We did Andrew Gelman’s candy weighing demonstration in class on Friday. This was the first time I’ve had a chance to teach sampling in some time and really wanted to do it. These were not traditional statistics students, however. We did this in research methods. So these are students interested in management and getting drug through a statistics course. And that might be putting it nicely. Nevertheless, they were good about the experiment. We also had some slight modifications to Gelman’s procedure. First, we used the measurements (in grams) printed right on the side of the packaging. And everyone did
I am teaching intermediate algebra this spring and I wanted to post the syllabus and give a bit of a comment here. You can find the syllabus on my math education page. The course catalog gives a description: A study of problem-solving techniques in intermediate-level algebra. The goal is to demonstrate number sense and estimation skills; interpret mathematical ideas using appropriate terminology; manipulate, evaluate, and simplify real-number and algebraic expressions; and translate, solve, and interpret applied problems. Emphasis is on numbers and algebraic properties, graphing skills, and applications drawn from a variety of areas (such as finance, science, and the
One thing I am sure my students hate is that I don’t give extra credit. I’ve talked about this before, and there is no reason to rehash them. But I do like to add something when I can use extra credit to push their boundaries, or something neat. Last semester, while teaching precalculus, Nina decided to make a dress for Ducky. She made a small math error when she first did the calculations and the results were clearly incorrect at the end. Fortunately, we caught that before she started working from them, and we got it together on round two.
I don’t usually post my math syllabi, but I have realized this is probably a bad habit. Or a good habit I don’t have. Or something. Anyway, I’ve posted my syllabus for MATH 106 – Finite Mathematics, at UMUC.
For about two years, I’ve been working on a book called Computational Methods for Numerical Analysis with R (CMNA), which will present an outline of numerical analysis topics with original (and simplified) implementations in R at a level appropriate for a graduate student or advanced undergraduate. Last night, I sent the latest draft to my editors, and I am quite pleased to say it should be heading into production, soon. The organizational structure of the text is based roughly on the organizational structure of MAPL 460 15-20 years ago: Introduction to Numerical Analysis Error Analysis Linear Equations Interpolation and Extrapolation