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  2. Second partial derivative test - Wikipedia

    en.wikipedia.org/wiki/Second_partial_derivative_test

    At the remaining critical point (0, 0) the second derivative test is insufficient, and one must use higher order tests or other tools to determine the behavior of the function at this point. (In fact, one can show that f takes both positive and negative values in small neighborhoods around (0, 0) and so this point is a saddle point of f .)

  3. Derivative test - Wikipedia

    en.wikipedia.org/wiki/Derivative_test

    After establishing the critical points of a function, the second-derivative test uses the value of the second derivative at those points to determine whether such points are a local maximum or a local minimum. [1] If the function f is twice-differentiable at a critical point x (i.e. a point where f ′ (x) = 0), then:

  4. Continuous uniform distribution - Wikipedia

    en.wikipedia.org/wiki/Continuous_uniform...

    Any probability density function integrates to , so the probability density function of the continuous uniform distribution is graphically portrayed as a rectangle where ⁠ ⁠ is the base length and ⁠ ⁠ is the height. As the base length increases, the height (the density at any particular value within the distribution boundaries) decreases.

  5. Root-finding algorithm - Wikipedia

    en.wikipedia.org/wiki/Root-finding_algorithm

    [5] [page needed] It says that, if the topological degree of a function f on a rectangle is non-zero, then the rectangle must contain at least one root of f. This criterion is the basis for several root-finding methods, such as those of Stenger [6] and Kearfott. [7] However, computing the topological degree can be time-consuming.

  6. 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 ...

  7. Jacobian matrix and determinant - Wikipedia

    en.wikipedia.org/wiki/Jacobian_matrix_and...

    The Jacobian at a point gives the best linear approximation of the distorted parallelogram near that point (right, in translucent white), and the Jacobian determinant gives the ratio of the area of the approximating parallelogram to that of the original square. If m = n, then f is a function from R n to itself and the Jacobian matrix is a ...

  8. Chegg - Wikipedia

    en.wikipedia.org/wiki/Chegg

    In the same year, Chegg partnered with Varkey Foundation to launch Global Student Prize to recognise outstanding students that make an impact to local or international communities. [ 22 ] On a May 1, 2023, earnings call, CEO Dan Rosensweig identified the rise of ChatGPT as a potential obstacle for the company's growth.

  9. Function of several real variables - Wikipedia

    en.wikipedia.org/wiki/Function_of_several_real...

    The image of a function f(x 1, x 2, …, x n) is the set of all values of f when the n-tuple (x 1, x 2, …, x n) runs in the whole domain of f.For a continuous (see below for a definition) real-valued function which has a connected domain, the image is either an interval or a single value.