This course will introduce basic concepts in probability and statistical inference, and demonstrate their importance and practical usefulness in real life including via a case study.

Lectures:  Three times per week for one hour at a time to be arranged.

Computer labs: 5 two hour practicals, at times to be arranged.

Tutorials: Weekly for one hour at times to be arranged.

STATS1010 Statistics 1A: Applied Statistics

Written examination (one two-hour paper) - 75%

Continuous assessment - 25%

Reassessment opportunities are not available for the continuous assessment

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

The course aims to:

• show how to present data informatively and clearly;

• introduce students to basic concepts in probability;

• demonstrate the importance and practical usefulness of probability and statistics in real life via

  a case study;

• introduce students to fundamental ideas in statistics including confidence intervals and hypothesis tests;

• give students an appreciation of the limitations of these standard techniques;

• promote an interest in probability and statistics and hence encourage students to study the subject further.

• introduce students to a statistical programming language for data analysis

By the end of this course students will be able to:

• explain the key concept of natural variation;

• explain the concepts of sample space, event, probability, conditional probability, independence, random variable, probability distribution, probability density function, expected value and variance;

• describe and recognise some standard discrete and continuous probability distributions and

  their applications

• use standard statistical tables for the Normal and chi-squared distributions;

• define the terms population and sample, parameter and estimate;

• differentiate between common types of data, and display them appropriately;

• explain the benefits of calculating interval estimates for unknown parameters, and be able to interpret interval estimates correctly;

• conduct hypothesis tests for categorical data, and interpret their results;

• check the assumptions underlying these simple procedures, and recognise how a breakdown in these assumptions affects the usefulness of their answers.

•implement statistical methods using a statistical programming language

(i) attendance at the Degree(or resit) Examination;