Handbook

This is a third-year statistics course for undergraduate students in mathematics/statistics and data science. Many data applications are concerned with observations that are correlated and sophisticated methods are required to analyze such data.  This course extends statistical modeling from independently distributed data to modelling dependence in observed data and develops an understanding of basic distributions and models useful in practical situations. It continues with the development of statistical methods for analysis of dependent data arising in multivariate observations, longitudinal data, time series and spatial processes. Specific topics include the multivariate normal distribution, linear mixed models, time series models, ARMA and forecasting ARMA, spatial processes, sample semivariograms and Kriging.  The teaching activities include weekly lectures in which the methods and theory are explained and weekly tutorials in which the methods can be applied.