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Da Nang-10/2014 Natural Science Department – Duy Tan University Lecturer: Ho Xuan Binh MATHEMATICAL MODELS In this section, we will learn: Mathematical.

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Presentation on theme: "Da Nang-10/2014 Natural Science Department – Duy Tan University Lecturer: Ho Xuan Binh MATHEMATICAL MODELS In this section, we will learn: Mathematical."— Presentation transcript:

1 Da Nang-10/2014 Natural Science Department – Duy Tan University Lecturer: Ho Xuan Binh MATHEMATICAL MODELS In this section, we will learn: Mathematical models

2 MATHEMATICAL MODELS 1 Natural Science Department – Duy Tan University A mathematical model is a mathematical Description — often by means of a function or an equation — of a real-world phenomenon such as: *Size of a population *Demand for a product *Speed of a falling object *Life expectancy of a person at birth *Cost of emission reductions

3 PURPOSE 2 Natural Science Department – Duy Tan University The purpose of the model is to understand the phenomenon and, perhaps, to make predictions about future behavior. Mathematical models

4 PROCESS 3 Natural Science Department – Duy Tan University The figure illustrates the process of mathematical modeling. Mathematical models

5 STAGE 1 4 Natural Science Department – Duy Tan University Given a real-world problem, our first task is to formulate a mathematical model. Mathematical models

6 STAGE 2 5 Natural Science Department – Duy Tan University The second stage is to apply the mathematics that we know—such as the calculus that will be developed throughout this book—to the mathematical model that we have formulated in order to derive mathematical conclusions. Mathematical models

7 STAGE 3 6 Natural Science Department – Duy Tan University In the third stage, we take those mathematical conclusions and interpret them as information about the original real-world phenomenon—by way of offering explanations or making predictions. Mathematical models

8 STAGE 4 7 Natural Science Department – Duy Tan University The final step is to test our predictions by checking against new real data. If the predictions don’t compare well with reality, we need to refine our model or to formulate a new model and start the cycle again. Mathematical models

9 LOGO Thank you for your attention


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