ADM 2304 Lecture Notes - Lecture 3: Convenience Sampling, Sampling Distribution, Standard Deviation
13 May 2017
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> a 100(1- )% c. i. for , when sd is unknown: > where t /2,n-1 is the t-value with n - 1 degrees of freedom for which there is /2 probability in the right tail. We have replaced by s and the z-value by a t-value. > if is unknown and n is large, then we can approximate the. 100(1- )% con dence interval for the population mean by. Assumptions: random sample not a convenience sample, for small samples, the underlying population must be normally distributed. Why: for larger samples, the underlying distribution is not important. > since the sample proportion has a known sampling distribution namely it is approximately normal with mean p and standard deviation squrt (p(1-p)/n) provided n is large enough! > therefore, an approximate 100(1- )% c. i. for p has the form: e. g, of 56 students polled, 10 found the midterm easier than expected and 46 found it harder than expected.
