The software R is used to introduce computer skills needed for the statistical sciences. Reproducible research, visualisation, exploratory data analysis, optimisation, resampling, simulation and R programming.
| Paper title | Special Topic: Statistical Computing |
|---|---|
| Paper code | STAT399 |
| Subject | Statistics |
| EFTS | 0.1500 |
| Points | 18 points |
| Teaching period | Second Semester |
| Domestic Tuition Fees (NZD) | $886.35 |
| International Tuition Fees (NZD) | $3,766.35 |
- Prerequisite
- (STAT 241 or STAT 210 or FINC 203 or ECON 210) and 18 additional STAT points at 200-level or above
- Restriction
- STAT 260, STAT 380
- Schedule C
- Arts and Music, Science
- Contact
- Teaching staff
To be advised
- Textbooks
To be advised
- Graduate Attributes Emphasised
- Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy,
Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
Fundamental to modern statistical practice is proficiency in the use of specialised software packages. This paper introduces students to the world of statistical computing, which encompasses fundamental programming skills motivated by handling and manipulating data, and how this relates to exploratory data analysis, visualisation, model fitting, and numerical simulation. Focus is on implementation in the R language and associated markup tools. Many of the skills students learn are transportable to other statistics packages.
Upon successful completion of the paper, the student will possess a range of skills used in and motivated by modern data analysis. They will be able to:
Use R programming syntax and control flow.
Use R to read in, manipulate, tidy, subset, recode, and write out data sets.
Use R to implement user-specified functions and algorithms.
Create, interpret and customise common statistical plots.
Use R to fit and interpret a range of statistical models.
Conduct simulation of data and execute numerically intensive operations.
Write dynamic documents that include executable code in LaTeX and markdown.
Use R to optimise functions with statistical applications.
Timetable
The software R is used to introduce computer skills needed for the statistical sciences. Reproducible research, visualisation, exploratory data analysis, optimisation, resampling, simulation and R programming.
| Paper title | Special Topic: Statistical Computing |
|---|---|
| Paper code | STAT399 |
| Subject | Statistics |
| EFTS | 0.1500 |
| Points | 18 points |
| Teaching period | Not offered in 2020 |
| Domestic Tuition Fees | Tuition Fees for 2020 have not yet been set |
| International Tuition Fees | Tuition Fees for international students are elsewhere on this website. |
- Prerequisite
- (STAT 241 or STAT 210 or FINC 203 or ECON 210) and 18 additional STAT points at 200-level or above
- Restriction
- STAT 260, STAT 380
- Schedule C
- Arts and Music, Science
- Contact
- Teaching staff
To be advised
- Textbooks
To be advised
- Graduate Attributes Emphasised
- Lifelong learning, Scholarship, Communication, Critical thinking, Information literacy,
Research, Self-motivation.
View more information about Otago's graduate attributes. - Learning Outcomes
Fundamental to modern statistical practice is proficiency in the use of specialised software packages. This paper introduces students to the world of statistical computing, which encompasses fundamental programming skills motivated by handling and manipulating data, and how this relates to exploratory data analysis, visualisation, model fitting, and numerical simulation. Focus is on implementation in the R language and associated markup tools. Many of the skills students learn are transportable to other statistics packages.
Upon successful completion of the paper, the student will possess a range of skills used in and motivated by modern data analysis. They will be able to:
Use R programming syntax and control flow.
Use R to read in, manipulate, tidy, subset, recode, and write out data sets.
Use R to implement user-specified functions and algorithms.
Create, interpret and customise common statistical plots.
Use R to fit and interpret a range of statistical models.
Conduct simulation of data and execute numerically intensive operations.
Write dynamic documents that include executable code in LaTeX and markdown.
Use R to optimise functions with statistical applications.
