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    regression
    /rɪˈɡrɛʃn/

    noun

    • 1. a return to a former or less developed state: "it is easy to blame unrest on economic regression"
    • 2. a measure of the relation between the mean value of one variable (e.g. output) and corresponding values of other variables (e.g. time and cost).

    More definitions, origin and scrabble points

  2. Jun 12, 2024 · Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between a dependent variable and one...

  3. a return to a previous and less advanced or worse state, condition, or way of behaving: A regression has occurred in the overall political situation. regression to childhood. Fewer examples. This is simply a regression to outdated attitudes. She claims to be able to induce a past-life regression through hypnosis.

  4. In recent decades, new methods have been developed for robust regression, regression involving correlated responses such as time series and growth curves, regression in which the predictor (independent variable) or response variables are curves, images, graphs, or other complex data objects, regression methods accommodating various ...

  5. : a functional relationship between two or more correlated variables that is often empirically determined from data and is used especially to predict values of one variable when given values of the others. the regression of y on x is linear.

  6. May 20, 2024 · What is regression? If you’ve delved into machine learning, you’ve likely encountered this term buzzing around. In essence, regression is the compass guiding predictive analytics, helping us navigate the maze of data to uncover patterns and relationships.

  7. Regression analysis is a set of statistical methods used to estimate relationships between a dependent variable and one or more independent variables. Corporate Finance Institute Menu

  8. Feb 19, 2020 · Regression models describe the relationship between variables by fitting a line to the observed data. Linear regression models use a straight line, while logistic and nonlinear regression models use a curved line. Regression allows you to estimate how a dependent variable changes as the independent variable (s) change.

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