Explain why it is preferable to perform a model utility test before using an estimated regression model to make predictions or to estimate the mean y value for specified values of the independent variables

The answer is explained below

Step-by-step explanation:

The reason for making the regression is generally to make predictions. So one should ask in what situations is the resulting estimated regression line y = b0 + b1 * x useful. In other words, since there is a linear relationship between x and y, in what situations would it be valid to say that the knowledge of the value x is not useful to predict a corresponding y.

When the value of B1 (or, equivalently, of p) is zero. However, it is entirely possible that B1 is 0 while b1 (our estimate for) is not zero due to random fluctuations between samples. Therefore, we require an inference procedure.

One of the inference procedures is that the model utility test is used to determine if there is any useful relationship between the dependent variable and the specified values of the independent variables.

So before using an estimated regression model, we run a utility test of the model

1.4

2.3

step-by-step explanation:

the formula for the constant of variation is

y= kx

divide each side by x

y/x =kx/x

y/x =k

we know a and y

1.   y/x = k

4/1 =4

check the other values to make sure they are the same

8/2 = 4   12/3 =4   and 16/4 =4  

the constant of variation is 4

2.   y/x = k

3/1 =3

check the other values to make sure they are the same

6/2 = 3   9/3 =3   and 12/4 =3  

the constant of variation is 3

what is the graph

step-by-step explanation: