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Fractional Factorial Designs
| Content Provider | Scilit |
|---|---|
| Author | Lawson, John |
| Copyright Year | 2014 |
| Description | There are two benefits to studying several treatment factors simultaneously in a factorial design. First, the interaction or joint effects of the factors can be detected. Second, the experiments are more efficient. In other words, the same precision of effects can be achieved with fewer experiments than would be required if each of the factors was studied one-at-a-time in separate experiments. The more factors included in a factorial design, the greater the efficiency and the greater the number of interactions that may be detected. However, the more factors included in a factorial experiment, the greater the number of runs that must be performed. When many factors are included in a factorial experiment, one way to reduce the number of runs is to use only two levels of each factor and run only one experiment per cell or treatment combination. These ideas were discussed in Sections 3.7 and 3.7.5. Book Name: Design and Analysis of Experiments with R |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2010-0-49135-6&isbn=9780429154522&doi=10.1201/b17883-9&format=pdf |
| Ending Page | 284 |
| Page Count | 68 |
| Starting Page | 217 |
| DOI | 10.1201/b17883-9 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2014-12-17 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Design and Analysis of Experiments with R Mathematical Psychology Mathematical Social Sciences Treatment Precision Joint Efficient Benefits Words Factorial Design Simultaneously Fewer |
| Content Type | Text |
| Resource Type | Chapter |
