Perform the following steps to fit a generalized additive model into data:

1. First, load the mgcv package, which contains the gam function:

        > install.packages("mgcv")
        > library(mgcv)  

2. Then, install the MASS package and load the Boston dataset:

        > install.packages("MASS")
        > library(MASS)
        > attach(Boston)
        > str(Boston)  

3. Fit the regression using gam:

        > fit = gam(dis ~ s(nox))  

4. Get the summary information of the fitted model:

        > summary(fit)
        Output:
        Family: gaussian 
        Link function: identity 

        Formula:
        dis ~ s(nox)

        Parametric coefficients:
                    Estimate Std. Error t value Pr(>|t|) 
        (Intercept) 3.79504 0.04507 84.21 <2e-16 ***
        ---
        Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

        Approximate significance of smooth terms:
                 edf Ref.df F p-value 
        s(nox) 8.434 8.893 189 <2e-16 ***
        ---
        Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

        R-sq.(adj) = 0.768 Deviance explained = 77.2%
        GCV = 1.0472 Scale est. = 1.0277 n = 506