Why do we would like assumptions ?
Assumptions are conditions that should be met sooner than using a linear regression model to make predictions or draw inferences. These assumptions are important because of in the event that they aren’t met, the model’s accuracy may be significantly diminished.
- Linearity : The connection between the dependent and neutral variable have to be linear.
2. Independence : The observations X1,X2,X3 are neutral of each other.
3. Homoscedasticity : The variance of the errors is fastened all through all ranges of the neutral variable.
4. Normality : The errors observe an ordinary distribution.
5. No Multicollinearity : The neutral variables shouldn’t extraordinarily correlated with each other.
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