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Adjusted R R Square R Square .801 .641 564 a. Predictors: (Constant), X6, X5, X4, X3, X2, X1 Model Model 1 Model 1 Model Summary Regressio Residual Total n (Constant) Sum of Squares X1 X2 X3 X4 X5 X6 1.210 .677 1.887 a. Dependent Variable: Temperature change b. Predictors: (Constant), X6, X5, X4, X3, X2, X1 ANOVA B df .667 6 28 34 Unstandardized Coefficients Coefficients Std. Error .070 .108 226 400 -210 080 -086 a. Dependent Variable: Temperature change Std. Error of the Estimate 155526 .098 098 .098 .098 098 098 Mean Square 202 .024 Standardiz ed Coefficient S Beta 162 341 .603 -316 120 -.129 FL 8.335 9.590 1.096 2.300 4.065 -2.131 .811 -.872 Sig. .000⁰ Sig. .000 282 .029 .000 042 424 .390

Adjusted R R Square R Square .801 .641 564 a. Predictors: (Constant), X6, X5, X4, X3, X2, X1 Model Model 1 Model 1 Model Summary Regressio Residual Total n (Constant) Sum of Squares X1 X2 X3 X4 X5 X6 1.210 .677 1.887 a. Dependent Variable: Temperature change b. Predictors: (Constant), X6, X5, X4, X3, X2, X1 ANOVA B df .667 6 28 34 Unstandardized Coefficients Coefficients Std. Error .070 .108 226 400 -210 080 -086 a. Dependent Variable: Temperature change Std. Error of the Estimate 155526 .098 098 .098 .098 098 098 Mean Square 202 .024 Standardiz ed Coefficient S Beta 162 341 .603 -316 120 -.129 FL 8.335 9.590 1.096 2.300 4.065 -2.131 .811 -.872 Sig. .000⁰ Sig. .000 282 .029 .000 042 424 .390

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interpret the following output and come to a conclusion

Adjusted Model R Square R Square .641 1 .801ª .564 a. Predictors: (Constant), X6, X5, X4, X3, X2, X1 Model 1 Model 1 Regressio n Model Summary Residual Total (Constant) X1 X2 X3 Sum of Squares 1.210 X4 X5 X6 .677 1.887 a. Dependent Variable: Temperature change b. Predictors: (Constant), X6, X5, X4, X3, X2, X1 ANOVA B df .667 6 28 34 Unstandardized Coefficients Coefficients Std. Error .070 .108 .226 .400 -.210 .080 -.086 a. Dependent Variable: Temperature change .098 .098 .098 .098 .098 .098 Std. Error of the Estimate 155526 Mean Square 202 .024 Standardiz ed Coefficient S Beta 162 .341 .603 -316 120 -129 F 8.335 t 9.590 1.096 2.300 4.065 -2.131 .811 -.872 Sig. .000⁰ Sig. .000 282 .029 .000 042 .424 .390
Transcribed Image Text:Adjusted Model R Square R Square .641 1 .801ª .564 a. Predictors: (Constant), X6, X5, X4, X3, X2, X1 Model 1 Model 1 Regressio n Model Summary Residual Total (Constant) X1 X2 X3 Sum of Squares 1.210 X4 X5 X6 .677 1.887 a. Dependent Variable: Temperature change b. Predictors: (Constant), X6, X5, X4, X3, X2, X1 ANOVA B df .667 6 28 34 Unstandardized Coefficients Coefficients Std. Error .070 .108 .226 .400 -.210 .080 -.086 a. Dependent Variable: Temperature change .098 .098 .098 .098 .098 .098 Std. Error of the Estimate 155526 Mean Square 202 .024 Standardiz ed Coefficient S Beta 162 .341 .603 -316 120 -129 F 8.335 t 9.590 1.096 2.300 4.065 -2.131 .811 -.872 Sig. .000⁰ Sig. .000 282 .029 .000 042 .424 .390
From the above output, the coefficient of constant is 0.667, the coefficient of X1 is 0.108, the coefficient of X2 is 0.226, the coefficient of X3 is 0.400, the coefficient of X4 is -0.210, the coefficient of X5 is 0.080 and the coefficient of X6 is -0.086. Then, the regression equation is Temperature change = 0.667 +0.108.X₁ +0.226X₂ +0.400X; -0.210X4+0.080X, -0.086X, Solution The regression equation is Temperature change = 0.667 +0.108X₁ +0.226X₂ +0.400X3 -0.210X₁ +0.080X, -0.086X,
Transcribed Image Text:From the above output, the coefficient of constant is 0.667, the coefficient of X1 is 0.108, the coefficient of X2 is 0.226, the coefficient of X3 is 0.400, the coefficient of X4 is -0.210, the coefficient of X5 is 0.080 and the coefficient of X6 is -0.086. Then, the regression equation is Temperature change = 0.667 +0.108.X₁ +0.226X₂ +0.400X; -0.210X4+0.080X, -0.086X, Solution The regression equation is Temperature change = 0.667 +0.108X₁ +0.226X₂ +0.400X3 -0.210X₁ +0.080X, -0.086X,
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