Module Code - Title:
MS4034
-
APPLIED DATA ANALYSIS
Year Last Offered:
2025/6
Hours Per Week:
Grading Type:
N
Prerequisite Modules:
MS4222
Rationale and Purpose of the Module:
[Module replaces Numerical Computation MS4024]
This is a new module the aim of which is to give the students experience
building and using statistical models to analyse real data and formulate
conclusions based on interval estimates, hypothesis testing, model selection
and comparison.
The module serves to integrate the practice and theory of statistics.
The instructor and students are expected to analyse the data provided
with each lab in order to answer a scientific question posed by the
original researchers who collected the data.
To answer a question, statistical methods are introduced, and the
mathematical statistics underlying these methods are developed.
Syllabus:
Descriptive statistics; quantile plots, normal approximation.
Simple random sampling; confidence intervals.
Stratified sampling; parametric bootstrap allocation.
Estimation and testing; goodness-of-fit tests, information, asymptotic variance.
Contingency tables; experimental design.
Poisson counts and rates; Mantel-Haenszel test.
Regression; prediction, replicate measurements, transformations, inverse regression, weighted regression.
Multiple linear regression; model checking, projections.
Analysis of variance; unbalanced designs, indicator variables, factorial designs.
Learning Outcomes:
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)
On successful completion of this module, students should be able to:
Apply a range of standard statistical modelling approaches for the
analysis of real data sets using the statistical software package R.
Use sampling-resampling approaches for the purpose of characterising the sampling distribution of statistical estimates.
Apply a range of specialized regression models to real data sets.
Plan, design and analysis basic experimental factorial layouts.
Develop scientific report writing skills and presentation of project work.
Affective (Attitudes and Values)
n/a
Psychomotor (Physical Skills)
n/a
How the Module will be Taught and what will be the Learning Experiences of the Students:
Normal lecture/tutorial delivery, supported by two lab hours per week.
Recent and relevant statistical research and data sets will be incorporated as far as practicable.
Graduates will be expected to be proactive in the analysis of real data sets, and accurately articulate their findings in both the written word and oral presentations. This will be developed through weekly computer laboratory sessions, individual and group projects, leading to written reports and oral presentations.
Research Findings Incorporated in to the Syllabus (If Relevant):
Prime Texts:
Nolan, D., and Speed, T. (2000)
Stat Labs: Mathematical Statistics Through Applications
, Springer
Other Relevant Texts:
Venables and Ripley (2002)
Modern applied statistics with S
, Springer
Programme(s) in which this Module is Offered:
BSECMSUFA - Economics and Mathematical Sciences
BSMSCIUFA - MATHEMATICAL SCIENCES
BSSCCHUFA - Science Choice
BSFIMAUFA - FINANCIAL MATHEMATICS
Semester(s) Module is Offered:
Spring
Module Leader:
james.a.sweeney@ul.ie