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  2. Linear regression - Wikipedia

    en.wikipedia.org/wiki/Linear_regression

    Standard linear regression models with standard estimation techniques make a number of assumptions about the predictor variables, the response variable and their relationship. Numerous extensions have been developed that allow each of these assumptions to be relaxed (i.e. reduced to a weaker form), and in some cases eliminated entirely.

  3. Quantile regression - Wikipedia

    en.wikipedia.org/wiki/Quantile_regression

    Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional mean of the response variable across values of the predictor variables, quantile regression estimates the conditional median (or other quantiles) of the response variable.

  4. Binomial regression - Wikipedia

    en.wikipedia.org/wiki/Binomial_regression

    The response variable Y is assumed to be binomially distributed conditional on the explanatory variables X. The number of trials n is known, and the probability of success for each trial p is specified as a function θ(X). This implies that the conditional expectation and conditional variance of the observed fraction of successes, Y/n, are

  5. Regression analysis - Wikipedia

    en.wikipedia.org/wiki/Regression_analysis

    The response variable may be non-continuous ("limited" to lie on some subset of the real line). For binary (zero or one) variables, if analysis proceeds with least-squares linear regression, the model is called the linear probability model. Nonlinear models for binary dependent variables include the probit and logit model.

  6. Logistic regression - Wikipedia

    en.wikipedia.org/wiki/Logistic_regression

    x m,i (also called independent variables, explanatory variables, predictor variables, features, or attributes), and a binary outcome variable Y i (also known as a dependent variable, response variable, output variable, or class), i.e. it can assume only the two possible values 0 (often meaning "no" or "failure") or 1 (often meaning "yes" or ...

  7. Conditional variance - Wikipedia

    en.wikipedia.org/wiki/Conditional_variance

    In probability theory and statistics, a conditional variance is the variance of a random variable given the value(s) of one or more other variables. Particularly in econometrics , the conditional variance is also known as the scedastic function or skedastic function . [ 1 ]

  8. Conditional expectation - Wikipedia

    en.wikipedia.org/wiki/Conditional_expectation

    In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value evaluated with respect to the conditional probability distribution. If the random variable can take on only a finite number of values, the "conditions" are that the variable can only take on a subset of ...

  9. Classical conditioning - Wikipedia

    en.wikipedia.org/wiki/Classical_conditioning

    Classical conditioning occurs when a conditioned stimulus (CS) is paired with an unconditioned stimulus (US). Usually, the conditioned stimulus is a neutral stimulus (e.g., the sound of a tuning fork), the unconditioned stimulus is biologically potent (e.g., the taste of food) and the unconditioned response (UR) to the unconditioned stimulus is an unlearned reflex response (e.g., salivation).