knitr::opts_chunk$set(echo = TRUE)
Perfroming two sample t test
a95<-c(11.176,7.089,8.097,11.739,11.291,10.759,6.467,8.315)
b100<-c(5.263,6.748,7.461,7.015,8.133,7.418,3.772,8.963)
summary(a95)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.467 7.845 9.537 9.367 11.205 11.739
summary(b100)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.772 6.377 7.216 6.847 7.629 8.963
t.test(log(a95),log(b100),var.equal = TRUE, alternative = 'greater')
##
## Two Sample t-test
##
## data: log(a95) and log(b100)
## t = 2.5046, df = 14, p-value = 0.01262
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 0.09507682 Inf
## sample estimates:
## mean of x mean of y
## 2.213906 1.893530
p value for the two sample t test is 0.01262
95% confidence interval is 0.09507 to ∞
As the p value is less than 0.05, so we can reject the null hypothesis.
Here we can say that the null hypothesis is recjected and we can conclude that the higher banking temperature results in wafers with a lower mean photoresist thickness.
Plotting Normal probability plots
qqnorm(a95,main = "Normal probability plot for 95°C")
qqline(a95)
qqnorm(b100,main = "Normal probability plot for 100°C")
qqline(b100)
From the normal probability plots we can say that the data for 95°C and 100°C is apporximately normally distributed.As some of the data is close to line.
a95<-c(11.176,7.089,8.097,11.739,11.291,10.759,6.467,8.315)
b100<-c(5.263,6.748,7.461,7.015,8.133,7.418,3.772,8.963)
summary(a95)
summary(b100)
t.test(a95,b100,var.equal = TRUE)
qqnorm(a95)
qqline(a95)
qqnorm(b100)
qqline(b100)