Fr. Clinic II
• Begins with carefully considering what the
objectives (or goals)are
– How do our filters work?
– Which filter is best?
• Ease of Use
• Process variables include both inputs and
outputs - i.e. factors and responses
• The selection of these variables is best done
as a team effort
– Something that can be varied, e.g.,temperature,
pressure, material …
– the results
– Each variable can be “set” to or measured at different
– How many times each unique combinations of factors
and levels will be tested
What are our factors?
• How do filters work?
– Could be the membrane/porous filter, the
activated carbon, the viral guard…
• Comparative Assessment
– We have one factor, the portable water filters
Water are our possible responses?
• Removal of contaminants
• Produced water quality
• Ease of use
• Choosing an experimental design depends
on the objectives of the experiment and the
number of factors to be investigated.
Experimental Design Objectives
• Comparative objective
– 1 or several factors under investigation
– primary goal to make conclusion about 1 important factor
• in the presence of, and/or in spite of the other factors
• Screening objective
– select or screen out few important main effects from many
lesser important ones
• Response Surface (method) objective
– estimate response to multiple factors
• find improved or optimal process settings, troubleshoot process
problems and weak points, or make a product or process more robust
against external and non-controllable influences.
• Completely Randomized Designs
– Comparative objective, 1 factor
• Randomized Block Designs
– Comparative objective, multiple factors
• Full or fractional factorial
– Screening objective, multiple factors
• Many more!…
Completely Randomized Designs
• One factor, with multiple levels of interest
– For example – effect of temperature on a chemical
• three levels, two runs each gives 90 unique orders to
conduct experiment (e.g., T1, T1, T2, T2, T3, T3;…)
• In a completely randomized design, you would
randomly select the order of runs
Randomized Block Designs
• One factor or variable is of primary interest.
However, there are also several other
nuisance factor variables
– Nuisance variables are those which may affect
the measured result, but are not considered of
• For example: specific operator who prepared the
treatment, the time of day the experiment was run,
and the room temperature.
• All experiments have nuisance factors.
Randomized Block Designs (cont.)
– Run every level of the primary factor with the nuisance
factor(s) held the same
– Minimizes total # of runs
– Run with nuisance factors selected randomly.
– More runs may be required
– Likely to get more variability (error) in results
• May be able to block some nuisance factors, but
Randomized Block Design Example
• Engineers at semiconductor manufacturing facility
want to test effect of 4 different wafer implant
material dosages using 3 runs for each level
• The nuisance factor is "furnace run", since it is
known that each furnace run differs from the last
and impacts many process parameters
– Block: run all 4x3=12 wafers in the same furnace run
– Completely Random: randomly select order and and
include each run each on a different furnace run
• Run all combinations of factors and levels.
– For example: A basic experimental design is
one with all input factors set at two levels each
• These levels are called ‘high’ and ‘low’ or ‘+1’
• A design with all possible high/low combinations of
all the input factors is called a “full factorial design
in two levels”
• A factorial experiment in which only an
adequately chosen fraction of the treatment
combinations required for the complete
factorial experiment is selected to be run
What should we do?
• For our competitive assessment, we have
one factor – portable water filters
• The levels are the different filters
• What are our nuisance factors?
– Properties of pond water?
– Day of experiment?
– Operators of experiment?
• We will run a randomized block design
• However, we may only be able to do one
run per level
• We’ll try to block as many of the nuisance
factors as possible
• There are some that we won’t be able to
block or randomize