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Lecture 7 Accuracy of sample mean X Var (X)= Var (X) divided by sample size n What is X bar ? Called sample mean. Standard error of the mean =SD(X) = SD (X) divided by squared root of n As sample size increases, the sample mean become more and more accurate in estimating the population mean Sample size needed to meet accuracy requirement

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Sum of independent random variables from box A, mean , SD Sample size n =2 Let X 1, X 2 be the length of two independent phone calls (the phone call example : mean=2.5 minutes, SD=1 minute) Let T=X 1 +X 2 What is the variance of T? (=1+1) SD of T? (=sqrt 2) Sample size n=4 T= X 1 + X 2 + X 3 +X 4 Var (T) = 1 + 1 + 1 + 1 =4 SD(T)= sqrt 4 =2

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T= sum of n independent draws X= average of n independent draws Var (T) = n SD(T)= sqrt n times X = T/n SD(X)= SD(T) / n = / sqrt n =Squared root = sqrt

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Difference of two sample means Assume independence Example: Does body position for epidural injections affect labor pain? Page 303, textbook, National Women’s Hospital in Auckland : number of spinal segments blocked by the anesthetic Lying group : n1 =48, mean=8.8, SD=4.4 Sitting group : n2=35, mean=7.1, SD=4.5 Question : do we have enough evidence to tell which is better? SD of difference in mean =.9909, two-standard error interval : [-0.3, 3.7]. Ans. No.

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Another example: from page 305 of book Individual 2-SE interval 2-SE interval for the difference Overlapping case Mean X=14, SE =1.2 Mean Y=10, SE=1.1 SE for difference of mean = sqrt (1.2 2 +1.1 2 )=1.627 SD(X-Y) = sqrt [ SD(X) 2 + SD(Y) 2 ] < SD(X) + SD (Y)

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Lecture 13 Chi-square and sample variance Finish the discussion of chi-square distribution from lecture 12 Expected value of sum of squares equals n-1.

Lecture 13 Chi-square and sample variance Finish the discussion of chi-square distribution from lecture 12 Expected value of sum of squares equals n-1.

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