You are here:

Math and Science Solutions for Businesses/Business Mathematics and Statistics

Advertisement


dipanjali gogoi wrote at 2014-02-19 09:51:36
For this problem we need to calculate the probability, using the Poisson distribution, of getting 0, 1, 2 and 3 defects per packet and then multiplying by 10,000 to get the expected value of the number of defective packets per consignment.



The probability of a defect per blade is p = 1/500 = 0.002. This means that for a packet of 10, the mean number of defects L = 10p = 0.02. The parameter L is used in the Poisson distribution to give the probability of the number of defects, n, in a packet of 10:



P(n) = exp(-L)・(L^n)/n!



For n = 0, I get P(0) = 0.9802, which means that the expected value for the number of packets with no defects is 9802. For n = 1, P(1) = 0.0002. You can do the arithmetic for the other values of n; the probabilities are very small.



I'm not sure if the problem asks for just the individual number for each n or if it wants the total sum, but you can just add them up yourself. This actually might cause some confusion because you might notice that the sum over all 11 possible values for n in a packet (including 0) using the Poisson distribution formula will not be quite 1; it should be exactly 1 of course since it is the sum over all possible outcomes. The error is really small, however. The sum is exactly one if the binomial distribution is used; the Poisson distribution is an approximation to the binomial distribution.  


Math and Science Solutions for Businesses

All Answers


Answers by Expert:


Ask Experts

Volunteer


Randy Patton

Expertise

Questions regarding application of mathematical techniques and knowledge of physics and engineering principles to product and services design, optimization, prediction, feasibility and implementation. Examples include sales and product performance projections based on math/physics models in addition to standard regression; practical and cost effective sensor design and component configuration; optimal resource allocation using common tools (eg., MS Office); advanced data analysis techniques and implementation; simulation and "what if" analysis; and innovative applications of remote sensing.

Experience

26 years as professional physical scientist and project manager for elite research company providing academic quality basic and applied research for government and defense industry clients (currently retired). Projects I have been involved in include: - Notional sensor performance predictions for detecting underwater phenomena - Designing and testing guidance algorithms for multi-component system - Statistical analysis of ship tracking data and development of anomaly detector - Deployed vibration sensors in Arctic ice floes; analysis of data - Developed and tested ocean optical instrument to measure particles - Field testing of protoype sonar system - Analysis of synthetic aperture radar system data for ocean surface measurements - Redesigned dust shelters for greeters at Burning Man Festival Project management with responsibility for allocation and monitoriing of staff and equipment resources.

Publications
“A Numerical Model for Low-Frequency Equatorial Dynamics” (with Mark A. Cane), J. of Phys. Oceanogr., 14, No. 12, pp. 18531863, December 1984.

Education/Credentials
MIT, MS Physical Oceanography, 1981 UC Berkeley, BS Applied Math, 1976

Past/Present Clients
Am also an Expert in Advanced Math and Oceanography

©2016 About.com. All rights reserved.