Applied Statistics II- Winter 2017


Introduction to Probability and Statistics, 14th Edition by Mendenhall, Beaver and Beaver. You have two options to purchase the book. First, is to purchase a hard copy with “Web Assign” access code, or Second, purchase only “Web Assign” access code which comes with PDF copy of the book .

Meeting Information

Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: MWF 11:00 AM - 11:50AM
Email: kpokhrel(at)
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 introduces some of the fundamental inferential statistical techniques. Topics include hypothesis testing, Analysis of variance,Linear regression, Multiple Regression, Goodness of fit tests, Non-parametric tests and the use of statistical computer packages for data analysis.

Learning Objectives

This course aims to provide some fundamentals of inferential techniques. Students are expected to learn setting correct hypothesis and find reasonable conclusions out of those hypothesis. Most importantly, we focus on core techniques of statistical modeling. A successful completion of this course will make student statistically literate with tools to explore more about the subject.


Introduction to Probability and Statistics , 14th Edition, William Mendenhall III, Robert J. Beaver, Barbara M. Beaver; ISBN-13:978-1-133-10375-2,Brooks/Cole. We will be covering chapters 9, 10, 11, 12, 13, 14, and 15. Apart from text book we will use different resources for the classroom activities and homeworks.
Major Reference Books
  1. Peck, Olsen, and Devore (2012). Introduction to Statistics & Data Analysis. 4th Edition. Publisher: Duxbury.
  2. Diez, Barr, and Cetinkaya-Rundel, OpenIntro Statistics.
  3. McClave and Sincich (2012). Statistics, 12th edition, by Pearson.


Number of homework sets will be assigned using webassign. Some addition homework problems will periodically be assigned during the lecture. Good news! lowest homework grade will be dropped. For better exam results you need to master all the homework problems.


This is the most important part of the course. You will get a set of questions, mostly related to R-programming, almost every Friday. You will do these questions in group and submit your findings at the end of class.


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


There will be two mini-projects during the semester. You will do these projects in a group. Groups will be formed during the first four weeks of the semester. A good project describ the data, poses reasonable hypotheses,carries appropriate statistical tests, include test results, and explain the results in both in both techical and non-technical ways. Primary objective of these projects is to apply statistical methods in real life situations.


We will 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/Important Dates:
Exam I (20%) Monday, February 13
Exam II (20%) Friday, March 31
Mini Project I(7.5%): Due Feb 24
Mini Project II(7.5%): Due April 10
Homeworks (10%) Webassign
Rlabs (10%) TBD
Final Exam (25%) Monday, April 24(11:30AM-2:30PM)

Grade Distribution

Your final grade will be based on two mid-term exams, at least five set of graded homeworks, two mini-projects, and a comprehensive final. Lowest homework grade will be dropped. Your performance is measured by the weighted average of exams, homeworks, and projects. The lowest midterm grade will be replaced by final if your final examination score, in percentage, happens to be more than the lowest midterm score. The table below shows percentage intervals for the distributions of letter grades:
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 (Jan 9, 11, 13) Chapter 10 (sections: 10.1-10.4) Inferences from one sample
Week 2 (Jan 18, 20) Chapter 10 (sections: 10.4-10.8) Paired t-test, comparing two population variance
Week 3 (Jan 23, 25, 27) Chapter 9: revisit (sections: 9.5-9.6) Power of test, test of proportions
Week 4 (Jan 30, Feb 01, 03) Chapter 11 (sections: 11.1-10.5) The analysis of variance
Week 5 (Feb 06, 08, 10) Chapter 11, review
Week 6 (Feb 13, 15, 17) Chapter 12 (sections 12.1-12.3) Linear regression and correlation
Week 7 (Feb 20, 22, 24) Chapter 12 (12.4-12.8) Linear regression and correlation
Week 8 ( Feb 27, Mar 01, 03) Spring recess
Week 9(Mar 06, 08, 10) Chapter 13(13.1-13.4) Multiple regression Analysis
Week 10 (Mar 13, 15, 17) Chapter 13 (sections: 13.5, 13.8) Multiple regression Analysis
Week 11 (Mar 20, 22, 24) Chapter 13 Multiple Regression: exam 2, review
Week 12(Mar 27, 29, 31) Chapter 14 (14.1), Exam II review Exam II, categorical data Analysis
Week 13 (Apr 3, 5, 7) Chapter 14 (14.2, 14.4) Goodness of fit test
Week 14 (Apr 10, 12, 14) Chapter 14 (14.5, 14.6) contingency table, chi-square test
Week 15 (Apr 17, 19, 21) Chapter 15* Non-parametric statistics

Rmarkdown Examples

Description Remarks
review: Hypothesis Testing
Data Visualization
Measures of Data
Normal Distribution
Central Limit Theorem
Book Data Download
North Caolina Birth Data Download
salary data Download
Old Auto Data Download
Detergent Download
Galton's Height Data Download

Extra Resources 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.
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
R commander Perhaps one of the most valuable R-alternative to other statistical packages like SPSS.
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
Unlocking the power of data a very rich resource for introductory statistics with apps.
Twenty Big Data Sources A list of some big data repositories through Data Science Central.
Data Journalism Open data sets by British newspaper the guardian.
DataCamp A comprehensive resourse to learn Statistics and Data Science
Markdown Themes Appearance and Style themes to create HTML document using R Studio.
Spurious Correlation Interesting examples of correlation.
Limitations of R-squared Is R-squared Useless? Thought provoking discussion about limitations of R-sqaured.
Power of Test Dynamic visualization:Power of test.