QMB - Quantitative Methods in Business

An introductory course in business statistics and analytics that covers commonly used methods to support business problem-solving and decision-making. Descriptive and predictive analytics techniques are applied with an emphasis on the justification for their use, and the interpretation and validation of their results. Topics include, but are not limited to, descriptive statistics, probability, random variables and probability distributions, sampling distributions, interval estimation, hypothesis testing, regression, forecasting and the role of big data in organizations. A statistical software package is used to illustrate methods and techniques.

Building on basic statistical techniques, this course further explores managerial and business statistics. It covers multivariate regression and model building, forecasting, decision making, analysis of variance, non-parametric statistics and research design and methods. Critical thinking and problem solving skills are emphasized as students learn to evaluate various statistical models and methods. A contemporary business statistics product as well as Microsoft Excel is used to describe and analyze data. A semester project provides students with the opportunity to apply statistical concepts to business decision making. The managerial implications of each topic are provided along the way, and students have opportunities to develop on what they have learned through assignments and projects.

For graduate students only. This is an introductory course in statistical analysis as it applies to managerial decision-making. Topics include sampling techniques, descriptive statistics, probability, random variables and probability distributions, sampling distributions, interval estimation, one-sample and two-sample tests, simple and multiple linear regression, forecasting and statistical applications in quality management. A statistical software package is used to illustrate all methods and techniques. (CFA)

For graduate students only. This course is a survey of quantitative techniques used in business decision-making, including linear programming, forecasting, decision analysis and queuing analysis. Students will develop spreadsheet models that enable these techniques to utilize the data available to them and apply the results to business decisions.

Most business planning begins with a forecast or a prediction.  We cover the most useful predictive analytics models, whether you are a financial analyst, an operations manager, an accountant, a marketer, a human resources manager or an entrepreneur.  We use actual data much like the data you encounter in practice.  Models are explained as procedures that you may replicate with your own data.  These include moving-average, exponential smoothing, S-curve, event, advanced regression, time-series decomposition, ARIMA, data mining, ensemble, and text mining models.  We employ Excel-based ForecastX and Analytic Solver, two widely-used forecasting and data/text mining software in practice.