COURSE OVERVIEW

  This practical and highly-interactive course includes various practical sessions and exercises. Theory learnt will be applied using “MS Excel” applications.

 

Continuous improvement in business efficiencies and reducing operations and maintenance costs require managers, engineers and analysts face an ever-challenging analytical problems and performance targets. Therefore, it is essential to base decisions on appropriate analysis and interpretation of numerical data.

 

The purpose of this course is to give you a working vocabulary such that you will be able to converse easily with people who are experts in the areas of statistics and probability and will be able to read and comprehend most basic textbooks and well-written journal articles. The course will give you the ability to recognize what category a particular problem falls into thereby narrowing the number of possible techniques which might apply. 

 

This course will provide you with a good working knowledge of several of the more common statistical techniques which should cover a high percentage of the types of problems you are likely to run into. 

 

It will give you enough background and theoretical knowledge to recognize when a technique is applicable and when assumptions from which the technique was derived have been so grossly violated that use of the technique will lead to incorrect conclusions.

 

The course will give you a feel for what to do when your particular problem has violated major assumptions but you need an answer anyway. It will provide you with a manual containing enough worked out examples to serve as a useful future reference. It will provide you with a reasonable reference guide so that you will be able to find more detailed information on the techniques we have introduced. Finally, the course will leave you with a comfortable feeling about the use of statistical and probability techniques.

 

This course contains detailed techniques for data analysis, experimental design and modelling using statistical and probability methodology. Topics included are: the importance of statistics; probability and Bayes’ theorem; applications of probability theory; random variables and their distributions; model fitting; understanding confidence limits; hypothesis testing; experimental design; regression analysis; statistical process control. The theory is extensively illustrated with practical applications.