Meeting Information
Instructor: Keshav P. Pokhrel, Ph.D.Meeting Times:
Email: kpokhrel(at)umich.edu
Meeting Location: Monday in 2048CB
Office: 2087CB
Office Hours:
Monday 12:30 PM-1:30 PM
Tuesday 12:30 PM- 1:30 PM
Thursday 5:00 PM- 5:50 PM
and by appointment
Course Description
A study of the fundamental concepts and methods of probability and statistics. Topics include counting problems, discrete probability, random variables and probability distributions, sampling distributions, the central limit theorem, introduction to hypothesis testing.
Program Goals
- Understand the fundamentals of probability theory.
- Understand statistical and inferential reasoning.
- Become proficient at statistical computing.
- Become skilled in the description, interpretation and exploratory analysis of data by graphical and non graphical methods.
Course Objectives
- Find Descriptive statistics and interpret the statistic in the context of the problem.
- Use technology to vilualize continuous and categorical data.
- Use counting techniques to compute probability and odds.
- Calculate conditional probabilities and check for independent events.
- Calculate probability for discrete random variable.
- Calculate probability of continuous random variable.
- Compute expected value and variance for different probability distribution.
- Find a set of significantly contributing factors for the subject response variable.
Textbook
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
- Peck, Olsen, and Devore (2012). Introduction to Statistics & Data Analysis. 4th Edition. Publisher: Duxbury.
- Diez, Barr, and Cetinkaya-Rundel, OpenIntro Statistics.
- McClave and Sincich (2012). Statistics, 12th edition, by Pearson.
Homework
At least one set of homework problems will be assigned from each chapter using webassign . Some additional homework problems will periodically be assigned during the lecture. Good news! lowest homework grade will be dropped. You are expected to spend an average of 3-5 hours of work per week outside of class. Late assignment is accepted with 20% penalty per day. For better exam results you need to master all the homework problems.
Exams
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.
In Class Work and Quizzes
You will get worksheets with problems in the class. Students can interact with the friends and look at the notes to solve the problems. I encourage everyone to solve the problems on the white board and interpret the results to the class. I urge you to find interesting problems from the areas (eg. business, sociology, biology, sports, public health etc.) of your interest, this will help you to prepare for your project and at the same you are higly likely to earn better score in quizzes.
Project
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 and encourage students to work in a group. Late submission of project will result in losing 10% of total points everyday.
Software
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 http://www.r-project.org. We will use 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 : | ||
| Exam I (20%) | Thursday, October 12 | |
| Exam II (20%) | November, November 21 | |
| Online homework (15%) | November, November 21 | |
| classwork (10%) | TBD | |
| Group project part I (5%): | Due October 31 | |
| Group project part II(5%): | Due December 05 | |
| Final Exam (25): | Tuesday, Dec 19(3:00 -6:00PM) | |
Grade Distribution
Your final grade will be based on two mid-term exams, a set of online graded homeworks, classwork, two mini-projects, and a comprehensive final exam. Lowest homework grade will be dropped. Your performance is measured by the weighted average of exams, homeworks, and projects. T The table below shows percentage intervals for the distribution 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: 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 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
Tentative Academic Calender
| Week | Chapters/Sections | Topics covered | Remarks |
| Week 1, 2(Sept 7, 12, 14) | Chapter 1 (sections 1.1, 1.2) | Introduction to Statistics, Data Visualization | |
| Week 3(Sept 12, 14) | Chapter 2 (sections: 2.2-2.7) | Descriptive Statistics | |
| Week 4(Sept 19, 21) | Chapter 3, 4(sections: 3.2, 4.1,4.2) | Basic concepts of probability | |
| Week 5(Sept 26, 28) | Chapter 4 (sections: 4.3-4.8) | probability rules | |
| Week 5 ( Oct 3, 5) | Chapter 5 , review (sections 5.2) | Binomial Distribution | |
| Week 6,7 (Oct 10, 12, 19) | Exam I, Chapter 5 (section 5.3) | ||
| Week 8 ( Oct 24, 26) | Chapter 6(section: 6.3-6.4) | Normal Probability Distribution | |
| Week 9( Oct 31, Nov 2) | Chapter 7 (section: 7.1-7.5) | Central Limit Theorem | |
| Week 10 (Nov 7,9) | Chapter 8 (section: 8.1-9.5) | CLarge Sample Estimation | |
| Week 11, 12 (Nov 14, 16, 21) | Chapter 8 (section: 9.3); Review ; Exam II | Large Sample Estimation | |
| Week 13 (Nov 28, 30) | Chapter 9 (section: 9.1) | Hypothesis testing | |
| Week 14 (Dec 5, 7) | Chapter 9 (9.2) | Type I and Type II errors, Power of Test | |
| Week 15(Dec 12) | Chapter 9 | Review |
Selected Practice Problems
| 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 | ||
| 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 | ||
| Ex 3.2: 3.2, 3.6, 3.8 Ex 3.4: 3.9, 3.11, 3.14, 3.17, 3.18 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 | ||
| Data I | ||
| Ex 4.6: 4.40, 4.46, 4.52, 4.56, 4.57, 4.60, 4.62, 4.65 Ex 4.8: 4.80, 4.83,4.84, 4.96 EX 6.1: 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 | ||
| Ex 7.5: 7.15, 7.17, 7.23, 7.26, 7.30 Ex 7.6: 7.35, 7.38, 7.43, 7.44,7.46 | ||
| 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 | ||
| Mini project II |
Practice Data Mini Project data | |
R-labs
| Description | Remarks | |
| Data Visualization | ||
| Measures of Data | ||
| Normal Distribution | ||
| Central Limit Theorem | ||
| Text book Data | Download | |
| Car Price Data | Download | |
| Smoking Data | Download |
Extra Resources
Interactive Visualization A collection of interactive data visualization platform.
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.
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.
Markdown Themes Appearance and Style themes to create HTML document using R Studio.