Statistics and data analysis is essential to insurance and finance. This module covers most of the foundational statistical knowledge, including statistical inference, linear and generalised linear model, regression and classification, and Bayesian statistics. This module will equip the students with a solid statistical background, which is fundamental for further studies in advanced statistical techniques applied in practical insurance and financial problems. This module also teaches students the skills and techniques to apply R computer-based package in different learning outcomes.
A. Explain and apply the methods of parameter estimation including maximum likelihood estimation/moment estimation/Bayesian estimation. B. Evaluate parameter estimation using efficiency, bias, consistency, mean-square error. C. Implement confidence interval and hypothesis testing for statistical inference. D. Analyze goodness of fit via p-value; R-square. E. Carry out and interpret (generalized) linear regression model with insurance data. F. Diagnose/improve regression analysis with data transformation/model refitting. G. Implement the concepts and techniques in Bayesian statistics. H. Perform linear regression, analysis of variance and generalized linear models analysis using computer package or outputs.
This module is delivered through formal lectures and tutorials.