STAT 1000Q Lecture 13: Stats Notes 13
1 Oct 2017
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Formula for the ci for when is known. If n < 30, the population sampled must be approximately normal. T /(cid:1006), (cid:374)-1 = invt (1 - /(cid:1006), (cid:374)-1) Whe(cid:374) is k(cid:374)ow(cid:374), (cid:894) - (cid:895) / (cid:894) (cid:374)(cid:895) = n(cid:894)(cid:1004),(cid:1005)(cid:895) On calc for invt: 2nd distribution, invt, area to the left of unknown point, comma, degrees of freedom, close parentheses, enter. T /(cid:1006), (cid:374)-1= invt(1- /(cid:1006), (cid:374)-1) = invt (1 + cc)/ 2, n - 1. Exa(cid:373)ple (cid:1005): (cid:374)= (cid:1005)(cid:1009), is u(cid:374)k(cid:374)ow(cid:374), a(cid:374)d we wa(cid:374)t to co(cid:373)pute a 99% ci for . N-(cid:1005)= (cid:1005)(cid:1008), (cid:894)(cid:1005) + cc(cid:895)/(cid:1006)= (cid:1004). 99(cid:1009), /(cid:1006)= (cid:1004). (cid:1004)(cid:1004)(cid:1009) T. 005, 14 = invt(o. 995, 15) = 2. 977. Example 2: n= 20 and we want a 90 % ci for . N-(cid:1005) = (cid:1006)(cid:1004), cc = (cid:1004). 9, (cid:894)(cid:1005)+cc(cid:895)/(cid:1006)= (cid:1004). 9(cid:1009), /(cid:1006)= (cid:1004). (cid:1004)(cid:1009) T. 05, 20 = invt (0. 95, 20) = 1. 725. Assume that data is from an approximately normal distribution.
