[MOSAIC-news] Mosaic M-Cast on Friday

Daniel Kaplan kaplan at macalester.edu
Sun Sep 5 07:57:48 CDT 2010

Our next Mosaic M-Cast comes this week:

*Friday, Sept 10*, at 12:30pm Eastern (11:30am Central, 9:30 Pacific)
 lasting 20 minutes.
Instructions for tuning in to an

Presented by Victor Addona, the M-Cast deals with models of a familiar
setting: body fat percentage and how it relates to other variables: height,
waist circumference, etc.

Victor's M-Cast is rooted in statistics, but provides a lovely illustration
applicable to those who teach calculus and modeling of how single-variable
descriptions can miss important (and in this case, intuitive) relationships.
 If you're looking for a compelling example of the differences between
partial and total derivatives, this is it!

So plan to join us this Friday. If you can't, watch the recording that will
be posted on the MOSAIC web site: www.mosaic-web.org.

See also the schedule of upcoming
held on the 2nd, 4th, and 5th Fridays of each month.

Danny Kaplan

September 10: Vittorio Addona*Helping Students Understand Regression
Coefficients: An Example of Modeling Body Fat Percentage*

Time: 11:30 CDT (12:30 Eastern, 9:30 Pacific)

Abstract: Once students have understood univariate regression models, the
move to multivariate models can be rather straightforward, if the
explanatory variables are uncorrelated. Quite commonly, however, it is
important to interpret coefficients in a multivariate model as partial
changes in the response variable, holding the other explanatory variables
fixed. This language is hard for most students to understand without a
specific context. We discuss a data set (freely available on StatLib) which
has successfully served as a tool for aiding students comprehension of
multiple regression coefficients. The data deals with measurements of male
body fat percentages, and we seek to build models for this response variable
using more easily measured quantities. At Macalester College, introductory
students learn about multiple regression in their first statistics course.
Many other institutions do not broach this material until a second course.
This talk will be helpful regardless of when students first encounter
multiple regression.
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