STAT20060 Statistics & Probability

Academic Year 2023/2024

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.

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Curricular information is subject to change

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
Lectures

24

Tutorial

8

Practical

4

Autonomous Student Learning

90

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 Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Assignment Throughout the Trimester n/a Standard conversion grade scale 40% No

10

Examination: End of trimester exam 2 hour End of Trimester Exam No Standard conversion grade scale 40% No

70

Continuous Assessment: labs and practical exam Varies over the Trimester n/a Standard conversion grade scale 40% No

20


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
Beatriz Barbero Lucas Tutor
Manisha Ganguly Tutor
Pedro Menezes De Araújo Tutor
Mr Matt Nagle Tutor
Lapo Santi 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
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Mon 09:00 - 09:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 09:00 - 09:50
External & School Exams Offering 1 Week(s) - 33 Wed 18:00 - 19:50
Practical Offering 1 Week(s) - 22, 24, 26, 30, 33 Mon 15:00 - 15:50
Practical Offering 2 Week(s) - 23, 25, 29, 31 Mon 15:00 - 15:50
Practical Offering 2 Week(s) - 33 Mon 15:00 - 15:50
Practical Offering 3 Week(s) - 22, 24, 26, 30, 33 Tues 15:00 - 15:50
Practical Offering 4 Week(s) - 23, 25, 29, 31, 33 Tues 15:00 - 15:50
Practical Offering 5 Week(s) - 22, 24, 26, 30, 33 Fri 11:00 - 11:50
Practical Offering 6 Week(s) - 23, 25, 29, 31, 33 Fri 11:00 - 11:50
Practical Offering 7 Week(s) - 23, 25, 29, 32, 33 Fri 15:00 - 15:50
Spring