Fo a simple linear regression, there is only one independent variable while there are multiple independent variables in a multiple linear regression.
Assumptions of linear equations:
i) It must have a linear relationship
ii) Number of sample size should be more than number of parameters
iii) It must have a normal distribution
iv) It must have multicollinearity
v) It must have auto correlation
Whenever you have a multiple regression data, suspect multicollinearity
Whenever it is a time series data, suspect auto correlation and
whenever it is a cross-sectional data, suspect Heteroscedasticity.
Four components of time series data are: Trend, Irregular, Seasonal and cyclical
