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Using the Regression Model to Make Predictions SUMMARY OUTPUT y = 0.1941 - 0.1485x1 + 1.0893x2 + 0.1612x3 - 0.0083x4 Regression Statistics Multiple R 0.9012 R Square 0.8122 Adjusted R Square 0.8087 Standard Error 1.1045 Observations 221 ANOVA df SS MS F Significance F Regression 4 1139.7513 284.9378 233.5660 3.1834E-77 Residual 216 263.5083 1.2199 Total 220 1403.2596 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 0.1941 0.1823 1.0646 0.2883 -0.1653 0.5534 Total Assets ($ Billions) -0.1485 0.0737 -2.0161 0.0450 -0.2937 -0.0033 Previous ROAA (%) 1.0893 0.0743 14.6614 0.0000 0.9429 1.2358 Previous EPS ($) 0.1612 0.0518 3.1115 0.0021 0.0591 0.2633 Previous ROAE (%) -0.0083 0.0046 -1.7878 0.0752 -0.0174 0.0008 Multiple Linear Regression (MLR) Multiple Linear Regression (MLR) By-Hand: Show step-by-step conditions, and calculations. Show Work: How do you predict the Current EPS for a company with $4B in assets, 2% in Previous ROAA, 2% in Previous ROAE, and Previous EPS of $1? y =_ + Assets + ROAA + EPS + ROAE (Step 1: find the estimated slope or coefficient for each x variable ) (Step 2: plug in numbers of the independent variables)