HW 11

df <- cars
head(df)

And lets preview this data:

plot(df[,"speed"],df[,"dist"], main="Speed vs Distance", xlab="speed", ylab="dist")

lm <- lm(dist ~ speed, data = df)

print(lm)
## 
## Call:
## lm(formula = dist ~ speed, data = df)
## 
## Coefficients:
## (Intercept)        speed  
##     -17.579        3.932
plot( dist ~ speed, data = df)
abline(lm)

summary(lm)
## 
## Call:
## lm(formula = dist ~ speed, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.069  -9.525  -2.272   9.215  43.201 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -17.5791     6.7584  -2.601   0.0123 *  
## speed         3.9324     0.4155   9.464 1.49e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.38 on 48 degrees of freedom
## Multiple R-squared:  0.6511, Adjusted R-squared:  0.6438 
## F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12
plot(fitted(lm),resid(lm))

summary(lm)
## 
## Call:
## lm(formula = dist ~ speed, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -29.069  -9.525  -2.272   9.215  43.201 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -17.5791     6.7584  -2.601   0.0123 *  
## speed         3.9324     0.4155   9.464 1.49e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 15.38 on 48 degrees of freedom
## Multiple R-squared:  0.6511, Adjusted R-squared:  0.6438 
## F-statistic: 89.57 on 1 and 48 DF,  p-value: 1.49e-12
plot(fitted(lm),resid(lm))

par(mfrow=c(2,2)) 
plot(lm)