Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the SU academic department to assign the appropriate course level, title, and grade for the student's transcript.

Basic statistical theory and data analysis methods. Describing data graphically and numerically. Probability distributions. Sampling. Statistical tests and intervals. Use of computer statistics programs. Emphasis on choice, limitations, and interpretation of methods for management use.

Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.

Review of important statistical concepts. Mathematical models for management data emphasizing correct use and interpretation of results. Linear and logistic regression. Time series analysis. Model assumptions and limitations.

Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.

Selected nonparametric statistical methods; those requiring few assumptions about the population. Related aspects of probability theory, hypothesis formulation and testing, and estimation for population location, dispersion, and functional form.

Survey methods applicable to management and other social science areas. Various sample designs: cost and accuracy, estimation for population location, dispersion, and functional form.

The course will examine how data mining methods and data-driven predictive models can help improve business decision-making. Data mining techniques covered: classification, clustering, association analysis, and optimization.

Participation in a discipline or subject related experience. Student must be evaluated by written or oral reports or an examination. Permission in advance with the consent of the department chairperson, instructor, and dean. Limited to those in good academic standing.

Basic concepts applied to management. Discrete time-series analysis for forecasting and control. Selected statistical models. Regression analysis. ARIMA methods, econometric modeling. Forecasting and business planning. Evaluation of forecasts. Case studies.

Exploration of a problem, or problems, in depth. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor(s) and the department.

Completion of an Honors Capstone Project under the supervision of a faculty member.

Individual study involving selected readings and reports in specific areas of interest.

Statistical Methods of relevance to accounting and auditing. Emphasis on the application of these methods to accounting and auditing problems, such as audits and determination of costs as functions of volume of output, and other factors.