16-1©2008 Raj JainCSE567MWashington University in St. LouisIntroduction to Introduction to Experimental DesignExperimental DesignRaj Jain Washington University in Saint LouisSaint Louis, MO [email protected] slides are available on-line at:http://www.cse.wustl.edu/~jain/cse567-08/16-2©2008 Raj JainCSE567MWashington University in St. LouisOverviewOverview! What is experimental design?! Terminology! Common mistakes! Sample designs16-3©2008 Raj JainCSE567MWashington University in St. LouisExperimental Design and AnalysisExperimental Design and AnalysisHow to:! Design a proper set of experiments for measurement or simulation.! Develop a model that best describes the data obtained.! Estimate the contribution of each alternative to the performance.! Isolate the measurement errors.! Estimate confidence intervals for model parameters.! Check if the alternatives are significantly different.! Check if the model is adequate.16-4©2008 Raj JainCSE567MWashington University in St. LouisExampleExamplePersonal workstation design1. Processor: 68000, Z80, or 8086.2. Memory size: 512K, 2M, or 8M bytes3. Number of Disks: One, two, three, or four4. Workload: Secretarial, managerial, or scientific.5. User education: High school, college, or post-graduate level.Five Factors at 3x3x4x3x3 levels16-5©2008 Raj JainCSE567MWashington University in St. LouisCartoonCartoon16-6©2008 Raj JainCSE567MWashington University in St. LouisTerminologyTerminology! Response Variable: Outcome.E.g., throughput, response time ! Factors: Variables that affect the response variable.E.g., CPU type, memory size, number of disk drives, workload used, and user's educational level.Also called predictor variables or predictors.! Levels: The values that a factor can assume, E.g., the CPU type has three levels: 68000, 8080, or Z80.# of disk drives has four levels.Also called treatment.! Primary Factors: The factors whose effects need to be quantified.E.g., CPU type, memory size only, and number of disk drives.16-7©2008 Raj JainCSE567MWashington University in St. LouisTerminology (Cont)Terminology (Cont)! Secondary Factors: Factors whose impact need not be quantified.E.g., the workloads. ! Replication: Repetition of all or some experiments. ! Design: The number of experiments, the factor level and number of replications for each experiment.E.g., Full Factorial Design with 5 replications: 3× 3 × 4 × 3 × 3 or 324 experiments, each repeated five times. ! Experimental Unit: Any entity that is used for experiments.E.g., users. Generally, no interest in comparing the units.! Goal - minimize the impact of variation among the units.16-8©2008 Raj JainCSE567MWashington University in St. LouisTerminology (Cont)Terminology (Cont)! Interaction ⇒ Effect of one factor depends upon the level of the other.16-9©2008 Raj JainCSE567MWashington University in St. LouisCommon Mistakes in ExperimentationCommon Mistakes in Experimentation! The variation due to experimental error is ignored.! Important parameters are not controlled.! Effects of different factors are not isolated! Simple one-factor-at-a-time designs are used! Interactions are ignored! Too many experiments are conducted.Better: two phases.16-10©2008 Raj JainCSE567MWashington University in St. LouisTypes of Experimental DesignsTypes of Experimental Designs! Simple Designs: Vary one factor at a time" Not statistically efficient." Wrong conclusions if the factors have interaction." Not recommended. ! Full Factorial Design: All combinations. " Can find the effect of all factors." Too much time and money." May try 2kdesign first.16-11©2008 Raj JainCSE567MWashington University in St. LouisTypes of Experimental Designs (Cont)Types of Experimental Designs (Cont)! Fractional Factorial Designs: Less than Full Factorial" Save time and expense." Less information." May not get all interactions." Not a problem if negligible interactions16-12©2008 Raj JainCSE567MWashington University in St. LouisA Sample Fractional Factorial DesignA Sample Fractional Factorial Design! Workstation Design:(3 CPUs)(3 Memory levels)(3 workloads)(3 ed levels) = 81 experiments16-13©2008 Raj JainCSE567MWashington University in St. LouisSummarySummary! Goal of proper experimental design is to get the maximum information with minimum number of experiments! Factors, levels, full-factorial designs16-14©2008 Raj JainCSE567MWashington University in St. LouisExercise 16.1Exercise 16.1The performance of a system being designed depends upon the following three factors:" CPU type: 68000, 8086, 80286" Operating System type: CPM, MS-DOS, UNIX" Disk drive type: A, B, Ca. There is significant interaction among factors.b. There is no interaction among factors.c. The interactions are small compared to main
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