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 MATH/STAT 101, STAT 160, STAT 218, MATH/STAT 220 (prior to spring 2018), BISC 198, ECON 103/SOC 190, POL 299, or PSYC 205. Not open to students who have received AP credit in Statistics. Note that this course cannot be used as a prerequisite for upper-level courses in statistics or econometrics including STAT 260 and ECON 203.

Instructor: Calvin Cochran, Charles Bu

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Degree Requirements: DL - Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL)

Typical Periods Offered: Fall and Spring

Semesters Offered this Academic Year: Spring

Notes:

An introduction to the fundamental ideas and methods of statistics for analyzing data. Topics include descriptive statistics, inference, and hypothesis testing. This course introduces statistical concepts from the perspective of statisticians and mathematicians, with concepts illustrated by simulation. Students will engage with statistics using the data analysis software R. Designed for students who plan to continue to study statistics and/or apply statistical methods to future work in the sciences or other fields. The course is accessible to those who have not yet had calculus.

Units: 1

Max Enrollment: 25

Prerequisites: Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement. Not open to students who have taken or are taking MATH 205, MATH 101/STAT 101, STAT 218, STAT 220, ECON 103/SOC 190, PSYC 205, BISC 198, POL 299, QR 260/STAT 260, STAT 318 or the QAI Summer Course.

Instructor: Calvin Cochran, Oscar Fernandez

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Degree Requirements: DL - Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL)

Typical Periods Offered: Spring

Semesters Offered this Academic Year: Spring

Notes:

This is a calculus-based introductory statistics course. Topics covered include data collection, data visualization, descriptive statistics, linear regression, sampling schemes, design of experiment, probability, random variables (both discrete and continuous cases), Normal model, statistical tests and inference (e.g. one-sample and two-sample z-tests and t-tests, chi-square test, etc). Statistical language R will be used throughout the course to realize data visualization, linear regression, simulations, and statistical tests and inference.

Units: 1

Max Enrollment: 25

Instructor: W. Wang

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Degree Requirements: DL - Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL)

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Spring; Fall

Notes:

Probability is the mathematics of uncertainty. We begin by developing the basic tools of probability theory, including counting techniques, conditional probability, and Bayes's Theorem. We then survey several of the most common discrete and continuous probability distributions (binomial, Poisson, uniform, normal, and exponential, among others) and discuss mathematical modeling using these distributions. Often we cannot calculate probabilities exactly, and we need to approximate them. A powerful tool here is the Central Limit Theorem, which provides the link between probability and statistics. Another strategy when exact results are unavailable is simulation. We examine Markov chain Monte Carlo methods, which offer a means of simulating from complicated distributions.

Units: 1

Max Enrollment: 25

Crosslisted Courses: STAT 220

Prerequisites: MATH 205

Instructor: Tannenhauser

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Semesters Offered this Academic Year: Fall

Notes:

This course introduces the theory of statistical inference: given a data set, how do we estimate the parameters of probabilistic models like those introduced in MATH 220/STAT 220? What is the optimal way to make use of the information in our data? Topics include the theories that underlie traditional hypothesis testing and confidence intervals, such as maximum likelihood inference and sufficiency. The course will also cover Bayesian techniques for point and interval estimation and resampling approaches, such as the bootstrap.

Units: 1

Max Enrollment: 25

Prerequisites: MATH 220/STAT 220. Not open to students who have completed MATH 221.

Instructor: Pattanayak

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Spring

Notes:

This is a course in multivariate data analysis. Students will be introduced to modern multivariate techniques, their applications and interpretations, and will learn how to use these methods to understand relationships between variables, extract patterns, or identify clusters or classifications in a rich data set involving multiple variables. Topics covered during the semester include both dependence techniques (e.g. multiple linear regression, binary logistic regression, multinomial logistic regression, principal component analysis, linear discriminant analysis, decision trees, etc) and interdependence techniques (e.g. factor analysis, cluster analysis, etc). A selection of topics in machine learning and data mining are also introduced during the semester. Statistical language R is used in this class.

Units: 1

Max Enrollment: 20

Prerequisites: MATH 205 and (STAT 218 or STAT 260 or STAT 318).

Instructor: W. Wang

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Semesters Offered this Academic Year: Spring

Notes:

Units: 1

Max Enrollment: 25

Prerequisites: Permission of the instructor. Open to juniors and seniors.

Instructor:

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Spring; Fall

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, missing data, and causal inference. 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, offered through Wellesley's Quantitative Analysis Institute, 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: 25

Crosslisted Courses: STAT 260

Prerequisites: Any introductory statistics course (any one of BISC 198, ECON 103/SOC 190, MATH/STAT 101, STAT 160, STAT 218, POL 299, 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.

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 QRDL); DL - Data Literacy (Formerly QRF)

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Not Offered

Notes:

This is an applied regression analysis course that involves hands-on data analysis. Topics covered during the semester include simple and multiple linear regression models, model diagnostics and remedial measures, matrix representation of linear regression models, model comparison and selection, generalized linear regression models (e.g. binary logistic regression, multinomial logistic regression, ordinal logistic regression, and Poisson regression), and basic time-series autoregressive AR(p) models. Statistical language R will be used throughout the course to realize fitting linear (or generalized linear) regressions models, model diagnostics, model comparison and selection, and simulations.

Units: 1

Max Enrollment: 15

Prerequisites: STAT 218, MATH 205 and MATH 206. (STAT 218 can be replaced by STAT 160, ECON 103, or STAT 260.)

Instructor: W. Wang

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Semesters Offered this Academic Year: Fall

Notes:

Units: 1

Max Enrollment: 25

Prerequisites: Permission of the instructor. Open to juniors and seniors.

Instructor:

Typical Periods Offered: Spring; Fall

Semesters Offered this Academic Year: Fall; Spring