The data were collected to investigate the determinants of pollution. For 41 cities in the United States, seven variables were recorded:

SO2: SO2 content of air in micrograms per cubic meter Temp: Average annual temperature in degrees Fahrenheit Manuf: Number of manufacturing enterprises employing 20 or more workers Pop: Population size (according to 1970 census) in thousands Wind: Average annual wind speed in miles per hour Precip: Average annual precipitation in inches Days: Average number of days with precipitation per year Construct a scatterplot matrix of the data and use it to guide the fitting of a multiple linear regression model with SO2 as the response variable and the remaining variables as explanatory. Find the variance inflation factor (VIF) for each explanatory variable and use the factors to decide if there are any problems in using all six of the explanatory variables. Use the procedure involving the AIC described in the text to search for a more parsimonious model for the data. For the final model chosen, use some regression diagnostics to investigate the assumptions made in fitting the model.