STAT4220 ch. 1-5 Notes - Review: - - Population: entire...

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Review: - Population: entire group of individuals interested in - Parameters: pop. mean (μ) and pop. standard deviation (σ) - Sample: small group from pop. that is actually tested - Statistics: sample mean (x) and sample standard deviation (s) - Location: mean (average), median (middle), mode (value with highest frequency) - Spread: variance, standard deviation, range - Normal distribution: 68-95-99.7 (important for inference) Observational Studies vs. Experiments - Observation Study: observes individuals and measures variables of interest but does not attempt to influence the response (no control, observe sample of pop., not possible to isolate effects of interest, no inference about causation, broader applications) - Experiment: imposes some treatment on individuals in order to observe their responses (control, impose treatments on samples, possible to isolate effects of interest, inference about causation, limited to labs/isolated environment) Introduction to Experimental Design - Planning an experiment: First, form a hypothesis from scientific questions of interest then translate them into specific plans; Decide on content (response- what to measure, treatments- conditions to study, units- experimental units); Sources of variability (due to conditions of interest (effects of treatment – wanted), due to the measurement process (measurement error – don’t want but care can be taken to avoid bias (systematic bias); avoid bias by being careful and keeping conditions constant), due to experimental material (don’t want but care can be taken to avoid bias (selection bias); randomly assign treatments to materials to minimize variability); Size of chance errors can be estimated by making several measurements under the same conditions; To isolate the effects of interest, control what you can and randomize the rest - Three basic principles and four experimental designs (how to assign conditions to material): Random assignment (assigning condition to material using a chance-like device); Randomizing brings two advantages (protects against possible bias, makes statistical analysis possible because analysis is based on a set of assumptions); Blocking (subdividing experimental material into groups/block of similar units then assigning conditions within each block. If variability between experimental units is known, then block); Factorial crossing (If there are two or more sets of conditions in the same experiment, take all possible combinations as treatments then randomize or block. Analysis includes interaction effects) - Factor structure of designs: Two universal factors in all designs: grand average , residual error ; structural factors for the four basic designs: one-way randomized basic factorial (randomly assign treatments to units, units are interchangeable; each treatment is assigned to same number of units – balanced; one structural factor – treatments/conditions); complete block (first sort experimental material into blocks of similar units and then randomly assign treatments to units separately
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This note was uploaded on 10/22/2008 for the course STAT 4220 taught by Professor Smith during the Fall '08 term at University of Georgia Athens.

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STAT4220 ch. 1-5 Notes - Review: - - Population: entire...

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