The correct answer is: A. Least square is an estimation tool
Least squares is a method of estimating the parameters of a statistical model. It is based on the principle of minimizing the sum of the squares of the residuals, which are the differences between the observed values and the values predicted by the model.
Least squares problems can be divided into three categories:
- Ordinary least squares (OLS): This is the most common type of least squares problem. It is used to estimate the parameters of a linear model.
- Generalized least squares (GLS): This is used to estimate the parameters of a linear model when the errors are not independent and identically distributed (iid).
- Nonlinear least squares: This is used to estimate the parameters of a nonlinear model.
Compound least squares is not one of the categories of least squares. It is a method of estimating the parameters of a compound model, which is a model that consists of two or more linear models.
Here is a brief explanation of each option:
A. Least square is an estimation tool
Least squares is a method of estimating the parameters of a statistical model. It is based on the principle of minimizing the sum of the squares of the residuals, which are the differences between the observed values and the values predicted by the model.B. Least square problems falls in to three categories
Least squares problems can be divided into three categories:Ordinary least squares (OLS): This is the most common type of least squares problem. It is used to estimate the parameters of a linear model.
- Generalized least squares (GLS): This is used to estimate the parameters of a linear model when the errors are not independent and identically distributed (iid).
Nonlinear least squares: This is used to estimate the parameters of a nonlinear model.
C. Compound least square is one of the category of least square
Compound least squares is not one of the categories of least squares. It is a method of estimating the parameters of a compound model, which is a model that consists of two or more linear models.D. None of the mentioned
This option is incorrect because it does not include the correct answer.