ST7001 – Base Module – Teaching and Learning

Module Code	ST7001
Module Name	Base Module (Post Graduate Certificate in Statistics)
ECTS Weighting[1]	15 ECTS – Derogation
Semester taught	Semester 1
Module Coordinator/s	Assistant Professor Mimi Zhang

Module Learning Outcomes

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

Demonstrate a systematic understanding of the fundamental inferential ideas which underpin statistical methods.
Demonstrate a broad understanding of the role of statistical ideas and methods covering both data collection and data analysis.
Demonstrate a competence in the use of basic statistical tools.Have a sound basis on which to develop further their statistical skills.

Module Content

Specific topics addressed in this module include: random variablesNormal distribution and Normal probability plotsampling distributionsz-test, t-test, F-test, chi-square testconfidence interval, p-value, significance leveltype-I error, type-II error, power of a testdesign of experimentsanalysis of variance (ANOVA)linear regressionMinitab laboratory.

Teaching and learning Methods

The module will consist of 21 hours of lectures and 3 hours of labs

The base module is introductory and will lay down the foundations on which other modules will build. The fundamental statistical inferential ideas of significance tests and confidence intervals are the central topics. The various inferential methods will be unified through the concept of a statistical model, which is an abstract representation of the quantity we wish to describe. For example, we may choose to represent the weights of filled containers by a Normal distribution with a particular centre (mean) and measure of spread (standard deviation). This would allow us to introduce formal tests to determine when the process average weight changes. Of course, the value of any formal procedure will depend on how well the underlying model represents the characteristics of the practical problem. When models are fitted, good statistical practice requires the assessment of the models used; this is done mainly by use of graphical procedures. These may be simple scatterplots of two characteristics of a number of individuals (e.g., heights and weights of a sample of people) to determine whether or not the assumption of a linear relationship between the two characteristics is reasonable. Alternatively, the graph might be a Normal probability plot (quantile-quantile plot) of residuals (differences between observed and predicted values) after a complex multiple regression model has been fitted to the data. Many questions can be answered by simple plots, so the course will emphasis practical methods that can be applied across many empirical disciplines.

Assessment Details

Assessment Component Brief Description Learning Outcomes Addressed % of total Week set Week due Examination 3-hour written examination LO1-LO4 100% n/a n/a

Assessment Component	Brief Description	Learning Outcomes Addressed	% of total	Week set	Week Due

Reassessment Details

Examination (3 hours, 100%)

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:	48 hours
Lecture	44 hours
Laboratory	4 hours
Tutorial or seminar	0 hours
Other	0 hours
Independent study (outside scheduled contact hours), broken down by:	120 hours
Preparation for classes and review of material (including preparation for examination, if applicable	0 hours
completion of assessments (including examination, if applicable)	0 hours
Total Hours	168 hours

Module Pre-requisites

Prerequisite modules: NA

Other/alternative non-module prerequisites: NA

Module Co-requisites

None

Module Website

Blackboard