# Question: Using the data of Exercise 14 99 In exercise a Create a

Using the data of Exercise 14.99,

In exercise

(a) Create a new variable, x22.

(b) Fit a surface of the form

(c) Find the correlation matrix of the three independent variables. Is there evidence of multicollinearity?

(d) Standardize each of the independent variables, x1 and x2, and create a new variable that is the square of the standardized value of x2.

(e) Fit a surface of the same form as in part (b) to the standardized variables. Compare the goodness of fit of this surface to that of the linear surface fitted in Exercise 14.99.

(f) Plot the residuals of this regression analysis against the values of and compare this plot to the one obtained in Exercise 14.99.

In exercise

(a) Create a new variable, x22.

(b) Fit a surface of the form

(c) Find the correlation matrix of the three independent variables. Is there evidence of multicollinearity?

(d) Standardize each of the independent variables, x1 and x2, and create a new variable that is the square of the standardized value of x2.

(e) Fit a surface of the same form as in part (b) to the standardized variables. Compare the goodness of fit of this surface to that of the linear surface fitted in Exercise 14.99.

(f) Plot the residuals of this regression analysis against the values of and compare this plot to the one obtained in Exercise 14.99.

## Answer to relevant Questions

Using the data of Exercise 14.100, In exercise (a) Create a new variable, x1x2. (b) Fit a surface of the form (c) Find the correlation matrix of the four independent variables. Is there evidence of multicollinearity? (d) ...When the x’s are equally spaced, the calculation of and can be simplified by coding the x’s by assigning them the values . . . ,- 3,- 2,- 1, 0, 1, 2, 3, . . . when n is odd, or the values . . . ,- 5,- 3,- 1, 1, 3, 5, ...Use Theorem 4.15 show that Theorem 4.15 If X1, X2, . . . , Xn are random variables and Where a1, a2, . . . , an, b1, b2, . . . , bn are constants, then Given the joint destiny Find µY|x and µX|y. With x01, x02, . . . , x0k and X0 as defined in Exercise 14.39 and Y0 being a random variable that has a normal distribution with the mean β0 + β1x01 + · · · + βkx0k and the variance σ2, it can be shown that Is a ...Post your question