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Course Criteria
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3.00 Credits
No course description available.
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3.00 Credits
Time series analysis is an effective statistical methodology for modelling time series data (a series of observations collected over time) and forecasting future observations in many areas; economics, the social sciences, the physical and environmental sciences, medicine, and signal processing. For example, monthly unemployment rates in economics, yearly birth rates in social science, global warming trends in environmental studies, and magnetic resonance imaging of brain waves in medicine. This course presents the fundamental principles of time series analysis including mathematical modeling of time series data and methods for statistical inference. Topics covered will include modeling and inference for linear autoregressive time series models; i.e., autoregressive (AR) and autoregressive moving a verage (ARMA) models, (nonseasonal/seasonal) autoregressive integrated moving average (ARIMA) models, unit root and differencing, transfer function noise models, intervention analysis and state-space models. If time permits, additional topics will include spectral analysis, (generalized) autoregressive conditionally heteroscedastic (ARCH) models, Kalman filtering and smoothing, and signal extraction. Prerequisites: Probability and Statistics at a calculus-based graduate level such as Stat 607 and Stat 608 (concurrent), a previous course on regression analysis covering multiple linear regression (e.g., Stat 505, BioEpi744, RESEC702) with some exposure to regression models in matrix form. Prior computing experience with R is desirable.
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3.00 Credits
First semester of two-semester sequence in the theory of linear models. Basic results on the multivariate normal distribution; linear and quadratic forms; noncentral Chi-square and F distributions; inference in linear models, including point and interval estimation, hypothesis testing, etc. Prerequisites: Statistc 607-608 or equivalent; linear algebra.
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3.00 Credits
Second semester of sequence in theory of linear models with focus on "analysis of variance/design of experiments" models. Includes factorial experiments (balanced and unbalanced designs, notions of interaction, etc.), randomized block designs, incomplete designs (incomplete block designs and latin squares), random effects, nested models, and mixed models.
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1.00 - 6.00 Credits
Contact department for description.
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3.00 Credits
Measurement error (errors-in-variables) which is ubiquitous, occurs when one or more of the variables of interest in a model cannot be observed exactly. In ecology this is referred to as observation error, while in economics it is called the problem of unobservable variables/use of proxy variables. This is typically due to sampling error, instrument error or a combination of the two and includes misclassification of categorical variables as a special case. Examples abound across epidemiology, ecology, economics, the physical sciences as well as most other disciplines. This course examines the impact of measurement errors on standard statistical analyses which ignore them (so-called "naive" analyses) and describes methods of correcting for measurement error using additional information or data about t he measurement error process (usually arising from replication or validation data). We examine these questions for i) misclassification in estimating one or more proportions and in two-way contingency tables; ii) measurement error in predictors and/or the response in simple and multiple linear regression as well as error in the response in estimating and comparing one or many means; iii) measurement error in nonlinear regression, including binary regression (e.g., logistic or probit) and Poisson type models. The focus is on understanding models and methods and applying them to examples from a variety of disciplines. Computing will use STATA and SAS, the two software packages for which measurement error programs have been developed. Prior experience with SAS or STATA is not required. ST797ME will have more of a theory component. Prerequisites: A background in probability and statistics at the level of STATISTC 515-516 or equivalent, some familiarity with regression analysis including experience with regression models in matrix form (e.g., ST505 or equivalent). Prior exposure to nonlinear and logistic regression is not essential, we will review the usual methods there when we get to these topics. ST797ME requires ST607-608 and ST705 (or current enrollment) or equivalent.
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1.00 - 6.00 Credits
Contact department for description.
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1.00 Credits
Independent preparation for the state pesticide certification examination and licensure. The State Pesticide Exam Study Manual is used and available for purchase either online or at the UMass Extension Bookstore. Students must apply to take the exam; applications must be submitted by the deadline date (one week prior to the exam). Examinations are given at various times throughout the state.
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1.00 Credits
Highly interactive and participatory introduction to the field of Sustainable Food and Farming focused on academic preparation and possible careers.
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1.00 - 6.00 Credits
Independent work related to some area of the equine, food crops, and green industries. Prerequisite: consent of program coordinator and instructor.
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