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A 2 full day course: 21st - 22nd January 2019

 

This course introduces the concepts of statistical learning  focusing on predictive models.

It covers modern statistical modelling methods, including logistic regression, multinomial regression, and classification and regression trees, as well as assessment methods such as prediction error, deviance and AUC. The focus will be on validation and cross-validation methods, to ensure that  selected models are not overfitting the data. Real life examples are used, and there is time for practical use of the methods described using the R package.

 

Important please note:

We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.

 

Details of course fees.

Payment: Once you have registered, please pay at the online shop 

https://online-payments.lancaster-university.co.uk/product-catalogue/courses/mathematics-and-statistics/short-courses-and-cpd/applied-statistics-psc-short-courses-20182019

Accommodation Details: Can be found here.

Cancellation Policy

Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.


Date:
Monday, January 21, 2019
Time:
All Day Event
Location:
Postgraduate Statistics Centre
Presenter:
Professor Brian Francis
Type:
Taught Session - For Externals, Staff & Students

Topics covered:

 

Introduction to statistical learning

The ideas of statistical modelling

Logistic regression.

Calibration and validation samples.

Multinomial regression.

Classification and regression trees.

Cross-validation

Ensemble methods.




Non-attendance