introduction to machine learning with R 

data<-read.csv("data1.csv")

x<-data[,names(data)=='pelvic_incidence']
y<-data[,names(data)=='sacral_slope']

df<- data.frame(x,y)

linear <- lm(y ~ x, data = df)

summary(linear)

## 
## Call:
## lm(formula = y ~ x, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.884  -4.480   0.838   4.781  34.470 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  4.55916    1.61742   2.819  0.00513 ** 
## x            0.63466    0.02572  24.680  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7.792 on 308 degrees of freedom
## Multiple R-squared:  0.6642,	Adjusted R-squared:  0.6631 
## F-statistic: 609.1 on 1 and 308 DF,  p-value: < 2.2e-16

predicted= predict(linear, newdate =df$x)

plot(x,y,pch = 16, cex = 1.3,col=rainbow(5))
abline(linear,lw=3)
plot of chunk unnamed-chunk-7