discrete random variables:
- specification of probability distributions for discrete random variables using graphs, tables and probability mass functions
- calculation and interpretation of mean, 𝜇, variance, 𝜎^2, and standard deviation of a discrete random variable and their use
- Bernoulli trials and the binomial distribution, Bi(𝑛, 𝑝), as an example of a probability distribution for a discrete random variable
- effect of variation in the value(s) of defining parameters on the graph of a given probability mass function for a discrete random variable
- calculation of probabilities for specific values of a random variable and intervals defined in terms of a random variable, including conditional probability
U34.AoS4.10
calculate and interpret the probabilities of various events associated with a given probability distribution, by hand in cases where simple arithmetic computations can be carried out
U34.AoS4.11
apply probability distributions to modelling and solving related problems
