APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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independence of residual
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linearity of residual
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multicollinearity
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homocedasticity
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Detailed explanation-1: -Multicollinearity is the occurrence of high intercorrelations among two or more independent variables in a multiple regression model.
Detailed explanation-2: -Multicollinearity is a situation in which the dependent variable is highly correlated with two or more of the independent variables in a multiple regression.
Detailed explanation-3: -The independent variables may also be referred to as the predictor variables or regressors. There are 3 major uses for multiple linear regression analysis. First, it might be used to identify the strength of the effect that the independent variables have on a dependent variable.
Detailed explanation-4: -Multicollinearity refers to a situation in which more than two explanatory variables in a multiple regression model are highly linearly related. There is perfect multicollinearity if, for example as in the equation above, the correlation between two independent variables equals 1 or −1.