Loading data set
bmi.and.chol <- read.csv("/cloud/project/bmi and chol.txt", sep="")
#cor.test()?
#View(bmi.and.chol)
#bmi.and.chol
#View(bmi.and.chol) # function View() show as table
age <- bmi.and.chol$age # set age as avai from bmi.and.chol
chol <- bmi.and.chol$chol # set chol as avai from bmi.and.chol
cor.test(age,chol) # read cor.test () function
##
## Pearson's product-moment correlation
##
## data: age and chol
## t = 10.704, df = 16, p-value = 1.058e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8350463 0.9765306
## sample estimates:
## cor
## 0.9367261
cor.test(age,chol, method = "spearman")
## Warning in cor.test.default(age, chol, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: age and chol
## S = 51.158, p-value = 2.57e-09
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.947205
plot(age,chol)# plot(x,y) x,y numberical
cor.test(age,chol, method = "kendall")
## Warning in cor.test.default(age, chol, method = "kendall"): Cannot compute exact
## p-value with ties
##
## Kendall's rank correlation tau
##
## data: age and chol
## z = 4.755, p-value = 1.984e-06
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.8333333
lm(chol~age) # lm(y~x) as linear mode
##
## Call:
## lm(formula = chol ~ age)
##
## Coefficients:
## (Intercept) age
## 1.08922 0.05779
reg <- lm(chol~age) #set reg as ...
summary(reg) # show inf of reg
##
## Call:
## lm(formula = chol ~ age)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.40729 -0.24133 -0.04522 0.17939 0.63040
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.089218 0.221466 4.918 0.000154 ***
## age 0.057788 0.005399 10.704 1.06e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3027 on 16 degrees of freedom
## Multiple R-squared: 0.8775, Adjusted R-squared: 0.8698
## F-statistic: 114.6 on 1 and 16 DF, p-value: 1.058e-08
fitted(reg) # calculate Y^ (predict Y)
## 1 2 3 4 5 6 7 8
## 3.747483 2.244985 4.094214 2.822869 4.383156 2.533927 2.707292 3.169600
## 9 10 11 12 13 14 15 16
## 2.360562 3.574118 4.383156 2.996234 2.360562 4.729886 3.400753 3.863060
## 17 18
## 2.707292 3.920849
resid(reg) # calculate residual (phan du) of reg
## 1 2 3 4 5 6
## -0.247483426 -0.344985415 -0.094213736 -0.222869265 0.116844338 0.466072660
## 7 8 9 10 11 12
## 0.192707505 0.630400424 -0.260562185 0.225881729 -0.283155662 0.003765579
## 13 14 15 16 17 18
## 0.139437815 -0.129885972 -0.200753116 0.336939804 -0.407292495 0.079151419
#op <- par(mfrow= c(2,2)) # chia man hinh ra 4 phan #op <-par(mar=c(1,1,1,1))
op <-par(mar=c(1,1,1,1)) # lenh chia man hinh
plot(reg) # ve cac do thi (co the co) trong reg
#abline(chol~age)
#op <-par(mar=c(1,0,0,0)) # chua biet tra ve sao cho nguyen o
#graphics.off()
plot(chol ~ age, pch=16)
abline(reg,col="red")
