# Statistics Courses

STAT5293*Applied Statistics3 ch
Introduction to the use of statistical computer packages as a tool for analysing data. Brief review of basic statistics. Topics chosen from analysis of variance and covariance, regression, multivariate methods, analysis of categorical data, non-parametric methods. Concentration on the use of one of MINITAB, SAS, SPSS, BMDP, with reference to the other packages. Each student must complete a statistical analysis and report on a data-set taken from the student's discipline.
STAT5473Experimental Design and Data Analysis in Biology and Forestry3 ch
An introduction to the practice and pitfalls of experimental design and data analysis in biology and forestry. Topics will be selected from sampling designs, experimental designs, parametric and non-parametric analysis, power analysis, and regression. The course will include discussion of examples in the literature. Students will analyse and interpret data sets arising from their field of research. (This course may not be taken for credit by graduate students in Mathematics & Statistics degree programs).
STAT6043Sample Survey Theory I3 ch
Simple random sampling; stratified sampling; systematic sampling; multi-stage sampling; double sampling; ratio and regression estimates; sources of error in surveys.
STAT6053Regression Analysis3 ch
Likelihood ratio tests; distribution of quadratic forms, noncentral chi square, noncentral F; independence of quadratic forms; linear models, model classification; general linear hypothesis of full rank, Gauss-Markov theorem, normal equations, tests of hypotheses; polynomial models; orthogonal polynomials; regression models; experimental design models; estimable functions.
STAT6073Categorical Analysis Data3 ch

Logistic regression models for binary response variables, log-linear models for contingency tables, Poisson regression models for count response variables, multinomial regression models for categorical response variables, cumulative logic, and continuation-ratio regression models for ordinal response variables, model selection, some special topics in generalized linear models. Emphasis will be on computer implementation and applications in social sciences, psychology, education, medicine, sciences and engineering.

STAT6083Multivariate Methods for Statistical Learning3 ch
Multivariate normal distribution; estimation of the mean vector and covariance matrix; partial and multiple correlation coefficients; multiple regression; the T2 statistics; tests of hypotheses; discriminant analysis; principal components; factor analysis.

Prerequisites: STAT 2593 (at least B), or STAT 3093 and 1 of MATH 1503 or MATH 2213.

STAT6211Mathematical Statistics3 ch
Distribution functions; mean values and moments of random variables; sequences of random variables; characteristic functions and generating functions; special distributions; sampling theory; statistical parametric estimation; testing parametric statistical hypotheses.
STAT6212Sample Survey Theory II3 ch
Review of fundamental survey sampling; simple random sampling, stratified sampling, systematic sampling. Cluster sampling and subsampling; unequal clusters; control of subsampel size. Optimum designs. Area sampling. Multistage sampling; sampling from imperfest framses; selection techniques. Bias and response errors.
STAT6221Survival Analysis3 ch
This course will cover the following topics: concepts, models, and techniques in survival analysis including types of censoring and truncation, Kaplan-Meier estimators, log-rank statistics, parametric models, proportional hazards models, extended PH models, competing risks, recurrent events and frailty models

Prerequisites: STAT 2593 or STAT 3083

STAT6222Linear Models3 ch
Brief review of generalized inverse matrices; distribution of quadratic forms. Full rank models; models not of full rank, estimable functions, testable hypothesis; classification models; variance components.
STAT6251Stochastic Process II3 ch
Brief review of discrete time processes, random walks and Markov Chains.Martingales, stationary processes and ergodicity, Markov processes,Browian motion, boundaries. Diffusion processes.
STAT6262Stochastic Models in Reliability3 ch
Course Desc
STAT6291Statistical Interference3 ch
Course Desc
STAT6323Dynamic Programming3 ch
Deterministic and probabilistic dynamic programming. Markovian decision models. Applications of dynamic programming technique in replacement policies and multiperiod dynamic inventory models.
STAT6333Applied Longitudinal Data Analysis3 ch
This course will cover the following topics: graphical and tabular displays of longitudinal data, analyses of various types of longitudinal data including normal and non-normal continuous, semi-continuous, count, nominal and ordinal responses, marginal and conditional inferences, random effects models, extensive use of statistical software, emphasis on applications.

Prerequisites: STAT 4053 or consent of instructor

STAT6372Non-parametric Statistics II3 ch
Methods of non-parametric statistics, order statistics, critical points, tolerance regions, and their applications; use of incomplete Beta function, Kolmogorov-Smirnov statistics and the Cramer statistics.
STAT6383Stochastic Processes I3 ch
Exact contents may vary from year to year, e.g.: counting processes and Poisson processes; renewal processes (discrete); finite state Markov chains; stationary covariance processes.
STAT6392Seminar in Statistics and Operations Researchcr
Students in this course will review the literature in one or more areas of statistics or O.R. and present a minimum of four but not more than six seminars throughout the year.
STAT6402Multivariate Statistical Analysis3 ch
Course Desc
STAT6433Applied Statistical Methods with R3 ch
This course will cover the following topics: data input and manipulation in R. Basic R coding. Visualization. Simulation of random variables. Simulation experiments to evaluate estimators and tests. Using optimization to fit models. Data smoothing with splines and kernel density estimation. Bootstrapping and other resampling methods. Data analysis will be undertaken by means of R packages and R coding.

Prerequisites: STAT 2593 (at least B) or STAT 3093

STAT6443Time Series Analysis and Applications3 ch
Discrete time series and stochastic processes; autocorrelation and partial correlation functions; white noise; moving averages; autoregressive, mixed and integrated processes; stochastic models, fitting, estimation and diagnostic checkup; forecasting; forecasting in seasonal time series; applications would include problems from Economics, Engineering, Physics.
STAT6473Experimental Design3 ch
Randomization, one and two way classifications. Latin squares, factorial experiments, nesting, incomplete blocks, linear regression. Emphasis on applications. Extensive use of a statistical computer package.
Students in the PhD program will review the literature in one or more areas of pure or applied mathematics and present a minimum of four but not more than six seminars throughout the year.
Topics to be chosen by instructor with approval of MATH/STAT department.
A continuation of topics offered in STAT 6801 .