Examples of 'ordinary least squares' in a sentence
Meaning of "ordinary least squares"
Ordinary least squares (OLS) is a statistical method used for estimating the parameters of a linear regression model. It is a commonly used technique in econometrics and other disciplines to analyze the relationship between a dependent variable and one or more independent variables. OLS aims to minimize the sum of the squared differences between the observed and predicted values, allowing for the estimation of the regression coefficients
How to use "ordinary least squares" in a sentence
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ordinary least squares
Relations were analysed by ordinary least squares regression.
Or ordinary least squares regression and.
One method of estimation is ordinary least squares.
The ordinary least squares and optimization approach.
It is estimated by the method of ordinary least squares.
Ordinary least squares regression.
The results are from an ordinary least squares regression.
The results presented below correspond to estimates using ordinary least squares.
Effect in ordinary least squares.
Bienaymé had generalized the method of ordinary least squares.
Estimation is by ordinary least squares with standard errors clustered by journal.
The model was estimated by the ordinary least squares method.
The ordinary least squares results for the first period are the following.
Formulation as an extended form of ordinary least squares prediction problem.
Variables were transformed into logarithms and the estimation was conducted using the ordinary least squares.
See also
This further legitimated the use of ordinary least squares estimation in this setting.
Ordinary Least Squares regression was used in conducting the analysis.
The remainder of the article assumes an ordinary least squares regression.
Ordinary least squares Weighted least squares Generalized least squares.
The regression second plane is fitted by using ordinary least squares.
Estimation methods such as Ordinary least squares and Generalized least squares.
Research method is hedonic regression and estimation is conducted by ordinary least squares method.
DA are estimated using pooled ordinary least squares OLS with the following equation.
Regression The regression analysis estimates a linear regression model with ordinary least squares OLS.
It may not be best to use the ordinary least squares methods in such cases.
Ordinary least squares regression assumes that φ ∞.
Own price elasticities were estimated by ordinary least squares using time series data.
In these cases ordinary least squares can be statistically inefficient, or even give misleading inferences.
Examining residual plots helps you determine if the ordinary least squares assumptions are being met.
In this situation, ordinary least squares produces biased and inconsistent estimates.
Analyses were performed using principal component factor analysis and ordinary least squares linear regression models.
In yet another embodiment, ordinary least squares regression or unconditional logistic regression is used.
The vector of estimated polynomial regression coefficients ( using ordinary least squares estimation ) is.
Estimated by ordinary least squares except for the second equations for the U.S. and Japan.
This familiar loss function is used in Ordinary Least Squares regression.
Contrasting ordinary least squares with least absolute deviations [ edit ].
Table 3 presents the results from ordinary least squares regressions.
The ordinary least squares ( OLS ) estimation is used, and robust standard errors are recorded parenthetically.
To calculate the results, the ordinary least squares method was used.
Estimation of equation ( 3 ) can be undertaken in a straightforward way using ordinary least squares.
For the outcomes measured in dollars, ordinary least squares regressions are estimated.
Using ordinary least squares ( OLS ), the earnings equation is estimated separately for men and women.
To this end, this study adopts a multiple ordinary least squares regression method.
In the Ordinary Least Squares ( OLS ) estimation, replacement rate is positively associated with usage.
Table 1 presents the set of estimates from ordinary least squares regressions of variations of equation 1.
In most studies this equation is log-linearised and estimated using ordinary least squares OLS.
That 's called the ordinary least squares method!
The model can be estimated equation-by-equation using standard ordinary least squares OLS.
Model 1 is an ordinary least squares regression,.
A method according to claim 1, wherein the regression analysis is an ordinary least squares regression analysis.
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