Grover, DeepakShekhar, Shashi2016-11-192016-11-192008http://krishikosh.egranth.ac.in/handle/1/86493The search for better method of estimation is everlasting. Assumptions of ordinary least squares provided numerous opportunities of study when its assumptions are violated in multiple linear regression. The present study is related to the violation of normality assumption. Any process which can give relatively better estimates even after the assumption is violated is a robust estimation process. Modified maximum likelihood method is one such tool which is applied in the present study. Normality assumption of errors is checked with the help of Q-Q plot. Plots of standardized residuals against each independent variable were utilized to detect the outlier cases. Deviation from normal plot gives deviation of each point from normal distribution. Exclusion of outliers caused considerable changes in the values of R2, adjusted R2 and standard error of estimates. We considered a distribution which is in reasonable proximity of error distribution and estimated the unknown parameter of the distribution. The distribution of errors which violate normality assumption can broadly be divided into two parts i.e. symmetric and skewed. Modified maximum likelihood estimates are calculated for a series of value of unknown parameter and the value having maximum value of logarithm of likelihood function is selected as the most plausible value. In present study, two estimation procedures i.e. MMLE and OLS were compared in terms of standard error of estimates. MMLE was found to have lesser standard errors than OLS even when normality assumption was violated and outliers were present. With the help of datasets it is concluded that MML estimates are robust.enHeterosis, Sowing, Developmental stages, Yields, Planting, Crops, Hybrids, Biological phenomena, Crossing over, OilsThesis