47666

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.

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.

A second course in single-variable calculus. Applications of integration, techniques of integration, numerical integration, indeterminate forms, improper integrals, parametrized curves, polar coordinates, infinite sequences and series, and power series.

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.

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.

Set theory, logic, proof techniques, mathematical induction, relations and functions, recursion, combinatorics, elementary number theory, trees and graphs, analysis of algorithms. Emphasis on topics of relevance to mathematics and computer science majors.