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), CCTV denotes the number of CCTV cameras installed in the district, and Prison denotes the number of prison inmates. The following table shows the results of a few regressions of the total cost to the state. Dependent variable: total cost to the state (in thousand dollars) Regressor (1) (2) LEOP 12.32 17.99 (0.52) (0.84) DTP 0.73 (0.06) CCTV (3) 14.55 (2.25) 0.59 (0.12) 18.1 (0.82) 0.75 (0.04) 0.73 (0.13) 2.11 (0.39) 288.5 (4.14) 0.75 2.12 Prison (0.5) 182.5 191.6 219.95 Intercept (11.52) (6.68) (5.26) R 0.12 0.75 0.64 n 220 220 220 Heteroskedasticity-robust standard errors are given in parentheses under coefficients. 220 Which of the following statements correctly describe the reasons behind the differences observed in the coefficients in the given specifications? (Check all that apply.) B. A. According to the 4th specification, reducing the number of law enforcement officers on patrol by one officer is estimated to decrease total cost to the state by approximately $18.10, holding constant other factors. The value of in the 1st specification suggests that the number of law enforcement officers on patrol alone explains a large fraction of the variation in total cost to the state. C. The significant rise in the coefficient on LEOP from the 1st specification to the 4th shows the presence of omitted variable bias in the 1st specification D. The number of CCTV cameras installed in the district appears to be redundant. As reported in regression (4), adding it to regression (2) has a negligible effect on the estimated coefficients on LEOP and DTP or their standard errors