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  2. Dummy variable (statistics) - Wikipedia

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

    Dummy variables are commonly used in regression analysis to represent categorical variables that have more than two levels, such as education level or occupation. In this case, multiple dummy variables would be created to represent each level of the variable, and only one dummy variable would take on a value of 1 for each observation.

  3. Continuous or discrete variable - Wikipedia

    en.wikipedia.org/.../Continuous_or_discrete_variable

    A variable of this type is called a dummy variable. If the dependent variable is a dummy variable, then logistic regression or probit regression is commonly employed. In the case of regression analysis, a dummy variable can be used to represent subgroups of the sample in a study (e.g. the value 0 corresponding to a constituent of the control ...

  4. Mutual exclusivity - Wikipedia

    en.wikipedia.org/wiki/Mutual_exclusivity

    The number of dummy variables is always one less than the number of categories: with the two categories black and white there is a single dummy variable to distinguish them, while with the three age categories two dummy variables are needed to distinguish them. Such qualitative data can also be used for dependent variables. For example, a ...

  5. List of statistics articles - Wikipedia

    en.wikipedia.org/wiki/List_of_statistics_articles

    Jackknife (statistics) – redirects to Resampling (statistics) Jackson network; Jackson's theorem (queueing theory) Jadad scale; James–Stein estimator; Jarque–Bera test; Jeffreys prior; Jensen's inequality; Jensen–Shannon divergence; JMulTi – software; Johansen test; Johnson SU distribution; Joint probability distribution; Jonckheere's ...

  6. Linear predictor function - Wikipedia

    en.wikipedia.org/wiki/Linear_predictor_function

    For example, a four-way discrete variable of blood type with the possible values "A, B, AB, O" would be converted to separate two-way dummy variables, "is-A, is-B, is-AB, is-O", where only one of them has the value 1 and all the rest have the value 0. This allows for separate regression coefficients to be matched for each possible value of the ...

  7. Multiple correspondence analysis - Wikipedia

    en.wikipedia.org/wiki/Multiple_correspondence...

    MCA is performed by applying the CA algorithm to either an indicator matrix (also called complete disjunctive table – CDT) or a Burt table formed from these variables. [citation needed] An indicator matrix is an individuals × variables matrix, where the rows represent individuals and the columns are dummy variables representing categories of the variables. [1]

  8. Nominal category - Wikipedia

    en.wikipedia.org/wiki/Nominal_category

    From a quantitative analysis perspective, one of the most common operations to perform on nominal data is dummy variable assignment, a method earlier introduced. For example, if a nominal variable has three categories (A, B, and C), two dummy variables would be created (for A and B) where C is the reference category, the nominal variable that ...

  9. Fixed effects model - Wikipedia

    en.wikipedia.org/wiki/Fixed_effects_model

    One is to add a dummy variable for each individual > (omitting the first individual because of multicollinearity). This is numerically, but not computationally, equivalent to the fixed effect model and only works if the sum of the number of series and the number of global parameters is smaller than the number of observations. [ 10 ]