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  2. Multinomial logistic regression - Wikipedia

    en.wikipedia.org/wiki/Multinomial_logistic...

    Suppose the odds ratio between the two is 1 : 1. Now if the option of a red bus is introduced, a person may be indifferent between a red and a blue bus, and hence may exhibit a car : blue bus : red bus odds ratio of 1 : 0.5 : 0.5, thus maintaining a 1 : 1 ratio of car : any bus while adopting a changed car : blue bus ratio of 1 : 0.5.

  3. Omitted-variable bias - Wikipedia

    en.wikipedia.org/wiki/Omitted-variable_bias

    The second term after the equal sign is the omitted-variable bias in this case, which is non-zero if the omitted variable z is correlated with any of the included variables in the matrix X (that is, if X′Z does not equal a vector of zeroes).

  4. Hosmer–Lemeshow test - Wikipedia

    en.wikipedia.org/wiki/Hosmer–Lemeshow_test

    The addition of the quadratic term caffeine 2 to the regression model would allow for the increasing and then decreasing relationship of grade to caffeine dose. The logistic model including the caffeine 2 term indicates that the quadratic caffeine^2 term is significant (p = 0.003) while the linear caffeine term is not significant (p = 0.21).

  5. Error correction model - Wikipedia

    en.wikipedia.org/wiki/Error_correction_model

    The first term in the RHS describes short-run impact of change in on , the second term explains long-run gravitation towards the equilibrium relationship between the variables, and the third term reflects random shocks that the system receives (e.g. shocks of consumer confidence that affect consumption). To see how the model works, consider two ...

  6. Seemingly unrelated regressions - Wikipedia

    en.wikipedia.org/wiki/Seemingly_unrelated...

    Here i represents the equation number, r = 1, …, R is the individual observation, and we are taking the transpose of the column vector. The number of observations R is assumed to be large, so that in the analysis we take R → ∞ {\displaystyle \infty } , whereas the number of equations m remains fixed.

  7. Quadratic form (statistics) - Wikipedia

    en.wikipedia.org/wiki/Quadratic_form_(statistics)

    Since the quadratic form is a scalar quantity, = ⁡ (). Next, by the cyclic property of the trace operator, ⁡ [⁡ ()] = ⁡ [⁡ ()]. Since the trace operator is a linear combination of the components of the matrix, it therefore follows from the linearity of the expectation operator that

  8. Kernel (statistics) - Wikipedia

    en.wikipedia.org/wiki/Kernel_(statistics)

    In statistics, especially in Bayesian statistics, the kernel of a probability density function (pdf) or probability mass function (pmf) is the form of the pdf or pmf in which any factors that are not functions of any of the variables in the domain are omitted. [1] Note that such factors may well be functions of the parameters of the pdf or pmf.

  9. Jacobi symbol - Wikipedia

    en.wikipedia.org/wiki/Jacobi_symbol

    Quadratic residues are highlighted in yellow — note that no entry with a Jacobi symbol of −1 is a quadratic residue, and if k is a quadratic residue modulo a coprime n, then (⁠ k / n ⁠) = 1, but not all entries with a Jacobi symbol of 1 (see the n = 9 and n = 15 rows) are quadratic residues.