Скачать с ютуб Analysis of Completely Randomized Design (CRD) Using R studio в хорошем качестве

Analysis of Completely Randomized Design (CRD) Using R studio

A completely randomized design (CRD) is one assigned completely at random so that each experimental unit has the same chance of receiving any one treatment. For the CRD, any difference among experimental units receiving the same treatment is considered an experimental error. Hence, the CRD is only appropriate for experiments with homogeneous experimental units, such as laboratory experiments, where environmental effects are relatively easy to control. For field experiments, where there is generally large variation among experimental plots, in such environmental factors as soil, the CRD is rarely used. Advantages of a CRD 1. Very flexible design (i.e. number of treatments and replicates is only limited by the available number of experimental units). 2. Statistical analysis is simple compared to other designs. 3. Loss of information due to missing data is small compared to other designs due to the larger number of degrees of freedom for the error source of variation. Disadvantages 1. If experimental units are not homogeneous and you fail to minimize this variation using blocking, there may be a loss of precision. 2. Usually the least efficient design unless experimental units are homogeneous. 3. Not suited for a large number of treatments.