Question

Data Set A:

1.32, 1.01, 0, 2.21, 1.69, 1.73, 2.01, 0, 0.73, 0.91, 0, 3.03, 2.22, 1.23, 3.71, 0, 0.45, 2.18, 3.12, 1.91

Data Set B:

0, 0.63, 2.11, 1.37, 0, 1.11, 2.93, 0, 3.11, 2.61, 0, 0.38, 0.98, 1.55, 1.83, 0, 3.46, 2.31, 0, 1.49

- 2-sample parametric estimates

- Using SAS or R, perform the appropriate 95% confidence interval for the difference in means, ratios of variances, and the modified-Levene test.

Answer 1

x1 <- c(1.32, 1.01, 0, 2.21, 1.69, 1.73, 2.01, 0, 0.73, 0.91, 0, 3.03, 2.22, 1.23, 3.71, 0, 0.45, 2.18, 3.12, 1.91)
> x2 <- c(0, 0.63, 2.11, 1.37, 0, 1.11, 2.93, 0, 3.11, 2.61, 0, 0.38, 0.98, 1.55, 1.83, 0, 3.46, 2.31, 0, 1.49)
> t.test(x1,x2)

        Welch Two Sample t-test

data: x1 and x2
t = 0.49899, df = 37.884, p-value = 0.6207
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.548808 0.907808
sample estimates:
mean of x mean of y
   1.4730    1.2935

> var.test(x1,x2)

        F test to compare two variances

data: x1 and x2
F = 0.89508, num df = 19, denom df = 19, p-value = 0.8116
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.3542844    2.2613811
sample estimates:
ratio of variances
         0.8950822