Assumption of linear regression. Linear regression is a widely used…

Linearity: This assumption states that there is a linear relationship between the independent and dependent variables. The relationship should be expressed as a straight line on a scatter plot, and the residuals (the difference between the actual and predicted values) should be randomly dispersed around zero. If this assumption is violated, the regression results may be misleading and the model will not generalize well to new data. Independence: The observations in the data should be independent of each other, meaning that the value of one observation should not affect the value of another observation. If the observations are dependent, the standard errors of the regression coefficients will be biased and the results will not be valid.