Monday, February 1 (pdf of Notes pages 0–10)

• Includes Section 1.1 and Section 1.2 to middle of page 18
• What is Mathematical Modeling?
• Steps of the Modeling Process

• Includes Section 1.3 to top of page 26 and pages 151-152
• Plotting data, including scatterplots, proportionality
• Fitting linear data visually.
• Fitting y=Cx^k.
• Functions you should know on sight.

• In Mathematica lab, Kiely 236.
  
• Tutorial 1.

• Includes Section 1.4 and ideas from Section 3.2
• Modeling exponential data
• Residuals
• Regression and Least Squares

• In Mathematica lab, Kiely 236.
  
• Tutorials 2 and 3.

• Includes Section 2.3.3 and some of Section 3.2.
• More Least Squares examples
• Interpolation vs. Extrapolation

• Includes Sections 3.4 and 3.3
• Correlation Coefficient
• Multiple Linear Regression
• Correlation is not Causation

• In Mathematica lab, Kiely 236.
  
• Tutorial 4.

• Includes Section 3.1
• How can a mathematical model be good?
• Definitions: Accuracy, Descriptive Realism, Precision, Robustness, General, Fruitfulness
• College enrollment examples
• The advantages of inaccuracy: Traveling Salesman Problem
• Definition: Error, Fractional Error, Percentage Error

• Modeling automobile stopping distance, start to finish

• Question and Answer Session

• Covers all topics to date including and not limited to: steps of the modeling process, plotting data, fitting curves to data, linear regression, correlation coefficient, extrapolation, interpolation, how a mathematical model can be good.
• Covers the following sections: 1.1, 1.2 (to page 18), 1.3 (to page 26), 1.4, 2.3.3, 2.3.4, 3.1, 3.2, 3.3, and 3.4.
• Covers the topics in Mathematica tutorials 1–4; know the important concepts and the following commands:
• Table
• Plot, ListPlot, ListLinePlot, Show
• Fit, FindFit
• For informational purposes only, here is a copy of a first exam from the past. No guarantees of similarity are assured.

• Includes Section 5.3A and probability
• Introduction to probability
• Determining probability of events
• Independent events
• (Notes completed on Wed 3/23)

• Includes Section 2.1 and probability
• Sources of Error
• Error Boggle
• Simulation models
• Monte Carlo simulation
• Using Mathematica to run simulations

• Independent events
• Component Reliability
• Includes ideas from Section 5.1 and 5.3A
• Using Mathematica to run simulations
• If statements
• For loops

• Includes ideas from Section 5.1
• Queuing simulations

• In Mathematica Lab, Kiely 236
• Tutorial 5

• Collecting, plotting, and visualizing data

• Includes ideas from Sections 1.7 and 4.1
• Optimization using calculus
• Optimization: Inventory Policy
• The language of optimization

• Includes ideas from Section 4.2
• Linear Optimization
• Graphical interpretation
• Solving graphically
• Using Mathematica to optimize

• Includes ideas from Section 4.2 and 4.4
• Additional examples of linear programming
• Integer programming

• Includes ideas from Section 4.2
• Sensitivity analysis in linear optimization
• Group work on sensitivity analysis

• Question and Answer Session

• Covers all topics since the first exam, including and not limited to: sources of error, probability, Monte Carlo models, computer simulations, linear optimization, and sensitivity analysis.
• Covers the following sections: 1.7, 2.1, 4.1, 4.2, 4.4, 5.1, and 5.3A.
• Covers the topics in Mathematica tutorials 5-6; be sure to completely understand the waiting room simulation and the following commands:
• RandomInteger, RandomReal
• If, For
  
• Histogram
• Maximize, Minimize
• For informational purposes only, here is a copy of a second exam from the past. No guarantees of similarity are assured. (You did not see Markov Chains in this class so ignore Question 1.)