Refer to the data presented in exercise 2.

The estimated regression equation for these data is Here SST 15,182.9, SSR 14,052.2, .2471, and s .9484. b2 sb1 yˆ 18.37 2.01x1 4.74x2 sb2 sb1 yˆ 29.1270 .5906x1 .4980x2 SELF test NOTES AND COMMENTS Ordinarily, multicollinearity does not affect the way in which we perform our regression analysis or interpret the output from a study. However, when multicollinearity is severe—that is, when two or more of the independent variables are highly correlated with one another—we can have difficulty interpreting the results of t tests on the individual parameters. In addition to the type of problem illustrated in this section, severe cases of multicollinearity have been shown to result in least squares estimates that have the wrong sign. That is, in simulated studies where researchers created the underlying regression model and then applied the least squares technique to develop estimates of 0, 1, 2, and so on, it has been shown that under conditions of high multicollinearity the least squares estimates can have a sign opposite that of the parameter being estimated. For example, b2 might actually be 10 and 2, its estimate, might turn out to be 2. Thus, little faith can be placed in the individual coefficients if multicollinearity is present to a high degree.

a. Test for a significant relationship among x1, x2, and y. Use α .05.

b. Is 1 significant? Use α .05.

c. Is 2 significant? Use α .05.