Mathematical Modeling in Business and Economics: A Data-Driven Approach

The use of mathematical models to analyze and solve problems can provide businesses with a competitive edge. Useful models need not be complex. Simple, basic models can provide decision makers with valuable insight into a wide spectrum of issues such as sales growth and decline, inventory and revenue management, break-even and cost-benefit analysis, forecasting, resource sustainability, population growth and government budget trends. Employees who are skilled in even the most basic mathematical models are valuable to both the private and public sectors. Data collection is often the first step in the decision-making process. This text presents a parallel instructive approach to modeling; the first step is the use of data to determine the best model. Linear, quadratic, polynomial, rational, exponential and logarithmic models are each introduced by first presenting a data set that displays the basic pattern and properties of the underlying function. Curve fitting, using Microsoft Excel, enables the student to translate actual data relationships into an appropriate functional model as well as see the applicability of the model's mathematical properties. Data sets used cover a wide range of topics such as consumer spending, federal budget deficits, income, health care costs and global carbon emissions. The required mathematical background does not extend beyond basic algebra. Students should be proficient in Excel.