Unit information: Linear Models in 2016/17

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Unit name Linear Models MATH35110 10 H/6 Teaching Block 1A (weeks 1 - 6) Dr. Cho Not open MATH11300 Probability 1 and MATH 11400 Statistics 1; MATH 20800 Statistics 2 is desirable but not essential. None School of Mathematics Faculty of Science

Description

Please refer to the unit description and unit aims on the School of Mathematics website

Intended learning outcomes

Learning Objectives

By the end of the unit the student should be able to:

• Express a range of linear models in different forms: R syntax, matrix notation, etc.
• Prove fundamental results from linear model theory: calculation of least-squares estimates and their sampling properties, the Gauss Markov theorem, partitioning results for analysis of variance, etc.
• Calculate least-squares estimates in low-dimensional problems.
• Carry out simple analyses of variance.
• Fit models in R, discriminate between models and check goodness-of-model fit.
• Use analysis of variance techniques to check goodness-of-fit in the simple linear model case with replicates.
• Formulate simple factorial experimental results as an analysis of variance.
• Derive standard results for linear models from the likelihood function.

Transferable Skills

Computing skills (use of an advanced package, simple programming, interpretation of computational results in problem context). Relation of numerical results to mathematical and statistical theory. Building models for uncertain phenomena. Data analysis. Self assessment by working through examples sheets and using solutions provided.

Teaching details

Lectures supported by problem and solution sheets.

Assessment Details

100% Examination

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.