Course Catalogue

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The course provides the basic theory of normal linear models and generalized linear models.

Timetable information is available from the University of Bologna.
http://corsi.unibo.it/1Cycle/StatisticalSciences/Pages/course-timetable.aspx?CodiceCorso=8873&Indirizzo=A32&AnnoCorso=2

This course is only available to students on the Double Degree programme in Statistics with the University of Bologna.

Statistics 3L: Linear Models [STATS3016]

Linear Models 3 [STAST4015]
Regression Models (Level M) [STATS5025]

Statistics 3G: Generalised Linear Models [STATS3014]

Generalised Linear Models [STATS4043]

Generalised Linear Models (Level M) [STATS5019]

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End-of-course examination, carried out in accordance with the assessment procedures and regulations of the University of Bologna.

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

This course aims 

■ to establish a solid understanding of (generalised) linear models and their practical relevance;

■ to train students in estimating and testing the significance of parameters in (generalised) linear regression models introduce students to the multivariate normal distribution; and

■ to expose students to variable selection procedures for these models.

By the end of the course students will be able to:

■ formulate a normal linear model, estimate its parameters and test their significance;

■ use the variable selection procedures;

■ define a generalised linear model, by combining a random component with a linear predictor with a proper link function;

■ estimate and test the significance of the parameter of a generalised linear model; and

■ evaluate the goodness of fit of a model and detect violations of model assumptions.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.