This unit will be offered for the first time in 2022.
Data Handling and Statistics 2 is the second of three applied statistics units offered by the School of Natural Sciences (Mathematics). This unit is designed to extend students' knowledge of statistical data analysis. It builds on the concepts introduced in Data Handling & Statistics 1 and gives a detailed treatment of regression and ANOVA within a general linear modelling framework. Students will become proficient in the use of a contemporary statistical package R, the interpretation of its output and reporting of statistical analyses to statisticians and to the general public. Students will be able to evaluate the appropriateness of experimental designs and statistical methods and suggest changes to designs and analyses.
|Unit name||Data Handling and Statistics 2|
|College/School||College of Sciences and Engineering
School of Natural Sciences
|Coordinator||Professor Barbara Holland|
|Available as student elective?||Yes|
|Delivered By||University of Tasmania|
|Location||Study period||Attendance options||Available to|
|Hobart||Semester 2||On-Campus||Off-Campus||International International||Domestic Domestic|
- International students
- Domestic students
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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.
|Study Period||Start date||Census date||WW date||End date|
* 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).
Unit census dates currently displaying for 2021 are indicative and subject to change. Finalised census dates for 2021 will be available from the 1st October 2020. Note census date cutoff is 11.59pm AEST (AEDT during October to March).
- understand both the need for and the limitations of statistical analyses applied to data from diverse sources including both observational studies and experimental designs.
- apply theoretical and practical knowledge of modern statistical analysis to construct statistical models for a wide range of problems in various disciplines. implement and test these models in the statistical software R.
- interpret results of the statistical analyses and communicate the design of the experiment, the goal of the experiment and the analytical results to both statisticians and non-statisticians.
|Field of Education||Commencing Student Contribution 1||Grandfathered Student Contribution 1||Approved Pathway Course Student Contribution 2||Domestic Full Fee|
- Available as a Commonwealth Supported Place
- HECS-HELP is available on this unit, depending on your eligibility3
- FEE-HELP is available on this unit, depending on your eligibility4
1 Please refer here more information on student contribution amounts.
2 Information on eligibility and Approved Pathway courses can be found here
3 Please refer here for eligibility for HECS-HELP
4 Please refer here for eligibility for FEE-HELP
Please note: international students should refer to this page to get an indicative course cost.
Prerequisites(KMA153 - Data Handling and Statistics 1 OR KMA154 - Mathematics 1B OR JEE104 - Mathematics II)
You cannot enrol in this unit as well as the following:KMA653
1 x 1-hour short pre-recorded videos, 1 x 1-hour live lecture (online), 1 x 2-hour computer lab (1 x 1-hour for distance offering), 1 x 1-hour optional Q&A session.
|Assessment||Test (35%)|Online quizzes (20%)|Project 1 (15%)|Project 2 (15%)|Project 3 (15%)|
|Timetable||View the lecture timetable | View the full unit timetable|
Required readings will be listed in the unit outline prior to the start of classes.
|Links||Booktopia textbook finder|
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.