In linear regression analysis, there are several assumptions that need to be met for the results to be reliable. These assumptions include:
Linearity: The relationship between the independent variable and dependent variable is linear.
Independence: The observations are independent of each other.
Homoscedasticity: The variance of the errors is constant across all levels of the independent variable.
Normality: The errors are normally distributed.