What is the difference between Least Squares Method and the maximum likelihood

Question

Accepted Answer

Least Squares Method and Maximum Likelihood are both used to estimate parameters, but differ in their approach.

Least Squares Method minimizes the sum of squared errors between the observed and predicted values.
Maximum Likelihood estimates the parameters that maximize the likelihood of observing the given data.
Least Squares Method assumes that the errors are normally distributed and independent.
Maximum Likelihood does not make any assumptions about the distribution of errors.
Least Squares Method is commonly used in linear regression.
Maximum Likelihood is commonly used in logistic regression and other models where the response variable is binary or categorical.

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