Ginger Holmes Rowell

Multivariable parametric cost model for ground optical telescope assembly

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter are derived.

Introductory Statistics Education and the National Science Foundation.

This paper describes 27 National Science Foundation supported grant projects that have innovations designed to improve teaching and learning in introductory statistics courses. The characteristics of these projects are compared with the six recommendations given in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2005 for teaching an introductory course in statistics. Through this analysis, we are able to see how NSF-supported introductory statistics education projects during the last decade achieve the GAISE ideals. Thus, materials developed from many of these projects provide resources for first steps in implementing GAISE recommendations.

Multivariable parametric cost model for ground-based telescopes

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

Undergraduate Statistics Education and the National Science Foundation.

This paper describes 25 National Science Foundation supported projects that have innovations designed to improve education for students majoring or minoring in statistics. The characteristics of these projects and the common themes which emerge are compared with the American Statistical Associations (ASA) guidelines for developing statistics education curricula for majors and minors and for teaching the corresponding statistics courses. Through this analysis, we are able to see how the last decade of NSF supported projects in statistics education exemplify these ASA guidelines.

Team-based Introductory Research Experiences in Mathematics

Abstract As part of Middle Tennessee State University’s (MTSU’s) initiative to improve retention of at-risk STEM majors, they recruit first-time, full-time freshman STEM majors with mathematics ACT scores of 19 to 23 to participate in MTSU’s “Mathematics as a FirstSTEP to Success in STEM” project (FirstSTEP). This article overviews MTSU’s FirstSTEP research approach and demonstrates that introductory, early research experiences can be successful for mathematics projects. At the end of their freshman year, mathematics students can develop a foundation to conduct mathematical research, increase depth of content knowledge, and build confidence in their technical abilities.

Assessment of Student Understanding of the Central Limit Theorem

What is classroom or action research? In the spring semester of 2004, we used a classroom research model (see supplemental material at www.amstat.org/ publications/chance) to investigate our students’ understanding of concepts related to sampling distributions of sample means and the Central Limit Theorem (CLT). Our goal, when implementing our teaching methods and assessing our students, was to build on the work of Robert delMas, Joan Garfield, and Beth Chance. We applied their “classroom research model” to Math 385, the first course of a two-semester, post-calculus mathematical probability and statistics sequence taught at the University of Alabama in Huntsville (a small engineeringand science-oriented, PhD-granting university in the southeastern United States with an approximate undergraduate enrollment of 5,000). The CLT is one of the most fundamental and important results in probability and statistics. For students who are learning statistics, it provides a gateway from probability and descriptive statistics to inferential statistics. Recall that, from properties of mathematical expectation, we know the sample mean statistic, , is an unbiased estimator of the population