Use R to develop a Poisson model and a negative binomial model for MI crash frequencies Validate your models and compare their performance Select three explanatory variables to interpret their effects MI Count of possible injury and non incapacitating injury crashes Use R Zone ID SIF MI PDO TruckRatio Street mile RoadDensity SIF MI PDO VMT TruckRatio Freeway mile Ave mile MI PDO TruckRatio 1 8 19 12 0 011603 0 164401 216 2768 193 2495 170 2222 147 1949 124 1677 101 1404 78 1131 37 0233 60 0505 106 105 2 28 275 181 0 092104 0 807822 1970 344 1757 201 1544 057 1330 914 1117 77 904 6269 691 483 374 234 587 377 1013 66 3 4 54 24 0 025337 0 261658 601 2713 538 1738 475 0763 411 9788 348 8813 285 7838 222 686 92 8012 155 899 282 094 4 0 5 5 0 039438 0 146013 162 4087 145 845 129 2813 112 7176 96 15396 79 59028 63 0266 19 7918 36 3554 69 4828 5 7 33 17 0 084979 0 443269 427 1601 382 2364 337 3127 292 3891 247 4654 202 5417 157 618 67 0003 111 924 201 771 6 11 125 254 0 052308 1 105728 2741 746 2456 792 2171 837 1886 883 1601 929 1316 974 1032 02 392 751 677 706 1247 61 7 1 43 25 0 072882 0 371901 940 5741 843 9917 747 4092 650 8267 554 2443 457 6618 361 079 121 833 218 415 411 58

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Question: ***Use R to develop a Poisson model and a negative binomial model for MI crash frequencies. Validate your models and compare their performance. Select three

***Use R to develop a Poisson model and a negative binomial model for MI crash frequencies. Validate your models and compare their performance. Select three explanatory variables to interpret their effects. *MI= Count of possible injury and non-incapacitating injury crashes

*Use R*

Zone_ID	SIF	MI	PDO	TruckRatio	Street_mile	RoadDensity	SIF	MI	PDO	VMT	TruckRatio	Freeway_mile	Ave_mile	MI	PDO	TruckRatio
1	8	19	12	0.011603	0.164401		216.2768	193.2495	170.2222	147.1949	124.1677	101.1404	78.1131	-37.0233	-60.0505	-106.105
2	28	275	181	0.092104	0.807822		1970.344	1757.201	1544.057	1330.914	1117.77	904.6269	691.483	-374.234	-587.377	-1013.66
3	4	54	24	0.025337	0.261658		601.2713	538.1738	475.0763	411.9788	348.8813	285.7838	222.686	-92.8012	-155.899	-282.094
4	0	5	5	0.039438	0.146013		162.4087	145.845	129.2813	112.7176	96.15396	79.59028	63.0266	-19.7918	-36.3554	-69.4828
5	7	33	17	0.084979	0.443269		427.1601	382.2364	337.3127	292.3891	247.4654	202.5417	157.618	-67.0003	-111.924	-201.771
6	11	125	254	0.052308	1.105728		2741.746	2456.792	2171.837	1886.883	1601.929	1316.974	1032.02	-392.751	-677.706	-1247.61
7	1	43	25	0.072882	0.371901		940.5741	843.9917	747.4092	650.8267	554.2443	457.6618	361.079	-121.833	-218.415	-411.58

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