The article “Estimating Resource Requirements at Conceptual Design Stage Using Neural Networks” (A. Elazouni, I. Nosair, et al., Journal of Computing in Civil Engineering, 1997:217–223) suggests that certain resource requirements in the construction of concrete silos can be predicted from a model. These include the quantity of concrete in m3 (y), the number of crew-days of labor (z), or the number of concrete mixer hours (w ) needed for a particular job. Table SE23A defines 23 potential independent variables that can be used to predict y, z, or w. Values of the dependent and independent variables, collected on 28 construction jobs, are presented in Table SE23B (page 655) and Table SE23C (page 656). Unless otherwise stated, lengths are in meters, areas in m2, and volumes in m3.

a. Using best subsets regression, find the model that is best for predicting y according to the adjusted R2 criterion.

b. Using best subsets regression, find the model that is best for predicting y according to the minimum Mallows Cp criterion.

c. Find a model for predicting y using stepwise regression. Explain the criterion you are using to determine which variables to add to or drop from the model.

d. Using best subsets regression, find the model that is best for predicting z according to the adjusted R2 criterion.

e. Using best subsets regression, find the model that is best for predicting z according to the minimum Mallows Cp criterion.

f. Find a model for predicting z using stepwise regression. Explain the criterion you are using to determine which variables to add to or drop from the model.

g. Using best subsets regression, find the model that is best for predicting w according to the adjusted R2 criterion.

h. Using best subsets regression, find the model that is best for predicting w according to the minimum Mallows Cp criterion.

i. Find a model for predicting w using stepwise regression. Explain the criterion you are using to determine which variables to add to or drop from the model.