In real-world data analysis, a skilled statistician will utilize and adapt existing methodology to suit research goals at hand. This paper illustrates and provides background on a raft of specialized techniques in applied statistics, motivated by real case studies in statistics.
About this paper
| Paper title | Case Studies in Statistics |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees ( NZD ) | $1,206.91 |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- STAT 401 or (STAT 270 and STAT 310)
- Eligibility
Enrolments for this paper require departmental permission.
Pre-requisites are STAT401, or STAT270 and STAT310, or equivalent.- Contact
- Teaching staff
- Paper Structure
Content and case studies form 5 to 6 modules drawn from:
- Univariate smoothing
- Time series
- Multivariate smoothing
- Spatial regression
- Generalised additive models
- Non-parametric testing
- Teaching Arrangements
Lectures (2 per week); practicals/tutorials (1 per fortnight); student seminars (1 per fortnight).
- Textbooks
Recommended reading:
A full course book/reader will be provided.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Ethics, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
Students successfully completing this course will be able to demonstrate the following:
- Understanding of the relationship between theory and application of specialized statistical techniques.
- Identification of research question and ability to adapt statistical methods to problems without standard solutions.
- Ability to apply methodology and statistical computing to analyse data using specialized techniques, and interpret results in a logical manner.
- Display autonomy and judgement in presenting results to others, including non-scientists.
Timetable
In real-world data analysis, a skilled statistician will utilise and adapt existing methodology to suit research goals at hand. This paper illustrates and provides background on a raft of specialised techniques in applied statistics, motivated by real case studies in statistics.
About this paper
| Paper title | Case Studies in Statistics |
|---|---|
| Subject | Statistics |
| EFTS | 0.1667 |
| Points | 20 points |
| Teaching period | Semester 2 (On campus) |
| Domestic Tuition Fees | Tuition Fees for 2024 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- STAT 401 or (STAT 270 and STAT 310)
- Eligibility
Enrolments for this paper require departmental permission.
Pre-requisites are STAT 401, or STAT 270 and STAT 310, or equivalent.- Contact
- Teaching staff
- Paper Structure
Content and case studies form 5 to 6 modules drawn from:
- Univariate smoothing
- Time series
- Multivariate smoothing
- Spatial regression
- Generalised additive models
- Non-parametric testing
- Teaching Arrangements
Lectures (2 per week); practicals/tutorials (1 per fortnight); student seminars (1 per fortnight).
- Textbooks
Recommended reading:
A full course book/reader will be provided.
- Graduate Attributes Emphasised
- Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Ethics, Information literacy, Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
Students successfully completing this course will be able to demonstrate the following:
- Understanding of the relationship between theory and application of specialized statistical techniques
- Identification of research question and ability to adapt statistical methods to problems without standard solutions
- Ability to apply methodology and statistical computing to analyse data using specialized techniques, and interpret results in a logical manner
- Display autonomy and judgement in presenting results to others, including non-scientists