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  • Leicester celebrates opening access to higher education

    Our University is celebrating its links with local colleges from across the Midlands and beyond. The Local to Leicester partnership brings together colleges associated with our University both locally and in the West Midlands.

  • Statistics for Data Science

    Module code: MA7023 The most important method in statistical analysis is the natural extension of simple linear regression models to include several explanatory variables, thus giving general linear models.

  • News and events

    The latest news from the AIDAIM Centre.

  • Museum Studies Connect

    Find out how graduates in Museum Studies at the University of Leicester are vital to our continued success and about our graduate network, Museum Studies Connect.

  • Meet the research team

    Learn more about the research team based in the School of Healthcare at the University of Leicester.

  • Global celebration

    On Monday 4 October 2021, we celebrated the official birthday of the University, marking 100 years since we opened our doors to our first students in 1921. As well as events on campus, we wanted to ensure that our global community was included.

  • Social Class and Inequalities

    Module code: SY2075 Debates around the Channel 4 programme 'Benefits Street' (2014) highlighted some of the problems with making 'poverty entertainment'.

  • Climate Change: Impacts, Vulnerability and Adaptation

    Module code: GY3432 "Climate change is destroying our path to sustainability. Ours is a world of looming challenges and increasingly limited resources." So said the then-UN Secretary-General Ban Ki-moon in 2012.

  • Statistics for Data Science

    Module code: MA7023 The most important method in statistical analysis is the natural extension of simple linear regression models to include several explanatory variables, thus giving general linear models.

  • Linear Statistical Models

    Module code: MA2261 The most important method in statistical analysis is the natural extension of simple linear regression models to include several explanatory variables, thus giving general linear models.

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