This preview shows page 1. Sign up to view the full content.
Unformatted text preview: probability distribution that applies to occurrences of some event over a specified interval. P1x2 = mx # e-m x! where e L 2.71828 C n-1 or n1 g x22 - 1 g x22 Mean: x - E 6 m 6 x + E E = za>2 # E = ta>2 # s 1s known2 1n s 1s not known2 1n 1n - 12s2 x2 R 1n - 12s2 C n1n - 12 A n1 + n2 St. dev. from frequency dist.: s= n C g 1f # x22 D - C g 1f # x2 D 2 E
2 t= 1for s not known2 n1n - 12 Sample variance: s Population st. dev.: = n! 1n - r2! ISBN-13: 978-0-321-57080-2 ISBN-10: 0-321-57080-4 Standard Deviation: Alternative case when S1 and S2 are not known, but it is assumed that S1 = S2: Pool variances and use test statistic t= 1x1 - x22 - 1m1 - m22 Explained Variation: r 2 is the proportion of the variation in y that is explained by the linear association between x and y. Hypothesis test 1. Using r as test statistic: If ƒ r ƒ 7 critical value (from table), then there is sufficient evidence to support a claim of linear correlation. If ƒ r ƒ … critical value, there is not sufficient evidence to support a claim of linear correlation. 2. Using t as test statistic: r t...
View Full Document
This note was uploaded on 06/08/2010 for the course MATH 1123 taught by Professor Serpa during the Spring '10 term at Hawaii Pacific.
- Spring '10