Assumptions under which ordinary least square estimates are valid.

** 1. ****Model is Linear in Parameters.**

It means model should be in the form of

Y=B0 + B1X1 + B2X2

&

it should not be of the form

Y = B0 + B1²X1 + B2²X2

** 2. ****The data are a random sample of the population.**

The errors are not co- related , they are statistically independent from one another.

** 3. ****The Expected values of errors is always zero.**

Average of errors is zero.

**4. Independent variables are not too strongly collinear.**

Independent variables should not be highly co-related among themselves. Independent variables should not be co-related among themselves. As collinearity between variables brings very less additional information coming from 2nd variable.

**5. Independent variables are measured precisely.**

X variables (Independent variables) are measured precisely.

**6. Residuals have constant variance – homoscedasticity.**

Variance of the error should be constant.

**7. Errors are normally distributed with mean = 0 .**

To check take the residuals and generate a histogram.

**8. Model is correctly specified.**

Meaning we are using correct variable in the right type and we are not missing on any information.

**If all these conditions hold, then OLS estimators are BLUE – Best Linear Unbiased Estimators.**