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dat1 <- c(16.03,16.01,16.04,15.96,16.05,15.98,16.05,16.02,16.02,15.99)
dat2 <- c(16.02,16.03,15.97,16.04,15.96,16.02,16.01,16.01,15.99,16.00)

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

qqnorm(dat1)
qqline(dat1)

qqnorm(dat2)
qqline(dat2)

data1 shows that its normally distributed.

data2 shows that its normally distributed.

boxplot(dat1,dat2,col=c("light blue","red"),names=c("machine1","machine2"))

### the result of the box plot clearly says the variance are not equal. ###alternative hypothesis is tested

t.test(dat1,dat2,alternative = c("two.sided","less","greater"))

## 
##  Welch Two Sample t-test
## 
## data:  dat1 and dat2
## t = 0.79894, df = 17.493, p-value = 0.435
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.01635123  0.03635123
## sample estimates:
## mean of x mean of y 
##    16.015    16.005

Since the p-value is 0.435 which is above 0.05, so we fail to reject the null hypothesis and so we can conclude that the mean values are equal.

95 percent confidence interval on the difference in mean fill volume for the two machines is -0.01635123-0.03635123

dat1 <- c(16.03,16.01,16.04,15.96,16.05,15.98,16.05,16.02,16.02,15.99)
dat2 <- c(16.02,16.03,15.97,16.04,15.96,16.02,16.01,16.01,15.99,16.00)
library(dplyr)
qqnorm(dat1)
qqline(dat1)
qqnorm(dat2)
qqline(dat2)
boxplot(dat1,dat2,col=c("light blue","red"),names=c("machine1","machine2"))
t.test(dat1,dat2,alternative = c("two.sided","less","greater"))