Study information

Probability, Statistics and Data - 2023 entry

MODULE TITLEProbability, Statistics and Data CREDIT VALUE30
MODULE CODEMTH1004 MODULE CONVENERDr Stefan Siegert (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11 11 0
Number of Students Taking Module (anticipated) 275
DESCRIPTION - summary of the module content
Our ability to collect and analyse data is increasingly driving our world. Statistics is concerned with both the practice of analysing data to learn about the world, and the theory that underpins the methods and models used for data collection and analysis. This theory is itself based on probability, the mathematics of chance and uncertainty. In this module, you will learn about the mathematics of combinatorics and probability, and the key ideas of statistical modelling and inference, in which probability is used to quantify uncertainty. You will also gain experience of employing these ideas to analyse data using statistical software such as the R programming environment. The module develops key ideas and techniques that form the foundation of modules such as MTH2006 Statistical Modelling and Inference.
 
AIMS - intentions of the module

The aim of this module is to introduce you to basic topics in probability, statistics and data analysis. This module provides the foundation for the second-year stream in Statistical Modelling and Inference, and subsequent modules in statistics in years 3 and 4.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and  Knowledge:

  1. demonstrate a sound understanding of selected essential topics in probability theory, including the ability to apply those concepts in tackling an appropriate range of problems;
  1. demonstrate a knowledge of the basic ideas of statistical inference, including probability distributions, point and interval estimation and hypothesis tests;
  2. use the statistical programming environment R to manipulate, visualise and analyse data.

Discipline Specific Skills and Knowledge:

  1. show sufficient knowledge of fundamental mathematical and statistical concepts, manipulations and results.

Personal and Key Transferable/ Employment Skills and Knowledge:

  1. reason using abstract ideas, formulate and solve problems and communicate reasoning and solutions effectively in writing;
  2. use learning resources appropriately;
  3. exhibit self-management and time-management skills.

 

SYLLABUS PLAN - summary of the structure and academic content of the module

the nature of data;

data visualisation

probability theory and applications;

random variables and moments;

discrete and continuous distributions;

bivariate and multivariate distributions;

parametric statistical models;

prediction and simulation;

applications of models;

combinations of random variables;

transformation of random variables;

point estimation;

interval estimation;

hypothesis testing.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 88.00 Guided Independent Study 212.00 Placement / Study Abroad
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 66 Lectures
Scheduled learning and teaching activities 11 Practical classes in a computer lab
Scheduled learning and teaching activities 12 Tutorials
Guided independent study 211 Guided independent study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Weekly theoretical and practical exercises 1 hour each week All Class feedback
Report 1 practice 4 hours All Class feedback
Report 2 practice 4 hours All  Class feedback
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 30 Written Exams 70 Practical Exams
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written exam – closed book 60 2 hours (Summer) 1,2,4-7 Via SRS
Report 1 15 Short report, about two pages All Feedback sheet
Report 2 15 Short report, about four pages All Feedback sheet
Mid-term Tests 10 2x40 minutes All Via SRS

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-reassessment
Written Exam* Written exam (2 hours) 1,2,4-7 August Ref/Def period
Report 1* Report 1 All August Ref/Def period
Report 2* Report 2 All August Ref/Def period
Mid-term test 1* Mid term test 1 All August Ref/Def period
Mid-term test 2* Mid term test 2 All August Ref/Def period

*Please refer to reassessment notes for details on deferral vs. Referral reassessment 

RE-ASSESSMENT NOTES

Deferrals: Reassessment will be by coursework and/or exam in the failed or deferred element only. For deferred candidates, the module mark will be uncapped.

Referrals: Reassessment will be by a single written exam worth 100% of the module only. As it is a referral, the mark will be capped at 40%. 
 

 

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

ELE – http://vle.exeter.ac.uk


 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set McColl, J. Probability Arnold 1995 0000340614269 [Library]
Set Grolemund, G. and Wickham, H. R for Data Science O'Reilly Media 2016 978-1491910399 [Library]
Set Rice, J A Mathematical Statistics and Data Analysis 3rd Brooks Cole 2007 978-0495118688 [Library]
CREDIT VALUE 30 ECTS VALUE 15
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 4 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Friday 03 February 2023
KEY WORDS SEARCH Probability; probability distributions; random variables; statistics; inference; estimation; prediction; simulation; data analysis; data visualisation; R.

Please note that all modules are subject to change, please get in touch if you have any questions about this module.