Topics in Statistics: Advanced Methods

(3,4 Credit Hours) · Undergraduate

IT On successful completion of this module, students will be able to: LO1. Understand and put into practice merging and cleaning of datasets LO2. Understand and put into practice use of inbuilt and user written functions LO3. Understand the graphics capabilities of R and use these methods to visualise data and create reports LO4. Understand the use of clustering methods and their application to different data types LO5. Understand the use of Generalized linear models and their application to different data types LO6. Understand methods of Classification and their application to different data types LO7. Principles of effective report writing and how to present research and analysis. BP Statistics teaches us how to behave in the face of uncertainties, according to the famous mathematician, Abraham Wald. Theoretically, we will learn strategies of treating chances in everyday life, where our inference is based on a randomly selected sample from a large population, and hence, we intensively use concepts of probability (laws of large numbers, Bayes rule). Estimation theory and hypothesis testing are introduced on a theoretical basis, but applications are also discussed. Methods of supervised and unsupervised learning are outlined; former include regression and discriminant analysis, while latter ones factor and cluster analysis. The students are also made capable of selecting the methods and making inference on real-life data, while outputs of a program package for medical data are analyzed.