91·çÔÂÂ¥

Descriptive statistics: organization of data, sample surveys, experiments and observational studies, measures of central tendency and dispersion, correlation, regression lines, and analysis of variance (ANOVA). Probability theory. Random variables: expected value, variance, independence, probability distributions, normal approximation. Sampling: sampling distributions, and statistical inference, estimating population parameters, interval estimation, standard tests of hypotheses.

Complete both precalculus algebra and trigonometry by taking this single class.? Topics covered include real functions and their graphs; one-to-one and inverse functions; algebraic, exponential and logarithmic, and trigonometric functions; complex numbers and zeros of polynomials; matrices; transformations and conic sections; discrete mathematics; polar coordinates; and applications of trigonometric identities.

Support for students who are concurrently enrolled in MATH 90, Precalculus Algebra. Topics include concepts and skills from elementary and intermediate algebra that are needed to understand the basics of precalculus algebra. Concepts are taught in the context of the linked Math 90 course.

Support for students who are concurrently enrolled in MATH 80, Probability and Statistics. Topics include concepts and skills from arithmetic, pre-algebra, elementary and intermediate algebra, and descriptive statistics that are needed to understand the basics of college-level statistics. Concepts are taught in the context of the linked Math 80 course.

Descriptive statistics: organization of data, sample surveys, experiments and observational studies, measures of central tendency and dispersion, correlation, regression lines, and analysis of variance (ANOVA). Probability theory. Random variables: expected value, variance, independence, probability distributions, normal approximation. Sampling: sampling distributions, and statistical inference, estimating population parameters, interval estimation, standard tests of hypotheses.

Descriptive statistics: organization of data, sample surveys, experiments and observational studies, measures of central tendency and dispersion, correlation, regression lines, and analysis of variance (ANOVA). Probability theory. Random variables: expected value, variance, independence, probability distributions, normal approximation. Sampling: sampling distributions, and statistical inference, estimating population parameters, interval estimation, standard tests of hypotheses.

Descriptive statistics: organization of data, sample surveys, experiments and observational studies, measures of central tendency and dispersion, correlation, regression lines, and analysis of variance (ANOVA). Probability theory. Random variables: expected value, variance, independence, probability distributions, normal approximation. Sampling: sampling distributions, and statistical inference, estimating population parameters, interval estimation, standard tests of hypotheses.

Support for students who are concurrently enrolled in MATH 75, Mathematical Analysis for Business. Topics include concepts and skills from elementary and intermediate algebra that are needed to understand the basics of Mathematical Analysis for Business. Concepts are taught in the context of the linked Math 75 course.

Topics include real vector spaces, subspaces, linear dependence, span, matrix algebra, determinants, basis, dimension, inner product spaces, linear transformations, eigenvalues, eigenvectors, and proofs. Ordinary differential equations and first-order linear systems of differential equations; explicit solutions; qualitative analysis of solution behavior; linear structure, existence, and uniqueness of solutions. Partial differential equations.

Real vector spaces, subspaces, linear dependence and span, matrix algebra and determinants, basis and dimension, inner product spaces, linear transformations, eigenvalues and eigenvectors, proofs of basic results.