Christine Franklin

A method for predicting maximal strength in collegiate women athletes.

The purpose of this study was to develop a regression equation capable of accurately predicting a 1 repetition maximum bench press in collegiate women athletes. The findings of this study could benefit future women athletes by providing coaches and trainers with an easy method of determining maximum upper body strength in women athletes. Sixty-five University of Georgia NCAA Division 1 women athletes from 9 different sports were measured prior to the start of their season utilizing 2 repetition tests to fatigue (25 kg: REPS55; 31.8 kg: REPS70) and a 1 repetition maximum (1RM) bench press test in random order. Other independent variables that were used with a submaximal weight to predict 1RM were total body weight, lean body mass (LBM), height, and percent body fat. The variables of REPS70 and LBM were the best predictors of 1RM utilizing Pearson product correlations (r = 0.909, p = 0.000; r = 0.445, p = 0.000) and multiple regression results (R2 = 0.834, p = 0.000) for this population. The results from this study indicate muscular endurance repetitions using an absolute weight of 31.8 kg in conjunction with LBM can be used to accurately predict 1RM bench press strength in collegiate women athletes.

The Effects of the Environment on Physical Activity Patterns of Children with Mental Retardation

For children with mental retardation, the prevailing attitude is that fitness and overall functioning is lower because they are not as active during the course of the school day and have limited opportunities to participate in physical activities available to their peers. Children with mental retardation tend to be less fit compared to children without disabilities (Horvat & Croce, 1995; Pitetti & Fernhall, 1997). In addition, it has been suggested that children with mental retardation tend to be more sedentary and participate in fewer active play and leisure pursuits compared to children without mental retardation (Schleien, Ray,& Green, 1997; Horvat, Malone, & Deener, 1993). A rational for this inactivity may involve the lack of role models or opportunities for play activity or placement in segregated settings (Block, 1994; Levinson & Reid, 1991; Schleien, et al., 1997). In contrast, when children are involved in structured exercise programs, training responses are similar to those in children without mental retardation (Croce & Horvat, 1992; Horvat, Croce, & MeGhee, 1993). Currently, the status of physical activity patterns in children with mental retardation is not sufficiently documented. We need to present a clear picture not only of how and where they move but also of the energy costs and intensity levels of their activity.To facilitate activity for children with mental retardation, a systematic method ofobserving children should be used to measure their activity patterns. Physical activity must be measured in the natural environment to obtain an accurate assessment and cost

A Capstone Course for Undergraduate Statistics Majors

Many undergraduate statistics students receive limited exposure to real data and the challenges of real data analysis. To help improve our undergraduate program at the University of Georgia, we developed a Statistics Capstone Course. The course has three main threads: (1) teaching advanced/modern statistical methods to undergraduate statistics students; (2) giving these students an intensive, year-long data-analysis experience; and (3) providing the students with an opportunity to improve their written and oral communication skills. In this article, we describe the philosophy behind the Capstone Course, detail its implementation, and informally evaluate the success of our endeavor.

Assessment of Learning, for Learning, and as Learning in Statistics Education

Assessing student learning of statistics poses unique challenges to mathematics teachers at the elementary and secondary level. This chapter describes some guiding principles for developing or selecting assessment items, building on general pillars of good assessment practice as well as important features of the discipline of statistics. The chapter concludes with some specific recommendations regarding the improvement of assessment of student learning of statistics.

AP Statistics: Building Bridges Between High School and College Statistics Education

After providing a brief history of the AP Statistics program and a description of the AP Statistics course content, exam and grading, the paper presents a discussion of current challenges for statistics education in the schools and a look at opportunities for the statistics profession, especially college faculty, to aid the AP Statistics program so as to improve statistics teaching in both venues and thus strengthen the quantitative literacy of future generations of high school or college graduates. This article has supplementary material online.

Predicting Strength in High School Women Athletes

The purpose of this study was to investigate the hypothesis that a repetitions-to-maximum test is a predicator of a 1 repetition maximum (1RM) performance for evaluating upper and lower body strength in women high school athletes. Fifty-seven high school athletes ages 14–18 participated in this study. All of the participants completed a 1RM bench (1RMBP) and leg press (1RMLP) test, as well as leg press repetitions-to-fatigue (91 kg; LPRTF91) and bench press repetitions-to-fatigue (27 kg; BPRTF27) tests. A Pearson product correlation and regression analysis was used to determine relationships between 1RM strength and the repetitions-to-fatigue for upper and lower body strength. On the basis of the data analysis, it was concluded that BPRTF27 had a high correlation with 1RMBP strength (r = 0.802) and LPRTF91 had a correlation with 1RMLP strength (r = 0.793), indicating that these test measures were viable alternatives to 1RM testing for strength assessment. A regression analysis further confirmed that BPRTF27 was a significant variables in developing the model 1RMBP = 28.88 ° (0.68)BPRTF27 for predicting upper body strength (p − 0.001). Similar results occurred 1RMLP = 106.3 ° (2.4)LPRTF91 in developing the lower body model (p − 0.001). From data analysis, it was concluded that repetitions-to-fatigue testing can be used to evaluate upper and lower body strength in women high school athletes.

Simulation of the Sampling Distribution of the Mean Can Mislead.

Although the use of simulation to teach the sampling distribution of the mean is meant to provide students with sound conceptual understanding, it may lead them astray. We discuss a misunderstanding that can be introduced or reinforced when students who intuitively understand that “bigger samples are better” conduct a simulation to explore the effect of sample size on the properties of the sampling distribution of the mean. From observing the patterns in a typical series of simulated sampling distributions constructed with increasing sample sizes, students reasonably—but incorrectly—conclude that, as the sample size, n, increases, the mean of the (exact) sampling distribution tends to get closer to the population mean and its variance tends to get closer to ś2/n, where ś2 is the population variance. We show that the patterns students observe are a consequence of the fact that both the variability in the mean and the variability in the variance of simulated sampling distributions constructed from the means of N random samples are inversely related, not only to N, but also to the size of each sample, n. Further, asking students to increase the number of repetitions, N, in the simulation does not change the patterns.

The Statistical Education of Teachers: Preparing Teachers to Teach Statistics

19 In an increasingly data-driven world, statistical literacy is becoming an essential competency, not only for researchers conducting formal statistical analyses, but also for informed citizens making everyday decisions based on data. As Mark van der Laan recently wrote in Amstat News (see http://magazine. amstat.org/blog/2015/02/01/statscience_feb2015), “We need to take the field of statistics (i.e., the science of learning from data) seriously.” Because statistics and data are everywhere, it is imperative that we educate a population of data-literate people. If not in school, where are individuals expected to learn to be data literate? Teachers need to be able to teach students how to navigate the data world. They are the gatekeepers of the transfer of knowledge to young people. If teachers are the key, then they must be educated in statistics and on how to teach the subject at the different school levels. The Statistical Education of Teachers (SET) report (www.amstat.org/education/ SET/SET.pdf), commissioned by the American Statistical Association (ASA), addresses how teachers should be prepared in statistics. The purpose of this article is to describe the recommendations put forth in the report.

Interview with Christine Franklin.

freely shared among individuals, but it may not be republished in any medium without express written consent from the authors and advance notification of the editor. Beginnings AR: Thanks very much, Chris, for agreeing to be interviewed for the Journal of Statistics Education. How did you come to study statistics? CF: Allan, I am so appreciative of this opportunity to share my devotion to statistics education with you and the readers of JSE. My plan as an undergraduate student was to pursue a career in law. As a political science major, I found that the elegant problem solving in mathematics and statistics courses (initially taken as electives) was a refreshing alternative to the nuances of politics and law. I also began to understand the importance of statistics in the field of political science. A month away from attending University of North Carolina Law School, I made the decision to attend graduate school in mathematics and statistics. My goal with that decision was to pursue a career in public administration utilizing my undergraduate studies in political science