Y = β0 + β1x1 + β2 x12+ ε is known as a Group of answer choices simple first-order model with two predictor variables. simple first-order model with one predictor variable second-order model with two predictor variables. second-order model with one predictor variable.

(A)Simple First Order Model with Two Predictor Variables.

Step-by-step explanation:

A multiple linear regression model with k predictor variables X₁, X₂, ..., Xₖ and a response Y, can be written as:

y = βₒ + β₁x₁ + β₂x₂ + ··· βₖxₖ + ∈.

Where:

x₁ , x₂,...xₖ are the predictor variables.

βₒ ,β₁, β₂ ··· βₖ are the Regression Coefficients.

and ∈ are the residual terms of the model

Multiple regression models describe how a single response variable y depends linearly on a number of predictor variables x.

The model:

y = βₒ + β₁x₁ + β₂x₂ + ∈ has two predictor variables. It is also of first order since there is no quadratic term, therefore it is a Simple First Order Model with Two Predictor Variables.

if the eqn of the parabola is f(x) = (x-4)(x+2), we need to mult. this out to find the coordinates of the vertex.

then f(x) = x^2 + 2x - 4x - 8, and f(x) = x^2 - 2x - 8.

one way in which you can find the vertex is to calculate x = -b / (2a).   here, that comes to

        )

x = = 1.   f(1) = 1^2 - 2(1) - 8 = - 9.   vertex is at (1, -9).

        2(1) 

find another point on the curve:   let x=0, for which y=-8.   then (0, -8) is on the curve.   plot the vertex and this point and draw a smooth parabola thru both.

hope this

x/3-10=8

x/3=18

x=48