**Ques1. Assumptions in logistic Regression**

- Logistic regression is used to make relationship between categorical target variable and independent variable (continuous and categorical)
- Logistic regression does not make any assumptions of normality, linearity, and homogeneity of variance for the independent variables.
- Equation of model is Log (p/ (1-p)) = a + b1 x1 + b2 x2 +….. +bn xn + e
- The value produced by logistic regression is a probability value varies between 0 & 1
- The independent variables should not be multicollinearity(numeric variables)
- All the independent categorical values should be dummy coded.

**Ques 2. Assumptions of Multivariate Linear Regression**

- There should be linear relationship between the target variable and independent variables.
- The expected value of the error term, conditional on the independent variables is zero.
- The error terms are homoskedastic, i.e. the variance of the error terms should be constant for all the observations.
- Error terms should be uncorrelated with each other. We can check it by checking the product of error terms, It should be zero
- The error terms should be normally distributed.
- The independent variables should not have any linear relationships between each other. It means they should not be multi-collinear.