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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.

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.

“A Numerical Model for Low-Frequency Equatorial Dynamics” (with Mark A. Cane), J. of Phys. Oceanogr., 14, No. 12, pp. 1853−1863, December 1984.

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

Am also an Expert in Advanced Math and Oceanography

I like being able to apply the knowledge I have of mathematics and science to problems that arise in business applications where the client feels a solution is possible but does not have the background to readily supply it. I also like to advise people as to the feasibiltiy of innovative ideas and applications to allow them move forward.

Real world applications are "the mother of invention". Developing an expanding knowledge-base of practical applications of advanced concepts is an exciting goal.

Predicting the amount of sales over time given a limited inventory can be modeled much like a population where the number of sales (birth rate) is related to the number of items available for display (size of population). This situation corresponds to a well known and simple differential equation whose solution can be used to predict future sales.

Prevent bad decisions based on seemingly obvious but erroneous intuition. If a fair coin flip comes up tails 10 times in a row, most people would assume that it is most likely that a heads will come up on the next toss. This is not true; it is equally likely for a tail to come up as it is a heads, no matter how many times tails came up before. Betting heavily on a heads coming up could cost a lot.

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David | 06/12/16 | 8 | 8 | 10 | Well, at least you helped me get ..... |

Joe | 01/26/16 | 10 | 9 | 10 | |

abe | 11/04/15 | 10 | 10 | 10 | Thanks for the detailed response. I appreaciate ..... |

Alex | 11/03/15 | 10 | 10 | 10 | thanks for the input |

AJ | 10/27/15 | 10 | 10 | 10 |

Stephen, this is a great question. I have worked on something similar before. Let me collect my notes and get back to you with some hopefully useful results. Meanwhile, your problem has a couple of

I just discovered that I do have Pages on my Macbook Pro! to make the chart (see attached) I just opened a blank document, chose Chart from the toolbar, hit "edit chart", deleted the placeholder numbers

Hey David, I think what you are asking is pretty simple to do. The chart you want is called a column chart. I've attached a screen shot of the one I created with your numbers. To create it, I used the

Looks like we want to use an exponential to model the elephant's weight gain. This can be written as W(t) = B･exp(at) where t is time (in days), A = growth constant and B = initial weight

Sofi, To compare variances, you need to use the F distribution. FYI, this is the distribution of the ratio of the 2 variances you are comparing (which are presumably chi-square distributed). You also

Math and Science Solutions for Businesses

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