STAT20060 Statistics & Probability

Academic Year 2020/2021

This module introduces the basic concepts of probability and statistical modelling. Strong emphasis is placed on using the material covered in problem-solving scenarios. 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

In addition students are required to complete a sequence of computer laboratory sessions using the R software package. Students will learn how to perform exploratory data analyses using graphical and numerical descriptive statistics, how to calculate probabilities and simulate from common probability distributions, how to calculate confidence intervals and perform hypothesis tests and finally to fit linear regression models.

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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

4
Practical

4
Autonomous Student Learning

90
Total

122
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

Additional Information:
Econ & Fin students should not take STAT10010


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Class Test: mid-term exam Week 7 n/a Standard conversion grade scale 40% No

10
Examination: End of trimester exam 2 hour End of Trimester Exam Yes 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

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