MIS171 Lecture Notes - Lecture 5: Collectively Exhaustive Events, Probability Distribution, Mutual Exclusivity
Document Summary
Probability and probability distributions enables us to develop models that take into account uncertainty. 0 < p(x) > 1 (cid:862)p(cid:894)x(cid:895) (cid:373)ust (cid:271)e e(cid:395)ual o(cid:396) g(cid:396)eate(cid:396) tha(cid:374) (cid:1004) a(cid:374)d less tha(cid:374) o(cid:396) e(cid:395)ual to (cid:1005)(cid:863) Probabilities of all events (collectively exhaustive) must add to exactly 1. P(cid:894)x(cid:895) = (cid:1005) (cid:862)the su(cid:373) of all p(cid:894)x(cid:895)(cid:859)s (cid:373)ust e(cid:395)ual (cid:1005)(cid:863) Three main ways: e. g. number of insurance claims per day rolling a dice landing on 4 = 1/6 rolling a dice of odd number = 3/6. = the number of ways the event can occur / total number of possible outcomes. Relies on data from past surveys or observations (historical) to provide insights to probabilities of what could occur in the future. e. g. = p (exactly 2) = p(x = 2) =0. 45. P (3 or more in a day) = p(x = > 3) = 0. 25 + 0. 05 = 0. 3.
