# Mathematics

## MTH3028 - Statistical Inference: Theory and Practice (2019)

MODULE TITLE CREDIT VALUE Statistical Inference: Theory and Practice 15 MTH3028 Dr Stefan Siegert (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 weeks 0
 Number of Students Taking Module (anticipated) 80
DESCRIPTION - summary of the module content

Statistical models help us to describe and predict the real world, and are used in sectors as diverse as finance, insurance, economics, marketing, pharmaceuticals, sport, environment and government to name only a few. Statistical inference is the way that we use data and other information to learn about and apply our models. This module introduces you to some of the main approaches to statistical inference and explains their associated procedures. It is designed for students who want to understand the ideas and mathematical theory that lie behind many modern statistical methods. The module establishes key theoretical concepts and results alongside explanations of their practical purpose and application. We will use computer simulations to illustrate basic concepts and as a tool for comparing procedures. You will gain practical experience with the methods through a series of worked examples and exercises.

Prerequisite module: MTH2006 Statistical Modelling and Inference or equivalent

AIMS - intentions of the module

This module aims to help you to develop a thorough understanding of statistical inference from a frequentist perspective. This includes understanding the underlying concepts, the mathematical theory, and how to apply the inferential methods to a range of statistical models. Such understanding is important for any job that involves conducting statistical investigations.

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 an understanding of the purpose of statistical inference, different approaches to statistical inference, and the key theoretical results and inferential procedures associated with these approaches;

2 apply these procedures to draw inferences about parametric statistical models, and compare different procedures critically.

Discipline Specific Skills and Knowledge:

3 demonstrate an understanding of the ways in which statistical inferential procedures and their performances may differ;

4 demonstrate an understanding of inferential concepts integral to statistical science;

5 progress to study a wider range of statistical inferential approaches in more detail.

Personal and Key Transferable/ Employment Skills and  Knowledge:

6 demonstrate an understanding of key mathematical arguments, statistical concepts and practical issues important for advanced study, application and development of statistical science;

7 use the statistical programming environment 'R' to implement generic inferential procedures and to conduct simulation studies.

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

1. Classical Inference:

- The principles and methods of classical frequentist inference are explained. These include point estimators, bias and efficiency; hypothesis tests, the Neyman-Pearson Theorem and uniformly most powerful tests; confidence sets and their construction from hypothesis tests; prediction intervals and their construction from ancillary statistics.

2. Likelihood Inference:

- Inferential approaches based on the likelihood are introduced. These include maximum likelihood estimators and their asymptotic properties; likelihood-based hypothesis tests and confidence sets; and pseudo-likelihoods.

3. Computational Inference:

- Inferential approaches based on resampling are introduced. These include Monte Carlo and bootstrap tests; the jackknife and bootstrap estimates of bias and variance; bootstrap confidence sets; and bootstrap prediction intervals.

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
 Scheduled Learning & Teaching Activities Guided Independent Study 33 117
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
 Category Hours of study time Description Scheduled learning and teaching activities 33 Lectures/example classes Guided independent study 20 Study of lecture notes Guided independent study 50 Unassessed and formative exercises Guided independent study 27 Revision Guided independent study 20 Summative Assessment

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
Coursework - set questions 10 hours (1 hour each week) All Oral feedback in tutorial and office hour.

SUMMATIVE ASSESSMENT (% of credit)
 Coursework Written Exams 20 80
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Written exam – closed book

80

2 hours 1-6 Written/verbal on request

Coursework – set questions

20 20 hours All Written feedback on script and oral feedback in office hour

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
All above Written exam (100%) All August Ref/Def period

RE-ASSESSMENT NOTES

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.

If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

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