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MATH1617: Statistics I

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Type	Open
Level	1
Credits	10
Availability	Available in 2024/2025
Module Cap	None.
Location	Durham
Department	Mathematical Sciences

Prerequisites

Normally, A level Mathematics at grade A or better, orequivalent

Corequisites

Calculus I (Maths Hons) (MATH1081) or Calculus I (MATH1061) and Probablity I (MATH1597)

Excluded Combinations of Modules

Mathematics for Engineers and Scientists (MATH1551), SingleMathematics A (MATH1561), Single Mathematics B (MATH1571) may not be taken with or after thismodule.

Aims

To introduce the principles and procedures of frequentist and Bayesian statistics, and illustrate them with canonical examples. This lays the foundations for all subsequent study of statistics.
To present frequentist and Bayesian principles as alternative approaches to doing statistics; to compare frequentist and Bayesian procedures and results.
To demonstrate the relevance of these principles and procedures to real problems.

Content

Introduction: applications; the nature of statistics; two schools of thought: frequentist and Bayesian.
Frequentist inference: principles and procedures of frequentist statistics; statistics and sampling distributions, confidence intervals, hypothesis testing; examples.
Bayesian inference: principles and procedures of Bayesian statistics; posterior distributions, credible intervals, decisions; examples.
Comparison between frequentist and Bayesian inference.
Demonstration of how the principles and procedures apply to real problems.

Learning Outcomes

Subject-specific Knowledge:

Knowledge of the principles and procedures of both frequentist and Bayesian inference as approaches to doing statistics.
Understanding of how to apply these principles and procedures, both in general, and in canonical examples.
Knowledge of the strengths and weaknesses of each approach.
Knowledge of the similarities and differences between the two approaches.
Understanding of the relevance of these principles and procedures to real problems.

Subject-specific Skills:

Ability to solve in principle and in practice arange of both routine and more challenging problems instatistics.

Key Skills:

Students will have basic mathematical skills in the followingareas: problem solving, modelling, computation.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

Lectures demonstrate what is required to be learned and theapplication of the theory to practical examples.
Tutorials provide the practice and support in applying themethods to relevant situations as well as active engagement and feedbackto the learning process.
Problem classes show how to solve example problems in an ideal way, revealing also the thought processes behind such solutions.
Weekly homework problems provide formative assessment to guide students in the correct development of their knowledge and skills. They are also an aid in developing students' awareness of standardsrequired.
The examination provides a final assessment of the achievementof the student.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total	Monitored
Lectures	27	3 pw in wks 11,12,13,15,17,19; 2 pw in wks 14, 16, 18, 20; (alternating with Problems Classes), 1 revision in wk 21	1 Hour	27
Tutorials	6	1 pw in wks 12, 14, 16, 18, 20; 1 revision in wk 21	1 Hour	6	Yes
Problem classes	4	1 pw in wks 14, 16, 18, 20	1 Hour	4
Preparation and Reading				63
Total				100

Summative Assessment

Component: Examination		Component Weighting: 90%
Element	Length / Duration	Element Weighting	Resit Opportunity
Written examination	2 hours	100	Yes

Component: Continuous Assessment		Component Weighting: 10%
Element	Length / Duration	Element Weighting	Resit Opportunity
Weekly written or electronic assignments to be assessed and returned. Other assignments are set for self-study and complete solutions are made available to students. Students will have about one week to complete each assignment. Students will have about one week to complete each assignment.		100	Yes

Formative Assessment

More information

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