GS507

Graduate Studies Form for Modules attached to Structured PhD and/or Research Masters Programmes                                                               

Title

Statistical Methods for Research

Credits  (ECTS)

5

Module Places

Module Code: Please indicate if generic (GS) or specialised

GS507

Teaching Period

Semester 1   

Module Owner

Professor John Newell

Module Discipline:  MA_ST_AM - School of Mathematics, Statistics and Applied Mathematics

Module Description:

This course will introduce students to statistical concepts and thinking by providing a practical introduction to data analysis. The importance and practical usefulness of statistics in biomedical and clinical environments will be demonstrated through a large array of case studies. Students attending this course will be encouraged and equipped to apply simple statistical techniques to design, analyse and interpret studies in a wide range of disciplines. Introduction to Biostatistics Statistics can be a very important and interesting subject as it is an integral part of almost all areas of practical research both inside and outside the University. The main theme of this course for students is that they should meet and understand many of the basic statistical ideas they may meet and use in their future research. The emphasis throughout the course is on the application of Statistics and will rely heavily on a statistical computing package called MINITAB. The course concentrates on how, in any research context, to pose answerable and generalisable questions, design an experiment to answer such, carry out the appropriate statistical procedures on the resulting data from the experiment and finally to interpret and report the conclusions/answers to the questions posed on the basis of this analysis.

Learning Outcomes:

On successful completion of this module, the learner will be able to:;

LO1:  Understand the key concept of variability;

LO2: Understand the ideas of population, sample, parameter, statistic and probability;

LO3: Understand simple ideas of point estimation;

LO4: Recognise the additional benefits of calculating interval estimates for unknown parameters and be able to interpret interval estimates correctly;

LO5: Carry out a variety of commonly used hypothesis tests

LO6: Understand the difference between paired and independent data and be able to recognise both in practice;

LO7: Understand the aims and desirable features of a designed experiment;

LO8: calculate the sample size needed for one and two sample problems.

Indicative Content: 

Written Assessment

Assessment Type

Assessment Description

Outcome addressed

% of total

Marks Out of

Pass Marks

Sitting

Assessment Period

Assessment Date

Duration

Mandatory

Paper 1 - Written

n/a

1,2,3,4,5,6,7,8

70.00

100

40

First Sitting

Semester 1

n/a

2:00

True

Assessment is marked as bondable but has no matching assessments

Continuous Assessment

Assessment Type

Assessment Description

Outcome addressed

% of total

Marks Out of

Pass Marks

Sitting

Assessment Period

Assessment Date

Duration

Mandatory

Essay 1

n/a

1,2,3,4,5,6,7,8

30.00

100

40

First Sitting

Semester 1

n/a

0

True

Workload Type

Workload Description

Learning Outcomes

Hours

Frequency

Average Weekly Learner Workload

Lecture

1 hour duration

1,2,3,4,5,6,7,8

24

Per semester

2.00

Tutorial

1 hour duration

1,2,3,4,5,6,7,8

12

Per semester

1.00

Independent and Directed Learning

No description

1,2,3,4,5,6,7,8

110

Per semester

9.17

Total Hours = 146

Workload: Full Time

Workload Type

WorkLoad Description

Learning Outcomes

Hours

Frequency

Average Weekly Learner Workload

Lecture

24 hours

1,2,3,4,5,6,7,8

24

Every Week

24.00

Tutorial

12 hours of tutorials

1,2,3,4,5,6,7,8

12

Every Week

12.00

Independent & Directed Learning (Non-contact)

110 hours

1,2,3,4,5,6,7,8

110

Per Semester

9.17

Total Hours

146.00

Total Weekly Learner Workload

45.17

Total Weekly Contact Hours

36.00