Overview
Statistical model building, motivated by real applications. Topics include regularisation, lasso, splines, non-linear regression, generalised linear models, model checking and introduction to mixed models.
The ability to fit statistical models to data is an important part of statistical practice. This course builds on modelling approaches introduced in STAT 210. The emphasis will be on fitting models to real datasets, understanding the assumptions and limitations of the method, and the interpretation of the results. R will be the primary language used, but students will also learn how to carry out selected analyses in SAS. Students will also learn to write up the analysis in the form of a report from a statistical consultant, i.e. in a way that can be understood by a general scientist or business manager.
About this paper
| Paper title | Statistical Modelling |
|---|---|
| Subject | Statistics |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees ( NZD ) | $981.75 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- One of (ECON 210 or FINC 203 or STAT 210 or STAT 241) and STAT 260
- Restriction
- STAT 341
- Schedule C
- Arts and Music, Science
- Eligibility
Students are expected to have completed a 200 level statistical modelling paper (STAT 210 or STAT241 or ECON 210 or FINC 203) and STAT 260
- Contact
- Teaching staff
Professor Martin Hazelton
Dr Conor Kresin
- Paper Structure
Main topics:
- Regularisation and the lasso
- Splines and non-linear models
- Generalized linear models
- Model checking
- Introduction to mixed effects models
- Textbooks
Textbooks are not required for this paper.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Communication, Critical thinking, Information literacy, Research.
View more information about Otago's graduate attributes. - Learning Outcomes
Upon successful completion of the course, the student should be able to:
- Select an appropriate and useful model, from a range of models widely used in modern statistical practice, to address the objectives of a study
- Fit the models using the R software package
- Check the assumptions underlying these models
- Prepare a report communicating the results of an analysis
Timetable
Overview
Statistical model building, motivated by real applications. Topics include regularisation, lasso, splines, non-linear regression, generalised linear models, model checking and introduction to mixed models.
The ability to fit statistical models to data is an important part of statistical practice. This course builds on modelling approaches introduced in STAT 210. The emphasis will be on fitting models to real datasets, understanding the assumptions and limitations of the method, and the interpretation of the results. R will be the primary language used, but students will also learn how to carry out selected analyses in SAS. Students will also learn to write up the analysis in the form of a report from a statistical consultant, i.e. in a way that can be understood by a general scientist or business manager.
About this paper
| Paper title | Statistical Modelling |
|---|---|
| Subject | Statistics |
| EFTS | 0.15 |
| Points | 18 points |
| Teaching period | Semester 1 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2025 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- One of (ECON 210 or FINC 203 or STAT 210 or STAT 241) and STAT 260
- Restriction
- STAT 341
- Schedule C
- Arts and Music, Science
- Eligibility
Students are expected to have completed a 200 level statistical modelling paper (STAT 210 or STAT241 or ECON 210 or FINC 203) and STAT 260
- Contact
- Teaching staff
Professor Martin Hazelton
Dr Conor Kresin
- Paper Structure
Main topics:
- Regularisation and the lasso
- Splines and non-linear models
- Generalized linear models
- Model checking
- Introduction to mixed effects models
- Textbooks
Textbooks are not required for this paper.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Communication, Critical thinking, Information literacy, Research.
View more information about Otago's graduate attributes. - Learning Outcomes
Upon successful completion of the course, the student should be able to:
- Select an appropriate and useful model, from a range of models widely used in modern statistical practice, to address the objectives of a study
- Fit the models using the R software package
- Check the assumptions underlying these models
- Prepare a report communicating the results of an analysis