Abstract:
The classical linear model is commonly used to model the relationship between a response variable and a set of
explanatory variables. The normality assumption is usually required so as to ease the hypothesis testing for the various linear
regression models but it can be misleading for a proportional response variable that is bounded. This makes the ordinary least
squares regression inappropriate for a regression model with a bounded dependent variable. This research proposes the fractional
beta regression model as an alternative to help examine the determinants of post-harvest loss management of maize produce for
farmers in Kenya. The response variable (Post-Harvest Loss Coefficient (PHLC)) is assumed to have a mixed
continuous-discrete distribution with probability mass between zero and one. The fractional beta distribution is used to describe
the continuous component of the model, since its density has a wide range of different shapes depending on the values of the two
parameters that index the distribution. The study uses a suitable parameterization of the beta law in terms of its mean and a
precision parameter, the parameters of the mixture distribution shall be modeled as functions of regression parameters. The
considered parameters are Agriculture, Storage, Education, Fumigation and Transport. Inference on parameters, model
diagnostics and model selection tools for the fractional beta regression is also be provided. Data used for this research was purely
primary data which was collected from Uasin Gishu County, Kenya maize farmers through administration of a research
questionnaire.