Modeling, in general logic

refers to the establishment of a description of a system in mathematical terms,

which describes the behaviour of the original organism. Such a design of

mathematical representation is called a mathematical model, of the physical system.

In numerous realistic disciplines such as medicine, engineering and ?nance,

amongst others, modeling and investigating lifespan data is essential.

Researchers in mathematics

are in the habit of dividing the universe into two parts: mathematics, and

everything else,that is, the rest of the world, sometimes called “the real

world”. As soon as you practice mathematics to know a situationin the actual world,and

then feasibly practice it totake an action or event of forecast the future, together

the actual world condition and the resultant mathematics methods are taken seriously.The

circumstances and the queries related with them can be any extent from enormous

to tiny. The enormous ones may lead to lifetime careers for those who study

them deeply and special curricula or whole university departments may be set up

to prepare people for such careers. Bioorganism, hormones study, medical

imaging, and cryptography are some such examples. At the another end of the extent,

there are slight circumstances and equivalent interrogations, although they may

be of great importance to the individuals involved: planning a trip, scheduling

the time-table, man requirtment methods, or bidding in an auction. Whether the problem is enormous or

tiny, the procedure of “interface” between the mathematics and the physical world

is the same: the actual circumstances frequently has numerous sides that you

can’t take all into account, so you choose which characteristics are most

significant and retain those. At this instant, you have an perfect description

of the actual condition, which you can then interpret into mathematical

relations. Now you have a mathematical model of the idealized question. Then

you relate your mathematical characters and facts to the model, and gain

exciting understandings, examples, designs, formulas, and algorithms. You

decode all this back into the actual situation, and you assurance to have a

model for the idealized question. But you have to check back: the results are practical,

the answers are reasonable, the consequences are acceptable? If so, then we

have the mathematical model for the actual world problem, If not, take another

look at the choices you made at the beginning, and try again. This entire

process is called mathematical modeling.