Requirements for an M.S. in Statistics

UNL offers three (3) options for a Masters of Science degree:

1. A thesis option
2. A non-thesis with minor option
3. A non-thesis with special problem or area of intensive study.

Please refer to the Graduate Studies Bulletin for specific details. Most Statistics MS students will take options 2 or 3.

The primary aim of the Statistics MS program is to provide students with an education sufficient to allow them to be competent practitioners of applied statistics, especially applications involving agricultural, biological, or environmental sciences. Competence includes mastery of statistical theory and practice, significant exposure to disciplines with which statisticians interact, proficiency with statistical computing tools, and training and experience in statistical consulting.

Specific requirements are designed to allow each student flexibility in designing a program suited to his or her specific needs. Students will be expected to take a common core curriculum consisting of:

• One year of mathematical statistics (STAT 882 and STAT 883)
• One year of linear and statistical modeling (STAT 970 and STAT 971)
• One semester of design of experiments (STAT 802)
• One semester of multivariate methods (STAT 873)

In addition, students will be required to:

1. Demonstrate proficiency in a statistical computing language.
2. Gain statistical consulting experience.
3. Become familiar with a discipline (e.g. agricultural, biological, environmental, etc.) to which statistics is applied.

Generally, students will become proficient in SAS to fulfill (1) but other languages (e.g. SYSTAT, SPSS, GENSTAT, GLIM, R, S-Plus, etc. may be deemed more appropriate by the student and committee). Requirement (2) will generally be fulfilled by taking a statistical consulting course and practicum (STAT 997). Requirement (3) will generally be met by taking one or more courses in appropriate disciplines outside the mathematical sciences. Specific needs, goals or past experiences of the students may result in these requirements being fulfilled in alternative ways.

Additionally, students will be expected to take a variety of statistical methods courses. Options available to the student include advanced experimental design, survey sample design, regression, nonparametric methods, categorical data, variance component analysis, agricultural decision making, statistical quality/process control, statistical ecology, repeated measures, and other special topics depending on student interest and faculty availability.

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