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Consider the multiple regression output shown: The regression equation is Predicted response: i Y = 43.46.82X1 + 1.48X2 +1.43X3 + 0.458X4. Residual: i Predictor Coef SE Coef T Constant X1 X2 X3 X4 Regression 43.39 Residual Error Total Round your answers to one decimal place. Analysis of Variance Source 1.483 -6.821 1.059 -6.44 0.001 S = 2.04939 R - Sq = 99.8% R - Sq (adj) = 99.6% 1.4253 0.5036 2.83 0.030 0.4576 0.1520 3.01 0.024 18.78 2.31 0.060 DF SS P 1.037 1.43 0.202 6 25.2 4 10844.0 2711.0 650.66 0.000 10 10869.2 MS F 4.2 One case in the sample has Y = 14, X1 = 9, X2 = 7, X3 = 2, and X4 = 48. What is the predicted response for this case? What is the residual? P

Consider the multiple regression output shown: The regression equation is Predicted response: i Y = 43.46.82X1 + 1.48X2 +1.43X3 + 0.458X4. Residual: i Predictor Coef SE Coef T Constant X1 X2 X3 X4 Regression 43.39 Residual Error Total Round your answers to one decimal place. Analysis of Variance Source 1.483 -6.821 1.059 -6.44 0.001 S = 2.04939 R - Sq = 99.8% R - Sq (adj) = 99.6% 1.4253 0.5036 2.83 0.030 0.4576 0.1520 3.01 0.024 18.78 2.31 0.060 DF SS P 1.037 1.43 0.202 6 25.2 4 10844.0 2711.0 650.66 0.000 10 10869.2 MS F 4.2 One case in the sample has Y = 14, X1 = 9, X2 = 7, X3 = 2, and X4 = 48. What is the predicted response for this case? What is the residual? P

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Consider the multiple regression output shown: The regression equation is Y = 43.4 6.82X1 + 1.48X2 + 1.43X3 + 0.458X4. Predicted response: i Predictor Residual: i Constant X1 X2 X3 X4 Source Coef SE Coef T Total Round your answers to one decimal place. 43.39 Analysis of Variance Regression Residual Error -6.821 1.059 1.483 1.037 S = 2.04939 R - Sq = 99.8% R - Sq (adj) = 99.6% 18.78 2.31 0.060 1.4253 0.5036 2.83 0.030 0.4576 0.1520 3.01 0.024 DF SS 6 P -6.44 0.001 1.43 0.202 25.2 10 10869.2 MS 4 10844.0 2711.0 650.66 0.000 4.2 F One case in the sample has Y = 14, X1 = 9, X2 = 7, X3 = 2, and X4 = 48. What is the predicted response for this case? What is the residual? P
Transcribed Image Text:Consider the multiple regression output shown: The regression equation is Y = 43.4 6.82X1 + 1.48X2 + 1.43X3 + 0.458X4. Predicted response: i Predictor Residual: i Constant X1 X2 X3 X4 Source Coef SE Coef T Total Round your answers to one decimal place. 43.39 Analysis of Variance Regression Residual Error -6.821 1.059 1.483 1.037 S = 2.04939 R - Sq = 99.8% R - Sq (adj) = 99.6% 18.78 2.31 0.060 1.4253 0.5036 2.83 0.030 0.4576 0.1520 3.01 0.024 DF SS 6 P -6.44 0.001 1.43 0.202 25.2 10 10869.2 MS 4 10844.0 2711.0 650.66 0.000 4.2 F One case in the sample has Y = 14, X1 = 9, X2 = 7, X3 = 2, and X4 = 48. What is the predicted response for this case? What is the residual? P
Consider the multiple regression output shown: 00000 The regression equation is Y = 60+ 4.85X10.09X2 + 6.42X3 + 1.65X4 - 1.16X5. O X1 X2 UX3 OX4 O X5 Predictor Constant X1 X2 X3 X4 X5 Coef SE Coef T Total -60.2 105.6 -0.57 0.575 4.851 Regression Residual Error -0.093 0.443 -0.21 0.833 2.412 2.66 0.019 1.6511 0.5431 3.04 0.009 6.415 Analysis of Variance Source -1.1623 0.7907 -1.47 0.163 S = 32.33309 R - Sq = 77.9% R - Sq (adj) = 70.0% 2.299 2.11 0.053 DF SS P 5 51590 Select all of the variables which are significant at the 1% level. 19 66226 14 14636 1045.4 MS F P 10318 9.87 0.000
Transcribed Image Text:Consider the multiple regression output shown: 00000 The regression equation is Y = 60+ 4.85X10.09X2 + 6.42X3 + 1.65X4 - 1.16X5. O X1 X2 UX3 OX4 O X5 Predictor Constant X1 X2 X3 X4 X5 Coef SE Coef T Total -60.2 105.6 -0.57 0.575 4.851 Regression Residual Error -0.093 0.443 -0.21 0.833 2.412 2.66 0.019 1.6511 0.5431 3.04 0.009 6.415 Analysis of Variance Source -1.1623 0.7907 -1.47 0.163 S = 32.33309 R - Sq = 77.9% R - Sq (adj) = 70.0% 2.299 2.11 0.053 DF SS P 5 51590 Select all of the variables which are significant at the 1% level. 19 66226 14 14636 1045.4 MS F P 10318 9.87 0.000
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