This course will introduce students to summarising patterns and relationships in data, and to using these simple statistical methods to answer real-world questions of interest in 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

STATS1002 Statistics 1Y: Introduction to Statistics: Learning from Data

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

Continuous assessment - 25%

Reassessment opportunities are not available for continuous assessment

The course aims to

• develop students understanding of confidence intervals and hypothesis tests and apply these to   continuous data; 

• enable students to apply correlation coefficients and simple linear regression models to quantify relationships in data; 

• enable students to use other linear models to explore relationships and variability in data

• enable students to use bivariate probability models to assess the dependence between two random variables; 

• demonstrate the importance and usefulness of statistical methods in real life via a case study; 

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

• develop students' skills in using a statistical programming language for data analysis

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

• carry out hypothesis tests and calculate interval estimates with continuous data, and interpret their results; 

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

• describe the difference between paired and independent data, and be able to recognise both in practice; 

• calculate and interpret the sample correlation coefficient between 2 variables; 

• fit a straight line using linear regression; 

• construct interval estimates and carry out hypothesis tests in the context of correlation and linear regression, and interpret their results correctly and in non-technical language; 

• explain the importance of checking assumptions wherever possible; 

• outline statistical modelling strategies used in different application domains; 

• interpret the output of simple statistical models illustrating these modelling strategies; 

• explain for some of these models how the calculations are performed and perform these calculations for simple examples.

• fit statistical models using a statistical programming language