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Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

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Module Title
Module Code (ITS)
Faculty School
Module Co-ordinatorSemester 1: Lubani Nondo
Semester 2: Lubani Nondo
Autumn: Lubani Nondo
Module TeachersAnn Largey
Hiroyuki Kawakatsu
declan curran
Pia Helbing
Sandeep Keshava Rao
Mahdavi Ardekani Seyed Aref
Lubani Nondo
NFQ level 6 Credit Rating
Pre-requisite Not Available
Co-requisite Not Available
Compatibles Not Available
Incompatibles Not Available
None
Description

The purpose of this module is to provide students with an introduction to data analysis and interpretation. Students will learn about sources of data, methods of data presentation and description, and how to conduct simple hypothesis tests and make inferences. On completion of the module, students should be able to draw on statistics appropriately to support their own arguments and be able to better understand and critique statistical analysis they encounter in academic papers in subsequent courses. Students are expected to attend lectures and to actively participate in workshops and tutorials. Term exercises will be assigned in order to focus students' study and ensure timely review of course material.

Learning Outcomes

1. Define and calculate basic statistics used to describe distributions.
2. Present data in a meaningful way, using graphs and tables.
3. Perform calculations and manipulate data using a spreadsheet package, including estimation of a single variable regression.
4. Explain what a hypothesis test is, conduct simple hypothesis tests and interpret statistical significance.
5. Comment on statistical analysis in academic papers and identify shortcomings.



Workload Full-time hours per semester
Type Hours Description
Lecture18No Description
Lecture6Workshops - Two of these sessions to be used as computer practicals
Independent Study67Review of lecture material, additional reading, preparation for workshops
Independent Study8Completion of Assessment 1
Independent Study6Completion of Assessment 2
Independent Study20Completion of Assessment 3
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Data description & presentation
Topics will include: Types of data; Displaying data graphically; Descriptive statistics – definitions, uses, examples; Correlation; Applications using data relevant to programme disciplines; Sourcing data.

Statistical Estimation
Statistical decision theory; Hypothesis tests; Interpretation of test statistics (p values, F tests etc); Regression output interpretation – using firstly statistical estimation, then reviewing data analysis section (eg. to show importance of causation v correlation)

Critiquing statistical analysis
Drawing on papers from relevant disciplines, show the different ways in which statistics are used to inform and augment debate. Examine the statistics used and conclusions drawn. Highlight potential shortcomings (eg. external factors outside scope of project that may have impact, reverse causation.)

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
AssignmentTake home exercise15%Week 6
AssignmentTake home computer based exercise15%Week 8
ParticipationConstructive participation10%Every Week
AssignmentFinal term assignment60%Sem 2 End
Indicative Reading List

  • by Gary Koop: 0, Analysis of economic data, 978-0470713891
  • Beverly Dretzke,: 0, Statistics with Microsoft Excel, 978-0136043874
  • Deborah Rumsey,: 0, Statistics II for Dummies, 978-0470466469
  • by Deborah Rumsey: 2003, Statistics for dummies, Wiley, Hoboken, N.J., 978-0764554230
  • Deborah Rumsey: 2005, Statistics workbook for dummies, Wiley, Hoboken, N.J., 978-0764584664
Other Resources

None

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