QMB - Quantitative Methods in Business

An introductory course in business statistics. Topics include sampling techniques, descriptive statistics, probability, random variables and probability distributions, normal distribution, 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.

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.

For graduate students only. Most business planning begins with a forecast. We cover the most helpful forecasting models, whether you are a financial analyst, an operations manager, an accountant, a marketer, a human resources manager or an entrepreneur. We use actual historical data much like the data you encounter in practice. The models are explained as procedures that you may replicate with your own data. In particular, we study moving-average, exponential smoothing, regression (simple and multiple), time-series decomposition and ARIMA models, as well as techniques for combining forecasts and data mining. We use Excel-based ForecastX, one of the most widely used forecasting software in practice.