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A variable is considered dependent if it depends on an independent variable. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of ...
Dead code elimination: If no side effected operation depends on a variable, this variable is considered dead and can be removed. Dynamic graph analytics: GraphBolt [2] and KickStarter [3] capture value dependencies for incremental computing when graph structure changes. Spreadsheet calculators. They need to derive a correct calculation order ...
For example, in the IS-LM graph shown here, the IS curve shows the amount of the dependent variable spending (Y) as a function of the independent variable the interest rate (i), while the LM curve shows the value of the dependent variable, the interest rate, that equilibrates the money market as a function of the independent variable income ...
Graphs that are appropriate for bivariate analysis depend on the type of variable. For two continuous variables, a scatterplot is a common graph. When one variable is categorical and the other continuous, a box plot is common and when both are categorical a mosaic plot is common. These graphs are part of descriptive statistics.
For continuous variables, multiple alternative measures of dependence were introduced to address the deficiency of Pearson's correlation that it can be zero for dependent random variables (see [9] and reference references therein for an overview). They all share the important property that a value of zero implies independence.
Example graph of a logistic regression curve fitted to data. The curve shows the estimated probability of passing an exam (binary dependent variable) versus hours studying (scalar independent variable). See § Example for worked details.
Typically, path models consist of independent and dependent variables depicted graphically by boxes or rectangles. Variables that are independent variables, and not dependent variables, are called 'exogenous'. Graphically, these exogenous variable boxes lie at outside edges of the model and have only single-headed arrows exiting from them.
In the formula above we consider n observations of one dependent variable and p independent variables. Thus, Y i is the i th observation of the dependent variable, X ij is i th observation of the j th independent variable, j = 1, 2, ..., p. The values β j represent parameters to be estimated, and ε i is the i th independent identically ...