STAT -555-Environmental Statistics- Winter 2017
Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: W 6:00 PM - 9:00PM
Meeting Location: 2048CB
Monday 12:30 PM-1:30 PM
Wednesday 4:30PM- 5:45 PM
and by appointments
This course primarily focuses in fundamentals of statistics with emphasis on environmental problems and their relevance in everyday life. The topics covered are types of data, data visualization, parametric and non-parametric statistical inferences including mutiple linear regression, analysis of bivariate measurements, contingency table, goodness of fit tests, and comparison of several groups, and ANOVA testing.
Primary objective of the course is to introduce , graduate students in evinonmental and biologicals sciences, statical techniques to make data driven decision making. This course aims to nurture
importance of statistical method to enhance the understanding of issues related to environmental sciences. A one semester course can not be exaustive in depth and width of literature but could create interest and encourage to delve more in to the subject. Creating a group of learners in environmental statistics is the main
goal of this course.
Student Leanrning Outcomes:
At the end of the course, the student will be able to
understand types of data
visualize data using approproate graphing techniques
find inferences using parametric and statistical models
learn some techniques of non-parametric metnod for statistical inferences.
compare, statistically, different groups and find differences and similarities between the groups.
Using Statistics to understand the Environment, C. Philip Wheater and Penny A. Cook; ISBN-13: 978-0415198882, Taylor and Farncis Group . We will be covering chapters 1, 2, 3, 3, 4, 5, 6 and 7*. Apart from text book we will use different resources for the classroom activities and homeworks.
Major Reference Books
- Song S. Qian, Environmental and Ecological Statistics with R, CRC Press
- Diez, Barr, and Cetinkaya-Rundel, OpenIntro Statistics.
At least five sets of homework problems will be assigned. Some addition homework problems will periodically be assigned during the lecture. Lowest homework grade will be dropped. For a better grade you need to master all the homework problems.
There will be two mid-term exams, two mini projects and a final exam. To answer the exam questions, you are expected to have a clear mathematical reasoning of the statistical methods used to solve the subject problems.
You will be assigned two mini projects during the semester. For a good project, you need to describe the data, pose reasonable hypotheses, select appropriate statistical model/s, compute the test results, present a clear model diagnostics, and explain the results in both statistical terms and in plain English. Primary objective of these projects is to apply statistical methods in the real life situations.
We use a software called "R". R is a programming language for statistical computing and visualizing data. It can be downloaded for free from http://www.r-project.org. We will R Studio for regular classroom activities. R studio is an open source Integrated development Environment(IDE) for R.
To download R click here for windows and here for Mac.
After Installing R: click R Studio to download R studio.
| Evaluations and Important Dates:|
|Exam I (20%) || Wednesday, February 15|
|Exam II (20%)|| Wednesday, March 29 |
| Mini Project I(10%) || Due, February 22 |
| Mini Project II(10%) || Due, April 05 |
| 5 Homeworks (15%) || TBD |
| Final Exam (25%) || Wednesday, April 26(6:30PM-9:30 PM) |
Your final grade will be based on two mid-term exams, five sets of graded homeworks, two mini-projects, and a final project. Lowest homework grade will be dropped.
The University will make reasonable accommodations for persons with documented disabilities. Student need to register with Disability Resource Services (DSR) every semester they are enrolled for classes. DRS is located in counseling & Support Services, 2157 UC. To be assured of having services when they are needed, students should register no later than the end of add/ drop deadline of each term. Visit the DSR website at: webapps.umd.umich.edu/aim. If you have disability that necessitates an accommodation or adjustment to the academic requirements stated in this syllabus, you must register with DRS as directed above and notify me. Upon receipt of your notification, we will make accommodation as directed by DRS.
￼The University of Michigan-Dearborn values academic honesty and integrity. Each student has a responsibility to understand, accept, and comply with the University's standards of academic conduct as set forth by the Code of Academic Conduct ￼(mdearborn.edu/policies_st-rights), as well as policies established by each college. Cheating , collusion, misconduct, fabrication, and plagiarism are considered serious offenses, and may be monitored using tools including but not limited to TurnItIn. Violations can result in penalties up to and including expulsion from the University. At the instructor's direction, the penalty may be a grade zero on the assignment up to and including recommending that student be expelled from the University. It is the sole responsibility of the student to understand and follow academic guidelines regarding plagiarism. The University of Michigan-Dearborm has an online academic integrity tutorial that can be accessed at: umdearborn.edu/umemergencyalert
All students are encouraged to program 911 and UM-Dearborn’s University Police phone number (313) 593-5333 into personal cell phones. In case of emergency, first dial 911 and then if the situation allows call University Police.
The Emergency Alert Notification (EAN) system is the official process for notifying the campus community for emergency events. All students are strongly encouraged to register in the campus EAN, for communications during an emergency. The following link includes information on registering as well as safety and emergency procedures information: .
If you hear a fire alarm, class will be immediately suspended, and you must evacuate the building by using the nearest exit. Please proceed outdoors to the assembly area and away from the building. Do not use elevators. It is highly recommended that you do not head to your vehicle or leave campus since it is necessary to account for all persons and to ensure that first responders can access the campus.
If the class is notified of a shelter-in-place requirement for a tornado warning or severe weather warning, your instructor will suspend class and shelter the class in the lowest level of this building away from windows and doors.
If notified of an active threat (shooter) you will Run (get out), Hide (find a safe place to stay) or Fight (with anything available). Your response will be dictated by the specific circumstances of the encounter.
Tentative Academic Calender
| Week ||Chapters/Sections||Topics covered ||Remarks |
| Week 1 (Jan 11) || Chapter 1 || Introduction to Statistics |
| Week 2 (Jan 18) || Chapter 2 || Descriptive Statistics|
| Week 3 (Jan 25) || Chapter 2 || Descriptive Statistics || |
| Week 4 (Feb 01) || Chapter 3 || Hypothesis Testing, Data Transformation || |
| Week 5 (Feb 08) || Chapter 3 || Hypothesis Testing, Data Transformation|| |
| Week 6 (Feb 15) || Chapter 1,2, 3 || Review; Exam I || |
| Week 7 (Feb 22) || Chapter 4 || Differences between two Samples || |
| Week 8 ( Feb Mar 01) || || Spring recess || |
| Week 9(Mar 08) || Chapter 5 || Differences between two Samples || |
| Week 10 (Mar 15) || Chapter 5 || Linear and Multiple regression || |
| Week 11 (Mar 22) || Chapter 6 || Logistic Regressionn || |
| Week 12(Mar 29) || Cchapte 4, 5 || Review; Exam II || |
| Week 13 (Apr 5) ||Cchapte 6 || Analysis of Frequency Data || |
| Week 14 (Apr 12) || Chapter 6 || Analysis of Frequency Data || |
| Week 15 (Apr 19) || Chapter 7* || Differences Between more than one Samples || |
StatSci.org. A good resource for varieties of data sets. These data sets are open to public and you can use these data sets for your own projects. If you happen to use these data please do not forget to mention the source.
UNSD United Nations Statistics Division Environmental Indicators
Fuel Economy Data Fuel economy data by U.S. department of energy
Environment and Public Health Collection of data by National Center for Environmental Health
OpenItro This is an excellent resource for introductory statistics. Apart from lecture notes they also have well explained examples with R code.
Distribution Calculator An excellent App to understand binomial distribution normal approximation to binomial
Install R and R Commander Guideline to download and install R
Exploratory Data Analysis Wide range of statistical topics are covered in this web page with video lectures and other supplementary materials.
Statistics Online Computational resources interactive apps to calculate different statistical measures.
More Stat Apps Wonderful collection of Statistics Apps for data visualization
Some Environmental Data
Markdown Themes Appearance and Style themes to create HTML document using R Studio.
Shiny Apps A comprehensive Resource of Shiny Apps
Shiny Apps A comprehensive Resource of Shiny Apps
DataCamp A comprehensive resourse to learn Statistics and Data Science