Blocking OMARS designs and definitive screening designs
status: published
Analysis of data from orthogonal minimally aliased response surface designs
status: published
ORTHOGONAL MINIMALLY ALIASED RESPONSE SURFACE DESIGNS FOR THREE-LEVEL QUANTITATIVE FACTORS AND TWO-LEVEL CATEGORICAL FAC...
status: published
Follow-up Designs to Resolve Confounding in Split-Plot Experiments
Split-plot designs are effective in industry due to time and/or cost constraints, restriction on randomization of the treatment combinations of the hard-to-change factors, and different sizes of experimental units. Some of the results of fractional factorial split-plot experiments can be ambiguous and a need may arise to conduct follow-up experimen...
Follow-up Designs to Resolve Confounding in Split-Plot Experiments
Split-plot designs are effective in industry due to time and/or cost constraints, restriction on randomization of the treatment combinations of the hard-to-change factors, and different sizes of experimental units. Some of the results of fractional factorial split-plot experiments can be ambiguous and a need may arise to conduct follow-up experimen...
Follow-up Designs to Resolve Confounding in Split-Plot Experiments
Split-plot designs are effective in industry due to time and/or cost constraints, restriction on randomization of the treatment combinations of the hard-to-change factors, and different sizes of experimental units. Some of the results of fractional factorial split-plot experiments can be ambiguous and a need may arise to conduct follow-up experimen...
Follow-up Designs to Resolve Confounding in Split-Plot Experiments
Split-plot designs are effective in industry due to time and/or cost constraints, restriction on randomization of the treatment combinations of the hard-to-change factors, and different sizes of experimental units. Some of the results of fractional factorial split-plot experiments can be ambiguous and a need may arise to conduct follow-up experimen...
Follow-up Designs to Resolve Confounding in Split-Plot Experiments
Split-plot designs are effective in industry due to time and/or cost constraints, restriction on randomization of the treatment combinations of the hard-to-change factors, and different sizes of experimental units. Some of the results of fractional factorial split-plot experiments can be ambiguous and a need may arise to conduct follow-up experimen...
Follow-up Designs to Resolve Confounding in Split-Plot Experiments
Split-plot designs are effective in industry due to time and/or cost constraints, restriction on randomization of the treatment combinations of the hard-to-change factors, and different sizes of experimental units. Some of the results of fractional factorial split-plot experiments can be ambiguous and a need may arise to conduct follow-up experimen...
Follow-up Designs to Resolve Confounding in Split-Plot Experiments
Split-plot designs are effective in industry due to time and/or cost constraints, restriction on randomization of the treatment combinations of the hard-to-change factors, and different sizes of experimental units. Some of the results of fractional factorial split-plot experiments can be ambiguous and a need may arise to conduct follow-up experimen...
