Statistical Foundations

SOST70151 Statistical Foundations 15 Semester 1 Postgraduate Taught Level 7 Philip Leifeld Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 To give students: (a) a firm grounding in the basics of statistical inference and probability, (b) an understanding of how model considerations affect the kinds of inferences that can be drawn from different kinds of social science data, (c) the confidence and ability to draw different kinds of statistical inferences from real data, and (d) having a working knowledge of modelling and inferential assumptions of linear models and their extensions. The course consists of six main parts: (1) fundamental concepts in probability theory (2) Common probability distributions and their properties (3) Population, samples and the principles of design-based inference (4) Estimators (5) Hypothesis testing (6) How 1-5 fits into statistical modelling and practical considerations in social science To give students: (a) a firm grounding in the basics of statistical inference and probability, (b) an understanding of how model considerations affect the kinds of inferences that can be drawn from different kinds of social science data, (c) the confidence and ability to draw different kinds of statistical inferences from real data, and (d) having a working knowledge of modelling and inferential assumptions of linear models and their extensions. On successful completion of this course unit, students will• Understand and do calculus involving fundamental concepts in probability theory such as independence and conditional probabilities• Have working handle on random variables and their properties• Have a basic understanding of estimators and how these relate to a model for data• Being able to perform basic tests of hypothesis and being able to generalise this understanding beyond the standard cases• Critically assess the extent to which a statistical analysis meets required assumptions• Have a broad knowledge general issues in statistical inference Twelve teaching occasions comprising a lecture component and a practical. The practical element may involve computer based activities and/or discussion sessions. Computer exercises will be done using the R environment and will not be scheduled every week. A number of extra tutorials led by the course TAs will be scheduled in addition. 0 Other 100% Weekly tests (x8, 30% total)Exam (70%) Feedback available via Turnitin Y Preliminary main reading• Agresti, A. (2018) Statistical Methods for the Social Sciences (5th Edition). Pearson International Edition. Online learning modules on R • https://www.datacamp.com/swirl-r-tutorial • http://eclr.humanities.manchester.ac.uk/index.php/R  Additional readings may include excerpts from• Bluman, A. G. (2012). Probability demystified.• McGraw-Hill.Gill, J. (2006)Essential Mathematics for Political and Social Research. Cambridge University Press. (Electronic version available in UoM library) Lectures 20 Tutorials 8 0 122 Information Compulsory for SRMS