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  2. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    The capital asset pricing model uses linear regression as well as the concept of beta for analyzing and quantifying the systematic risk of an investment. This comes directly from the beta coefficient of the linear regression model that relates the return on the investment to the return on all risky assets.

  3. Simple linear regression - Wikipedia

    en.wikipedia.org/wiki/Simple_linear_regression

    Deming regression (total least squares) also finds a line that fits a set of two-dimensional sample points, but (unlike ordinary least squares, least absolute deviations, and median slope regression) it is not really an instance of simple linear regression, because it does not separate the coordinates into one dependent and one independent ...

  4. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    The response variable may be non-continuous ("limited" to lie on some subset of the real line). For binary (zero or one) variables, if analysis proceeds with least-squares linear regression, the model is called the linear probability model. Nonlinear models for binary dependent variables include the probit and logit model.

  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. Coefficient of determination - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_determination

    Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

  7. Leverage (statistics) - Wikipedia

    en.wikipedia.org/wiki/Leverage_(statistics)

    Consider the linear regression model = +, =,, …,.That is, = +, where, is the design matrix whose rows correspond to the observations and whose columns correspond to the independent or explanatory variables.

  8. Mallows's Cp - Wikipedia

    en.wikipedia.org/wiki/Mallows's_Cp

    In statistics, Mallows's, [1] [2] named for Colin Lingwood Mallows, is used to assess the fit of a regression model that has been estimated using ordinary least squares.It is applied in the context of model selection, where a number of predictor variables are available for predicting some outcome, and the goal is to find the best model involving a subset of these predictors.

  9. Fraction of variance unexplained - Wikipedia

    en.wikipedia.org/wiki/Fraction_of_variance...

    When trying to predict Y, the most naive regression function that we can think of is the constant function predicting the mean of Y, i.e., () = ¯. It follows that the MSE of this function equals the variance of Y ; that is, SS err = SS tot , and SS reg = 0.