Module: Probability and Statistics

Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title	Probability and Statistics
Module Code	MS223
School	School of Mathematical Sciences
Module Co-ordinator	Semester 1: Mingchuan Zhao Semester 2: Ho Man Tai Autumn: Ho Man Tai
Module Teachers	Gurpreet Singh Thorsten Neuschel Ho Man Tai Mingchuan Zhao
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Repeat examination
Array

Description

The aim of this module is to give a thorough grounding in probability, statistics and calculus of several variables as required for the successful understanding and solution of problems in science. Students will learn how mathematics can be used as a tool for solving scientific problems and a language for communicating information. This is a know-how and skills module. Students will participate in the following learning activities: Lectures: Students will attend two one-hour lectures per week. These lectures are designed to introduce learners to the mathematical principles and problem solving techniques that underpin this module. Tutorials: Each student will attend one one-hour tutorial per week. Problem sheets based on lecture content are distributed to the students and they are strongly advised to attempt all tutorial questions in advance of the tutorial.Reading: Students are expected to fully utilise the textbooks recommended.

Learning Outcomes

1. apply the rules of probability and assign probabilities to events.
2. know how to obtain expectations of discrete and continuous random variables.
3. use the normal and student t-distributions to test statistical hypotheses and to compute confidence intervals.
4. perform the calculations that arise when the calculus of several variables is used to solve problems.

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	24	Two lectures per week
Directed learning	2	End of Semester Exam
Independent Study	64	Study over 16 weeks-including exam preiod
Tutorial	10	Weekly Tutorial
Directed learning	25	Solving Tutorial questions
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Sets
Definitions, set operations; set identities. Russell's paradox.

Probability
Random experiments; axioms of probability; independent events; conditional probability; Bayes' theorem

Random variables and probability densities
Discrete and continuous random variables; characteristics of random variables; probability distributions and densities.

Some important probability densities
Basic combinatorics, the binomial, Poisson, Pascal and normal distributions.

Statistical inference
Point estimates and confidence intervals; the central limit theorem; hypothesis tests.

Vector calculus and functions of several variables
Vectors; scalar and cross product; applications. Scalar and vector fields; partial derivatives; div, grad and curl; surfaces; optimization problems.

Assessment Breakdown
Continuous Assessment	20%	Examination Weight	80%

Type	Description	% of total	Assessment Date
Course Work Breakdown
In Class Test	Short Test	10%	Once per semester
In Class Test	Short Test	10%	Sem 2 End

Reassessment Requirement Type
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 3

Indicative Reading List

J.J. Kinney: 2002, Statistics for Science and Engineering., AddisonWesley,
R.L. Finney and G. B. Thomas: 1994, Calculus, Addison Wesley,

Other Resources

None

Programme or List of Programmes

AC	BSc in Chemical and Pharmaceutical Sc.
AI	Chemistry with Artificial Intelligence
AS	BSc in Analytical Science
BSSA	Study Abroad (DCU Business School)
BSSAO	Study Abroad (DCU Business School)
ECSA	Study Abroad (Engineering & Computing)
ECSAO	Study Abroad (Engineering & Computing)
EST	BSc in Environmental Science & Tech
HMSA	Study Abroad (Humanities & Soc Science)
HMSAO	Study Abroad (Humanities & Soc Science)
IESA	Study Abroad (Institute of Education)
IESAO	Study Abroad (Institute of Education)
PAN	BSc Physics with Data Analytics
SAMPM	Professional Development Modules Maths
SHSA	Study Abroad (Science & Health)
SHSAO	Study Abroad (Science & Health)
SMPSC	Single Module Prof. Science and Health

Date of Last Revision

26-SEP-07

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