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Masters Degree in Statistics, Newton (Boston Area)
All statistics courses are offered in a flexible format (flexible learning with all classes offered both in-person and remotely). Classes are offered from 6:00-8:30 p.m. once per week from Monday to Thursday at the Newton campus.
All statistics courses can be taken remotely.
Students can also take elective courses in other graduate programs at UMass-Amherst including Computer Science, Biostatistics, Business & Analytics and Geosciences, among many others, for the M.S. Statistics Degree; many of these courses are offered remotely/online, with some being offered in person at Newton.
Statistics Courses, Fall 2024
The following statistics courses are offered at Newton in Fall 2024.
See Spire for more
information, including the course schedule and instructor names
here.
Further details on courses are available below and
here.
- Stat 535, Section 2: Statistical Computing
- Stat 607, Section 2: Mathematical Statistics I
- Stat 625, Section 2: Regression Modeling
- Stat 631: Categorical Data Analysis
- Stat 691P: Project Seminar
Statistics Courses, Spring 2025
The following statistics courses will be offered at Newton in Spring 2025.
See Spire for more
information, including the course schedule and instructor names
here.
Further details on courses are available below and
here.
- Stat 526: Design of Experiments
- Stat 608, Section 2: Mathematical Statistics II
- Stat 610: Bayesian Statistics
- Stat 630: Statistical Methods for Data Science
- Stat 633: Data Visualization
Course Descriptions, All Courses
Stat 526: Design of Experiments
An applied statistics course on planning, statistical analysis
and interpretation of experiments of various types. Coverage includes
factorial designs, randomized blocks, incomplete block designs, nested
designs, crossover designs and mixed models. Has a strong applied component
involving the use of a statistical package for data analysis.
Prerequisites: Stat 516 or must have prior knowledge of hypothesis tests including t-tests, z-tests, F-tests, confidence intervals and p-values.
Stat 535: Statistical Computing
This course provides an introduction to fundamental computer science concepts
relevant to the statistical analysis of large-scale data sets. Students will
collaborate in a team to design and implement analyses of real-world data sets,
and communicate their results using mathematical, verbal and visual means.
Students will learn how to analyze computational complexity and how to choose
an appropriate data structure for an analysis procedure. Students will learn
and use the python language to implement and study data structure and
statistical algorithms.
Prerequisites: Stat 516 and CS 121 or equivalent.
Stat 607: Mathematical Statistics I
The first part of a two-semester graduate level sequence in probability
and statistics, this course develops probability theory at an intermediate
level (i.e., non measure-theoretic - Stat 605 is a course in measure-theoretic
probability) and introduces the basic concepts of statistics.
Topics include: general probability concepts; discrete probability;
random variables (including special discrete and continuous distributions)
and random vectors; independence; laws of large numbers; central limit
theorem; statistical models and sampling distributions; and a brief
introduction to statistical inference. Statistical inference will be
developed more fully in Stat 608.
This course is also suitable for graduate students in a wide variety of
disciplines and will give strong preparation for further courses in areas
such as statistics, econometrics, stochastic processes, time series,
decision theory, operations research.
Prerequisites: Multivariable calculus and linear algebra.
Stat 608: Mathematical Statistics II
This is the second part of a two semester sequence on probability
and mathematical statistics. Stat 607 covered probability,
discrete/continuous distributions, basic convergence theory and basic
statistical modelling. In Stat 608 we cover an introduction to the basic
methods of statistical inference, pick up some additional probability topics
as needed and examine further issues in methods of inference including more
on likelihood based methods, optimal methods of inference, more large sample
methods, Bayesian inference and Resampling methods. The theory is utilized
in addressing problems in parametric/nonparametric methods, two and
multi-sample problems, and regression. As with Stat 607, this is primarily
a theory course emphasizing fundamental concepts and techniques.
Prerequisites: Stat 607 or equivalent.
Stat 610: Bayesian Statistics
This course will introduce students to Bayesian data analysis, including
modeling and computation. We will begin with a description of the components
of a Bayesian model and analysis (including the likelihood, prior, posterior,
conjugacy and credible intervals). We will then develop Bayesian approaches
to models such as regression models, hierarchical models and ANOVA. Computing
topics include Markov chain Monte Carlo methods. The course will have students
carry out analyses using statistical programming languages and software
packages.
Prerequisites: Stat 608 or permission of instructor
Stat 625: Regression Modeling
Regression is the most widely used statistical technique. In addition to
learning about regression methods this course will also reinforce basic
statistical concepts and introduce students to "statistical thinking" in
a broader context. This is primarily an applied statistics course. While
models and methods are written out carefully with some basic derivations,
the primary focus of the course is on the understanding and presentation of
regression models and associated methods, 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,
regression models and methods in matrix form. With time permitting, further
topics include an introduction to weighted least squares, regression with
correlated errors and nonlinear (including binary) regression.
Prerequisites: Linear algebra and Stat 516 or equivalent (knowledge of
estimation, confidence intervals, and hypothesis testing and its use in
at least one-sample and two-sample problems).
Stat 630: Statistical Methods for Data Science
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.
Prerequisites: Stat 516 or Stat 608 or permission of instructor.
Stat 631: Categorical Data Analysis
Distribution and inference for binomial and multinomial variables with
contingency tables, generalized linear models, logistic regression for
binary responses, logit models for multiple response categories,
loglinear models, inference for matched-pairs and correlated clustered data.
Prerequisites: Stat 516 and Stat 525 or equivalent (knowledge of
distribution theory, estimation, confidence intervals,
hypothesis testing and multiple linear regression).
Stat 632: Applied Multivariate Statistics
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: Stat 516 or Stat 608 or equivalent.
Stat 633: Data Visualization
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 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.
Prerequisites: Stat 516 or Stat 608 or equivalent.
Stat 639: Time Series Analysis and Applications
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.
Prerequisites: Stat 608 or equivalent.
Stat 691P: Project Seminar
This course is designed for students to complete the master's project
requirement in statistics, with guidance from faculty. The course will
begin with determining student topics and groups. Each student will
complete a group project. Each group will work together for one semester
and be responsible for its own schedule, work plan, and final report.
Regular class meetings will involve student presentations on progress of
projects, with input from the instructor. Students will learn about the
statistical methods employed by each group. Students in the course will
learn new statistical methods, how to work collaboratively,
how to use R and other software packages, and how to present oral and
written reports.
Prerequisites: Permission of instructor.
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