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Linear regression is the most basic and commonly used predictive analysis. Regression estimates are used to describe data and to explain the relationship between one dependent variable and one or more independent variables.
1 dependent variable (nominal), 1+ independent variable(s) (interval or ratio) When selecting the model for the analysis another important consideration is the model fitting. Adding independent variables to a linear regression model will always increase the explained variance of the model (typically expressed as RÂ²).