Courses & Units

Statistical Analysis Using R KMA711

Hobart

Note:

You are expected to have access to a laptop with sufficient system requirements. It is recommended that you have a recent version of the R programming language (https://www.r-project.org) and RStudio (https://rstudio.com) installed before the unit begins.

Introduction

Statistics is the science of decision making and forms a key foundation of scientific research. This unit will introduce students to a broad range of quantitative data analysis techniques. Students will learn aspects of collecting, processing, analysing, and presenting, quantitative information. Topics include: experimental design, data exploration and presentation, fitting linear models and their extensions (e.g. generalised linear modelling, and mixed effects modelling), model selection, and inference. Students will gain hands-on experience conducting statistical analyses using the R programming language within the RStudio environment, including the use of R Markdown for promoting reproducible research. Examples will be drawn from the biological, physical and social sciences.

Summary

Unit name Statistical Analysis Using R
Unit code KMA711
Credit points 12.5
Faculty/School College of Sciences and Engineering
School of Natural Sciences
Discipline Mathematics
Coordinator

Shane Richards

Teaching staff

Barbara Holland, Michael Charleston

Level Postgraduate
Available as student elective? Yes
Breadth Unit? No

Availability

Location Study period Attendance options Available to
Hobart Intensive Session Jun On-Campus International Domestic

Key

On-campus
Off-Campus
International students
Domestic students
Note

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Units are offered in attending mode unless otherwise indicated (that is attendance is required at the campus identified). A unit identified as offered by distance, that is there is no requirement for attendance, is identified with a nominal enrolment campus. A unit offered to both attending students and by distance from the same campus is identified as having both modes of study.

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TNE Program units special approval requirements.

Key Dates

Study Period Start date Census date WW date End date
Intensive Session Jun 22/6/2020 26/6/2020 3/7/2020 12/7/2020

* The Final WW Date is the final date from which you can withdraw from the unit without academic penalty, however you will still incur a financial liability (see withdrawal dates explained for more information).

About Census Dates

Fees

Domestic

Band CSP Student Contribution Full Fee Paying (domestic) Field of Education
2 2020: $1,190.00 2020: $2,402.00 010103

Fees for next year will be published in October. The fees above only apply for the year shown.

Please note: international students should refer to this page to get an indicative course cost.

Requisites

Students must be enrolled in a PhD, Masters or Honours

Prerequisites

Mutual Exclusions

You cannot enrol in this unit as well as the following:

KMA253

Teaching

Teaching Pattern

On-campus component - 8 days (over 3 weeks) 2-3hr lecture & 2-3hr practical, self study before and after on-campus sessions.

Assessment

2 assignments worth 100%: portfolio exercises (60%), project report (40%)

TimetableView the lecture timetable | View the full unit timetable

Textbooks

Required

Recommended

Fränzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jérôme Guélat, Bettina Almasi, and Pius Korner-Nievergelt (2015) Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. Academic Press.

Claus Thorn Ekstrom (2017) The R primer. Second edition. CRC Press.

Booktopia textbook links

The University reserves the right to amend or remove courses and unit availabilities, as appropriate.