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1Dependent types and explicit substitutions

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“Dependent types and explicit substitutions” Metadata:

  • Title: ➤  Dependent types and explicit substitutions
  • Author:
  • Language: English
  • Publisher: ➤  National Technical Information Service, distributor - Institute for Computer Applications in Science and Engineering, NASA Langley Research Center
  • Publish Date:
  • Publish Location: Hampton, VA - Springfield, VA

“Dependent types and explicit substitutions” Subjects and Themes:

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Access and General Info:

  • First Year Published: 1999
  • Is Full Text Available: No
  • Is The Book Public: No
  • Access Status: No_ebook

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    Source: Wikipedia

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    Dependent and independent variables

    A variable is considered dependent if it depends on (or is hypothesized to depend on) an independent variable. Dependent variables are studied under the

    Regression analysis

    a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more independent variables (often

    Control variable

    test the relative relationship of the dependent variable (DV) and independent variable (IV). The control variables themselves are not of primary interest

    Instrumental variables estimation

    that affect both the dependent and explanatory variables, or the covariates are subject to measurement error. Explanatory variables that suffer from one

    Multivariate analysis of variance

    there are two or more dependent variables, and is often followed by significance tests involving individual dependent variables separately. Without relation

    Mediation (statistics)

    understanding the relationship between an independent variable and a dependent variable when these variables do not have an obvious direct connection. Baron

    Dummy variable (statistics)

    known as one-hot encoding. Dummy variables are commonly used in regression analysis to represent categorical variables that have more than two levels,

    Omitted-variable bias

    independent variable that is a determinant of the dependent variable and correlated with one or more of the included independent variables. Suppose the

    Logistic regression

    single binary dependent variable, coded by an indicator variable, where the two values are labeled "0" and "1", while the independent variables can each be

    Linear regression

    response (dependent variable) and one or more explanatory variables (regressor or independent variable). A model with exactly one explanatory variable is a


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