STAT -530- Applied regression Analysis- Fall 2016


Applied Linear Regression , Fourth Edition by Kutner, Nachtsheim and Neter

Meeting Information

Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: TR 12:30 PM - 1:45PM
Email: kpokhrel(at)
Meeting Location: 2046CB
Fi Office: 2087CB
Office Hours:
Monday 2:00PM - 3:15 PM
Tuesday 2:00PM - 3:00 PM
Wednesday 2:00PM - 3:15 PM
and by appointments

Course Description and Objectives

In this course, we will discuss single variable linear regression, multiple linear regression including polynomial regression, logistic regression and Poission regression. Model checking techniques based on analysis of residuals will be emphasized. Remedies to model inadequacies such as transformations will be covered. We will extensively use computer software to analyze data with emphasis on interpretation of estimated parameters. The major computing workhorse for this course is a software "R".

One of the major objective of this course to develop understanding of theory behind the statistical methods and develop successful applications of these models. The course seeks to blend theory and applications while avoiding extremes of theoritical isolations. Course aims to fit and interpret parameters in a linear or non-linear regression models using statistical software R.

Student Leanrning Outcome:
  • Increase students’ command of problem-solving tools and facility in using problem-solving strategies, through classroom exposure and through experience with problems within and outside of mathematics.
  • Increase students’ ability to communicate and work cooperatively.
  • Increase students’ ability to use technology and to learn from the use of technology, including improving their ability to make calculations and appropriate decisions about the type of calculations to make.
  • Increase students’ knowledge of the history and nature of statistics. Provide students with an understanding of how statistics is done and learned so that students become self-reliant learners and effective users of statistics.


    Applied Regression Models-4th Edition by Kutner, Nachtsheim, and Neter
    Major Reference Books
    1. 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. Majority of the homework problems will be from book but be prepared to solve any problems of that streamlines with our course content. Good news! Lowest homework grade will be dropped. For better exam results you need to master all the homework problems.


    There will be two mid-term exams, and a final project. To answer the exam questions, you are expected to have a clear mathematical reasoning of the statistical methods used to solve the subject problems.


    There will be two mini-projects and a semester project. For a good project, you need to describe the data, pose reasonable hypotheses, estimate parameters, select appropriate regression model/s, and explain the results in both statistical terms and in a nontechnical language. Primary objective of these projects is to apply statistical methods in the real life situations and come up with logical reasonings and explanations of the statistical methods.


    We use a software called "R". R is a programming language for statistical computing and visualizing data. It can be downloaded for free from 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%) Thursday, October 06
    Exam II (20%) Thursday, November 10
    Mini Project I(10%) Due, October 27
    Mini Project II(10%) Due, December 01
    5 Homeworks (15%) TBD
    Final Project(25%) Presentation, December 13
    Final paper due December 19

    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: 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 (, 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:


    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 and 2 (Sept 8, 13, 15) Chapter 1 (sections: 1.1-1.8) Linear Regression with One Predictor
    Week 3 and 4 (Sept 20, 22, 27, 29 ) Chapter 2 (sections: 2.1-2.10) Inferences in Regression and Correlation Rlab
    Week 5 ( Oct 4, 6) Review; Exam I
    Week 6,7 (Oct 11, 13, 20) Chapter 3 (sections 3.1-3.10) Diagnostics and Remedial Measures
    Week 8 ( Oct 25) Chapter 4 (section: 4.4) Regression through Origin
    Week 8 and 9(Oct 27, Nov 1, 3) Chapter 6 (section: 6.1, 6.5-6.8) Multiple Regression Models
    Week 10 (Nov 8, 10) Review ; Exam II
    Week 11, 12 (Nov 15, 17) Chapter 7 and 8 (section: 7.1-7.3, 8.1-8.3) Multicollinearity, Polynomial regression
    Week 13 (Nov 22, 29) Chapter 9 (section: 9.1, 9.4, 9.6) Model Selection and Validation
    Week 14 (Dec 1, 6) Chapter 13 (section: 13.1, 13.2, 13.6) Nonlinear Regression
    Week 15(Dec 8, 13) Chapter 14 (section: 14.1, 14.2) Logistic Regression


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