Applied Statistics I- Winter 2016


Introduction to Probability and Statistics, 14th Edition by Mendenhall, Beaver and Beaver.

Meeting Information

Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: MWF 12:30 PM - 1:45PM
Email: kpokhrel(at)
Meeting Location: 2046CB
Office: 2087CB
Office Hours:
Monday 10:30 AM- 12:00PM
Wednesday 5:00 PM- 6:00PM
Friday 10:30 AM- 12:00PM
and by appointments

Implicit Course Objectives

  1. Learn the concepts and language of probability
  2. Apply abstract mathematical concept in real data.
  3. Understand the scope of statistics and it applications.
  4. Study fundamental statistical methods and employ these methods in real life problems.

Explicit Course Objectives

  1. Use counting techniques to compute probability and odds
  2. Calculate conditional probabilities and check for independent events
  3. Calculate probability for discrete random variable
  4. Calculate probability of continuous random variable
  5. Find confidence interval of a population mean for one sample and two sample data
  6. Find a set of significantly contributing factors for the subject response variable


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 1, 2, 3, 4, 6, 7, 8, and 9*. 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.


At least five sets of homework problems will be assigned. 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. The single most important part of this course is doing your homework and review questions.


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. 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 these projects is to apply statistical methods in the 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 15
Exam II (20%) Monday, April 04
Mini Project I(10%): Due 24
Mini Project II(10%): Due, April 13
Homeworks (15%) TBD
Final Exam (25%) Wednesday, April 27(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 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:

Tentative Academic Calender

Date SectionsTopics covered Remarks
January 6 Role of Statistics and Data Analysis Process
January 8 1.2, 1.3 Types of Variables, Graphs of Categorical Data
January 11 1.4 Graphs of Quantitative Data Rlab
January 13 1.5 Frequency Distribution, Histograms
January 15 2.1, 2.2 Measures of center
January 18 No class MLK DAY
January 20 2.4, 2.5 Measure Variability, Chebyshev's and Emperical Rule
January 22 2.6, 2.7 Measures of Relative Standing, Five Point Summary, Box Plots
January 25 3.1, 3.2 Graphs for Categorical Variables
January 27 3.3 Grpahs for Bivariate Data
January 29 3.4 Numerical Measures for Quantitative Bivariate Data
February 1 4.1, 4.2, 4.3 Basics of Probability, Additive Rule
February 3 4.4 Counting Rules
February 5 4.5 Event Relations and Probability Rules
February 8 4.6 Independence and Conditional Probability
February 10 4.8 Discrete Random Variables
February 12 5.1, 5.2 Binomial Probability Distribution
February 15 Exam 1
February 17 5.3 Poisson Probability Distribution
*February 19 5.4 Hypergeometric Distribution Rlab
February 24 6.3 Normal Probability Distribution
February 26 7.1, 7.2, 7.3 Sampling Distributions Rlab
February 28-March 06 Spring recess
March 07 7.4 Central Limit Theorem
March 09 7.5 Sampling Distribution of the Sample Mean
March 11 7.6 Sampling Ditribution of the Sample Proportion
March 14 8.1,8.2, 8.3 Statistical Inference
March 16 8.4 Point Estimation
March 18 8.5 Interval Estimation
March 21 8.6 Estimating the Difference between Populatin Means
March 23 8.7 Estimating the Difference between Binomial Proportions
March 25 8.8 One Sided Confidence Bounds
March 28 8.9 Sample Size Calculation
March 29 Review: Confidence Interval
April 1 Review: Exam
April 4 Exam II
April 6 9.1 Hypothesis Testing: Introduction
April 8 9.3 Large Sample Test about Population Mean contd..
April 11 9.4 Hypothesis testing: Difference between two population means
April 13 9.5 Hypothesis Testing: difference between binomial proportion Mini Project II
April 15 Review
April 18 Review
April 27 Final Exam (11:30AM- 2:30 PM)


Description Remarks
Ex 1.3: 1.1, 1.4, 1.5, 1.8, 1.11, 1.12, 1.13
Ex 1.5: 1.16, 1.18, 1.22, 1.23, 1.25, 1.26, 1.33
Due Jan 22
Ex 2.2: 2.1, 2.3, 2.4, 2.8, 2.12
Ex 2.3: 2.13, 2.15, 2.16, 2.18
Ex 2.5: 2.19, 2.20, 2.22, 2.23, 2.25 , 2.27, 2.28, 2.33
Ex 2.7: 2.40, 2.42, 2.47, 2.48, 2.51
Due Friday Jan 29
Chapter 3: 3.4: 3.11, 3.14, 3.17, 3.26, 3.29, 3.41 Due Feb 05
Ex 4.3: 4.1, 4.2, 4.4, 4.6, 4.9, 4.16 Ex 4.4: 4.17, 4.19,4.20, 4.35, 4.36, 4.26,4.39
Ex 4.6: 4.40, 4.46, 4.52, 4.56, 4.57, 4.60, 4.62, 4.65
Exam 1 covers upto excercise 4.6
Ex4.8: 4.8: 4.80, 4.83,4.84, 4.96
Ex 5.2: 5.1, 5.3, 5.12, 5.23, 5.27,5.28
Ex 5.3: 5.35, 5.37, 5.44,5.45, 5.47
Quiz on March 07
EX 6.3: 6.1 to 6.11, 6.13, 6.17, 6.19, 6.27, 6.33
Ex 6.4: 6.35, 6.38, 6.43,6.47, 6.49, 6.53
Quiz on March 16th
Ex 7.5: 7.15, 7.17, 7.23, 7.26, 7.27, 7.30, 7.32, 7.34
Ex 7.6: 7.35, 7.38, 7.43, 7.44,7.46, 7.47
Homework Due: Wednesday March 23
Ex8.4: 8.1, 8.4, 8.8, 8.9, 8.12, 8.16, 8.19
EX8.5: 8.24, 8.27, 8.30, 8.32, 8.34 , 8.35
EX 8.6: 8.41, 8.44, 8.45, 8.47, 8.50
EX 8.7: 8.54, 8.58, 8.59, 8.64, 8.66
Ex 8.9: 8.67, 8.69, 8.73, 8.76, 8.82, 8.83
Do not need to submit but will be a part of final


Description Remarks
Data Visualization
Measures of Data
Normal Distribution
Central Limit Theorem
Book Data Download
Fisher's Data
This data is one of the most celebrated data by Sir Ronald Fisher (measurements flower dimensions and types): petal width (PW), petal length (PL), sepal width (SW), and sepal length (SL) for a sample of 150 irises. The lengths are measured in millimeters. Type 0 is Setosa; type 1 is Verginica; and type 2 is Versicolor.
Salary Data Download
All London 2012 athletes and
medal data - Medal Winners

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.
Markdown Themes Appearance and Style themes to create HTML document using R Studio.