Q 2 i) From the Excel output, fit a regression model using wire length as X1 and die height as X2. ii) Interpret the coefficient of X1 and X2. iii) Calculate the adjusted R square for this regression model. iv) Test the overall significance of this regression model at a = 0.05 v) Test the significance of X1 (wire length) and X2 (die height) respectively in the regression model at a = 0.05 vi) Estimate the pull strength of a wire bond with wire length is 3 and die height is 250. c) Comparing regression model in (a) and (b), which regression model should be used by the researcher? (?? Marks)Q2 The following data is the pull strength of a wire bond in a semiconductor manufacturing process, wire length, and die height. Observation number Pull strength (Y) Wire length (X1) Die Height (X2) 9.95 2 50 24.45 8 110 3 31.75 11 120 4 35.00 10 550 5 25.02 8 295 6 16.86 4 200 7 14.38 2 375 8 9.60 2 52 9 24.35 9 100 10 27.50 8 300 a i) Fit a regression line using pull strength as Y and wire length as X. Use calculator to find the a and b to do so. ii) Find the coefficient of correlation in this regression model, Interpret its meaning. iii) Find the coefficient of determination in this regression model. Interpret its meaning. b) Now the researcher wanted to include one more variable, Die Height, in the regression model. Thus he obtained the following Excel output using both wire length and die height as independent variables: SUMMARY OUTPUT Regression Statistics Multiple R 0.994525658 R Square 0.989081284 Adjusted R Square Standard Error 1.043637866 Observations 10 ANOVA MS Significance F Regression 2 690.65018 Residual 7 7.624259961 Total 698.27444 Coefficients Standard Error Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 4.544350574 0.774648328 Wire length (X1) 2 215835576 0.101913929 Die Height (X2) 0.014685417 0.002211316