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277 4.7 Taking a Second Look at Statistics (Monte Carlo Simulations) MTB > random 1 c1; SUBC > exponential 1.33. MTB > print c1 c1 1.15988 0.8 0.6 fY (y) 0.4 0.2 y 0 1 MTB > random 1 c1; SUBC > normal 100 20. MTB > print c1 c1 127.199 4 fC (c) 100 c 140 0.8 0.6 fY (y) 0.4 0.2 y 0 1 MTB > random 1 c1; SUBC > normal 100 20. MTB > print c1 c1 98.6673 2 3 4 fC (c) 0.01 60 MTB > random 1 c1; SUBC > exponential 1.33. MTB > print c1 c1 1.46394 3 0.01 60 MTB > random 1 c1; SUBC > exponential 1.33. MTB > print c1 c1 0.284931 2 100 c 140 0.8 0.6 fY (y) 0.4 0.2 y 0 1 2 3 4 Figure 4.7.5 Running those commands twice produced c-values of 127.199 and 98.6673 (see Figure 4.7.5), corresponding to repair bills of $127.20 and $98.67, meaning that a total of $225.87 (= $127.20 + $98.67) would have been spent on maintenance during the ï¬rst two years. In that case, the $200 warranty would have been a good investment. The ï¬nal step in the Monte Carlo analysis is to repeat many times the sampling process that led to Figure 4.7.5âthat is, to generate a series of yi âs whose sum (in days) is less than or equal to 730, and for each yi in that sample, to generate a corresponding cost, ci . The sum of those ci âs becomes a simulated value of the maintenance-cost random variable, W .