Skip to second half of course

Monday, February 1 (pdf of Notes pages 0–8)

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

• Includes Section 1.3 to page 26 and Section 3.2 to page 153
• Definition: Descriptively realistic
• Plotting data, including scatterplots, proportionality
• Fitting linear data visually.
• Functions you should know on sight.
• Group discussion of fitting y=Cx^k.

• Held in computer labs I-201 and I-212.
• Tutorial 1. Introduction to Mathematica
• Tutorial 2. Using lists in Mathematica

• Snow day, no class

• Includes Section 1.4 and Section 2.3.3.
• Why plot data visually?
• Exponential growth
• Definitions: birth rate, death rate, growth rate, Mathusian model, Regression, Method of Least Squares
• Regression and the method of least squares

• Includes Section 2.3.4 and the remainder of Section 3.2.
• More Least Squares examples
• Interpolation vs. Extrapolation

• Held in computer labs I-201 and I-212.
• Tutorial 3. Plotting functions and data in Mathematica
• Tutorial 4. Fitting curves to data in Mathematica

• 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

• Includes Sections 3.3 and 3.4
• Positive and Negative Correlation
• Types of Causality: Simple, Reverse, Mutual Causality, Confounding Variable, Coincidence
• Correlation is not causation
• R² Calculations

• Includes Section 3.4 and an example on automobile stopping distance
• R² Calculations
• Multiple Linear Regression
• 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, extrapolation, interpolation, descriptive realism, and difference equations.
• 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.

• Includes Section 1.5 and matrices
• Introduction to vectors and matrices
• Transition matrix interpretation

• Leslie matrices for modeling population change
• Includes Section 5.3A and probability
• Introduction to probability

• Independent events
• Determining probability of events
• Component Reliability

• Markov Chains
• Sources of Error
• Error Boggle

Wednesday, April 7 (pdf of Notes pages 90–101)

• Simulation models
• Monte Carlo simulation
• Using Mathematica to run simulations
• Monte Carlo simulations to calculate area

Monday, April 12 (pdf of Notes pages 102–110)

• Queuing simulations
• Collecting, plotting, and visualizing data

Wednesday, April 14 (No new notes)

• Queuing simulations
• Collecting, plotting, and visualizing data

• Held in computer labs I-201 and I-212.
• Tutorial 5: Introduction to Random Numbers and Simulation Models

Wednesday, April 21 (pdf of Notes pages 111–120)

• Linear Optimization
• Linear Programs
• Graphical interpretation
• Solving graphically
• Using Mathematica to optimize

Monday, April 26 (pdf of Notes pages 121–128)

• Additional examples of linear programming
• Integer programming

• Peer Review Day

Monday, May 3 (pdf of Notes pages 129–130)

• 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: Leslie matrices, simple probability, Markov chains, sources of error, simulation models, queuing models, and linear optimization (including sensitivity analysis).
• Covers the following sections: 1.5, 2.1, 4.2, 4.4, 5.1, and 5.3A.
• Covers the topics in Mathematica tutorial 5.

• Project Presentations