When should a regression model be used to make a prediction? When should it not be...

When should a regression model be used to make a prediction? When should it not be used to make a prediction? What value tells us how good the prediction will be?

Expert Solution

use regression model to make prediction when :

Your two variables should be measured at the continuous level (i.e., they are either interval or ratio variables). Examples of continuous variables include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg)
There needs to be a linear relationship between the two variables
There should be no significant outliers. An outlier is an observed data point that has a dependent variable value that is very different to the value predicted by the regression equation.
You should have independence of observations.
Your data needs to show homoscedasticity.

Do not use regression model to make prediction when:

Your data are heteroscedasticity.
multi collinearity ( correlation between independent variables) occurs.

coefficient of determination R² value tells us how good the prediction will be

orchestra answered 2 years ago

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