This preview shows pages 1–4. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: ≤ ≤ 109 P (103 ≤ ≤ 109) = P (1.44 ≤ z ≤ 1.44) = .9251  .0749 = .8502( 4pts ) 46. ( Total 15pts ) μ = 41,979 σ = 5000 a. ( 2pts ) b. P (> 41,979) = P ( z > 0) = .50( 4pts ) c. P ( z ≤ 1.41) = .9207 P ( z < 1.41) = .0793 P (40,979 ≤ ≤ 42,979) = P (1.41 ≤ z ≤ 1.41) = .9207  .0793 = .8414( 4pts ) d. P ( z ≤ 2.00) = .9772 P ( z < 2.00) = .0228 P (40,979 ≤ ≤ 42,979) = P (2 ≤ z ≤ 2) = .9772  .0228 = .9544( 5pts ) 37. ( Total 15pts ) a. Normal distribution E () = .50 ( 2pts ) b. P ( z ≤ 1.94) = .9738 P ( z < 1.94) = .0262 P (.46 ≤≤ .54) = .9738  .0262 = .9476 ( 4pts ) c. P ( z ≤ 1.46) = .9279 P ( z < 1.46) = .0721 P (.47 ≤≤ .53) = .9279  .0721 = .8558 ( 4pts ) d. P ( z ≤ .97) = .8340 P ( z < .97) = .1660 P (.48 ≤≤ .52) = .8340  .1660 = .6680 ( 5pts )...
View
Full
Document
This note was uploaded on 01/31/2011 for the course MGMT 305 taught by Professor Priya during the Spring '08 term at Purdue University.
 Spring '08
 Priya

Click to edit the document details