Design of Experiments – Full Factorial Designs

December 1st, 2009

In designs where there are multiple factors, all with a discrete group of level settings, the full enumeration of all combinations of factor levels is referred to as a full factorial design. As the number of factors increases, potentially along with the settings for the factors, the total number of experimental units increases rapidly. Read the rest of this entry »

Design of Experiments – Optimal Designs

November 29th, 2009

When designing an experiment it is not always possible to generate a regular, balanced design such as a full or fractional factorial design plan. There are usually restrictions of the total number of experiments that can be undertaken or constraints on the factor settings both individually or in combination with each other. Read the rest of this entry »

Design of Experiments – Power Calculations

November 18th, 2009

Prior to conducting an experiment researchers will often undertake power calculations to determine the sample size required in their work to detect a meaningful scientific effect with sufficient power. In R there are functions to calculate either a minimum sample size for a specific power for a test or the power of a test for a fixed sample size. Read the rest of this entry »

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