# Statistics Program

The Five College Statistics Program was created in 2011 to enable statistics faculty members at the five campuses to coordinate and integrate resources to better serve our statistics and data science students.

**“Distinguishing the signal from the noise requires both scientific knowledge and self-knowledge: the serenity to accept the things we cannot predict, the courage to predict the things we can, and the wisdom to know the difference.” - ***Nate Silver (Five Thirty Eight), The Signal and the Noise: Why So Many Predictions Fail - But Some Don't*

**"Statisticians now collaborate with the scientists generating the data to develop innovative new theory and methods to tackle problems never envisioned." **- *Marie Davidian (North Carolina State University)*

**"Data is the sword of the 21st century, those who wield it well, the Samurai."***- Jonathan Rosenberg* *(former Google Inc. Senior Vice President)*

The time to become statistically literate is now. The Five College Statistics Program was created in 2011 to enable statistics faculty members at the five campuses to coordinate and integrate resources to better serve our statistics and data science students.

Whether you want to take an introductory statistics or data science class or pursue elective course offerings, the Five Colleges has courses and programs of study just waiting for you. There are undergraduate majors in statistics and data science at Smith, Mount Holyoke, and Amherst Colleges, undergraduate and graduate programs at the University of Massachusetts, and growth in faculty staffing and enrollments at all of the institutions that make up the Five Colleges.

On this page, you can find resources, including links to statistics courses at each school, statistics faculty on each campus, news and events, announcements and more.

The Five College Statistics Program is committed to fostering closer ties between the faculty members teaching statistics and facilitating additional curricular cooperation to continue the strong statistical presence in the Valley. The Five College Statisticians meet on a regular basis to coordinate activities and curricular offerings.

## Faculty

## Courses

### Fall 2021 Courses

Amy Wagaman

MWF 09:00AM-09:50AM

WEBS 102

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111 or STAT 135. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected

Requisite: STAT 111 or 135. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Fall Professor Liao, Spring Professor Liao, Professor Matheson

Amy Wagaman

MWF 11:00AM-11:50AM

WEBS 102

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111 or STAT 135. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment. Topics covered will include ethics, experimental design, resampling approaches, analysis of variance models, multiple regression, model selection, and logistic regression. No prior experience with statistical software is expected

Requisite: STAT 111 or 135. Limited to 24 students. Four spots reserved for incoming first-year students in each Fall section. Fall and Spring semester. Fall Professor Liao, Spring Professor Liao, Professor Matheson

Brittney Bailey

TTH 08:30AM-09:50AM

WEBS 102

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools

Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department.

Brittney Bailey

TTH 11:30AM-12:50PM

WEBS 102

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools

Requisite: STAT 111 or STAT 135 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department.

Kevin Donges

MWF 01:30PM-02:20PM

SMUD 206

(Offered as STAT 360 and MATH 360) This course explores the nature of probability and its use in modeling real world phenomena. There are two explicit complementary goals: to explore probability theory and its use in applied settings, and to learn parallel analytic and empirical problem-solving skills. The course begins with the development of an intuitive feel for probabilistic thinking, based on the simple yet subtle idea of counting. It then evolves toward the rigorous study of discrete and continuous probability spaces, independence, conditional probability, expectation, and variance. Distributions covered include the binomial, hypergeometric, Poisson, normal, Gamma, Beta, multinomial, and bivariate normal. Other topics include generating functions, order statistics, and limit theorems.

Requisite: MATH 121 or consent of the instructor. Limited to 24 students. Fall semester. Professor Donges.

Kevin Donges

MWF 03:00PM-03:50PM

SMUD 206

(Offered as STAT 360 and MATH 360) This course explores the nature of probability and its use in modeling real world phenomena. There are two explicit complementary goals: to explore probability theory and its use in applied settings, and to learn parallel analytic and empirical problem-solving skills. The course begins with the development of an intuitive feel for probabilistic thinking, based on the simple yet subtle idea of counting. It then evolves toward the rigorous study of discrete and continuous probability spaces, independence, conditional probability, expectation, and variance. Distributions covered include the binomial, hypergeometric, Poisson, normal, Gamma, Beta, multinomial, and bivariate normal. Other topics include generating functions, order statistics, and limit theorems.

Requisite: MATH 121 or consent of the instructor. Limited to 24 students. Fall semester. Professor Donges.

Ryan McShane

TTH 01:00PM-02:20PM

SCCE E208

Competitions, which can include individual and team sports, eSports, tabletop gaming, preference formation, and elections, produce data dependent on interrelated competitors and the decision, league, or tournament format. In this course, students will learn to think about the ways a wide variety of statistical methodologies can be applied to the complex and unique data that emerge through competition, including paired comparisons, decision analysis, rank-based and kernel methods, and spatio-temporal methods. The course will focus on the statistical theory relevant to analyzing data from contests and place an emphasis on simulation and data visualization techniques. Students will develop data collection, wrangling,combination, exploration, analysis, and interpretation skills individually and in groups. Applications may include rating players and teams, assessing shot quality, animating player tracking data, roster construction, comparing alternative voting systems, developing optimal strategies for games, and predicting outcomes. Prior experience with probability such as STAT 360 may be helpful, but is not required.

Requisite: STAT 230 and STAT 231. Limited to 24 students. Fall semester. Professor McShane.

Nicholas Horton

MWF 10:00AM-10:50AM

SMUD 205

Our world is awash in data. To allow decisions to be made based on evidence, there is a need for statisticians to be able to make sense of the data around us and communicate their findings. In this course, students will be exposed to advanced statistical methods and will undertake the analysis and interpretation of complex and real-world datasets that go beyond textbook problems. Course topics will vary from year to year depending on the instructor and selected case studies but will include static and dynamic visualization techniques to summarize and display high dimensional data, advanced topics in design and linear regression, ethics, and selected topics in data mining. Other topics may vary but might include nonparametric analysis, spatial data, and analysis of network data. Through a series of case studies, students will develop the capacity to think and compute with data, undertake and assess analyses, and effectively communicate their results using written and oral presentation.

Requisite: STAT 230, STAT 231, STAT 370, and the computing requirement; or consent of the instructor. Recommended requisite: STAT 231. Limited to 20 students. Fall semester. Professor Wagaman.

Elizabeth Conlisk

10:30AM-11:50AM TU;10:30AM-11:50AM TH

Cole Science Center 316;Cole Science Center 316

Amy Nussbaum

TTH 10:00AM-11:15AM

Clapp Laboratory 407

Amy Nussbaum

TTH 11:30AM-12:45PM

Clapp Laboratory 407

Pramesh Subedi

TTH 10:00AM-11:15AM

Clapp Laboratory 402

Marie Ozanne

TTH 01:45PM-03:00PM

Clapp Laboratory 402

Marie Ozanne

TTH 08:30AM-09:45AM

Clapp Laboratory 402

Laurie Tupper

MWF 01:45PM-03:00PM

Clapp Laboratory 402

Laurie Tupper

MWF 11:30AM-12:45PM

Clapp Laboratory 401

Jordan Crouser

M W 1:20 PM - 2:35 PM

Stoddard G2

Scott J. LaCombe

TU TH 1:20 PM - 2:35 PM

Burton 219

Massachusetts’s decision to legalize recreational marijuana influence Vermont’s marijuana policies?

From declarations of war to the decision of who congressmembers will vote with, social scientists are

increasingly looking to political networks to recognize the inter-connectedness of the world around us.

This course will overview the essentials of social network analysis and how they are applied to give us

a better understanding of American politics. Prerequisites: SDS 220 or an equivalent introductory statistics course.

Katherine Taylor Halvorsen

TU TH 10:50 AM - 12:05 PM

Burton 301

Jordan Crouser

M W 1:20 PM - 2:35 PM

Stoddard G2

Albert Y. Kim

M W F 10:50 AM - 12:05 PM

Stoddard G2

Albert Y. Kim

M W F 9:25 AM - 10:40 AM

Sabin-Reed 220

William Hopper

M W F 9:25 AM - 10:40 AM

McConnell 404

William Hopper

TU 9:25 AM - 10:40 AM

Sabin-Reed 301

William Hopper

TU 10:50 AM - 12:05 PM

Sabin-Reed 301

Katherine M. Kinnaird

M W F 10:50 AM - 12:05 PM

Sabin-Reed 301

Fatou Sanogo

M W F 1:20 PM - 2:35 PM

Sabin-Reed 301

Katherine M. Kinnaird

TH 9:25 AM - 10:40 AM

Sabin-Reed 301

Katherine M. Kinnaird

TH 10:50 AM - 12:05 PM

Sabin-Reed 301

Fatou Sanogo

TH 1:20 PM - 2:35 PM

Sabin-Reed 301

Fatou Sanogo

TH 2:45 PM - 4:00 PM

Sabin-Reed 301

Lindsay Poirier

TU TH 9:25 AM - 10:40 AM

Seelye 106

Randi Garcia

TU TH 10:50 AM - 12:05 PM

McConnell 103

William Hopper

TU TH 1:20 PM - 2:35 PM

Sabin-Reed 220

Randi Garcia

TU TH 2:45 PM - 4:00 PM

Sabin-Reed 220

Scott J. LaCombe

TU TH 1:20 PM - 2:35 PM

Burton 219

Massachusetts’s decision to legalize recreational marijuana influence Vermont’s marijuana policies?

From declarations of war to the decision of who congressmembers will vote with, social scientists are

increasingly looking to political networks to recognize the inter-connectedness of the world around us.

This course will overview the essentials of social network analysis and how they are applied to give us

a better understanding of American politics. Prerequisites: SDS 220 or an equivalent introductory statistics course.

Anna Liu,Krista Gile

TU TH 2:30PM 3:45PM

Hasbrouck Laboratory room 130

Haben Michael

M W 2:30PM 3:45PM

Lederle Grad Res Tower Rm 177

Joanna Jeneralczuk

TU TH 11:30AM 12:45PM

Lederle Grad Res Ctr rm A301

Yueqiao Zhang

M W 2:30PM 3:45PM

Lederle Grad Res. Ctr rm A201

Yalin Rao

M W F 11:15AM 12:05PM

Lederle Grad Res. Ctr rm A201

Yalin Rao

M W F 10:10AM 11:00AM

Lederle Grad Res. Ctr rm A201

Luc Rey-Bellet

TU TH 10:00AM 11:15AM

Hasbrouck Laboratory room 137

Brian Van Koten

TU TH 2:30PM 3:45PM

Tobin Hall room 204

Jiayu Zhai

M W 4:00PM 5:15PM

Hasbrouck Laboratory room 137

Budhinath Padhy

TU TH 4:00PM 5:15PM

Lederle Grad Res. Ctr rm A201

Haben Michael

M W 4:00PM 5:15PM

Lederle Grad Res. Ctr rm A201

Shixiao Zhang

TU TH 8:30AM 9:45AM

Lederle Grad Res. Ctr rm A201

Shixiao Zhang

TU TH 2:30PM 3:45PM

Lederle Grad Res. Ctr rm A201

Daeyoung Kim

TU TH 8:30AM 9:45AM

Integ. Learning Center S231

Budhinath Padhy

TU TH 1:00PM 2:15PM

Lederle Grad Res Ctr rm A203

Patrick Flaherty

TU TH 2:30PM 3:45PM

Morrill 1 N 347

Shai Gorsky

W 6:00PM 8:30PM

Sch of Design@MountIda Rm 105

Krista Gile,Anna Liu

TU 1:00PM 2:15PM

Lederle Grad Res Tower Rm 143

Hyunsun Lee

M 6:00PM 8:30PM

Sch of Design@MountIda Rm 105

John Staudenmayer

M W F 11:15AM 12:05PM

Lederle Grad Res Tower Rm 173

Krista Gile

TU TH 10:00AM 11:15AM

Hasbrouck Laboratory room 230

Shai Gorsky

TU 6:00PM 8:30PM

Sch of Design@MountIda Rm 105

Erin Conlon

SA 1:00PM 3:30PM

Sch of Design@MountIda Rm 105

Zijing Zhang

TH 6:00PM 8:30PM

Sch of Design@MountIda Rm 105

Patrick Flaherty

TU TH 1:00PM 2:15PM

Lederle Grad Res Tower rm 1334

Hyunsun Lee

W 6:00PM 8:30PM

Sch of Design@MountIda Rm 101

John Staudenmayer

M W F 10:10AM 11:00AM

Lederle Grad Res Tower Rm 173

Theodore Westling

TU TH 2:30PM 3:45PM

Hasbrouck Laboratory room 136

## News

Winners for the Five College Statistics award for 2021 are:

- Amherst College: Breanna Richards ’21 and Maria-Cristiana (Kitty) Gîrjău ’21
- Hampshire College: Molly Dent and Flynn Hibbs
- Mount Holyoke College: Huong (Amelia) Tran '21
- Smith College: Yujia (Starry) Zhou '21 and Hannah Snell '21
- UMass Statistics: Erica Laider and Ka Wing Cheung

Congratulations to all!

Professor Nick Horton, Beitzel Professor of Technology and Society (Statistics and Data Science), Department of Mathematics and Statistics, Amherst College, has been elected as vice president of the American Statistical Association (ASA). Professor Horton's term begins in January 2021; he will serve with ASA president-elect Dionne Price, who will become the first African-American president of the ASA.

Professor Miles Ott, Assistant Professor of Statistical and Data Sciences is the 2021 LGBTQ+ Educator of the Year. This award is given an educator for significant impact on STEM students "through teaching, advocacy, and role modeling."

Congratulations to Professor Ott on this honor!

The Five Colleges had a strong showing in the Fall 2020 Undergraduate Statistics Project Competition, with winners in the Electronic Undergraduate Statistics Research Conference (eUSR), the Undergraduate Statistics Class Project (USCLAP), and the Undergraduate Statistics Research Project (USRESP).

Congratulations to our Fall 2020 competition winners:

- Amelia Tran (Mount Holyoke) - eUSR Best Video Presentation
- Xian Ye, Hannah Snell, Dianne Caravela, and Natalia Iannucci (Smith) - USCLAP First Standing in Intermediate Statistics
- Juliet Ramey-Lariviere, Ivy Chen, and Kathleen Hablutzel (Smith) - USCLAP Honorable Mention in Intermediate Statistics
- Tyler Marshall (Amherst) - USRESP Third Standing

## Events

**DataFest 2021**

- DATE: April 9-11, 2021
- TIME: TBA
- LOCATION: virtual

DataFest is a nationally-coordinated undergraduate competition in which teams of up to 5 students work over a weekend to extract insight from a rich and complex data set. The mission of DataFest is to expose undergraduate students to challenging questions with immediate real-world significance that can be addressed through data analysis. Apart from developing data analysis and team building skills, students can win cash prizes, fame, glory, or some combination thereof… and will get a free t-shirt!

**Recurring Events**

## Resources

The Lorna M. Peterson Award supports scholarly and creative work by undergraduate students taking part in Five College programs. The prize is awarded annually based on nominations from Five College programs.

#### Campus Curricula

## Contact Us

**Five College Statistics Program Representatives:**

**Nicholas (Nick) Horton**, Beitzel Professor in Technology and Society, Department of Mathematics & Statistics, Amherst College (+ **Program Chair**)

**Marie Ozanne, **Clare Boothe Luce Assistant Professor of Statistics, Department of Mathematics & Statistics, Mount Holyoke College (+ **Webmaster**)

**Ben Baumer**, Associate Professor, Department of Statistical & Data Sciences, Smith College (+ **Secretary/Treasurer**)

**Evan Ray****, **Assistant Professor, Department of Biostatistics, UMass

**Haben Michael**, Assistant Professor, Department of Statistics, UMass

**Liz Conlisk**, Dean of Natural Science, Cognitive Science and Critical Social Inquiry and Professor of Public Health, Hampshire College

**Five College Staff Liaison:**

**Ray Rennard**, Director of Academic Programs