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  2. Polynomial regression - Wikipedia

    en.wikipedia.org/wiki/Polynomial_regression

    A drawback of polynomial bases is that the basis functions are "non-local", meaning that the fitted value of y at a given value x = x 0 depends strongly on data values with x far from x 0. [9] In modern statistics, polynomial basis-functions are used along with new basis functions, such as splines, radial basis functions, and wavelets. These ...

  3. Linear trend estimation - Wikipedia

    en.wikipedia.org/wiki/Linear_trend_estimation

    All have the same trend, but more filtering leads to higher r 2 of fitted trend line. The least-squares fitting process produces a value, r-squared (r 2), which is 1 minus the ratio of the variance of the residuals to the variance of the dependent variable. It says what fraction of the variance of the data is explained by the fitted trend line.

  4. 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.

  5. Polynomial and rational function modeling - Wikipedia

    en.wikipedia.org/wiki/Polynomial_and_rational...

    A polynomial function is one that has the form = + + + + + where n is a non-negative integer that defines the degree of the polynomial. A polynomial with a degree of 0 is simply a constant function; with a degree of 1 is a line; with a degree of 2 is a quadratic; with a degree of 3 is a cubic, and so on.

  6. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    A trend line could simply be drawn by eye through a set of data points, but more properly their position and slope is calculated using statistical techniques like linear regression. Trend lines typically are straight lines, although some variations use higher degree polynomials depending on the degree of curvature desired in the line.

  7. Autoregressive moving-average model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_moving...

    In order for the model to remain stationary, the roots of its characteristic polynomial must lie outside the unit circle. For example, processes in the AR(1) model with | φ 1 | ≥ 1 {\displaystyle |\varphi _{1}|\geq 1} are not stationary because the root of 1 − φ 1 B = 0 {\displaystyle 1-\varphi _{1}B=0} lies within the unit circle.

  8. NYT ‘Connections’ Hints and Answers Today, Friday, December 13

    www.aol.com/nyt-connections-hints-answers-today...

    Read no further until you really want some clues or you've completely given up and want the answers ASAP. Get ready for all of today's NYT 'Connections’ hints and answers for #551 on Friday ...

  9. Time series - Wikipedia

    en.wikipedia.org/wiki/Time_series

    Alternatively polynomial interpolation or spline interpolation is used where piecewise polynomial functions are fitted in time intervals such that they fit smoothly together. A different problem which is closely related to interpolation is the approximation of a complicated function by a simple function (also called regression ).