## Availabilities:

Not currently available in 2014

## Unit Summary

### Unit type

PG Coursework Unit

12

9

Introductory

### Former School/College

Former School of Environment, Science and Engineering

## Unit aim

Develops statistical methods required for environmental management. Students are required to understand the different types of variables, use SPSS to describe and graphically represent quantitative data, perform tests of significance (chi square, t test, correlation and regression and analysis of variance). Students will be introduced to multivariate statistics and develop a research proposal that requires multivariate statistical analysis, develop a model to analyse the data and analyse and interpret a mock data set.

## Unit content

1. Introduction to statistics: data types, number skills, rounding, significant figures, errors and use of scientific calculators and Microsoft Excel
2. Descriptive statistics
3. Simple hypotheses testing, Student's t-test and chi square, correlation, linear regression,one-way analysis of variance
4. Introduction to SPSS to (Descriptive statistics and simple hypothesis testing).
5. Two-way analysis of variance
6. Multivariate statistics (The form of multivariate statistics covered will depend on the needs and interest of individual students).

## Learning outcomes

Unit Learning Outcomes express learning achievement in terms of what a student should know, understand and be able to do on completion of a unit. These outcomes are aligned with the graduate attributes. The unit learning outcomes and graduate attributes are also the basis of evaluating prior learning.

On completion of this unit, students should be able to:

1. Demonstrate how different types of variables are used in environmental science/management
• GA1:
2. Interpret, summarise and graphically represent the different types of environmental data -calculate means, standard deviations using a scientific calculator, Microsoft Excel and SPSS
• GA1:
3. Analyse an environmental problem and design an appropriate experiment/survey
• GA2:
4. Demonstrate an understanding of at least two forms of multivariate analysis.
• GA3:
5. Develop a statistical model that can be used to analyse data requiring multivariate analysis.
• GA2:
6. Interpret the results from multivariate statistical analysis.
• GA2:
• GA3: