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    Tools for Quantitative Reasoning
    MATH2118
    Progress0 / 27 topics
    Topics
    1. Logic, Logical and Critical Reasoning: Introduction and importance of logic2. Inductive, deductive and abductive approaches of reasoning3. Propositions4. Argutnents (valid and invalid5. Logical connectives6. Truth tables and propositional equivalences7. Logical fallacies8. Venn Diagrams9. Predicates and quantifiers10. Quantitative reasoning exercises using logical reasoning concepts and techniques11. Mathematical Modeling and Analyses12. Introduction to deterministic models13. Use of linear functions for modeling in real-world situations14. Modeling with the system of linear equations and their solutions15. Elementary introduction to derivatives in mathematical modeling16. Linear and exponential growth and decay models17. Quantitative reasoning exercises using mathematical modeling18. Statistical Modeling and Analyses19. Introduction to probabilistic models20. Bivariate analysis, scatter plots21. Simple linear regression model and correlation analysis22. Basics of estimation and confidence interval23. Testing of hypothesis24. z-test25. t-test26. Statistical inference in decision making27. Quantitative reasoning exercises using statistical modeling
    MATH2118›Modeling with the system of linear equations and their solutions
    Tools for Quantitative ReasoningTopic 14 of 27

    Modeling with the system of linear equations and their solutions

    5 minread
    932words
    Intermediatelevel

    Modeling with systems of linear equations involves creating mathematical representations of real-world situations where multiple conditions or constraints must be satisfied simultaneously. A system of linear equations consists of two or more linear equations with the same set of variables. Solving these systems helps find values for the variables that satisfy all equations in the system.

    Key Concepts

    1. Formulation of the System:

      • A system of linear equations can be written in standard form: a1x+b1y=c1a2x+b2y=c2⋮anx+bny=cna_1x + b_1y = c_1 \\ a_2x + b_2y = c_2 \\ \vdots \\ a_nx + b_ny = c_na1​x+b1​y=c1​a2​x+b2​y=c2​⋮an​x+bn​y=cn​
      • Here, ai,bi,cia_i, b_i, c_iai​,bi​,ci​ are constants, and x,yx, yx,y are the variables.
    2. Types of Solutions:

      • Unique Solution: The system has exactly one solution where the lines intersect at a single point.
      • Infinite Solutions: The equations represent the same line, resulting in infinitely many solutions.
      • No Solution: The equations represent parallel lines that never intersect.

    Example Applications

    1. Business and Economics

    Example: Cost and Revenue Analysis

    • Suppose a company produces two products, xxx and yyy. The profit from product xxx is 5 per unit, and from product $$ y $$ is 8 per unit. The company wants to maximize profit given the constraints of production capabilities and market demand.

    Formulate the System:

    • Let:
      • xxx = number of product xxx produced
      • yyy = number of product yyy produced

    Assume:

    • The company can produce at most 100 units of product xxx and 50 units of product yyy: x+y≤100(1)y≤50(2)x + y \leq 100 \quad (1) \\ y \leq 50 \quad (2)x+y≤100(1)y≤50(2)

    The profit function to maximize is:

    P=5x+8yP = 5x + 8yP=5x+8y

    Solve the System:

    1. Set up the equations based on constraints.
    2. Graph the inequalities.
    3. Find the corner points of the feasible region.
    4. Evaluate the profit at each corner point to determine the maximum profit.

    2. Engineering

    Example: Electrical Circuits

    • In a simple electrical circuit, we can use Kirchhoff’s laws to model the currents in the circuit. Assume we have a circuit with three loops.

    Formulate the System:

    • Using Kirchhoff's laws, we might arrive at the following equations: I1+I2=10(1)I2+I3=5(2)2I1+I3=15(3)I_1 + I_2 = 10 \quad (1) \\ I_2 + I_3 = 5 \quad (2) \\ 2I_1 + I_3 = 15 \quad (3)I1​+I2​=10(1)I2​+I3​=5(2)2I1​+I3​=15(3)

    Here, I1,I2,I3I_1, I_2, I_3I1​,I2​,I3​ are the currents in different branches of the circuit.

    Solve the System:

    • You can solve the system using substitution, elimination, or matrix methods (like row reduction or using the inverse of a matrix).

    3. Environmental Science

    Example: Resource Allocation

    • A city allocates its water resources for two main purposes: agriculture and domestic use. Let xxx be the water allocated to agriculture and yyy to domestic use.

    Formulate the System:

    • Assume the total available water is 5000 liters, and the demands are: x+y=5000(1)2x+3y=12000(2)x + y = 5000 \quad (1) \\ 2x + 3y = 12000 \quad (2)x+y=5000(1)2x+3y=12000(2)

    Here, the second equation reflects the water demand based on usage rates.

    Solve the System:

    1. Use substitution or elimination to find xxx and yyy.
    2. Analyze the results to ensure they meet the constraints of the problem.

    Methods for Solving Systems of Linear Equations

    1. Graphical Method:

      • Plot each equation on a graph. The point(s) where the lines intersect represent the solution(s).
    2. Substitution Method:

      • Solve one equation for one variable and substitute that expression into the other equation(s).
    3. Elimination Method:

      • Add or subtract equations to eliminate one variable, making it easier to solve for the other.
    4. Matrix Method:

      • Use matrices to represent the system and apply methods such as Gaussian elimination or finding the inverse of a matrix to solve the system.

    Conclusion

    Modeling with systems of linear equations is a powerful technique that can be applied across various disciplines to solve problems involving multiple variables and constraints. By accurately formulating the system and employing appropriate solution methods, one can derive meaningful insights and make informed decisions based on the results. Understanding these concepts is essential for effective problem-solving in mathematics, science, economics, and engineering.

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    Use of linear functions for modeling in real-world situations
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    Elementary introduction to derivatives in mathematical modeling

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