a)
sample mean, xbar = 105
sample standard deviation, s = 10
sample size, n = 15
degrees of freedom, df = n - 1 = 14
Given CI level is 95%, hence α = 1 - 0.95 = 0.05
α/2 = 0.05/2 = 0.025, tc = t(α/2, df) = 2.145
CI = (xbar - tc * s/sqrt(n) , xbar + tc * s/sqrt(n))
CI = (105 - 2.145 * 10/sqrt(15) , 105 + 2.145 * 10/sqrt(15))
CI = (99.46 , 110.54)
b)
sample size, n = 29
degrees of freedom, df = n - 1 = 28
Given CI level is 95%, hence α = 1 - 0.95 = 0.05
α/2 = 0.05/2 = 0.025, tc = t(α/2, df) = 2.048
CI = (xbar - tc * s/sqrt(n) , xbar + tc * s/sqrt(n))
CI = (105 - 2.048 * 10/sqrt(29) , 105 + 2.048 * 10/sqrt(29))
CI = (101.2 , 108.8)
c)
Given CI level is 96%, hence α = 1 - 0.96 = 0.04
α/2 = 0.04/2 = 0.02, tc = t(α/2, df) = 2.264
CI = (xbar - tc * s/sqrt(n) , xbar + tc * s/sqrt(n))
CI = (105 - 2.264 * 10/sqrt(15) , 105 + 2.264 * 10/sqrt(15))
CI = (99.15 , 110.85)
d)
No, as the baseline assumption is that data should be selected from
normally distributed population
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