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  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. Binomial regression - Wikipedia

    en.wikipedia.org/wiki/Binomial_regression

    In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is the number of successes in a series of ⁠ ⁠ independent Bernoulli trials, where each trial has probability of success ⁠ ⁠. [1]