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Masters Option in Statistics, Newton (Boston Area)

The following courses are offered at Newton in Fall 2022.

See Spire for more information, including the course schedule and instructor names here.

Further details on courses are available here.

  • Stat 535, Section 2: Statistical Computing
  • Stat 607, Section 2: Mathematical Statistics I
  • Stat 625, Section 2: Regression Modeling
  • Stat 691P: Project Seminar
  • Stat 697L: Categorical Data Analysis
  • Stat 697TS: Time Series Analysis and Applications

The following courses will be offered at Newton in Spring 2023.

See Spire for more information, including the course schedule and instructor names here.

Further details on courses are available here.
  • Stat 526: Design of Experiments
  • Stat 608, Section 2: Mathematical Statistics II
  • Stat 610: Bayesian Statistics
  • Stat 697DS: Statistical Methods for Data Science
  • Stat 697MV: Applied Multivariate Statistics
  • Stat 697V: Data Visualization

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.

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.

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.

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.

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

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).

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.

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.

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.

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.

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.

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.


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