Probability and Statistics

(3 Credit Hours) · Undergraduate

Summarising and displaying statistical data in R; Introduction to probability: discrete sample spaces; axioms; addition and multiplication laws; conditional probability and independence; reliability of systems; Bayes theorem; Discrete Random Variables: Bernouilli, hypergeometric, binomial, geometric and Poisson distributions; expectation; Sampling Inspection Schemes: Single and double sampling; operating characteristic function; average outgoing quality; consumers and producers risks. Continuous Random Variables: Uniform, exponential and normal distributions; normal approximation to binomial. Tchebechevs and Markovs inequalities Aims: To introduce the basic probability concepts and their applications to computer disciples; To provide an understanding of discrete and continuous distributions; To cover the essentials of the statistical computing system R. To introduce the essentials of statistical analysis using R.