Question

Describe the two assumptions used in class to derive the ordinary least squares estimates of the intercept and slope. Why are these assumptions important and what do they mean? Which assumption is needed to estimate each parameter?

Answer 1

## Q ) Describe the two assumptions used in class to derive the ordinary least squares estimates of the intercept and slope Why are these assumptions important and what do they mean? Which assumption is needed to estimate each parameter?

Answer : there are more than two assumptions used for ols

## Assumption 1 : The regression model is linear in the coefficients and the error term

This assumption addresses the functional form of the model .in Statistics , a regression model is linear when all terms in the model are either constant or a parameter multiplied by an independent variable . we can build the model equation only by adding the terms together .

## Assumption 2 :The error terms has a population mean of zero

The error term accounts for the variation in the dependent variable that the independent variables do not explain . Random chance should determine the values of the error term . for your model to be unbiased the average value of the error term must equal to zero and unknown variance .

## once above assumption satisfy then we can get parameters there are formula for parameters and it is free from error that and it is unbiased for unbiased ness we need to check all required assumption satisfy. once it satisfy we get unbiased parameters .

for each parameter we need several assumption but above two can be work .