 0.42664358128895 F-TEST (value) 22.7447178321266 F-TEST (DF numerator) 9 F-TEST (DF denominator) 254 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.85966979909574 Sum Squared Residuals 878.426427463874 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 16.0700655900209 -3.07006559002095 2 16 15.6942675757612 0.305732424238781 3 19 17.0415769521614 1.95842304783860 4 15 12.1576657479351 2.84233425206487 5 14 16.2994894725434 -2.29948947254338 6 13 14.7950060414932 -1.79500604149322 7 19 15.3389991824186 3.66100081758143 8 15 17.0235065935887 -2.02350659358869 9 14 16.0003395028786 -2.00033950287857 10 15 14.4038856504104 0.596114349589618 11 16 14.9516848951879 1.04831510481213 12 16 16.1473625864268 -0.147362586426829 13 16 15.4913428783582 0.508657121641839 14 16 15.4405198313003 0.559480168699663 15 17 17.8853580370055 -0.88535803700552 16 15 15.4353591932854 -0.435359193285363 17 15 14.5253722248192 0.474627775180776 18 20 16.3214775769141 3.67852242308589 19 18 15.4749244030792 2.52507559692076 20 16 15.5302501747197 0.469749825280298 21 16 15.3232119982948 0.676788001705164 22 16 15.1454786232591 0.854521376740856 23 19 16.4214951769553 2.57850482304467 24 16 15.0962327241620 0.903767275837978 25 17 16.0514895310405 0.948510468959527 26 17 16.1576406167872 0.842359383212759 27 16 14.8262868325144 1.17371316748563 28 15 16.6963966553163 -1.69639665531634 29 16 15.5806714859803 0.41932851401971 30 14 14.1985115803063 -0.198511580306322 31 15 15.6695809188536 -0.66958091885361 32 12 12.7906452676818 -0.79064526768175 33 14 14.7261551758147 -0.726155175814723 34 16 15.8859522313880 0.114047768611978 35 14 15.3940210428864 -1.39402104288639 36 10 12.9674151644339 -2.96741516443386 37 10 12.9563513608649 -2.95635136086492 38 14 15.6454169049801 -1.64541690498012 39 16 14.4547712055122 1.54522879448782 40 16 14.4162824886488 1.58371751135121 41 16 14.6352327129597 1.36476728704028 42 14 15.5781446705218 -1.57814467052182 43 20 17.3891420698026 2.61085793019745 44 14 14.0784398144220 -0.0784398144219514 45 14 14.3789815766678 -0.378981576667822 46 11 15.3881083294370 -4.38810832943697 47 14 16.5151148807080 -2.51511488070805 48 15 15.0303079753592 -0.0303079753592149 49 16 15.2783176603198 0.721682339680205 50 14 15.519755748483 -1.51975574848301 51 16 16.8799334836024 -0.879933483602363 52 14 14.0353329346706 -0.0353329346706483 53 12 14.8936198510576 -2.89361985105759 54 16 15.9067455912898 0.093254408710175 55 9 11.2791460966546 -2.27914609665465 56 14 12.2786815412273 1.72131845877269 57 16 15.7161769220831 0.283823077916920 58 16 15.4025201627707 0.597479837229299 59 15 14.9945385656531 0.00546143434686897 60 16 14.0985125449901 1.90148745500994 61 12 11.4773016697539 0.522698330246092 62 16 15.503517312822 0.496482687177994 63 16 16.3953957461175 -0.395395746117497 64 14 14.5856409302666 -0.585640930266565 65 16 15.1810557145335 0.818944285466491 66 17 16.2047372497908 0.795262750209219 67 18 16.4748169837709 1.52518301622910 68 18 14.5675923749950 3.43240762500498 69 12 16.0458555294013 -4.04585552940125 70 16 15.7887669278641 0.211233072135857 71 10 13.5861095198426 -3.58610951984262 72 14 15.0685754612093 -1.06857546120934 73 18 17.0338373096417 0.966162690358343 74 18 17.2666487619529 0.733351238047127 75 16 15.3791524815831 0.620847518416886 76 17 13.6397272891058 3.36027271089416 77 16 16.5367045060885 -0.536704506088515 78 16 14.6708544911275 1.32914550887252 79 13 15.3391689355289 -2.33916893552889 80 16 15.3216902385524 0.678309761447573 81 16 15.7773983285827 0.222601671417309 82 16 15.8947463699324 0.105253630067610 83 15 15.7687684346207 -0.768768434620745 84 15 15.0119944674752 -0.0119944674751563 85 16 14.2949824477905 1.70501755220947 86 14 14.2832878161251 -0.283287816125141 87 16 15.4919413967119 0.508058603288128 88 16 15.0058396847770 0.99416031522303 89 15 14.6361451105540 0.363854889445985 90 12 14.0240021293728 -2.02400212937277 91 17 16.8752884338016 0.124711566198416 92 16 15.8968278523366 0.103172147663437 93 15 15.1564981641270 -0.156498164126972 94 13 15.1377201193380 -2.13772011933795 95 16 14.8553597614483 1.14464023855169 96 16 15.8900910611324 0.109908938867559 97 16 13.8065484257596 2.19345157424036 98 16 15.8659027151001 0.134097284899881 99 14 14.5391552576154 -0.5391552576154 100 16 17.0743775085394 -1.07437750853939 101 16 14.7792755272878 1.22072447271217 102 20 17.5113570477099 2.48864295229012 103 15 14.3725729912835 0.627427008716497 104 16 15.0304615737166 0.969538426283401 105 13 14.9476625190988 -1.94766251909879 106 17 15.7896130589679 1.21038694103205 107 16 15.7771351160272 0.222864883972845 108 16 14.4793131532877 1.52068684671229 109 12 12.4247306347661 -0.424730634766116 110 16 15.3469187736929 0.653081226307068 111 16 16.0648243519389 -0.0648243519388998 112 17 15.1218879167947 1.87811208320529 113 13 14.3977845272317 -1.39778452723171 114 12 14.6596712566202 -2.65967125662023 115 18 16.2266270861001 1.77337291389988 116 14 15.9393449157931 -1.93934491579306 117 14 13.32241795909 0.677582040910008 118 13 14.8839669096552 -1.88396690965521 119 16 15.5673387430657 0.43266125693432 120 13 14.5186679408697 -1.51866794086971 121 16 15.4507221944478 0.549277805552234 122 13 15.8673321688520 -2.86733216885203 123 16 16.9098249193686 -0.909824919368622 124 15 15.9104307251192 -0.910430725119216 125 16 16.8293600377629 -0.829360037762884 126 15 14.7263211912627 0.273678808737307 127 17 15.5532913665631 1.44670863343687 128 15 14.1000504647674 0.899949535232634 129 12 14.748138474433 -2.74813847443299 130 16 14.0178144602426 1.9821855397574 131 10 13.7843416092875 -3.78434160928755 132 16 13.5356139612752 2.46438603872483 133 12 14.2217235432609 -2.22172354326086 134 14 15.5859809278217 -1.58598092782170 135 15 15.1154055258866 -0.115405525886594 136 13 12.1934992670644 0.806500732935584 137 15 14.5867670780997 0.413232921900267 138 11 13.5689474543954 -2.56894745439539 139 12 13.0848157644353 -1.08481576443525 140 11 13.4033024434723 -2.40330244347227 141 16 12.9423224077991 3.05767759220089 142 15 13.6683385872839 1.33166141271611 143 17 16.8406031814283 0.159396818571737 144 16 14.1766054141107 1.82339458588930 145 10 13.3169539393641 -3.3169539393641 146 18 15.5255159640444 2.47448403595558 147 13 14.9960505112883 -1.99605051128828 148 16 14.8839432289554 1.11605677104456 149 13 12.8754521491037 0.124547850896257 150 10 12.9339030961619 -2.93390309616189 151 15 15.8823334933492 -0.882333493349214 152 16 13.8641567962093 2.13584320379074 153 16 11.9117740705224 4.08822592947764 154 14 12.1478743978375 1.85212560216245 155 10 12.5114300675364 -2.51143006753643 156 17 16.4587774343048 0.541222565695222 157 13 11.7514931675940 1.24850683240602 158 15 13.9078146188458 1.09218538115424 159 16 14.5611656307325 1.43883436926753 160 12 12.704212629745 -0.704212629745011 161 13 12.7207844852095 0.279215514790499 162 13 12.7169044636284 0.283095536371560 163 12 12.4426974334534 -0.442697433453368 164 17 16.2042613597248 0.795738640275188 165 15 13.7039365204601 1.29606347953990 166 10 11.6747846515357 -1.67478465153568 167 14 14.3757003960369 -0.375700396036854 168 11 14.2342154864931 -3.23421548649315 169 13 14.8072513414194 -1.80725134141935 170 16 14.4021428566751 1.5978571433249 171 12 10.5661030498728 1.43389695012720 172 16 15.3536013523164 0.646398647683617 173 12 13.8966712518605 -1.89667125186049 174 9 11.4024498263592 -2.40244982635922 175 12 14.9322562649427 -2.93225626494266 176 15 14.5011891902514 0.498810809748587 177 12 12.3519181544552 -0.351918154455236 178 12 12.6823000987845 -0.682300098784452 179 14 13.8290430481456 0.170956951854436 180 12 13.3221032278085 -1.32210322780853 181 16 15.0394835251507 0.960516474849282 182 11 11.5257114005642 -0.525711400564246 183 19 16.6874954053371 2.31250459466286 184 15 15.1240470477292 -0.124047047729155 185 8 14.5815747753204 -6.5815747753204 186 16 14.6773642610343 1.3226357389657 187 17 14.3935335217317 2.60646647826829 188 12 12.3733411536194 -0.373341153619402 189 11 11.4657753769959 -0.465775376995942 190 11 10.4877543505679 0.512245649432125 191 14 14.4712423242103 -0.47124232421031 192 16 15.3678610959226 0.632138904077387 193 12 9.76447869879284 2.23552130120716 194 16 14.0923364101279 1.90766358987211 195 13 13.6297538082259 -0.629753808225923 196 15 14.9932964361314 0.00670356386858984 197 16 12.8981133734385 3.10188662656153 198 16 14.9481508787370 1.05184912126304 199 14 12.4231490931405 1.57685090685951 200 16 14.4356890921409 1.56431090785908 201 16 14.0248811503992 1.97511884960085 202 14 13.2867429909819 0.713257009018119 203 11 13.3067570218082 -2.30675702180818 204 12 14.493290634375 -2.49329063437499 205 15 12.6781371774303 2.32186282256971 206 15 14.3882471078060 0.61175289219397 207 16 14.4936607205281 1.50633927947185 208 16 14.8183152156677 1.18168478433225 209 11 13.5667794673052 -2.5667794673052 210 15 13.8528718135307 1.14712818646934 211 12 14.1839074212464 -2.18390742124639 212 12 15.6604102083986 -3.6604102083986 213 15 14.0456750091148 0.95432499088521 214 15 12.0816248858050 2.91837511419496 215 16 14.4263463318439 1.57365366815615 216 14 13.0653460848600 0.934653915140025 217 17 14.5162856802113 2.48371431978866 218 14 13.8679068963144 0.132093103685559 219 13 11.8155168348847 1.18448316511532 220 15 15.0571742797099 -0.0571742797098508 221 13 14.5339634850250 -1.53396348502497 222 14 13.8640988277851 0.135901172214907 223 15 14.1513232086972 0.84867679130279 224 12 13.0285658747232 -1.02856587472319 225 13 12.2570352157982 0.742964784201789 226 8 11.6272488560834 -3.62724885608335 227 14 13.6455756405834 0.354424359416577 228 14 12.7903734854848 1.20962651451519 229 11 12.1537442990202 -1.15374429902023 230 12 12.7956608051062 -0.795660805106198 231 13 11.1692162232697 1.83078377673027 232 10 13.1695174126745 -3.16951741267448 233 16 11.3391134718976 4.66088652810242 234 18 15.6920746670863 2.30792533291370 235 13 13.6766507663951 -0.676650766395077 236 11 13.1781146783976 -2.17811467839762 237 4 10.9584446281236 -6.95844462812359 238 13 14.1454748455463 -1.14547484554629 239 16 14.0793794177226 1.92062058227743 240 10 11.5688475086550 -1.56884750865497 241 12 12.1029187904657 -0.102918790465723 242 12 13.3423212406224 -1.34232124062235 243 10 8.80155569122024 1.19844430877976 244 13 10.9397523849159 2.06024761508412 245 15 13.4984089562216 1.50159104377838 246 12 11.7307657213995 0.269234278600498 247 14 12.6734439260472 1.32655607395279 248 10 12.3174962338769 -2.31749623387687 249 12 10.6570841657677 1.34291583423234 250 12 11.4788667027969 0.521133297203091 251 11 11.6499964267704 -0.649996426770351 252 10 11.4436163470791 -1.44361634707906 253 12 11.2251995045076 0.774800495492435 254 16 12.6327358402385 3.36726415976152 255 12 13.1233615268255 -1.12336152682551 256 14 13.6313734463998 0.368626553600234 257 16 14.0205792923089 1.97942070769111 258 14 11.4865985763956 2.51340142360441 259 13 13.9532280855537 -0.953228085553718 260 4 9.30524253866037 -5.30524253866037 261 15 13.4612630342946 1.53873696570543 262 11 14.6588914363487 -3.65889143634869 263 11 11.0942706014226 -0.0942706014226423 264 14 12.5370472764224 1.46295272357763 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.917403150010602 0.165193699978797 0.0825968499893984 14 0.85682309439675 0.286353811206499 0.143176905603249 15 0.806455660823021 0.387088678353957 0.193544339176979 16 0.836164817124294 0.327670365751412 0.163835182875706 17 0.774902414049014 0.450195171901973 0.225097585950986 18 0.932137792348776 0.135724415302449 0.0678622076512243 19 0.913328021041501 0.173343957916998 0.0866719789584989 20 0.877506156964089 0.244987686071823 0.122493843035911 21 0.828716670851821 0.342566658296357 0.171283329148179 22 0.7955460578284 0.4089078843432 0.2044539421716 23 0.762164164675009 0.475671670649983 0.237835835324991 24 0.73189480712185 0.536210385756299 0.268105192878149 25 0.673513862410823 0.652972275178354 0.326486137589177 26 0.624787689063159 0.750424621873683 0.375212310936841 27 0.564973144246464 0.870053711507072 0.435026855753536 28 0.611400339772781 0.777199320454437 0.388599660227219 29 0.550499935542302 0.899000128915396 0.449500064457698 30 0.556628869654638 0.886742260690723 0.443371130345362 31 0.504560109564391 0.990879780871218 0.495439890435609 32 0.496822025315197 0.993644050630395 0.503177974684803 33 0.474006983526621 0.948013967053242 0.525993016473379 34 0.412571358747727 0.825142717495454 0.587428641252273 35 0.396862823393609 0.793725646787217 0.603137176606392 36 0.49617048677849 0.99234097355698 0.50382951322151 37 0.572158069669473 0.855683860661053 0.427841930330527 38 0.54560551083894 0.90878897832212 0.45439448916106 39 0.590779184802592 0.818441630394815 0.409220815197408 40 0.571323792101886 0.857352415796228 0.428676207898114 41 0.535885602596867 0.928228794806267 0.464114397403133 42 0.502633773343334 0.994732453313332 0.497366226656666 43 0.520200882294691 0.959598235410618 0.479799117705309 44 0.471050166339388 0.942100332678775 0.528949833660612 45 0.429396411455162 0.858792822910324 0.570603588544838 46 0.645357706483004 0.709284587033992 0.354642293516996 47 0.658790113188579 0.682419773622842 0.341209886811421 48 0.621062935923807 0.757874128152386 0.378937064076193 49 0.608035134727208 0.783929730545583 0.391964865272792 50 0.579420730091979 0.841158539816042 0.420579269908021 51 0.534229518119586 0.931540963760828 0.465770481880414 52 0.491438971769037 0.982877943538073 0.508561028230963 53 0.497723623098151 0.995447246196303 0.502276376901849 54 0.466751374597205 0.93350274919441 0.533248625402795 55 0.46601125529817 0.93202251059634 0.53398874470183 56 0.479803896687619 0.959607793375238 0.520196103312381 57 0.437413942360113 0.874827884720226 0.562586057639887 58 0.421304301954618 0.842608603909236 0.578695698045382 59 0.380217388195282 0.760434776390565 0.619782611804718 60 0.405372306741337 0.810744613482674 0.594627693258663 61 0.377129995058411 0.754259990116822 0.622870004941589 62 0.340275236852553 0.680550473705107 0.659724763147447 63 0.304335902769782 0.608671805539563 0.695664097230218 64 0.272223504220934 0.544447008441868 0.727776495779066 65 0.245832772574535 0.491665545149069 0.754167227425465 66 0.213374540491958 0.426749080983916 0.786625459508042 67 0.187961703085738 0.375923406171476 0.812038296914262 68 0.205203238424325 0.410406476848649 0.794796761575675 69 0.434631298648678 0.869262597297357 0.565368701351322 70 0.393373491752949 0.786746983505898 0.606626508247051 71 0.501301217240459 0.997397565519083 0.498698782759541 72 0.465185202165617 0.930370404331234 0.534814797834383 73 0.448463786789094 0.896927573578188 0.551536213210906 74 0.419194899583888 0.838389799167776 0.580805100416112 75 0.381714616499878 0.763429232999756 0.618285383500122 76 0.442938195257134 0.885876390514268 0.557061804742866 77 0.406560644772833 0.813121289545665 0.593439355227167 78 0.379393562314290 0.758787124628579 0.62060643768571 79 0.402363301341251 0.804726602682503 0.597636698658749 80 0.368263036856716 0.736526073713432 0.631736963143284 81 0.333157445808359 0.666314891616717 0.666842554191641 82 0.29844948046575 0.5968989609315 0.70155051953425 83 0.273337000121177 0.546674000242354 0.726662999878823 84 0.241558177119962 0.483116354239923 0.758441822880038 85 0.234207814148184 0.468415628296367 0.765792185851816 86 0.205001382316996 0.410002764633991 0.794998617683004 87 0.180407098885288 0.360814197770577 0.819592901114712 88 0.163874819134812 0.327749638269624 0.836125180865188 89 0.14164830183006 0.28329660366012 0.85835169816994 90 0.13820298785957 0.27640597571914 0.86179701214043 91 0.118527462221925 0.237054924443849 0.881472537778075 92 0.102804297801198 0.205608595602395 0.897195702198802 93 0.087984500621734 0.175969001243468 0.912015499378266 94 0.0929078092543183 0.185815618508637 0.907092190745682 95 0.0837449464318781 0.167489892863756 0.916255053568122 96 0.0699937473601535 0.139987494720307 0.930006252639846 97 0.0745596235984085 0.149119247196817 0.925440376401592 98 0.0620597673471895 0.124119534694379 0.93794023265281 99 0.0515373430660422 0.103074686132084 0.948462656933958 100 0.0450400254862771 0.0900800509725542 0.954959974513723 101 0.0401933347613839 0.0803866695227678 0.959806665238616 102 0.0495430251545656 0.0990860503091312 0.950456974845434 103 0.0417510540994371 0.0835021081988742 0.958248945900563 104 0.0376779412637723 0.0753558825275446 0.962322058736228 105 0.0437531875379646 0.0875063750759291 0.956246812462035 106 0.0390276773124664 0.0780553546249328 0.960972322687534 107 0.0318915381696423 0.0637830763392847 0.968108461830358 108 0.0306361822565303 0.0612723645130605 0.96936381774347 109 0.0248356138498717 0.0496712276997435 0.975164386150128 110 0.0207570420374206 0.0415140840748412 0.97924295796258 111 0.0165400526093474 0.0330801052186949 0.983459947390653 112 0.0178530085159647 0.0357060170319295 0.982146991484035 113 0.0154071311615533 0.0308142623231066 0.984592868838447 114 0.0197344985445317 0.0394689970890634 0.980265501455468 115 0.0193809448617952 0.0387618897235904 0.980619055138205 116 0.0186549589306562 0.0373099178613125 0.981345041069344 117 0.0158850579860182 0.0317701159720363 0.984114942013982 118 0.0155809810446777 0.0311619620893553 0.984419018955322 119 0.0126220836605096 0.0252441673210192 0.98737791633949 120 0.0112143685277325 0.0224287370554651 0.988785631472268 121 0.00911678212160006 0.0182335642432001 0.9908832178784 122 0.012695495388135 0.02539099077627 0.987304504611865 123 0.0103850207251372 0.0207700414502745 0.989614979274863 124 0.00851195140233952 0.0170239028046790 0.99148804859766 125 0.00680926686960897 0.0136185337392179 0.993190733130391 126 0.00537574013200975 0.0107514802640195 0.99462425986799 127 0.00506935489162455 0.0101387097832491 0.994930645108375 128 0.00438279399760094 0.00876558799520189 0.995617206002399 129 0.00564947726093263 0.0112989545218653 0.994350522739067 130 0.00637079290718485 0.0127415858143697 0.993629207092815 131 0.0122021996813014 0.0244043993626029 0.987797800318699 132 0.0152081492476919 0.0304162984953838 0.984791850752308 133 0.0158789841424019 0.0317579682848038 0.984121015857598 134 0.0145406604972728 0.0290813209945456 0.985459339502727 135 0.0115246866452312 0.0230493732904625 0.988475313354769 136 0.0100136638990043 0.0200273277980087 0.989986336100996 137 0.00822731898492272 0.0164546379698454 0.991772681015077 138 0.0103493490562661 0.0206986981125321 0.989650650943734 139 0.00880196515237905 0.0176039303047581 0.99119803484762 140 0.0106541890476265 0.021308378095253 0.989345810952374 141 0.017171396201654 0.034342792403308 0.982828603798346 142 0.016346820686937 0.032693641373874 0.983653179313063 143 0.0135619452203443 0.0271238904406887 0.986438054779656 144 0.0140800236654396 0.0281600473308791 0.98591997633456 145 0.0229160505845723 0.0458321011691446 0.977083949415428 146 0.0286385743555829 0.0572771487111659 0.971361425644417 147 0.0299761563608853 0.0599523127217706 0.970023843639115 148 0.0265866137656251 0.0531732275312503 0.973413386234375 149 0.0215921358603525 0.043184271720705 0.978407864139648 150 0.0296181844354454 0.0592363688708908 0.970381815564555 151 0.0249218511100782 0.0498437022201564 0.975078148889922 152 0.0268016182916603 0.0536032365833206 0.97319838170834 153 0.0546281259337347 0.109256251867469 0.945371874066265 154 0.0516763671331743 0.103352734266349 0.948323632866826 155 0.0594357476359056 0.118871495271811 0.940564252364094 156 0.050405019901643 0.100810039803286 0.949594980098357 157 0.0446170368371092 0.0892340736742184 0.95538296316289 158 0.0387311762817014 0.0774623525634028 0.961268823718299 159 0.0378865252182045 0.075773050436409 0.962113474781795 160 0.0316539838819782 0.0633079677639563 0.968346016118022 161 0.0258090765919910 0.0516181531839821 0.974190923408009 162 0.0207805987514569 0.0415611975029138 0.979219401248543 163 0.0172822395702499 0.0345644791404998 0.98271776042975 164 0.0147360200033734 0.0294720400067469 0.985263979996627 165 0.0124406114886362 0.0248812229772724 0.987559388511364 166 0.0134920073651075 0.0269840147302150 0.986507992634893 167 0.0107479067646855 0.0214958135293710 0.989252093235314 168 0.0156639487156670 0.0313278974313341 0.984336051284333 169 0.015357169918249 0.030714339836498 0.984642830081751 170 0.0146710104408366 0.0293420208816731 0.985328989559163 171 0.0134258096921439 0.0268516193842877 0.986574190307856 172 0.0107886038962103 0.0215772077924207 0.98921139610379 173 0.0103357002502192 0.0206714005004384 0.98966429974978 174 0.0115765114043448 0.0231530228086896 0.988423488595655 175 0.0140287029504975 0.0280574059009951 0.985971297049502 176 0.0113420518216896 0.0226841036433792 0.98865794817831 177 0.00881462360454005 0.0176292472090801 0.99118537639546 178 0.0073117329905807 0.0146234659811614 0.99268826700942 179 0.00569223116767329 0.0113844623353466 0.994307768832327 180 0.0048286645537371 0.0096573291074742 0.995171335446263 181 0.00400399906562101 0.00800799813124202 0.99599600093438 182 0.00302234583175246 0.00604469166350491 0.996977654168248 183 0.00340504761476052 0.00681009522952104 0.99659495238524 184 0.00255643688393327 0.00511287376786654 0.997443563116067 185 0.0724574722446438 0.144914944489288 0.927542527755356 186 0.065408020311588 0.130816040623176 0.934591979688412 187 0.0756270189848483 0.151254037969697 0.924372981015152 188 0.062836742439957 0.125673484879914 0.937163257560043 189 0.0555413082289088 0.111082616457818 0.944458691771091 190 0.0467518182993391 0.0935036365986783 0.95324818170066 191 0.0400589201744297 0.0801178403488594 0.95994107982557 192 0.0331486233528123 0.0662972467056247 0.966851376647188 193 0.0336672682210864 0.0673345364421728 0.966332731778914 194 0.0321637530609283 0.0643275061218566 0.967836246939072 195 0.0276192254776479 0.0552384509552958 0.972380774522352 196 0.0217120185772111 0.0434240371544222 0.978287981422789 197 0.0291667971184513 0.0583335942369025 0.970833202881549 198 0.0237283661730167 0.0474567323460335 0.976271633826983 199 0.0212161896077298 0.0424323792154597 0.97878381039227 200 0.0179596654628295 0.0359193309256589 0.98204033453717 201 0.0163891968464966 0.0327783936929931 0.983610803153503 202 0.0135753458386863 0.0271506916773726 0.986424654161314 203 0.0150002675980504 0.0300005351961007 0.98499973240195 204 0.0198467434413163 0.0396934868826325 0.980153256558684 205 0.0209685909524739 0.0419371819049479 0.979031409047526 206 0.0161738821314557 0.0323477642629115 0.983826117868544 207 0.0143123054089823 0.0286246108179647 0.985687694591018 208 0.0113045338639903 0.0226090677279807 0.98869546613601 209 0.0130918815238780 0.0261837630477560 0.986908118476122 210 0.0107129317594557 0.0214258635189114 0.989287068240544 211 0.0130910170218174 0.0261820340436349 0.986908982978183 212 0.0230878998464113 0.0461757996928227 0.976912100153589 213 0.0177846108410096 0.0355692216820192 0.98221538915899 214 0.0222645737074489 0.0445291474148978 0.977735426292551 215 0.0187314919132261 0.0374629838264523 0.981268508086774 216 0.0142637960967183 0.0285275921934365 0.985736203903282 217 0.0159606915406003 0.0319213830812006 0.9840393084594 218 0.0132966682835629 0.0265933365671259 0.986703331716437 219 0.0122696591625391 0.0245393183250782 0.98773034083746 220 0.00900420892815034 0.0180084178563007 0.99099579107185 221 0.00730431888468475 0.0146086377693695 0.992695681115315 222 0.00519319190106094 0.0103863838021219 0.99480680809894 223 0.00392599392817616 0.00785198785635232 0.996074006071824 224 0.00293116982658556 0.00586233965317111 0.997068830173414 225 0.00199657946387335 0.0039931589277467 0.998003420536127 226 0.00416243124356273 0.00832486248712545 0.995837568756437 227 0.00323673190482169 0.00647346380964338 0.996763268095178 228 0.00229780883778965 0.00459561767557929 0.99770219116221 229 0.00157622213545567 0.00315244427091135 0.998423777864544 230 0.00107729394084913 0.00215458788169826 0.99892270605915 231 0.00116408999436304 0.00232817998872608 0.998835910005637 232 0.00414260836127780 0.00828521672255561 0.995857391638722 233 0.0168064379097609 0.0336128758195218 0.98319356209024 234 0.0262600131386674 0.0525200262773348 0.973739986861333 235 0.0184974150143350 0.0369948300286699 0.981502584985665 236 0.0145560950697861 0.0291121901395721 0.985443904930214 237 0.157747989249828 0.315495978499657 0.842252010750172 238 0.121209603936203 0.242419207872405 0.878790396063797 239 0.0986267129874016 0.197253425974803 0.901373287012598 240 0.0746453672494463 0.149290734498893 0.925354632750554 241 0.0642555539517069 0.128511107903414 0.935744446048293 242 0.101261067950775 0.20252213590155 0.898738932049225 243 0.0810638081600756 0.162127616320151 0.918936191839924 244 0.0791917948558791 0.158383589711758 0.92080820514412 245 0.0655218651527268 0.131043730305454 0.934478134847273 246 0.0536671159515352 0.107334231903070 0.946332884048465 247 0.0324486793959173 0.0648973587918347 0.967551320604083 248 0.0262310874902121 0.0524621749804241 0.973768912509788 249 0.0174048056452720 0.0348096112905441 0.982595194354728 250 0.04472468595569 0.08944937191138 0.95527531404431 251 0.0299664327414376 0.0599328654828753 0.970033567258562 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.0669456066945607 NOK 5% type I error level 102 0.426778242677824 NOK 10% type I error level 133 0.556485355648536 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/10dtn01289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/10dtn01289986122.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/1pa8p1289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/1pa8p1289986122.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/2zk7s1289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/2zk7s1289986122.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/3zk7s1289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/3zk7s1289986122.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/4sbpd1289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/4sbpd1289986122.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/5sbpd1289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/5sbpd1289986122.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/6sbpd1289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/6sbpd1289986122.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/73k6g1289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/73k6g1289986122.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/8dtn01289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/8dtn01289986122.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/9dtn01289986122.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/17/t1289986052iuu6wbiswsl22l0/9dtn01289986122.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal 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