Notes: Adjusted estimates are based on ordinary least square regressions.

We used ordinary least square technique to analyze our data.

cNormal regression, Poisson regression, and logistic regression are all special cases of the generalized linear model.

Source: IMF staff calculations.Note: All regressions are estimated using ordinary least squares.

A linearized version of the model is estimated using standard OLS and annual data.

The preliminary analysis applies the ordinary least square (OLS) regression on the panel sample data.

This allows the use of a conventional OLS estimator to yield estimates of the logarithm of the coefficients.

The significance of the various factors was examined via logistic regression and based on ordinary least squares regression.

This method only requires the use of ordinary least squares regression after ordering the sample data.

An ordinary least squares model estimates the mean of the dependent variable, conditional on various explanatory variables.

Online sources have stated that the data that best fits the ordinary least squares minimizes the sum of squared residuals.

Then the nonlinear model is approximated with linear terms and ordinary least squares are employed to estimate the parameters.

This application allows you to calculate the straight line of ordinary least squares regression (OLS) in samples of small size.

Linear regression is also known as multiple regression, multivariate regression, ordinary least squares (OLS), and regression.

By using robust ordinary least squares (OLS), some of these extreme observations would have been treated as outliers and not considered.