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  2. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    In the more general multiple regression model, there are independent variables: = + + + +, where is the -th observation on the -th independent variable.If the first independent variable takes the value 1 for all , =, then is called the regression intercept.

  3. Simple linear regression - Wikipedia

    en.wikipedia.org/wiki/Simple_linear_regression

    The above equations are efficient to use if the mean of the x and y variables (¯ ¯) are known.If the means are not known at the time of calculation, it may be more efficient to use the expanded version of the ^ ^ equations.

  4. Multinomial logistic regression - Wikipedia

    en.wikipedia.org/wiki/Multinomial_logistic...

    Multinomial logistic regression is used when the dependent variable in question is nominal (equivalently categorical, meaning that it falls into any one of a set of categories that cannot be ordered in any meaningful way) and for which there are more than two categories.

  5. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    Consider a situation where a small ball is being tossed up in the air and then we measure its heights of ascent h i at various moments in time t i.Physics tells us that, ignoring the drag, the relationship can be modeled as

  6. Polynomial regression - Wikipedia

    en.wikipedia.org/wiki/Polynomial_regression

    Polynomial regression models are usually fit using the method of least squares.The least-squares method minimizes the variance of the unbiased estimators of the coefficients, under the conditions of the Gauss–Markov theorem.

  7. Partial least squares regression - Wikipedia

    en.wikipedia.org/wiki/Partial_least_squares...

    Partial least squares (PLS) regression is a statistical method that bears some relation to principal components regression and is a reduced rank regression [1]; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space of maximum ...

  8. Multivariate adaptive regression spline - Wikipedia

    en.wikipedia.org/wiki/Multivariate_adaptive...

    In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.

  9. Passing–Bablok regression - Wikipedia

    en.wikipedia.org/wiki/Passing–Bablok_regression

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