### MAS2342 : Introduction to Statistics (Inactive)

• Inactive for Year: 2016/17
• Module Leader(s): Dr Malcolm Farrow
• Teaching Location: Newcastle City Campus
##### Semesters
 Semester 2 Credit Value: 10 ECTS Credits: 5.0
##### Pre Requisites
Code Title
MAS1041Mathematical Methods A
MAS1042Mathematical Methods B
###### Pre Requisite Comment

A-level Mathematics (or equivalent)

##### Co Requisites
Code Title
MAS2341Introduction to Probability
###### Co Requisite Comment

MAS2041, MAS2042 are acceptable in place of MAS1041, MAS1042 respectively

#### Aims

To develop ideas and methods essential for the study of probability and statistics. To develop a familiarity with ideas of continuous probability models and interpretation, and inference through likelihood. To further develop use of the statistical package R. To further develop writing skills through project work.

Module Summary

The course will be mainly concerned with using probability theory and observed data together in order to estimate the properties of a population. For example, we will see how we can use information on the actual survival rates of a group of patients to make statements about survival rates of patients in general, and how confident we can be about the accuracy of such statements. Key statistical ideas will be introduced in the examination of these questions.

#### Outline Of Syllabus

Continuous probability models. Calculation and interpretation of mean and variance. Practical illustrations and calculation of probabilities using R. Introduction to statistical inference: estimation of population quantities and properties of estimators. Introduction to likelihood inference: maximum likelihood estimation and the motivation and use of the asymptotic properties of the maximum likelihood estimator. Introduction to hypothesis tests: simple hypotheses, critical regions and power. Motivation and use of a one-sample t-test.

#### Learning Outcomes

##### Intended Knowledge Outcomes

Students will be familiar with ideas of statistical modelling, data analysis, interpretation and introductory likelihood methods. They will know the rudiments of hypothesis testing, such as size, power and critical regions.

##### Intended Skill Outcomes

Students will be able to use probability distributions to model real-life situations. They will be able to apply elementary statistical methods such as the one-sample t-test, in the analysis of data. They will be able to use likelihood methods in the analysis of data.

• Cognitive/Intellectual Skills
• Data Synthesis : Assessed
• Numeracy : Assessed
• Information Literacy
• Use Of Computer Applications : Assessed
• Self Management
• Personal Enterprise
• Problem Solving : Assessed
• Interaction
• Communication
• Written Other : Assessed

#### Teaching Methods

##### Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion24:008:00Project
Guided Independent StudyAssessment preparation and completion42:008:00CBAs
Scheduled Learning And Teaching ActivitiesLecture201:0020:00Formal lectures
Guided Independent StudyAssessment preparation and completion28:0016:00Written assignments
Scheduled Learning And Teaching ActivitiesLecture21:002:00Revision lectures
Scheduled Learning And Teaching ActivitiesLecture41:004:00Problem classes
Guided Independent StudyAssessment preparation and completion111:0011:00Revision for unseen Exam
Guided Independent StudyAssessment preparation and completion11:301:30Unseen Exam
Scheduled Learning And Teaching ActivitiesPractical21:002:00N/A
Scheduled Learning And Teaching ActivitiesDrop-in/surgery21:002:00Tutorials in the lecture room
Scheduled Learning And Teaching ActivitiesDrop-in/surgery120:000:00Office Hours in a staff office
Guided Independent StudyIndependent study22:004:00Assignment review
Guided Independent StudyIndependent study121:3021:30Studying, practising and gaining understanding of course material
Total100:00
##### Jointly Taught With
Code Title
MAS1342Introduction to Statistics
##### Teaching Rationale And Relationship

Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the studentsâ€™ abilities at applying the theory to solving problems. Tutorials are used to identify and resolve specific queries raised by students and to allow students to receive individual feedback on marked work. Practicals are used both for solution of problems and work requiring extensive computation and for simulation to give insight into the ideas/methods studied. Office hours provide an opportunity for more direct contact between individual students and the lecturer.

#### Assessment Methods

The format of resits will be determined by the Board of Examiners

##### Exams
Description Length Semester When Set Percentage Comment
Written Examination902A70unseen
##### Exam Pairings
Module Code Module Title Semester Comment
2N/A
##### Other Assessment
Description Semester When Set Percentage Comment
Written exercise2M10Project work
Prob solv exercises2M10N/A
Computer assessment2M10CBAs
##### Assessment Rationale And Relationship

A substantial formal unseen examination is appropriate for the assessment of the material in this module. Approximately two written assignments of approximately equal weight (worth approximately 10% in total), approximately two computer based assessments of approximately equal weight (worth approximately 10% in total), and project work (worth 10%) allow the students to develop their problem solving techniques, to practise the methods learnt in the module and to receive feedback; this is thus formative as well as summative assessment.

#### General Notes

N/A

Disclaimer: The information contained within the Module Catalogue relates to the 2016/17 academic year. In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described. Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2017/18 entry will be published here in early-April 2017. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.