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Mathematics for Modeling

Mathematics for Modeling. Presentation to Joint Math Meetings, January 5, 2017 Mary Parker, Austin Community College Hunter Ellinger , Austin, Texas Lindsay Orlando, Austin Community College. Background.

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Mathematics for Modeling

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  1. Mathematics for Modeling Presentation to Joint Math Meetings, January 5, 2017 Mary Parker, Austin Community College Hunter Ellinger, Austin, Texas Lindsay Orlando, Austin Community College

  2. Background A revision of a course taught from 2001-2011 at Austin Community College: Mathematics for Measurement Level: Same level as Math for Liberal Arts. (No college-level math prerequisite.) Materials for that course were developed and revised by Mary Parker and Hunter Ellinger. Three “strands”: Modeling, Approximate Numbers, and Trigonometry Audience: Mainly workforce students.

  3. Why change? ACC joined a project in which we had to develop materials to have a full degree plan for which the students had no-cost textbooks. The Math Department’s contribution for that was to develop a Math for Liberal Arts course. (In Texas: MATH 1332)

  4. Our analysis • Math for Measurement had been a successful course. Both the students and faculty were impressed with the level of sophistication the students developed in using math and computers in their other courses. • In our Math for Liberal Arts course, one of the most engaging parts for our students seemed to be the Personal Finance part, especially when Lindsay had used a project where students developed a budget to help a young person figure out how to live within his $35,000 salary, pay off his debts, and start a savings plan. And then Mary had used that project where she had students learn to use a spreadsheet as well.

  5. Solution From the Math for Measurement course, drop the Trigonometry and include the Personal Finance materials we had developed for our Math for Liberal Arts course. We will teach this course for the first time this coming semester. The report here is based on our success in using these materials in previous courses.

  6. What do students learn about modeling data with spreadsheets? • Fit linear, quadratic, and exponential models to data by fitting parameters “by eye.” • Then fit them by using “Solver” in Excel. • Change the criteria for fitting and note the similarities and differences in the solutions: (1) minimize sum of squared errors, (2) minimize sum of absolute errors, (2) minimize maximum absolute error. • Use residual plots (as well as just visual inspection) to determine which of these types of models fit better. • Then on to other types of models: power functions and more.

  7. How do we get them started? • Develop basic spreadsheet skills • Develop the idea of estimating parameters and checking them graphically. This really supports their understanding the meaning of the parameters.

  8. Example for students to estimate slope and intercept.

  9. Since the slope of the formula line was too high, lower the slope to 2.

  10. Since the intercept was too high, lower it. But I didn’t lower it enough.

  11. Kept lowering the intercept until the formula line matched the line through the two points.

  12. Next step Give the students a spreadsheet already set up to make it easy for them to start estimating the parameters of the formula from data. By the end of the course, they are expected to start with a blank spreadsheet, copy the data into it and create all the columns and formulas themselves, as well as the graphs and find good parameter values.

  13. Page 1 of the workbook for the first lesson in fitting data

  14. Page 3 of the workbook, as provided to students • The labels of the columns • The formulas • The places to enter the parameter values (Red arrows here) • Labels in cells G1 and G2 that are more elaborate than we will expect students to do, but useful in the beginning.

  15. Page 2 of workbook, as students started • They copied data to the sheet • They “pulled down” the formulas for the prediction and the deviations • They made a graph • Then they estimated the slope and intercept

  16. Page 5 of workbook. Note the parameterization of the quadratic model.

  17. Later in the course. For these data, chose whether to fit a linear, quadratic, or exponential model. Start from a blank spreadsheet.

  18. Note the adjusted x-values and the growth-rate parameterization.

  19. What do students learn to do with spreadsheets in the Finance portion? • Develop spreadsheets to compute accumulated savings with simple and compound interest. • Students use an annuity calculator to explore effective interest rate changes or withdrawals. • Develop a loan amortization spreadsheet. • Modify the loan amortization spreadsheet to easily see the effect of occasional extra payments.

  20. We give the framework, students enter formulas and pull down.

  21. Students model various types of growth.

  22. Students use an annuity calculator to explore the effects of various interest rates and various choices.

  23. Students develop a loan amortization sheet.

  24. Financial Advice Project • Students are given Joe’s income, monthly spending pattern, and debts and asked to give him advice. • They are expected to use an online debt-reduction calculator to compute his optimal loan payment strategy, including choosing which method to use to decide how much to pay on each loan and when (snowball, avalanche, etc.) • They are also expected to use a spreadsheet to summarize all of this. • On the final exam in the course, they are expected to use their spreadsheet to make some additional adjustments and give the resulting values.

  25. One student’s workbook

  26. Her revised budget for Joe

  27. Why spreadsheets? • Requires active participation. • Different type of engagement in this from working with algebra (symbolic manipulation.) Usually students have a better attitude about trying to make it make sense. • Generally speaking, everyone’s product looks good – considerably more so than when doing work by hand.

  28. Why spreadsheets? • Making successive adjustments to improve their estimates and their solutions shows students a different (and useful) aspect of quantitative thinking than in previous math courses. • Students successfully do considerably more complex problems than they expect to be able to do.

  29. Links to the materials as well as the information about this talk are available at http://www.austincc.edu/mparker/talks/ mparker@austincc.edu lorlando@austincc.edu

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