Question

Anthony Stark, the manager of South Carolina Iron Industries ISCHI thinks the number of contracts istirm receives for bridge construction is directly related to the volume of highway construction in his geographic He wonders if this is really so, and if it is can this information help him plan his operations better by forecasting the demand for SCI engineering services required in each year? Help Tony create a good forecast for the number of contracts SCI might get it righway construction expenditures are expected to be $200 next year. (dollars in milions) SCHI Contracts SS Highway Construction 153 172 197 176 185 Regression Analysis 0.82 0.90 n= 8 k= 1 Dep. Var. df MS F p-value SS ANOVA Table Source Regressio 26.56 .0021 1 6 7 44.76 10.11 54.88 44.76 1.69 Residual Total Regression output confidence interval 95% lower 95% upper std. error (df-6) p-value variables coefficients Intercept -9.34 0.06 0.17 5.153 0.112 0.0218

Answer 1

In this case the dependent variable is the number of SCII contracts as Tony thinks that the number of contracts he get is dependent on the volume of highway construction. Hence number of SCII contracts is the dependent variable and volume of highway construction ($$ highway construction) is the independent variable.

Hence the regression equation from the given regression analysis becomes as below

Number of SCII contracts = -9.34 + 0.112 *(Highway construction expenditure)

For an expenditure of $200, the number of SCII contracts are

Number of SCII contracts = -9.34 + 0.112*200 = -9.34+22.4 = 13.06

Hence the number of SCII contracts that Tony can expect with $200 million expenditure is 13.06 or 13 (rounded)