Monday, January 28 

• Includes Section 1.1 and Section 1.2 to middle of page 18 (pdf of Notes pages 0–10) 
• What is Mathematical Modeling?
• Steps of the Modeling Process
• Group worksheet: Choosing a problem statement

• Includes Section 1.3 to top of page 26, pages 114–119, and pages 151–152 (pdf of Notes pages 11–28) 
• Situating Fitting data in the modeling process
• Functions you should know on sight
• Plotting data, scatterplots, proportionality
• Fitting linear data visually
• Transformations for fitting y=Cx^k. and y=Ck^x.

• No notes to download.
• Class meets in Kiely 236.
• Introduction to Mathematica; tutorial 1.
• Before class, download Mathematica to your home computer. (If desired.) Also read the Mathematica part of this website.

• Modeling exponential data and population growth
• Residuals, interpreting residual plots
• Interpolation and Extrapolation

• Class meets in Kiely 236.
• Tutorials 2 and 3.

• Includes Section 1.4 and ideas from Sections 2.3.3, 2.3.4, and 3.2. (pdf of Notes pages 29–36)  
• Regression and Least Squares
• Least Squares Examples
  
• The R² statistic
• Mathematica Tutorial 4

• Includes Section 3.4. (pdf of Notes pages 37–44) 
• Includes Section 3.3. (pdf of Notes pages 45–51) 
• Multiple linear regression
• Types of Causation
• Correlation is not causation

• Includes Chapter 3 (pdf of Notes pages 52–63) 
• Ways in which a model can 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

• Question and Answer Session.

• Exam 1

• Includes Section 5.3A and probability (pdf of Notes pages 64–73) 
• Introduction to probability
• Determining probability of events
• Independent events
• Component Reliability

• Includes Section 1.5 and matrix models (pdf of Notes pages 74–85)  (Class Mathematica worksheet)
• Vectors and Matrices
• Leslie matrices for modeling population change

• Markov chains
• Includes ideas from Sections 5.1 and 5.3A (pdf of Notes pages 86–100)  (Class Mathematica worksheet)
• Simulation models
• Using Mathematica to run simulations

• If statements
• For loops

• Monte Carlo simulations
• Includes ideas from Section 5.1 (pdf of Notes pages 101–111)  (Class Mathematica worksheet)
• Queuing simulations
• Collecting, plotting, and visualizing data

• In Mathematica Lab, Kiely 236
• Tutorial 5

• Includes ideas from Section 2.1 and 2.2.2 (pdf of Notes pages 112–119)  (Class Mathematica worksheet 1 worksheet 2)
• Errors inherent to the modeling process
• Random walks
• Gambler's ruin
• Color mixing game
• (See also: background and additional topics)

• Includes ideas from Sections 1.7 and 4.1 (pdf of Notes pages 120–124) 
• Inventory Policy
• The language of optimization

• Peer Review Day. Bring in three copies of the final draft of your paper.

• Includes ideas from Section 4.2 (pdf of Notes pages 125–134)  (Class Mathematica worksheet)
• Linear Optimization

• Includes ideas from Section 4.2 and 4.4 (pdf of Notes pages 135–144) 
• Linear Optimization Examples
• Integer Programming

• Sensitivity Analysis (No new notes)

• To be determined

• Question and Answer Day

• Exam 2

• Presentations

• Presentations

• Presentations