# Statistics

Students should note that in the Science Faculty the minimum acceptable grade in a course which is required by a particular program or is used to meet a prerequisite, is a "C". Any student who fails to attain a "C" or better in such a course must repeat the course (at the next regular session) until a grade of "C" or better is attained. Students will not be eligible for graduation until such deficiencies are removed. The only exception will be granted for a single course with a "D" grade that is a normal part of the final year of that program, and is being taken for the first time in the final year.

STAT 2*** courses may not be taken by students who have passed a higher level STAT course.

NOTE: See the beginning of Section H for abbreviations, course numbers and coding.

STAT2043Statistics for Social Sciences I.3 ch (3C)

Topics from survey statistics: simple random sampling; systematic sampling, question composition, scaling techniques. Topics from basic statistics: descriptive statistics; estimating/testing means, standard deviations, proportions, paired data versus two independent samples, chi-square tests.

Prerequisite: Successful completion of at least one year of program. NOTE: Credit can be obtained in only one of STAT 2043, STAT 2253, STAT 2263, STAT 2264 or STAT 2593. Not to be taken for credit by CS, MATH or STAT majors.

STAT2253Introductory Statistics for Forestry Students 3 ch (2C 2L)

Emphasis on applications to forestry and biology, using a statistical package. Graphical and numerical summaries of data; Populations, samples, sampling techniques; Normal distribution; Estimation and tests for means, medians, proportions; Individual versus mean behaviour; Matched pairs, independent samples, analysis of variance; Regression; Chi-squared tests for categorical data. NOTE: Credit can be obtained for only one of STAT 2043, STAT 2253, STAT 2263, STAT 2264 or STAT 2593

STAT2263Statistics for Non-Science Majors3 ch (3C)

An introductory course in statistics. Experiments, sampling, basic descriptive statistics. Probability, random variables, Normal distribution. Confidence intervals for means and proportions. Tests of hypotheses. Paired samples vs. two independent samples. Contingency tables. Regression, correlation. Introduction to analysis of variance. Examples drawn from the health sciences. Use of a statistical computer package.

Prerequisite: A New Brunswick high school mathematics course, either Pre-Calculus 110 or Foundations of Mathematics 120, or equivalent. NOTE: Credit can be obtained for only one of STAT 2043, STAT 2253, STAT 2263, STAT 2264, or STAT 2593

STAT2264Statistics for Biology3 ch (3C)

An introductory course in statistics. Probability, Bayes' Theorem, applications of probability to genetics, random variable, expectation, binomial and normal random variables, confidence intervals for means and proportions, prediction intervals, tests of hypotheses, paired data versus two independent samples, brief introduction to analysis of variance, regression, correlation, contingency tables, examples drawn from medicine and biology, use of a statistical computer package.

Prerequisite: MATH 1003 or MATH 1053. NOTE: Credit can be obtained for only one of STAT 2043, STAT 2253, STAT 2263, STAT 2264, or STAT 2593

STAT2593Probability and Statistics for Engineers3 ch (3C)

Probability spaces: combinatorial probability; conditional probability and independence. Random variables: discrete distributions; continuous distributions; expectation, variance, and covariance; linear combinations. Statistics: descriptive and graphical statistics; sampling distributions. Inference: point estimation; confidence intervals; hypothesis tests; paired data designs; two sample inference.

Prerequisite: MATH 1013. NOTE: Credit can be obtained for only one of STAT 2043, STAT 2253, STAT 2263, STAT 2264, or STAT 2593.

STAT3043Statistics for Social Scientists II3 ch (3C)

Topics from survey statistics: stratified sampling; cluster sampling. Questionnaires: construction, administration, interpretation and reporting. Topics from basic statistics: regression; one way and two way analysis of variance.

Prerequisite: STAT 2043. Not to be taken for credit by CS, MATH or STAT majors. NOTE: Credit can be obtained for only one of STAT 2253, STAT 2263, STAT 2264, STAT 2593, or STAT 3043

STAT3083Probability and Mathematical Statistics I3 ch (3C)

The first half of a two-part sequence covering various topics in probability and statistics. This course provides an introduction to probability theory and the theory of random variables and their distributions. Probability laws. Discrete and continuous random variables. Means, variances, and moment generating functions. Sums of random variables. Joint discrete distributions. Central Limit Theorem. Examples drawn from engineering, science, computing science and business.

Prerequisite: MATH 1013. NOTE: Credit can be obtained in only one of STAT 2593 or STAT 3083.

STAT3093Probability and Mathematical Statistics II3 ch (3C)
The second half of a two-part sequence covering various topics in probability and statistics. This course provides an introduction to essential techniques of statistical inference. Samples and statistics versus populations and parameters. Brief introduction to method of moments and maximum likelihood. Tests and intervals for means, variances and proportions (one and two-sample). Multiple regression, residual plots. Analysis of variance, brief introduction to experimental design. Chi-squared tests. Examples drawn from engineering, science, computing science and business. Use of a statistical computer package.

Prerequisite: STAT 3083. Students with exceptional standings in STAT 2593 may seek permission from the instructor.

STAT3303Survival Analysis 3 ch (3C)
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.
STAT3373Elementary Experimental Design3 ch (3C)

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.

Prerequisites: STAT 2263, STAT 2264, STAT 2593, or STAT 3093, MATH 1503 or MATH 2213

STAT3383Introduction to Stochastic Processes (A)3 ch (3C)

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.

Prerequisites: STAT 2593 or STAT 3083 and one of MATH 2013 or MATH 2213

STAT4043Sample Survey Theory3 ch (3C)

Simple random sampling; stratified sampling; systematic sampling; multi-stage sampling; double sampling; ratio and regression estimates; sources of error in surveys.

Prerequisite: STAT 3093

STAT4053Regression Analysis3 ch (3C)

Simple and multiple linear regression. Regression diagnostics. Prediction and model testing. Qualitative variables as predictors. Transformation of variables. Analysis of collinear data. Variable Selection and model reduction procedures. Data analysis using software. NOTE: Credit may be obtained for only one of STAT 4053 or ECON 4625.

Prerequisites: STAT 3093 and one of MATH 1503 or MATH 2213

STAT4073Categorical Data Analysis 3 ch (3C)

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.

Prerequisite: STAT 3093 or the permission of the instructor.

STAT4083Multivariate Methods for Statistical Learning3 ch (3C)

Multivariate normal distribution; variance and correlation matrices. Visualization of multivariate data. Dimension reduction for numerical and categorical data. Simple modelling of covariances by means of exploratory and confirmatory factor analysis. Methodology and techniques of supervised and unsupervised statistical learning.

Prerequisites: STAT 2503 (at least B) or STAT 3093 and one of MATH 1503 or MATH 2213.

STAT4100Honours Project 6 ch [W]

Statistics Honours students must complete a project under the supervision of a faculty member. The project is to include a written report and an oral presentation. Prior to being admitted into STAT 4100, the student must have been admitted to the Honours Program and have submitted an acceptable project proposal to the department. Normally students would begin preparation and research for the project during their third year of study, submit the proposal by October of their fourth (final) year of study, and complete the written and oral presentation by the end of the winter term, to graduate in May of that year. Honours students in an interdepartmental program with statistics may choose to complete their honours project in statistics.

STAT4293Applied Statistics Methods with R3 ch (3C)
Data input and manipulation in R. Basic R programming. 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 programming.

Prerequisite
: STAT 2593 (at least B) or STAT 3093.

STAT4303Mathmatical Statistics3 ch (3C)
Common families of distributions, convergence concepts, sufficiency, completeness, detailed discussion on methods of finding and evaluating point estimators, interval estimators and hypothesis tests.

Prerequisites:
STAT 3083 or permission of the instructor.
STAT4333Applied Longitudial Data Analysis3 ch (3C)
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.

Prerequisite
STAT 4053 or permission of the instructor.

STAT4443Time Series Analysis and Applications (A)3 ch (3C)

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.

Prerequisite: STAT 3093.

STAT4903Independent Study in Statistics3 ch

Advanced topic in Statistics to be chosen jointly by student, advisor, and Department Chair. May be taken for credit more than once. Title of topic chosen will appear on transcript.

Prerequisite: Permission of Department.