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(a) Eight experiments were carried out a various condition of Saturation (X1) and Transisomers (X2). The values of the response Y are listed below together with the corresponding levels of X1 and X2.
yi
Saturation xi1
Transisomers xi2
66.0
38
47.5
43.0
41
21.3
36.0
34
36.5
23.0
35
18.0
22.0
31
29.5
14.0
14.2
12.0
29
21.0
7.6
32
10.0
Some results from fitting a linear model to this data set are given below.
Estimates:
(Intercept)
-94.552
X1
2.802
X2
1.073
Var-cov:
99.270
-2.910
0.056
0.091
-0.008
0.56
0.009
(i) Using a significance level of 5%, test whether the two regressors Saturation and Transisomers have the same effect on the response.
(ii) Find the 95% confidence interval for the expected value of Y corresponding to X1 = 30 and X2 = 15.
(b) Suppose that a regression model with two regressors X1 and X2 which have been centred is being fitted. Show that
where ρ is the correlation coefficient between X1 and X2 and SX_iX_j is the corrected sum of products (or squares if i = j) of Xi and Xj.
(c) Use the result in part (d) to find the variance inflation factors of the least squares estimates of the regression coefficients, b^1 and b^2, and show that the standard errors of b^1 and b^2 tend to infinity as ρ tends to -1 or 1. What does this indicate about regression analysis if there is collinearity?
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