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.
(Offered as STAT 370 and MATH 370) This course examines the theory underlying common statistical procedures including visualization, exploratory analysis, estimation, hypothesis testing, modeling, and Bayesian inference. Topics include maximum likelihood estimators, sufficient statistics, confidence intervals, hypothesis testing and test selection, non-parametric procedures, and linear models.
Requisite: STAT 111 or STAT 135 and STAT 360, or consent of the instructor. Limited to 25 students. Spring semester. Professor Donges.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Ryan P. McShane
TTH 01:00 PM-02:20 PM
Amherst College
STAT-225-01-2122S
rmcshane@amherst.edu
This course is an introduction to nonparametric and distribution-free statistical procedures and techniques. These methods rely heavily on counting and ranking techniques and will be explored through both theoretical and applied perspectives. One- and two-sample procedures will provide students with alternatives to traditional parametric procedures, such as the t-test. We will also investigate correlation and regression in a nonparametric setting. A variety of other topics may be explored in the nonparametric setting including resampling techniques (for example, bootstrapping), categorical data and contingency tables, density estimation, and the one-way and two-way layouts for analysis of variance. The course will emphasize data analysis (with appropriate use of statistical software) and the intuitive nature of nonparametric statistics.
Requisite: STAT 111 or STAT 135. Limited to 24 students. Professor McShane.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Pamela B. Matheson
MWF 09:00 AM-09:50 AM
Amherst College
STAT-230-01-2122S
pmatheson@amherst.edu
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
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Shu-Min Liao
MWF 11:00 AM-11:50 AM
Amherst College
STAT-230-02-2122S
sliao@amherst.edu
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
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Amy S. Wagaman
TTH 08:30 AM-09:50 AM
Amherst College
STAT-231-01-2122S
awagaman@amherst.edu
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.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Amy S. Wagaman
TTH 11:30 AM-12:50 PM
Amherst College
STAT-231-02-2122S
awagaman@amherst.edu
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.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Kevin A. Donges
MWF 10:00 AM-10:50 AM
Amherst College
STAT-370-01-2122S
kdonges@amherst.edu
STAT-370-01, MATH-370-01
(Offered as STAT 370 and MATH 370) This course examines the theory underlying common statistical procedures including visualization, exploratory analysis, estimation, hypothesis testing, modeling, and Bayesian inference. Topics include maximum likelihood estimators, sufficient statistics, confidence intervals, hypothesis testing and test selection, non-parametric procedures, and linear models.
Requisite: STAT 111 or STAT 135 and STAT 360, or consent of the instructor. Limited to 25 students. Spring semester. Professor Donges.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Brittney E. Bailey
MW 08:30 AM-09:50 AM
Amherst College
STAT-456-01-2122S
bebailey@amherst.edu
Linear regression and logistic regression are powerful tools for statistical analysis, but they are only a subset of a broader class of generalized linear models. This course will explore the theory behind and practical application of generalized linear models for responses that do not have a normal distribution, including counts, categories, and proportions. We will also delve into extensions of these models for dependent responses such as repeated measures over time.
Requisite: STAT 230 and STAT 360. Limited to 20 students. Spring semester. Professor Bailey.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Laurie Tupper
MWF 11:30AM-12:45PM
Mount Holyoke College
116913
ltupper@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Marie Ozanne
MWF 01:45PM-03:00PM
Mount Holyoke College
116916
mozanne@mtholyoke.edu
In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Amy Nussbaum
TTH 10:00AM-11:15AM
Mount Holyoke College
116918
anussbau@mtholyoke.edu
The methods taught in traditional statistics courses are based on assumptions that are often not satisfied by real data sets. In this course we will learn about approaches that require fewer assumptions, known as nonparametric methods. After taking this course, students will be able to examine assumptions for different approaches to statistical inference, compare nonparametric statistical tests such as sign and Wilcoxon tests to their parametric equivalents, and implement non-parametric approaches using R. In addition, the course will incorporate computational techniques for statistical analysis, including simulation, permutation tests, and bootstrapping.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Marie Ozanne
MWF 03:15PM-04:30PM
Mount Holyoke College
116930
mozanne@mtholyoke.edu
This course is an introduction to the mathematical theory of statistics and to the application of that theory to the real world. Topics include probability, random variables, special distributions, introduction to estimation of parameters, and hypothesis testing.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Amy Nussbaum
TTH 01:45PM-03:00PM
Mount Holyoke College
117152
anussbau@mtholyoke.edu
John Tukey once said "the best thing about being a statistician is that you get to play in everyone's backyard" -- but when do statisticians learn how to play nice with others? In Statistical Consulting and Communication, students will implement techniques and methods they have learned elsewhere while simultaneously developing skills for communicating results to peers, collaborators, and clients, including best practices for reproducible research, technical writing, and public speaking. Furthermore, students will learn how to respond to questions commonly asked of statistical consultants, such as study design, sample size computations, and dealing with missing data. Finally, students will consider several aspects of ethics in statistics, including questions on informed consent and data manipulation.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Katherine Taylor Halvorsen
TU TH 9:25 AM - 10:40 AM
Smith College
MTH-320-01-202203
khalvors@smith.edu
SDS 320-01, MTH 320-01
Offered as MTH 320 and SDS 320. An introduction to the mathematical theory of statistics and to the application of that theory to the real world. Topics include functions of random variables, estimation, likelihood and Bayesian methods, hypothesis testing and linear models. Prerequisites: a course in introductory statistics, MTH 212 and MTH 246, or permission of the instructor. Enrollment limited to 12. Instructor permission required.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Randi Garcia
M W 1:20 PM - 2:35 PM
Smith College
PSY-364-02-202203
rgarcia@smith.edu
SDS 364-02, PSY 364-02
Offered as PSY 364 and SDS 364. Research on intergroup relationships and an exploration of theoretical and statistical models used to study mixed interpersonal interactions. Example research projects include examining the consequences of sexual objectification for both women and men, empathetic accuracy in interracial interactions, and gender inequality in household labor. A variety of skills including, but not limited to, literature review, research design, data collection, measurement evaluation, advanced data analysis, and scientific writing will be developed. Prerequisites: PSY 201, SDS 201, SDS 220 or equivalent and PSY 202.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Lindsay Poirier
M W F 10:50 AM - 12:05 PM
Smith College
SDS-192-01-202203
lpoirier@smith.edu
An introduction to data science using Python, R and SQL. Students learn how to scrape, process and clean data from the web; manipulate data in a variety of formats; contextualize variation in data; construct point and interval estimates using resampling techniques; visualize multidimensional data; design accurate, clear and appropriate data graphics; create data maps and perform basic spatial analysis; and query large relational databases. No prerequisites, but a willingness to write code is necessary.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
5.00
William Hopper
M W F 9:25 AM - 10:40 AM
Smith College
SDS-201-01-202203
whopper@smith.edu
(Formerly MTH/PSY 201). An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data. This course satisfies the basic requirement for the psychology major. Students who have taken MTH 111 or the equivalent should take SDS 220, which also satisfies the basic requirement. Normally students receive credit for only one of the following introductory statistics courses: SDS 201; PSY 201; ECO 220, GOV 190, SDS 220 or SOC 201.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
L01
0.00
William Hopper
TU 9:25 AM - 10:40 AM
Smith College
SDS-201-L01-202203
Sabin-Reed 301
whopper@smith.edu
(Formerly MTH/PSY 201). An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data. This course satisfies the basic requirement for the psychology major. Students who have taken MTH 111 or the equivalent should take SDS 220, which also satisfies the basic requirement. Normally students receive credit for only one of the following introductory statistics courses: SDS 201; PSY 201; ECO 220, GOV 190, SDS 220 or SOC 201.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
L02
0.00
William Hopper
TU 10:50 AM - 12:05 PM
Smith College
SDS-201-L02-202203
Sabin-Reed 301
whopper@smith.edu
(Formerly MTH/PSY 201). An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data. This course satisfies the basic requirement for the psychology major. Students who have taken MTH 111 or the equivalent should take SDS 220, which also satisfies the basic requirement. Normally students receive credit for only one of the following introductory statistics courses: SDS 201; PSY 201; ECO 220, GOV 190, SDS 220 or SOC 201.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
5.00
Albert Y. Kim
M W F 10:50 AM - 12:05 PM
Smith College
SDS-220-01-202203
Sabin-Reed 301
akim04@smith.edu
(Formerly MTH/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Prerequisite: MTH 111 or the equivalent, or permission of the instructor. Lab sections limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
5.00
Fatou Sanogo
M W F 1:20 PM - 2:35 PM
Smith College
SDS-220-02-202203
Sabin-Reed 301
fsanogo@smith.edu
(Formerly MTH/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics; random variables; probability and sampling distributions; point and interval estimates; hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. Classes meet for lecture/discussion and for a required laboratory that emphasizes analysis of real data. SDS 220 satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 190, ECO 220, SDS 220 or SOC 201. Exceptions may be allowed in special circumstances and require the permission of the adviser and the instructor. Prerequisite: MTH 111 or the equivalent, or permission of the instructor. Lab sections limited to 20.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Lindsay Poirier
M W 9:25 AM - 10:40 AM
Smith College
SDS-237-01-202203
lpoirier@smith.edu
This course introduces the theory and practice of data ethnography, demonstrating how qualitative data collection and analysis can be used to study of data settings and artifacts. Students will learn techniques in field-note writing, participant observation, in-depth interviewing, documentary analysis, and archival research and how they may be used to contextualize the cultural underpinnings of datasets. Students will learn how to visualize datasets in ways that foreground their sociopolitical provenance in R. Students will also learn how ethnographic methods can be leveraged to improve data documentation and communication. The course will introduce debates regarding the politics of technoscientific fieldwork. Prerequisites: SDS 192 is recommended.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Ben Baumer
TU TH 10:50 AM - 12:05 PM
Smith College
SDS-270-01-202203
bbaumer@smith.edu
This course is not about data analysis—rather, students will learn the R programming language at a deep level. Topics may include data structures, control flow, regular expressions, functions, environments, functional programming, object-oriented programming, debugging, testing, version control, documentation, literate programming, code review, and package development. The major goal for the course is to contribute to a viable, collaborative, open-source, publishable R package. Prerequisites: SDS 192 and CSC 111, or the equivalent.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Scott J. LaCombe
TU TH 1:20 PM - 2:35 PM
Smith College
SDS-291-01-202203
slacombe@smith.edu
(Formerly MTH/SDS 291). Theory and applications of regression techniques; linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences. Prerequisite: one of the following: SDS 201, PSY 201, GOV 190, SDS 220, ECO 220, or the equivalent or a score of 4 or 5 on the AP Statistics examination. Enrollment limited to 40.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Fatou Sanogo
M W 9:25 AM - 10:40 AM
Smith College
SDS-293-02-202203
Sabin-Reed 301
fsanogo@smith.edu
In the era of “big data,” statistical models are becoming increasingly sophisticated. This course begins with linear regression models and introduces students to a variety of techniques for learning from data, as well as principled methods for assessing and comparing models. Topics include bias-variance trade-off, resampling and cross-validation, linear model selection and regularization, classification and regression trees, bagging, boosting, random forests, support vector machines, generalized additive models, principal component analysis, unsupervised learning and k-means clustering. Emphasis is placed on statistical computing in a high-level language (e.g. R or Python).
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Katherine Taylor Halvorsen
TU TH 9:25 AM - 10:40 AM
Smith College
SDS-320-01-202203
khalvors@smith.edu
SDS 320-01, MTH 320-01
Offered as MTH 320 and SDS 320. An introduction to the mathematical theory of statistics and to the application of that theory to the real world. Topics include functions of random variables, estimation, likelihood and Bayesian methods, hypothesis testing and linear models. Prerequisites: a course in introductory statistics, MTH 212 and MTH 246, or permission of the instructor. Enrollment limited to 12. Instructor permission required.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Randi Garcia
M W 1:20 PM - 2:35 PM
Smith College
SDS-364-02-202203
rgarcia@smith.edu
SDS 364-02, PSY 364-02
Offered as PSY 364 and SDS 364. Research on intergroup relationships and an exploration of theoretical and statistical models used to study mixed interpersonal interactions. Example research projects include examining the consequences of sexual objectification for both women and men, empathetic accuracy in interracial interactions, and gender inequality in household labor. A variety of skills including, but not limited to, literature review, research design, data collection, measurement evaluation, advanced data analysis, and scientific writing will be developed. Prerequisites: PSY 201, SDS 201, SDS 220 or equivalent and PSY 202.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Ben Baumer
TU TH 1:20 PM - 2:35 PM
Smith College
SDS-410-01-202203
Bass 002
bbaumer@smith.edu
This one-semester course leverages students’ previous coursework to address a real-world data analysis problem. Students collaborate in teams on projects sponsored by academia, government, and/or industry. Professional skills developed include: ethics, project management, collaborative software development, documentation, and consulting. Regular team meetings, weekly progress reports, interim and final reports, and multiple presentations are required. Open only to majors. Prerequisites: SDS 192, SDS 291 and CSC 111.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Carol Bigelow
W 4:00PM 6:30PM
UMass Amherst
26816
Hasbrouck Laboratory room 137
cbigelow@schoolph.umass.edu
Principles of statistics applied to analysis of biological and health data. Continuation of Bioepi 540 including analysis of variance, regression, nonparametric statistics, sampling, and categorical data analysis.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Chi Hyun Lee
TU TH 10:00AM 11:15AM
UMass Amherst
26839
Morrill 1 N 347
chihyunlee@umass.edu
The course introduces advanced central topics in biostatistics and health data science including survival analysis, design and analysis of clinical trials, models for correlated data, bayesian modeling, and causal inference. The course motivates statistical reasoning and methods through substantive research questions and features of data typically available in public health and biomedical research. Students will obtain hands-on experience in applying selected methods on real data using the statistical programming language R.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Scott Chasan-Taber
TU TH 8:30AM 9:45AM
UMass Amherst
26832
Integ. Learning Center N255
scottct@umass.edu
An introduction to data management for research projects in the biomedical sciences using microcomputers. Topics include design of data collection forms, data entry, computer managed documentation and statistical computing using SAS.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Scott Chasan-Taber
TU TH 8:30AM 9:45AM
UMass Amherst
38437
Mount Ida Classroom
scottct@umass.edu
An introduction to data management for research projects in the biomedical sciences using microcomputers. Topics include design of data collection forms, data entry, computer managed documentation and statistical computing using SAS.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Scott Chasan-Taber
W 5:30PM 8:00PM
UMass Amherst
37812
Engineering Lab II Room 115
scottct@umass.edu
Major designs used in clinical investigations; alternative approaches to the analysis of gathered data. Prerequisite: BIOST&EP 640.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Scott Chasan-Taber
W 5:30PM 8:00PM
UMass Amherst
38438
Mount Ida Classroom
scottct@umass.edu
Major designs used in clinical investigations; alternative approaches to the analysis of gathered data. Prerequisite: BIOST&EP 640.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Zhichao Jiang
TU TH 2:30PM 3:45PM
UMass Amherst
26835
Arnold Room 104
zhichaojiang@umass.edu
This course will introduce students to both statistical theory and practice of causal inference. We will review the basics of causal inference, introduce a missing data perspective of causal inference and instrumental variable methods. We then cover 3 advanced topics based on a survey to students. Tentative topics include randomization inference, mediation analysis, principal stratification, measurement error, natural experiments, and causal inference with interference.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Serena Houghton
M W F 9:05AM 9:55AM
UMass Amherst
34590
Thompson Hall room 106
shoughto@umass.edu
This introductory course is designed to give students the basic skills to organize and summarize data, along with an introduction to the fundamental principles of statistical inference. The course emphasizes an understanding of statistical concepts and interpretation of numeric data summaries along with basic analysis methods, using examples and exercises from medical and public health studies. The course does not require a high-level mathematics background, and will highlight the use and integration of statistical software, spreadsheets and word processing software in conducting and presenting data summaries and analyses.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Ken Kleinman,Peter Larson
M W F 11:15AM 12:05PM
UMass Amherst
34713
Lederle Grad Res Tower rm 123
kkleinman@umass.eduplarson@umass.edu
How do epidemics happen? How do we respond? What is the intersection between practice and policy? What happens when epidemiologic desirability meets political and cultural reality? We?ll explore epidemic diseases around the world and in history, and the role that we can play in their management.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Nicholas Reich
TU TH 10:00AM 11:15AM
UMass Amherst
34640
Integ. Learning Center N101
nick@umass.edu
The aim of this course is to provide students with the skills necessary to tell interesting and useful stories in real-world encounters with data. Specifically, they will develop the statistical and programming expertise necessary to analyze datasets with complex relationships between variables. Students will gain hands-on experience summarizing, visualizing, modeling, and analyzing data. Students will learn how to build statistical models that can be used to describe and evaluate multidimensional relationships that exist in the real world. Specific methods covered will include linear, logistic, and Poisson regression. This course will introduce students to the R statistical computing language and by the end of the course will require substantial independent programming. To the extent possible, the course will draw on real datasets from biological and biomedical applications. This course is designed for students who are looking for a second course in applied statistics/biostatistics (e.g. beyond PUBHLTH 391B or STAT 240), or an accelerated introduction to statistics and modern statistical computing.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Yuyu Tang
TU TH 10:00AM 11:15AM
UMass Amherst
35805
Lederle Grad Res Tower rm 121
yuyu@umass.edu
The field of Data Science encompasses methods, processes, and systems that enable the extraction of useful knowledge from data. Foundations of Data Science introduces core data science concepts including computational and inferential thinking, along with core data science skills including computer programming and statistical methods. The course presents these topics in the context of hands-on analysis of real-world data sets, including economic data, document collections, geographical data, and social networks. The course also explores social issues surrounding data analysis such as privacy and design. (Gen. Ed. R2)
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01LL
F 9:05AM 9:55AM
UMass Amherst
38220
Lederle Grad Res Tower Rm 173
The field of Data Science encompasses methods, processes, and systems that enable the extraction of useful knowledge from data. Foundations of Data Science introduces core data science concepts including computational and inferential thinking, along with core data science skills including computer programming and statistical methods. The course presents these topics in the context of hands-on analysis of real-world data sets, including economic data, document collections, geographical data, and social networks. The course also explores social issues surrounding data analysis such as privacy and design. (Gen. Ed. R2)
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01LM
F 10:10AM 11:00AM
UMass Amherst
38221
Lederle Grad Res Tower Rm 173
The field of Data Science encompasses methods, processes, and systems that enable the extraction of useful knowledge from data. Foundations of Data Science introduces core data science concepts including computational and inferential thinking, along with core data science skills including computer programming and statistical methods. The course presents these topics in the context of hands-on analysis of real-world data sets, including economic data, document collections, geographical data, and social networks. The course also explores social issues surrounding data analysis such as privacy and design. (Gen. Ed. R2)
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Cynthia Cabrera
TU TH 8:30AM 9:45AM
UMass Amherst
35734
Hasbrouck Lab Add room 20
cabrera@umass.edu
Descriptive statistics, elements of probability theory, and basic ideas of statistical inference. Topics include frequency distributions, measures of central tendency and dispersion, commonly occurring distributions (binomial, normal, etc.), estimation, and testing of hypotheses. Prerequisite: high school algebra. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AA
W 8:00AM 8:50AM
UMass Amherst
35735
Lederle Grad Res Tower Rm 177
Descriptive statistics, elements of probability theory, and basic ideas of statistical inference. Topics include frequency distributions, measures of central tendency and dispersion, commonly occurring distributions (binomial, normal, etc.), estimation, and testing of hypotheses. Prerequisite: high school algebra. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AB
W 9:05AM 9:55AM
UMass Amherst
35736
Lederle Grad Res Tower Rm 173
Descriptive statistics, elements of probability theory, and basic ideas of statistical inference. Topics include frequency distributions, measures of central tendency and dispersion, commonly occurring distributions (binomial, normal, etc.), estimation, and testing of hypotheses. Prerequisite: high school algebra. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AC
W 5:30PM 6:20PM
UMass Amherst
35737
Lederle Grad Res Tower Rm 173
Descriptive statistics, elements of probability theory, and basic ideas of statistical inference. Topics include frequency distributions, measures of central tendency and dispersion, commonly occurring distributions (binomial, normal, etc.), estimation, and testing of hypotheses. Prerequisite: high school algebra. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AD
W 4:00PM 4:50PM
UMass Amherst
35738
Lederle Grad Res Tower Rm 173
Descriptive statistics, elements of probability theory, and basic ideas of statistical inference. Topics include frequency distributions, measures of central tendency and dispersion, commonly occurring distributions (binomial, normal, etc.), estimation, and testing of hypotheses. Prerequisite: high school algebra. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AE
W 12:20PM 1:10PM
UMass Amherst
35739
Lederle Grad Res Tower Rm 141
Descriptive statistics, elements of probability theory, and basic ideas of statistical inference. Topics include frequency distributions, measures of central tendency and dispersion, commonly occurring distributions (binomial, normal, etc.), estimation, and testing of hypotheses. Prerequisite: high school algebra. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AF
W 5:30PM 6:20PM
UMass Amherst
35740
Lederle Grad Res Tower rm 202
Descriptive statistics, elements of probability theory, and basic ideas of statistical inference. Topics include frequency distributions, measures of central tendency and dispersion, commonly occurring distributions (binomial, normal, etc.), estimation, and testing of hypotheses. Prerequisite: high school algebra. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AG
W 2:30PM 3:20PM
UMass Amherst
35741
Lederle Grad Res Tower Rm 145
Descriptive statistics, elements of probability theory, and basic ideas of statistical inference. Topics include frequency distributions, measures of central tendency and dispersion, commonly occurring distributions (binomial, normal, etc.), estimation, and testing of hypotheses. Prerequisite: high school algebra. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AJ
W 11:15AM 12:05PM
UMass Amherst
35742
Lederle Grad Res Tower Rm 177
Descriptive statistics, elements of probability theory, and basic ideas of statistical inference. Topics include frequency distributions, measures of central tendency and dispersion, commonly occurring distributions (binomial, normal, etc.), estimation, and testing of hypotheses. Prerequisite: high school algebra. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
4.00
Joanna Jeneralczuk
TU TH 1:00PM 2:15PM
UMass Amherst
35743
Morrill Sci Ctr (1) Room N375
jeneral@math.umass.edu
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AA
W 9:05AM 9:55AM
UMass Amherst
35768
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AB
W 10:10AM 11:00AM
UMass Amherst
35744
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AC
W 2:30PM 3:20PM
UMass Amherst
35745
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AD
W 4:00PM 4:50PM
UMass Amherst
35746
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AE
W 1:25PM 2:15PM
UMass Amherst
35747
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AF
W 2:30PM 3:20PM
UMass Amherst
35748
Engineering Laboratory rm 327
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AG
W 5:30PM 6:20PM
UMass Amherst
35749
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01AJ
W 4:00PM 4:50PM
UMass Amherst
35750
Lederle Grad Res Tower Rm 145
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
4.00
Joanna Jeneralczuk
TU TH 4:00PM 5:15PM
UMass Amherst
35769
Hasbrouck Lab Add room 20
jeneral@math.umass.edu
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02AA
M 4:00PM 4:50PM
UMass Amherst
35770
Lederle Grad Res Tower Rm 173
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02AB
M 10:10AM 11:00AM
UMass Amherst
35771
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02AC
M 4:00PM 4:50PM
UMass Amherst
35772
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02AD
M 5:30PM 6:20PM
UMass Amherst
35773
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02AE
M 9:05AM 9:55AM
UMass Amherst
35774
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02AF
M 2:30PM 3:20PM
UMass Amherst
35775
Lederle Grad Res Tower Rm 173
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02AG
M 11:15AM 12:05PM
UMass Amherst
35776
Lederle Grad Res Tower Rm 177
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02AJ
M 5:30PM 6:20PM
UMass Amherst
35777
Lederle Grad Res Tower Rm 173
Basics of probability, random variables, binomial and normal distributions, central limit theorem, hypothesis testing, and simple linear regression. (Gen.Ed. R2)
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Krista Gile
TU TH 2:30PM 3:45PM
UMass Amherst
35813
Lederle Grad Res Tower rm 206
gile@cns.umass.edu
This course is an introduction to the fundamental principles of statistical science. It does not rely on detailed derivations of mathematical concepts, but does require mathematical sophistication and reasoning. It is an introduction to statistical thinking/reasoning, data management, statistical analysis, and statistical computation. Concepts in this course will be developed in greater mathematical rigor later in the statistical curriculum, including in STAT 515, 516, 525, and 535. It is intended to be the first course in statistics taken by math majors interested in statistics. Concepts covered include point estimation, interval estimation, prediction, testing, and regression, with focus on sampling distributions and the properties of statistical procedures. The course will be taught in a hands-on manner, introducing powerful statistical software used in practical settings and including methods for descriptive statistics, visualization, and data management.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Joanna Jeneralczuk
TU TH 11:30AM 12:45PM
UMass Amherst
35759
Lederle Grad Res Ctr rm A301
jeneral@math.umass.edu
For graduate and upper-level undergraduate students, with focus on practical aspects of statistical methods.Topics include: data description and display, probability, random variables, random sampling, estimation and hypothesis testing, one and two sample problems, analysis of variance, simple and multiple linear regression, contingency tables. Includes data analysis using a computer package. Prerequisites: high school algebra; junior standing or higher.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Yalin Rao
TU TH 10:00AM 11:15AM
UMass Amherst
35760
Lederle Grad Res Tower rm 219
yalin@schoolph.umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Jiayu Zhai
M W F 12:20PM 1:10PM
UMass Amherst
35784
Lederle Grad Res. Ctr rm A201
jiayuzhai@umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
03
3.00
Jiayu Zhai
M W F 1:25PM 2:15PM
UMass Amherst
35787
Lederle Grad Res. Ctr rm A201
jiayuzhai@umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
04
3.00
Yueqiao Zhang
M W F 9:05AM 9:55AM
UMass Amherst
35788
Lederle Grad Res. Ctr rm A201
yueqiaozhang@umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
05
3.00
Yueqiao Zhang
M W F 10:10AM 11:00AM
UMass Amherst
35795
Lederle Grad Res. Ctr rm A201
yueqiaozhang@umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
06
3.00
Yalin Rao
TU TH 1:00PM 2:15PM
UMass Amherst
35801
Lederle Grad Res. Ctr rm A201
yalin@schoolph.umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
07
3.00
Brian Van Koten
TU TH 2:30PM 3:45PM
UMass Amherst
35808
Hasbrouck Laboratory room 137
bvankoten@umass.edu
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Theodore Westling
TU TH 8:30AM 9:45AM
UMass Amherst
35761
Lederle Grad Res. Ctr rm A201
twestling@umass.edu
Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Shixiao Zhang
M W F 11:15AM 12:05PM
UMass Amherst
35785
Lederle Grad Res. Ctr rm A201
shixiaozhang@umass.edu
Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
03
3.00
Shixiao Zhang
M W F 10:10AM 11:00AM
UMass Amherst
35786
Lederle Grad Res Tower rm 202
shixiaozhang@umass.edu
Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
04
3.00
Budhinath Padhy
TU TH 2:30PM 3:45PM
UMass Amherst
35812
Lederle Grad Res Tower Rm 141
bpadhy@math.umass.edu
Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent.
[Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
John Staudenmayer
M W F 10:10AM 11:00AM
UMass Amherst
35789
Lederle Grad Res Tower Rm 171
jstauden@math.umass.edu
Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection; expressing regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear regression. Extensive data analysis using R or SAS (no previous computer experience assumed). Requires prior coursework in Statistics, preferably ST516, and basic matrix algebra. Satisfies the Integrative Experience requirement for BA-Math and BS-Math majors.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Michael Sullivan
TU TH 10:00AM 11:15AM
UMass Amherst
35793
Lederle Grad Res Ctr rm A203
sullivan@math.umass.edu
Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection; expressing regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear regression. Extensive data analysis using R or SAS (no previous computer experience assumed). Requires prior coursework in Statistics, preferably ST516, and basic matrix algebra. Satisfies the Integrative Experience requirement for BA-Math and BS-Math majors.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
03
3.00
Budhinath Padhy
TU TH 1:00PM 2:15PM
UMass Amherst
35800
Lederle Grad Res Tower Rm 141
bpadhy@math.umass.edu
Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection; expressing regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear regression. Extensive data analysis using R or SAS (no previous computer experience assumed). Requires prior coursework in Statistics, preferably ST516, and basic matrix algebra. Satisfies the Integrative Experience requirement for BA-Math and BS-Math majors.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Zijing Zhang
TH 6:00PM 8:30PM
UMass Amherst
35807
Mount Ida Classroom
zhangzijingapply@gmail.com
Planning, statistical analysis and interpretation of experiments. Designs considered include factorial designs, randomized blocks, latin squares, incomplete balanced blocks, nested and crossover designs, mixed models. Has a strong applied component involving the use of a statistical package for data analysis. Prerequisite: previous coursework in statistics.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Patrick Flaherty
M W 2:30PM 3:45PM
UMass Amherst
35791
Lederle Grad Res. Ctr rm A201
pflaherty@umass.edu
This course will introduce computing tools needed for statistical analysis including data acquisition from database, data exploration and analysis, numerical analysis and result presentation. Advanced topics include parallel computing, simulation and optimization, and package creation. The class will be taught in a modern statistical computing language.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Krista Gile,Anna Liu
TH 11:30AM 12:45PM
UMass Amherst
35792
Lederle Grad Res Tower rm 219
gile@cns.umass.eduanna@math.umass.edu
This course provides a forum for training in statistical consulting. Application of statistical methods to real problems, as well as interpersonal and communication aspects of consulting are explored in the consulting environment. Students enrolled in this class will become eligible to conduct consulting projects as consultants in the Statistical Consulting and Collaboration Services group in the Department of Mathematics and Statistics. Consulting projects arising during the semester will be matched to students enrolled in the course according to student background, interests, and availability. Taking on consulting projects is not required, although enrolled students are expected to have interest in consulting at some point. The class will include some presented classroom material; most of the class will be devoted to discussing the status of and issues encountered in students' ongoing consulting projects.
Instructor Permission: Permission is required for interchange registration during all registration periods.
01
3.00
Daeyoung Kim
TU TH 1:00PM 2:15PM
UMass Amherst
35763
Lederle Grad Res Tower rm 219
dkim1@umass.edu
Point and interval estimation, hypothesis testing, large sample results in estimation and testing; decision theory; Bayesian methods; analysis of discrete data. Also, topics from nonparametric methods, sequential methods, regression, analysis of variance. Prerequisite: Statistc 607 or equivalent.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
02
3.00
Hyunsun Lee
M 6:00PM 8:30PM
UMass Amherst
35806
Mount Ida Classroom
hyunsunlee@umass.edu
Point and interval estimation, hypothesis testing, large sample results in estimation and testing; decision theory; Bayesian methods; analysis of discrete data. Also, topics from nonparametric methods, sequential methods, regression, analysis of variance. Prerequisite: Statistc 607 or equivalent.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Hyunsun Lee
W 6:00PM 8:30PM
UMass Amherst
35809
Mount Ida Classroom
hyunsunlee@umass.edu
This course provides an introduction to the statistical techniques that are most applicable to data science. Topics include regression, classification, resampling, linear model selection and regularization, tree-based methods, support vector machines and unsupervised learning. The course includes a computing component using statistical software. Students must have prior experience with a statistical programming language such as R, Python or MATLAB.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Shai Gorsky
TH 6:00PM 8:30PM
UMass Amherst
35811
Mount Ida Classroom
sgorsky@umass.edu
This course provides an introduction to the more commonly-used multivariate statistical methods. Topics include principal component analysis, factor analysis, clustering, discrimination and classification, multivariate analysis of variance (MANOVA), and repeated measures analysis. The course includes a computing component. Prerequisites: Probability and Statistics at a calculus-based level such as Stat 607 and Stat 608 (concurrent) or Stat 515 and Stat 516 (concurrent). Students must have prior experience with a statistical programming language such as R, Python or MATLAB.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Maryclare Griffin
M W F 12:20PM 1:10PM
UMass Amherst
35799
Lederle Grad Res Tower Rm 171
maryclaregri@umass.edu
This course will cover several workhorse models for analysis of time series data. The course will begin with a thorough and careful review of linear and general linear regression models, with a focus on model selection and uncertainty quantification. Basic time series concepts will then be introduced. Having built a strong foundation to work from, we will delve into several foundational time series models: autoregressive and vector autoregressive models. We will then introduce the state-space modeling framework, which generalizes the foundational time series models and offers greater flexibility. Time series models are especially computationally challenging to work with - throughout the course we will explore and implement the specialized algorithms that make computation feasible in R and/or STAN. Weekly problem sets, two-to-three short exams, and a final project will be required.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
01
3.00
Shai Gorsky
TU 6:00PM 8:30PM
UMass Amherst
35810
Mount Ida Classroom
sgorsky@umass.edu
The increasing production of descriptive data sets and corresponding software packages has created a need for data visualization methods for many application areas. Data visualization allows for informing results and presenting findings in a structured way. This course provides an introduction to graphical data analysis and data visualization. Topics covered include exploratory data analysis, data cleaning, examining features of data structures, detecting unusual data patterns, and determining trends. The course will also introduce methods to choose specific types of graphics tools and understanding information provided by graphs. The statistical programming language R is used for the course. Prerequisites: Probability and Statistics at a calculus-based level such as Stat 607 and Stat 608 (concurrent) or Stat 515 and Stat 516 (concurrent). Students must have prior experience with a statistical programming language such as R, Python or MATLAB.
Instructor Permission: Permission is required for interchange registration during the add/drop period only.
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:
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!
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.
