ExperimentalDesign - CPE 619 Experimental Design Aleksandar...

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CPE 619 Experimental Design Aleksandar Milenković The LaCASA Laboratory Electrical and Computer Engineering Department The University of Alabama in Huntsville http://www.ece.uah.edu/~milenka http://www.ece.uah.edu/~lacasa
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2 PART IV: Experimental Design and Analysis How 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
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3 Introduction Goal is to obtain maximum information with minimum number of experiments Proper analysis will help separate out the factors Statistical techniques will help determine if differences are caused by variations from errors or not No experiment is ever a complete failure. It can always serve as a negative example. Arthur Bloch The fundamental principle of science, the definition almost, is this: the sole test of the validity of any idea is experiment. Richard P. Feynman
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4 Introduction (cont’d) Key assumption is non-zero cost Takes time and effort to gather data Takes time and effort to analyze and draw conclusions Minimize number of experiments run Good experimental design allows you to: Isolate effects of each input variable Determine effects due to interactions of input variables Determine magnitude of experimental error Obtain maximum info with minimum effort
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5 Introduction (cont’d) Consider Vary one input while holding others constant Simple, but ignores possible interaction between two input variables Test all possible combinations of input variables Can determine interaction effects, but can be very large Ex: 5 factors with 4 levels 4 5 = 1024 experiments Repeating to get variation in measurement error 1024x3 = 3072 There are, of course, in-between choices… Chapter 19
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6 Outline Introduction Terminology General Mistakes Simple Designs Full Factorial Designs 2 k Factorial Designs 2 k r Factorial Designs
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7 Terminology Consider an example: Personal workstation design CPU choice: 6800, z80, 8086 Memory size: 512 KB, 2 MB, 8 MB Disk drives: 1-4 Workload: secretarial, managerial, scientific User’s education: high school, college, graduate Response variable – the outcome or the measured performance E.g.: throughput in tasks/min or response time for a task in seconds
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8 Terminology (cont’d) Factors – each variable that affects response E.g., CPU, memory, disks, workload, user’s ed. Also called
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ExperimentalDesign - CPE 619 Experimental Design Aleksandar...

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