Data on 220 reported crimes is collected from district X in 2016. Suppose CS denotes the total cost to the state of offering crime protection services to this district (in thousand dollars), LEOP denotes the number of law enforcement officers on patrol, DTP denotes the damage to public and private property (in thousand dollars), and Prison denotes the number of prison inmates. An estimated regression yields: CS 188.5+13.5 LEOP+0.65 DTP+3.11 Prison. Suppose that as a result of a major drug cartel bust in the district, the number of inmates in the district's prisons drives up by 60 The expected change in the cost to the state, keeping other factors constant, would be $ (Express your answer in dollars) Suppose the researcher realises that she may have missed an important variable while processing the data. She now estimates the following regression equation: CS-245 25+19.5 LEOP+0.85 DTP+0.5 CCTV+1.25 Prison. where CCTV denotes the number of CCTV cameras installed in the district. With the revised estimates, the expected change in the cost to the state due to the increase in the number of inmates would be $ (Express your answer in dollars.) The initial regression model suffered from