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STAT399 Special Topic: Statistical Computing

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

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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

katrina.sharples@otago.ac.nz

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:

  1. Use R programming syntax and control flow.

  2. Use R to read in, manipulate, tidy, subset, recode, and write out data sets.

  3. Use R to implement user-specified functions and algorithms.

  4. Create, interpret and customise common statistical plots.

  5. Use R to fit and interpret a range of statistical models.

  6. Conduct simulation of data and execute numerically intensive operations.

  7. Write dynamic documents that include executable code in LaTeX and markdown.

  8. Use R to optimise functions with statistical applications.

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Timetable

Second Semester

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

Computer Lab

Stream Days Times Weeks
Attend one stream from
A1 Tuesday 13:00-13:50 28-34, 36-41
A2 Tuesday 14:00-14:50 28-34, 36-41
A3 Tuesday 15:00-15:50 28-34, 36-41
AND one stream from
B1 Thursday 13:00-13:50 28-34, 36-41
B2 Thursday 14:00-14:50 28-34, 36-41
B3 Thursday 15:00-15:50 28-34, 36-41
AND
C1 Friday 10:00-10:50 28-34, 36-41

Lecture

Stream Days Times Weeks
Attend
A1 Tuesday 10:00-10:50 28-34, 36-41
Thursday 10:00-10:50 28-34, 36-41

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.

^ Top of page

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

katrina.sharples@otago.ac.nz

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:

  1. Use R programming syntax and control flow.

  2. Use R to read in, manipulate, tidy, subset, recode, and write out data sets.

  3. Use R to implement user-specified functions and algorithms.

  4. Create, interpret and customise common statistical plots.

  5. Use R to fit and interpret a range of statistical models.

  6. Conduct simulation of data and execute numerically intensive operations.

  7. Write dynamic documents that include executable code in LaTeX and markdown.

  8. Use R to optimise functions with statistical applications.

^ Top of page

Timetable

Not offered in 2020

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