Yearbooks

Introduction to statistical learning 720


 
Modulekode EKT 720
Kwalifikasie Postgraduate
Fakulteit Faculty of Economic and Management Sciences
Module-inhoud

The emphasis is on the theoretical understanding and practical application of advances in statistical modelling. The following topics are covered: Single equation models: Nonparametric regression. Bootstrap procedures within regression analysis, k-nearest neighbour classification. Modelling categorical dependent variables - Logit/Probit models. Multiple outputs. Linear regression of an indicator matrix. Ridge regression. Non-linear regression modelling.  Some new developments in regression and classification.
Simultaneous equation models: Specification, identification and estimation of simultaneous equation models.

Modulekrediete 15.00
NQF Level 08
Service modules Faculty of Natural and Agricultural Sciences
Prerequisites RAL 780 or WST 311, 312, 321
Contact time 1 lecture per week, 1 web-based period per week
Language of tuition Module is presented in English
Department Statistics
Period of presentation Semester 2

The regulations and rules for the degrees published here are subject to change and may be amended after the publication of this information.

The General Academic Regulations (G Regulations) and General Student Rules apply to all faculties and registered students of the University, as well as all prospective students who have accepted an offer of a place at the University of Pretoria. On registering for a programme, the student bears the responsibility of ensuring that they familiarise themselves with the General Academic Regulations applicable to their registration, as well as the relevant faculty-specific and programme-specific regulations and information as stipulated in the relevant yearbook. Ignorance concerning these regulations will not be accepted as an excuse for any transgression, or basis for an exception to any of the aforementioned regulations.

Copyright © University of Pretoria 2024. All rights reserved.

FAQ's Email Us Virtual Campus Share Cookie Preferences