OLS stands for ordinary least squares, and MLE stands for maximum likelihood estimation. The ordinary least squares may also be referred to as the linear least squares, and this is a technique for approximately surmising the unknown parameters situated in the linear regression model. The ordinary least squares are procured by reducing the total of squared vertical distances between the detected responses within the data set and the responses which are premeditated by linear approximation.
MCE is a method used in determining the parameters of a statistical model, and for exhibiting a statistical model to information. Using the maximum likelihood estimation, you can determine the mean and variance of the height of your subjects. The MCE would set the mean and variance as parameters in selecting the specific parametric values in a given model.
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