STAT C 3997x and y Independent Research

May be repeated for credit. The student participates in the current research of a member of the department and prepares a report on the work.

STAT W 1001x and y Introduction to Statistical Reasoning

A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance.
Prerequisites: Some high school algebra. General Education Requirement: Quantitative and Deductive Reasoning (QUA).
3 points

STAT W 1111x and y Introduction To Statistics (without calculus)

Designed for students in fields that emphasize quantitative methods. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, confidence intervals and hypothesis testing. Quantitative reasoning and data analysis. Practical experience with statistical software. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.
Prerequisites: Intermediate high school algebra. General Education Requirement: Quantitative and Deductive Reasoning (QUA).
3 points

STAT W 1211x and y Introduction To Statistics (with calculus)

Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Topics of STAT W1111 are covered in greater depth. Also covered is maximum likelihood estimation. Practical experience with statistical software. Illustrations are taken from a variety of fields.
Prerequisites: one semester of calculus. General Education Requirement: Quantitative and Deductive Reasoning (QUA).
3 points

STAT W 2110y Introduction to Applied Statistics

This course is an introduction to data analysis and model building. Instruction in statistical methods will be coupled with intensive practical experience with a statistical software package. Topics to be covered include: linear models; random effects models; nonparametric methods; and variance components methods. The course culminates with guest lectures illustrating the fundamental role of statistics in a variety of interdiscsiplinary research areas.

STAT W 3105x Introduction To Probability

A calculus-based introduction to probability theory. A quick review of multivariate calculus is provided. Topics covered include random variables, conditional probability, expectation, independence, Bayes' rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markov's inequality.
Prerequisites: MATH V1101 and V1102 or the equivalent General Education Requirement: Quantitative and Deductive Reasoning (QUA).
3 points

STAT W 3107y Introduction to Statistical Inference

Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.
Prerequisites: STAT W3105 or W4105, or the equivalent. General Education Requirement: Quantitative and Deductive Reasoning (QUA).
3 points

SIEO W 4150x and y Introduction To Probability and Statistics

A quick calculus-based tour of the fundamentals of probability theory and statistical inference. Probabilistic models, random variables, useful distributions, expectations, laws of large numbers, central limit theorem. Statistical inference: point and confidence interval estimation, hypothesis tests, linear regression. Students seeking a more thorough introduction to probability and statistics should consider STAT W3105 and W3107.

STAT W 4315x and y Linear Regression Models

Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

STAT W 4201x and y Advanced Data Analysis

This is a course on getting the most out of data. The emphasis will be on hands-on experience, involving case studies with real data and using common statistical packages. The course covers, at a very high level, exploratory data analysis, model formulation, goodness of fit testing, and other standard and non-standard statistical procedures, including linear regression, analysis of variance, nonlinear regression, generalized linear models, survival analysis, time series analysis, and modern regression methods. Students will be expected to propose a data set of their choice for use as case study material.

STAT W 4240x Data Mining

Data Mining is a dynamic and fast growing field at the interface of Statistics and Computer Science. The emergence of massive datasets containing millions or even billions of observations provides the primary impetus for the field. Such datasets arise, for instance, in large-scale retailing, telecommunications, astronomy, computational and statistical challenges.

This course will provide an overview of current research in data mining and will be suitable for graduate students from many disciplines. Specific topics covered with include databases and data warehousing, exploratory data analysis and visualization, descriptive modeling, predictive modeling, pattern and rule discovery, text mining, Bayesian data mining, and causal inference.

STAT W 4290y Statistical Methods in Finance

A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

STAT W 4325y Generalized Linear Models

Statistical methods for rates and proportions, ordered and nominal categorical responses, contingency tables, odds-ratios, exact inference, logistic regression, Poisson regression, generalized linear models.

STAT W 4330x Multilevel Models

Theory and practice, including model-checking, for random and mixed-effects models (also called hierarchical, multi-level models). Extensive use of the computer to analyse data.

STAT W 4335x Sample Surveys

Introductory course on the design and analysis of sample surveys. How sample surveys are conducted, why the designs are used, how to analyze survey results, and how to derive from first principles the standard results and their generalizations. Examples from public health, social work, opinion polling, and other topics of interest.

STAT W 4413y Nonparametric Statistics

Statistical inference without parametric model assumption. Hypothesis testing using ranks, permutations, and order statistics. Nonparametric analogs of analysis of variance. Non-parametric regression, smoothing and model selection.

STAT W 4437x and y Time Series Analysis

Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

STAT W 4543y Survival Analysis

Survival distributions, types of censored data, estimation for various survival models, nonparametric estimation of survival distributions, the proportional hazard and accelerated lifetime models for regression analysis with failure-time data. Extensive use of the computer.

STAT W 4606x and y Elementary Stochastic Processes

Review of elements of probability theory. Poisson processes. Renewal theory. Wald's equation. Introduction to discrete and continuous time Markov chains. Applications to queueing theory, inventory models, branching processes.

STAT W 4635y Stochastic Processes for Finance

This course covers theory of stochastic processes applied to finance. It covers concepts of Martingales, Markov chain models, Brownian motion. Stochastic Integration, Ito's formula as a theoretical foundation of processes used in financial modeling. It also introduces basic discrete and continuous time models of asset price evolutions in the context of the following problems in finance: portfolio optimization, option pricing, spot rate interest modeling.