The following result perspective in RapidMiner shows a multiple linear regression model Based on the diagram, the model for our dependent variable Y is Predicted Y = (Insulation *0.420)+(Temperature *0.071)+(Avg_Age*0.065)+(Home_Size *0.311)+7.589 Attribute Insulation Temperature Avg Age Home Size (Intercept) O True Coefficient 3.323 -0.869 1.968 3.173 134.511 Std. Error 0.420 0.071 0.065 0.311 7.589 Std. Coefficient 0.164 -0.262 0.527 0.131 ? Tolerance 0.431 0.405 0.491 0.914 ? t-Stat 7.906 -12.222 30.217 10.210 17.725

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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The following result perspective in RapidMiner shows a multiple linear regression model.
Based on the diagram, the model for our dependent variable Y is Predicted Y=
(Insulation *0.420)+(Temperature *0.071)+(Avg_Age*0.065)+(Home_Size *0.311)+7.589
Attribute
Insulation
Temperature
Avg Age
Home Size
(Intercept)
O True
O False
Coefficient
3.323
-0.869
1.968
3.173
134.511
Std. Error
0.420
0.071
0.065
0.311
7.589
Std. Coefficient
0.164
-0.262
0.527
0.131
?
Tolerance
0.431
0.405
0.491
0.914
?
t-Stat
7.906
-12.222
30.217
10.210
17.725
Transcribed Image Text:The following result perspective in RapidMiner shows a multiple linear regression model. Based on the diagram, the model for our dependent variable Y is Predicted Y= (Insulation *0.420)+(Temperature *0.071)+(Avg_Age*0.065)+(Home_Size *0.311)+7.589 Attribute Insulation Temperature Avg Age Home Size (Intercept) O True O False Coefficient 3.323 -0.869 1.968 3.173 134.511 Std. Error 0.420 0.071 0.065 0.311 7.589 Std. Coefficient 0.164 -0.262 0.527 0.131 ? Tolerance 0.431 0.405 0.491 0.914 ? t-Stat 7.906 -12.222 30.217 10.210 17.725
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