STU11002 – Statistical Analysis I

Module CodeSTU11002
Module NameStatistical Analysis I
ECTS Weighting[1]5 credits
Semester taughtSemester 2
Module Coordinator/s  Dr Susan Connolly

Module Learning Outcomes

On successful completion of this module, students will be able to:

LO1: Explain the nature of data using appropriate descriptive statistics and graphical techniques     

LO2: Discuss ethical issues in statistics using relevant examples

LO3: Calculate and explain simple probabilities

LO4: Understand how various statistical distributions are used

LO5: Explain types of sampling, including how to choose a lower threshold for sample size.

LO6: Create estimates and confidence intervals of population parameters from samples

LO7: Carry out and interpret the results of statistical tests including independent t-tests and the chi-square test

LO8: Explain the ideas behind simple linear regression

LO9: Explain the ideas behind bootstrap and simulation studies in statistical analysis

Module Content

The aim of the course is to introduce the students to basic statistical concepts. In this module students will learn how to explain basic statistical theory and apply the techniques to data.  Students will be able to describe and interpret the results of their analyses in a detailed fashion. R studio will be used to analyse data.  

Teaching and learning Methods

2 hours of lectures/prerecorded videos per week and 1 hour of labs every second week. Lectures will introduce theory, methods, and examples. Labs will put these methods into practice in R Studio.

Assessment Details

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of totalWeek setWeek Due
Lab Work15%TBDTBD
ExaminationOnline examination – Time-limited60%TBDTBD

Reassessment Details

Supplemental assessment is by online time-limited examination ONLY (100%). Students repeating ‘off-books’ (OBA) are also assessed by examination ONLY (100%) in all examination sessions.

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:25 hours
 Lecture20 hours
 Laboratory5 hours
 tutorial or seminar0 hours
 Other0 hours
Independent study (outside scheduled contact hours), broken down by:79 hours
 preparation for classes and review of material
(including preparation for examination, if applicable)
70 hours
 completion of assessments (including examination, if applicable)9 hours
Total Hours104 hours

Recommended Reading List

Stuart, M. An Introduction to Statistical Analysis for Business and Industry A problem Solving approach.  London: Hodder Arnold, 2003

Moore, D.S, McCabe G.P & Craig, B.A. An Introduction to the practice of Statistics 6th ed.  New York: W. H. Freeman, 2009  

R for Data Science (available free online at

Module Pre-requisites

Prerequisite modules: None

Other/alternative non-module prerequisites:

Module Co-requisites


Module Website