This two day short course which has been designed to provide professionals in the environment sector with skills and knowledge in the application of statistics to aid the analysis of environmental problems. Read more Read less

The objective of this course is to present a set of statistical techniques that can be used to analyse a range of problems. Statistical concepts will be explained and supported with real practical examples.

At a glance

  • Duration2 days
  • LocationCranfield campus
  • Cost:£580 - Standard (20% discount for Cranfield alumni, 10% discount for colleagues of alumni) £550 - Professional/trade association discount £530 - Multiple bookings* * Minimum of 5 delegates

Course structure

Two day short course with hands on case studies based on current environmental practice within the Department of Environmental Science and Technology.

What you will learn

The course is designed for environmental professionals who want to review and/or develop their statistical knowledge and use appropriate methods for analysing their data. On successful completion of this course the delegate will be able to:

  • Decide which statistical method is most appropriate for the problems they encounter
  • Apply the methods appropriately to investigate their data.

Core content

  • Data cleaning, identification of outliers & extreme values (use of data  transformation techniques)
  • Simple and multiple linear regression
  • General Linear Moldels (Analysis of Variance)
  • Multivariate statistics (Principal Component Analysis).

Timetable

Day one

The first day of the course will cover the main statistical concepts with hands-on experience, illustrated by examples from real environmental problems.

Programme:

08:30 - 9:00      Registration
09:00 - 09:15     Introduction to the course
09:15 - 10:30     Explore and understand your data
11:00 - 12:30     Identify statistical differences between groups of data
12:30 - 13:30     Lunch
13:30 - 15:00     Investigate relationships between quantitative variables, build models and make predictions
15:30 - 17:30     Practical session

Day two

The second day will take the form of a statistical workshop with practical case studies, demonstrating the application of a range of techniques to environmental data, including the topics covered during day one and also introducing more advanced statistical techniques such as principal component analysis.

Programme:

09:00 - 12.30      Case Study one: How can I show a statistical significant difference between these datasets or varieties or treatments or scenarios? (use of ANOVA and t-test)
                      Case study two: How can I build a model to predict a target variable? (use of regression models)
12:30 - 13:30     Lunch
13:30 - 17.30      Case study three: How do I investigate the relationships between variables in large data sets? (use of principal component analysis).

Who should attend

  • Environmental consultants
  • Engineers working in the environment sector
  • Environmental professionals working in government (eg. EA, Defra, FSA)
  • Environmental scientists
  • Research students.

Accommodation options and prices

This is a non-residential course. If you would like to book accommodation on campus, please contact Mitchell Hall or Cranfield Management Development Centre directly. Further information regarding our accommodation on campus can be found here.

Alternatively you may wish to make your own arrangements at a nearby hotel.

Location and travel

Cranfield University is situated in Bedfordshire close to the border with Buckinghamshire. The University is located almost midway between the towns of Bedford and Milton Keynes and is conveniently situated between junctions 13 and 14 of the M1.

London Luton, Stansted and Heathrow airports are 30, 90 and 90 minutes respectively by car, offering superb connections to and from just about anywhere in the world.

For further location and travel details

Location address

Cranfield University
College Road 
Cranfield 
Bedford
MK43 0AL

Read our Professional development (CPD) booking conditions.