Logistic regression (LoR) is a foundational supervised machine learning algorithm and yet, unlike linear regression, appears rarely taught early on, where analogy and proximity to linear regression would be an advantage. A random sample of 50 syllabi from undergraduate business statistics courses shows only two percent of the courses included LoR.…

Using data from 499 students over 12 sections, 2 courses, and 3 instructors, we estimate the effect of loss aversion on the probability of turning in extra credit assignments and the effect on the overall grade. Regression results indicate no effect of loss aversion on the probability of turning in extra credit assignments and no effect on a…

The authors apply a regression-discontinuity design to identify the causal impact of letter grades on student effort within a course, subsequent credit hours taken, and the probability of majoring in economics. Their methodology addresses key issues in identifying the causal impact of letter grades: correlation with unobservable factors, such as…

The quantitative results of Sources of Self-Efficacy in Science Courses-Physics (SOSESC-P) are presented as a logistic regression predicting the passing of students in introductory Physics with Calculus I, overall as well as disaggregated by gender. Self-efficacy as a theory to explain human behavior change [Bandura [1977] "Psychological…

Stochastic understanding of probability distribution undergirds development of conceptual connections between probability and statistics and supports development of a principled understanding of statistical inference. This study investigated the impact of an instructional course intervention designed to support development of stochastic…

We present a data set consisting of user profile data for 59,946 San Francisco OkCupid users (a free online dating website) from June 2012. The data set includes typical user information, lifestyle variables, and text responses to 10 essay questions. We present four example analyses suitable for use in undergraduate introductory probability and…

This investigation sought to determine when colleges should conduct assessments to identify first-year students at risk of dropping out. Thirty-five variables were used to predict the persistence of 2,024 first-year students from four universities in the southeastern United States. The predictors were subdivided into groups according to when they…

The author employs a regression discontinuity design to provide direct evidence on the effects of grades earned in economics principles classes on the decision to major in economics and finds a differential effect for male and female students. Specifically, for female students, receiving an A for a final grade in the first economics class is…

Considering the range of changes in the instruction and learning of statistics, several questions emerge regarding how those changes influence students' attitudes. Equally, other questions emerge to reflect that statistics is a fundamental course in the university academic programs because of its relevance to the professional development of the…

This research examines the effect of undergraduate course letter grades on future course selection and major choice. Using a Regression-Discontinuity design, we exploit the fact that the probability of earning a particular letter grade jumps discontinuously around letter grade cutoffs. This variation in letter grades allows us to isolate the…