The general information of the dataset[¹].
#datasets::ToohtGrowth
head(ToothGrowth)
## len supp dose
## 1 4.2 VC 0.5
## 2 11.5 VC 0.5
## 3 7.3 VC 0.5
## 4 5.8 VC 0.5
## 5 6.4 VC 0.5
## 6 10.0 VC 0.5
str(ToothGrowth)
## 'data.frame': 60 obs. of 3 variables:
## $ len : num 4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
## $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
## $ dose: num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
summary(ToothGrowth)
## len supp dose
## Min. : 4.20 OJ:30 Min. :0.500
## 1st Qu.:13.07 VC:30 1st Qu.:0.500
## Median :19.25 Median :1.000
## Mean :18.81 Mean :1.167
## 3rd Qu.:25.27 3rd Qu.:2.000
## Max. :33.90 Max. :2.000
library(ggplot2)
ggplot(ToothGrowth, aes(x=factor(dose), y=len, color=supp))+geom_boxplot()+labs(x="Vitamin C dose (mg)",y="Teeth length (mm)")
From the exploratory analysis the following observations might be done:
In the following hyphotesis study, the previous two observations are further investigated. The departing hyphothesis according to each observation are:
For the observation 1:
a<-t.test(x=ToothGrowth[ToothGrowth$dose==1,]$len,y=ToothGrowth[ToothGrowth$dose==0.5,]$len,alternative="greater", var.equal=TRUE)
For the observation 2:
library(knitr)
b <- t.test(x=ToothGrowth[ToothGrowth$dose==0.5 &ToothGrowth$supp=="VC",]$len,y=ToothGrowth[ToothGrowth$dose==0.5 &ToothGrowth$supp=="OJ",]$len, alternative="less", mu=0, var.equal = TRUE)
c <- t.test(x=ToothGrowth[ToothGrowth$dose==1 &ToothGrowth$supp=="VC",]$len,y=ToothGrowth[ToothGrowth$dose==1 &ToothGrowth$supp=="OJ",]$len, alternative="less", mu=0, var.equal = TRUE)
d <- t.test(x=ToothGrowth[ToothGrowth$dose==2 &ToothGrowth$supp=="VC",]$len,y=ToothGrowth[ToothGrowth$dose==2 &ToothGrowth$supp=="OJ",]$len, alternative="less", mu=0, var.equal = TRUE)
test <- c("Dose vs teeth length",
"Orange Juice vs Ascorbic Acid (dose=0.5mg)",
"Orange Juice vs Ascorbic Acid (dose 1 mg)",
"Orange Juice vs Ascorbic Acid (dose = 2 mg)")
Lower.CI<-c(a$conf[1],b$conf[1],c$conf[1],d$conf[1])
Higher.CI<-c(a$conf[2],b$conf[2],c$conf[2],d$conf[2])
pvalue<-c(a$p.value,b$p.value,c$p.value,d$p.value)
final=data.frame(test,Lower.CI,Higher.CI,pvalue)
From the table below, the following conclusions might be drawn:
kable(final)
| test | Lower.CI | Higher.CI | pvalue |
|---|---|---|---|
| Dose vs teeth length | 6.753344 | Inf | 0.0000001 |
| Orange Juice vs Ascorbic Acid (dose=0.5mg) | -Inf | -2.377886 | 0.0026518 |
| Orange Juice vs Ascorbic Acid (dose 1 mg) | -Inf | -3.380140 | 0.0003904 |
| Orange Juice vs Ascorbic Acid (dose = 2 mg) | -Inf | 3.086866 | 0.5181451 |
[¹]: C. I. Bliss (1952) The Statistics of Bioassay. Academic Press.