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Posted: **Wed May 11, 2022 8:15 am**

: Consider The Multiple Regression Output Shown The Regression Equation Is Y 42 6 6 57x1 1 44x2 1 89x3 0 425x4 1 (21.59 KiB) Viewed 27 times

: Consider The Multiple Regression Output Shown The Regression Equation Is Y 42 6 6 57x1 1 44x2 1 89x3 0 425x4 2 (23.06 KiB) Viewed 27 times

Consider the multiple regression output shown: The regression equation is Y = 42.6 -6.57X1 + 1.44X2 + 1.89X3 + 0.425X4. Predictor Coef SE Coef T P Constant 42.56 18.42 2.31 0.060 X1 -6.567 1.020 -6.44 0.001 X2 1.439 1.006 1.43 0.202 X3 1.8923 0.6687 2.83 0.030 X4 0.4251 0.1412 3.01 0.024 S = 2.07364 R - Sq = 99.8% R - S (adj) = 99.6% Analysis of Variance

Analysis of Variance Source DF SS MS FP Regression 4 11120.0 2780.0 650.66 0.000 Residual Error 6 25.8 4.3 Total 10 11145.8 One case in the sample has Y = 26.X1 = 8.X2 = 5.x3 = 4, and X4 = 48. What is the predicted response for this case? What is the residual? Round your answers to one decimal place. Predicted response i Residual: eTextbook and Media Save for Later Attempts: 0 of 3 used Submit Answer