Analytical chemistry is the branch of chemistry dealing with measurement, both qualitative and quantitative. This discipline is also concerned with the chemical composition of samples. In the field, analytical chemistry is applied when detecting the presence and determining the quantities of chemical compounds, such as lead in water samples or arsenic in tissue samples. It also encompasses many different spectrochemical techniques, all of which are used under various experimental conditions. This branch of chemistry teaches the general theories behind the use of each instrument as well analysis of experimental data. Upon successful completion of this course, the student will be able to: Demonstrate a mastery of various methods of expressing concentration; Use a linear calibration curve to calculate concentration; Describe the various spectrochemical techniques as described within the course; Use sample data obtained from spectrochemical techniques to calculate unknown concentrations or obtain structural information where applicable; Describe the various chromatographies described within this course and analyze a given chromatogram; Demonstrate an understanding of electrochemistry and the methods used to study the response of an electrolyte through current of potential. (Chemistry 108)

Introduces statistical data analysis, concentrating on techniques used in management science and finance. Topics chosen from: statistical graphics, basics of sampling, estimation, hypothesis testing, linear and logistic regression, analysis of variance, contingency tables, forecasting, statistical quality control, principal components, and factor analysis. SAS or similar package used for data analysis. This course is an introduction to applied statistics and data analysis. Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and statistical computing. It is not a course in mathematical statistics, but provides a balance between statistical theory and application. Prerequisites are calculus, probability, and linear algebra. We would like to acknowledge the contributions that Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof. Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee) have made to the course material.

Covers computational and data analysis techniques for environmental engineering applications. First third of subject introduces MATLAB and numerical modeling. Second third emphasizes probabilistic concepts used in data analysis. Final third provides experience with statistical methods for analyzing field and laboratory data. Numerical techniques such as Monte Carlo simulation are used to illustrate the effects of variability and sampling. Concepts are illustrated with environmental examples and data sets. This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

This module discusses the concept of modeling data with linear functions in Algebra.

This course is designed to provide the student with a simple and straightforward introduction to econometrics. Econometrics is a set of research tools employed in the business disciplines of accounting, finance, marketing, and management. It is also used by social scientists, specifically researchers in history, political science, and sociology, and it even plays an important role in such diverse fields as forestry and agricultural economics. Studying econometrics will help the student transition from being a student of economics to a practicing economist. By taking this course, the student will gain an overview of what econometrics is about and develop some 'intuition' about how things work. Upon successful completion of this course, students will be able to: Explain the fundamental probability concepts used in econometric analysis; Discuss the issues and pitfalls involved in testing theories; Demonstrate an understanding of the formulation of an empirical economic model; Perform data collection, interpretation, organization, and analysis for economics; Identify the desirable properties of estimators; Identify key classical assumptions in the field of Econometrics, explain their significance, and describe the effects that violations of the classical assumptions can have; Demonstrate an understanding of the basics of econometric analysis focusing on the least squares methodology for single explanatory and multiple explanatory variables; Extend to the regression 'family' to handle important special cases; Interpret key statistics and diagnostics typically generated by software. (Economics 203; See also: Mathematics 301)

Introduction to econometric models and techniques, emphasizing regression. Advanced topics include instrumental variables, panel data methods, measurement error, and limited dependent variable models. Includes problem sets. May not count toward HASS requirement.

This module is a quiz containing 10 multiple choice questions covering topics related to linear regression and correlation. This module is part of a set of companion resources to Collaborative Statistics (col10522) by Barbara Illowsky and Susan Dean.

Elementary introduction with applications. Basic probability models. Combinatorics. Random variables. Discrete and continuous probability distributions. Statistical estimation and testing. Confidence intervals. Introduction to linear regression.

This is a collection of labs from Collaborative Statistics by Illowski and Dean which have been edited to include Minitab activities. In addition the labs are to be done as individual activities.

Principles, techniques, and algorithms in machine learning from the point of view of statistical inference; representation, generalization, and model selection; and methods such as linear/additive models, active learning, boosting, support vector machines, hidden Markov models, and Bayesian networks.

Explores the theory and practice of scientific modeling in the context of auditory and speech biophysics. Based principally on seminar-style discussions of the research literature, subject draws on examples from hearing and speech (e.g., cochlear and vocal-fold mechanics) to explore general, meta-theoretical issues that transcend the particular subject matter. Examples include: What is a model? What is the process of model building? What are the different approaches to modeling? What is the relationship between theory and experiment? How are models tested? What constitutes a good model?

In this computer-assisted exercise first-year students explore the fundamental concept of allometry: the study of size and its consequences. Students examine the relationship between size and shape and learn how to quantify changes in proportions. They investigate how North American mammals of various sizes change proportions to compensate for changes of surface area and volume. Interactive computer programs aid each student in calculating standard dimensions from an Audubon illustration, process class data, identify lines of best-fit (using linear regression), and statistically test whether relationships between selected morphological variables exhibit isometric or allometric change.

Descriptive and inferential statistics for the behavioral and neurological sciences are considered. Techniques such as t-tests, factorial analysis of (co)variance, correlation, multiple regression, and nonparametric tests are introduced. Subject provides an introductory overview of some advanced methods such as path analysis, factor analysis, discriminant analysis, and analysis of functional MRI data. Basic issues of research design and methodology intimately associated with data analysis are discussed.

This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

This course introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and introduces the fundamentals of probability. Presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests. Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression. Introduces and employs the freely-available statistical software, R, to explore and analyze data.

This module briefly introduces linear regression. It also includes an example and an exercise.