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  2. Polynomial regression - Wikipedia

    en.wikipedia.org/wiki/Polynomial_regression

    Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression. [1]

  3. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    Polynomial regression; General linear model; ... although the expression on the right hand side is quadratic in the independent variable , it is linear in the ...

  4. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    Optimal instruments regression is an extension of classical IV regression to the situation where E[ε i | z i] = 0. Total least squares (TLS) [6] is an approach to least squares estimation of the linear regression model that treats the covariates and response variable in a more geometrically symmetric manner than OLS. It is one approach to ...

  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. Local regression - Wikipedia

    en.wikipedia.org/wiki/Local_regression

    Local regression or local polynomial regression, [1] also known as moving regression, [2] is a generalization of the moving average and polynomial regression. [3] Its most common methods, initially developed for scatterplot smoothing, are LOESS (locally estimated scatterplot smoothing) and LOWESS (locally weighted scatterplot smoothing), both pronounced / ˈ l oʊ ɛ s / LOH-ess.

  7. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    The basic idea of logistic regression is to use the mechanism already developed for linear regression by modeling the probability p i using a linear predictor function, i.e. a linear combination of the explanatory variables and a set of regression coefficients that are specific to the model at hand but the same for all trials.

  8. AOL

    search.aol.com

    The search engine that helps you find exactly what you're looking for. Find the most relevant information, video, images, and answers from all across the Web.

  9. Regularized least squares - Wikipedia

    en.wikipedia.org/wiki/Regularized_least_squares

    Polynomial regression; General linear model ... Which of these regimes is more relevant depends on the specific data set at hand. ... Lasso regression is the minimal ...