Question

Fixed versus random effects - define each approach and provide one fictional example for each case

please give a complete and detailed answer

Answer 1

Fixed effect models have the data frame with the distinct groups or distinct treatments, the effects analyzed for the variations among the groups and within group in one way ANOVA model. There the response variable is measured for the each treatment in groups. The response are measure here in one way. So the effect size of these type of models are fixed.

In randomized effect models, the effect due to the variations caused due to the variations in the method of measurements used us randomized blocks. Usually the raters are used to measure the effect under each treatments analyzed as measurement system analysis. The randomized block design in the design of experiments is the random effect size model.

For example, the mean value of the effect of a drug in three different groups measured and analyzed for determining the effect of the treatments mean values and if there is any statistically significant difference among the groups.

Random effect model example, you wish to block the inspectors who inspected a dimension in manufactured from five different processes. Suppose there are six raters who measured a critical dimension of a sample of size 10. Then the 50 data points are analyzed for the among the group variations and within group variation and if there is any difference in the mean values of the 5 process for the 6 operators being different who measured the same dimension.