Subset the data to include any one county of your choice. Develop a logistic regression model to analyze the probability of a head-on crash using predictor variables such as the weather condition, amount of daylight, and whether or not the accident takes place on a highway. Use the appropriate cutoff point to analyze the accuracy, sensitivity, and specificity of the estimated model.

 

ID	County	City	Weekday	Severity	ViolCat	ClearWeather	Month	CrashType	Highway	Daylight
1	SAN DIEGO	SAN DIEGO	7	1	8	0	1	A	0	1
2	HUMBOLDT	UNINCORPORATED	4	1	8	1	1	A	1	0
3	VENTURA	OXNARD	2	1	12	1	2	A	0	1
4	STANISLAUS	UNINCORPORATED	4	1	1	1	1	A	0	0
5	MENDOCINO	UNINCORPORATED	5	1	1	1	1	A	1	0
6	LOS ANGELES	LONG BEACH	7	1	3	1	3	A	0	0
7	LOS ANGELES	LOS ANGELES	4	1	3	0	3	A	0	1
8	CALAVERAS	UNINCORPORATED	1	1	1	1	2	A	1	1
9	SAN BERNARDINO	HESPERIA	2	1	1	1	1	A	0	0
10	VENTURA	OXNARD	5	0	8	1	1	A	0	0
11	VENTURA	OXNARD	6	0	8	0	1	A	0	0
12	ORANGE	FULLERTON	4	0	9	1	2	A	0	0
13	SAN DIEGO	CHULA VISTA	1	0	3	1	1	A	0	0
14	ALAMEDA	OAKLAND	6	0	1	1	1	A	0	0
15	LOS ANGELES	LOS ANGELES	5	0	9	1	3	A	0	0
16	SANTA CLARA	MORGAN HILL	4	0	8	1	3	A	0	0
17	LOS ANGELES	LOS ANGELES	3	0	9	1	3	A	0	1
18	SAN JOAQUIN	UNINCORPORATED	3	0	8	1	3	A	0	1
19	LOS ANGELES	LOS ANGELES	5	0	9	1	4	A	0	1
20	ORANGE	FOUNTAIN VALLEY	4	0	8	1	1	A	0	1
21	RIVERSIDE	RIVERSIDE	5	0	9	1	1	A	0	1
22	ORANGE	ANAHEIM	2	0	9	1	1	A	0	0
23	CONTRA COSTA	WALNUT CREEK	4	0	1	1	1	A	0	0
24	SOLANO	FAIRFIELD	7	0	1	1	1	A	0	0
25	CONTRA COSTA	UNINCORPORATED	7	0	9	1	1	A	0	1
26	BUTTE	CHICO	5	0	9	1	1	A	0	1
27	EL DORADO	UNINCORPORATED	7	0	3	1	1	A	0	1
28	ORANGE	GARDEN GROVE	6	0	9	1	1	A	0	1
29	TUOLUMNE	UNINCORPORATED	3	0	8	1	1	A	1	0