state.df<-as.data.frame(state.x77)
View(state.df)
names(state.df)[4]<-"Life.Exp"
names(state.df)[6]<-"HS.Grad"

fit1<-lm(Life.Exp~.,data=state.df)
summary(fit1)

## 
## Call:
## lm(formula = Life.Exp ~ ., data = state.df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.48895 -0.51232 -0.02747  0.57002  1.49447 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  7.094e+01  1.748e+00  40.586  < 2e-16 ***
## Population   5.180e-05  2.919e-05   1.775   0.0832 .  
## Income      -2.180e-05  2.444e-04  -0.089   0.9293    
## Illiteracy   3.382e-02  3.663e-01   0.092   0.9269    
## Murder      -3.011e-01  4.662e-02  -6.459 8.68e-08 ***
## HS.Grad      4.893e-02  2.332e-02   2.098   0.0420 *  
## Frost       -5.735e-03  3.143e-03  -1.825   0.0752 .  
## Area        -7.383e-08  1.668e-06  -0.044   0.9649    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7448 on 42 degrees of freedom
## Multiple R-squared:  0.7362, Adjusted R-squared:  0.6922 
## F-statistic: 16.74 on 7 and 42 DF,  p-value: 2.534e-10

fit2<-lm(Life.Exp~HS.Grad+Murder,state.df)
summary(fit2)

## 
## Call:
## lm(formula = Life.Exp ~ HS.Grad + Murder, data = state.df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.66758 -0.41801  0.05602  0.55913  2.05625 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 70.29708    1.01567  69.213  < 2e-16 ***
## HS.Grad      0.04389    0.01613   2.721  0.00909 ** 
## Murder      -0.23709    0.03529  -6.719 2.18e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.7959 on 47 degrees of freedom
## Multiple R-squared:  0.6628, Adjusted R-squared:  0.6485 
## F-statistic:  46.2 on 2 and 47 DF,  p-value: 8.016e-12