# Minor in Mathematics

The Math Minor is designed to equip students with the quantitative skills that are often required for entrance into top graduate schools. The Math Minor also improves the student’s future employability in a competitive work environment. This minor is particularly popular with students who major in Business Management, Finance, or Economics and wish to improve their quantitative skill set at both the theoretical and practical level.

This course provides a sound understanding of the concepts of calculus and their applications to business and economics. Emphasis in providing the theory side by side with practical applications and with numerous examples. Topics include co-ordinate geometry of straight lines, quadratic curves, exponential and logarithmic functions; elementary differentiation and integration; and applications to maxima, minima, and optimisation. It also deals with differentiation and integration of trigonometric and inverse trigonometric functions.

An introductory course in probability primarily designed for business economics and psychology majors. The course coverage will include: descriptive statistics, elementary probability theory, random variables and expectations, discrete probability distributions (Binomial and Poisson distributions), continuous probability distribution (Normal distribution), linear regression analysis and correlations, elementary hypothesistesting and Chi-square tests, non-parametric methods and SPSS lab sessions targeting applications of statistical concepts to business, economics and psychology and interpretations of hardcopies. All practical work will be produced using SPSS statistical software.

Plus four of the following:

This Course provides a detailed coverage of the analytical and geometrical properties of exponential functions, logarithmic functions, hyperbolic functions; complex numbers; TaylorMacLaurin expansion; methods of integration; infinite series; and co-ordinate geometry of the conic sections and calculus of functions of several variables to include partial derivatives, solving linear differential equations of first order; multiple integrals, Jacobians, line and surface integrals and the theorems of Green and Stokes; and continuity and analyticity of functions of complex variables.

This Course is a continuation of MTH125 and is concerned with inferential statistics. It covers sampling distributions, interval estimations and estimating confidence intervals for populations and proportions, hypothesis and significance testing, goodness-of-fit test and Chi-square test, one-way analysis of variance (ANOVA), applications of non-parametric statistics and linear regression analysis. All practical work will be done on SPSS statistical software.

This Course provides an introduction to game theory and its relation to decision methods in business. The course will cover the core principles of game theory and its role in the process of decision making in business. The use of game algebra and the analyses of the structure of various types of practical statistical decision problems as applied to business will be emphasized. The areas to be studied will include decision making under uncertainty, risk analysis, Baye’s strategies, decision trees, linear programming, Markov Processes, game strategies, classification of games, game trees, the Nash equilibrium, zero-sum games, mixed strategy games, the prisoner’s dilemma and repeated games, collective action games and evolutionary games in the context of hawk-dove games. Applications to specific strategic situation such as in bargaining, bidding and market competition will be explored.

This course provides an introduction to Linear Algebra and Real Analysis. In Linear Algebra the course will cover: Systems of linear equations, the algebra of matrices, determinants and determinant functions, inner products, canonical forms, the theory of vector spaces, linear mappings and transformations, eigenvectors and eigenvalues. In Real Analysis the course will cover: Properties of real numbers (ℝ), sequences and series, limits, properties of continuous functions, differentiability, The Riemann integral, Lebesgue integral, sequences of functions, infinite series, measure theory and Lebesgue measure, properties of vector, metric and topological spaces.

This course provides an introduction to differential and integral calculus of several variables, functions of complex variables, ordinary and partial differential equations, infinite series and convergence, Fourier and orthogonal functions. Analysis of linear differential equations, non-homogeneous, boundary value problems, various methods of solving differential equations e.g. separation of variables, variation of parameters, Laplace transform, Inverse transforms, Power Series solutions and Fourier series. Methods studied will be shown how they can be applied to problem in business, finance and economics.

This course will cover: Essential mathematics (calculus, differential equations, linear algebra and elementary probability theory), mathematics in finance (Central Limit Theorem and Brownian motion, Stochastic calculus and random behaviour, Markov Processes and Martingales, Wiener process, Monte Carlo simulation of pricing and simple trading models), Binomial and Black-Scholes Models and their significance in asset pricing and analysis of financial derivatives.