Cascade Pharmaceuticals Company developed the following regression model, using time-series data from the past 33 quarters, for one of its nonprescription cold remedies: Y=1.04+0.24X10.27X2 where Y= quarterly sales (in thousands of cases) of the cold remedy X1= Cascade's quarterly advertising ($1,000) for the cold remedy X2= competitors' advertising for similar products ($10,000) Here is additional information concerning the regression model: sb1=0.052,sb2=0.080,R2=0.680,se=1.63,F-statistic =31.402, and Durbin-Watson (d) statistic =0.499. Which of the independent variables (if any) appears to be statistically significant (at the 0.05 level) in explaining sales of the cold remedy? (Hint: t0.05/2,333=2.042.) Check all that apply. X1 X2 What proportion of the total variation in sales is explained by the regression equation? 0.052 0.132 0.680 0.080 The given F-value shows that you reject the null hypothesis that neither of the independent variables explains a significant (at the 0.05 level) proportion of the variation in income. (Hint: F0.05,2,3321=3.316.)