Question 1

library(lawstat)
Aspirin_A <-c(15,26,13,28,17,20,7,36,12,18)
Aspirin_B <-c(13,20,10,21,17,22,5,30,7,11)

t.test(Aspirin_A, Aspirin_B,paired=TRUE, level=0.05) #reject Ho
## 
##  Paired t-test
## 
## data:  Aspirin_A and Aspirin_B
## t = 3.6742, df = 9, p-value = 0.005121
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  1.383548 5.816452
## sample estimates:
## mean of the differences 
##                     3.6
t.test(Aspirin_A, Aspirin_B, var.equal = TRUE) 
## 
##  Two Sample t-test
## 
## data:  Aspirin_A and Aspirin_B
## t = 0.9802, df = 18, p-value = 0.34
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -4.116103 11.316103
## sample estimates:
## mean of x mean of y 
##      19.2      15.6

Conclusion

There is significative differences between mean of Aspirin A in comparison with Aspirin B with a 95% of significance

Question 2

\[ Ho:\mu T,male= \mu T,female\] \[ Ha:\mu T,male \ne\ \mu T,female\]

temp <- read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv")
male <- temp[1:65,]
female <- temp[66:130,]
boxplot(male$Temp,female$Temp, names = c("Male", "Female"), ylab="Temperature")

t.test(male$Temp,female$Temp,var.equal=TRUE)
## 
##  Two Sample t-test
## 
## data:  male$Temp and female$Temp
## t = -2.2854, df = 128, p-value = 0.02393
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.53963938 -0.03882216
## sample estimates:
## mean of x mean of y 
##  98.10462  98.39385
t.test(male$Temp,female$Temp,var.equal=FALSE)
## 
##  Welch Two Sample t-test
## 
## data:  male$Temp and female$Temp
## t = -2.2854, df = 127.51, p-value = 0.02394
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.53964856 -0.03881298
## sample estimates:
## mean of x mean of y 
##  98.10462  98.39385

Conclusion

The mean for males is lower in comparison with the mean for females, also there some outliers points for the temperature for females

R CODE Used

library(lawstat)
Aspirin_A <-c(15,26,13,28,17,20,7,36,12,18)
Aspirin_B <-c(13,20,10,21,17,22,5,30,7,11)

t.test(Aspirin_A, Aspirin_B,paired=TRUE, level=0.05) #reject Ho

#Conclusions: there is significative differences between mean of Aspirin A in
# in comparison with Aspirin B with a 95% of significance 

t.test(Aspirin_A, Aspirin_B, var.equal = TRUE) 

#Conclusion: p-value iqual to 0.34 and fail to reject H0 

#Question 2
temp <- read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv")
male <- temp[1:65,]
female <- temp[66:130,]
boxplot(male$Temp,female$Temp, names = c("Male", "Female"), ylab="Temperature")

#The mean for males is lower in comparison with the mean for females, also there some outliers points for the temperature for females

#H0: uT,male =uT,females
#Ha: uT,male =/ uT,females

t.test(male$Temp,female$Temp,var.equal=TRUE)

#since p-value = 0.02394 reject H0 which means that male and female means are different with a 95% of significance 

t.test(male$Temp,female$Temp,var.equal=FALSE)