Linear Regression

data(iris)

response=iris[,-5]

model<-lm(Sepal.Length ~ Petal.Length, data=response)

plot(Sepal.Length~ Petal.Length, data=response,main="linear regression",col=iris$Species)
 abline(model,response)

# coefficent 
# intercetp
summary(model)

## 
## Call:
## lm(formula = Sepal.Length ~ Petal.Length, data = response)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1.24675 -0.29657 -0.01515  0.27676  1.00269 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   4.30660    0.07839   54.94   <2e-16 ***
## Petal.Length  0.40892    0.01889   21.65   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4071 on 148 degrees of freedom
## Multiple R-squared:   0.76,  Adjusted R-squared:  0.7583 
## F-statistic: 468.6 on 1 and 148 DF,  p-value: < 2.2e-16