Courses / Module

On successful completion of the module, students should be able to:

• Analyse data using linear models and analysis of variance.
• Assess assumptions.
• Use model diagnostics.
• Choose and construct appropriate data visualisations.

Delivery methods	Hours
Lectures	24
Labs / Practicals	4
Tutorials	0
Planned learning activities	0
Independent student activities	97
Total	125

• Module Graded (numeric value) or Ungraded (Pass/Not Passed): Graded

Assessment type	Options	Weighting	Duration
Continuous Assessment		100%
University scheduled written examination	No	0%
Other		0%
Total		100%

• Pass standard: 40%
• Could the result of this module be capped? No

• Are supplemental/resit registrations permitted? Yes
• Students who score less than 40% on this module may repeat the final assessment (only) on a date determined by the Department. In this case, the module mark will be capped at 40%.

• DS663 or equivalent

For a full timetable search, go to NUIM Timetable. Venue locations can be found here.