Module Information

Module Identifier
Module Title
Applied Statistics
Academic Year
Semester 1 (Taught over 2 semesters)
Mutually Exclusive
MA10310 or MA11310 or MT10310 or MT11310
External Examiners
  • Dr Theodore Kypraios (Associate Professor - University of Nottingham)

Course Delivery



Assessment Type Assessment length / details Proportion
Semester Exam 2 Hours   (Written Examination)  60%
Semester Assessment Written Course Work 1 report on a given dataset and 1 assessed practical.  40%
Supplementary Exam 2 Hours   Supplementary Exam  (Written Examination)  100%

Learning Outcomes

On successful completion of this module students should be able to:

Summarise effectively a set of data;

Identify the statistical techniques appropriate to analysing a given situation;

Carry out standard statistical procedures for one and two samples;

State and check the assumptons required by their analyses;

Draw conclusions from an analysis of data and write reports concerning these conclusions;

Interpret one-way and two-way ANOVA tables, and interaction diagrams;

Use the statistical package 'R' to practically manipulate and analyse datasets.

Brief description

There is an increasing demand in the workplace for staff skilled in the analysis and interpretation of data. The module, includes extensive use of the statistical package 'R', covers the basic skills of a practising statistian from both theoretical and practical viewpoints. It also includes the preparation of reports.


1. DESCRIPTIVE STATISTICS: Interpretation of data presented in numerical and graphical forms. Report writing.

2. REGRESSION: Fitting straight lines, correlation coefficient, multiple regression.

3. DISTRIBUTIONS: Bionomial, Poisson, Normal etc. Distribution of the sample mean. Graphical assessment of goodness of fit; Q-Q plots.

4. BASIC IDEAS: Estimates and standard errors of means, proportions, sums and differences.

5. CONFIDENCE STATEMENTS: Construction and interpretation of confidence intervals for means, rates and proportions. Bootstrap confidence intervals. Confidence limits for the difference between parameters.

6. HYPOTHESIS TESTING: Hypothesis tests; interpretation of p-values. Paired an unpaired two sample t-tests. Bootstrap p-values.

7. GOODNESS-OF-FIT: Chi-squared test.

8. DESIGNED EXPERIMENTS: One-way ANOVA. Two-way ANOVA. Interactions.

Module Skills

Skills Type Skills details
Application of Number Omnipresent in a statistics module
Communication Written answers to assignment exercises must be clear and well structured.
Improving own Learning and Performance Students are expected to develop their own approach to time-management regarding the completion of assignments on time and preparation between lectures.
Information Technology Dataset manipulation and analysis in this module include extensive use of R. Word processing skills required for professional presentation of results, especially for the statistical report assignment.
Personal Development and Career planning Development of proficiency in R and familiarity with statistical methods in a strong skill set for anyone wishing to use statistics in their future career.
Problem solving Omnipresent in a statistics module.
Research skills Students will be encouraged to independently find and assimilate useful resources
Subject Specific Skills Broadens exposure of students to methods of vital importance in the study of statistics.
Team work Students will be encouraged to work together on questions during practical sessions


This module is at CQFW Level 6