In this course, students develop and apply mathematical, logical, and statistical skills to solve problems in authentic contexts. The quantitative skills emphasized include algebra, geometry, probability, statistics, estimation, and mathematical modeling. Throughout the course, these skills are used to solve real world problems, from personal finance to medical decision-making. A student passing this course satisfies the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement. This course is required for students who do not satisfy the QR component of the QR & DL requirement via the Quantitative Reasoning Assessment. Those who satisfy the QR Assessment, but still want to enroll in this course must receive permission of instructor.

This course is intended to provide students with the skills necessary to digest, critique, and express every-day statistics and to use statistical thinking to answer questions in their own lives. Students will be exposed to and produce descriptive statistics, including measures of central tendency & spread, as well as common visual representations of data. The bulk of the class will be devoted to giving students the tools needed to analyze and critique statistical claims, including an understanding of the dangers of confounding variables and bias, the advantages and limitations of various study designs and statistical inference, and how to carefully read and parse claims which attempt to use numbers to sway their audience. The class will examine this material in authentic contexts such as political polling, medical decision making, online dating, and personal finance. This course is primarily aimed at students whose majors do not require mathematics or statistics.

This is an intermediate statistics course focused on fundamentals of statistical inference and applied data analysis tools. Emphasis on thinking statistically, evaluating assumptions, and developing practical skills for real-life applications to fields such as medicine, politics, education, and beyond. Topics include t-tests and non-parametric alternatives, analysis of variance, linear regression, model refinement and missing data. Students can expect to gain a working knowledge of the statistical software R, which will be used for data analysis and for simulations designed to strengthen conceptual understanding. This course can be counted toward the major or minor in Mathematics, Statistics, Data Science, Economics, Environmental Studies, Psychology or Neuroscience. Students who earned a Quantitative Analysis Institute Certificate are not eligible for this course.

This course focuses on statistical methods for causal inference, with an emphasis on how to frame a causal (rather than associative) research question and design a study to address that question. What implicit assumptions underlie claims of discrimination? Why do we believe that smoking causes lung cancer? We will cover both randomized experiments – the history of randomization, principles for experimental design, and the non-parametric foundations of randomization-based inference – and methods for drawing causal conclusions from non-randomized studies, such as propensity score matching. Students will develop the expertise necessary to assess the credibility of causal claims and master the conceptual and computational tools needed to design and analyze studies that lead to causal inferences. Examples will come from economics, psychology, sociology, political science, medicine, and beyond. Previous exposure to the statistical software R is expected; students who have not previously coded in R may enroll with permission of the instructor but should expect to put in additional effort to learn this skill.