Meeting Information
Instructor: Keshav P. Pokhrel, Ph.D.Meeting Times:
Email: kpokhrel(at)umich.edu
Meeting Location: 2046 CB
Office: 2087CB
Office Hours:
Tuesday 11:30 PM- 12:30 PM
Wednesday 12:30 PM- 1:45 PM
Thursday 11:30 AM-12:30 PM
and by appointment
Course Description
This course is primarily designed for those who want to apply quantitative reasoning in different areas of social and natural sciences. After a successful completion of this course, students can think critically, make data driven decisions, understand quantitative news articles and draw relevant inferences. Topics covered include: Frequency distribution, descriptive measures, sampling distributions, elementary probability theory, statistical inference, and linear regression. Statistical computer packages will be used to analyze the data.
Course Objectives:
Upon successful completion of the course, the students should be able to:
- Recognize the use and misuse of statistics in real life situations, in the news, and in surveys.
- Properly Visualize the data and connect the visuals with the subject domains.
- Explore the concepts of basic probability theory in decision making processes.
- Demonstrate understanding of the fundamental statistical concepts: Descriptive statistics, sampling distribution, normal distribution, confidence intervals, hypothesis testing, and linear regression.
Dearborn Discovery Goals:
Please click on the following link for Dearborn Discovery Goals:Program Goals:
Please click on the following link for for program Goals:Textbook
Elementary Statistics: Picturing the World 6th edition, Larson Ron and Farber Besty, 2012; ISBN: View Larger Image 9780321911216, Prentice-Hall. We will be cover chapters 1, 2, 3, 5, 6, 7, 8 ,9 and 10*.
Homework
Homework problems from each chapter will be assigned through Mystatlab. Some additional homework problems will periodically be assigned during the lecture. Primary ingredient for a good grade is to have a solid understanding of all the assigned homework problems. I strongly encourage you to see me for help if you are unable to solve the assigned problems. You are expected to invest an average of 3-5 hours of work per week outside of class. Late assignment is accepted with 20% penalty per day.
Exams
There will be two mid-term exams, and a comprehensive final exam. To answer the exam questions, you are expected to have clear understanding of the statistical procedures and interpret the results in the context of the question.
In Class Assignments and Quizzes
In Class Assignments: You will get worksheets with problems in the class. You can interact with the friends and look at the notes, books and other online resources to solve the problems. Quizzes: Quizzes will be closed book and closed notes. In most cases- structures, styles and diffculty levels of quiz questions will be similar to that of exams.
Project
There will be a project during the semester. For a good project, you need to describe the data, pose reasonable hypotheses, select appropriate statistical tests, compute the test results, and explain the results in both statistical terms and in plain English. Primary objective of the projects is to apply the statistical methods in the real life situations, learn and strengthen a habit of working in a group. Late submission of project will result in losing 20% of total points everyday.
Software
We will use a software called Minitab. It can be downloaded for free from the information technology services (ITS) wegpage. To download Minitab click here and use your unique UM-ID and password.| Important Dates: | ||
| Exam I | Thursday, October 04 | |
| Exam II | Thursday, November 20 | |
| Quiz | Sept 20, Oct 04, Oct 23, Nov 08, Nov 29, Dec 06 | |
| Group project part I : | Due October 25 | |
| Group project part II: | Due December 06 | |
| Final Exam | Thursday, Dec 13(11:30 AM-2:30PM) | |
Evaluations:
Your performance is measured by the weighted average of two in class exams, homework, in-class assignments, a group project and a comprehensive final exam. If you have any grade disputes you need to notify me within a week of grade posted date. Weight distribution of different assesment categories is given below.
- (40%)- Two Mid-Term Exams
- (15%) - homework
- (5%) - In-class assignment
- (10%)- Quizz
- (5%) - group project
- (25%)- Final Exam
| 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 |
Important Dates
University Attendance Policy:
A student enrolled in a course (lecture, laboratory, recitation, colloquium, seminar, or other university approved format) is expected to attend every scheduled session of the course. The instructor of each course will make known to the students the course attendance policy with respectto student absences. It is the student’s responsibility to be aware of this policy. The instructor makes the final decision to excuse or not to excuse an absence.Presence or participation is also expectedin online courses. Participation in online courses can take various forms; it is the instructor who determines what form of presence or participation is expected. Students enrolled in online courses are responsible for being aware of that policy/expectation. An instructor is entitled to give a failing gradefor excessive absences or for a student who stops participating in class at some point during the semester.
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 includiing 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 strongly encouraged to register in the campus Emergency Alert System, used to communicate with campus community during emergency. More information on the system and how it works, along with enrollment information can be found at: webapps.umd.umich.edu/aim
Harassment, Sexual Violence, Bias, and Discrimination:
The University of Michigan-Dearborn recognizes that students have a right to study in a safe atmosphere free of sexual violence, harassment, bias and discrimination. Should you wish to report an incident of sexual assault, harassment, bias and discrimination, visit https://umdearborn.edu/offices/enrollment-management-student-life/incident-and-complaint-reporting.
Tentative Academic Calender
| Week | Chapters/Sections | Topics covered | Remarks |
| Week 1(Sept 6) | Chapter 1 (sections 1.1, 1.2) | Introduction to Statistics | |
| Week 2(Sept 11, 13) | Chapter 2 (sections: 2.1-2.5) | Descriptive Statistics | |
| Week 3(Sept 18, 20) | Chapter 3 (sections: 3.1-3.3) | Basic concepts of probability | |
| Week 4 (Sept 25, 27) | Chapter 5 (sections: 5.3-5.4) | Central Limit Theorem | |
| Week 5 ( Oct 2, 4) | Chapter 6 , review (sections 6.1) | Confidence Intervals | |
| Week 6(Oct 9, 11) | Exam I, Chapter 6 (sections 6.2-6.4) | ||
| Week 7, 8 ( Oct 18, 23, 25) | Chapter 7(section: 7.1-7.3) | Hypothesis Testing (one Sample) | |
| Week 9( Oct 30, Nov 1) | Chapter 8 (section: 8.1-8.3) | Hypothesis testing(Two Samples) | |
| Week 10 (Nov 6,8) | Chapter 9 (section: 9.1-9.2) | Correlation and regression | |
| Week 11, 12 (Nov 13, 15, 20) | Chapter 9 (section: 9.3); Review ; Exam II | Measures of linear regression | |
| Week 13 (Nov 27, 29) | Chapter 9 (section: 9.4) | Multiple Linear Regression | |
| Week 14 (Dec 4, 6) | Chapter 4, Chapter 10 (4.2, 10.1) | Binomial Distribution, Goodness of Fit test | |
| Week 15(Dec 11) | Chapter 10 (section: 10.2) | Test of Independence |
Selected Practice Problems
| Section | Selected Problems | Remarks |
| 2.1 | 7,8, 9, 10,15, 18, 19,21, 24, 25, 28, 33, 35, 40, 47 | |
| 2.2 | 3, 5, 7, 10, 12, 13, 14, 15, 19, 30, 32, 34 | |
| 2.3 | 5, 7, 8,10, 12, 14, 17, 18, 19, 22, 24, 29, 32, 35, 38, 40, 41, 42, 44, 47, 49, 52, 55, 58 | |
| 2.4 | 5, 8,11, 14, 15, 16, 19, 20, 29, 31 | |
| 2.5 | 1, 3, 5, 7, 10, 13, 14, 21, 30, 35, 36, 39, 40, 42, 44, 48, 50 | |
| 4.1 | 1, 3, 5, 8, 9, 12, 13, 15, 17, 20, 22, 25, 27 | |
| 4.2* | 1, 2, 3, 7-10, 11, 14, 15, 16, 18, 20, 27, 30, 32 | |
| 5.1 | 1, 3, 4, 6, 10, 11, 12, 14, 19, 20, 22, 24, 25, 26, 29, 30, 33, 37,39, 43, 45-50, 51, 54, 56 | |
| 5.2 | 1, 4, 6, 7, 11, 13, 15, 16, 17, 20 | |
| 5.3 | 1, 7, 11, 15, 16, 19, 20, 22, 24, 27, 31, 33, 34, 35, 37 | |
| 5.4 | 1, 7, 9, 10, 13, 15, 18, 22, 24, 25, 27, 31, 33, 35, 37, 38 | |
| 6.1 | 1, 3, 5, 9, 12, 17, 21, 23, 25, 28, 32, 35, 37, 39, 43, 46, 50, 54 | |
| 6.2 | 1, 4, 6, 8, 9, 12, 17, 20, 27, 29 | |
| 6.3 | 1, 2, 4, 6, 11, 13, 15, 18, 20 | |
| 7.1 | 1, 2, 5, 7, 10, 21, 23, 24, 27, 29, 33, 37, 40, 43, 46, 49, 50, 51 | |
| 7.2 | 1, 2, 3, 6, 8, 11, 14, 17, 20, 22, 23, 27, 28, 29 | |
| 7.3 | 1, 3, 6, 8, 9, 13, 15, 16, 18, 21, 25 | |
| 7.4 | 3,6,8, 10, 11, 12, 14, 25, 16, 17 | |
| 8.1 | 1, 4, 7, 9, 10, 13, 16, 17, 18, 21 | |
| 8.2 | 3, 5, 6, 9, 11, 13, 15, 17 | |
| 8.3 | 3, 4, 6, 9, 11, 13, 19 | |
| 9.1 | 1-18, 22, 24, 27, 29, 30 | |
| 9.2 | 7, 8, 9, 11, 12, 17, 19, 20, 22, 24, 30, 31 | |
| 9.3 | 1, 2, 4, 5, 7, 12, 13, 15, 16, 17, 18 | |
| 10.1 | 3, 6, 8, 10, 15, 17 | |
| 10.1 | 7,8, 10, 13, 17, 18, 21, 22 |
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.
Online Statistics Education This a very helpful resource for introductory statistics.
Distribution Calculator An excellent Apps to understand binomial distribution normal approximation to binomial
Exploratory Data Analysis Wide range of statistical topics are covered in this web page with video lectures and other supplementary materials.
Unlocking the power of data a very rich resource for introductory statistics with apps.
Why to study Statistics? Applications of Statistics in different areas of study.
Data Search Engine Data Search engine by Google