This preview shows pages 1–2. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: Factorial Experiments When something we wish to predict depends on two or more factors what we do is fit a multi-linear model i.e. make predictions of the form ˆ y i = b p x p,i + b p- 1 x p- 1 ,i + ··· + b 1 x 1 ,i + b by calculating b p , b p- 1 , . . . , b to minimise ∑ n i =1 ( y i- ˆ y i ) 2 by using some software package. We now have to deal with the possibility that different factors interact as their levels are changed. To fit any model we need data and this should come from an experiment which is properly designed i.e. the values of the x j,i must be chosen appropriately. An experiment in which several controllable factors may affect the results should use a factorial design – all possible combinations of factor levels (the different possible values of the x j,i ) should be investigated in the replicates (indexed by i ) of the experiment. The purpose of the experiment is to discover how varying the levels of controllable factors in any combination affects output. Where possible more than one trial should be run at each selected combination of factor levels – this replication makes possible the estimation of the variation caused by uncontrolled factors. Combinations should be tested in randomised order to avoid confounding with uncontrolled factors. Designs that do some fraction e.g. one half or one sixteenth of the total number of combinations exist and are called fractional factorial designs but there won’t be time to discuss them in this set of lectures....
View Full Document
This note was uploaded on 05/12/2010 for the course APPLIED ST 2010 taught by Professor Various during the Spring '10 term at Universidad Nacional Agraria La Molina.
- Spring '10