Summary statistics for person dataset : by Jean Roth , jroth@nber.org , 18 Apr 2016 Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- state | 102,102 26.8757 16.09664 1 56 county | 102,102 87.06235 89.75961 1 840 month | 102,102 6.791317 3.33325 1 12 day | 102,102 15.53341 8.879653 1 99 hour | 102,102 13.60001 8.921802 0 99 -------------+--------------------------------------------------------- minute | 102,102 27.92128 18.04473 0 99 ve_forms | 102,102 1.826634 1.485173 1 32 road_fnc | 102,102 8.239701 8.109782 1 99 harm_ev | 102,102 14.86163 10.54822 1 99 man_coll | 102,102 1.631594 1.796329 0 9 -------------+--------------------------------------------------------- sch_bus | 102,102 .0055729 .0744436 0 1 veh_no | 102,102 1.337564 1.017941 0 32 make | 94,624 26.96291 21.41077 1 99 body_typ | 94,624 17.78314 21.59041 1 99 mod_year | 94,624 87.65093 6.037072 13 99 -------------+--------------------------------------------------------- rollover | 94,624 .3428623 .700877 0 2 tow_veh | 94,624 .088244 .6291884 0 9 spec_use | 94,624 .0464998 .5640581 0 9 emer_use | 94,624 .0017754 .0420989 0 1 impact1 | 94,624 10.41659 13.01751 0 99 -------------+--------------------------------------------------------- impact2 | 94,624 10.57073 13.53485 0 99 impacts | 94,624 1.205085 .6912862 0 9 fire_exp | 94,624 .0271707 .1625815 0 1 wgtcd_tr | 30,839 3.317034 3.099583 1 9 per_no | 102,102 1.781121 1.847478 1 43 -------------+--------------------------------------------------------- n_mot_no | 102,102 .0746606 .2690679 0 4 age | 102,102 36.12775 22.01939 0 99 sex | 102,102 1.456113 1.044287 1 9 per_typ | 102,102 1.691691 1.244814 1 99 seat_pos | 102,102 14.58233 13.65486 0 99 -------------+--------------------------------------------------------- location | 102,102 .8159781 3.474817 0 99 ejection | 102,102 .1963723 .7781802 0 9 extricat | 102,102 .1698008 .9774751 0 9 alc_det | 102,102 8.152455 2.296025 1 9 drinking | 102,102 4.422666 3.979963 0 9 -------------+--------------------------------------------------------- inj_sev | 102,102 2.581634 1.643127 0 9 hospital | 102,102 .7728938 1.382895 0 9 death_mo | 102,102 2.825292 4.48454 0 99 death_da | 102,102 6.412646 9.759465 0 99 death_yr | 102,102 38.90952 46.71823 0 99 -------------+--------------------------------------------------------- death_hr | 102,102 6.936906 15.38028 0 99 death_mn | 102,102 12.60683 21.39565 0 99 lag_hrs | 102,102 622.6971 477.912 -22 999 lag_mins | 102,102 68.03896 40.86291 0 99 p_cf1 | 102,102 .6388905 5.377518 0 99 -------------+--------------------------------------------------------- p_cf2 | 102,102 .3828035 4.695639 0 99 p_cf3 | 102,102 .2436583 4.226425 0 99 work_inj | 102,102 5.028256 3.894749 0 9 st_case | 102,102 269496.7 160821.8 10001 560138 mak_mod | 94,624 27243.99 21538.57 1001 99999 -------------+--------------------------------------------------------- vin_wgt | 66,161 3610.569 2367.875 101 9999 whlbs_sh | 90,689 1680.723 2215.71 90 9999 whlbs_lg | 90,689 953.3041 2483.408 0 9999 mcycl_ds | 6,201 6389.102 4481.672 49 9999 death_tm | 102,102 706.2975 1554.288 0 9999 -------------+--------------------------------------------------------- cert_no | 0 ser_tr | 0 vina_mod | 0 vin_bt | 0 rest_use | 102,102 10.82679 28.34549 0 99 -------------+--------------------------------------------------------- air_bag | 102,102 8.075934 2.573758 0 9 ej_path | 102,102 .983683 2.730417 0 9 alc_res | 102,102 66.19849 42.70778 0 99 drugs | 102,102 6.835361 2.896717 0 9 drug_det | 102,102 7.829553 1.058955 1 8 -------------+--------------------------------------------------------- drugtst1 | 102,102 1.658587 3.236141 0 9 drugres1 | 102,102 178.3946 375.9025 0 999 drugtst2 | 102,102 .7884077 2.513841 0 9 drugres2 | 102,102 89.77933 282.2793 0 999 drugtst3 | 102,102 .7658616 2.500763 0 9 -------------+--------------------------------------------------------- drugres3 | 102,102 85.91399 279.1764 0 999 by Jean Roth , jroth@nber.org , 18 Apr 2016