ASSUMPTIONS - For a successful regression analysis, it's essential to validate these assumptions. 1. Linearity: The relationship between dependent and independent variables should be linear. 2. Homoscedasticity: (constant variance) of the errors should be maintained. 3. Multivariate Normality: Multiple regression assumes that the residuals are normally distributed. 4. Lack of Multicollinearity: It is assumed that there is little or no multicollinearity in the data. Source: Avik Jain