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.
Units: 1
Max Enrollment: 13
Prerequisites: Open to First-Year students who did not satisfy the QR component of the QR & DL requirement via the QR Assessment.
Instructor: Staff
Distribution Requirements: MM - Mathematical Modeling and Problem Solving
Degree Requirements: QR - Quantitative Reasoning (Formerly QRB)
Typical Periods Offered: Spring; Fall
Semesters Offered this Academic Year: Spring; Fall
Notes:
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.
Units: 1
Max Enrollment: 25
Crosslisted Courses: STAT 150
Prerequisites: Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement. Not open to students who have completed another introductory statistics course at Wellesley, including STAT 160, STAT 218, BISC 198, ECON 103/SOC 190, POL 299, PSYC 105 or PSYC 205. Not open to students who have received AP credit in Statistics.
Instructor: Bu, Schultz
Distribution Requirements: MM - Mathematical Modeling and Problem Solving
Degree Requirements: DL - Data Literacy (Formerly QRDL); DL - Data Literacy (Formerly QRF)
Typical Periods Offered: Fall and Spring
Semesters Offered this Academic Year: Fall; Spring
Notes: Note that this course cannot be used as a prerequisite for upper-level courses in statistics or econometrics including STAT 260 and ECON 203.
Epidemiology is the study of the distribution and determinants of disease and health in human populations and the application of this understanding to the solution of public health problems. Topics include measurement of disease and health, the outbreak and spread of disease, reasoning about cause and effect with attention to study designs and sources of bias, analysis of risk, and the evaluation of trade-offs. The course will emphasize women’s health topics such as mammography and breast cancer. The course is designed to fulfill and extend the professional community’s consensus definition of undergraduate epidemiology. In addition to the techniques of modern epidemiology, the course emphasizes the historical evolution of ideas of causation, treatment, and prevention of disease.
Units: 1
Max Enrollment: 20
Prerequisites: Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement.
Instructor: Polito
Distribution Requirements: NPS - Natural and Physical Sciences
Degree Requirements: DL - Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL)
Typical Periods Offered: Spring
Semesters Offered this Academic Year: Not Offered
Notes:
Units: 1
Max Enrollment: 10
Prerequisites: Permission of the instructor.
Instructor:
Typical Periods Offered: Spring; Fall
Semesters Offered this Academic Year: Fall; Spring
Notes:
Units: 0.5
Max Enrollment: 10
Prerequisites: Permission of the instructor.
Instructor:
Typical Periods Offered: Spring; Fall
Semesters Offered this Academic Year: Spring; Fall
Notes: Mandatory Credit/Non Credit.
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, multiple comparisons, 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 as a 200-level course toward the major or minor in Mathematics, Statistics, Economics, Environmental Studies, Psychology or Neuroscience. Students who earned a Quantitative Analysis Institute Certificate are not eligible for this course.
Units: 1
Max Enrollment: 24
Crosslisted Courses: STAT 260
Prerequisites: Any introductory statistics course (BISC 198, ECON 103/SOC 190, STAT 160, STAT 218, POL 299, PSYC 105 or PSYC 205).
Instructor: Pattanayak
Distribution Requirements: MM - Mathematical Modeling and Problem Solving
Typical Periods Offered: Fall
Semesters Offered this Academic Year: Fall
Notes:
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 but should expect to put in additional effort to learn this skill.
Units: 1
Max Enrollment: 15
Crosslisted Courses: STAT 30 9
Prerequisites: Any one of QR 260/STAT 260, STAT 318, ECON 203, SOC 290, PSYC 305 or a Psychology 300-level R course; or a Quantitative Analysis Institute Certificate; or permission of the instructor.
Instructor: Pattanayak
Distribution Requirements: SBA - Social and Behavioral Analysis
Degree Requirements: DL - Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL)
Typical Periods Offered: Every other year
Semesters Offered this Academic Year: Spring
Notes: