| Module Code | STU11002 |
| Module Name | Statistical Analysis I |
| ECTS Weighting [1] | 5 ECTS |
| Semester Taught | Semester 2 |
| Module Coordinator/s | Silivia D’Angelo |
Module Learning Outcomes
On successful completion of this module, students will be able to:
- Explain the nature of data using appropriate descriptive statistics and graphical techniques;
- Discuss ethical issues in statistics using relevant examples;
- Calculate and explain simple probabilities;
- Understand how various statistical distributions are used;
- Explain types of sampling, including how to choose a lower threshold for sample size;
- Create estimates and confidence intervals of population parameters from samples;
- Carry out and interpret the results of statistical tests including independent t-tests and the chi-square test;
- Explain the ideas behind simple linear regression;
- 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 analyze data.
Teaching and Learning Methods
2 hours of lectures/pre-recorded 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 Component | Brief Description | Learning Outcomes Addressed | % of Total | Week Set | Week Due |
| Coursework | 30% | TBD | TBD | ||
| Examination | In person 2hrs | 70% | TBD | TBD |
Reassessment Details
Supplemental assessment is by In person 2 hr examination (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 |
| Lecture | 20 hours |
| Laboratory | 5 hours |
| Tutorial or seminar | 0 hours |
| Other | 0 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 Hours | 104 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 https://r4ds.had.co.nz/)
Module Pre-requisites
Prerequisite modules: N/A
Other/alternative non-module prerequisites: N/A
Module Co-requisites
N/A