Chapter 7 - Moving Beyond Linearity, Question 7

In [1]:
library(ISLR)
summary(Wage)

      year           age               sex                    maritl    
 Min.   :2003   Min.   :18.00   1. Male  :3000   1. Never Married: 648  
 1st Qu.:2004   1st Qu.:33.75   2. Female:   0   2. Married      :2074  
 Median :2006   Median :42.00                    3. Widowed      :  19  
 Mean   :2006   Mean   :42.41                    4. Divorced     : 204  
 3rd Qu.:2008   3rd Qu.:51.00                    5. Separated    :  55  
 Max.   :2009   Max.   :80.00                                           
                                                                        
       race                   education                     region    
 1. White:2480   1. < HS Grad      :268   2. Middle Atlantic   :3000  
 2. Black: 293   2. HS Grad        :971   1. New England       :   0  
 3. Asian: 190   3. Some College   :650   3. East North Central:   0  
 4. Other:  37   4. College Grad   :685   4. West North Central:   0  
                 5. Advanced Degree:426   5. South Atlantic    :   0  
                                          6. East South Central:   0  
                                          (Other)              :   0  
           jobclass               health      health_ins      logwage     
 1. Industrial :1544   1. <=Good     : 858   1. Yes:2083   Min.   :3.000  
 2. Information:1456   2. >=Very Good:2142   2. No : 917   1st Qu.:4.447  
                                                           Median :4.653  
                                                           Mean   :4.654  
                                                           3rd Qu.:4.857  
                                                           Max.   :5.763  
                                                                          
      wage       
 Min.   : 20.09  
 1st Qu.: 85.38  
 Median :104.92  
 Mean   :111.70  
 3rd Qu.:128.68  
 Max.   :318.34
In [79]:
par(mfrow=c(2,2))
plot(Wage$jobclass,Wage$wage)
plot(Wage$maritl,Wage$wage)
plot(Wage$health_ins,Wage$wage)
In [80]:
library(gam)
gam.model = gam(wage~maritl+jobclass+health_ins,data=Wage)
par(mfrow=c(2,2))
plot(gam.model)

According to the Generalized Additive Model plots we can deduce that the three factors mentioned above do have an affect on the wage.

In [ ]: