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2. Beta regression - Wikipedia

   en.wikipedia.org/wiki/Beta_regression

   Beta regression is a form of regression which is used when the response variable, , takes values within (,) and can be assumed to follow a beta distribution. [1] It is generalisable to variables which takes values in the arbitrary open interval ( a , b ) {\displaystyle (a,b)} through transformations. [ 1 ]

3. Standardized coefficient - Wikipedia

   en.wikipedia.org/wiki/Standardized_coefficient

   In statistics, standardized (regression) coefficients, also called beta coefficients or beta weights, are the estimates resulting from a regression analysis where the underlying data have been standardized so that the variances of dependent and independent variables are equal to 1. [1]

4. Regression analysis - Wikipedia

   en.wikipedia.org/wiki/Regression_analysis

   First, regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Second, in some situations regression analysis can be used to show causal relationships between the independent and dependent variables. Importantly, regressions by themselves only reveal ...

5. Ordinary least squares - Wikipedia

   en.wikipedia.org/wiki/Ordinary_least_squares

   In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...

6. Linear regression - Wikipedia

   en.wikipedia.org/wiki/Linear_regression

   Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. [4] This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the ...

7. Generalized beta distribution - Wikipedia

   en.wikipedia.org/wiki/Generalized_Beta_distribution

   The beta family includes the beta of the first and second kind [7] (B1 and B2, where the B2 is also referred to as the Beta prime), which correspond to c = 0 and c = 1, respectively. Setting c = 0 {\displaystyle c=0} , b = 1 {\displaystyle b=1} yields the standard two-parameter beta distribution .

8. Least squares - Wikipedia

   en.wikipedia.org/wiki/Least_squares

   The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...

9. Generalized least squares - Wikipedia

   en.wikipedia.org/wiki/Generalized_least_squares

   In statistics, generalized least squares (GLS) is a method used to estimate the unknown parameters in a linear regression model.It is used when there is a non-zero amount of correlation between the residuals in the regression model.