For each date below, you will find the homework assignment that is due that day, any lecture notes for downloading, and the key topics that are covered that day.
This schedule is approximate and subject to change!

• Includes Section 1.1 and Section 1.2 to middle of page 18. (Notes pages 0–11) '
• What is Mathematical Modeling?
• Steps of the Modeling Process
• Groupwork: Choosing a problem statement

• Email me at chanusa@qc.cuny.edu with the following four things.: (1) Your name, (2) Your class (Math 245) (3) the email address where you are best contacted, (4) your graduation year.
• Thoroughly read the class web page including the syllabus and schedule. This should answer all the questions that you may have about the class. Next, take the syllabus quiz on Blackboard. Retake the quiz as necessary to earn a score of 100%.

• Includes pieces of Sections 2.1, 2.2, 3.1, and 3.2 (Notes pages 12–18) '
• The mathematical definition of error
• Errors inherent to the modeling process
• Groupwork: Simplifying assumptions and formulation errors
• When is a model descriptively realistic?

• Includes Section 3.1 (Notes pages 19–28) '
• 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

• Homework #1 is due on Monday, February 9.
• Link to the homework guidelines.

• Includes Section 1.3 to top of page 26, pages 114–119, and pages 151–152 (Notes pages 29–46) '
• 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 new notes to download
• Modeling exponential data and population growth
• Residuals, interpreting residual plots
• Interpolation and Extrapolation

• Read the Mathematica part of this website.
• If desired, download Mathematica to your home computer.
• Choose who you want to work with on the group project.
  If you are without a group, see me at the beginning of class.

• Class meets in Mathematica Lab, Kiely 236. (Enter through Kiely 220; stay outside until I arrive.)
• Introduction to Mathematica; tutorial 1.

• Explore Mathematica further outside of class.
• Meet with your group to discuss possible project topics.

• Includes ideas from Sections 2.3.3, 2.3.4, and 3.2. (Notes pages 47–54) '
• Includes Section 3.4. (Notes pages 55–62) '
• Regression and Least Squares
• The R² statistic
• Multiple linear regression

• Project statements are due today.
  Email Prof. Chris before class and bring a hard copy to class.

• Class meets in Mathematica Lab.
• Mathematica tutorials 2 and 3.
• Prof. Chris will meet with each group to discuss your project statement

• Includes Section 3.3. (Notes pages 63–69) '
• Types of Causation
• Correlation is not causation

• Class meets in Mathematica Lab.
• Mathematica tutorial 4.

• The first exam of the semester will take place on Monday, March 16 (and not Wednesday, March 11 as originally scheduled).
• The exam covers all material covered since the beginning of the semester.
• Here are more details about the first exam.
• My students often ask for an example of the style of exam that I am liable to give. I am including my exam from last year. The topics covered by the exam are the same topics, but you should expect your exam to be very different because there are many ways for me to ask questions that test your knowledge on these topics. Click here for last year's exam.

• Correlation is not causation (Notes were posted for March 3.)
• The modeling process from start to end (Notes pages 70–77) '

• Question and Answer Day

• Exam 1

• Includes Section 5.3A and probability (Notes pages 78–87) '
• Introduction to probability
• Determining probability of events
• Independent events
• Expected Value
• Component Reliability

• Includes Vectors, Matrices, and Section 1.5 (Notes pages 88–95) '
• Vectors, Matrices, and multiplication
• Transition Matrices
• Leslie Matrices
• Vectors and Matrices in Mathematica

• Markov Chains and Random Walks (Notes pages 96–103) ' (Class Mathematica worksheet) (Class Mathematica worksheet)
• Random walks
• Gambler's ruin
• Color mixing game
• (See also: background and additional topics)

• In-class Project Work Day in Kiely 061.

• In-class Project Work Day in Kiely 061.

• Includes ideas from Sections 5.1 and 5.3A (Notes pages 104–118) ' (Class Mathematica worksheet)
• Simulation models
• Using Mathematica to run simulations

• For loops
• Includes ideas from Section 5.1 (Notes pages 119–129) ' (Class Mathematica worksheet)

• In Mathematica Lab, Kiely 236
• In-class Draft Critique Day.
• Tutorial 6 / Homework #4 work day.

• In normal room, Kiely 317
• Queuing simulations
• Collecting, plotting, and visualizing data

• In Mathematica Lab, Kiely 236
• Tutorial 7 / Homework 4 work day.

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

• Question and Answer Day

•••••• Please fill out the college-wide course evaluations, distinct from the course evaluations that will be given out in class. Thank you for your feedback! ••••••

• The second exam of the semester will take place on Wednesday, May 6.
• The exam covers all material in the course notes from page 77–147. (March 5 through May 5.)
• Here are more details about the second exam.
• My students often ask for an example of the style of exam that I am liable to give. I am including my exam from last year. The topics covered by the exam are the same topics, but you should expect your exam to be very different because there are many ways for me to ask questions that test your knowledge on these topics. Click here for last year's exam.

• Exam 2

• Presentations

• Presentations

•••••• Please fill out the college-wide course evaluations, distinct from the course evaluations that will be given out in class. Thank you for your feedback! ••••••

• Presentations