00:01
So in statistics, ordinary leased squares or linear leased squares, both represents the same concept.
00:14
It is a method for estimating the unknown parameters, unknown parameters in the linear regression method, in terms of linear regression model.
00:27
So this method minimizes the sum of the square, minimizes the sum of the squares vertical distance between the observed response in the database, the dataset and the response predicted by the linear approximation.
00:48
So the resulting estimator can be expressed by a simple formula, especially in the case of a single regression, on the right hand side.
01:00
So the ols estimator is consistent when the regression or exonious and there is no perfect miscollinearity and optimal in the class of linear unbiased estimator when the errors are homostatic and serially uncorrelated.
01:26
Therefore, under these conditions the method of ols provides minimum variance, minimum invariance mean of an unbiased estimation when the error have finite variance.
01:47
Under the additional assumption that the errors be normally distributed.
01:53
So, ols is the maximum likelihood estimators.
02:01
It is the maximum likelihood estimators.
02:05
Therefore, there may be some relationship between the regression.
02:11
There are several different frameworks in which the linear regression models can be cast in order to make the ols technique applicable.
02:21
Each of these settings produces...