Ph.D. Track in Environmental Statistics
Goals:
(1) To give the graduates of this program an appropriate combination of statistical and environmental systems backgrounds so that they may have successful technical careers in environmental organizations and companies or successful academic careers doing research in environmental statistics
(2) To train academic environmental statisticians who can serve as better bridges between the academic and corporate worlds.
Program:
Masters degree in Statistics with an Environmental Statistics Option: A Masters degree in statistics with an environmental statistics option will provide the student with strong program in statistics with an emphasis on special methods used in environmental analysis. The basis of this will be core set of courses required for Masters degree in Statistics plus addition courses on environmental statistics. The degree will require 30 semester hours, including at least 25 semester hours of work within the department. Students should expect to complete the master's degree in 18 months of graduate study.
The program will require a course on "Environmental Problem-Solving Using Statistics" (tentative title) in the fall of the second year and will restrict the possible electives to Bayesian Statistics, Bioassay, Spatial Statistics, Computation Statistics or Multivariate Methods. The purpose of the problem-solving course is to prepare students to lead improvement projects in environmental sciences.
Ph.D. degree in Statistics with an Environmental Statistics emphasis: For the Ph.D. program, each student must complete a minimum of 90 credit hours of graduate study (beyond the baccalaureate) of which approximately 30 will be research and dissertation credits, including a master's thesis if appropriate. This will usually require four to five years for a student with no previous graduate training in statistics. In addition to the required core courses for the Masters Degree, the core courses for the Ph.D. program are Advanced Topics in Statistical Inference. Also, the student will be expected to complete advanced course work in appropriate areas of concentration, to be chosen by the student in conjunction with his other advisory committee.
Students are required to take four 6000-level courses in statistics. Only STAT 6114 (advanced Inference) is required. The written Ph.D. preliminary exams can both be on combinations of courses in Group C: Methods and Applications. We anticipate students will take 6000 level classes in Multivariate Methods, Bayesian Statistics or Regression. Other combinations may be substituted at the discretion of the student's committee. As an additional requirement, students are required to take three 5000-level courses in the electives from partnering departments. Possible courses are listed below.
Students should finish all coursework in the fourth year. Exceptional students should finish their Ph.D. by the end of the fourth year. More typically, they will finish sometime in the fifth year.
Two objectives will help strengthen the program:
Objective 1: set up an advisory panel on environmental statistics consisting of members from the statistics department and other departments such as forestry, fisheries and wildlife, civil engineering and biological systems engineering.
Objective 2: develop an environmental statistics problem solving course. The intention is to design a course centered on solving problems in environmental statistics. Examples would include projects such as designing statistically based standards, helping Virginia's Department of Environmental Quality to design a biological monitoring program, etc. The course is intended to be designed around group projects rather than individual assessments.
New courses: The program calls for establishment or re-design of several courses. These include Advanced Bayesian Analysis and Advanced Multivariate Methods.
Environmental Statistics Ph.D. Curriculum (Entry in Odd Year)
First Year
| Fall (Odd) | Spring (Even) | Summer (Even) | |||
| STAT 5034 | Inference Fundamentals | STAT 5114 | Stat. Inference | STAT 5984 | Special Topics (1) |
| STAT 5044 | Reg. and ANOVA | STAT 5124 | Linear Models Theory | ||
| STAT 5014 | Intro. Stat. Prog. Pack | STAT 5204 | Exp. Des. & Analysis I | ||
| STAT 5104 | Prob. & Dist. Theory | STAT 5024A | Seminar in Environmental Statistics | ||
Second Year
| Fall (Even) | Spring (Odd) | Summer (Odd) | |||
| STAT 5514 | Regression Analysis | STAT | Elective | STAT 5594 | Bioassay |
| STAT 5XXX | Problem Solving Course | STAT 6514 | Advanced Regression | ||
| STAT 5504 | Multivariate Methods | STAT 6424 | Advanced Multivariate | ||
| STAT 5904 | Special Topics (1) | ||||
Third Year
| Fall (Odd) | Spring (Even) | Summer (Even) | |||
| STAT 5444 | Bayesian I | STAT 6444 | Bayesian II | STAT | Elective |
| Elective | STAT 6114 | Advanced Inference | |||
| STAT | Elective | STAT | Elective | ||
| STAT 5984 | Special Topics (1) | ||||
Fourth Year
| Fall (Even) | Spring (Odd) | Summer | |||
| Elective | STAT | Elective | STAT 7994 | Research & Dissertation | |
| STAT 7994 | Research & Dissertation | STAT 7994 | Research & Dissertation | ||
Environmental Statistics Ph.D. Curriculum (Entry in Even Year)
First Year
| Fall (Even) | Spring (Odd) | Summer (odd) | |||
| STAT 5034 | Inference Fundamentals | STAT 5114 | Stat. Inference | STAT 5594 | Bioassay |
| STAT 5044 | Reg. and ANOVA | STAT 5124 | Linear Models Theory | STAT 5984 | Special Topics (1) |
| STAT 5014 | Intro. Stat. Prog. Pack | STAT 5204 | Exp. Des. & Analysis I | ||
| STAT 5104 | Prob. & Dist. Theory | STAT 5024 | Seminar in Stat. Consulting | ||
Second Year
| Fall (Odd) | Spring (Even) | Summer (Even) | |||
| Elective | STAT | Elective | STAT or ISE | Elective | |
| STAT 5XXX | Problem Solving Course | STAT 6114 | Advanced Inference | ||
| Elective | STAT 5984 | Special Topics (1) | |||
| STAT 5984 | Special Topics (1) | Elective | |||
Third Year
| Fall (Even) | Spring (Odd) | Summer (Odd) | ||
| STAT 5504 | Multivariate | STAT 6424 | Advanced Multivariate | |
| STAT 5514 | Regression Analysis | STAT 6514 | Advanced Regression | |
| STAT 5444 | Bayesian | STAT 6444 | Advanced Bayesian | |
Fourth Year
| Fall (Odd) | Spring (Even) | Summer (Even) | |||
| Elective | STAT | Elective | STAT 7994 | Research & Dissertation | |
| STAT 7994 | Research & Dissertation | STAT 7994 | Research & Dissertation | ||
New Courses:
5XXX “ Problem-Solving Using Statistics”
(The intention of the course is to look at data that arises in environmental studies. The course will be based on group projects. Specialized statistical methods will be discussed to deal with problems arising in the analysis of environmental data such as censoring, zero-heavy data, biological-environmental relationships and missing values).
5YYY Environmental Internship
Students may opt to work for eight months at an Environmental Partner under the supervision of an environmental mentor. Students must submit a written report of their project to his/her supervisor and to his/her advisory committee. The student must also make formal oral presentations at both the company and at Virginia Tech. The advisory committee and environmental mentor will determine the grade jointly. This elective will substitute for electives in other departments.
Graduate Courses in Environmental Studies from different departments:
Courses that may be used to satisfy requirements
Biology
5024: Population & Community Ecology
5034: Ecosystem Dynamics
5044: Aquatic Ecotoxicology
5054: Hazard Evaluation Of Toxic Chemicals
Civil Engineering
5104: Environmental Chemistry
5714: Surface Water Quality Modeling
5184: Techniques For Environmental Analysis
5194: Environmental Engineering Microbiology
5204: Gis Applications In Civil Engineering
5214: Analysis Of Imaging Systems
5224: Adv. Gis Applications In Civil & Environmental Engr .
5324: Advanced Hydrology
5334: Analysis Of Water Resources Systems
5344: Environmental Systems Optimization
5354 (Geol 5814): Numerical Modeling Of Groundwater
5364: Water Law
Biochemistry
4204: Biochemical Toxicology
Biological Systems Engineering
5124: Probability Models In Agricultural Engineering
5144 (Cee 5064): Knowledge-Based Expert Systems
5304: Nonpoint Source Poll
5354: Nonpoint Source Pollution Modeling
4144: Biological Systems Simulation
4304: Nonpoint Source Pollution Modeling & Management
Crop Soil And Environmental Science
5634: Soil Chemistry
5694 (Biol 5694): Soil Biochemistry
4134: Soil Genesis & Classification
4734 (Ensc 4734): Environmental Soil Chemistry
Entomology
4354 (Biol 4354): Aquatic Entomology
6164: Insecticide Toxicology
6254: Population Modelling Of Insect Systems
Fisheries
5214: Wildlife Population & Habitat Analysis
5224: Wildlife Population Dynamics
5734: Fisheries & Wildlife Planning
5514: Fish Population Dynamics & Modeling
5624: Fish Health
Forestry
5104 (Geog 5104): Seminar In Remote Sensing & Geographic Informationsystems
5214: Advanced Forest Inventory
5224: Forest Biometry
5254: Remote Sensing Of Natural Resources
Geography
5034: Analysis Of Spatial Data
5104 (For 5104): Seminar In Remote Sensing & Geographic Information Systems
5314: Advanced Spatial Analysis In Geographic Information Systems