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.
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
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
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  7.09853081 0.588367569
-12.82864915 0.184667098
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  0.37379146 0.557720134
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  0.71574466 0.564570338
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  2.52620466 0.306875011
  5.37718673 0.366807049
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  6.04848363 0.644501799
  4.57402403 0.121419591
  8.55606848 0.373011464
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  1.55798880 0.822587656
  5.22810831 0.333747878
  9.50280477 0.068100934
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  1.36045068 0.851827791
  4.41604088 0.197207162
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 -5.36123334 0.906513529
  3.61594583 0.414340595
-10.01952852 0.140372658
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-18.00437582 0.461438059
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  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
 17.02906651 0.996849502
-10.18073139 0.012543080
 12.72088788 0.910600764
 10.45328185 0.331285901
  7.14370922 0.896312020
 -2.81754334 0.048741266
  6.40217095 0.075796756
 -3.18030478 0.666325307
  8.64585957 0.120549153
  1.37952764 0.899991932
-11.81143886 0.601949630
  0.03899706 0.363808260
-10.63828243 0.031092967
 -6.66940972 0.246204205
 -5.07374962 0.951272057
  4.82281566 0.063928187
-21.93693564 0.050972680
 -4.54569883 0.225839693
 -0.92422779 0.437796785
 -1.11683029 0.740215139
 16.77765554 0.851072372
  9.73614597 0.388180586
 14.05345168 0.063760129
  1.20512012 0.665964184
  8.00307080 0.102447114
  8.01252623 0.580929209
-13.54924183 0.438420739
  9.87164361 0.970859344
 17.63437095 0.250501797
 -3.42503574 0.873290220
 -2.45873197 0.847756049
 17.29212092 0.411683187
  1.15496098 0.530658504
 -2.14438907 0.592255367
 -1.79942021 0.517773009
 -1.30677990 0.830860762
  1.70233874 0.291826660
 -3.05532536 0.801767829
 -4.06732625 0.092294501
  6.34665476 0.270426235
  9.46946411 0.196915311
 14.50919907 0.480357167
  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
  9.76114237 0.769375983
-10.25662038 0.508137553
  0.11155446 0.468504431
 -8.06824580 0.414098962
  3.10031660 0.327130207
 -3.33393146 0.756896774
 -3.96276749 0.530956360
 14.53610268 0.846474699
  1.70505918 0.754662464
 -1.93495001 0.656650411
  5.01974522 0.745337633
 13.41249973 0.489362476
 11.49288744 0.335924476
 12.59019763 0.155560469
-10.17947298 0.677318449
  0.05556115 0.655090105
  3.82092860 0.051838719
  8.23041456 0.918272190
 -0.50314649 0.772015826
 20.05162157 0.880265258
  8.98816884 0.666646668
 -6.28312120 0.138534416
  3.68589909 0.274559458
  0.59699510 0.253180863
 -2.74783135 0.983525221
  0.32515065 0.839969577
 -3.60606166 0.330646732
 -0.82037740 0.129591173
  6.12444860 0.098536516
 10.95671074 0.033546728
 -2.84911174 0.720288722
  6.04597572 0.577061422
 -0.60147150 0.674096868
 -5.30458364 0.291468008
  2.68044943 0.379853840
  0.85986585 0.984214339
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  7.21420144 0.550884826
  2.31817022 0.231021556
 11.60161950 0.888496654
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 12.39893259 0.674693704
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 17.30761707 0.380542812
  5.72738968 0.105447516
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-15.95683040 0.618526462
  8.76889573 0.950243069
 -1.13482624 0.113477080
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  4.95508365 0.612511543
  5.36934491 0.979213258
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-11.58131144 0.183341373
  4.46809041 0.796330582
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  0.49990913 0.732519306
 12.82688360 0.109400213
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  5.43148331 0.670904647
  7.14649727 0.028190029
  0.25410683 0.421535783
-12.41047826 0.086404379
-10.64180909 0.229659236
 -6.40185653 0.876365242
 15.63063324 0.667672536
  1.94280423 0.799266628
 -5.76507450 0.367344192
  8.60895533 0.154109357
  9.38306751 0.788742770
  3.43573528 0.284535277
  4.81848966 0.872283177
 11.65839314 0.234109111
 -5.57884822 0.030363060
 -3.94238060 0.325320686
  9.38133340 0.201141788
 -7.65003459 0.647734396
 11.23091019 0.084927159
 -6.07705432 0.037273791
  7.46380750 0.506897136
  7.42034855 0.869351148
 -4.43031973 0.231191152
 -1.07351537 0.480234836
 -1.40653281 0.690620421
 -3.82710168 0.990191328
  5.04583490 0.543427375
-11.54265099 0.270542185
  0.49059479 0.991447248
 -1.40871469 0.555998766
  3.64241437 0.743840673
-18.30031589 0.357478210
  4.27487959 0.770619738
  1.28805821 0.654787106
 -3.19542768 0.218110139
 12.53375654 0.011857644
 11.78889419 0.054127726
 -5.38392310 0.839309080
 16.38024181 0.228801038
 -0.59622631 0.134381782
 -0.74107258 0.258146632
-12.31429450 0.020524447
 -0.79785028 0.968028764
  6.39899711 0.038162566
  7.42024044 0.716163692
 -3.62470664 0.018201813
 -2.55049724 0.162446610
-10.79888854 0.683070478
 10.18490144 0.546461234
 -2.76979044 0.198830067
  4.85164813 0.094100357
  0.96477200 0.381801756
  8.13344336 0.639730450
  9.04684412 0.786084368
 10.41746272 0.828304181
  0.94334368 0.798419831
 10.13116556 0.191715972
 -4.12728628 0.575178239
 -9.59222379 0.876405375
  1.64680258 0.391003085
 -4.58897613 0.039176486
  0.38394379 0.511577564
 -4.80428215 0.222785463
  0.35363661 0.681658725
 -9.63685708 0.183035382
  3.54363414 0.766127414
  6.89610808 0.967514568
 -2.03781105 0.464416752
  8.67956196 0.421424078
 -1.09959038 0.061231448
  7.12587456 0.028601318
 -6.93064672 0.402561175
  8.57989199 0.925089270
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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