TwoFactorExperimentsReps

# TwoFactorExperimentsReps - CPE 619 Two-Factor Full...

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CPE 619 Two-Factor Full Factorial Design With Replications 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 Overview Model Computation of Effects Estimating Experimental Errors Allocation of Variation ANOVA Table and F-Test Confidence Intervals For Effects
3 Model Replications allow separating out the interactions from experimental errors Model: With r replications Where

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4 Model (cont’d) The effects are computed so that their sum is zero: The interactions are computed so that their row as well as column sums are zero: The errors in each experiment add up to zero:
5 Computation of Effects Averaging the observations in each cell: Similarly, Use cell means to compute row and column effects

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6 Example 22.1: Code Size
7 Example 22.1: Log Transformation

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8 Example 22.1: Computation of Effects An average workload on an average processor requires a code size of 10 3.94 (8710 instructions) Proc. W requires 10 0.23 (=1.69) less code than avg processor Processor X requires 10 0.02 (=1.05) less than an average processor … The ratio of code sizes of an average workload on processor W and X is 10 0.21 (= 1.62 ).
Example 22.1: Interactions Check: The row as well column sums of interactions are zero Interpretation: Workload I on processor W requires 0.02 less log

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## TwoFactorExperimentsReps - CPE 619 Two-Factor Full...

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