# Statistics BSc/MSci

• School: School of Mathematics and Statistics
• Credits: 10
• Level: Level 4 (SCQF level 10)
• Typically Offered: Semester 1
• Available to Visiting Students: No

### Short Description

This course will enable students to develop advanced expertise in data analysis for a wide variety of subject areas using the statistical package R.

### Timetable

20 two hour practical computing sessions (weekly, at times to be arranged)

### Excluded Courses

STATS5051 Advanced Data Analysis (Level M)

### Assessment

Laboratory work (100%)

Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.

### Course Aims

To develop advanced expertise in formulating and implementing statistical approaches to practical problems in a wide variety of subject areas.

To integrate material covered in various lecture courses with skills developed through practical work in order to solve real-world problems.

To train students in more advanced aspects of statistical computing through the statistical package R.

To further develop written skills of presentation and communication.

To provide the opportunity for critical thinking and independent learning.

### Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ formulate questions of interest and identify relevant informal and formal statistical methodology for a wide variety of practical contexts;

■ implement the various stages of advanced statistical analysis appropriately in R;

■ interpret the output of an R procedure;

■ critically collate results and conclusions;

■ present the main results and conclusions in the form of concise summaries;

■ present results of analyses in the form of written reports;

■ work independently (and as a group) on practical data analysis problems;

■ write R code that can be utilised by another user to produce their own statistical analyses.

### Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.