STAT463-23S1 (C) Semester One 2023

# Advanced Multivariable Statistical Methods and Applications

15 points

Details:
 Start Date: Monday, 20 February 2023 End Date: Sunday, 25 June 2023
Withdrawal Dates
Last Day to withdraw from this course:
• Without financial penalty (full fee refund): Sunday, 5 March 2023
• Without academic penalty (including no fee refund): Sunday, 14 May 2023

## Description

Advanced Multivariable Statistical Methods and Applications

STAT315 and STAT463 are courses in data modelling. The emphasis is on real world examples, and how you can use models to understand patterns and relationships in data. Multivariable statistical methods are used to extract information from data where there are multiple variables measured, for example in biology, health, engineering, finance, economics, and marketing.

In the course we will cover the application of common statistical methods used to fit models to data including multiple regression and dimension reduction methods like principal component analysis, factor analysis and clustering methods, generalised linear models (GLMs), mixed models (LMM and GLMM), generalised additive models (GAMs), and multiple comparison tests.

The course will introduce and use the statistical analysis software R, which is a powerful tool when dealing with large multivariate datasets. Special attention will be given to practical applications and the interpretation of the results.

## Learning Outcomes

• Students from the courses will be able to:

•            choose appropriate method when analysing data
•            define the problem in a statistical framework
•            fit a multivariable regression model to data and interpret the results
•            understand and apply other dimension reduction methods such as principle component analysis (PCA), factor analysis (FA) and clustering
•            use a generalised linear model (GLM) with data that does not follow a normal distribution
•            identify when a mixed-model should be used and apply this to linear and generalised linear models
•            use a generalised additive model (GAM) with non-linear data
•            undertake appropriate statistical tests when multiple comparisons are used
•            use the statistical analysis software R
•            write scientific and technical reports

This course will provide students with an opportunity to develop the Graduate Attributes specified below:

 Critically competent in a core academic discipline of their award Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject. Employable, innovative and enterprising Students will develop key skills and attributes sought by employers that can be used in a range of applications. Biculturally competent and confident Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree. Engaged with the community Students will have observed and understood a culture within a community by reflecting on their own performance and experiences within that community. Globally aware Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.

## Prerequisites

Subject to approval of the Head of School.

## Assessment

Assessment Due Date Percentage
Assignments (x4) 40%
Project Report and presentation 10%
Final Examination 50%

STAT463 (C) and (D)
Assignments (x6)                                  48%
Project Report and presentation              12%
Final Examination                                  40%

## Indicative Fees

Domestic fee \$1,045.00

* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.

For further information see Mathematics and Statistics .

## All STAT463 Occurrences

• STAT463-23S1 (C) Semester One 2023
• STAT463-23S1 (D) Semester One 2023 (Distance)