designs with one source of variation phd seminar 31/01/2014
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Designs with One Source of Variation
PhD seminar31/01/2014
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• Introduction• Randomization• Model for a Completely Randomized Design• Estimation of parameters• One-Way Analysis of Variance• Sample Sizes
Contents
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• Treatment: a level of a factor in a single factor experiment (or a combination in a factorial experiment).
Introduction (1)
Algorithms(Treatments)
Dataset(Experimental Unit)
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Introudction (2)
• Experimental Design
– the rule that determines the assignment of the experimental units to treatments.
– The simplest: completely randomized design
• One Source of Variation– The variation of the factor of interest (levels).
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• Randomization Design :– Consists of assigning the experimental units to the
treatments in a random manner.
– Reduces (and with a bit of luck, removes) the selection bias and/or accidental bias and tends to produce comparable groups [Suresh KP 2011]
– It is best used when relatively homogeneous experimental units are available
– This design will normally be analysed using a one-way analysis of variance [Michael FW Festing]
Randomized Design
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Model for a Completely Randomized Design
Cause X “” Effect Y
Observed
Y = X + ε
Controlled variable
•Independent Variable•Factor•Treatment
•Dependent Variable•Observations•Response variable
•External factors•Not controlled •Ignored
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Model for a Completely Randomized Design
Experimental Unit 1
y1
y2
yr
ObservationsTreatment 1
ε
Experimental Unit 2
y1
y2
yr
ObservationsTreatment 2
ε
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• Linear statistical model
Yit -is the observation of the t-th repetition of the i-th treatment
μi - is the mean of the i-th treatment
ϵit - experimental error
Model for a Completely Randomized Design
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• Full modelEach population defined by the treatment has his own mean
• Reduced model
There is no difference between the means of the populations
Figure from: [http://www.dpye.iimas.unam.mx/patricia/indexer/completamente_al_azar.pdf]
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Model for a Completely Randomized Design
• Linear statistical model
μ - is the general mean (common to all experimental units - homogeneous)
τi - effect of the i-th treatment
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• Knows as: One-way analysis of variance model– The model includes only one major source of
variation (treatment)– The analysis of data involves a comparition of
measures of variation.
Model for a Completely Randomized Design
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Estimation of parameters
Figure from: [Dean, A. Voss, D. ,Design and Analysis of Experiments]
• Least Squares (balanced model)
• Maximum-Likelihood Estimation (MLE) (unbalanced model)
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• In an experiment involving several treatments the treatments differ at all in terms of their effects on the response variable?
H0:{τ1= τ2=…= τv}
HA:{at least two of the τi’s differ}
One-Way Analysis of Variance
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• Compare the sum of squares for errors (ssE) between the full and reduced model.
H0 is false if ssE(fullModel) << ssE(reducedModel)
H0 is true if ssE(fullModel) ≈ ssE(reducedModel)
• ANOVA
One-Way Analysis of Variance
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• Before an experiment can be run, it is necessary to determine the number of observations that should be taken on each treatment.
• Consider time and money
• Two methods: – Specifying the desired length of confidence intervals– Specifying the power required of the analysis of
variance.
Sample sizes
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• Sample Sizes using power of a test– The power of a test at Δ, denoted π(Δ), is the
probability of rejecting H0 when the effects of two treatments differ by Δ.
Sample sizes
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• Dean, A.M., Voss, D., Design and Analysis of Experiments, Spring-Verlag, 1999
• http://www.3rs-reduction.co.uk/html/about.html• http://www.ugr.es/~bioestad/guiaspss/practica7/
ArchivosAdjuntos/EfectosFijos.pdf• http://www.dpye.iimas.unam.mx/patricia/indexer/
completamente_al_azar.pdf• http://www.ugr.es/~bioestad/guiaspss/practica7/
ArchivosAdjuntos/EfectosFijos.pdf