Respuesta :
The following method is employed to ascertain whether the inclusion of an independent variable improves the linear regression model.
What is a regression model?
A regression model, which offers a function, establishes the inclusion between an independent variable and a dependent variable. You can forecast how the independent variable will affect the dependent one by doing a regression analysis.
For example, age and height may be represented using a linear regression model. The link between height and age is linear since height grows with age.
Regression models are frequently employed as statistical support for statements about commonplace truths.
How to solve?
Generally speaking, and without a doubt, you are referring to a new and separate variable that may also have a significant relationship with certain other elements. See, if it has a low correlation, it will surely provide a significant amount of accuracy to the model [accuracy in the sense of correctness; you can also compare before and after adding the variable using any metric, such as R-square or any other coefficient of determination]. Whatever the new variable is, it will surely enhance our comprehension of the data and improve the regression line's ability to accurately match the data.
If it closely resembles another variable and doesn't add any new information to the data, we usually ignore it. If there is little to no similarity between it and what we can, however, even this reduces the model's accuracy.
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