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diff --git a/Legalization and Drug Deaths/data/ORIGINAL_drug_abuse_data.csv b/Legalization and Drug Deaths/data/ORIGINAL_drug_abuse_data.csv
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+++ /dev/null
@@ -1,919 +0,0 @@
-state,state_code,year,drug_deaths,population,labor_force,employment,unemployment,unemployment_rate,cpi_all_urban_consumers,gdp_per_capita
-Alabama,1,1999,195,4430141,2140296,2038889,101407,4.7,166.6,33106
-Alabama,1,2000,232,4447100,2133223,2035594,97629,4.6,172.2,33106
-Alabama,1,2001,253,4467634,2115401,2006884,108517,5.1,177.1,33312
-Alabama,1,2002,248,4480089,2106161,1981919,124242,5.9,179.9,34068
-Alabama,1,2003,255,4503491,2120225,1992732,127493,6.0,184.0,34855
-Alabama,1,2004,327,4530729,2136458,2014889,121569,5.7,188.9,36702
-Alabama,1,2005,332,4569805,2146025,2049791,96234,4.5,195.3,37578
-Alabama,1,2006,466,4628981,2167809,2080233,87576,4.0,201.6,37735
-Alabama,1,2007,554,4672840,2175612,2089127,86485,4.0,207.342,37522
-Alabama,1,2008,646,4718206,2176489,2053477,123012,5.7,215.303,36976
-Alabama,1,2009,688,4757938,2162999,1924747,238252,11.0,214.537,35376
-Alabama,1,2010,585,4779736,2196042,1964559,231483,10.5,218.056,35913
-Alabama,1,2011,605,4802740,2202670,1990413,212257,9.6,224.939,36201
-Alabama,1,2012,632,4822023,2176337,2003290,173047,8.0,229.594,36425
-Alabama,1,2013,648,4833722,2174000,2017043,156957,7.2,232.957,36660
-Alabama,1,2014,800,4849377,2161618,2015087,146531,6.8,236.736,36455
-Alabama,1,2015,810,4858979,2157376,2025949,131427,6.1,237.017,36911
-Alabama,1,2016,855,4863300,2173175,2045624,127551,5.9,240.007,37402
-Alaska,2,1999,56,624779,317943,297354,20589,6.5,166.6,58609
-Alaska,2,2000,54,626932,319511,299146,20365,6.4,172.2,57184
-Alaska,2,2001,72,633714,321393,300731,20662,6.4,177.1,58547
-Alaska,2,2002,85,642337,326401,302580,23821,7.3,179.9,60101
-Alaska,2,2003,86,648414,331669,305723,25946,7.8,184.0,59008
-Alaska,2,2004,92,659286,336743,311643,25100,7.5,188.9,60137
-Alaska,2,2005,85,666946,344559,320811,23748,6.9,195.3,61125
-Alaska,2,2006,85,675302,349691,326542,23149,6.6,201.6,64349
-Alaska,2,2007,75,680300,350785,328579,22206,6.3,207.342,67228
-Alaska,2,2008,132,687455,356109,332285,23824,6.7,215.303,67527
-Alaska,2,2009,133,698895,359647,331792,27855,7.7,214.537,72204
-Alaska,2,2010,84,710231,361913,333416,28497,7.9,218.056,69564
-Alaska,2,2011,107,722718,365913,338161,27752,7.6,224.939,70573
-Alaska,2,2012,132,731449,365519,339474,26045,7.1,229.594,73478
-Alaska,2,2013,110,735132,365292,339801,25491,7.0,232.957,69700
-Alaska,2,2014,127,736732,365704,340585,25119,6.9,236.736,67188
-Alaska,2,2015,127,738432,363872,340132,23740,6.5,237.017,67302
-Alaska,2,2016,138,741894,363047,337947,25100,6.9,240.007,63317
-Arizona,4,1999,557,5023823,2473013,2363084,109929,4.4,166.6,38300
-Arizona,4,2000,559,5130632,2509883,2410581,99302,4.0,172.2,39185
-Arizona,4,2001,598,5273477,2585284,2461498,123786,4.8,177.1,39170
-Arizona,4,2002,667,5396255,2679421,2516006,163415,6.1,179.9,39323
-Arizona,4,2003,739,5510364,2736000,2578854,157146,5.7,184.0,40769
-Arizona,4,2004,803,5652404,2796269,2655428,140841,5.0,188.9,41063
-Arizona,4,2005,849,5839077,2883226,2748372,134854,4.7,195.3,43018
-Arizona,4,2006,959,6029141,2990441,2864385,126056,4.2,201.6,44086
-Arizona,4,2007,981,6167681,3034016,2917117,116899,3.9,207.342,44168
-Arizona,4,2008,853,6280362,3104863,2913903,190960,6.2,215.303,41800
-Arizona,4,2009,1041,6343154,3128110,2817577,310533,9.9,214.537,38232
-Arizona,4,2010,1141,6392017,3089705,2769454,320251,10.4,218.056,37935
-Arizona,4,2011,1118,6482505,3037017,2748470,288547,9.5,224.939,38257
-Arizona,4,2012,1206,6553255,3028878,2776349,252529,8.3,229.594,38559
-Arizona,4,2013,1304,6626624,3029425,2794697,234728,7.7,232.957,38303
-Arizona,4,2014,1274,6731484,3087942,2878611,209331,6.8,236.736,38450
-Arizona,4,2015,1351,6828065,3153040,2962245,190795,6.1,237.017,38647
-Arizona,4,2016,1500,6931071,3225703,3052788,172915,5.4,240.007,38985
-Arkansas,5,1999,121,2651860,1255032,1197320,57712,4.6,166.6,31877
-Arkansas,5,2000,151,2673400,1258301,1204695,53606,4.3,172.2,31834
-Arkansas,5,2001,131,2691571,1253456,1191273,62183,5.0,177.1,31685
-Arkansas,5,2002,190,2705927,1270997,1201422,69575,5.5,179.9,32514
-Arkansas,5,2003,203,2724816,1271696,1196299,75397,5.9,184.0,33678
-Arkansas,5,2004,249,2749686,1300318,1226123,74195,5.7,188.9,34936
-Arkansas,5,2005,286,2781097,1345913,1275448,70465,5.2,195.3,35649
-Arkansas,5,2006,307,2821761,1365410,1294906,70504,5.2,201.6,35803
-Arkansas,5,2007,326,2848650,1369284,1296572,72712,5.3,207.342,35205
-Arkansas,5,2008,390,2874554,1375257,1300017,75240,5.5,215.303,34957
-Arkansas,5,2009,384,2896843,1358911,1252399,106512,7.8,214.537,33837
-Arkansas,5,2010,374,2915918,1353338,1242496,110842,8.2,218.056,34671
-Arkansas,5,2011,375,2937979,1362682,1249514,113168,8.3,224.939,35146
-Arkansas,5,2012,398,2949131,1342753,1241127,101626,7.6,229.594,34965
-Arkansas,5,2013,343,2959373,1322576,1226859,95717,7.2,232.957,35865
-Arkansas,5,2014,377,2966369,1316595,1237316,79279,6.0,236.736,36274
-Arkansas,5,2015,425,2978204,1332255,1265166,67089,5.0,237.017,36338
-Arkansas,5,2016,423,2988248,1342561,1289549,53012,3.9,240.007,36524
-California,6,1999,3091,33499204,16416600,15555278,861322,5.2,166.6,45541
-California,6,2000,2301,33871648,16867808,16033179,834629,4.9,172.2,48223
-California,6,2001,1507,34479458,17128430,16197671,930759,5.4,177.1,47216
-California,6,2002,3418,34871843,17257097,16108685,1148412,6.7,179.9,47880
-California,6,2003,3614,35253159,17277618,16102840,1174778,6.8,184.0,49501
-California,6,2004,3681,35574576,17383631,16303996,1079635,6.2,188.9,51520
-California,6,2005,3821,35827943,17530064,16582651,947413,5.4,195.3,53320
-California,6,2006,4026,36021202,17654109,16789422,864687,4.9,201.6,54842
-California,6,2007,4178,36250311,17893080,16931590,961490,5.4,207.342,55154
-California,6,2008,4147,36604337,18178123,16854482,1323641,7.3,215.303,54454
-California,6,2009,4290,36961229,18215140,16182572,2032568,11.2,214.537,51733
-California,6,2010,4258,37253956,18336271,16091945,2244326,12.2,218.056,51871
-California,6,2011,4429,37691912,18415100,16258133,2156967,11.7,224.939,52099
-California,6,2012,4304,38041430,18523793,16602672,1921121,10.4,229.594,52974
-California,6,2013,4747,38332521,18624992,16958403,1666589,8.9,232.957,53855
-California,6,2014,4816,38802500,18758399,17351318,1407081,7.5,236.736,55598
-California,6,2015,5025,39144818,18896477,17724799,1171678,6.2,237.017,57596
-California,6,2016,5094,39250017,19093658,18048827,1044831,5.5,240.007,59117
-Colorado,8,1999,375,4226018,2344282,2272197,72085,3.1,166.6,49040
-Colorado,8,2000,372,4301261,2359515,2294408,65107,2.8,172.2,51524
-Colorado,8,2001,444,4425687,2391048,2300113,90935,3.8,177.1,51113
-Colorado,8,2002,474,4490406,2442992,2307685,135307,5.5,179.9,50870
-Colorado,8,2003,529,4528732,2486229,2338207,148022,6.0,184.0,50743
-Colorado,8,2004,548,4575013,2532361,2393559,138802,5.5,188.9,50083
-Colorado,8,2005,640,4631888,2563900,2435082,128818,5.0,195.3,51473
-Colorado,8,2006,660,4720423,2622243,2509729,112514,4.3,201.6,51515
-Colorado,8,2007,747,4803868,2664677,2565218,99459,3.7,207.342,52094
-Colorado,8,2008,760,4889730,2716625,2585243,131382,4.8,215.303,51651
-Colorado,8,2009,784,4972195,2722982,2524443,198539,7.3,214.537,49731
-Colorado,8,2010,676,5029196,2724417,2486404,238013,8.7,218.056,49254
-Colorado,8,2011,852,5116796,2736079,2507265,228814,8.4,224.939,49285
-Colorado,8,2012,826,5187582,2757222,2539941,217281,7.9,229.594,49639
-Colorado,8,2013,864,5268367,2767153,2577556,189597,6.9,232.957,50475
-Colorado,8,2014,917,5355866,2799491,2659474,140017,5.0,236.736,52064
-Colorado,8,2015,893,5456574,2824759,2715059,109700,3.9,237.017,52858
-Colorado,8,2016,973,5540545,2893268,2798928,94340,3.3,240.007,52567
-Connecticut,9,1999,330,3386401,1754565,1704134,50431,2.9,166.6,57724
-Connecticut,9,2000,336,3405565,1764126,1721913,42213,2.4,172.2,61355
-Connecticut,9,2001,326,3432835,1759829,1704774,55055,3.1,177.1,61872
-Connecticut,9,2002,377,3458749,1774152,1697127,77025,4.3,179.9,61233
-Connecticut,9,2003,338,3484336,1784260,1687924,96336,5.4,184.0,61419
-Connecticut,9,2004,360,3496094,1774193,1686213,87980,5,188.9,65034
-Connecticut,9,2005,352,3506956,1796976,1709382,87594,4.9,195.3,66032
-Connecticut,9,2006,458,3517460,1828651,1749191,79460,4.3,201.6,67458
-Connecticut,9,2007,444,3527270,1856209,1773159,83050,4.5,207.342,70096
-Connecticut,9,2008,397,3545579,1881454,1774681,106773,5.7,215.303,68777
-Connecticut,9,2009,397,3561807,1891077,1740971,150106,7.9,214.537,65574
-Connecticut,9,2010,372,3574097,1911712,1737449,174263,9.1,218.056,64906
-Connecticut,9,2011,416,3580709,1914775,1745967,168808,8.8,224.939,63638
-Connecticut,9,2012,451,3590347,1887419,1729838,157581,8.3,229.594,63502
-Connecticut,9,2013,600,3596080,1863337,1717764,145573,7.8,232.957,62550
-Connecticut,9,2014,639,3596677,1892451,1767282,125169,6.6,236.736,62171
-Connecticut,9,2015,827,3590886,1895832,1787434,108398,5.7,237.017,63464
-Connecticut,9,2016,997,3576452,1904556,1806575,97981,5.1,240.007,63636
-Delaware,10,1999,55,774990,398783,385190,13593,3.4,166.6,66794
-Delaware,10,2000,60,783600,413500,398027,15473,3.7,172.2,68992
-Delaware,10,2001,70,795699,421786,407033,14753,3.5,177.1,71155
-Delaware,10,2002,85,806169,416783,400019,16764,4,179.9,67853
-Delaware,10,2003,88,818003,415160,397472,17688,4.3,184.0,67956
-Delaware,10,2004,92,830803,420384,403599,16785,4,188.9,69500
-Delaware,10,2005,67,845150,432409,414465,17944,4.1,195.3,67525
-Delaware,10,2006,83,859268,441184,425399,15785,3.6,201.6,67857
-Delaware,10,2007,102,871749,443573,428312,15261,3.4,207.342,66344
-Delaware,10,2008,125,883874,447041,424914,22127,4.9,215.303,62535
-Delaware,10,2009,139,891730,437500,401312,36188,8.3,214.537,64313
-Delaware,10,2010,147,897934,434419,397869,36550,8.4,218.056,62837
-Delaware,10,2011,166,907135,443346,410086,33260,7.5,224.939,63793
-Delaware,10,2012,150,917092,445572,413457,32115,7.2,229.594,62174
-Delaware,10,2013,170,925749,442728,413119,29609,6.7,232.957,60719
-Delaware,10,2014,204,935614,453312,427386,25926,5.7,236.736,63368
-Delaware,10,2015,208,945934,467110,444404,22706,4.9,237.017,64516
-Delaware,10,2016,288,952065,473881,452434,21447,4.5,240.007,64054
-District of Columbia,11,1999,55,570213,306131,286650,19481,6.4,166.6,138460
-District of Columbia,11,2000,84,572059,310469,293086,17383,5.6,172.2,138628
-District of Columbia,11,2001,111,574504,310515,290811,19704,6.3,177.1,144598
-District of Columbia,11,2002,70,573158,306099,286496,19603,6.4,179.9,148686
-District of Columbia,11,2003,119,568502,306422,285454,20968,6.8,184.0,152623
-District of Columbia,11,2004,109,567754,312027,287683,24344,7.8,188.9,160065
-District of Columbia,11,2005,94,567136,315616,295484,20132,6.4,195.3,163100
-District of Columbia,11,2006,129,570681,316083,297786,18297,5.8,201.6,164366
-District of Columbia,11,2007,90,574404,322237,304426,17811,5.5,207.342,167668
-District of Columbia,11,2008,77,580236,330544,309192,21352,6.5,215.303,170687
-District of Columbia,11,2009,58,592228,335672,304500,31172,9.3,214.537,166178
-District of Columbia,11,2010,90,601723,346065,313508,32557,9.4,218.056,168030
-District of Columbia,11,2011,94,617996,350778,315171,35607,10.2,224.939,166870
-District of Columbia,11,2012,85,632323,364989,331984,33005,9.0,229.594,163274
-District of Columbia,11,2013,113,646449,374126,342351,31775,8.5,232.957,159497
-District of Columbia,11,2014,106,658893,378380,348981,29399,7.8,236.736,159745
-District of Columbia,11,2015,130,672228,388057,361324,26733,6.9,237.017,159881
-District of Columbia,11,2016,276,681170,394586,370521,24065,6.1,240.007,160643
-Florida,12,1999,1061,15759421,7657159,7359460,297699,3.9,166.6,37913
-Florida,12,2000,1237,15982378,7856895,7565981,290914,3.7,172.2,38860
-Florida,12,2001,1833,16356966,8043714,7669117,374597,4.7,177.1,39445
-Florida,12,2002,2041,16689370,8114409,7656349,458060,5.6,179.9,40254
-Florida,12,2003,2153,17004085,8210696,7783148,427548,5.2,184.0,40983
-Florida,12,2004,2408,17415318,8440428,8049908,390520,4.6,188.9,41736
-Florida,12,2005,2664,17842038,8720970,8398974,321996,3.7,195.3,43326
-Florida,12,2006,2928,18166990,9000204,8709522,290682,3.2,201.6,43926
-Florida,12,2007,2936,18367842,9157124,8789770,367354,4.0,207.342,43506
-Florida,12,2008,3097,18527305,9215524,8637164,578360,6.3,215.303,41241
-Florida,12,2009,3193,18652644,9094825,8148123,946702,10.4,214.537,38695
-Florida,12,2010,3181,18801310,9212066,8193659,1018407,11.1,218.056,38604
-Florida,12,2011,3033,19057542,9300542,8371638,928904,10.0,224.939,37876
-Florida,12,2012,2697,19317568,9382993,8588669,794324,8.5,229.594,37705
-Florida,12,2013,2603,19552860,9453747,8770084,683663,7.2,232.957,38023
-Florida,12,2014,2804,19893297,9581083,8979399,601684,6.3,236.736,38477
-Florida,12,2015,3377,20271272,9629632,9104357,525275,5.5,237.017,39289
-Florida,12,2016,4963,20612439,9846000,9373059,472941,4.8,240.007,39506
-Georgia,13,1999,347,8045965,4153499,3990393,163106,3.9,166.6,44677
-Georgia,13,2000,429,8186453,4222253,4071557,150696,3.6,172.2,45447
-Georgia,13,2001,591,8377038,4262041,4089559,172482,4.0,177.1,45181
-Georgia,13,2002,631,8508256,4327691,4110874,216817,5.0,179.9,44771
-Georgia,13,2003,684,8622793,4394437,4182521,211916,4.8,184.0,45453
-Georgia,13,2004,730,8769252,4451800,4239065,212735,4.8,188.9,46599
-Georgia,13,2005,820,8925922,4586420,4341223,245197,5.3,195.3,47610
-Georgia,13,2006,912,9155813,4710775,4489128,221647,4.7,201.6,47013
-Georgia,13,2007,973,9349988,4815818,4597640,218178,4.5,207.342,46374
-Georgia,13,2008,977,9504843,4879253,4575010,304243,6.2,215.303,44239
-Georgia,13,2009,1043,9620846,4787749,4311854,475895,9.9,214.537,42052
-Georgia,13,2010,1124,9687653,4696676,4202052,494624,10.5,218.056,42029
-Georgia,13,2011,1112,9815210,4748754,4263305,485449,10.2,224.939,42140
-Georgia,13,2012,1115,9919945,4787367,4348083,439284,9.2,229.594,42191
-Georgia,13,2013,1160,9992167,4756157,4366374,389783,8.2,232.957,42500
-Georgia,13,2014,1268,10097343,4752639,4416145,336494,7.1,236.736,43465
-Georgia,13,2015,1370,10214860,4788858,4503150,285708,6.0,237.017,44299
-Georgia,13,2016,1461,10310371,4926945,4662849,264096,5.4,240.007,45140
-Hawaii,15,1999,83,1210300,602907,570394,32513,5.4,166.6,43398
-Hawaii,15,2000,64,1211537,605947,580214,25733,4.2,172.2,44260
-Hawaii,15,2001,83,1225948,610630,583187,27443,4.5,177.1,43778
-Hawaii,15,2002,85,1239613,600924,575594,25330,4.2,179.9,44658
-Hawaii,15,2003,102,1251154,603122,578323,24799,4.1,184.0,46437
-Hawaii,15,2004,128,1273569,610912,590156,20756,3.4,188.9,48373
-Hawaii,15,2005,143,1292729,626912,608957,17955,2.9,195.3,50145
-Hawaii,15,2006,131,1309731,638260,621573,16687,2.6,201.6,50631
-Hawaii,15,2007,142,1315675,638395,620535,17860,2.8,207.342,51245
-Hawaii,15,2008,129,1332213,639691,612120,27571,4.3,215.303,50833
-Hawaii,15,2009,148,1346717,631690,586522,45168,7.2,214.537,48549
-Hawaii,15,2010,154,1360301,647249,602282,44967,6.9,218.056,49418
-Hawaii,15,2011,189,1374810,660253,615319,44934,6.8,224.939,49331
-Hawaii,15,2012,159,1392313,647185,608302,38883,6,229.594,49513
-Hawaii,15,2013,167,1404054,651557,619691,31866,4.9,232.957,49539
-Hawaii,15,2014,174,1419561,666674,637661,29013,4.4,236.736,49716
-Hawaii,15,2015,175,1431603,675506,651459,24047,3.6,237.017,50994
-Hawaii,15,2016,207,1428557,684167,664032,20135,2.9,240.007,51819
-Idaho,16,1999,66,1275674,651207,619237,31970,4.9,166.6,31920
-Idaho,16,2000,70,1293953,659824,628844,30980,4.7,172.2,34851
-Idaho,16,2001,99,1319962,677665,643280,34385,5.1,177.1,33390
-Idaho,16,2002,123,1340372,684591,646549,38042,5.6,179.9,33638
-Idaho,16,2003,118,1363380,690005,651335,38670,5.6,184.0,34034
-Idaho,16,2004,113,1391802,703062,668441,34621,4.9,188.9,34665
-Idaho,16,2005,118,1428241,731670,702291,29379,4.0,195.3,35895
-Idaho,16,2006,147,1468669,747377,721346,26031,3.5,201.6,36931
-Idaho,16,2007,133,1505105,754438,731235,23203,3.1,207.342,37080
-Idaho,16,2008,152,1534320,755153,716653,38500,5.1,215.303,36691
-Idaho,16,2009,181,1554439,757131,690722,66409,8.8,214.537,34664
-Idaho,16,2010,184,1567582,761056,692826,68230,9.0,218.056,34620
-Idaho,16,2011,199,1584985,765178,701466,63712,8.3,224.939,34270
-Idaho,16,2012,188,1595728,769256,713704,55552,7.2,229.594,34094
-Idaho,16,2013,217,1612136,770833,723636,47197,6.1,232.957,34748
-Idaho,16,2014,218,1634464,779631,741525,38106,4.9,236.736,35103
-Idaho,16,2015,224,1654930,794675,761009,33666,4.2,237.017,35738
-Idaho,16,2016,260,1683140,812856,782279,30577,3.8,240.007,36056
-Illinois,17,1999,872,12359020,6467988,6186543,281445,4.4,166.6,47665
-Illinois,17,2000,910,12419293,6493466,6211404,282062,4.3,172.2,49145
-Illinois,17,2001,930,12488445,6457922,6113677,344245,5.3,177.1,48849
-Illinois,17,2002,1030,12525556,6349239,5933897,415342,6.5,179.9,48918
-Illinois,17,2003,872,12556006,6300221,5874608,425613,6.8,184.0,49495
-Illinois,17,2004,1080,12589773,6325447,5933127,392320,6.2,188.9,50479
-Illinois,17,2005,1140,12609903,6397808,6033913,363895,5.7,195.3,51237
-Illinois,17,2006,1423,12643955,6526273,6230845,295428,4.5,201.6,52486
-Illinois,17,2007,1239,12695866,6665601,6334010,331591,5.0,207.342,52856
-Illinois,17,2008,1412,12747038,6657227,6238611,418616,6.3,215.303,51406
-Illinois,17,2009,1427,12796778,6618658,5943229,675429,10.2,214.537,49859
-Illinois,17,2010,1344,12830632,6625321,5937047,688274,10.4,218.056,50304
-Illinois,17,2011,1459,12869257,6586893,5948366,638527,9.7,224.939,51198
-Illinois,17,2012,1667,12875255,6581867,5990644,591223,9.0,229.594,52172
-Illinois,17,2013,1607,12882135,6549473,5956749,592724,9.0,232.957,51963
-Illinois,17,2014,1736,12880580,6508356,6047243,461113,7.1,236.736,52966
-Illinois,17,2015,1872,12859995,6507228,6119271,387957,6.0,237.017,53729
-Illinois,17,2016,2455,12801539,6549991,6169560,380431,5.8,240.007,54404
-Indiana,18,1999,251,6044969,3124851,3029834,95017,3.0,166.6,40222
-Indiana,18,2000,289,6080485,3126379,3029073,97306,3.1,172.2,41205
-Indiana,18,2001,348,6127760,3140899,3007507,133392,4.2,177.1,40138
-Indiana,18,2002,345,6155967,3171168,3006811,164357,5.2,179.9,40969
-Indiana,18,2003,498,6196638,3182988,3014655,168333,5.3,184.0,42099
-Indiana,18,2004,606,6233007,3167797,2998068,169729,5.4,188.9,43143
-Indiana,18,2005,665,6278616,3205436,3029959,175477,5.5,195.3,42826
-Indiana,18,2006,773,6332669,3235110,3072113,162997,5.0,201.6,43279
-Indiana,18,2007,827,6379599,3207687,3061042,146645,4.6,207.342,44145
-Indiana,18,2008,869,6424806,3232097,3041828,190269,5.9,215.303,43515
-Indiana,18,2009,929,6459325,3193989,2864985,329004,10.3,214.537,40572
-Indiana,18,2010,964,6483802,3175192,2845608,329584,10.4,218.056,42996
-Indiana,18,2011,1006,6516922,3181991,2891945,290046,9.1,224.939,43058
-Indiana,18,2012,1056,6537334,3169835,2905549,264286,8.3,229.594,43064
-Indiana,18,2013,1122,6570902,3188406,2944275,244131,7.7,232.957,43866
-Indiana,18,2014,1233,6596855,3224871,3032693,192178,6.0,236.736,44784
-Indiana,18,2015,1310,6619680,3267121,3109362,157759,4.8,237.017,45012
-Indiana,18,2016,1574,6633053,3327139,3180104,147035,4.4,240.007,45977
-Iowa,19,1999,59,2917634,1596414,1555090,41324,2.6,166.6,37971
-Iowa,19,2000,79,2926324,1590453,1548636,41817,2.6,172.2,39747
-Iowa,19,2001,90,2931997,1612964,1559241,53723,3.3,177.1,38988
-Iowa,19,2002,101,2934234,1637909,1572560,65349,4.0,179.9,40072
-Iowa,19,2003,110,2941999,1606798,1534489,72309,4.5,184.0,41665
-Iowa,19,2004,132,2953635,1601788,1529761,72027,4.5,188.9,44605
-Iowa,19,2005,154,2964454,1629764,1560169,69595,4.3,195.3,45554
-Iowa,19,2006,199,2982644,1657584,1596530,61054,3.7,201.6,45908
-Iowa,19,2007,211,2999212,1660677,1599332,61345,3.7,207.342,47504
-Iowa,19,2008,214,3016734,1679293,1608695,70598,4.2,215.303,46413
-Iowa,19,2009,213,3032870,1687411,1579248,108163,6.4,214.537,45195
-Iowa,19,2010,258,3046355,1678281,1577447,100834,6.0,218.056,45884
-Iowa,19,2011,265,3062309,1662376,1570177,92199,5.5,224.939,46523
-Iowa,19,2012,268,3074186,1653141,1569879,83262,5.0,229.594,48001
-Iowa,19,2013,287,3090416,1674190,1594891,79299,4.7,232.957,48002
-Iowa,19,2014,273,3107126,1700756,1628505,72251,4.2,236.736,49566
-Iowa,19,2015,332,3123899,1701317,1637118,64199,3.8,237.017,51305
-Iowa,19,2016,338,3134693,1696113,1634964,61149,3.6,240.007,51912
-Kansas,20,1999,100,2678338,1425599,1378867,46732,3.3,166.6,39206
-Kansas,20,2000,112,2688418,1406580,1356147,50433,3.6,172.2,39923
-Kansas,20,2001,148,2702162,1390620,1332034,58586,4.2,177.1,39745
-Kansas,20,2002,189,2713535,1413014,1341603,71411,5.1,179.9,40169
-Kansas,20,2003,197,2723004,1437027,1357867,79160,5.5,184.0,40944
-Kansas,20,2004,238,2734373,1463707,1383867,79840,5.5,188.9,40748
-Kansas,20,2005,284,2745299,1465640,1392024,73616,5,195.3,41736
-Kansas,20,2006,292,2762931,1467095,1402796,64299,4.4,201.6,43593
-Kansas,20,2007,294,2783785,1483458,1420449,63009,4.2,207.342,45313
-Kansas,20,2008,241,2808076,1499676,1430628,69048,4.6,215.303,46050
-Kansas,20,2009,318,2832704,1518207,1413873,104334,6.9,214.537,43770
-Kansas,20,2010,288,2853118,1500764,1394958,105806,7.1,218.056,44243
-Kansas,20,2011,317,2871238,1491087,1394082,97005,6.5,224.939,45476
-Kansas,20,2012,341,2885905,1485220,1400122,85098,5.7,229.594,45514
-Kansas,20,2013,356,2893957,1485917,1407217,78700,5.3,232.957,45470
-Kansas,20,2014,349,2904021,1491710,1424016,67694,4.5,236.736,46269
-Kansas,20,2015,349,2911641,1489829,1427731,62098,4.2,237.017,46658
-Kansas,20,2016,333,2907289,1485336,1425413,59923,4,240.007,46217
-Kentucky,21,1999,215,4018053,1959402,1870873,88529,4.5,166.6,36847
-Kentucky,21,2000,257,4041769,1965688,1883714,81974,4.2,172.2,35513
-Kentucky,21,2001,370,4068132,1958409,1857238,101171,5.2,177.1,35497
-Kentucky,21,2002,451,4089875,1944020,1833535,110485,5.7,179.9,36161
-Kentucky,21,2003,588,4117170,1960045,1839873,120172,6.1,184.0,36731
-Kentucky,21,2004,559,4146101,1963875,1857816,106059,5.4,188.9,37463
-Kentucky,21,2005,662,4182742,1999777,1881943,117834,5.9,195.3,38205
-Kentucky,21,2006,766,4219239,2029231,1913472,115759,5.7,201.6,38856
-Kentucky,21,2007,722,4256672,2032082,1922220,109862,5.4,207.342,38159
-Kentucky,21,2008,779,4289878,2030738,1900683,130055,6.4,215.303,37875
-Kentucky,21,2009,786,4317074,2060162,1847126,213036,10.3,214.537,36209
-Kentucky,21,2010,1036,4339367,2054375,1844650,209725,10.2,218.056,37579
-Kentucky,21,2011,1099,4369356,2056410,1862928,193482,9.4,224.939,37926
-Kentucky,21,2012,1126,4380415,2059127,1891162,167965,8.2,229.594,38052
-Kentucky,21,2013,1049,4395295,2055442,1890879,164563,8.0,232.957,38247
-Kentucky,21,2014,1128,4413457,2003842,1874516,129326,6.5,236.736,38334
-Kentucky,21,2015,1332,4425092,1976967,1872326,104641,5.3,237.017,38610
-Kentucky,21,2016,1525,4436974,2012279,1909158,103121,5.1,240.007,38950
-Louisiana,22,1999,246,4460811,2039045,1935457,103588,5.1,166.6,42192
-Louisiana,22,2000,335,4468976,2037075,1929056,108019,5.3,172.2,40679
-Louisiana,22,2001,351,4477875,2028302,1911818,116484,5.7,177.1,41302
-Louisiana,22,2002,468,4497267,2004222,1881146,123076,6.1,179.9,41756
-Louisiana,22,2003,630,4521042,2018324,1888974,129350,6.4,184.0,43275
-Louisiana,22,2004,656,4552238,2037176,1916536,120640,5.9,188.9,44706
-Louisiana,22,2005,752,4576628,2069403,1921385,148018,7.2,195.3,47004
-Louisiana,22,2006,800,4302665,1988513,1899981,88532,4.5,201.6,49164
-Louisiana,22,2007,862,4375581,2030434,1944038,86396,4.3,207.342,46878
-Louisiana,22,2008,686,4435586,2084835,1982381,102454,4.9,215.303,46578
-Louisiana,22,2009,623,4491648,2064768,1923884,140884,6.8,214.537,46949
-Louisiana,22,2010,616,4533372,2086076,1919852,166224,8.0,218.056,48599
-Louisiana,22,2011,618,4574836,2073217,1911021,162196,7.8,224.939,45712
-Louisiana,22,2012,588,4601893,2080280,1933137,147143,7.1,229.594,45482
-Louisiana,22,2013,836,4625470,2101416,1960539,140877,6.7,232.957,43725
-Louisiana,22,2014,810,4649676,2153613,2016083,137530,6.4,236.736,44453
-Louisiana,22,2015,901,4670724,2162410,2025661,136749,6.3,237.017,44503
-Louisiana,22,2016,1036,4681666,2125934,1997358,128576,6.0,240.007,44451
-Maine,23,1999,70,1266808,668497,642085,26412,4.0,166.6,35300
-Maine,23,2000,62,1274923,678164,655349,22815,3.4,172.2,36503
-Maine,23,2001,92,1285692,674319,648545,25774,3.8,177.1,36995
-Maine,23,2002,147,1295960,675232,645935,29297,4.3,179.9,37584
-Maine,23,2003,134,1306513,683535,649571,33964,5.0,184.0,38136
-Maine,23,2004,145,1313688,686000,654161,31839,4.6,188.9,39089
-Maine,23,2005,168,1318787,697153,663132,34021,4.9,195.3,38961
-Maine,23,2006,166,1323619,701541,669554,31987,4.6,201.6,39288
-Maine,23,2007,161,1327040,700468,667781,32687,4.7,207.342,38946
-Maine,23,2008,162,1330509,701646,663158,38488,5.5,215.303,38508
-Maine,23,2009,182,1329590,696219,639954,56265,8.1,214.537,37910
-Maine,23,2010,140,1328361,695182,638630,56552,8.1,218.056,38352
-Maine,23,2011,160,1328188,699281,644085,55196,7.9,224.939,37780
-Maine,23,2012,157,1329192,702636,650117,52519,7.5,229.594,37705
-Maine,23,2013,183,1328302,705417,658522,46895,6.6,232.957,37477
-Maine,23,2014,227,1330089,696298,657066,39232,5.6,236.736,38055
-Maine,23,2015,278,1329328,683369,653330,30039,4.4,237.017,38415
-Maine,23,2016,369,1331479,692154,665911,26243,3.8,240.007,38956
-Maryland,24,1999,660,5254509,2766119,2667341,98778,3.6,166.6,44507
-Maryland,24,2000,657,5296486,2803685,2703284,100401,3.6,172.2,45619
-Maryland,24,2001,676,5374691,2837985,2724184,113801,4.0,177.1,46792
-Maryland,24,2002,759,5440389,2869954,2742501,127453,4.4,179.9,47714
-Maryland,24,2003,834,5496269,2882219,2754523,127696,4.4,184.0,48471
-Maryland,24,2004,721,5546935,2889324,2766475,122849,4.3,188.9,50547
-Maryland,24,2005,696,5592379,2924546,2803487,121059,4.1,195.3,51960
-Maryland,24,2006,805,5627367,2963866,2849354,114512,3.9,201.6,52697
-Maryland,24,2007,807,5653408,2970094,2867348,102746,3.5,207.342,52646
-Maryland,24,2008,731,5684965,3001953,2874987,126966,4.2,215.303,52877
-Maryland,24,2009,768,5730388,3032700,2820245,212455,7.0,214.537,52515
-Maryland,24,2010,674,5773552,3073826,2838492,235334,7.7,218.056,53675
-Maryland,24,2011,731,5828289,3096561,2872084,224477,7.2,224.939,54209
-Maryland,24,2012,860,5884563,3119647,2902307,217340,7.0,229.594,54018
-Maryland,24,2013,908,5928814,3129352,2923498,205854,6.6,232.957,53765
-Maryland,24,2014,1095,5976407,3129933,2947943,181990,5.8,236.736,54108
-Maryland,24,2015,1320,6006401,3151142,2990942,160200,5.1,237.017,54908
-Maryland,24,2016,2089,6016447,3178646,3037763,140883,4.4,240.007,56070
-Massachusetts,25,1999,512,6317345,3353865,3247012,106853,3.2,166.6,50987
-Massachusetts,25,2000,484,6349097,3330177,3240245,89932,2.7,172.2,54736
-Massachusetts,25,2001,714,6397634,3381287,3255134,126153,3.7,177.1,55114
-Massachusetts,25,2002,736,6417206,3431082,3250085,180997,5.3,179.9,55234
-Massachusetts,25,2003,876,6422565,3422409,3226981,195428,5.7,184.0,56380
-Massachusetts,25,2004,704,6412281,3395511,3220838,174673,5.1,188.9,57699
-Massachusetts,25,2005,867,6403290,3384116,3220089,164027,4.8,195.3,58776
-Massachusetts,25,2006,1021,6410084,3413339,3246316,167023,4.9,201.6,59706
-Massachusetts,25,2007,1003,6431559,3426009,3268096,157913,4.6,207.342,60955
-Massachusetts,25,2008,885,6468967,3452468,3261408,191060,5.5,215.303,60723
-Massachusetts,25,2009,916,6517613,3470382,3189010,281372,8.1,214.537,59178
-Massachusetts,25,2010,836,6547629,3480083,3190818,289265,8.3,218.056,60808
-Massachusetts,25,2011,1017,6587536,3469308,3217754,251554,7.3,224.939,61769
-Massachusetts,25,2012,976,6646144,3485161,3252531,232630,6.7,229.594,62456
-Massachusetts,25,2013,1200,6692824,3512827,3276792,236035,6.7,232.957,61882
-Massachusetts,25,2014,1402,6745408,3566237,3361811,204426,5.7,236.736,62571
-Massachusetts,25,2015,1851,6794422,3588241,3415874,172367,4.8,237.017,64592
-Massachusetts,25,2016,2379,6811779,3611418,3471112,140306,3.9,240.007,65281
-Michigan,26,1999,708,9897116,5115757,4925999,189758,3.7,166.6,41764
-Michigan,26,2000,871,9938444,5162774,4976322,186452,3.6,172.2,42225
-Michigan,26,2001,955,9991120,5119956,4854630,265326,5.2,177.1,40883
-Michigan,26,2002,1080,10015710,5017141,4702399,314742,6.3,179.9,41907
-Michigan,26,2003,1062,10041152,5027859,4667103,360756,7.2,184.0,42581
-Michigan,26,2004,1208,10055315,5062376,4705591,356785,7.0,188.9,42384
-Michigan,26,2005,1398,10051137,5083130,4738902,344228,6.8,195.3,42919
-Michigan,26,2006,1661,10036081,5076656,4721085,355571,7.0,201.6,42206
-Michigan,26,2007,1542,10001284,5011120,4658939,352181,7.0,207.342,41925
-Michigan,26,2008,1575,9946889,4921466,4529289,392177,8.0,215.303,39863
-Michigan,26,2009,1750,9901591,4903544,4233803,669741,13.7,214.537,36676
-Michigan,26,2010,1723,9883640,4798954,4194041,604913,12.6,218.056,38763
-Michigan,26,2011,1702,9876187,4685164,4198349,486815,10.4,224.939,39757
-Michigan,26,2012,1623,9883360,4672695,4246658,426037,9.1,229.594,40463
-Michigan,26,2013,1812,9895622,4723945,4308030,415915,8.8,232.957,40993
-Michigan,26,2014,2048,9909877,4753626,4409394,344232,7.2,236.736,41566
-Michigan,26,2015,2316,9922576,4751234,4493447,257787,5.4,237.017,42778
-Michigan,26,2016,2701,9928300,4840281,4599266,241015,5.0,240.007,43665
-Minnesota,27,1999,169,4873481,2756145,2680029,76116,2.8,166.6,45258
-Minnesota,27,2000,161,4919479,2812947,2724118,88829,3.2,172.2,47577
-Minnesota,27,2001,226,4982796,2845203,2737961,107242,3.8,177.1,47177
-Minnesota,27,2002,241,5018935,2859602,2731080,128522,4.5,179.9,47944
-Minnesota,27,2003,296,5053572,2874663,2734287,140376,4.9,184.0,49769
-Minnesota,27,2004,302,5087713,2880428,2745615,134813,4.7,188.9,51256
-Minnesota,27,2005,338,5119598,2879760,2762733,117027,4.1,195.3,52246
-Minnesota,27,2006,378,5163555,2887832,2772115,115717,4.0,201.6,51598
-Minnesota,27,2007,359,5207203,2906390,2773704,132686,4.6,207.342,51351
-Minnesota,27,2008,398,5247018,2925088,2766342,158746,5.4,215.303,51234
-Minnesota,27,2009,441,5281203,2941976,2713426,228550,7.8,214.537,48884
-Minnesota,27,2010,427,5303925,2938795,2721194,217601,7.4,218.056,50148
-Minnesota,27,2011,557,5344861,2946279,2755264,191015,6.5,224.939,50875
-Minnesota,27,2012,539,5379139,2946356,2781141,165215,5.6,229.594,51272
-Minnesota,27,2013,579,5420380,2958596,2811762,146834,5.0,232.957,51999
-Minnesota,27,2014,586,5457173,2973073,2849017,124056,4.2,236.736,53105
-Minnesota,27,2015,653,5489594,2998352,2887683,110669,3.7,237.017,53634
-Minnesota,27,2016,750,5519952,3036278,2919097,117181,3.9,240.007,54414
-Mississippi,28,1999,101,2828408,1286502,1220702,65800,5.1,166.6,29257
-Mississippi,28,2000,123,2844658,1319266,1248240,71026,5.4,172.2,29166
-Mississippi,28,2001,183,2852994,1294134,1222500,71634,5.5,177.1,28856
-Mississippi,28,2002,202,2858681,1286236,1201191,85045,6.6,179.9,29056
-Mississippi,28,2003,225,2868312,1301250,1219145,82105,6.3,184.0,30139
-Mississippi,28,2004,245,2889010,1313102,1232187,80915,6.2,188.9,30509
-Mississippi,28,2005,263,2905943,1317392,1218825,98567,7.5,195.3,30813
-Mississippi,28,2006,368,2904978,1290171,1205906,84265,6.5,201.6,31513
-Mississippi,28,2007,334,2928350,1303514,1224059,79455,6.1,207.342,32041
-Mississippi,28,2008,321,2947806,1306772,1220991,85781,6.6,215.303,33128
-Mississippi,28,2009,345,2958774,1269219,1148930,120289,9.5,214.537,31658
-Mississippi,28,2010,353,2967297,1306608,1170879,135729,10.4,218.056,31688
-Mississippi,28,2011,317,2978512,1342808,1208747,134061,10.0,224.939,31167
-Mississippi,28,2012,328,2984926,1316536,1198196,118340,9.0,229.594,31786
-Mississippi,28,2013,327,2991207,1272477,1163752,108725,8.5,232.957,31923
-Mississippi,28,2014,361,2994079,1248014,1154058,93956,7.5,236.736,31613
-Mississippi,28,2015,369,2992333,1263925,1182779,81146,6.4,237.017,31700
-Mississippi,28,2016,374,2988726,1278960,1204481,74479,5.8,240.007,32102
-Missouri,29,1999,294,5561948,2904971,2809325,95646,3.3,166.6,41051
-Missouri,29,2000,327,5595211,2959946,2853891,106055,3.6,172.2,41920
-Missouri,29,2001,381,5641142,3001465,2862838,138627,4.6,177.1,41346
-Missouri,29,2002,443,5674825,3000908,2839467,161441,5.4,179.9,41421
-Missouri,29,2003,550,5709403,3020821,2850987,169834,5.6,184.0,42043
-Missouri,29,2004,574,5747741,3015650,2838872,176778,5.9,188.9,42643
-Missouri,29,2005,640,5790300,3009334,2847006,162328,5.4,195.3,42806
-Missouri,29,2006,765,5842704,3029689,2882757,146932,4.8,201.6,42792
-Missouri,29,2007,730,5887612,3034579,2879647,154932,5.1,207.342,42561
-Missouri,29,2008,779,5923916,3028857,2842845,186012,6.1,215.303,43118
-Missouri,29,2009,881,5961088,3049563,2766711,282852,9.3,214.537,42012
-Missouri,29,2010,1024,5988927,3056484,2763535,292949,9.6,218.056,42204
-Missouri,29,2011,990,6010688,3047786,2789224,258562,8.5,224.939,41598
-Missouri,29,2012,961,6021988,3025309,2815275,210034,6.9,229.594,41926
-Missouri,29,2013,1041,6044171,3020202,2817435,202767,6.7,232.957,42487
-Missouri,29,2014,1107,6063589,3048576,2861721,186855,6.1,236.736,42483
-Missouri,29,2015,1098,6083672,3077535,2923107,154428,5.0,237.017,42995
-Missouri,29,2016,1418,6093000,3079559,2939309,140250,4.6,240.007,43004
-Montana,30,1999,48,897507,464373,439993,24380,5.3,166.6,31395
-Montana,30,2000,47,902195,467293,444137,23156,5.0,172.2,31899
-Montana,30,2001,62,906961,466063,444909,21154,4.5,177.1,32272
-Montana,30,2002,85,911667,467203,446640,20563,4.4,179.9,32851
-Montana,30,2003,116,919630,471489,449266,22223,4.7,184.0,33848
-Montana,30,2004,119,930009,480281,457423,22858,4.8,188.9,34870
-Montana,30,2005,119,940102,486054,464907,21147,4.4,195.3,36120
-Montana,30,2006,121,952692,493614,476377,17237,3.5,201.6,36769
-Montana,30,2007,132,964706,502070,484189,17881,3.6,207.342,38063
-Montana,30,2008,141,976415,509163,483347,25816,5.1,215.303,37636
-Montana,30,2009,147,983982,501014,466713,34301,6.8,214.537,36667
-Montana,30,2010,123,989415,500525,463998,36527,7.3,218.056,37733
-Montana,30,2011,150,998199,501225,466403,34822,6.9,224.939,38542
-Montana,30,2012,129,1005141,506485,476174,30311,6.0,229.594,38537
-Montana,30,2013,156,1015165,510781,483071,27710,5.4,232.957,38476
-Montana,30,2014,145,1023579,511501,487629,23872,4.7,236.736,39174
-Montana,30,2015,152,1032949,516845,495298,21547,4.2,237.017,39725
-Montana,30,2016,136,1042520,521628,499990,21638,4.1,240.007,39763
-Nebraska,31,1999,41,1704764,937559,911444,26115,2.8,166.6,40474
-Nebraska,31,2000,52,1711263,944973,918370,26603,2.8,172.2,41761
-Nebraska,31,2001,69,1719836,950764,921003,29761,3.1,177.1,42227
-Nebraska,31,2002,69,1728292,954224,919401,34823,3.6,179.9,42746
-Nebraska,31,2003,88,1738643,964258,926315,37943,3.9,184.0,44904
-Nebraska,31,2004,91,1749370,969981,932515,37466,3.9,188.9,45488
-Nebraska,31,2005,129,1761497,972992,935906,37086,3.8,195.3,46062
-Nebraska,31,2006,131,1772693,970052,939874,30178,3.1,201.6,47056
-Nebraska,31,2007,92,1783440,978763,949494,29269,3.0,207.342,47501
-Nebraska,31,2008,113,1796378,989757,956759,32998,3.3,215.303,47770
-Nebraska,31,2009,118,1812683,991583,945648,45935,4.6,214.537,47973
-Nebraska,31,2010,130,1826341,993398,947360,46038,4.6,218.056,49569
-Nebraska,31,2011,148,1842641,1003256,959059,44197,4.4,224.939,51360
-Nebraska,31,2012,154,1855525,1014980,974428,40552,4.0,229.594,50631
-Nebraska,31,2013,137,1868516,1018294,979937,38357,3.8,232.957,51565
-Nebraska,31,2014,140,1881503,1011564,978377,33187,3.3,236.736,53101
-Nebraska,31,2015,139,1896190,1007585,977279,30306,3.0,237.017,53859
-Nebraska,31,2016,146,1907116,1008715,977622,31093,3.1,240.007,53949
-Nevada,32,1999,236,1934718,1020300,979646,40654,4,166.6,49517
-Nevada,32,2000,281,1998257,1067022,1022462,44560,4.2,172.2,49607
-Nevada,32,2001,268,2098399,1112079,1054262,57817,5.2,177.1,48733
-Nevada,32,2002,332,2173791,1129668,1066121,63547,5.6,179.9,48489
-Nevada,32,2003,350,2248850,1147036,1088787,58249,5.1,184.0,49271
-Nevada,32,2004,395,2346222,1177634,1126943,50691,4.3,188.9,52310
-Nevada,32,2005,464,2432143,1223831,1173709,50122,4.1,195.3,54790
-Nevada,32,2006,471,2522658,1285318,1233287,52031,4,201.6,54797
-Nevada,32,2007,515,2601072,1330396,1270572,59824,4.5,207.342,52818
-Nevada,32,2008,529,2653630,1363574,1272187,91387,6.7,215.303,49286
-Nevada,32,2009,555,2684665,1349299,1196758,152541,11.3,214.537,44774
-Nevada,32,2010,581,2700551,1358578,1174774,183804,13.5,218.056,44589
-Nevada,32,2011,642,2723322,1373115,1194264,178851,13,224.939,44555
-Nevada,32,2012,611,2758931,1376381,1222710,153671,11.2,229.594,43382
-Nevada,32,2013,622,2790136,1381157,1248122,133035,9.6,232.957,43075
-Nevada,32,2014,555,2839099,1394758,1284292,110466,7.9,236.736,43067
-Nevada,32,2015,629,2890845,1413739,1318024,95715,6.8,237.017,43803
-Nevada,32,2016,678,2940058,1430344,1348265,82079,5.7,240.007,43557
-New Hampshire,33,1999,62,1222014,675195,656962,18233,2.7,166.6,42172
-New Hampshire,33,2000,49,1235786,687908,669621,18287,2.7,172.2,44460
-New Hampshire,33,2001,83,1255517,697760,673778,23982,3.4,177.1,44540
-New Hampshire,33,2002,114,1269089,702280,670880,31400,4.5,179.9,45311
-New Hampshire,33,2003,137,1279840,707524,677030,30494,4.3,184.0,46621
-New Hampshire,33,2004,128,1290121,716186,689150,27036,3.8,188.9,47472
-New Hampshire,33,2005,152,1298492,725849,700026,25823,3.6,195.3,48357
-New Hampshire,33,2006,160,1308389,731854,706777,25077,3.4,201.6,48857
-New Hampshire,33,2007,187,1312540,737942,712008,25934,3.5,207.342,48618
-New Hampshire,33,2008,129,1315906,742781,714104,28677,3.9,215.303,47842
-New Hampshire,33,2009,172,1316102,744227,697802,46425,6.2,214.537,47265
-New Hampshire,33,2010,164,1316470,738257,695135,43122,5.8,218.056,48388
-New Hampshire,33,2011,212,1318194,736302,696532,39770,5.4,224.939,48504
-New Hampshire,33,2012,177,1320718,741097,700371,40726,5.5,229.594,48652
-New Hampshire,33,2013,217,1323459,741285,703534,37751,5.1,232.957,48871
-New Hampshire,33,2014,348,1326813,740667,708977,31690,4.3,236.736,49544
-New Hampshire,33,2015,433,1330608,741937,716874,25063,3.4,237.017,50781
-New Hampshire,33,2016,495,1334795,746452,725154,21298,2.9,240.007,51411
-New Jersey,34,1999,757,8359592,4287886,4094559,193327,4.5,166.6,51571
-New Jersey,34,2000,795,8414350,4282069,4123717,158352,3.7,172.2,53701
-New Jersey,34,2001,796,8492671,4288826,4106225,182601,4.3,177.1,54000
-New Jersey,34,2002,885,8552643,4346237,4095174,251063,5.8,179.9,54842
-New Jersey,34,2003,792,8601402,4347154,4093686,253468,5.8,184.0,55897
-New Jersey,34,2004,711,8634561,4349171,4138830,210341,4.8,188.9,56261
-New Jersey,34,2005,966,8651974,4391579,4194895,196684,4.5,195.3,56768
-New Jersey,34,2006,1009,8661679,4445879,4236485,209394,4.7,201.6,57666
-New Jersey,34,2007,797,8677885,4441797,4251815,189982,4.3,207.342,57860
-New Jersey,34,2008,808,8711090,4504432,4263965,240467,5.3,215.303,57784
-New Jersey,34,2009,343,8755602,4550645,4138570,412075,9.1,214.537,55067
-New Jersey,34,2010,903,8791894,4555330,4121455,433875,9.5,218.056,55023
-New Jersey,34,2011,1042,8821155,4565272,4138464,426808,9.3,224.939,54302
-New Jersey,34,2012,1268,8864590,4588033,4159986,428047,9.3,229.594,55161
-New Jersey,34,2013,1331,8899339,4548560,4173828,374732,8.2,232.957,55750
-New Jersey,34,2014,1303,8938175,4527181,4221267,305914,6.8,236.736,55677
-New Jersey,34,2015,1506,8958013,4537232,4274695,262537,5.8,237.017,56241
-New Jersey,34,2016,2132,8944469,4530784,4305516,225268,5.0,240.007,56565
-New Mexico,35,1999,275,1808082,836237,789677,46560,5.6,166.6,37395
-New Mexico,35,2000,281,1819046,845755,804103,41652,4.9,172.2,37773
-New Mexico,35,2001,277,1831690,857949,815642,42307,4.9,177.1,37915
-New Mexico,35,2002,301,1855309,874496,826086,48410,5.5,179.9,38421
-New Mexico,35,2003,373,1877574,889730,837667,52063,5.9,184.0,39578
-New Mexico,35,2004,331,1903808,902265,852612,49653,5.5,188.9,41558
-New Mexico,35,2005,385,1932274,918156,871248,46908,5.1,195.3,41193
-New Mexico,35,2006,435,1962137,928094,889448,38646,4.2,201.6,41206
-New Mexico,35,2007,471,1990070,934027,898998,35029,3.8,207.342,40694
-New Mexico,35,2008,534,2010662,944548,902411,42137,4.5,215.303,41207
-New Mexico,35,2009,447,2036802,940352,869491,70861,7.5,214.537,40670
-New Mexico,35,2010,487,2059179,936088,860154,75934,8.1,218.056,40297
-New Mexico,35,2011,537,2082224,930356,860305,70051,7.5,224.939,40167
-New Mexico,35,2012,502,2085538,927795,861617,66178,7.1,229.594,40094
-New Mexico,35,2013,471,2085287,923899,859804,64095,6.9,232.957,39659
-New Mexico,35,2014,559,2085572,927142,865229,61913,6.7,236.736,40814
-New Mexico,35,2015,516,2085109,927999,867387,60612,6.5,237.017,41509
-New Mexico,35,2016,525,2081015,928732,866704,62028,6.7,240.007,41559
-New York,36,1999,1102,18882725,9126567,8654586,471981,5.2,166.6,52368
-New York,36,2000,901,18976457,9133869,8718749,415120,4.5,172.2,53827
-New York,36,2001,1280,19082838,9151745,8709897,441848,4.8,177.1,55375
-New York,36,2002,1118,19137800,9275515,8705352,570163,6.1,179.9,55039
-New York,36,2003,1209,19175939,9263401,8672909,590492,6.4,184.0,54756
-New York,36,2004,1068,19171567,9355953,8812618,543335,5.8,188.9,56259
-New York,36,2005,1175,19132610,9460866,8986859,474007,5.0,195.3,58502
-New York,36,2006,1949,19104631,9508149,9077529,430620,4.5,201.6,59961
-New York,36,2007,1909,19132335,9522056,9088207,433849,4.6,207.342,60018
-New York,36,2008,1856,19212436,9664773,9139080,525693,5.4,215.303,58532
-New York,36,2009,1797,19307066,9647485,8844486,802999,8.3,214.537,59481
-New York,36,2010,1760,19378102,9595362,8769723,825639,8.6,218.056,61267
-New York,36,2011,2149,19465197,9517361,8728057,789304,8.3,224.939,61185
-New York,36,2012,2262,19570261,9612232,8793385,818847,8.5,229.594,62841
-New York,36,2013,2483,19651127,9659170,8913788,745382,7.7,232.957,62444
-New York,36,2014,2510,19746227,9591309,8984074,607235,6.3,236.736,63372
-New York,36,2015,3009,19795791,9644615,9136151,508464,5.3,237.017,64573
-New York,36,2016,3894,19745289,9668687,9200266,468421,4.8,240.007,64810
-North Carolina,37,1999,401,7949361,4057006,3927179,129827,3.2,166.6,41811
-North Carolina,37,2000,531,8049313,4138190,3986151,152039,3.7,172.2,42193
-North Carolina,37,2001,584,8210122,4198338,3966798,231540,5.5,177.1,42388
-North Carolina,37,2002,684,8326201,4184713,3906570,278143,6.6,179.9,42443
-North Carolina,37,2003,833,8422501,4255631,3984596,271035,6.4,184.0,42871
-North Carolina,37,2004,918,8553152,4253126,4019809,233317,5.5,188.9,43866
-North Carolina,37,2005,1061,8705407,4317428,4091039,226389,5.2,195.3,45042
-North Carolina,37,2006,1120,8917270,4444967,4234095,210872,4.7,201.6,46408
-North Carolina,37,2007,1125,9118037,4512856,4300304,212552,4.7,207.342,45440
-North Carolina,37,2008,1217,9309449,4560059,4281713,278346,6.1,215.303,45567
-North Carolina,37,2009,1223,9449566,4570789,4087105,483684,10.6,214.537,43160
-North Carolina,37,2010,1125,9535483,4616691,4115629,501062,10.9,218.056,43141
-North Carolina,37,2011,1303,9656401,4633072,4157543,475529,10.3,224.939,43223
-North Carolina,37,2012,1346,9752073,4680057,4245675,434382,9.3,229.594,42659
-North Carolina,37,2013,1308,9848060,4692940,4318114,374826,8.0,232.957,42945
-North Carolina,37,2014,1435,9943964,4699058,4402450,296608,6.3,236.736,43442
-North Carolina,37,2015,1636,10042802,4768806,4495190,273616,5.7,237.017,44172
-North Carolina,37,2016,2040,10146788,4854828,4608229,246599,5.1,240.007,44511
-North Dakota,38,1999,15,644259,343452,332180,11272,3.3,166.6,33710
-North Dakota,38,2000,16,642200,342568,332407,10161,3.0,172.2,35067
-North Dakota,38,2001,17,639062,341816,331820,9996,2.9,177.1,35939
-North Dakota,38,2002,24,638168,340631,327957,12674,3.7,179.9,37893
-North Dakota,38,2003,27,638817,344676,331852,12824,3.7,184.0,40213
-North Dakota,38,2004,21,644705,352466,340403,12063,3.4,188.9,40099
-North Dakota,38,2005,13,646089,355545,343508,12037,3.4,195.3,41272
-North Dakota,38,2006,16,649422,361459,349821,11638,3.2,201.6,42762
-North Dakota,38,2007,37,652822,367234,355766,11468,3.1,207.342,44385
-North Dakota,38,2008,48,657569,371025,359333,11692,3.2,215.303,48379
-North Dakota,38,2009,28,664968,368665,353455,15210,4.1,214.537,48858
-North Dakota,38,2010,26,672591,378342,364053,14289,3.8,218.056,52185
-North Dakota,38,2011,22,683932,388634,375153,13481,3.5,224.939,57066
-North Dakota,38,2012,27,699628,397867,385674,12193,3.1,229.594,68105
-North Dakota,38,2013,27,723393,406193,394392,11801,2.9,232.957,67651
-North Dakota,38,2014,48,739482,414413,403362,11051,2.7,236.736,70986
-North Dakota,38,2015,65,756927,414210,402667,11543,2.8,237.017,67632
-North Dakota,38,2016,86,757952,414431,401671,12760,3.1,240.007,64136
-Ohio,39,1999,534,11335454,5754992,5508439,246553,4.3,166.6,42013
-Ohio,39,2000,625,11353140,5787343,5556757,230586,4.0,172.2,42678
-Ohio,39,2001,808,11387404,5816832,5567130,249702,4.3,177.1,41941
-Ohio,39,2002,1025,11407889,5852985,5516645,336340,5.7,179.9,42818
-Ohio,39,2003,852,11434788,5872372,5505858,366514,6.2,184.0,43445
-Ohio,39,2004,1266,11452251,5870479,5502444,368035,6.3,188.9,44434
-Ohio,39,2005,1382,11463320,5890046,5541082,348964,5.9,195.3,44684
-Ohio,39,2006,1606,11481213,5945482,5624435,321047,5.4,201.6,44532
-Ohio,39,2007,1691,11500468,5990292,5657718,332574,5.6,207.342,44399
-Ohio,39,2008,1811,11515391,5965166,5580843,384323,6.4,215.303,43548
-Ohio,39,2009,1340,11528896,5906768,5297098,609670,10.3,214.537,41593
-Ohio,39,2010,1911,11536504,5846886,5247050,599836,10.3,218.056,42667
-Ohio,39,2011,2127,11544951,5771469,5261238,510231,8.8,224.939,44192
-Ohio,39,2012,2207,11544225,5705591,5284001,421590,7.4,229.594,44896
-Ohio,39,2013,2450,11570808,5716730,5290609,426121,7.5,232.957,45254
-Ohio,39,2014,2832,11594163,5701477,5370560,330917,5.8,236.736,46662
-Ohio,39,2015,3418,11613423,5694303,5414872,279431,4.9,237.017,47146
-Ohio,39,2016,4477,11614373,5739296,5451315,287981,5.0,240.007,47633
-Oklahoma,40,1999,189,3437147,1656205,1597894,58311,3.5,166.6,33045
-Oklahoma,40,2000,250,3450654,1660528,1610099,50429,3.0,172.2,34015
-Oklahoma,40,2001,282,3467100,1675935,1614572,61363,3.7,177.1,35162
-Oklahoma,40,2002,257,3489080,1686568,1608106,78462,4.7,179.9,35316
-Oklahoma,40,2003,411,3504892,1685572,1593521,92051,5.5,184.0,35780
-Oklahoma,40,2004,527,3525233,1689608,1607421,82187,4.9,188.9,36634
-Oklahoma,40,2005,533,3548597,1703293,1627110,76183,4.5,195.3,37662
-Oklahoma,40,2006,619,3594090,1715794,1647098,68696,4.0,201.6,39391
-Oklahoma,40,2007,687,3634349,1726259,1655490,70769,4.1,207.342,39716
-Oklahoma,40,2008,585,3668976,1746466,1681081,65385,3.7,215.303,40048
-Oklahoma,40,2009,766,3717572,1764432,1652023,112409,6.4,214.537,38640
-Oklahoma,40,2010,728,3751351,1768284,1648138,120146,6.8,218.056,38303
-Oklahoma,40,2011,715,3791508,1772666,1668418,104248,5.9,224.939,39961
-Oklahoma,40,2012,782,3814820,1803863,1709258,94605,5.2,229.594,41861
-Oklahoma,40,2013,800,3850568,1802073,1706861,95212,5.3,232.957,43288
-Oklahoma,40,2014,809,3878051,1796183,1715600,80583,4.5,236.736,45414
-Oklahoma,40,2015,751,3911338,1830358,1749626,80732,4.4,237.017,46447
-Oklahoma,40,2016,840,3923561,1829413,1741277,88136,4.8,240.007,44356
-Oregon,41,1999,329,3393941,1793699,1694881,98818,5.5,166.6,35552
-Oregon,41,2000,306,3421399,1818559,1725744,92815,5.1,172.2,38193
-Oregon,41,2001,321,3467937,1823916,1706904,117012,6.4,177.1,37161
-Oregon,41,2002,411,3513424,1835325,1697237,138088,7.5,179.9,37669
-Oregon,41,2003,482,3547376,1845544,1695906,149638,8.1,184.0,38870
-Oregon,41,2004,482,3569463,1838112,1703703,134409,7.3,188.9,42196
-Oregon,41,2005,513,3613202,1845761,1731845,113916,6.2,195.3,42558
-Oregon,41,2006,584,3670883,1885385,1785044,100341,5.3,201.6,45752
-Oregon,41,2007,564,3722417,1921766,1822772,98994,5.2,207.342,46504
-Oregon,41,2008,521,3768748,1955121,1827352,127769,6.5,215.303,48508
-Oregon,41,2009,576,3808600,1976638,1753682,222956,11.3,214.537,47530
-Oregon,41,2010,576,3831074,1984039,1773076,210963,10.6,218.056,49601
-Oregon,41,2011,596,3871859,1993889,1804320,189569,9.5,224.939,51266
-Oregon,41,2012,568,3899353,1953439,1780790,172649,8.8,229.594,49395
-Oregon,41,2013,545,3930065,1910702,1760302,150400,7.9,232.957,48094
-Oregon,41,2014,617,3970239,1935884,1804417,131467,6.8,236.736,48466
-Oregon,41,2015,609,4028977,1978226,1867407,110819,5.6,237.017,50058
-Oregon,41,2016,651,4093465,2049480,1951218,98262,4.8,240.007,51066
-Pennsylvania,42,1999,1049,12263805,6076778,5810162,266616,4.4,166.6,41016
-Pennsylvania,42,2000,1215,12281054,6106892,5854551,252341,4.1,172.2,41857
-Pennsylvania,42,2001,1021,12298970,6178617,5881688,296929,4.8,177.1,42458
-Pennsylvania,42,2002,1159,12331031,6218643,5868607,350036,5.6,179.9,42756
-Pennsylvania,42,2003,1441,12374658,6170359,5821711,348648,5.7,184.0,43553
-Pennsylvania,42,2004,1600,12410722,6213778,5876997,336781,5.4,188.9,44585
-Pennsylvania,42,2005,1680,12449990,6251402,5940570,310832,5.0,195.3,45035
-Pennsylvania,42,2006,1834,12510809,6294504,6006634,287870,4.6,201.6,45021
-Pennsylvania,42,2007,1812,12563937,6342997,6064063,278934,4.4,207.342,46330
-Pennsylvania,42,2008,1898,12612285,6451535,6109645,341890,5.3,215.303,46862
-Pennsylvania,42,2009,1983,12666858,6400263,5885351,514912,8.0,214.537,45312
-Pennsylvania,42,2010,1980,12702379,6380949,5840887,540062,8.5,218.056,46387
-Pennsylvania,42,2011,2342,12742886,6395506,5888745,506761,7.9,224.939,46872
-Pennsylvania,42,2012,2459,12763536,6462939,5957314,505625,7.8,229.594,47540
-Pennsylvania,42,2013,2525,12773801,6442411,5967811,474600,7.4,232.957,48278
-Pennsylvania,42,2014,2829,12787209,6396041,6021005,375036,5.9,236.736,49214
-Pennsylvania,42,2015,3376,12802503,6420702,6080219,340483,5.3,237.017,50357
-Pennsylvania,42,2016,4762,12784227,6452812,6105223,347589,5.4,240.007,50665
-Rhode Island,44,1999,61,1040402,538096,515760,22336,4.2,166.6,40034
-Rhode Island,44,2000,76,1048319,543561,521313,22248,4.1,172.2,41395
-Rhode Island,44,2001,116,1057142,543862,519044,24818,4.6,177.1,41989
-Rhode Island,44,2002,112,1065995,550256,522832,27424,5.0,179.9,43041
-Rhode Island,44,2003,157,1071342,560483,530567,29916,5.3,184.0,44625
-Rhode Island,44,2004,109,1074579,558688,529893,28795,5.2,188.9,46310
-Rhode Island,44,2005,163,1067916,565735,537194,28541,5.0,195.3,47176
-Rhode Island,44,2006,179,1063096,572601,544357,28244,4.9,201.6,48259
-Rhode Island,44,2007,142,1057315,573173,543401,29772,5.2,207.342,47137
-Rhode Island,44,2008,193,1055003,570328,525941,44387,7.8,215.303,45746
-Rhode Island,44,2009,168,1053646,567280,504951,62329,11.0,214.537,45280
-Rhode Island,44,2010,176,1052567,566704,503216,63488,11.2,218.056,46332
-Rhode Island,44,2011,193,1051302,560056,498248,61808,11.0,224.939,46010
-Rhode Island,44,2012,201,1050292,558487,500434,58053,10.4,229.594,46188
-Rhode Island,44,2013,252,1051511,556926,505370,51556,9.3,232.957,46356
-Rhode Island,44,2014,253,1055173,555909,513121,42788,7.7,236.736,46723
-Rhode Island,44,2015,318,1056298,554486,521255,33231,6.0,237.017,47541
-Rhode Island,44,2016,330,1056426,553555,524745,28810,5.2,240.007,47739
-South Carolina,45,1999,168,3974682,1981546,1897056,84490,4.3,166.6,35728
-South Carolina,45,2000,289,4012012,1993562,1918583,74979,3.8,172.2,36070
-South Carolina,45,2001,265,4064995,1949685,1847939,101746,5.2,177.1,35902
-South Carolina,45,2002,238,4107795,1949135,1835823,113312,5.8,179.9,36341
-South Carolina,45,2003,310,4150297,1997977,1859765,138212,6.9,184.0,37283
-South Carolina,45,2004,383,4210921,2033310,1894141,139169,6.8,188.9,37124
-South Carolina,45,2005,486,4270150,2068599,1929233,139366,6.7,195.3,37397
-South Carolina,45,2006,641,4357847,2109097,1973337,135760,6.4,201.6,37326
-South Carolina,45,2007,584,4444110,2125891,2005686,120205,5.7,207.342,37724
-South Carolina,45,2008,588,4528996,2142232,1996409,145823,6.8,215.303,36834
-South Carolina,45,2009,631,4589872,2152745,1910670,242075,11.2,214.537,34955
-South Carolina,45,2010,697,4625364,2155668,1915045,240623,11.2,218.056,35126
-South Carolina,45,2011,641,4679230,2175523,1945900,229623,10.6,224.939,35609
-South Carolina,45,2012,611,4723723,2186878,1985618,201260,9.2,229.594,35346
-South Carolina,45,2013,648,4774839,2190968,2023642,167326,7.6,232.957,35701
-South Carolina,45,2014,726,4832482,2222978,2079565,143413,6.5,236.736,36301
-South Carolina,45,2015,793,4896146,2269339,2134244,135095,6,237.017,36917
-South Carolina,45,2016,927,4961119,2293666,2179521,114145,5,240.007,37075
-South Dakota,46,1999,20,750412,406105,395047,11058,2.7,166.6,33574
-South Dakota,46,2000,25,754844,408658,398618,10040,2.5,172.2,35601
-South Dakota,46,2001,25,757972,412778,400031,12747,3.1,177.1,36327
-South Dakota,46,2002,24,760020,417517,404239,13278,3.2,179.9,40062
-South Dakota,46,2003,25,763729,423035,408088,14947,3.5,184.0,40909
-South Dakota,46,2004,51,770396,427664,411762,15902,3.7,188.9,42046
-South Dakota,46,2005,47,775493,430606,414209,16397,3.8,195.3,42762
-South Dakota,46,2006,43,783033,435007,421483,13524,3.1,201.6,42825
-South Dakota,46,2007,34,791623,442499,430011,12488,2.8,207.342,44017
-South Dakota,46,2008,57,799124,446618,432925,13693,3.1,215.303,45516
-South Dakota,46,2009,51,807067,446010,423993,22017,4.9,214.537,45457
-South Dakota,46,2010,48,814180,441339,419355,21984,5.0,218.056,45605
-South Dakota,46,2011,60,824082,440934,420054,20880,4.7,224.939,47972
-South Dakota,46,2012,45,833354,442442,423501,18941,4.3,229.594,46965
-South Dakota,46,2013,57,844877,443079,426284,16795,3.8,232.957,46869
-South Dakota,46,2014,70,853175,446475,431139,15336,3.4,236.736,46874
-South Dakota,46,2015,72,858469,449408,435450,13958,3.1,237.017,47894
-South Dakota,46,2016,75,865454,451934,438377,13557,3.0,240.007,47808
-Tennessee,47,1999,374,5638706,2852445,2739189,113256,4.0,166.6,38930
-Tennessee,47,2000,426,5689283,2843069,2733281,109788,3.9,172.2,38892
-Tennessee,47,2001,469,5750789,2861343,2730988,130355,4.6,177.1,38631
-Tennessee,47,2002,524,5795918,2906591,2756086,150505,5.2,179.9,39514
-Tennessee,47,2003,713,5847812,2912187,2748140,164047,5.6,184.0,40463
-Tennessee,47,2004,822,5910809,2878736,2725108,153628,5.3,188.9,41777
-Tennessee,47,2005,936,5991057,2904794,2743383,161411,5.6,195.3,41868
-Tennessee,47,2006,1030,6088766,3036029,2878455,157574,5.2,201.6,42177
-Tennessee,47,2007,1035,6175727,3063669,2920352,143317,4.7,207.342,41172
-Tennessee,47,2008,977,6247411,3054785,2853746,201039,6.6,215.303,40949
-Tennessee,47,2009,1000,6306019,3052678,2733113,319565,10.5,214.537,39096
-Tennessee,47,2010,1132,6346105,3090795,2792063,298732,9.7,218.056,39370
-Tennessee,47,2011,1123,6403353,3125307,2844662,280645,9.0,224.939,40193
-Tennessee,47,2012,1221,6456243,3100671,2857945,242726,7.8,229.594,41091
-Tennessee,47,2013,1284,6495978,3067431,2828479,238952,7.8,232.957,41487
-Tennessee,47,2014,1330,6549352,3022927,2824016,198911,6.6,236.736,41875
-Tennessee,47,2015,1546,6600299,3062706,2890640,172066,5.6,237.017,42803
-Tennessee,47,2016,1740,6651194,3134725,2987074,147651,4.7,240.007,43688
-Texas,48,1999,1250,20558220,10245857,9767851,478006,4.7,166.6,43825
-Texas,48,2000,1211,20851820,10374053,9929387,444666,4.3,172.2,44432
-Texas,48,2001,1522,21319622,10532732,10011046,521686,5.0,177.1,44784
-Texas,48,2002,1780,21690325,10748810,10065870,682940,6.4,179.9,44661
-Texas,48,2003,1985,22030931,10914664,10185312,729352,6.7,184.0,44330
-Texas,48,2004,2039,22394023,10992359,10338484,653875,5.9,188.9,45691
-Texas,48,2005,2152,22778123,11124240,10523257,600983,5.4,195.3,45966
-Texas,48,2006,2452,23359580,11327995,10774490,553505,4.9,201.6,47595
-Texas,48,2007,2343,23831983,11431631,10941413,490218,4.3,207.342,48954
-Texas,48,2008,2199,24309039,11664390,11104115,560275,4.8,215.303,48282
-Texas,48,2009,2509,24801761,11910799,11008903,901896,7.6,214.537,47034
-Texas,48,2010,2492,25145561,12241970,11244632,997338,8.1,218.056,47417
-Texas,48,2011,2690,25674681,12504498,11535095,969403,7.8,224.939,48354
-Texas,48,2012,2546,26059203,12670455,11818675,851780,6.7,229.594,50266
-Texas,48,2013,2606,26448193,12857595,12052646,804949,6.3,232.957,52018
-Texas,48,2014,2727,26956958,13024701,12360368,664333,5.1,236.736,52875
-Texas,48,2015,2732,27469114,13074570,12493197,581373,4.4,237.017,54134
-Texas,48,2016,2965,27862596,13317176,12702122,615054,4.6,240.007,53129
-Utah,49,1999,212,2203482,1120920,1081276,39644,3.5,166.6,38084
-Utah,49,2000,224,2233169,1142044,1103807,38237,3.3,172.2,38695
-Utah,49,2001,216,2283715,1164077,1112607,51470,4.4,177.1,38849
-Utah,49,2002,300,2324815,1184751,1116067,68684,5.8,179.9,38764
-Utah,49,2003,368,2360137,1200236,1132554,67682,5.6,184.0,39094
-Utah,49,2004,411,2401580,1231160,1169795,61365,5.0,188.9,40089
-Utah,49,2005,481,2457719,1272445,1220617,51828,4.1,195.3,41657
-Utah,49,2006,481,2525507,1318450,1279328,39122,3.0,201.6,44004
-Utah,49,2007,546,2597746,1359129,1324060,35069,2.6,207.342,45293
-Utah,49,2008,483,2663029,1371201,1322089,49112,3.6,215.303,43375
-Utah,49,2009,508,2723421,1365850,1266009,99841,7.3,214.537,41563
-Utah,49,2010,457,2763885,1356097,1249814,106283,7.8,218.056,41500
-Utah,49,2011,520,2817222,1350444,1259337,91107,6.7,224.939,41992
-Utah,49,2012,612,2855287,1372971,1298807,74164,5.4,229.594,41929
-Utah,49,2013,612,2900872,1405630,1341192,64438,4.6,232.957,42267
-Utah,49,2014,617,2942902,1428293,1373801,54492,3.8,236.736,43145
-Utah,49,2015,667,2995919,1464173,1411194,52979,3.6,237.017,44174
-Utah,49,2016,685,3051217,1511279,1459309,51970,3.4,240.007,44893
-Vermont,50,1999,31,604683,336946,326979,9967,3.0,166.6,35087
-Vermont,50,2000,37,608827,331404,322129,9275,2.8,172.2,36622
-Vermont,50,2001,53,612223,337065,325889,11176,3.3,177.1,37618
-Vermont,50,2002,54,615442,343139,329427,13712,4.0,179.9,38541
-Vermont,50,2003,73,617858,345971,331122,14849,4.3,184.0,39886
-Vermont,50,2004,52,619920,347673,334899,12774,3.7,188.9,41165
-Vermont,50,2005,55,621215,350766,338553,12213,3.5,195.3,41588
-Vermont,50,2006,83,622892,357007,343865,13142,3.7,201.6,41754
-Vermont,50,2007,68,623481,353739,339547,14192,4.0,207.342,41472
-Vermont,50,2008,76,624151,354899,338273,16626,4.7,215.303,41764
-Vermont,50,2009,57,624817,359836,336104,23732,6.6,214.537,40855
-Vermont,50,2010,68,625741,359402,337488,21914,6.1,218.056,42170
-Vermont,50,2011,87,626431,358108,338463,19645,5.5,224.939,43124
-Vermont,50,2012,81,626011,354857,337284,17573,5.0,229.594,43042
-Vermont,50,2013,99,626630,350447,334964,15483,4.4,232.957,42914
-Vermont,50,2014,90,626562,347172,333438,13734,4.0,236.736,43120
-Vermont,50,2015,111,626042,344520,332199,12321,3.6,237.017,43571
-Vermont,50,2016,131,624594,344572,333399,11173,3.2,240.007,43984
-Virginia,51,1999,391,7000174,3540081,3443020,97061,2.7,166.6,46223
-Virginia,51,2000,441,7078515,3605811,3522865,82946,2.3,172.2,47313
-Virginia,51,2001,503,7198362,3670137,3551375,118762,3.2,177.1,48194
-Virginia,51,2002,527,7286873,3726468,3569570,156898,4.2,179.9,47962
-Virginia,51,2003,580,7366977,3753703,3599264,154439,4.1,184.0,49191
-Virginia,51,2004,595,7475575,3795246,3650269,144977,3.8,188.9,50400
-Virginia,51,2005,617,7577105,3897042,3757592,139450,3.6,195.3,52305
-Virginia,51,2006,670,7673725,3978641,3855633,123008,3.1,201.6,52866
-Virginia,51,2007,713,7751000,4036835,3914087,122748,3.0,207.342,52657
-Virginia,51,2008,730,7833496,4133443,3970428,163015,3.9,215.303,51985
-Virginia,51,2009,700,7925937,4118171,3842516,275655,6.7,214.537,51389
-Virginia,51,2010,571,8001024,4157658,3860386,297272,7.1,218.056,51946
-Virginia,51,2011,817,8096604,4211802,3934326,277476,6.6,224.939,51754
-Virginia,51,2012,755,8185867,4223844,3967987,255857,6.1,229.594,51546
-Virginia,51,2013,890,8260405,4237277,3995182,242095,5.7,232.957,51106
-Virginia,51,2014,1002,8326289,4244109,4022160,221949,5.2,236.736,50879
-Virginia,51,2015,1070,8382993,4216974,4029043,187931,4.5,237.017,51667
-Virginia,51,2016,1444,8411808,4242650,4069139,173511,4.1,240.007,51643
-Washington,53,1999,596,5842564,3085544,2935202,150342,4.9,166.6,50591
-Washington,53,2000,599,5894121,3059339,2901492,157847,5.2,172.2,50063
-Washington,53,2001,547,5985722,3039367,2847875,191492,6.3,177.1,48408
-Washington,53,2002,694,6052349,3082839,2854762,228077,7.4,179.9,48855
-Washington,53,2003,752,6104115,3127404,2895863,231541,7.4,184.0,49149
-Washington,53,2004,888,6178645,3197095,2996795,200300,6.3,188.9,49407
-Washington,53,2005,931,6257305,3263703,3082399,181304,5.6,195.3,51956
-Washington,53,2006,970,6370753,3323938,3156626,167312,5.0,201.6,52900
-Washington,53,2007,1003,6461587,3403163,3243308,159855,4.7,207.342,55348
-Washington,53,2008,1058,6562231,3478577,3291309,187268,5.4,215.303,54939
-Washington,53,2009,1031,6667426,3535200,3211649,323551,9.2,214.537,52264
-Washington,53,2010,962,6724540,3511326,3160544,350782,10.0,218.056,52679
-Washington,53,2011,1050,6830038,3461428,3140190,321238,9.3,224.939,52500
-Washington,53,2012,1033,6897012,3471282,3189271,282011,8.1,229.594,53439
-Washington,53,2013,1042,6971406,3463869,3219842,244027,7.0,232.957,54161
-Washington,53,2014,1058,7061530,3489666,3275753,213913,6.1,236.736,55197
-Washington,53,2015,1189,7170351,3545904,3345496,200408,5.7,237.017,56407
-Washington,53,2016,1212,7288000,3635200,3444126,191074,5.3,240.007,57727
-West Virginia,54,1999,80,1811799,804343,752379,51964,6.5,166.6,32208
-West Virginia,54,2000,122,1808344,809063,764711,44352,5.5,172.2,32144
-West Virginia,54,2001,217,1801481,809128,768661,40467,5.0,177.1,32263
-West Virginia,54,2002,237,1805414,799998,752548,47450,5.9,179.9,32506
-West Virginia,54,2003,279,1812295,786552,738990,47562,6.0,184.0,32396
-West Virginia,54,2004,347,1816438,783658,741890,41768,5.3,188.9,32862
-West Virginia,54,2005,197,1820492,790982,750561,40421,5.1,195.3,33628
-West Virginia,54,2006,378,1827912,806168,766399,39769,4.9,201.6,34009
-West Virginia,54,2007,405,1834052,811160,773990,37170,4.6,207.342,33892
-West Virginia,54,2008,468,1840310,812905,777560,35345,4.3,215.303,34679
-West Virginia,54,2009,235,1847775,814027,751165,62862,7.7,214.537,34564
-West Virginia,54,2010,520,1852994,811125,740910,70215,8.7,218.056,35368
-West Virginia,54,2011,645,1855364,807021,741972,65049,8.1,224.939,36084
-West Virginia,54,2012,576,1855413,807917,747384,60533,7.5,229.594,35509
-West Virginia,54,2013,583,1854304,798530,744410,54120,6.8,232.957,35755
-West Virginia,54,2014,646,1850326,787845,735511,52334,6.6,236.736,36059
-West Virginia,54,2015,750,1844128,780428,727758,52670,6.7,237.017,36461
-West Virginia,54,2016,912,1831102,781600,733980,47620,6.1,240.007,36244
-Wisconsin,55,1999,237,5332666,2947247,2855212,92035,3.1,166.6,41053
-Wisconsin,55,2000,278,5363675,2973221,2868382,104839,3.5,172.2,41911
-Wisconsin,55,2001,297,5406835,3011703,2875585,136118,4.5,177.1,42078
-Wisconsin,55,2002,364,5445162,3024319,2861621,162698,5.4,179.9,42694
-Wisconsin,55,2003,433,5479203,3053642,2879779,173863,5.7,184.0,43568
-Wisconsin,55,2004,483,5514026,3034581,2882064,152517,5.0,188.9,44455
-Wisconsin,55,2005,561,5546166,3021086,2878086,143000,4.7,195.3,45131
-Wisconsin,55,2006,642,5577655,3058935,2914150,144785,4.7,201.6,45515
-Wisconsin,55,2007,639,5610775,3087828,2936452,151376,4.9,207.342,45464
-Wisconsin,55,2008,622,5640996,3091796,2940438,151358,4.9,215.303,44622
-Wisconsin,55,2009,641,5669264,3100348,2834335,266013,8.6,214.537,43215
-Wisconsin,55,2010,635,5686986,3081512,2814393,267119,8.7,218.056,44126
-Wisconsin,55,2011,712,5711767,3079759,2840996,238763,7.8,224.939,44906
-Wisconsin,55,2012,714,5726398,3073981,2857418,216563,7.0,229.594,45382
-Wisconsin,55,2013,874,5742713,3079305,2871997,207308,6.7,232.957,45845
-Wisconsin,55,2014,874,5757564,3082564,2915848,166716,5.4,236.736,46423
-Wisconsin,55,2015,894,5771337,3094351,2953622,140729,4.5,237.017,47325
-Wisconsin,55,2016,1103,5778708,3130520,3005478,125042,4.0,240.007,47833
-Wyoming,56,1999,21,491780,262758,250605,12153,4.6,166.6,49380
-Wyoming,56,2000,27,493782,266808,256414,10394,3.9,172.2,50814
-Wyoming,56,2001,25,494657,269359,259089,10270,3.8,177.1,53656
-Wyoming,56,2002,36,500017,269546,258650,10896,4.0,179.9,53921
-Wyoming,56,2003,37,503453,273620,261970,11650,4.3,184.0,54930
-Wyoming,56,2004,64,509106,274794,264321,10473,3.8,188.9,55848
-Wyoming,56,2005,45,514157,276527,266630,9897,3.6,195.3,57642
-Wyoming,56,2006,64,522667,281247,272336,8911,3.2,201.6,63428
-Wyoming,56,2007,68,534876,286560,278486,8074,2.8,207.342,65471
-Wyoming,56,2008,74,546043,293279,284310,8969,3.1,215.303,69182
-Wyoming,56,2009,63,559851,300120,281150,18970,6.3,214.537,66320
-Wyoming,56,2010,88,563626,303297,283744,19553,6.4,218.056,64603
-Wyoming,56,2011,88,568158,306815,289019,17796,5.8,224.939,63985
-Wyoming,56,2012,99,576412,307267,290932,16335,5.3,229.594,60777
-Wyoming,56,2013,99,582658,306608,292131,14477,4.7,232.957,60770
-Wyoming,56,2014,112,584153,306331,293657,12674,4.1,236.736,60900
-Wyoming,56,2015,99,586107,304775,291686,13089,4.3,237.017,60979
-Wyoming,56,2016,103,585501,300922,285052,15870,5.3,240.007,60004
\ No newline at end of file
diff --git a/Legalization and Drug Deaths/data/v1_drug_abuse_data.numbers b/Legalization and Drug Deaths/data/v1_drug_abuse_data.numbers
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diff --git a/Legalization and Drug Deaths/CS506 Project Proposal.pdf b/The Effects of Recreational Marijuana Legalization/CS506 Project Proposal.pdf
similarity index 100%
rename from Legalization and Drug Deaths/CS506 Project Proposal.pdf
rename to The Effects of Recreational Marijuana Legalization/CS506 Project Proposal.pdf
diff --git a/Legalization and Drug Deaths/Deliverable 1.pdf b/The Effects of Recreational Marijuana Legalization/Deliverable 1.pdf
similarity index 100%
rename from Legalization and Drug Deaths/Deliverable 1.pdf
rename to The Effects of Recreational Marijuana Legalization/Deliverable 1.pdf
diff --git a/The Effects of Recreational Marijuana Legalization/Deliverable 2.pdf b/The Effects of Recreational Marijuana Legalization/Deliverable 2.pdf
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diff --git a/The Effects of Recreational Marijuana Legalization/Final Paper.pdf b/The Effects of Recreational Marijuana Legalization/Final Paper.pdf
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diff --git a/The Effects of Recreational Marijuana Legalization/Project Step-by-Step Outline.pdf b/The Effects of Recreational Marijuana Legalization/Project Step-by-Step Outline.pdf
new file mode 100644
index 00000000..5757169d
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diff --git a/The Effects of Recreational Marijuana Legalization/Weekly Timeline and SCRUM.pdf b/The Effects of Recreational Marijuana Legalization/Weekly Timeline and SCRUM.pdf
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diff --git a/The Effects of Recreational Marijuana Legalization/data/data.csv b/The Effects of Recreational Marijuana Legalization/data/data.csv
new file mode 100644
index 00000000..a93f7414
--- /dev/null
+++ b/The Effects of Recreational Marijuana Legalization/data/data.csv	
@@ -0,0 +1,24 @@
+Year,Population (C),"Population (US, millions)",Drug Deaths (C),Drug Deaths Rate,,# Suicides (C),Suicide Rate,,Percent of Adult Smokers,,Alcohol Consumption (gallons) (C),Alcohol Consumption (gallons/population),,,Alcohol-Related Driving Fatalities,Alcohol-Related Driving Fatality Rate (% of pop.),,Rehab Admission (% of population),,Income per Capita,,Tax Revenue per Capita,
+1999,4226018,278.57,375,0.0089,0.0061,,,,22.5,22.8,3384222,0.8008063383,223099823,0.8008752665,229,0.005418812698,0.004620023692,0.01396113315,0.006157647988,31477,34720,,
+2000,4301261,281.71,372,0.0086,0.0062,,,,20.0,23.2,3467312,0.8061152299,225930423,0.8019964609,268,0.006230730941,0.0048241099,0.01279136514,0.006211029072,34187,36800,,
+2001,4425687,284.61,444,0.0100,0.0068,,,,22.3,23.2,3551248,0.8024173422,228578068,0.8031273251,328,0.007411278746,0.004760900882,0.009686857656,0.006209985594,35023,37700,,
+2002,4490406,287.28,474,0.0106,0.0082,,,,20.4,23.2,3608580,0.8036199845,231194672,0.8047712058,314,0.006992686185,0.004768866611,0.01507302458,0.006563095238,34608,38430,,
+2003,4528732,289.82,529,0.0117,0.0089,,,,18.6,22.0,3642128,0.8042268785,233701167,0.8063665965,246,0.005431984052,0.004595956111,0.01399420412,0.006423483542,34935,39740,,
+2004,4575013,292.35,548,0.0120,0.0094,792,0.0173,0.0110,20.0,20.9,3689253,0.806391807,236561755,0.8091730973,259,0.005661186099,0.004586967676,0.01510137785,0.006185972293,35870,42060,3170,3451
+2005,4631888,294.99,640,0.0138,0.0101,795,0.0172,0.0109,19.8,20.6,3739118,0.8072557022,239350769,0.8113860436,244,0.00526783031,0.004708634191,0.0166010059,0.006427055154,37841,44570,3365,3717
+2006,4720423,297.76,660,0.0140,0.0115,724,0.0153,0.0110,17.9,20.1,3815012,0.8081928251,242171653,0.8133115697,207,0.004385200225,0.004621171413,0.01662033254,0.006591462923,40140,47160,3635,4041
+2007,4803868,300.61,747,0.0155,0.0119,805,0.0168,0.0113,18.7,19.8,3883503,0.8084116799,244809096,0.8143744253,199,0.004142495173,0.004407704335,0.01652918024,0.007194960247,42024,48280,3872,4260
+2008,4889730,303.49,760,0.0155,0.0119,801,0.0164,0.0116,17.6,18.4,3953614,0.8085546646,247384676,0.8151328742,202,0.004131107444,0.003954001779,0.01755332094,0.007464750074,42689,48290,4006,4372
+2009,4972195,306.31,784,0.0158,0.0119,940,0.0189,0.0118,17.1,17.9,4021971,0.8088924509,249869828,0.8157416604,178,0.003579907868,0.003512781169,0.01775332625,0.00725942346,39982,46920,3905,4180
+2010,5029196,309.01,676,0.0134,0.0123,867,0.0172,0.0121,16.0,17.3,4086694,0.8125939017,252270877,0.816384185,142,0.002823512943,0.003236141225,0.01574704983,0.006762706709,40689,48900,3858,4134
+2011,5116796,311.58,852,0.0167,0.0132,910,0.0178,0.0123,18.3,21.2,4154402,0.8119147216,254542312,0.8169404711,173,0.00338102203,0.003113165158,0.01520795435,0.006695731433,43575,50820,3980,4314
+2012,5187582,314.04,826,0.0159,0.0131,1053,0.0203,0.0125,17.7,19.6,4223262,0.8141099264,256895820,0.8180353458,167,0.003219226221,0.003216150809,0.01694488877,0.0063312094,45669,53120,4075,4422
+2013,5268367,316.40,864,0.0164,0.0138,1004,0.0191,0.0126,17.7,19.0,4297634,0.8157430946,259137618,0.8190190202,170,0.003226806333,0.003103666245,0.0166850563,0.006061472819,50711,54360,4265,4604
+2014,5355866,318.67,917,0.0168,0.0147,1058,0.0198,0.0130,15.7,18.1,4376701,0.8171789585,261564480,0.8208004519,187,0.003491498854,0.002996830577,0.01672820044,0.005668914551,52254,56730,4363,4693
+2015,5456574,320.88,893,0.01593,0.0163,1093,0.0200,0.0133,15.6,17.5,4472741,0.8196976711,263925279,0.8225046092,,,,0.01607034011,0.005736144353,55270,58340,4520,4877
+2016,5540545,323.02,973,0.01701,0.0198,1156,0.0209,0.0134,15.6,17.1,4559826,0.822992323,266191088,0.8240699895,,,,0.01588724575,0.005933140363,57184,58970,4640,4951
+2017,5607154,325.08,,0.01707,0.0217,1175,0.0210,0.0140,14.6,17.1,4637587,0.827083936,268308621,0.8253618217,,,,0.01485477303,0.006168927649,58728,61020,4902,5073
+2018,5697155,327.10,,0.01711,0.0207,,,,14.5,16.1,4718558,0.8282305818,270449061,0.826808502,,,,,,59930,63780,,
+,,,,,,,,,,,,,,,,,,,,,,,
+,Colorado,,,,,,,,,,,,,,,,,,,,,,
+,USA,,,,,,,,,,,,,,,,,,,,,,
\ No newline at end of file
diff --git a/The Effects of Recreational Marijuana Legalization/modeling.py b/The Effects of Recreational Marijuana Legalization/modeling.py
new file mode 100644
index 00000000..c25bafb3
--- /dev/null
+++ b/The Effects of Recreational Marijuana Legalization/modeling.py	
@@ -0,0 +1,1294 @@
+import pandas as pd 
+import numpy as np
+import matplotlib.pyplot as plt 
+import matplotlib.ticker as ticker
+from sklearn.linear_model import LinearRegression
+
+# DRUG DEATHS
+df = pd.read_csv("./data/drugdeaths.csv")
+
+years = df.iloc[:,0].values
+years = years.reshape(-1,1)
+col = df.iloc[:,1].values
+percentchange = df.iloc[:,3].values
+usa = df.iloc[:,2].values
+percentchangeusa = df.iloc[:,4].values
+
+# Fitting a model for the data before legalization
+print('Linear Regression for Colorado Drug Deaths before legalization:')
+preyears = years[:14]
+prevals = col[:14]
+preyears = preyears.reshape(-1, 1)
+reg = LinearRegression().fit(preyears, prevals)
+pre_int = reg.intercept_
+pre_slope = reg.coef_
+print("Intercept is:", pre_int)
+print("Slope is:", pre_slope[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for Colorado Drug Deaths after legalization:')
+postyears = years[13:]
+postvals = col[13:]
+postyears = postyears.reshape(-1, 1)
+reg1 = LinearRegression().fit(postyears, postvals)
+post_int = reg1.intercept_
+post_slope = reg1.coef_
+print("Intercept is:", post_int)
+print("Slope is:", post_slope[0])
+print()
+
+# Fitting a model for years 1999-2005
+print('Linear Regression for Colorado Drug Deaths between 1999-2003:')
+firstyears = years[:4]
+firstvals = col[:4]
+firstyears = firstyears.reshape(-1,1)
+reg2 = LinearRegression().fit(firstyears, firstvals)
+f_int = reg2.intercept_
+f_slope = reg2.coef_
+print("Intercept is:", f_int)
+print("Slope is:", f_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Drug Deaths between 2004-2008:')
+secyears = years[4:9]
+secvals = col[4:9]
+secyears = secyears.reshape(-1,1)
+reg3 = LinearRegression().fit(secyears, secvals)
+s_int = reg3.intercept_
+s_slope = reg3.coef_
+print("Intercept is:", s_int)
+print("Slope is:", s_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Drug Deaths between 2009-2012:')
+thirdyears = years[9:14]
+thirdvals = col[9:14]
+thirdyears = thirdyears.reshape(-1,1)
+reg4 = LinearRegression().fit(thirdyears, thirdvals)
+t_int = reg4.intercept_
+t_slope = reg4.coef_
+print("Intercept is:", t_int)
+print("Slope is:", t_slope[0])
+print()
+
+# Fitting a model for USA
+# Fitting a model for the data before legalization
+print('Linear Regression for NATIONAL Drug Deaths before legalization:')
+preyears = years[:14]
+prevals = usa[:14]
+preyears = preyears.reshape(-1, 1)
+reg5 = LinearRegression().fit(preyears, prevals)
+pre_int_us = reg5.intercept_
+pre_slope_us = reg5.coef_
+print("Intercept is:", pre_int_us)
+print("Slope is:", pre_slope_us[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for NATIONAL Drug Deaths after legalization:')
+postyears = years[13:]
+postvals = usa[13:]
+postyears = postyears.reshape(-1, 1)
+reg6 = LinearRegression().fit(postyears, postvals)
+post_int_us = reg6.intercept_
+post_slope_us = reg6.coef_
+print("Intercept is:", post_int_us)
+print("Slope is:", post_slope_us[0])
+print()
+
+# Basic analysis
+change = (post_slope[0]-pre_slope[0])/(pre_slope[0])*100
+print("The percent change in slopes in Colorado before and after 2012 was ", change, "percent")
+prechange = np.mean(percentchange[1:14])
+postchange = np.mean(percentchange[13:])
+prechangeusa = np.mean(percentchangeusa[1:14])
+postchangeusa = np.mean(percentchangeusa[14:])
+print("The average percentage increase of drug deaths each year in Colorado before 2012 legalization was ", prechange, "percent")
+print("The average percentage increase of drug deaths each year in Colorado after 2012 legalization was ", postchange, "percent")
+print("The average percentage increase of drug deaths each year in the US before 2012 legalization was ", prechangeusa, "percent")
+print("The average percentage increase of drug deaths each year in the US after 2012 legalization was ", postchangeusa, "percent")
+
+# Plotting the linear regression model
+plt.xticks(years[::2])
+plt.title('Linear Regression of Drug Death Rate in Colorado')
+plt.xlabel('Year')
+plt.ylabel('Drug Death Rate')
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization')
+plt.plot(firstyears, reg2.predict(firstyears), color='magenta', label='Short-term trends')
+plt.plot(secyears, reg3.predict(secyears), color='magenta')
+plt.plot(thirdyears, reg4.predict(thirdyears), color='magenta')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+# Plotting the difference between what we expected (assuming no legalization) and what we found with legalization
+coords = []
+postpredictions = reg1.predict(postyears)
+prepredictions = reg.predict(years[13:])
+for i in range(len(postpredictions)):
+    coords.append((prepredictions[i], postpredictions[i]))
+predyears = []
+for j in range(len(postyears)):
+    predyears.append(postyears[j][0])
+plt.plot((predyears,predyears),([i for (i,j) in coords], [j for (i,j) in coords]),c='green')
+plt.legend()
+plt.show()
+# Plotting national trendline
+plt.xticks(years[::2])
+plt.title('Comparing Colorado with National Trend')
+plt.xlabel('Year')
+plt.ylabel('Drug Death Rate')
+plt.scatter(years, usa, color='orange', s=5)
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization (Colorado)')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization (Colorado)')
+plt.plot(years, reg5.predict(years),color='orange', label='Long-term trend before legalization (USA)')
+plt.plot(postyears, reg6.predict(postyears),color='purple', label='Trend after legalization (USA)')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+plt.legend()
+plt.show()
+
+# GDP PER CAPITA
+df = pd.read_csv("./data/gdp.csv")
+
+years = df.iloc[:,0].values
+years = years.reshape(-1,1)
+col = df.iloc[:,1].values
+percentchange = df.iloc[:,3].values
+usa = df.iloc[:,2].values
+percentchangeusa = df.iloc[:,4].values
+
+# Fitting a model for the data before legalization
+print('Linear Regression for Colorado GDP/Capita before legalization:')
+preyears = years[:14]
+prevals = col[:14]
+preyears = preyears.reshape(-1, 1)
+reg = LinearRegression().fit(preyears, prevals)
+pre_int = reg.intercept_
+pre_slope = reg.coef_
+print("Intercept is:", pre_int)
+print("Slope is:", pre_slope[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for Colorado GDP/Capita after legalization:')
+postyears = years[13:]
+postvals = col[13:]
+postyears = postyears.reshape(-1, 1)
+reg1 = LinearRegression().fit(postyears, postvals)
+post_int = reg1.intercept_
+post_slope = reg1.coef_
+print("Intercept is:", post_int)
+print("Slope is:", post_slope[0])
+print()
+
+# Fitting a model for years 1999-2005
+print('Linear Regression for Colorado GDP/Capita between 1999-2003:')
+firstyears = years[:4]
+firstvals = col[:4]
+firstyears = firstyears.reshape(-1,1)
+reg2 = LinearRegression().fit(firstyears, firstvals)
+f_int = reg2.intercept_
+f_slope = reg2.coef_
+print("Intercept is:", f_int)
+print("Slope is:", f_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado GDP/Capita between 2004-2008:')
+secyears = years[4:9]
+secvals = col[4:9]
+secyears = secyears.reshape(-1,1)
+reg3 = LinearRegression().fit(secyears, secvals)
+s_int = reg3.intercept_
+s_slope = reg3.coef_
+print("Intercept is:", s_int)
+print("Slope is:", s_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado GDP/Capita between 2009-2012:')
+thirdyears = years[9:14]
+thirdvals = col[9:14]
+thirdyears = thirdyears.reshape(-1,1)
+reg4 = LinearRegression().fit(thirdyears, thirdvals)
+t_int = reg4.intercept_
+t_slope = reg4.coef_
+print("Intercept is:", t_int)
+print("Slope is:", t_slope[0])
+print()
+
+# Fitting a model for USA
+# Fitting a model for the data before legalization
+print('Linear Regression for NATIONAL GDP/Capita before legalization:')
+preyears = years[:14]
+prevals = usa[:14]
+preyears = preyears.reshape(-1, 1)
+reg5 = LinearRegression().fit(preyears, prevals)
+pre_int_us = reg5.intercept_
+pre_slope_us = reg5.coef_
+print("Intercept is:", pre_int_us)
+print("Slope is:", pre_slope_us[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for NATIONAL GDP/Capita after legalization:')
+postyears = years[13:]
+postvals = usa[13:]
+postyears = postyears.reshape(-1, 1)
+reg6 = LinearRegression().fit(postyears, postvals)
+post_int_us = reg6.intercept_
+post_slope_us = reg6.coef_
+print("Intercept is:", post_int_us)
+print("Slope is:", post_slope_us[0])
+print()
+
+# Basic analysis
+change = (post_slope[0]-pre_slope[0])/(pre_slope[0])*100
+print("The percent change in slopes was ", change, "percent")
+prechange = np.mean(percentchange[1:14])
+postchange = np.mean(percentchange[13:])
+prechangeusa = np.mean(percentchangeusa[1:14])
+postchangeusa = np.mean(percentchangeusa[13:])
+print("The average percentage increase of GDP/Capita each year in Colorado before 2012 legalization was ", prechange, "percent")
+print("The average percentage increase of GDP/Capita each year in Colorado after 2012 legalization was ", postchange, "percent")
+print("The average percentage increase of GDP/Capita each year in the US before 2012 legalization was ", prechangeusa, "percent")
+print("The average percentage increase of GDP/Capita each year in the US after 2012 legalization was ", postchangeusa, "percent")
+
+# Plotting the linear regression model
+plt.xticks(years[::2])
+plt.title('Linear Regression of GDP/Capita in Colorado')
+plt.xlabel('Year')
+plt.ylabel('GDP/Capita')
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization')
+plt.plot(firstyears, reg2.predict(firstyears), color='magenta', label='Short-term trends')
+plt.plot(secyears, reg3.predict(secyears), color='magenta')
+plt.plot(thirdyears, reg4.predict(thirdyears), color='magenta')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+# Plotting the difference between what we expected (assuming no legalization) and what we found with legalization
+coords = []
+postpredictions = reg1.predict(postyears)
+prepredictions = reg.predict(years[13:])
+for i in range(len(postpredictions)):
+    coords.append((prepredictions[i], postpredictions[i]))
+predyears = []
+for j in range(len(postyears)):
+    predyears.append(postyears[j][0])
+plt.plot((predyears,predyears),([i for (i,j) in coords], [j for (i,j) in coords]),c='green')
+plt.legend()
+plt.show()
+# Plotting national trendline
+plt.xticks(years[::2])
+plt.title('Comparing Colorado with National Trend')
+plt.xlabel('Year')
+plt.ylabel('GDP/Capita')
+plt.scatter(years, usa, color='orange', s=5)
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization (Colorado)')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization (Colorado)')
+plt.plot(years, reg5.predict(years),color='orange', label='Long-term trend before legalization (USA)')
+plt.plot(postyears, reg6.predict(postyears),color='purple', label='Trend after legalization (USA)')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+plt.legend()
+plt.show()
+
+# TAX REVENUE PER CAPITA
+df = pd.read_csv("./data/tax.csv")
+
+years = df.iloc[:,0].values
+years = years.reshape(-1,1)
+col = df.iloc[:,1].values
+percentchange = df.iloc[:,3].values
+usa = df.iloc[:,2].values
+percentchangeusa = df.iloc[:,4].values
+
+# Fitting a model for the data before legalization
+print('Linear Regression for Colorado Tax Revenue/Capita before legalization:')
+preyears = years[:9]
+prevals = col[:9]
+preyears = preyears.reshape(-1, 1)
+reg = LinearRegression().fit(preyears, prevals)
+pre_int = reg.intercept_
+pre_slope = reg.coef_
+print("Intercept is:", pre_int)
+print("Slope is:", pre_slope[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for Colorado Tax Revenue/Capita after legalization:')
+postyears = years[8:]
+postvals = col[8:]
+postyears = postyears.reshape(-1, 1)
+reg1 = LinearRegression().fit(postyears, postvals)
+post_int = reg1.intercept_
+post_slope = reg1.coef_
+print("Intercept is:", post_int)
+print("Slope is:", post_slope[0])
+print()
+
+# Fitting a model for years 2004-2008
+print('Linear Regression for Colorado Tax Revenue/Capita between 2004-2008:')
+firstyears = years[:4]
+firstvals = col[:4]
+firstyears = firstyears.reshape(-1,1)
+reg2 = LinearRegression().fit(firstyears, firstvals)
+f_int = reg2.intercept_
+f_slope = reg2.coef_
+print("Intercept is:", f_int)
+print("Slope is:", f_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Tax Revenue/Capita between 2008-2012:')
+secyears = years[4:9]
+secvals = col[4:9]
+secyears = secyears.reshape(-1,1)
+reg3 = LinearRegression().fit(secyears, secvals)
+s_int = reg3.intercept_
+s_slope = reg3.coef_
+print("Intercept is:", s_int)
+print("Slope is:", s_slope[0])
+print()
+
+# Fitting a model for USA
+# Fitting a model for the data before legalization
+print('Linear Regression for NATIONAL Tax Revenue/Capita before legalization:')
+preyears = years[:9]
+prevals = usa[:9]
+preyears = preyears.reshape(-1, 1)
+reg5 = LinearRegression().fit(preyears, prevals)
+pre_int_us = reg5.intercept_
+pre_slope_us = reg5.coef_
+print("Intercept is:", pre_int_us)
+print("Slope is:", pre_slope_us[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for NATIONAL Tax Revenue/Capita after legalization:')
+postyears = years[8:]
+postvals = usa[8:]
+postyears = postyears.reshape(-1, 1)
+reg6 = LinearRegression().fit(postyears, postvals)
+post_int_us = reg6.intercept_
+post_slope_us = reg6.coef_
+print("Intercept is:", post_int_us)
+print("Slope is:", post_slope_us[0])
+print()
+
+# Basic analysis
+change = (post_slope[0]-pre_slope[0])/(pre_slope[0])*100
+print("The percent change in slopes was ", change, "percent")
+prechange = np.mean(percentchange[1:9])
+postchange = np.mean(percentchange[8:])
+prechangeusa = np.mean(percentchangeusa[1:9])
+postchangeusa = np.mean(percentchangeusa[8:])
+print("The average percentage increase of Tax Revenue/Capita each year in Colorado before 2012 legalization was ", prechange, "percent")
+print("The average percentage increase of Tax Revenue/Capita each year in Colorado after 2012 legalization was ", postchange, "percent")
+print("The average percentage increase of Tax Revenue/Capita each year in the US before 2012 legalization was ", prechangeusa, "percent")
+print("The average percentage increase of Tax Revenue/Capita each year in the US after 2012 legalization was ", postchangeusa, "percent")
+# Plotting the linear regression model
+plt.xticks(years[::2])
+plt.title('Linear Regression of Tax Revenue/Capita in Colorado')
+plt.xlabel('Year')
+plt.ylabel('Tax Revenue/Capita')
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization')
+plt.plot(firstyears, reg2.predict(firstyears), color='magenta', label='Short-term trends')
+plt.plot(secyears, reg3.predict(secyears), color='magenta')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+# Plotting the difference between what we expected (assuming no legalization) and what we found with legalization
+coords = []
+postpredictions = reg1.predict(postyears)
+prepredictions = reg.predict(years[8:])
+for i in range(len(postpredictions)):
+    coords.append((prepredictions[i], postpredictions[i]))
+predyears = []
+for j in range(len(postyears)):
+    predyears.append(postyears[j][0])
+plt.plot((predyears,predyears),([i for (i,j) in coords], [j for (i,j) in coords]),c='green')
+plt.legend()
+plt.show()
+# Plotting national trendline
+plt.xticks(years[::2])
+plt.title('Comparing Colorado with National Trend')
+plt.xlabel('Year')
+plt.ylabel('Tax Revenue/Capita')
+plt.scatter(years, usa, color='orange', s=5)
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization (Colorado)')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization (Colorado)')
+plt.plot(years, reg5.predict(years),color='orange', label='Long-term trend before legalization (USA)')
+plt.plot(postyears, reg6.predict(postyears),color='purple', label='Trend after legalization (USA)')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+plt.legend()
+plt.show()
+
+
+# UNEMPLOYMENT RATE
+df = pd.read_csv("./data/unemployment.csv")
+
+years = df.iloc[:,0].values
+years = years.reshape(-1,1)
+col = df.iloc[:,1].values
+percentchange = df.iloc[:,3].values
+usa = df.iloc[:,2].values
+percentchangeusa = df.iloc[:,4].values
+
+# Fitting a model for the data before legalization
+print('Linear Regression for Colorado Unemployment Rate before legalization:')
+preyears = years[:14]
+prevals = col[:14]
+preyears = preyears.reshape(-1, 1)
+reg = LinearRegression().fit(preyears, prevals)
+pre_int = reg.intercept_
+pre_slope = reg.coef_
+print("Intercept is:", pre_int)
+print("Slope is:", pre_slope[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for Colorado Unemployment Rate after legalization:')
+postyears = years[13:]
+postvals = col[13:]
+postyears = postyears.reshape(-1, 1)
+reg1 = LinearRegression().fit(postyears, postvals)
+post_int = reg1.intercept_
+post_slope = reg1.coef_
+print("Intercept is:", post_int)
+print("Slope is:", post_slope[0])
+print()
+
+# Fitting a model for years 1999-2005
+print('Linear Regression for Colorado Unemployment Rate between 1999-2003:')
+firstyears = years[:4]
+firstvals = col[:4]
+firstyears = firstyears.reshape(-1,1)
+reg2 = LinearRegression().fit(firstyears, firstvals)
+f_int = reg2.intercept_
+f_slope = reg2.coef_
+print("Intercept is:", f_int)
+print("Slope is:", f_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Unemployment Rate between 2004-2008:')
+secyears = years[4:9]
+secvals = col[4:9]
+secyears = secyears.reshape(-1,1)
+reg3 = LinearRegression().fit(secyears, secvals)
+s_int = reg3.intercept_
+s_slope = reg3.coef_
+print("Intercept is:", s_int)
+print("Slope is:", s_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Unemployment Rate between 2009-2012:')
+thirdyears = years[9:14]
+thirdvals = col[9:14]
+thirdyears = thirdyears.reshape(-1,1)
+reg4 = LinearRegression().fit(thirdyears, thirdvals)
+t_int = reg4.intercept_
+t_slope = reg4.coef_
+print("Intercept is:", t_int)
+print("Slope is:", t_slope[0])
+print()
+
+# Fitting a model for USA
+# Fitting a model for the data before legalization
+print('Linear Regression for NATIONAL Unemployment Rate before legalization:')
+preyears = years[:14]
+prevals = usa[:14]
+preyears = preyears.reshape(-1, 1)
+reg5 = LinearRegression().fit(preyears, prevals)
+pre_int_us = reg5.intercept_
+pre_slope_us = reg5.coef_
+print("Intercept is:", pre_int_us)
+print("Slope is:", pre_slope_us[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for NATIONAL Unemployment Rate after legalization:')
+postyears = years[13:]
+postvals = usa[13:]
+postyears = postyears.reshape(-1, 1)
+reg6 = LinearRegression().fit(postyears, postvals)
+post_int_us = reg6.intercept_
+post_slope_us = reg6.coef_
+print("Intercept is:", post_int_us)
+print("Slope is:", post_slope_us[0])
+print()
+
+# Basic analysis
+change = (post_slope[0]-pre_slope[0])/(pre_slope[0])*100
+print("The percent change in slopes was ", change, "percent")
+prechange = np.mean(percentchange[1:14])
+postchange = np.mean(percentchange[13:])
+prechangeusa = np.mean(percentchangeusa[1:14])
+postchangeusa = np.mean(percentchangeusa[13:])
+print("The average percentage increase of the UE rate each year in Colorado before 2012 legalization was ", prechange, "percent")
+print("The average percentage increase of the UE rate each year in Colorado after 2012 legalization was ", postchange, "percent")
+print("The average percentage increase of the UE rate each year in the US before 2012 legalization was ", prechangeusa, "percent")
+print("The average percentage increase of the UE rate each year in the US after 2012 legalization was ", postchangeusa, "percent")
+
+# Plotting the linear regression model
+plt.xticks(years[::2])
+plt.title('Linear Regression of Unemployment Rate in Colorado')
+plt.xlabel('Year')
+plt.ylabel('Unemployment Rate')
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization')
+plt.plot(firstyears, reg2.predict(firstyears), color='magenta', label='Short-term trends')
+plt.plot(secyears, reg3.predict(secyears), color='magenta')
+plt.plot(thirdyears, reg4.predict(thirdyears), color='magenta')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+# Plotting the difference between what we expected (assuming no legalization) and what we found with legalization
+coords = []
+postpredictions = reg1.predict(postyears)
+prepredictions = reg.predict(years[13:])
+for i in range(len(postpredictions)):
+    coords.append((prepredictions[i], postpredictions[i]))
+predyears = []
+for j in range(len(postyears)):
+    predyears.append(postyears[j][0])
+plt.plot((predyears,predyears),([i for (i,j) in coords], [j for (i,j) in coords]),c='green')
+plt.legend()
+plt.show()
+# Plotting national trendline
+plt.xticks(years[::2])
+plt.title('Comparing Colorado with National Trend')
+plt.xlabel('Year')
+plt.ylabel('Unemployment Rate')
+plt.scatter(years, usa, color='orange', s=5)
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization (Colorado)')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization (Colorado)')
+plt.plot(years, reg5.predict(years),color='orange', label='Long-term trend before legalization (USA)')
+plt.plot(postyears, reg6.predict(postyears),color='purple', label='Trend after legalization (USA)')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+plt.legend()
+plt.show()
+
+# CIGARETTE SALES
+df = pd.read_csv("./data/cig.csv")
+
+years = df.iloc[:,0].values
+years = years.reshape(-1,1)
+col = df.iloc[:,1].values
+percentchange = df.iloc[:,3].values
+usa = df.iloc[:,2].values
+percentchangeusa = df.iloc[:,4].values
+
+# Fitting a model for the data before legalization
+print('Linear Regression for Colorado Cigarette Sales before legalization:')
+preyears = years[:14]
+prevals = col[:14]
+preyears = preyears.reshape(-1, 1)
+reg = LinearRegression().fit(preyears, prevals)
+pre_int = reg.intercept_
+pre_slope = reg.coef_
+print("Intercept is:", pre_int)
+print("Slope is:", pre_slope[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for Colorado Cigarette Sales after legalization:')
+postyears = years[13:]
+postvals = col[13:]
+postyears = postyears.reshape(-1, 1)
+reg1 = LinearRegression().fit(postyears, postvals)
+post_int = reg1.intercept_
+post_slope = reg1.coef_
+print("Intercept is:", post_int)
+print("Slope is:", post_slope[0])
+print()
+
+# Fitting a model for years 1999-2005
+print('Linear Regression for Colorado Cigarette Sales between 1999-2003:')
+firstyears = years[:4]
+firstvals = col[:4]
+firstyears = firstyears.reshape(-1,1)
+reg2 = LinearRegression().fit(firstyears, firstvals)
+f_int = reg2.intercept_
+f_slope = reg2.coef_
+print("Intercept is:", f_int)
+print("Slope is:", f_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Cigarette Sales between 2004-2008:')
+secyears = years[4:9]
+secvals = col[4:9]
+secyears = secyears.reshape(-1,1)
+reg3 = LinearRegression().fit(secyears, secvals)
+s_int = reg3.intercept_
+s_slope = reg3.coef_
+print("Intercept is:", s_int)
+print("Slope is:", s_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Cigarette Sales between 2009-2012:')
+thirdyears = years[9:14]
+thirdvals = col[9:14]
+thirdyears = thirdyears.reshape(-1,1)
+reg4 = LinearRegression().fit(thirdyears, thirdvals)
+t_int = reg4.intercept_
+t_slope = reg4.coef_
+print("Intercept is:", t_int)
+print("Slope is:", t_slope[0])
+print()
+
+# Fitting a model for USA
+# Fitting a model for the data before legalization
+print('Linear Regression for NATIONAL Cigarette Sales before legalization:')
+preyears = years[:14]
+prevals = usa[:14]
+preyears = preyears.reshape(-1, 1)
+reg5 = LinearRegression().fit(preyears, prevals)
+pre_int_us = reg5.intercept_
+pre_slope_us = reg5.coef_
+print("Intercept is:", pre_int_us)
+print("Slope is:", pre_slope_us[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for NATIONAL Cigarette Sales after legalization:')
+postyears = years[13:]
+postvals = usa[13:]
+postyears = postyears.reshape(-1, 1)
+reg6 = LinearRegression().fit(postyears, postvals)
+post_int_us = reg6.intercept_
+post_slope_us = reg6.coef_
+print("Intercept is:", post_int_us)
+print("Slope is:", post_slope_us[0])
+print()
+
+# Basic analysis
+change = (post_slope[0]-pre_slope[0])/(pre_slope[0])*100
+print("The percent change in slopes was ", change, "percent")
+prechange = np.mean(percentchange[1:14])
+postchange = np.mean(percentchange[13:])
+prechangeusa = np.mean(percentchangeusa[1:14])
+postchangeusa = np.mean(percentchangeusa[13:])
+print("The average percentage increase of cigarette sales each year in Colorado before 2012 legalization was ", prechange, "percent")
+print("The average percentage increase of cigarette sales each year in Colorado after 2012 legalization was ", postchange, "percent")
+print("The average percentage increase of cigarette sales each year in the US before 2012 legalization was ", prechangeusa, "percent")
+print("The average percentage increase of cigarette sales each year in the US after 2012 legalization was ", postchangeusa, "percent")
+
+# Plotting the linear regression model
+plt.xticks(years[::2])
+plt.title('Linear Regression of Cigarette Sales in Colorado')
+plt.xlabel('Year')
+plt.ylabel('Cigarette Sales')
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization')
+plt.plot(firstyears, reg2.predict(firstyears), color='magenta', label='Short-term trends')
+plt.plot(secyears, reg3.predict(secyears), color='magenta')
+plt.plot(thirdyears, reg4.predict(thirdyears), color='magenta')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+# Plotting the difference between what we expected (assuming no legalization) and what we found with legalization
+coords = []
+postpredictions = reg1.predict(postyears)
+prepredictions = reg.predict(years[13:])
+for i in range(len(postpredictions)):
+    coords.append((prepredictions[i], postpredictions[i]))
+predyears = []
+for j in range(len(postyears)):
+    predyears.append(postyears[j][0])
+plt.plot((predyears,predyears),([i for (i,j) in coords], [j for (i,j) in coords]),c='green')
+plt.legend()
+plt.show()
+# Plotting national trendline
+plt.xticks(years[::2])
+plt.title('Comparing Colorado with National Trend')
+plt.xlabel('Year')
+plt.ylabel('Cigarette Sales')
+plt.scatter(years, usa, color='orange', s=5)
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization (Colorado)')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization (Colorado)')
+plt.plot(years, reg5.predict(years),color='orange', label='Long-term trend before legalization (USA)')
+plt.plot(postyears, reg6.predict(postyears),color='purple', label='Trend after legalization (USA)')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+plt.legend()
+plt.show()
+
+
+# ALCOHOL CONSUMPTION
+df = pd.read_csv("./data/alccons.csv")
+
+years = df.iloc[:,0].values
+years = years.reshape(-1,1)
+col = df.iloc[:,1].values
+percentchange = df.iloc[:,3].values
+usa = df.iloc[:,2].values
+percentchangeusa = df.iloc[:,4].values
+
+# Fitting a model for the data before legalization
+print('Linear Regression for Colorado Alcohol Consumption before legalization:')
+preyears = years[:14]
+prevals = col[:14]
+preyears = preyears.reshape(-1, 1)
+reg = LinearRegression().fit(preyears, prevals)
+pre_int = reg.intercept_
+pre_slope = reg.coef_
+print("Intercept is:", pre_int)
+print("Slope is:", pre_slope[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for Colorado Alcohol Consumption after legalization:')
+postyears = years[13:]
+postvals = col[13:]
+postyears = postyears.reshape(-1, 1)
+reg1 = LinearRegression().fit(postyears, postvals)
+post_int = reg1.intercept_
+post_slope = reg1.coef_
+print("Intercept is:", post_int)
+print("Slope is:", post_slope[0])
+print()
+
+# Fitting a model for years 1999-2005
+print('Linear Regression for Colorado Alcohol Consumption between 1999-2003:')
+firstyears = years[:4]
+firstvals = col[:4]
+firstyears = firstyears.reshape(-1,1)
+reg2 = LinearRegression().fit(firstyears, firstvals)
+f_int = reg2.intercept_
+f_slope = reg2.coef_
+print("Intercept is:", f_int)
+print("Slope is:", f_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Alcohol Consumption between 2004-2008:')
+secyears = years[4:9]
+secvals = col[4:9]
+secyears = secyears.reshape(-1,1)
+reg3 = LinearRegression().fit(secyears, secvals)
+s_int = reg3.intercept_
+s_slope = reg3.coef_
+print("Intercept is:", s_int)
+print("Slope is:", s_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Alcohol Consumption between 2009-2012:')
+thirdyears = years[9:14]
+thirdvals = col[9:14]
+thirdyears = thirdyears.reshape(-1,1)
+reg4 = LinearRegression().fit(thirdyears, thirdvals)
+t_int = reg4.intercept_
+t_slope = reg4.coef_
+print("Intercept is:", t_int)
+print("Slope is:", t_slope[0])
+print()
+
+# Fitting a model for USA
+# Fitting a model for the data before legalization
+print('Linear Regression for NATIONAL Alcohol Consumption before legalization:')
+preyears = years[:14]
+prevals = usa[:14]
+preyears = preyears.reshape(-1, 1)
+reg5 = LinearRegression().fit(preyears, prevals)
+pre_int_us = reg5.intercept_
+pre_slope_us = reg5.coef_
+print("Intercept is:", pre_int_us)
+print("Slope is:", pre_slope_us[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for NATIONAL Alcohol Consumption after legalization:')
+postyears = years[13:]
+postvals = usa[13:]
+postyears = postyears.reshape(-1, 1)
+reg6 = LinearRegression().fit(postyears, postvals)
+post_int_us = reg6.intercept_
+post_slope_us = reg6.coef_
+print("Intercept is:", post_int_us)
+print("Slope is:", post_slope_us[0])
+print()
+
+# Basic analysis
+change = (post_slope[0]-pre_slope[0])/(pre_slope[0])*100
+print("The percent change in slopes was ", change, "percent")
+prechange = np.mean(percentchange[1:14])
+postchange = np.mean(percentchange[13:])
+prechangeusa = np.mean(percentchangeusa[1:14])
+postchangeusa = np.mean(percentchangeusa[13:])
+print("The average percentage increase of alcohol consumption each year in Colorado before 2012 legalization was ", prechange, "percent")
+print("The average percentage increase of alcohol consumption each year in Colorado after 2012 legalization was ", postchange, "percent")
+print("The average percentage increase of alcohol consumption each year in the US before 2012 legalization was ", prechangeusa, "percent")
+print("The average percentage increase of alcohol consumption each year in the US after 2012 legalization was ", postchangeusa, "percent")
+
+# Plotting the linear regression model
+plt.xticks(years[::2])
+plt.title('Linear Regression of Alcohol Consumption in Colorado')
+plt.xlabel('Year')
+plt.ylabel('Alcohol Consumption')
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization')
+plt.plot(firstyears, reg2.predict(firstyears), color='magenta', label='Short-term trends')
+plt.plot(secyears, reg3.predict(secyears), color='magenta')
+plt.plot(thirdyears, reg4.predict(thirdyears), color='magenta')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+# Plotting the difference between what we expected (assuming no legalization) and what we found with legalization
+coords = []
+postpredictions = reg1.predict(postyears)
+prepredictions = reg.predict(years[13:])
+for i in range(len(postpredictions)):
+    coords.append((prepredictions[i], postpredictions[i]))
+predyears = []
+for j in range(len(postyears)):
+    predyears.append(postyears[j][0])
+plt.plot((predyears,predyears),([i for (i,j) in coords], [j for (i,j) in coords]),c='green')
+plt.legend()
+plt.show()
+# Plotting national trendline
+plt.xticks(years[::2])
+plt.title('Comparing Colorado with National Trend')
+plt.xlabel('Year')
+plt.ylabel('Alcohol Consumption')
+plt.scatter(years, usa, color='orange', s=5)
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization (Colorado)')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization (Colorado)')
+plt.plot(years, reg5.predict(years),color='orange', label='Long-term trend before legalization (USA)')
+plt.plot(postyears, reg6.predict(postyears),color='purple', label='Trend after legalization (USA)')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+plt.legend()
+plt.show()
+
+# ADMISSION TO REHAB
+df = pd.read_csv("./data/rehab.csv")
+
+years = df.iloc[:,0].values
+years = years.reshape(-1,1)
+col = df.iloc[:,1].values
+percentchange = df.iloc[:,3].values
+usa = df.iloc[:,2].values
+percentchangeusa = df.iloc[:,4].values
+
+# Fitting a model for the data before legalization
+print('Linear Regression for Colorado admission to rehab before legalization:')
+preyears = years[:14]
+prevals = col[:14]
+preyears = preyears.reshape(-1, 1)
+reg = LinearRegression().fit(preyears, prevals)
+pre_int = reg.intercept_
+pre_slope = reg.coef_
+print("Intercept is:", pre_int)
+print("Slope is:", pre_slope[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for Colorado admission to rehab after legalization:')
+postyears = years[13:]
+postvals = col[13:]
+postyears = postyears.reshape(-1, 1)
+reg1 = LinearRegression().fit(postyears, postvals)
+post_int = reg1.intercept_
+post_slope = reg1.coef_
+print("Intercept is:", post_int)
+print("Slope is:", post_slope[0])
+print()
+
+# Fitting a model for years 1999-2005
+print('Linear Regression for Colorado admission to rehab between 1999-2003:')
+firstyears = years[:4]
+firstvals = col[:4]
+firstyears = firstyears.reshape(-1,1)
+reg2 = LinearRegression().fit(firstyears, firstvals)
+f_int = reg2.intercept_
+f_slope = reg2.coef_
+print("Intercept is:", f_int)
+print("Slope is:", f_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado admission to rehab between 2004-2008:')
+secyears = years[4:9]
+secvals = col[4:9]
+secyears = secyears.reshape(-1,1)
+reg3 = LinearRegression().fit(secyears, secvals)
+s_int = reg3.intercept_
+s_slope = reg3.coef_
+print("Intercept is:", s_int)
+print("Slope is:", s_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado admission to rehab between 2009-2012:')
+thirdyears = years[9:14]
+thirdvals = col[9:14]
+thirdyears = thirdyears.reshape(-1,1)
+reg4 = LinearRegression().fit(thirdyears, thirdvals)
+t_int = reg4.intercept_
+t_slope = reg4.coef_
+print("Intercept is:", t_int)
+print("Slope is:", t_slope[0])
+print()
+
+# Fitting a model for USA
+# Fitting a model for the data before legalization
+print('Linear Regression for NATIONAL admission to rehab before legalization:')
+preyears = years[:14]
+prevals = usa[:14]
+preyears = preyears.reshape(-1, 1)
+reg5 = LinearRegression().fit(preyears, prevals)
+pre_int_us = reg5.intercept_
+pre_slope_us = reg5.coef_
+print("Intercept is:", pre_int_us)
+print("Slope is:", pre_slope_us[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for NATIONAL admission to rehab after legalization:')
+postyears = years[13:]
+postvals = usa[13:]
+postyears = postyears.reshape(-1, 1)
+reg6 = LinearRegression().fit(postyears, postvals)
+post_int_us = reg6.intercept_
+post_slope_us = reg6.coef_
+print("Intercept is:", post_int_us)
+print("Slope is:", post_slope_us[0])
+print()
+
+# Basic analysis
+change = (post_slope[0]-pre_slope[0])/(pre_slope[0])*100
+print("The percent change in slopes was ", change, "percent")
+prechange = np.mean(percentchange[1:14])
+postchange = np.mean(percentchange[13:])
+prechangeusa = np.mean(percentchangeusa[1:14])
+postchangeusa = np.mean(percentchangeusa[13:])
+print("The average percentage increase of rehab admissions each year in Colorado before 2012 legalization was ", prechange, "percent")
+print("The average percentage increase of rehab admissions each year in Colorado after 2012 legalization was ", postchange, "percent")
+print("The average percentage increase of rehab admissions each year in the US before 2012 legalization was ", prechangeusa, "percent")
+print("The average percentage increase of rehab admissions each year in the US after 2012 legalization was ", postchangeusa, "percent")
+
+# Plotting the linear regression model
+plt.xticks(years[::2])
+plt.title('Linear Regression of Rehab Admissions in Colorado')
+plt.xlabel('Year')
+plt.ylabel('Rehab Admissions')
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization')
+plt.plot(firstyears, reg2.predict(firstyears), color='magenta', label='Short-term trends')
+plt.plot(secyears, reg3.predict(secyears), color='magenta')
+plt.plot(thirdyears, reg4.predict(thirdyears), color='magenta')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+# Plotting the difference between what we expected (assuming no legalization) and what we found with legalization
+coords = []
+postpredictions = reg1.predict(postyears)
+prepredictions = reg.predict(years[13:])
+for i in range(len(postpredictions)):
+    coords.append((prepredictions[i], postpredictions[i]))
+predyears = []
+for j in range(len(postyears)):
+    predyears.append(postyears[j][0])
+plt.plot((predyears,predyears),([i for (i,j) in coords], [j for (i,j) in coords]),c='green')
+plt.legend()
+plt.show()
+# Plotting national trendline
+plt.xticks(years[::2])
+plt.title('Comparing Colorado with National Trend')
+plt.xlabel('Year')
+plt.ylabel('Rehab Admissions')
+plt.scatter(years, usa, color='orange', s=5)
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization (Colorado)')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization (Colorado)')
+plt.plot(years, reg5.predict(years),color='orange', label='Long-term trend before legalization (USA)')
+plt.plot(postyears, reg6.predict(postyears),color='purple', label='Trend after legalization (USA)')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+plt.legend()
+plt.show()
+
+# SUICIDE RATES
+df = pd.read_csv("./data/suicide.csv")
+
+years = df.iloc[:,0].values
+years = years.reshape(-1,1)
+col = df.iloc[:,1].values
+percentchange = df.iloc[:,3].values
+usa = df.iloc[:,2].values
+percentchangeusa = df.iloc[:,4].values
+
+# Fitting a model for the data before legalization
+print('Linear Regression for Colorado Suicide Rates before legalization:')
+preyears = years[:9]
+prevals = col[:9]
+preyears = preyears.reshape(-1, 1)
+reg = LinearRegression().fit(preyears, prevals)
+pre_int = reg.intercept_
+pre_slope = reg.coef_
+print("Intercept is:", pre_int)
+print("Slope is:", pre_slope[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for Colorado Suicide Rates after legalization:')
+postyears = years[8:]
+postvals = col[8:]
+postyears = postyears.reshape(-1, 1)
+reg1 = LinearRegression().fit(postyears, postvals)
+post_int = reg1.intercept_
+post_slope = reg1.coef_
+print("Intercept is:", post_int)
+print("Slope is:", post_slope[0])
+print()
+
+# Fitting a model for years 2004-2008
+print('Linear Regression for Colorado Suicide Rates between 2004-2008:')
+firstyears = years[:4]
+firstvals = col[:4]
+firstyears = firstyears.reshape(-1,1)
+reg2 = LinearRegression().fit(firstyears, firstvals)
+f_int = reg2.intercept_
+f_slope = reg2.coef_
+print("Intercept is:", f_int)
+print("Slope is:", f_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Suicide Rates between 2008-2012:')
+secyears = years[4:9]
+secvals = col[4:9]
+secyears = secyears.reshape(-1,1)
+reg3 = LinearRegression().fit(secyears, secvals)
+s_int = reg3.intercept_
+s_slope = reg3.coef_
+print("Intercept is:", s_int)
+print("Slope is:", s_slope[0])
+print()
+
+# Fitting a model for USA
+# Fitting a model for the data before legalization
+print('Linear Regression for NATIONAL suicide rates before legalization:')
+preyears = years[:9]
+prevals = usa[:9]
+preyears = preyears.reshape(-1, 1)
+reg5 = LinearRegression().fit(preyears, prevals)
+pre_int_us = reg5.intercept_
+pre_slope_us = reg5.coef_
+print("Intercept is:", pre_int_us)
+print("Slope is:", pre_slope_us[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for NATIONAL suicide rates after legalization:')
+postyears = years[8:]
+postvals = usa[8:]
+postyears = postyears.reshape(-1, 1)
+reg6 = LinearRegression().fit(postyears, postvals)
+post_int_us = reg6.intercept_
+post_slope_us = reg6.coef_
+print("Intercept is:", post_int_us)
+print("Slope is:", post_slope_us[0])
+print()
+
+# Basic analysis
+change = (post_slope[0]-pre_slope[0])/(pre_slope[0])*100
+print("The percent change in slopes was ", change, "percent")
+prechange = np.mean(percentchange[1:9])
+postchange = np.mean(percentchange[8:])
+prechangeusa = np.mean(percentchangeusa[1:9])
+postchangeusa = np.mean(percentchangeusa[8:])
+print("The average percentage increase of suicide rates each year in Colorado before 2012 legalization was ", prechange, "percent")
+print("The average percentage increase of suicide rates each year in Colorado after 2012 legalization was ", postchange, "percent")
+print("The average percentage increase of suicide rates each year in the US before 2012 legalization was ", prechangeusa, "percent")
+print("The average percentage increase of suicide rates each year in the US after 2012 legalization was ", postchangeusa, "percent")
+
+# Plotting the linear regression model
+plt.xticks(years[::2])
+plt.title('Linear Regression of Suicide Rates in Colorado')
+plt.xlabel('Year')
+plt.ylabel('Suicide Rates')
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization')
+plt.plot(firstyears, reg2.predict(firstyears), color='magenta', label='Short-term trends')
+plt.plot(secyears, reg3.predict(secyears), color='magenta')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+# Plotting the difference between what we expected (assuming no legalization) and what we found with legalization
+coords = []
+postpredictions = reg1.predict(postyears)
+prepredictions = reg.predict(years[8:])
+for i in range(len(postpredictions)):
+    coords.append((prepredictions[i], postpredictions[i]))
+predyears = []
+for j in range(len(postyears)):
+    predyears.append(postyears[j][0])
+plt.plot((predyears,predyears),([i for (i,j) in coords], [j for (i,j) in coords]),c='green')
+plt.legend()
+plt.show()
+# Plotting national trendline
+plt.xticks(years[::2])
+plt.title('Comparing Colorado with National Trend')
+plt.xlabel('Year')
+plt.ylabel('Suicide Rates')
+plt.scatter(years, usa, color='orange', s=5)
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization (Colorado)')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization (Colorado)')
+plt.plot(years, reg5.predict(years),color='orange', label='Long-term trend before legalization (USA)')
+plt.plot(postyears, reg6.predict(postyears),color='purple', label='Trend after legalization (USA)')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+plt.legend()
+plt.show()
+
+# Alcohol-Related Driving Fatalities
+df = pd.read_csv("./data/alc.csv")
+
+years = df.iloc[:,0].values
+years = years.reshape(-1,1)
+col = df.iloc[:,1].values
+percentchange = df.iloc[:,3].values
+usa = df.iloc[:,2].values
+percentchangeusa = df.iloc[:,4].values
+
+# Fitting a model for the data before legalization
+print('Linear Regression for Colorado Alcohol-Related Driving Fatalities before legalization:')
+preyears = years[:14]
+prevals = col[:14]
+preyears = preyears.reshape(-1, 1)
+reg = LinearRegression().fit(preyears, prevals)
+pre_int = reg.intercept_
+pre_slope = reg.coef_
+print("Intercept is:", pre_int)
+print("Slope is:", pre_slope[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for Colorado Alcohol-Related Driving Fatalities after legalization:')
+postyears = years[13:]
+postvals = col[13:]
+postyears = postyears.reshape(-1, 1)
+reg1 = LinearRegression().fit(postyears, postvals)
+post_int = reg1.intercept_
+post_slope = reg1.coef_
+print("Intercept is:", post_int)
+print("Slope is:", post_slope[0])
+print()
+
+# Fitting a model for years 1999-2005
+print('Linear Regression for Colorado Alcohol-Related Driving Fatalities between 1999-2003:')
+firstyears = years[:4]
+firstvals = col[:4]
+firstyears = firstyears.reshape(-1,1)
+reg2 = LinearRegression().fit(firstyears, firstvals)
+f_int = reg2.intercept_
+f_slope = reg2.coef_
+print("Intercept is:", f_int)
+print("Slope is:", f_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Alcohol-Related Driving Fatalities between 2004-2008:')
+secyears = years[4:9]
+secvals = col[4:9]
+secyears = secyears.reshape(-1,1)
+reg3 = LinearRegression().fit(secyears, secvals)
+s_int = reg3.intercept_
+s_slope = reg3.coef_
+print("Intercept is:", s_int)
+print("Slope is:", s_slope[0])
+print()
+
+# Fitting a model for years 2006-2012
+print('Linear Regression for Colorado Alcohol-Related Driving Fatalities between 2009-2012:')
+thirdyears = years[9:14]
+thirdvals = col[9:14]
+thirdyears = thirdyears.reshape(-1,1)
+reg4 = LinearRegression().fit(thirdyears, thirdvals)
+t_int = reg4.intercept_
+t_slope = reg4.coef_
+print("Intercept is:", t_int)
+print("Slope is:", t_slope[0])
+print()
+
+# Fitting a model for USA
+# Fitting a model for the data before legalization
+print('Linear Regression for NATIONAL Alcohol-Related Driving Fatalities before legalization:')
+preyears = years[:14]
+prevals = usa[:14]
+preyears = preyears.reshape(-1, 1)
+reg5 = LinearRegression().fit(preyears, prevals)
+pre_int_us = reg5.intercept_
+pre_slope_us = reg5.coef_
+print("Intercept is:", pre_int_us)
+print("Slope is:", pre_slope_us[0]) 
+print()
+
+# Fitting a model for the data after legalization
+print('Linear Regression for NATIONAL Alcohol-Related Driving Fatalities after legalization:')
+postyears = years[13:]
+postvals = usa[13:]
+postyears = postyears.reshape(-1, 1)
+reg6 = LinearRegression().fit(postyears, postvals)
+post_int_us = reg6.intercept_
+post_slope_us = reg6.coef_
+print("Intercept is:", post_int_us)
+print("Slope is:", post_slope_us[0])
+print()
+
+# Basic analysis
+change = (post_slope[0]-pre_slope[0])/(pre_slope[0])*100
+print("The percent change in slopes was ", change, "percent")
+prechange = np.mean(percentchange[1:14])
+postchange = np.mean(percentchange[13:])
+prechangeusa = np.mean(percentchangeusa[1:14])
+postchangeusa = np.mean(percentchangeusa[13:])
+print("The average percentage increase of alcohol-related driving fatalities each year in Colorado before 2012 legalization was ", prechange, "percent")
+print("The average percentage increase of alcohol-related driving fatalities each year in Colorado after 2012 legalization was ", postchange, "percent")
+print("The average percentage increase of alcohol-related driving fatalities in the US before 2012 legalization was ", prechangeusa, "percent")
+print("The average percentage increase of alcohol-related driving fatalities in the US after 2012 legalization was ", postchangeusa, "percent")
+
+# Plotting the linear regression model
+plt.xticks(years[::2])
+plt.title('Linear Regression of Alcohol-Related Driving Fatalities in Colorado')
+plt.xlabel('Year')
+plt.ylabel('Alcohol-Related Driving Fatalities')
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization')
+plt.plot(firstyears, reg2.predict(firstyears), color='magenta', label='Short-term trends')
+plt.plot(secyears, reg3.predict(secyears), color='magenta')
+plt.plot(thirdyears, reg4.predict(thirdyears), color='magenta')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+# Plotting the difference between what we expected (assuming no legalization) and what we found with legalization
+coords = []
+postpredictions = reg1.predict(postyears)
+prepredictions = reg.predict(years[13:])
+for i in range(len(postpredictions)):
+    coords.append((prepredictions[i], postpredictions[i]))
+predyears = []
+for j in range(len(postyears)):
+    predyears.append(postyears[j][0])
+plt.plot((predyears,predyears),([i for (i,j) in coords], [j for (i,j) in coords]),c='green')
+plt.legend()
+plt.show()
+# Plotting national trendline
+plt.xticks(years[::2])
+plt.title('Comparing Colorado with National Trend')
+plt.xlabel('Year')
+plt.ylabel('Alcohol-Related Driving Fatalities')
+plt.scatter(years, usa, color='orange', s=5)
+plt.scatter(years, col, color='red', s=5)
+plt.plot(years, reg.predict(years),color='red', label='Long-term trend before legalization (Colorado)')
+plt.plot(postyears, reg1.predict(postyears),color='blue', label='Trend after legalization (Colorado)')
+plt.plot(years, reg5.predict(years),color='orange', label='Long-term trend before legalization (USA)')
+plt.plot(postyears, reg6.predict(postyears),color='purple', label='Trend after legalization (USA)')
+plt.axvline(x=2012, color='black', label='2012 (Legalization Year)')
+plt.legend()
+plt.show()
\ No newline at end of file