<P> In statistics , ordinary least squares ( OLS ) or linear least squares is a method for estimating the unknown parameters in a linear regression model . OLS chooses the parameters of a linear function of a set of explanatory variables by minimizing the sum of the squares of the differences between the observed dependent variable ( values of the variable being predicted ) in the given dataset and those predicted by the linear function . Geometrically , this is seen as the sum of the squared distances , parallel to the axis of the dependent variable , between each data point in the set and the corresponding point on the regression surface -- the smaller the differences , the better the model fits the data . The resulting estimator can be expressed by a simple formula , especially in the case of a single regressor on the right - hand side . </P>
<P> In statistics , ordinary least squares ( OLS ) or linear least squares is a method for estimating the unknown parameters in a linear regression model . OLS chooses the parameters of a linear function of a set of explanatory variables by minimizing the sum of the squares of the differences between the observed dependent variable ( values of the variable being predicted ) in the given dataset and those predicted by the linear function . Geometrically , this is seen as the sum of the squared distances , parallel to the axis of the dependent variable , between each data point in the set and the corresponding point on the regression surface -- the smaller the differences , the better the model fits the data . The resulting estimator can be expressed by a simple formula , especially in the case of a single regressor on the right - hand side . </P>
for what kind of models can we use ols
for what kind of models can we use ols
linear regression
linear regression