Home
Research
Teaching Archive
Links
STAT -555-Environmental Statistics- Winter 2017

spritelogo


Meeting Information

Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: W 6:00 PM - 9:00PM
Email: kpokhrel(at)umich.edu
Meeting Location: 2048CB
Office: 2087CB
Office Hours:
Monday 12:30 PM-1:30 PM
Wednesday 4:30PM- 5:45 PM
and by appointments

Course Description


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.

Course Objectives

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.

    Textbook


    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
    1. Song S. Qian, Environmental and Ecological Statistics with R, CRC Press
    2. Diez, Barr, and Cetinkaya-Rundel, OpenIntro Statistics.

    Homework


    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.

    Exams


    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.

    Project

    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.

    Software


    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)

    Grade Distribution

    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.
    Letter Grade E D- D D+ C- C C+ B- B B+ A- A A+
    Percentage 0-59 60-62 63-66 67-69 70-72 73-76 77-79 80-82 83-86 87-89 90-92 93-96 97-100

    Disability Statement


    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.

    Academic Integrity


    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

    Safety


    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/SectionsTopics 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

    Homework



    Description Remarks

    Rmarkdown Examples



    Description Remarks
    review: Hypothesis Testing
    Data Visualization
    Measures of Data
    Normal Distribution
    Central Limit Theorem
    Galton's Height Data Download

    Extra Resources


    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
    Auto Data

    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