Assumption Of The Linear Statistics Regression Model.

Hello and welcome to my blog today.
My name is @elaebi and I will discussing about a topic under Econometrics titled "Assumptions of the linear statistics regression model.** for all my fellow students in the house both fresh graduates or postgraduates in the house.

1. The error otherwise known as stochastic term(U) is a random variable.
   I.e y=a+bx is an ordinary mathematical equation but when U is added to the equation, it becomes an econometrics model, y=a+bx+U.

2. The mean of the value of U at any particular time is equal to zero. I.e E(U)=0.

3. The variance of the stochastic or error term(U) is finite and constant. This means that for all values of x, the U will show the same dispersion around the mean. In other words, extent of dispersion is actual variable x around the values is the same of all values. This is also called homoscedasticity.

4. The error term U is normally distributed.

5. The random variables or error terms(U's) for the various observation of the values ate uncorrelated Meaning the values of U to respect of X1 does boy have any correlation with the value of U with respect to X2. In other words, the value of U with respect to X1 is independent with the value of U to X2. This means absense of autocorrelation or absense of serial correlation.

6. The error term(U) is independent of the explanatory variables(X).

7. The explanatory variables are measured without the error term(U). It suggests that the model is error free and it is boy biased to the values of x.

8. The explanatory variables (Xs) are boy perfectly linearly correlated.

9. The relationship being estimated is identified.

Thanks for reading and God bless you all for your endless support so far. Please do well to upvote, comment and resteem.