In 1794, two years before Tennessee became a state, the legislature of the Southwest Territory chartered Blount College in Knoxville as one of the first three colleges established west of the Appalachian Mountains. In 1807, the school changed its name to East Tennessee College. The school relocated to a 40-acre tract, known today as "the Hill," in 1828 and was renamed East Tennessee University in 1840. The Civil War literally shut down the university. Students and faculty were recruited to serve on battlefields, and troops used campus facilities as hospitals and barracks. In 1869, East Tennessee University became the state's land-grant institution under the auspices of the 1862 Morrill Act. In 1879, the state legislature changed the name of the institution to the University of Tennessee. By the early 20th century, the university admitted women, hosted teacher institutes, and constructed new buildings. Since that time, the University of Tennessee has established campuses and programs across the state. Today, in addition to a rich sports tradition, the University of Tennessee provides Tennesseans with unparalleled opportunities.

Statistical Methods and the Improvement of Data Quality contains the proceedings of The Small Conference on the Improvement of the Quality of Data Collected by Data Collection Systems, held on November 11-12, 1982, in Oak Ridge, Tennessee. The conference provided a forum for discussing the use of statistical methods to improve data quality, with emphasis on the problems of data collection systems and how to handle them using state-of-the-art techniques. Comprised of 16 chapters, this volume begins with an overview of some of the limitations of surveys, followed by an annotated bibliography on frames from which the probability sample is selected. The reader is then introduced to sample designs and methods for collecting data over space and time; response effects to behavior and attitude questions; and how to develop and use error profiles. Subsequent chapters focus on principles and methods for handling outliers in data sets; influence functions, outlier detection, and data editing; and application of pattern recognition techniques to data analysis. The use of exploratory data analysis as an aid in modeling and statistical forecasting is also described. This monograph is likely to be of primary benefit to students taking a general course in survey sampling techniques, and to individuals and groups who deal with large data collection systems and are constantly seeking ways to improve the overall quality of their data.