Forecasting plays a central role in business decision making. Accurate forecasts are needed when making decisions about investments, resource allocations, schedules and inventory levels. We give an overview of useful quantitative forecasting tools, such as exponential smoothing, autoregressive time series methods, and regression/time series models which incorporate into the forecasts any additional information such as sales promotions and price reductions.

Case studies and computer software for efficient data analysis will be presented.

This course will help you with the quantitative component of your dissertation.

Students in this course gain a solid introduction to important applied statistical methods. Students learn how to work with uncertainty (distributions) and how to carry out the appropriate statistical inference including confidence intervals, standard tests of hypotheses, and regression analysis. Students gain a working knowledge of statistical software (such as Excel, Minitab, SAS, SPSS, or R). This course helps students master many of the quantitative/statistical components that are needed to write an empirically-focused dissertation.

Detailed lecture notes and supplementary materials are sent to students prior to the course. Students are required to read needed background materials prior to the lectures. Basic concepts are explained during the lectures, and case studies and computer software for efficient data analysis is presented. Students practice the concepts on homework assignments and they complete a paper that ties this course to their anticipated dissertation topic.

Attendance; homework; paper on a topic selected by the student (to be submitted by July 31, 2013).

A solid introduction to statistics that covers discrete and continuous probability distributions and how to work with them, statistical inference including confidence intervals and standard tests of hypotheses, regression analysis, and a working knowledge of statistical software (such as Excel, Minitab, SAS, SPSS, or R).

Prior to the start of the course, instructor and participants will interact via e-mail about readings and prerequisites. Interested students should contact the instructor via e-mail prior to May 1.