Missing data are a part of almost all research, and we all have to decide how to deal with it
from time to time. There are a number of alternative ways of dealing with missing data. The
problem of handling missing data has been treated adequately in various real world data sets.
Several statistical methods have been developed since the early 1970s, when the manipulation
of complicated numerical calculations became feasible with the advance of computers. The
purpose of this research is to estimate missing values by using artificial neural network
(ANN), fuzzy regression models, and approach in a complete randomized block design table
(analysis of variance) and to compare the computational results with two other methods,
namely the approximate analysis and exact regression method. It is concluded that ANN
provides much better estimation than the conventional approaches. The superiority of ANN is
shown through lower error estimations.
