This course provides a rigorous introduction to statistical inference, with a unique balance of theory and applications to problems in statistical computing. Topics include parametric point estimation, parametric interval estimation and testing hypothesis. The course prepares students for further studies in advanced statistics such as in linear models and multivariate analysis.
Nature of mathematics; logic and reasoning; sets; numbers and patterns; Euclidean and non-Euclidean geometries; mathematics in arts and humanities; mathematics in social sciences; issues and trends in mathematics.
Functions, limits, derivatives and their applications, antiderivatives and their applications, L'Hopital's rule.
Techniques of integration; improper integrals; conic sections; parametric and polar curves; calculus of vector-valued functions; curves, planes and surfaces in three-dimensional space; calculus of multivariate functions
First order equations; second and higher order linear equations; Laplace transforms; numerical methods; applications in physics, biology and other fields.