A first exposure to statistics that builds data handling skills and develops conceptual thinking through active participation in problems using real data, computer simulations and group work. STATS 100 makes full use of appropriate technology and prepares students for further study in Statistics.
A practical course in the statistical analysis of data. Interpretation and communication of statistical findings. Includes exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection.
A practical course in the statistical analysis of data. There is a heavy emphasis in this course on the interpretation and communication of statistical findings. Topics such as exploratory data analysis, the analysis of linear models including two-way analysis of variance, experimental design and multiple regression, the analysis of contingency table data including logistic regression, the analysis of time series data, and model selection will be covered.
An introduction to calculus that builds mathematical skills and develops conceptual thinking.
MATHS 102 works as a refresher course for those who haven’t studied Mathematics for some time, a confidence builder for those lacking Mathematical confidence and a preparation course for further study in Mathematics.
A general entry to Mathematics for commerce and the social sciences. MATHS 108 covers selected topics in algebra and calculus and their applications, including: linear functions, linear equations and matrices; functions, equations and inequalities; limits and continuity; differential calculus of one and two variables; integral calculus of one variable.
This sequel to MATHS 108 features applications from the theory of multi-variable calculus, linear algebra and differential equations to real-life problems in statistics, economics, finance, computer science, and operations research. Matlab is used to develop analytical and numerical methods of solving problems.
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test.
Basic mathematical tools and methods needed for computer science are introduced. Elementary mathematical skills for defining, analysing and reasoning with abstract objects used in programming are developed. Topics include integers and rational numbers, strings and sets, methods of proof (including induction), algorithms and functions, and elementary introductions to graphs, trees, counting and probability.
Application of the generalised linear model and extensions to fit data arising from a range of sources including multiple regression models, logistic regression models, and log-linear models. The graphical exploration of data.
Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing.
The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce.
Pre-requisite: MATH 115A.
Ring of integers, integral domains, fields, polynomial domains, unique factorization.
Pre-requisite: MATH 32B, 33B.
Rigorous introduction to foundations of real analysis; real numbers, point set topology in Euclidean space, functions, continuity.
Pre-requisite: MATH 31A.
Introduction to differential calculus of several variables, vector field theory.
Pre-requisite: MATH 32B, 33B.
Introduction to basic formulas and calculation procedures of complex analysis of one variable relevant to applications. Topics include Cauchy/Riemann equations, Cauchy integral formula, power series expansion, contour integrals, residue calculus.
Pre-requisite: Successful completion of Mathematics Diagnostic Test.
Differential calculus and applications; introduction to integration.
Pre-requisite: MATH 31A, 31B.
Discrete structures commonly used in computer science and mathematics, including sets and relations, permutations and combinations, graphs and trees, induction.
Pre-requisite: MATH 33A.
Techniques of proof, abstract vector spaces, linear transformations, and matrices; determinants; inner product spaces; eigenvector theory.
Pre-requisite: MATH 3B or 31B or 32A.
Introduction to linear algebra: systems of linear equations, matrix algebra, linear independence, subspaces, bases and dimension, orthogonality, least-squares methods, determinants, eigenvalues and eigenvectors, matrix diagonalization, and symmetric matrices.
Pre-requisite: MATH 33B.
Dynamical systems analysis of nonlinear systems of differential equations. One- and two- dimensional flows. Fixed points, limit cycles, and stability analysis. Bifurcations and normal forms. Elementary geometrical and topological results. Applications to problems in biology, chemistry, physics, and other fields.
Pre-requisite: MATH 115A.
Quantitative modeling of strategic interaction. Topics include extensive and normal form games, background probability, lotteries, mixed strategies, pure and mixed Nash equilibria and refinements, bargaining; emphasis on economic examples. Optional topics include repeated games and evolutionary game theory.
Pre-requisite: MATH 33A, 33B.
Introduction to fundamental principles and spirit of applied mathematics. Emphasis on manner in which mathematical models are constructed for physical problems. Illustrations from many fields of endeavor, such as physical sciences, biology, economics, and traffic dynamics.
Pre-requisite: MATH 33A, 33B.
Selected topics in differential equations. Laplace transforms, existence and uniqueness theorems, Fourier series, separation of variable solutions to partial differential equations, Sturm/Liouville theory, calculus of variations, two-point boundary value problems, Green’s functions.
Pre-requisite: MATH 3B.
Multivariable modeling, matrices and vectors, eigenvalues and eigenvectors, linear and nonlinear systems of differential equations, probabilistic applications of integration.
Pre-requisite: Successful completion of Mathematics Diagnostic Test.
Linear and polynomial functions and their graphs, applications to optimization. Inverse, exponential, and logarithmic functions. Trigonometric functions.
Basic statistical procedures and their application to research and practice in various areas of psychology.
Lecture, four hours. Requisites: course 10 with a grade of C or better, and one course from Mathematics 2, Program in Computing 10A, Statistics 10, or one term of calculus. Designed for pre-majors.
Pre-requisites: Mathematics 31A, 31B.
Introduction to probability and statistics for economists, with emphasis on rigorous arguments. Not open to students with credit for former Statistics 11.
