Lecture#14-15.ppt

# Lecture#14-15.ppt - Lecture#14-15 1 Experiments in Computer...

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Lecture#14-15 1

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2 Experiments in Computer Science Introduction Terminology General Mistakes Simple Designs Full Factorial Designs 2 k Factorial Designs 2 k r Factorial Designs
Some claim computer science is not an experimental science Computers are man-made, predictable Is a theoretical science (like Math) Some claim system development is computer science Building an OS or a federated database Rather, computer engineering, and the science comes after

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" The fundamental principle of science, the definition almost, is this: the sole test of the validity of any idea is experiment" Richard P. Feynman Tried and true experimental scientific methodology from Physics, Biology, Chemistry ... Often not followed in Computer Science Let's be better Computer S cientists !
Observe (Devise/invent solution) Hypothesize Design Experiment Analyze Report

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6 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
7 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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8 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
9 Experiments in Computer Science Introduction Terminology General Mistakes Simple Designs Full Factorial Designs 2 k Factorial Designs 2 k r Factorial Designs

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10 Study PC performance CPU choice: 6800, z80, 8086 Memory size: 512 KB, 2 MB, 8 MB Disk drives: 1-4 Workload: secretarial, managerial, scientific Users: high school, college, graduate Response variable – the outcome or the measured performance Ex: throughput in tasks/min or response time for a task in seconds
11 Factors – each variable that affects response Ex: CPU, memory, disks, workload, user Also called predictor variables or predictors

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