# Answer true or false to each of the following statements and explain your answers. a. Polynomial regression equations are useful for modeling more com

Isa Trevino 2021-01-30 Answered
Answer true or false to each of the following statements and explain your answers.
a. Polynomial regression equations are useful for modeling more complex curvature in regression equations than can be handled by using the method of transformations.
b. A polynomial regression equation can be estimated using the method of least squares, the same method used in multiple linear regression.
c. The term “linear” in “multiple linear regression” refers to using only first-degree terms in the predictor variables.
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Maciej Morrow
(a)
The polynomial regression equation is useful when a linear regression equation fails to model the curvature between the response variable and the predictor variables. In order to make the polynomial regression equation easier to handle, a suitable method of transformations can be used on the variables. Also, when there are more complex curvature between the response and the predictor variables then the polynomial regression equations can be used than method of transformations.
Thus, the statement “Polynomial regression equations are useful for modeling more complex curvature in regression equations that can be handled by using the method of transformations.” is True.
Step 2
(b)
The polynomial regression coefficients can be estimated using the method of least squares, by simply manipulating the variables.
Thus, the statement “A polynomial regression equation can be estimated using the method of least squares, the same method used in multiple linear regression.” is True.
Step 3
(c)
In “multiple linear regression”, the term “linear” refers to having only first degree coefficients in the equation, or in other words, the term “linear” means that the response variable is linearly related to the coefficients.
Thus, the statement “The term “linear” in “multiple linear regression” refers to using only first-degree terms in the predictor variables.” is False.