For example, a plant manager, in consultation with the quality control engineer, must decide whether or not to shut down a production line given the dab that three poorly manufactured parts were produced during the last two-hour period. Similarly, an investment manager must decide what portfolio of stocks should be purchased given data on the last six months' performance history of stocks under consideration. These are examples of applied statistics. Statistics deals with observations and reality, but random noise often masks the observations making reality difficult to discern. Applications of experimental design include computer simulation, human factors engineering, cost estimation, manufacturing process design and telecommunications usage estimation.
Statistics provides guidelines about how to use data to make decisions.
An important tool of applied probability is sampling theory, which forms the basis for much of decision making and estimation in the field of computer simulation. With the advent of interactive personal computers, numerical statistical methods have been extended to include graphical data analysis. The Department of Industrial and Systems Engineering maintains a microcomputer laboratory and current software for implementation of many new graphical data analysis methods. This laboratory supports educational and research needs of the department. The Department of Statistics teaches many courses that support the applied statistics area in operations research.