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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. Constant term - Wikipedia

    en.wikipedia.org/wiki/Constant_term

    If the constant term is 0, then it will conventionally be omitted when the quadratic is written out. Any polynomial written in standard form has a unique constant term, which can be considered a coefficient of . In particular, the constant term will always be the lowest degree term of the polynomial. This also applies to multivariate polynomials.

  4. 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).

  5. 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

  6. Ordered logit - Wikipedia

    en.wikipedia.org/wiki/Ordered_logit

    We assume that the probabilities of these outcomes are given by p 1 (x), p 2 (x), p 3 (x), p 4 (x), p 5 (x), all of which are functions of some independent variable(s) x. Then, for a fixed value of x, the logarithms of the odds (not the logarithms of the probabilities) of answering in certain ways are:

  7. Robust regression - Wikipedia

    en.wikipedia.org/wiki/Robust_regression

    The two regression lines are those estimated by ordinary least squares (OLS) and by robust MM-estimation. The analysis was performed in R using software made available by Venables and Ripley (2002). The two regression lines appear to be very similar (and this is not unusual in a data set of this size).

  8. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints. [ 4 ] [ 5 ] Curve fitting can involve either interpolation , [ 6 ] [ 7 ] where an exact fit to the data is required, or smoothing , [ 8 ] [ 9 ] in which a "smooth ...

  9. 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.