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Unformatted text preview: 6 of 10 1.4 2 population means: dep. samples / matched pairs R: x2 . bar = mean( females ) R: s2 = sd ( females ) R: n2 = length ( females ) R: delta . x . bar = x1 . bar- x2 . bar R: df = min(n1- 1 , n2- 1) R: t . c r i t = qt (1- 0.05 /2 , df ) R: E = t . c r i t * sqrt ( s1 ˆ2/n1 + s2 ˆ2/n2) x1.bar n1 s1 x2.bar n2 s2 delta.x.bar df t.crit 1 70.86 7 3.34 65.55 11 2.50 5.31 6.00 2.45 R: delta . x . bar [ 1 ] 5.3117 R: E [ 1 ] 3.5979 R: CI = c ( delta . x . bar- E, delta . x . bar + E) R: CI [ 1 ] 1.7137 8.9096 Question 12 . How does the CI compare with our hypothesis tests conclusions? REQUIRED SAMPLE SIZE t-test required sample size: power.t.test(delta= h , sd = σ , sig.level= α , power= 1- β , type ="two.sample", alternative="two.sided") delta minimum effect size of interest sd estimated standard for both groups (assumed equal). power desired power alternative set to either “one.sided” or “two.sided” R Command 1.4 2 population means: dep. samples / matched pairs HYPOTHESIS TEST Notation If you have two dependent samples with sample 1 in x i and sample 2 in y i where x and y are ordered vectors : Anthony Tanbakuchi MAT167 Testing a claim about two means 7 of 10 paired differences: d i = x i- y i (6) mean paired difference of sample: ¯ d = mean(d) (7) mean paired difference of population: μ d (8) standard deviation of differences: s d...
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This note was uploaded on 02/10/2012 for the course MAT 1670 taught by Professor Tanbakuchi during the Spring '11 term at University of Florida.
- Spring '11