MA11310 - STATISTICS

Module Identifier

MA11310

Module Title

STATISTICS

Academic Year

2004/2005

Co-ordinator

Dr John A Lane

Semester

Semester 2

Other staff

Glenda Roberts

Pre-Requisite

MA10310

Course delivery

Lecture

20 x 1 hour lectures

Seminars / Tutorials

5 x 1 hour tutorials

Other

Workshop. 2 x 1 hour workshops (including test)

Assessment

Assessment Type	Assessment Length/Details	Proportion
Semester Exam	2 Hours (written examination)	75%
Semester Assessment	Continuous Assessment:	25%
Supplementary Assessment	2 Hours (written examination)	100%

Learning outcomes

On completion of this module, a student should be able to:

describe the notion of covariance;
calculate means and variances of linear combinations of random variables;
identify a probability distribution appropriate to a given situation;
describe modelling in terms of Bernoulli trials and of random events;
manipulate distributions to obtain moments and to sketch curves;
assess a given value in relation to the scale of a given probability distribution;
estimate means and proportions from data;
explain the use of a statistical test;
construct and carry out simple tests.
use relevant statistical tables

Brief description

This module aims to develop common probability models, applicable to a variety of situations and to illustrate their use in statistical inference. It also includes an introduction to the theory of estimation.

Aims

To introduce the subject of Statistics to mathematics students.

Content

1. THE INFERENCE PROBLEM: The difference between probability and statistical inference. Assessing 'typical' values from a distribution. The idea of a statistic. Estimates and estimators. Accuracy and precision. Bias, sampling, variance and mean squared error. Comparison of estimators.
2. PROBABILISTIC (STOCHASTIC) MODELLING (INCLUDING EXAMPLES OF INFERENCE): Bernoulli trials and distributions based on them (Geometric, Binomial). Opinion polls. The ideas of covariance and correlation. Variances of linear combinations of random variables. Modelling random events. The Poisson and exponential distributions. Normality and the Central Limit Theorem. The Weak Law of Large Numbers.
3. INFERENCE: Sampling mean, sampling variance and standard deviation of a sample total and a sample average. Statistical testing. Tail areas. p-values. Examples of simple tests. The notion of a confidence interval.

Reading Lists

Books
** Supplementary Text
D D Wackerley, W Mendenhall & R L Scheaffer (2002) Mathematical Statistics with Applications 6th. Duxbury. 0534377416

Notes

This module is at CQFW Level 4