Courses

An introduction to financial accounting, involving its basic structure, the accounting cycles for service and merchandising enterprises, assets, liabilities, and accounting systems. (3 units; Fall, Spring, & Online)

This course has an emphasis on managerial accounting involved with corporations, control accounting, and decision making. Prerequisite: ACC 250. (3 units; Fall, Spring, & Online)

This course studies economic behavior at the individual level. Topics include decision-making processes of households and firms, supply and demand, elasticity, and marginal analysis. Market structure and public policy are examined to evaluate their impact on economic welfare and the allocation of scarce resources. (3 units; Fall, Spring, & Online)

This course offers a common sense approach to economics, covering basic economic laws and how they apply to our world and everyday life. The course focuses on the overall economy and economic theories that offer explanations for its fluctuations and changes. Also, the course is designed to give the student a sufficient grounding in the terminology, basic concepts, and issues of economics to stimulate interest in further study and provide background in business. (3 units; Fall, Spring, & Online)

In this course concepts of computer programming languages are presented. Attention is given to the common constructs of programming languages as well as a structured approach to program development. The programming language may change from semester to semester. (3 units; Spring)

Basic concepts of analytical geometry, limits and derivatives, differentials and rates, integration, definite and indefinite integrals, differentiation of logarithmic and exponential functions. Prerequisites: MAT 135, 145, EGR 182, or sufficient SAT, ACT or math placement exam scores and appropriate high school mathematics background. (4 units; Fall/Spring)

Continued study and applications of integration: volumes, lengths, surface of revolution; derivatives and integrals involving trigonometric functions, infinite series, expansion of functions, hyperbolic functions, law of the mean, partial fractions, polar coordinates, and conic sections. Prerequisite: MAT 245. (4 units; Fall/Spring)

A course emphasizing the empirical and scientific approaches to disciplines involved in statistical and data analytics sciences. Students learn the historical foundation of each departmental program area. Students will also familiarize themselves with the perspectives and specialties of our department faculty while systematically and critically reviewing the expanding roles of data-centric sciences. The course focuses on teaching, writing, developing a future internship, exploring potential graduate work, and engaging in professional associations from a Christian worldview. Note: This course is designed to introduce students in statistics-related majors (Applied Statistical Analysis, Statistics and Data Analytics, Actuarial Science, Sports Analytics, etc.) to the discipline. It is not designed as a major or general education course in statistics and does not fulfill the ‘introduction to statistics’ requirement for any major. Pass/Fail. (1 unit; Fall)

Mathematical theory and applications, development of formulae, principles of statistical decision theory, descriptive measurements, probability concepts, random variables, normal distribution, inferential statistics, sampling distributions, confidence intervals, hypothesis testing, chi-squared procedures, linear regression, and the use of computers in statistics. Prerequisite: MAT 115 or sufficient SAT, ACT or math placement exam scores and appropriate high school mathematics background. (3 units; Fall, Spring, & Online)

This course represents a basic concepts and methodology course in regression analysis using application of general linear regression models to real-life situations. Case studies are used to give practice in diagnosing practical problems, deciding on appropriate models, and knowing which inferential technique will answer the researcher’s questions for the purposes of description and prediction. Regression models and model building typical of problems used in the social and behavioral sciences, the natural and health sciences, and many other disciplines are covered. Prerequisite: STA 144. (3 units; Spring, even years)

An introduction to data mining, management and statistical programming techniques using comprehensive and widely available tools like SAGE, SPSS, SAS and R. Students learn exploratory data analysis, coding and manipulation of variables, database management applying statistical concepts. Modeling and simulation experiments on a variety of applied data sets. Pre- or Co- Requisite: STA 144. (3 units; Fall)

A continuation of Statistical Computing I using comprehensive and widely available tools like SAGE, SPSS, SAS and R. Advanced techniques will be covered including (but not limited to) numerical linear algebra, optimization and nonlinear equations, the EM algorithm, Laplace approximations, quadrature methods, simulation methodology, sampling, Monte Carlo and bootstrap methods. Prerequisite; STA 210. (3 units; Spring)