Checking for Normal Distribution

t-tests: examine if the difference in means is significant or not

normdis <- rnorm(n=1000, m=30, sd=3)
hist(normdis)

data("ToothGrowth")
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 ...

hist(ToothGrowth$len)

qqplot

examining distribution of quantitative numerical variable

qqnorm(ToothGrowth$len)
qqline(ToothGrowth$len)

Shapiro-Wilk normality test

H0: data are normarlly distributed

shapiro.test(ToothGrowth$len) #data are normally distributed

## 
##  Shapiro-Wilk normality test
## 
## data:  ToothGrowth$len
## W = 0.96743, p-value = 0.1091