ASTR 530B Practical Statistics for Astronomers 3 credit Prereqs: None, beyond meeting the admission requirements for UBC graduate astronomy programme Evaluation: Continuous assessment via weekly assignments. Heavy homework. The course is presented through data-analysis examples, with minimum time spent on discussion of statistical / probability theory. Bayesian, classical and non-parametric methods are covered. The goal is for students to obtain an analytical tool-box for astronomy data. Unit: Basics - decision and the way science works; how probability and statistical analysis developed in astronomy; the nature of probability; probability distributions, with emphasis on those most frequently encountered in astronomy; statistics versus expectation values. Unit: Random-number toolbox - how to generate random numbers uniformly or following a prescribed distribution; simulation of a model via random numbers; an example of a toy model universe. Unit: Correlation - the pitfalls; the tests, parametric, non-parametric, and Bayesian; partial correlation; Principle Component Analysis. Unit: Hypothesis-testing, data-modelling, and parameter estimation - use of classical, non-parametric and Bayesian methods; Bayesian model choice, and Markov-Chain Monte Carlo methods in integration and simulation. Unit: Detection, sky surveys and luminosity functions - the nature of detection; Malmquist and Eddington bias; luminosity functions and their evaluation, including analysis of censored data (survival analysis). Unit: Astronomical analyses - treatment of 1D data such as spectral scans; 2D (surface distribution) analysis including 2-point correlation, counts-in-cells, and power-spectrum analysis; recent astronomy and cosmology results from statistical analysis, including galaxy clustering and its cosmic evolution, and derivation of cosmological parameters from Cosmic Microwave Background data.