2. A linear regression model was fitted to predict the city's per-capita gross metropolitan product (pcgmp: in dollars per person per year) on an urban economies dataset. Five variables, including population of each city (pop), the fraction (not percentage) of each city's economy devoted to four industries: finance, "professional and technical" services (prof.tech), information and communications technologies (ICT), and management services (management), were used. Given the summary of the linear regression model in Figure.1, answer the following questions. Call: 1m (formula pcgmp MSA, data = bea) Residuals: 1Q Median 3Q Max Min -19160 -4813 -806 3087 25556 Coefficients: (Intercept) 2.17e+04 1.88e+03 11.52 pop Estimate Std. Error t value Pr(>|t|) < 2e-16 2.05 0.04249 2.05 0.04252 finance prof.tech 2.24e-03 2.42e+04 1.09e-03 1.18e+04 3.09e+04 3.75e+04 0.82 0.41168 ict 6.42e+04 1.61e+04 3.98 0.00011 management 1.95e+05 8.58e+04 2.28 0.02440 Residual standard error: 7250 on 127 degrees of freedom Multiple R-squared: 0.433, Adjusted R-squared: 0.411 F-statistic: 19.4 on 5 and 127 DF, p-value: 2.54e-14 a) Write the estimated model. Figure 1. b) What is the residual sum of squares (RSS) of the fitted model? c) What is the percentage of variance explained by this model? d) Is there a relationship between the city's per-capita gross metropolitan product and five variables? e) As of 2006 (when this data was collected), Pittsburgh had the following values for all the variables: