STAT310 Statistical Modelling

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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.

Paper title Statistical Modelling STAT310 Statistics 0.1500 18 points First Semester \$904.05 \$3,954.75
Prerequisite
(STAT 210 or STAT 241 or ECON 210 or FINC 203) 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

Ting Wang

Teaching staff

To be confirmed

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

Interdisciplinary perspective, Communication, Critical thinking, Information literacy, Research.
Learning Outcomes

Upon successful completion of the course, the student should be able to:

1. Select an appropriate and useful model, from a range of models widely used in modern statistical practice, to address the objectives of a study
2. Fit the models using the R software package
3. Check the assumptions underlying these models
4. Prepare a report communicating the results of an analysis

Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

Lecture

Stream Days Times Weeks
Attend
A1 Monday 16:00-16:50 9-13, 19-22
Tuesday 16:00-16:50 9-12, 17-22
Friday 15:00-15:50 9-12, 17-22

Tutorial

Stream Days Times Weeks
Attend
A1 Friday 16:00-16:50 9-12, 17-22

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.

Paper title Statistical Modelling STAT310 Statistics 0.15 18 points First Semester Tuition Fees for 2021 have not yet been set 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

To be confirmed, contact the department for further details office@maths.otago.ac.nz

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

Interdisciplinary perspective, Communication, Critical thinking, Information literacy, Research.
Learning Outcomes

Upon successful completion of the course, the student should be able to:

1. Select an appropriate and useful model, from a range of models widely used in modern statistical practice, to address the objectives of a study
2. Fit the models using the R software package
3. Check the assumptions underlying these models
4. Prepare a report communicating the results of an analysis

Timetable

First Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

Lecture

Stream Days Times Weeks
Attend
A1 Monday 16:00-16:50 9-13, 15-16, 18-22
Tuesday 16:00-16:50 9-13, 15-22
Friday 15:00-15:50 9-12, 15-22

Tutorial

Stream Days Times Weeks
Attend
A1 Friday 16:00-16:50 9-12, 15-22