Question

What is the assumption of linear regression ?

Answer 1

Hint: Linear regression attempts to show the relationship between two variables by applying a linear equation to observed data . One variable is supposed to be an independent variable , and the other is to be a dependent variable . For e.g ,the weight of the person is linearly related to his height . Hence this shows a linear relationship between the height and weight of the person. As the height is increased, the weight of the person also gets increased

Complete step-by-step answer:
The assumptions regarding the linear regression are as follows :
Linearity – There will exist a linear relation between the variable independent \[X\] and dependent variable \[Y\].
Homoscedasticity - The residuals have constant variance for any value of \[X\] .
Independence – The observations which are denoted are independent of each other .
Normality – The residual of the model is normally distributed for any value of \[X\] and \[Y\] .
If one or more of these assumptions are violated , then the results of our linear regression will lead to unreliable or even misleading results .

Note: Violations of linearity or additivity are extremely serious: if you fit a linear model to data which are non – linearly or non – additively related, your predictions are likely to be seriously in error, especially when you extrapolate beyond the range of the sample data.
Violations of normality often arise either because :-
I.The distributions of the dependent and/or independent variables are themselves significantly non-normal, and/or
II.The linearity assumption is violated.