Q1. Study the following output from a regression analysis to predict y from x. Predictor Coef SE Coef T P Constant 67.231 5 . 046 13 .32 0. 000 X -0 . 05650 0 . 01027 -5.50 0. 000 S = 10.32 R-Sq = 62.78 R-Sq (adj ) = 60.6: Analysis of Variance Source DF SS MS F P Regression 1 3222.9 3222 .9 30.25 0.000 Residual Error 18 1918 .0 106 .6 Total 19 5141 . 0 a. What is the equation of the regression model? b. What is the meaning of the coefficient of x? c. What is the result of the test of the slope of the regression model? Let a= . 10. Why is the t ratio negative? d. Comment on R2. e. The correlation coefficient for these two variables is -. 7918. Is this result surprising to you? Why or why not? Q2. Study the following Excel regression output for an analysis attempting to predict the number of union members in the United States by the size of the labour force for selected years over a 30-year period from data published by the U.S. Bureau of Labour Statistics. Analyse the computer output. Discuss the strength of the model in terms of proportion of variation accounted for, slope, and overall predictability. Using the equation of the regression line, attempt to predict the number of union members when the labour force is 110,000. Note that the model was developed with data already recoded in 1,000 units. Use the data in the model as is. SUMMARY OUTPUT Regression Statistics Multiple R 0.798 R Square 0.636 Adjusted R Square 0.612 Standard Error 258.632 Observations 17 ANOVA SS MS F Significance F Regression 1 1756035.629 1756036 26 25 0.00012 Residual 15 1003354.471 66890.3 Total 16 2759390 Coefficients Standard Error t Stat P-value Intercept 20704.3806 879.6067 23.54 0100000 Total Employment -0.0390 0.0076 -5.12 0.00012