Wednesday, February 1

• 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

• Includes Section 1.3 to top of page 26 and pages 151–152 (pdf of Notes pages 11–20) 
• Situating Fitting data in the modeling process
• Functions you should know on sight.
• Plotting data, scatterplots, proportionality
• Fitting linear data visually.
• Fitting y=Cx^k.
• Residuals, interpreting residual plots.

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

• In Mathematica lab, 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 21-36) 
• Modeling exponential data
• Interpolation and Extrapolation
• Regression and Least Squares

• Includes Section 3.4 (pdf of Notes pages 37-44) 
• Least Squares Examples
  
• The R² statistic
• Multiple linear regression
• Correlation is not causation
  

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

• Discussion of Homework 2
• Includes Section 3.3 (pdf of Notes pages 45-51) 
• 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

• Modeling automobile stopping distance, start to finish (pdf of Notes pages 64-71) 
• Discussion of Homework 3

• Question and Answer Session
• Discussion of Midterm Practice Homework
  

• Exam 1
• 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 (pdf of Notes pages 72-81) 
• Introduction to probability
• Determining probability of events
• Independent events
• Component Reliability

• Includes ideas from Sections 5.1 and 5.3A (pdf of Notes pages 82-96) (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 97-107) (Class Mathematica worksheet)
• Queuing simulations

• Collecting, plotting, and visualizing data  (no new notes)
  

• In Mathematica Lab, Kiely 236
• Tutorial 5

• Includes ideas from Sections 1.7 and 4.1 (pdf of Notes pages 108-112) 
• Inventory Policy
• The language of optimization

• Includes ideas from Section 4.2 (pdf of Notes pages 113-122) (Class Mathematica worksheet)
• Linear Optimization

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

• Includes ideas from Section 4.2 and 4.4 (pdf of Notes pages 123-132) 
• Linear Optimization Examples
• Integer Programming

• Sensitivity Analysis (No new notes)

• To be determined

• Question and Answer Day

• Exam 2

• Presentations

• Presentations

• Presentations