SFY0002 : Statistics
SFY0002 : Statistics
- Offered for Year: 2024/25
- Module Leader(s): Dr Aleksandra Svalova
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
GCSE Mathematics Grade C or equivalent
Co-Requisite
Modules you need to take at the same time
| Code | Title |
|---|---|
| SFY0001 | Basic Mathematics |
Co Requisite Comment
Normally SFY0001 or equivalent
Aims
To develop confidence in using and questioning statistical data.By the completion of the course students will understand the basic principles of statistical analysis. They will also have a basic knowledge of data interpretation, data analysis and statistical inference.
Students will have a basic understanding of the concepts of chance and probability. Students will be able to present data in numerical, graphical and tabular form. They will also have developed their skills in project work and report writing.
Outline Of Syllabus
Basic concepts of statistics
Summary statistics and distributions of data
Describing distributions with plots and tables
Bivariate data
Chance
The normal distribution
Sampling variation and confidence intervals
Thinking about and studying unknowns
Learning Outcomes
Intended Knowledge Outcomes
At the end of this module the students will know the fundamental principles of data collection, analysis, and statistical inference.
The students will distinguish between different types of summary statistics of data location and spread (e.g. mean and standard deviation).
The students will know how to think about bivariate relationships, how to display them, and how to test the strength of a linear bivariate relationship
The students will understand the concept of chance and probability, basic rules of probability, and the normal distribution
The students will know the fundamental concepts of the normal probability distribution.
The students will be able to distinguish between the uncertainty in the sample data and the uncertainty in sample data statistics e.g. sample mean.
The students will know different types of study, e.g. descriptive and analytical studies.
Intended Skill Outcomes
At the end of this module students will be able to present data in numerical, graphical and tabular form.
They will also have developed their skills in project work and report writing.
The students will be able to apply the appropriate summary statistics for different data types.
The students will be able to evaluate the strength of simple bivariate relationships.
The students will be able to select appropriate plots to display different types of data and different distribution shapes
The students will be able to evaluate the probabilities of events that are assumed to follow a normal distribution.
The students will be evaluate confidence intervals for e.g. sample means.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 9:00 | 9:00 | Exam Revision |
| Scheduled Learning And Teaching Activities | Lecture | 11 | 1:00 | 11:00 | N/A |
| Guided Independent Study | Assessment preparation and completion | 1 | 5:30 | 5:30 | Project preparation and writing |
| Guided Independent Study | Assessment preparation and completion | 1 | 8:00 | 8:00 | Completing Assignments 1 and 2 |
| Scheduled Learning And Teaching Activities | Practical | 6 | 2:00 | 12:00 | Present in person computer practicals. |
| Scheduled Learning And Teaching Activities | Small group teaching | 11 | 1:00 | 11:00 | In-Person Tutorials |
| Guided Independent Study | Skills practice | 1 | 1:30 | 1:30 | Writing the exam |
| Guided Independent Study | Independent study | 1 | 9:00 | 9:00 | Going through computer practical material individually, up to 90 minutes per practical |
| Guided Independent Study | Independent study | 1 | 33:00 | 33:00 | Going through lecture materials individually, up to 3 hours per lecture |
| Total | 100:00 |
Teaching Rationale And Relationship
Lectures will give the students all of the knowledge needed to achieve the learning outcomes. Computer practicals using appropriate statistical software will give the students the skills needed to perform statistical analyses rapidly, and reflect modern statistical practices in industry and academia.
Through independent study and asynchronous material the students will gain a deeper understanding of the in-course material.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 90 | 2 | A | 70 | N/A |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Prob solv exercises | 2 | M | 14 | Problem solving exercise |
| Written exercise | 2 | M | 16 | Project work. This includes using statistical software to perform analysis on a data set and writing a report (no more than 8 pages not including appendix). |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Prob solv exercises | 2 | M | Students will get feedback on their first set of problem solving exercises. |
Assessment Rationale And Relationship
The project and two assessed problem-solving exercises will give the students the opportunity to practise the methods introduced in the course. They will also develop their problem solving and report writing skills. The exam tests the student’s ability to apply the theory to relevant questions.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- SFY0002's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- SFY0002's past Exam Papers
General Notes
N/A
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The information contained within the Module Catalogue relates to the 2024 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2025/26 entry will be published here in early-April 2025. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.