It seems that the variables Fertility and Education are the closest related variables.

library(psych)
cor(swiss)
##                   Fertility Agriculture Examination   Education   Catholic
## Fertility         1.0000000  0.35307918  -0.6458827 -0.66378886  0.4636847
## Agriculture       0.3530792  1.00000000  -0.6865422 -0.63952252  0.4010951
## Examination      -0.6458827 -0.68654221   1.0000000  0.69841530 -0.5727418
## Education        -0.6637889 -0.63952252   0.6984153  1.00000000 -0.1538589
## Catholic          0.4636847  0.40109505  -0.5727418 -0.15385892  1.0000000
## Infant.Mortality  0.4165560 -0.06085861  -0.1140216 -0.09932185  0.1754959
##                  Infant.Mortality
## Fertility              0.41655603
## Agriculture           -0.06085861
## Examination           -0.11402160
## Education             -0.09932185
## Catholic               0.17549591
## Infant.Mortality       1.00000000
#We can analyze the correlations between the variables and find out which one is suitable for fertility
plot(swiss$Fertility~swiss$Education, xlab='Education', ylab='Fertility')
abline(lm(swiss$Fertility~swiss$Education))

plot(swiss$Fertility~swiss$Examination, xlab='Examination', ylab='Fertility')
abline(lm(swiss$Fertility~swiss$Examination))

sample1<-lm(Fertility~Education, data=swiss)
summary(sample1)
## 
## Call:
## lm(formula = Fertility ~ Education, data = swiss)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -17.036  -6.711  -1.011   9.526  19.689 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  79.6101     2.1041  37.836  < 2e-16 ***
## Education    -0.8624     0.1448  -5.954 3.66e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.446 on 45 degrees of freedom
## Multiple R-squared:  0.4406, Adjusted R-squared:  0.4282 
## F-statistic: 35.45 on 1 and 45 DF,  p-value: 3.659e-07
sample2<-lm(Fertility~Examination, data=swiss)
summary(sample2)
## 
## Call:
## lm(formula = Fertility ~ Examination, data = swiss)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -25.9375  -6.0044  -0.3393   7.9239  19.7399 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  86.8185     3.2576  26.651  < 2e-16 ***
## Examination  -1.0113     0.1782  -5.675 9.45e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 9.642 on 45 degrees of freedom
## Multiple R-squared:  0.4172, Adjusted R-squared:  0.4042 
## F-statistic: 32.21 on 1 and 45 DF,  p-value: 9.45e-07
plot(sample1)

plot(sample2)