Summary statistics for person dataset : by Jean Roth , jroth@nber.org , 18 Apr 2016 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- state | 101,100 27.51636 16.01804 1 56 county | 101,100 89.65885 93.75904 0 999 month | 101,100 6.769931 3.366995 1 12 day | 101,100 15.70671 8.904938 1 99 hour | 101,100 13.45572 8.738592 0 99 -------------+--------------------------------------------------------- minute | 101,100 28.22726 18.0865 0 99 ve_forms | 101,100 1.884965 1.878889 1 42 road_fnc | 101,100 7.773165 6.783731 1 99 harm_ev | 101,100 14.7933 10.47405 1 99 man_coll | 101,100 1.652987 1.800917 0 9 -------------+--------------------------------------------------------- sch_bus | 101,100 .0049555 .0702209 0 1 veh_no | 101,100 1.375905 1.247168 0 42 make | 94,241 27.46603 22.0697 1 99 body_typ | 94,241 18.55295 21.67334 1 99 rollover | 94,241 .3508558 .7018496 0 2 -------------+--------------------------------------------------------- tow_veh | 94,241 .0730043 .4702721 0 9 spec_use | 94,241 .0277798 .4279506 0 9 emer_use | 94,241 .0017614 .0419328 0 1 impact1 | 94,241 10.75771 14.10852 0 99 impact2 | 94,241 10.93038 14.58289 0 99 -------------+--------------------------------------------------------- impacts | 94,241 1.215946 .7285871 0 9 fire_exp | 94,241 .0276631 .1640066 0 1 wgtcd_tr | 45,082 3.12739 3.080565 1 9 per_no | 101,100 1.774975 2.062028 1 61 n_mot_no | 101,100 .0927893 1.550613 0 99 -------------+--------------------------------------------------------- age | 101,100 36.79177 22.08834 0 99 sex | 101,100 1.451731 .9963357 1 9 per_typ | 101,100 1.662868 1.103131 1 19 seat_pos | 101,100 14.50045 13.13688 0 99 location | 101,100 .7409298 3.305364 0 99 -------------+--------------------------------------------------------- ejection | 101,100 .1874777 .7473071 0 9 extricat | 101,100 .2120079 1.086501 0 9 alc_det | 101,100 8.17094 2.201763 1 9 drinking | 101,100 4.344965 4.00402 0 9 inj_sev | 101,100 2.56456 1.657881 0 9 -------------+--------------------------------------------------------- hospital | 101,100 .7343917 1.285644 0 9 death_mo | 101,100 2.977428 5.968558 0 99 death_da | 101,100 6.673373 10.70002 0 99 death_hr | 101,100 7.316914 16.46873 0 99 death_mn | 101,100 13.29236 22.37322 0 99 -------------+--------------------------------------------------------- lag_hrs | 101,100 626.4865 476.9906 0 999 lag_mins | 101,100 68.34837 40.79281 0 99 p_cf1 | 101,100 .5263304 4.828286 0 99 p_cf2 | 101,100 .3477943 4.433403 0 99 p_cf3 | 101,100 .2001385 3.843613 0 99 -------------+--------------------------------------------------------- work_inj | 101,100 5.064263 3.888715 0 9 atst_typ | 101,100 .9960336 2.245123 0 9 st_case | 101,100 275879.2 160117.2 10001 560129 mak_mod | 94,241 27739.42 22202.44 1001 99999 vin_wgt | 54,841 3715.08 2595.342 94 9999 -------------+--------------------------------------------------------- death_yr | 101,100 823.0972 998.1731 0 9999 whlbs_sh | 91,690 1999.832 2679.131 90 9999 whlbs_lg | 91,690 1272.11 2980.105 0 9999 mcycl_ds | 8,518 7315.978 4154.859 49 9999 death_tm | 101,100 744.9838 1664.191 0 9999 -------------+--------------------------------------------------------- mod_year | 94,241 2083.321 857.8773 1926 9999 cert_no | 0 ser_tr | 0 vina_mod | 0 vin_bt | 0 -------------+--------------------------------------------------------- rest_use | 101,100 10.70546 28.12209 0 99 air_bag | 101,100 35.24331 28.79051 0 99 ej_path | 101,100 .951276 2.689451 0 9 alc_res | 101,100 67.28718 42.43583 0 99 drugs | 101,100 6.451058 3.222183 0 9 -------------+--------------------------------------------------------- drug_det | 101,100 7.778813 1.160362 1 8 drugtst1 | 101,100 1.526113 3.106376 0 9 drugres1 | 101,100 171.3748 369.2235 0 999 drugtst2 | 101,100 .5080119 2.037921 0 9 drugres2 | 101,100 58.76965 230.6936 0 999 -------------+--------------------------------------------------------- drugtst3 | 101,100 .48182 2.011332 0 9 drugres3 | 101,100 54.63132 225.9252 0 999 by Jean Roth , jroth@nber.org , 18 Apr 2016