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Regression analysis employs a model that describes the relationships between the dependent variables and the independent variables in a simplified mathematical form. Prognostication: Risk factors that influence the outcome can be identified, and individual prognoses can be determined. Regression analysis is a type of statistical evaluation that enables three things:ĭescription: Relationships among the dependent variables and the independent variables can be statistically described by means of regression analysis.Įstimation: The values of the dependent variables can be estimated from the observed values of the independent variables. Graphical representation of a linear relationship:Ī negative relationship is represented by a falling regression line (regression coefficient b 0). R 0: positive relationship (high values of one variable tend to occur together with high values of the other variable)
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R = 0: no linear or monotone relationship The closer r is to 1 or –1, the stronger the relationship. R = ± 1: perfect linear and monotone relationship. No distinction between the explaining variable and the variable to be explained is necessary: Interpretation of the correlation coefficient (r)Ī monotone relationship is one in which the dependent variable either rises or sinks continuously as the independent variable rises.Ĭorrelation coefficients provide information about the strength and direction of a relationship between two continuous variables.