Department of Mathematics & Statistics Calendar Week of April 7 - 13, 2008 Monday, April 7, 2008 2:30 PM Analysis Seminar 459 MSc Ian DETERS, BGSU "Spectral Synthesis of Diagonal Operators" 3:30 PM Calculus Seminar 459 MSc Professor Neocles LEONTIS, Chemistry Dept., BGSU "Calculus Issues in Chemistry" ABSTRACT: I would like to engage in a broad discussion as to WHAT WE EXPECT students to remember AND BE ABLE TO APPLY in their dealings with the "real" world and in their other science courses! AFTER studying (and hopefully learning) calculus. I will argue that it is VERY simple: We want them to be able to determine: 1) when they should integrate (calculate an area) -- and over what range of the function they are graphing to do the integration and 2) when to calculate a slope = take a derivative to calculate what they need to solve a problem and Also we want them to: 3) be able to use a program like MATLAB to set up the problem, make a graph and then call the appropriate functions to get a numerical answer. Most people will never have to do an indefinite integral in their lives, but likely will need to integrate (or differentiate) numerically. That's it in a nutshell. To achieve this -- I will argue that the key to this is to teach students UNIT Analysis. I don't see any evidence that students come out of calculus having any idea what UNIT analysis is and how it can help them to decide between 1 and 2. At best they guess. At worst they don't even attempt to use calculus to solve problems. I want to be provocative -- what the heck are you folks doing over there! The students leave your courses without remembering for any significant period of time what calculus is for and totally unconvinced they should remember anything from that trail of tears! On our end -- we are also at fault -- we steer most students to physics without calculus (!) -- can you imagine French without grammar? In chemistry we don't even TRY to use calculus. We pray they can just do some algebra. So we reinforce the notion that you can do science in a meaningful way without understanding and using calculus. We are cheating our students -- and our standards are abysmally low! So my question and challenge is: How can we start focusing on the RESULTS -- i.e. evaluate student learning (or lack thereof) so we can make changes to make sure students LEARN -- not that faculty cover the material (whatever that means). Come to discuss, brainstorm, and argue with me. NOTE: Anyone who might teach calculus, especially advanced graduate students, are invited. Tuesday, April 8, 2008 10:45 AM MATH 112 Instructor's Meeting 459 MSc 11:30 AM Statistics Seminar 459 MSc Dr. Craig L. ZIRBEL, BGSU "Cubic Splines via Linear Regression" ABSTRACT: Cubic splines are fairly smooth continuous functions that can be used to exactly interpolate data points, or can be chosen to approximate data points in much the same way as we use regression to fit a line to data. This will be a self-contained talk drawn from Chapter 5 of the book we have been reading this semester, "Elements of Statistical Learning" by Tibshirani et al. You do not need to have attended previous talks to follow this one. Wednesday, April 9, 2008 2:30 PM L-Groups Seminar 459 MSc Dr. Wolf IBERKLEID Thursday, April 10, 2008 9:45A-11:15A Undergraduate Committee Meeting 400 MSc 10:30 AM MATH 122 Instructor's Meeting 447 MSc 1:30 PM Foundational Math Committee 400 MSc Friday, April 11, 2008 12:00 Noon Calendar Information Due to Cyndi for next week's calendar 2:30 PM Advisory Committee 400 MSc 3:30 PM Refreshments served prior to colloquium 459 MSc 3:45 PM Colloquium 459 MSc Dr. Brisa Sanchez, School of Public Health University of Michigan "Residual-based Diagnostics for Structural Equation Models" ABSTRACT: Classical diagnostics for structural equation models are based on aggregate forms of the data and are ill suited for checking distributional or linearity assumptions. We extend recently developed goodness of fit tests for correlated data based on subject-specific residuals to structural equation models with latent variables. The proposed tests lend themselves to graphical displays and are designed to detect misspecified distributional or linearity assumptions. To complement graphical displays, test statistics are defined; the null distributions of the test statistics are approximated using computationally efficient simulation techniques. The properties of the proposed tests are examined via simulation studies. We illustrate the methods using data from a study of in-utero lead exposure. Saturday, April 12, 2008 12:00 PM Kappa Mu Epsilon Banquet/Initiation Ceremony Campus Corner's Restaurant Sunday, April 13, 2008
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