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Discuss the view of perfect/non-perfect predication and perfect/non-perfect correlation in linear regression

Discuss the view of perfect/non-perfect predication and perfect/non-perfect correlation in linear regression

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The perfect/non-perfect predication depends upon the collinearity among the variables to check if there is a causation between the variables. The residuals should a constant variance in order to achieve a perfect prediction. In order to predict one value from the other, the linear regression model should explain a minimum of 70% variation in the data.

The perfect/non-perfect correlation in linear regression depends upon the nature of the variables. If the variables are strongly related, then the correlation will be higher but if the variables are weakly related, then there will be a low correlation between the variables. It is also possible to have an inverse correlation between the variables.

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