Statistical Inference - Course Project

Exploratory Data Analyses

data(ToothGrowth)
pairs(ToothGrowth)

Relevant Confidence Intervals and Tests

library(ggplot2)
plt<-ggplot(ToothGrowth, aes(as.factor(supp),len,color=as.factor(supp)))+geom_boxplot()
print(plt)

plt<-ggplot(ToothGrowth, aes(as.factor(dose),len,color=as.factor(dose)))+geom_boxplot()+facet_wrap(~supp)
print(plt)

t.test(len~supp, ToothGrowth[ToothGrowth$dose==.5,])

## 
##  Welch Two Sample t-test
## 
## data:  len by supp
## t = 3.1697, df = 14.969, p-value = 0.006359
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.719057 8.780943
## sample estimates:
## mean in group OJ mean in group VC 
##            13.23             7.98

t.test(len~supp, ToothGrowth[ToothGrowth$dose==1,])

## 
##  Welch Two Sample t-test
## 
## data:  len by supp
## t = 4.0328, df = 15.358, p-value = 0.001038
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  2.802148 9.057852
## sample estimates:
## mean in group OJ mean in group VC 
##            22.70            16.77

t.test(len~supp, ToothGrowth[ToothGrowth$dose==2,])

## 
##  Welch Two Sample t-test
## 
## data:  len by supp
## t = -0.0461, df = 14.04, p-value = 0.9639
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.79807  3.63807
## sample estimates:
## mean in group OJ mean in group VC 
##            26.06            26.14

Conclusion

• OJ is better with dose .5 and 1.5 mg.

• There is no significant difference between supplements when dose is 2mg.