Note to Reporters: A photo of Jean Opsomer is available with the news release at http://www.news.colostate.edu.
Jean Opsomer, chair of the Department of Statistics at Colorado State University, has been named to a two-year term on the U.S. Department of Agriculture’s Advisory Committee on Agriculture Statistics.
Opsomer is the only university professor from Colorado appointed to the committee.
Opsomer and 19 other scientists and policy experts appointed will advise Secretary Tom Vilsack on surveys of agriculture, periodic census of agriculture and types of agricultural information to obtain from respondents. The committee also prepares recommendations regarding the content of agricultural reports and presents the views and data needs of major suppliers and users of agriculture statistics.
The committee will advise Vilsack concerning agricultural data collected and the statistics issued by the National Agricultural Statistics Service. Members of the committee have a broad range of knowledge in such topics as agricultural economics, rural sociology, farm policy analysis and agricultural education.
The Department of Statistics at Colorado State University is one of the top in the nation and the only one of its kind in the Intermountain West offering advanced degrees.
Several members of the faculty serve on national committees including Opsomer, who is also a member of the Bureau of Labor Statistics Technical Advisory Committee, and Professor Jay Breidt, who serves on the Federal Economic Statistics Advisory Committee for U.S. Secretary of Commerce Gary Locke.
Opsomer joined CSU in 2007. Previously, he was a faculty member in the Department of Statistics at Iowa State University. As an expert in survey statistics, he has worked as a statistical consultant with the National Marine Fisheries Service to redesign coastal recreational surveys, and with the U.S. Census Bureau and the National Science Foundation on the redesign of the National Survey of College Graduates. As chair at CSU, he launched a popular master’s program in Applied Statistics to help train statisticians to work in a wide variety of industries.