MA1501: Statistical Inference

School Cardiff School of Mathematics
Department Code MATHS
Module Code MA1501
External Subject Code 100406
Number of Credits 10
Level L4
Language of Delivery English
Module Leader Professor Jonathan Gillard
Semester Spring Semester
Academic Year 2013/4

Outline Description of Module

The role of statistics in the modern world is ever increasing and applications can be found in a wide variety of areas including science, industry, government and commerce making a basic understanding of statistics an essential skill.  This is a lecture based module given at an introductory level on statistical inference to develop an understanding of the basic principles of mathematical statistics, used in situations where the full picture of a problem (population) is unknown and must be inferred from collected data (random sample).

This module will be accessible to those who have knowledge of A-level Pure Mathematics and an Introduction to Probability Theory.  It will prepare students for all modules with statistics and probability content in future years of the degree scheme.

Free Standing Module Requirements:  A pass in A-Level Mathematics of at least Grade A

Precursor Modules: MA1500 Introduction to Probability Theory

On completion of the module a student should be able to

  • Create sampling distributions for various sample statistics.
  • Determine confidence intervals for the sample mean, the difference between sample means, the variance and the ratio of two sample variances for a given large or small sample.
  • Understand the concept of hypothesis testing, used to help scientists choose between two possible alternatives, and be aware of different types of errors that occur.
  • Be able to perform a goodness of fit test to check the validity of an assumption that a distribution has a particular form and also be able to test for independence when looking at data which is split into different categories.

How the module will be delivered

27 - 50 minute lectures

5 - 50 minute tutorial classes

Some handouts will be provided in hard copy or via Learning Central, but students will be expected to take notes of lectures.

Students are also expected to undertake at least 50 hours private study including preparation of worked solutions for tutorial classes.

Skills that will be practised and developed

Be able to undertake a simple statistical analysis on random data sets and have an appreciation of the problems involved in interpreting results.  An essential skill in almost every career.

How the module will be assessed

Formative assessment is carried out by means of regular tutorial exercises.  Feedback to students on their solutions and their progress towards learning outcomes is provided during tutorial classes.  

The in-course element of summative assessment is an assessed exercise similar in form to the tutorial exercises. This allows students to demonstrate a level of knowledge and skills appropriate to that stage in the module.

The major component of summative assessment is the written examination at the end of the module.  This gives students the opportunity to demonstrate their overall achievement of learning outcomes.  It also allows them to give evidence of the higher levels of knowledge and understanding required for above average marks.

The examination paper has two sections of equal weight.  Section A contains a number of compulsory questions of variable length but normally short.  Section B has a choice of two from three equally weighted questions.

Assessment Breakdown

Type % Title Duration(hrs)
Written Assessment 15 Coursework N/A
Exam - Spring Semester 85 Statistical Inference 2

Syllabus content

  • Sampling Distributions
    • To be able to create sampling distributions of the sample mean, variance, median, maximum, etc., for small samples drawn both with replacement from a finite population. 
  • Specific Distributional Results
    • Students should be able to state the Central Limit Theorem, and derive and use properties of some important distributions.
  • Confidence Intervals
    • Students should be able to derive and use confidence intervals for means (and their differences), variances (and their ratios).
  • Hypothesis Testing
    • Students should be able to understand the concept of hypothesis testing and, in particular, the meaning of Type I error, Type II error and the p-value of a test.  Significance testing should be restricted to the binomial, Poisson and normal distributions.
  • Chi-Square Tests
    • Students should be able to perform a Chi-square goodness of fit test and be able to test for independence using contingency tables.

Essential Reading and Resource List

Probability and Random Processes, Grimmet G., & Stirzaker D., Oxford University Press, 2001

Probability and Statistical Inference, Hogg, R., & Tanis, E., Pearson Education


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