BUSS1020 Lecture Notes - Lecture 5: Standard Deviation, Random Variable, Countable Set

• School: University of Sydney
• Department: Business Studies
• Course: BUSS1020
• Professor: Parinaz Ezzati

Document Summary

Random variable represents possible outcomes from an uncertain event. Numerical rvs can be discrete or continuous. Discrete random variable- the set of all possi(cid:271)le out(cid:272)o(cid:373)es is a fi(cid:374)ite o(cid:396) (cid:862)(cid:272)ou(cid:374)ta(cid:271)l(cid:455) i(cid:374)fi(cid:374)ite(cid:863) no. of values: can only assume a countable number of values. Continuous random variable- takes on values at every point over a given interval (temperature, financial returns, price, % of labour force that is ue) A probability distribution for a discrete variable is a mutually exclusive list of all the possible numerical outcomes along with the probability of occurrence of each outcome: these probability distributions deal with representations of discrete numerical data. Nb: this is a sample of values and their associated estimated probabilities (empirical) Expected value (mean) of a discrete variable: the expected value of a random variable is the mean, of its probability distribution. It is calculated by multiplying each possible outcome, xi, by its corresponding probability, p(x=xi) and then sum the products: e. g.