1.

What are various assumptions used in linear regression? What would happen if they are violated?

Answer»

LINEAR regression is done under the following assumptions:

  • The sample data used for MODELING represents the entire population.
  • There EXISTS a linear relationship between the X-axis variable and the mean of the Y variable.
  • The residual variance is the same for any X values. This is called homoscedasticity
  • The observations are independent of one another.
  • Y is DISTRIBUTED normally for any value of X.

Extreme VIOLATIONS of the above assumptions lead to redundant results. Smaller violations of these result in greater variance or bias of the estimates.


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