Short course/CPD

Statistics for Environmental Professionals

This is a two day course

 

Course date: Please enquire

Course overview

Cranfield University offers 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.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.

 
Location
Course fee:

  • £580 - Standard. 20% discount for Cranfield alumni, 10% discount for colleagues of alumni
  • £550 - Professional/trade association discount
  • £530 - Multiple bookings*
*Minimum of five delegates.

Accommodation fee:

Accommodation is not included in the price.

Advice on booking accommodation

How to register

To request a place on this course, please complete the online Registration Form

 

If you have any queries please contact:

Academic Operations Unit.

Cranfield University
Cranfield
Bedfordshire
MK43 0AL, UK


T: +44 (0) 1234 754176
E: shortcourse@cranfield.ac.uk
F: +44 (0) 1234 751206

Please be aware that short courses/CPD are subject to:

Booking Conditions

Course description

Day 1

08.30 - 09.00 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/

15:00 - 17:30 Practical session

Day 2

09:00 - 12.30 Case Study 1: 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 2: 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 3: How do I investigate the relationships between variables in large data sets? (use of principal component analysis).

 

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