This unit is designed to extend the knowledge of statistical data analysis. It builds on the concepts of regression and ANOVA introduced in Data Handling & Statistics 1 and introduces analyses using multiple explanatory variables, mixed-effects models and generalized linear models (GLMs, such as logistic regression. 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 general public. Students will be able to evaluate the appropriateness of experimental designs and statistical methods and suggest changes to designs and analysis.
If studying this unit off-campus, please note there is a need to attend either the Cradle Coast, Launceston or Hobart campus for a test held in week 13 of semester.
|Unit name||Data Handling and Statistics 2|
|Faculty/School||College of Sciences and Engineering
School of Natural Sciences
|Available as student elective?||Yes|
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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.
Special approval is required for enrolment into TNE Program units.
TNE Program units special approval requirements.
* 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).
- KMA153 or KMA154 or JEE104
You cannot enrol in this unit as well as the following:
2x 1 hr face-to-face lectures, 2x1-hr computer lab sessions weekly.
2 in-class tests (50%), Projects (30%), Quizzes (15%), Class participation/computer labs (5%).
|Timetable||View the lecture timetable | View the full unit timetable|
The University reserves the right to amend or remove courses and unit availabilities, as appropriate.