Summary: reading a regression model$/$analysis$.$

This study aims to $$assess the energy efficiency of $$residential buildings by $$examining their heating and cooling load requirements. Using Ecotect software, twelve distinct building shapes are simulated, resulting in $$a dataset of$768$simulated building shapes with $8$distinct features. This dataset is $$used to $$predict two real$-$valued outcomes: heating load and cooling load. The variables present in $$the dataset, along with their descriptions, are summarized in $$below Table:

Variable Name vs $$Description

X$1$Relative Compactness

X$2$Surface Area

X$3$Wall Area

X$4$Roof Area

X$5$Overall Height

X$6$Orientation

X$7$Glazing Area

X$8$Glazing Area Distribution

Y$1$Heating Load

Y$2$Cooling Load

Questions:

$1)$After creating a regression analysis for the variables in response to heating load, what observations, conclusions and$/$or patterns can you make for the different variables from reviewing the regression images attached?

$2)$Secondly$,$ looking at the PValue for each of these variables in the image attached, what observations can you make? Which PValue is the most significant variable and why?

$3)$Lastly$,$ one of the images show the predicted regression formula. What conclusions can you make form the equation? How does this show the effect of one independent variable on the dependent variable?This study aims to $$assess the energy efficiency of $$residential buildings by $$examining their heating and cooling load requirements. Using Ecotect software, twelve distinct building shapes are simulated, resulting in $$a dataset of$768$simulated building shapes with $8$distinct features. This dataset is $$used to $$predict two real$-$valued outcomes: heating load and cooling load. The variables present in $$the dataset, along with their descriptions, are summarized in $$below Table:

Variable Name vs $$Description

X$1$Relative Compactness

X$2$Surface Area

X$3$Wall Area

X$4$Roof Area

X$5$Overall Height

X$6$Orientation

X$7$Glazing Area

X$8$Glazing Area Distribution

Y$1$Heating Load

Y$2$Cooling Load

Questions:

$1)$After creating a regression analysis for the variables in response to heating load, what observations, conclusions and$/$or patterns can you make for the different variables from reviewing the regression images attached?

$2)$Secondly$,$ looking at the PValue for each of these variables in the image attached, what observations can you make? Which PValue is the most significant variable and why?

$3)$Lastly$,$ one of the images show the predicted regression formula. What conclusions can you make form the equation? How does this show the effect of one independent variable on the dependent variable $($heating load$)?$