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Linear regression is an approach to modeling the association between a numeric dependent variable y and one or more independent variables denoted X. The case of one explanatory variable in regression model is called simple linear regression. For more than one explanatory variable, then the model is called multiple linear regression. The dependent variable should be a numeric variable in linear regression. It is recommended at least 10 times as many cases as the number of independent variables in regression model. And a statistically significant regression analysis does not imply causal relationship between independent and dependent variables.
Selim Kılıç (Tue,) studied this question.