The ultimate goal of the study was to find out statistical linear model which predictor variables exactly
discriminate or separate women and men groups in wage non-farm employment sector in Rwanda. Linear
discriminant method was used with numerical data given by third EICV conducted from 2010-2011 published by
NISR in 2012. Discriminant analysis assigns observations to one of the pre-defined groups based on the knowledge
of the multi-attributes. For this study I had a single classification variable as sex (male and female) that were
divided into two groups of male workers and female workers in non-farm works and the distribution with each
group was multivariate normal. The research’s sample was limited to the age between 18 and 65 years old by
which women and men who are engaged in the wage nonfarm employment sector. This implied that 7,353
individuals belonged to the actual sample size with 3,772 (51.3%) women and 3,581 (48.7%) men. Majority of
respondents were between 18 and 32 years old. 80% of the respondents had been to school and the level of Diploma
is at 1%, Bachelor with 0.7%. 55% of the NFE workers are in the trade businesses. In the Non-Farm Employment
sector. The SPSS was used to perform tests including the ANOVA test, test of variance, test of equality of group
means, the Box’s M test, the Wilks’ Lambda test and Canonical discriminant analysis. Dependent variable was the
sex type of male and female which was categorical and independent variables were: Type of non-farm activity
(enterprise group), Education level, Income, Income-Unit of time, Expenditure, Expenditure-Unit of time,
Duration, Urban/Rural location, and Poverty. The best predictors variables of the discrimination between women
and men in the NFE sector were: Education level, Income, Income-Unit of time, Expenditure, Expenditure-Unit of
time, Urban/Rural location, and Poverty and the weak predictor variables were: Duration in the business and
Industry group of jobs.