Statistics Major Courses

Required Courses

MATH120C P4 Calculus I (4)

This is a first course in calculus for mathematics and science majors. The topics include limits, derivatives, applications of the derivative, tangent lines, concavity, maxima and minima, mean value theorem, definite integral, fundamental theorem of calculus, and applications of the definite integral.
Prerequisite: Good command of precalculus concepts and skills.

MATH122C P4 Calculus II (4)

This is a second course in calculus, building on the material of MATH 120C. The subject matter includes transcendental functions, techniques of integration, polar coordinates, arc length, indeterminate forms, infinite series, power series, Taylor series, and improper integrals.
Prerequisite: MATH 120C with a grade of "C" or higher.

MATH232 Linear Algebra (3)

This is a first course in linear algebra. The content includes linear equations, matrices, vector spaces, determinants, linear transformations, and eigenvalues.
Prerequisite: MATH 122C with a grade of "C" or higher.

MATH301 Mathematical Statistics I (3)

The content includes probability models, finite sample spaces, conditional probability and independence, random variables, functions and sums of random variables, characterizations of random variables, and moment-generating functions.
Prerequisite: MATH 221C* with a grade of "C" or higher.

* To Statistics majors: The content of Math 301 which is based on content from Math 221 is minimal in scope, and will be handled on an individual basis.

MATH302 Mathematical Statistics II (3)

Estimation, maximum likelihood, confidence intervals, hypothesis testing, regression, and correlation are covered.
Prerequisite: MATH 301 with a grade of "C" or higher.

MATH410 Probability Models (3)

This course seeks to apply the mathematical concepts learned in Math 301 and 302 to various applied settings. Probability models will be discussed as they relate to the physical sciences, psychology, engineering, and computers. Topics will be chosen from discrete and continuous Markov chains, queueing theory, branching processes, Brownian motion, Monte Carlo methods, and applications of conditional probability.
Prerequisite: MATH232 and 302 with grades of "C" or higher.

STAT210 Regression Analysis (3)

This course covers basic and intermediate principles of applied linear regression. The course topics include least-squares estimation, assumptions underlying regression analysis and tests of regression assumptions, residuals analysis, regression with nominal/dummy-coded predictors, stepwise and hierarchical entry strategies, prediction, and testing interaction effects in regression analysis. Emphasis is placed on the analysis of behavioral data using regression methods, the interpretation of regression statistics, and the written communication of results of regression analysis.

PSYC386 Survey Design and Analysis (3)

The course considers the construction of questionnaires, survey item types and wording, sampling procedures, and data collection methods. Statistics for establishing inter-item and inter-rater reliability, convergent and construct validity, measurement error, and identifying latent factors in survey items are covered.
Prerequisites: PSYC 200 and 201 with grades of "C" or higher.

PSYC388 Testing and Measurement (3)

This course presents general principles of measurement, enabling students to make and select better tests and to develop an understanding of the use and interpretation of standardized tests and locally made tests. Topics include: purposes and types of tests, norms, reliability, validity, test selection and administration, and interpretation of test results.
Prerequisites: PSYC 200 and 201 with grades of "C" or higher.

STAT205 Design & Analysis of Experiments (3)

The course covers the logic of experimentation, experimental and quasi-experimental research designs, threats to internal, external, and statistical validity, control in experimentation, as well as a thorough introduction to the analysis of variance (ANOVA). Statistics and quantitative methods used in the analysis of experimental designs include variance partitioning, the F distribution, single-factor and factorial ANOVA, multiple comparison procedures, ANOVA in between-subjects and repeated-measures designs, main and interaction effects, and effect size estimation. Data analytic and statistical communication skills are developed in the course.

STAT490 Field Experience in Statistics (3)

Placement in an organization for applied statistical, research, and data-analytic experience.
Prerequisites: Junior standing in Statistics and permission of Program Director.

Elective Courses

CSCI161 Foundations of Computer Science I (3)
ECON222 Statistics II (3)
ECON314 Introduction to Econometrics (3)
MATH221 Calculus III (4)
MATH260 Applied Mathematical Statistics (3)
MATH421 Principles of Real Analysis (3)
STAT260 Introduction to Meta-analysis (3)
STAT390 Special Topics in Statistics (3)
STAT496 Independent Study (1-3)

Quantitative courses in other departments not listed here may be granted elective credit with permission of the Program Director.