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STAT20060

Academic Year 2024/2025

Statistics & Probability (STAT20060)

Subject:
Statistics & Actuarial Science
College:
Science
School:
Mathematics & Statistics
Level:
2 (Intermediate)
Credits:
5
Module Coordinator:
Dr Leonard Henckel
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module introduces the foundational and applied concepts of probability and statistical modelling for data science in engineering. Strong emphasis is placed on using the material covered to solve engineering problems, with a focus on the R statistical computing software. The main sections of the course are:

- Descriptive statistics; numerical and graphical methods
- Laws of probability
- Random variables; both discrete and continuous, properties of expectation and variance are also covered
- Statistical inference, sampling distributions, the central limit theorem, confidence intervals and hypothesis testing
- Simple linear regression, cognisance of key assumptions, correlation, least squares estimation, model diagnostics and prediction
- Statistical methods for quality control

In addition students are required to complete a sequence of computer laboratory sessions using the R statistical computing software. Students will learn how to perform key data analytic skills required in engineering problems e.g. exploratory data analyses using graphical and numerical descriptive statistics, how to calculate probabilities and simulate from common probability distributions, how to calculate confidence intervals, perform hypothesis tests and to fit statistical models e.g. linear regression.

About this Module

Learning Outcomes:

After completing this module the student will be able to:
- apply elementary combinatorics to traditional probability problems
- compute probabilities, expectations and variances for basic probability distributions
- compute confidence intervals for population parameters
- perform hypothesis tests on population parameters
- analyse a data set using a regression model
- do all of the above using the R statistical software package.

Indicative Module Content:

The main sections of the course are:
- Descriptive Statistics; numerical and graphical methods
- Laws of Probability
- Random variables; both discrete and continuous, properties of expectation and variance are also covered
- Statistical inference; sampling distributions, the central limit theorem, confidence intervals and hypothesis testing
- Simple linear regression; correlation, least squares estimation, hypothesis testing, model diagnostics and prediction
- Statistical methods for quality control
- Introduction to the statistical software R

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

90
Lectures

24
Tutorial

8
Practical

4
Total

126

Approaches to Teaching and Learning:
Lectures, tutorials and software lab practicals.

Requirements, Exclusions and Recommendations
Learning Requirements:

Elementary linear algebra and knowledge of differentiation and integration.


Module Requisites and Incompatibles
Incompatibles:
STAT20110 - Introduction to Probability, STAT20120 - Statistics & Prob for Econ&Fin, STAT20130 - Statistics & Probability, STAT20200 - Probability, STAT41060 - Probability and Statistics

Additional Information:
Econ & Fin students should not take STAT10010


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Written exam covering the entirety of the course material End of trimester
Duration:
2 hr(s)
 Standard conversion grade scale 40% No
70
 No
Assignment(Including Essay): Assignment writing a report analysing a data-set Week 10 Standard conversion grade scale 40% No
10
 No
Practical Skills Assessment: R-exam Week 12 Standard conversion grade scale 40% No
20
 No

Carry forward of passed components
No
 

Resit In Terminal Exam
Autumn Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Thiago Americo Da Silva Cardoso Tutor
Antonio Fozzati Tutor
Pedro Menezes De Araújo Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Tutorial Offering 1 Week(s) - 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Fri 10:00 - 10:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Mon 09:00 - 09:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 09:00 - 09:50
Spring External & School Exams Offering 1 Week(s) - 33 Wed 18:00 - 19:50
Spring Practical Offering 1 Week(s) - 22, 24, 26, 30, 33 Mon 15:00 - 15:50
Spring Practical Offering 2 Week(s) - 23, 25, 29, 31 Mon 15:00 - 15:50
Spring Practical Offering 2 Week(s) - 33 Mon 15:00 - 15:50
Spring Practical Offering 3 Week(s) - 22, 24, 26, 30, 33 Tues 15:00 - 15:50
Spring Practical Offering 4 Week(s) - 23, 25, 29, 31, 33 Tues 15:00 - 15:50
Spring Practical Offering 5 Week(s) - 22, 24, 26, 30, 33 Fri 11:00 - 11:50
Spring Practical Offering 6 Week(s) - 23, 25, 29, 31, 33 Fri 11:00 - 11:50
Spring Practical Offering 7 Week(s) - 23, 25, 29, 32, 33 Fri 15:00 - 15:50
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