AT_BANNER([LINEAR REGRESSION]) AT_SETUP([LINEAR REGRESSION - basic]) AT_DATA([regression.sps], [dnl set format = F22.3. data list notable list / v0 to v2. filter by v0. begin data 0.65377128 7.735648 -23.97588 -0.13087553 6.142625 -19.63854 0.34880368 7.651430 -25.26557 0.69249021 6.125125 -16.57090 -0.07368178 8.245789 -25.80001 -0.34404919 6.031540 -17.56743 0.75981559 9.832291 -28.35977 -0.46958313 5.343832 -16.79548 -0.06108490 8.838262 -29.25689 0.56154863 6.200189 -18.58219 end data regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 /method=enter /save=pred resid. list. ]) AT_CHECK([pspp -O format=csv regression.sps], [0], [dnl regression.sps:16: warning: REGRESSION: REGRESSION with SAVE ignores FILTER. All cases will be processed. Table: Model Summary (v2) ,R,R Square,Adjusted R Square,Std. Error of the Estimate ,.971,.942,.925,1.337 Table: ANOVA (v2) ,,Sum of Squares,df,Mean Square,F,Sig. ,Regression,202.753,2,101.376,56.754,.000 ,Residual,12.504,7,1.786,, ,Total,215.256,9,,, Table: Coefficients (v2) ,,Unstandardized Coefficients,,Standardized Coefficients,, ,,B,Std. Error,Beta,t,Sig. ,(Constant),2.191,2.357,.000,.930,.380 ,v0,1.813,1.053,.171,1.722,.129 ,v1,-3.427,.332,-1.026,-10.334,.000 Table: Data List v0,v1,v2,RES1,PRED1 .654,7.736,-23.976,-.84,-23.13 -.131,6.143,-19.639,-.54,-19.10 .349,7.651,-25.266,-1.87,-23.40 .692,6.125,-16.571,.97,-17.54 -.074,8.246,-25.800,.40,-26.20 -.344,6.032,-17.567,1.53,-19.10 .760,9.832,-28.360,1.77,-30.13 -.470,5.344,-16.795,.18,-16.97 -.061,8.838,-29.257,-1.05,-28.21 .562,6.200,-18.582,-.54,-18.04 ]) AT_CLEANUP AT_SETUP([LINEAR REGRESSION - one save]) AT_DATA([regression.sps], [dnl set format = F22.3. data list notable list / v0 to v2. begin data 0.65377128 7.735648 -23.97588 -0.13087553 6.142625 -19.63854 0.34880368 7.651430 -25.26557 0.69249021 6.125125 -16.57090 -0.07368178 8.245789 -25.80001 -0.34404919 6.031540 -17.56743 0.75981559 9.832291 -28.35977 -0.46958313 5.343832 -16.79548 -0.06108490 8.838262 -29.25689 0.56154863 6.200189 -18.58219 end data regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 /method=enter /save=resid. regression /variables=v0 v1 v2 /statistics defaults /dependent=v2 /method=enter /save=pred. list. ]) AT_CHECK([pspp -O format=csv regression.sps], [0], [dnl Table: Model Summary (v2) ,R,R Square,Adjusted R Square,Std. Error of the Estimate ,.971,.942,.925,1.337 Table: ANOVA (v2) ,,Sum of Squares,df,Mean Square,F,Sig. ,Regression,202.753,2,101.376,56.754,.000 ,Residual,12.504,7,1.786,, ,Total,215.256,9,,, Table: Coefficients (v2) ,,Unstandardized Coefficients,,Standardized Coefficients,, ,,B,Std. Error,Beta,t,Sig. ,(Constant),2.191,2.357,.000,.930,.380 ,v0,1.813,1.053,.171,1.722,.129 ,v1,-3.427,.332,-1.026,-10.334,.000 Table: Model Summary (v2) ,R,R Square,Adjusted R Square,Std. Error of the Estimate ,.971,.942,.925,1.337 Table: ANOVA (v2) ,,Sum of Squares,df,Mean Square,F,Sig. ,Regression,202.753,2,101.376,56.754,.000 ,Residual,12.504,7,1.786,, ,Total,215.256,9,,, Table: Coefficients (v2) ,,Unstandardized Coefficients,,Standardized Coefficients,, ,,B,Std. Error,Beta,t,Sig. ,(Constant),2.191,2.357,.000,.930,.380 ,v0,1.813,1.053,.171,1.722,.129 ,v1,-3.427,.332,-1.026,-10.334,.000 Table: Data List v0,v1,v2,RES1,PRED1 .654,7.736,-23.976,-.84,-23.13 -.131,6.143,-19.639,-.54,-19.10 .349,7.651,-25.266,-1.87,-23.40 .692,6.125,-16.571,.97,-17.54 -.074,8.246,-25.800,.40,-26.20 -.344,6.032,-17.567,1.53,-19.10 .760,9.832,-28.360,1.77,-30.13 -.470,5.344,-16.795,.18,-16.97 -.061,8.838,-29.257,-1.05,-28.21 .562,6.200,-18.582,-.54,-18.04 ]) AT_CLEANUP # Test to ensure that the /SAVE subcommand works properly when SPLIT is active AT_SETUP([LINEAR REGRESSION - SAVE vs SPLITS]) # Generate some test data based on a linear model AT_DATA([gen-data.sps], [dnl set seed = 1. input program. loop #c = 1 to 20. compute x0 = rv.normal (0,1). compute x1 = rv.normal (0,2). compute err = rv.normal (0,0.1). compute y = 4 - 2 * x0 + 3 * x1 + err. compute g = (#c > 10). end case. end loop. end file. end input program. print outfile='regdata.txt' /g x0 x1 y err *. execute. ]) AT_CHECK([pspp -O format=csv gen-data.sps], [0], [ignore]) # Use our test data to create a predictor and a residual variable # for G == 0 AT_DATA([regression0.sps], [dnl data list notable file='regdata.txt' list /g x0 x1 y err *. select if (g = 0). regression /variables = x0 x1 /dependent = y /statistics = all /save = pred resid. . print outfile='outdata-g0.txt' /g x0 x1 y err res1 pred1 *. execute. ]) AT_CHECK([pspp -O format=csv regression0.sps], [0], [ignore]) # Use our test data to create a predictor and a residual variable # for G == 1 AT_DATA([regression1.sps], [dnl data list notable file='regdata.txt' list /g x0 x1 y err *. select if (g = 1). regression /variables = x0 x1 /dependent = y /statistics = all /save = pred resid. . print outfile='outdata-g1.txt' /g x0 x1 y err res1 pred1 *. execute. ]) AT_CHECK([pspp -O format=csv regression1.sps], [0], [ignore]) # Use our test data to create a predictor and a residual variable # The data is split on G AT_DATA([regression-split.sps], [dnl data list notable file='regdata.txt' list /g x0 x1 y err *. split file by g. regression /variables = x0 x1 /dependent = y /statistics = all /save = pred resid. . print outfile='outdata-split.txt' /g x0 x1 y err res1 pred1 *. execute. ]) AT_CHECK([pspp -O format=csv regression-split.sps], [0], [ignore]) # The concatenation of G==0 and G==1 should be identical to the SPLIT data AT_CHECK([cat outdata-g0.txt outdata-g1.txt | diff outdata-split.txt - ], [0], []) AT_CLEANUP # Test that the procedure behaves sensibly when presented with # multiple dependent variables AT_SETUP([LINEAR REGRESSION multiple dependent variables]) AT_DATA([regression.sps], [dnl set seed = 2. input program. loop #c = 1 to 200. compute x0 = rv.normal (0, 1). compute x1 = rv.normal (0, 2). compute err = rv.normal (0, 0.8). compute y = 2 - 1.5 * x0 + 8.4 * x1 + err. compute ycopy = y. end case. end loop. end file. end input program. regression /variables = x0 x1 /dependent = y ycopy /statistics = default. ]) AT_CHECK([pspp -O format=csv regression.sps > output], [0], [ignore]) AT_CHECK([head -16 output > first], [0], []) AT_CHECK([tail -16 output > second], [0], []) AT_CHECK([sed -e 's/ycopy/y/g' second | diff first -], [0], []) AT_CLEANUP # Tests the QR decomposition used by the REGRESSION command. AT_SETUP([LINEAR REGRESSION test of QR decomposition]) AT_DATA([regression.sps], [dnl data list list / v0 to v1. begin data -12.84099361 0.873270778 16.64932538 0.371315664 -1.88061907 0.505503722 -6.20952354 0.734698282 0.33272576 0.891224610 -5.54912717 0.052318165 6.11832417 0.448853404 11.78124974 0.470447593 0.75960353 0.565082303 6.06432768 0.149316743 -2.64919436 0.752532411 -10.32250712 0.798263603 2.06355038 0.469129797 -9.71851742 0.927162270 4.65582553 0.250629262 9.54574474 0.847032310 7.35544368 0.197028541 -2.09609740 0.400584261 10.30101161 0.671546480 -5.24501039 0.929962876 1.73412473 0.758161354 -3.12732732 0.569785505 12.66261501 0.630640223 -2.90956805 0.576067804 4.89649177 0.624483995 13.64613114 0.591089881 14.03198397 0.544587572 2.23566810 0.967898139 5.37367760 0.916246929 9.01346888 0.451702743 0.75378683 0.235544137 -3.47470624 0.742668194 -1.02063266 0.860311687 -2.67132813 0.082460702 23.67661680 0.932553932 7.95061359 0.430161125 2.05300558 0.066331375 -2.01332644 0.163705417 20.00663784 0.587292630 3.06099417 0.161411889 -3.46115358 0.216684625 -6.85287183 0.548714855 -4.27923809 0.630997663 -0.94863395 0.880612945 4.47481747 0.359885215 -12.80962955 0.886070341 9.35753086 0.187176558 2.81002235 0.063035095 0.01532424 0.964327101 0.29867732 0.866408063 -2.89035649 0.812135868 4.17352811 0.608884061 18.15502183 0.920568258 -2.92662792 0.550792959 -6.08090449 0.965036595 -1.09135397 0.862548019 7.02816784 0.042277017 -21.20245068 0.430673493 -8.83397584 0.724976162 -0.89055843 0.017934904 7.03871587 0.308829557 3.84286316 0.685105924 4.50280692 0.447635420 11.39207346 0.875177896 10.86673874 0.518530912 7.09853081 0.588367569 -12.82864915 0.184667098 13.74888760 0.610891139 0.37379146 0.557720134 -9.79020267 0.942839981 0.71574466 0.564570338 -17.56040637 0.182061777 2.52620466 0.306875011 5.37718673 0.366807049 -1.83964300 0.465772898 6.04848363 0.644501799 4.57402403 0.121419591 8.55606848 0.373011464 -8.46827907 0.491176571 -1.77989798 0.734722847 -0.68661121 0.540984182 1.55798880 0.822587656 5.22810831 0.333747878 9.50280477 0.068100934 -3.74521465 0.248537644 1.36045068 0.851827791 4.41604088 0.197207162 -3.72568327 0.726916693 -5.36123334 0.906513529 3.61594583 0.414340595 -10.01952852 0.140372658 25.48681482 0.354309660 -3.34529093 0.090075388 -18.00437582 0.461438059 -5.29782460 0.004362856 2.79608522 0.861294398 -1.64076209 0.345775481 6.82802334 0.137933862 -0.45416818 0.404379208 -1.66868582 0.797685201 -10.02820292 0.075876582 5.68232031 0.404815042 8.25113850 0.769173748 -2.83544237 0.076583474 0.87659945 0.092751009 6.60270870 0.530444351 -12.63924989 0.362099960 -6.24451253 0.641993458 3.53339015 0.461991892 -0.74012232 0.437409755 15.37311996 0.974913038 -8.09464797 0.543308711 -9.61320222 0.221564578 0.21843662 0.856512540 -1.56958954 0.610709221 6.44977372 0.200382138 -13.29136274 0.093222309 6.46257214 0.024135196 -3.82727990 0.601335801 0.43081953 0.268230667 19.06654416 0.219972815 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8.93767237 0.778228613 1.90298854 0.903146151 18.50500507 0.598561307 4.45123027 0.555898218 11.37344114 0.616557707 -12.14693218 0.409187285 18.27198688 0.141619222 -5.75939569 0.056989619 -4.05515382 0.369281201 16.69882098 0.946885257 6.39050536 0.679704228 4.04213339 0.662792380 6.89608366 0.419877433 1.56496633 0.358227958 5.16679947 0.095144366 -3.06280456 0.883265975 2.76279175 0.866571973 1.84969249 0.264869828 21.79840498 0.702650979 1.42450528 0.719308635 0.96797046 0.111937435 18.26840323 0.075621738 13.38288377 0.573399086 2.41101500 0.766238677 3.83866337 0.499888953 -1.56577367 0.695244089 -0.90342790 0.671654151 10.83775583 0.026041124 -9.89767935 0.745297991 11.74840150 0.309144074 1.73069359 0.814063985 -5.27966183 0.591005828 3.33030043 0.559401806 1.31427975 0.520950237 -10.04588558 0.507008362 10.41228345 0.425867272 1.71961097 0.595783108 -17.54904427 0.328788939 -2.23545419 0.223377350 -8.68774333 0.980964240 -3.48048220 0.008877675 -3.69635326 0.090236718 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0.799940482 8.95497981 0.984663669 0.93288527 0.277914575 -17.35306978 0.455587022 -3.26914604 0.406757639 8.75871227 0.067059659 1.79914932 0.784879863 -0.67305388 0.006393497 1.66805704 0.039614073 9.03868439 0.601066847 4.29458670 0.015772820 -8.15564320 0.939633197 12.50538902 0.766347628 -0.45547258 0.464314122 -9.47180656 0.640114882 -13.25567198 0.125841930 2.87660101 0.381931128 7.37834152 0.648958712 -0.45874073 0.303139498 4.87941450 0.500090729 4.50344891 0.311329309 -6.14257896 0.061368838 5.98243271 0.873804882 -2.64694079 0.080493398 -17.79727796 0.188420116 -13.52552336 0.798403568 2.29086373 0.518700767 5.21493652 0.788828533 -8.09641615 0.775041349 5.87005782 0.079447757 10.74795720 0.955691540 -8.01115709 0.004508053 -7.53735064 0.054195934 -6.79130165 0.877193354 -1.26419539 0.837772170 8.31082852 0.967509866 21.83090247 0.261529880 11.20453234 0.913858875 7.19128396 0.541942489 -2.93623595 0.860095891 -3.61446403 0.022418065 -6.59997709 0.532998307 3.71486934 0.522669434 18.03420874 0.295064126 -8.75452291 0.390175021 -7.83680812 0.263760724 -1.10263921 0.501819826 2.05633484 0.338684642 5.25636848 0.558667384 -7.33260497 0.457327559 3.86721296 0.612182242 -15.94331373 0.478329365 3.71501899 0.264241588 18.26175822 0.023212661 -5.21093378 0.184378036 -2.44074986 0.297114134 -11.88339919 0.875956945 7.52127093 0.927322099 5.31597834 0.416344968 11.42012314 0.952491078 13.64950955 0.794183413 12.50861255 0.390723282 -3.48142207 0.538708662 14.32910902 0.085221990 5.76196699 0.313860477 2.63751452 0.917424732 2.99975231 0.208662214 7.09852825 0.798246964 -1.95742636 0.352166210 7.80534904 0.386523123 -4.47229047 0.290188493 -4.35535158 0.761527294 1.47083860 0.447897289 3.09504296 0.048513534 3.50446804 0.925072429 12.00487617 0.574499971 10.35171466 0.934193962 -5.63256003 0.968833982 7.15625220 0.467160468 -7.81378393 0.790220187 4.52101003 0.014459969 12.90773453 0.990835752 7.70737851 0.785329264 -3.37196794 0.066025357 -5.12793918 0.347459322 -7.96083724 0.216608294 12.81247279 0.287880308 4.63872463 0.426881173 -3.85439309 0.336532356 4.55633320 0.108001536 -2.40824634 0.135247519 1.65932541 0.005108006 3.26129578 0.093163961 -3.52114597 0.544041275 -9.08479260 0.111212700 7.75150456 0.942726234 7.44829768 0.396996218 14.44430576 0.525470762 -2.13457508 0.207577358 9.74871681 0.537177845 -4.53338693 0.625854028 16.15962824 0.947933141 -0.17711664 0.480940902 -7.21470818 0.006952612 -6.27644212 0.737909602 -0.81648165 0.230003567 -2.10429152 0.209671398 7.69291241 0.987903443 -0.32284504 0.183904658 -0.90833921 0.782169770 10.35542238 0.201758865 10.40788689 0.540802365 10.80011849 0.298263948 7.39943598 0.785716539 -12.71674257 0.154135834 16.67139866 0.116794235 12.47832985 0.998179468 3.24041348 0.653080096 -5.50381593 0.995396942 -10.41952811 0.576472768 4.33514092 0.146434686 4.41294276 0.507165968 -0.14746982 0.698144836 -1.33323051 0.466481571 -7.01201350 0.797150114 6.58669848 0.942287809 7.19444974 0.053569397 -5.66046997 0.435728340 -5.07828702 0.497727572 13.72045272 0.324222944 9.99111984 0.355713969 -4.42363728 0.071790181 -2.34300923 0.618434528 4.98594041 0.605667438 -2.45307721 0.894546647 -3.52276424 0.760086779 -3.69489441 0.960758209 13.04792817 0.511273320 21.61433486 0.236270637 -9.57303968 0.964235539 11.70400744 0.391045695 4.25170422 0.411577090 7.87516537 0.952858161 9.89202673 0.971106687 7.51554467 0.505791978 2.17944879 0.893835908 0.82420351 0.213912155 2.47121932 0.688019842 -14.88503628 0.640950883 8.16032283 0.277742858 -4.65776244 0.129415853 7.48274838 0.074213153 7.70537066 0.476778957 5.88202944 0.351838898 1.95618325 0.106331699 7.22064623 0.511434587 -18.64632081 0.009314188 -6.16794611 0.526204245 2.14042033 0.675800465 -1.89535048 0.916845690 -7.77156605 0.069742819 -6.84078801 0.865082345 8.17539904 0.095895629 10.75170309 0.821383078 14.31498805 0.117893208 -2.82264467 0.809086411 0.11117380 0.400587471 6.43898314 0.333163663 9.48110784 0.465173316 5.39395511 0.695273081 -2.05636570 0.508060862 0.68666117 0.647109494 -6.41880322 0.267530762 -0.12096589 0.986901165 6.46062643 0.588580914 -4.20926136 0.550783675 4.07354300 0.907963701 10.84045143 0.900920521 2.64504664 0.767700269 10.34578229 0.197810342 -0.19222273 0.281932395 3.47400952 0.977555902 11.04549389 0.694010579 6.79729856 0.056652433 9.28300628 0.598930531 -3.53453282 0.183412212 8.04028248 0.250746943 5.75964045 0.424692336 -4.98252741 0.867446071 12.00352175 0.289615080 7.53497791 0.350915526 2.54579776 0.655113837 -9.29572208 0.900136667 6.41659249 0.100570650 7.37095646 0.907179211 -12.78417775 0.262214556 0.87962107 0.624657444 -5.96939907 0.296725805 -2.56857339 0.604065931 4.27131811 0.962952479 21.72603838 0.442485270 6.10056565 0.418383130 10.48099521 0.333593221 19.28363092 0.382408442 2.12080726 0.601206970 -6.82450704 0.740158518 11.32395692 0.627015570 5.00040701 0.476274658 -11.64750733 0.105099095 5.77442654 0.576560214 0.31340364 0.516479036 -2.09881449 0.146089191 5.12411327 0.368130477 1.70530391 0.621828438 -12.95649749 0.355726301 8.43735652 0.275383759 -15.56161079 0.413160084 5.28942694 0.069125495 5.96040877 0.438716686 -2.59318107 0.571116303 6.95988992 0.650760909 14.00074797 0.623645969 1.66101456 0.558763985 -2.57968349 0.648185379 -5.47584253 0.716901151 6.37222581 0.060563130 2.83664864 0.842419730 1.48926558 0.620280308 0.33471689 0.170312461 5.21648412 0.317639631 0.51733642 0.843867329 9.86005834 0.306036746 -5.81145791 0.975655452 -5.43219061 0.303385368 5.87157118 0.677369776 2.08889926 0.310200439 -2.53433085 0.194730908 7.01359575 0.674259533 -2.00936260 0.682056466 -2.98240739 0.787899917 -7.43289210 0.357483044 -12.58905988 0.981387385 5.78095517 0.533526274 -1.23065889 0.687266774 -6.82309960 0.293249774 8.47000829 0.842056399 -5.81624772 0.303700280 -14.83571031 0.311387926 4.66808472 0.091222946 -2.90144463 0.438301785 10.62458662 0.828335698 7.88002491 0.990156110 10.27680283 0.251087079 -9.42498970 0.292462244 6.73027640 0.213065205 1.28169895 0.353152789 -14.29203733 0.264563048 20.35772711 0.265208837 3.55095071 0.242905653 -17.97067670 0.373951756 10.53141139 0.247520698 0.05293205 0.579940423 12.79674707 0.288031751 -5.44235185 0.075899079 14.29464811 0.960707538 -1.36753291 0.124265178 -4.25946974 0.521720352 -12.46519252 0.385503339 -6.65343143 0.540942219 5.55949184 0.143194404 -1.20480594 0.515905644 -4.13839908 0.164461445 -2.21345425 0.812969725 3.94223380 0.229238952 -10.78661097 0.395049514 3.06997341 0.791234255 24.82205477 0.110859039 6.28791249 0.867125744 -2.80296119 0.703583849 13.24274039 0.425951975 -0.19577471 0.361568727 -2.34894781 0.954814545 19.76339577 0.635462177 -1.87591480 0.149121567 -7.70962391 0.711708342 -2.46291902 0.390902746 end data regression /variables=v0 v1 /statistics defaults /dependent=v0 /method=enter. ]) AT_CHECK([pspp -O format=csv regression.sps], [0], [dnl Table: Reading free-form data from INLINE. Variable,Format v0,F8.0 v1,F8.0 Table: Model Summary (v0) ,R,R Square,Adjusted R Square,Std. Error of the Estimate ,.05,.00,.00,8.11 Table: ANOVA (v0) ,,Sum of Squares,df,Mean Square,F,Sig. ,Regression,235.23,1,235.23,3.58,.059 ,Residual,98438.40,1498,65.71,, ,Total,98673.63,1499,,, Table: Coefficients (v0) ,,Unstandardized Coefficients,,Standardized Coefficients,, ,,B,Std. Error,Beta,t,Sig. ,(Constant),1.24,.42,.00,2.95,.003 ,v1,1.37,.72,.05,1.89,.059 ]) AT_CLEANUP AT_SETUP([LINEAR REGRESSION no crash on all missing]) AT_DATA([regcrash.sps], [dnl data list list /x * y. begin data. . . . . . . . . . . . . . . . . . . . . end data. regression /variables=x y /dependent=y. ]) AT_CHECK([pspp -o pspp.csv regcrash.sps], [1], [ignore], [ignore]) AT_CLEANUP AT_SETUP([LINEAR REGRESSION missing dependent variable]) dnl Test for a bug where missing values in the dependent variable were not being dnl ignored like they should have been. AT_DATA([reg-mdv-ref.sps], [dnl data list notable list / v0 to v2. begin data 0.65377128 7.735648 -23.97588 -0.13087553 6.142625 -19.63854 0.34880368 7.651430 -25.26557 0.69249021 6.125125 -16.57090 -0.07368178 8.245789 -25.80001 -0.34404919 6.031540 -17.56743 0.75981559 9.832291 -28.35977 -0.46958313 5.343832 -16.79548 -0.06108490 8.838262 -29.25689 0.56154863 6.200189 -18.58219 end data regression /variables=v0 v1 /statistics defaults /dependent=v2 /method=enter. ]) AT_CHECK([pspp -o pspp-ref.csv reg-mdv-ref.sps]) AT_DATA([reg-mdv.sps], [dnl data list notable list / v0 to v2. begin data 0.65377128 7.735648 -23.97588 -0.13087553 6.142625 -19.63854 0.34880368 7.651430 -25.26557 0.69249021 6.125125 -16.57090 -0.07368178 8.245789 -25.80001 -0.34404919 6.031540 -17.56743 0.75981559 9.832291 -28.35977 -0.46958313 5.343832 -16.79548 -0.06108490 8.838262 -29.25689 0.56154863 6.200189 -18.58219 0.5 8 9 end data missing values v2 (9). regression /variables=v0 v1 /statistics defaults /dependent=v2 /method=enter. ]) AT_CHECK([pspp -o pspp.csv reg-mdv.sps]) AT_CHECK([diff pspp.csv pspp-ref.csv]) AT_CLEANUP AT_SETUP([LINEAR REGRESSION with invalid syntax (and empty dataset)]) AT_DATA([ss.sps], [dnl data list notable list / v0 to v2. begin data end data. regression /variables=v0 v1 /statistics r coeff anova /dependent=v2 /method=enter v2. ]) AT_CHECK([pspp ss.sps], [1], [ignore]) AT_CLEANUP dnl The following example comes from dnl http://www.ats.ucla.edu/stat/spss/output/reg_spss%28long%29.htm AT_SETUP([LINEAR REGRESSION coefficient confidence interval]) AT_DATA([conf.sps], [dnl set format = F22.3. data list notable list /math female socst read science * begin data. 41.00 .00 57.00 57.00 47.00 53.00 1.00 61.00 68.00 63.00 54.00 .00 31.00 44.00 58.00 47.00 .00 56.00 63.00 53.00 57.00 .00 61.00 47.00 53.00 51.00 .00 61.00 44.00 63.00 42.00 .00 61.00 50.00 53.00 45.00 .00 36.00 34.00 39.00 54.00 .00 51.00 63.00 58.00 52.00 .00 51.00 57.00 50.00 51.00 .00 61.00 60.00 53.00 51.00 .00 61.00 57.00 63.00 71.00 .00 71.00 73.00 61.00 57.00 .00 46.00 54.00 55.00 50.00 .00 56.00 45.00 31.00 43.00 .00 56.00 42.00 50.00 51.00 .00 56.00 47.00 50.00 60.00 .00 56.00 57.00 58.00 62.00 .00 61.00 68.00 55.00 57.00 .00 46.00 55.00 53.00 35.00 .00 41.00 63.00 66.00 75.00 .00 66.00 63.00 72.00 45.00 .00 56.00 50.00 55.00 57.00 .00 61.00 60.00 61.00 45.00 .00 46.00 37.00 39.00 46.00 .00 31.00 34.00 39.00 66.00 .00 66.00 65.00 61.00 57.00 .00 46.00 47.00 58.00 49.00 .00 46.00 44.00 39.00 49.00 .00 41.00 52.00 55.00 57.00 .00 51.00 42.00 47.00 64.00 .00 61.00 76.00 64.00 63.00 .00 71.00 65.00 66.00 57.00 .00 31.00 42.00 72.00 50.00 .00 61.00 52.00 61.00 58.00 .00 66.00 60.00 61.00 75.00 .00 66.00 68.00 66.00 68.00 .00 66.00 65.00 66.00 44.00 .00 36.00 47.00 36.00 40.00 .00 51.00 39.00 39.00 41.00 .00 51.00 47.00 42.00 62.00 .00 51.00 55.00 58.00 57.00 .00 51.00 52.00 55.00 43.00 .00 41.00 42.00 50.00 48.00 .00 66.00 65.00 63.00 63.00 .00 46.00 55.00 69.00 39.00 .00 47.00 50.00 49.00 70.00 .00 51.00 65.00 63.00 63.00 .00 46.00 47.00 53.00 59.00 .00 51.00 57.00 47.00 61.00 .00 56.00 53.00 57.00 38.00 .00 41.00 39.00 47.00 61.00 .00 46.00 44.00 50.00 49.00 .00 71.00 63.00 55.00 73.00 .00 66.00 73.00 69.00 44.00 .00 42.00 39.00 26.00 42.00 .00 32.00 37.00 33.00 39.00 .00 46.00 42.00 56.00 55.00 .00 41.00 63.00 58.00 52.00 .00 51.00 48.00 44.00 45.00 .00 61.00 50.00 58.00 61.00 .00 66.00 47.00 69.00 39.00 .00 46.00 44.00 34.00 41.00 .00 36.00 34.00 36.00 50.00 .00 61.00 50.00 36.00 40.00 .00 26.00 44.00 50.00 60.00 .00 66.00 60.00 55.00 47.00 .00 26.00 47.00 42.00 59.00 .00 44.00 63.00 65.00 49.00 .00 36.00 50.00 44.00 46.00 .00 51.00 44.00 39.00 58.00 .00 61.00 60.00 58.00 71.00 .00 66.00 73.00 63.00 58.00 .00 66.00 68.00 74.00 46.00 .00 51.00 55.00 58.00 43.00 .00 31.00 47.00 45.00 54.00 .00 61.00 55.00 49.00 56.00 .00 66.00 68.00 63.00 46.00 .00 46.00 31.00 39.00 54.00 .00 56.00 47.00 42.00 57.00 .00 56.00 63.00 55.00 54.00 .00 36.00 36.00 61.00 71.00 .00 56.00 68.00 66.00 48.00 .00 56.00 63.00 63.00 40.00 .00 41.00 55.00 44.00 64.00 .00 66.00 55.00 63.00 51.00 .00 56.00 52.00 53.00 39.00 .00 56.00 34.00 42.00 40.00 .00 31.00 50.00 34.00 61.00 .00 56.00 55.00 61.00 66.00 .00 46.00 52.00 47.00 49.00 .00 46.00 63.00 66.00 65.00 1.00 61.00 68.00 69.00 52.00 1.00 48.00 39.00 44.00 46.00 1.00 51.00 44.00 47.00 61.00 1.00 51.00 50.00 63.00 72.00 1.00 56.00 71.00 66.00 71.00 1.00 71.00 63.00 69.00 40.00 1.00 41.00 34.00 39.00 69.00 1.00 61.00 63.00 61.00 64.00 1.00 66.00 68.00 69.00 56.00 1.00 61.00 47.00 66.00 49.00 1.00 41.00 47.00 33.00 54.00 1.00 51.00 63.00 50.00 53.00 1.00 51.00 52.00 61.00 66.00 1.00 56.00 55.00 42.00 67.00 1.00 56.00 60.00 50.00 40.00 1.00 33.00 35.00 51.00 46.00 1.00 56.00 47.00 50.00 69.00 1.00 71.00 71.00 58.00 40.00 1.00 56.00 57.00 61.00 41.00 1.00 51.00 44.00 39.00 57.00 1.00 66.00 65.00 46.00 58.00 1.00 56.00 68.00 59.00 57.00 1.00 66.00 73.00 55.00 37.00 1.00 41.00 36.00 42.00 55.00 1.00 46.00 43.00 55.00 62.00 1.00 66.00 73.00 58.00 64.00 1.00 56.00 52.00 58.00 40.00 1.00 51.00 41.00 39.00 50.00 1.00 51.00 60.00 50.00 46.00 1.00 56.00 50.00 50.00 53.00 1.00 56.00 50.00 39.00 52.00 1.00 46.00 47.00 48.00 45.00 1.00 46.00 47.00 34.00 56.00 1.00 61.00 55.00 58.00 45.00 1.00 56.00 50.00 44.00 54.00 1.00 41.00 39.00 50.00 56.00 1.00 46.00 50.00 47.00 41.00 1.00 26.00 34.00 29.00 54.00 1.00 56.00 57.00 50.00 72.00 1.00 56.00 57.00 54.00 56.00 1.00 51.00 68.00 50.00 47.00 1.00 46.00 42.00 47.00 49.00 1.00 66.00 61.00 44.00 60.00 1.00 66.00 76.00 67.00 54.00 1.00 46.00 47.00 58.00 55.00 1.00 56.00 46.00 44.00 33.00 1.00 41.00 39.00 42.00 49.00 1.00 61.00 52.00 44.00 43.00 1.00 51.00 28.00 44.00 50.00 1.00 52.00 42.00 50.00 52.00 1.00 51.00 47.00 39.00 48.00 1.00 41.00 47.00 44.00 58.00 1.00 66.00 52.00 53.00 43.00 1.00 61.00 47.00 48.00 41.00 1.00 31.00 50.00 55.00 43.00 1.00 51.00 44.00 44.00 46.00 1.00 41.00 47.00 40.00 44.00 1.00 41.00 45.00 34.00 43.00 1.00 46.00 47.00 42.00 61.00 1.00 56.00 65.00 58.00 40.00 1.00 51.00 43.00 50.00 49.00 1.00 61.00 47.00 53.00 56.00 1.00 66.00 57.00 58.00 61.00 1.00 71.00 68.00 55.00 50.00 1.00 61.00 52.00 54.00 51.00 1.00 61.00 42.00 47.00 42.00 1.00 41.00 42.00 42.00 67.00 1.00 66.00 66.00 61.00 53.00 1.00 61.00 47.00 53.00 50.00 1.00 58.00 57.00 51.00 51.00 1.00 31.00 47.00 63.00 72.00 1.00 61.00 57.00 61.00 48.00 1.00 61.00 52.00 55.00 40.00 1.00 31.00 44.00 40.00 53.00 1.00 61.00 50.00 61.00 39.00 1.00 36.00 39.00 47.00 63.00 1.00 41.00 57.00 55.00 51.00 1.00 37.00 57.00 53.00 45.00 1.00 43.00 42.00 50.00 39.00 1.00 61.00 47.00 47.00 42.00 1.00 39.00 42.00 31.00 62.00 1.00 51.00 60.00 61.00 44.00 1.00 51.00 44.00 35.00 65.00 1.00 66.00 63.00 54.00 63.00 1.00 71.00 65.00 55.00 54.00 1.00 41.00 39.00 53.00 45.00 1.00 36.00 50.00 58.00 60.00 1.00 51.00 52.00 56.00 49.00 1.00 51.00 60.00 50.00 48.00 1.00 51.00 44.00 39.00 57.00 1.00 61.00 52.00 63.00 55.00 1.00 61.00 55.00 50.00 66.00 1.00 56.00 50.00 66.00 64.00 1.00 71.00 65.00 58.00 55.00 1.00 51.00 52.00 53.00 42.00 1.00 36.00 47.00 42.00 56.00 1.00 61.00 63.00 55.00 53.00 1.00 66.00 50.00 53.00 41.00 1.00 41.00 42.00 42.00 42.00 1.00 41.00 36.00 50.00 53.00 1.00 56.00 50.00 55.00 42.00 1.00 51.00 41.00 34.00 60.00 1.00 56.00 47.00 50.00 52.00 1.00 56.00 55.00 42.00 38.00 1.00 46.00 42.00 36.00 57.00 1.00 52.00 57.00 55.00 58.00 1.00 61.00 55.00 58.00 65.00 1.00 61.00 63.00 53.00 end data. regression /variables = math female socst read /statistics = coeff r anova ci (95) /dependent = science /method = enter ]) AT_CHECK([pspp -O format=csv conf.sps], [0], [dnl Table: Model Summary (science) ,R,R Square,Adjusted R Square,Std. Error of the Estimate ,.699,.489,.479,7.148 Table: ANOVA (science) ,,Sum of Squares,df,Mean Square,F,Sig. ,Regression,9543.721,4,2385.930,46.695,.000 ,Residual,9963.779,195,51.096,, ,Total,19507.500,199,,, Table: Coefficients (science) ,,Unstandardized Coefficients,,Standardized Coefficients,,,95% Confidence Interval for B, ,,B,Std. Error,Beta,t,Sig.,Lower Bound,Upper Bound ,(Constant),12.325,3.194,.000,3.859,.000,6.027,18.624 ,math,.389,.074,.368,5.252,.000,.243,.535 ,female,-2.010,1.023,-.101,-1.965,.051,-4.027,.007 ,socst,.050,.062,.054,.801,.424,-.073,.173 ,read,.335,.073,.347,4.607,.000,.192,.479 ]) AT_CLEANUP