Applied Statistics II- Winter 2018

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

Meeting Information

Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: TR 12:30 PM - 1:45PM
Email: kpokhrel(at)umich.edu
Meeting Location: 2046CB
Office: 2087CB
Office Hours:
Tuesday 2:00 PM- 3:00 PM
Wednesday 1:30 PM- 3:00 PM
Thursday 11:00AM-12:00 PM
and by appointments

Course Description


This course treats both the principles and applications of statistics. Elementary theory of estimation and hypothesis testing, the use of the normal, chi- square, F and t distributions in statistics problems will be covered. Other topics are selected from regression and correlation, the design of experiments, analysis of variance, analysis of categorical data, nonparametric inference.

Course 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.

Student Leanrning Outcome:
  • Learn to set up correct hypothesis and interpret the results of the test of hypotheses.
  • Analyze categorical data and perform test of independence.
  • Perform and interpret univariate testing using analysis of variance.
  • Develop, Analyse and critique linear regression model.
  • Develop, analyse , and critique multiple linear regression model.
  • Predict and measure the prediction accuracy in the realm of model assumptions.

    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 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.

    Homework


    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. Late assignment is accepted with 20% penalty per day. For better exam results you need to master all the homework problems.

    Rlabs and In class assignments


    You will get worksheets with problems in the class. For a majority of in-class assignments 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 time you are higly likely to earn better score in quizzes.

    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.

    Project


    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 describes the data, poses reasonable hypotheses, carries appropriate statistical tests, include test results, and explain the results in techical and non-technical ways. Primary objective of these projects is to apply statistical methods in real life situations. 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 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%) Tuesday, February 13
    Exam II (20%) Tuesday, March 27
    Mini Project I(5%): Due March 09
    Mini Project II(5%): Due April 10
    Homeworks (15%) Webassign
    In class Assignments/Rlabs (10%) TBD
    Final Exam (25%) Thursday, April 26 (11:30AM-2:30PM)

    Grade Distribution

    Your final grade will be based on the weighted average of two mid-term exams, online homeworks though webassign, in- class assignments, two mini-projects, and a comprehensive final. Lowest homework grade will be dropped. 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. If you have any grade disputes then you need to notify me within a week after grades are posted in canvas. 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: 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 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) Chapter 10 (sections: 10.1-10.4) Inferences from one sample
    Week 2 (Jan 16, 18) Chapter 10 (sections: 10.4-10.8) Paired t-test, comparing two population variance
    Week 3 (Jan 23, 25) Chapter 9: revisit (sections: 9.5-9.6) Power of test, test of proportions
    Week 4 (Jan 30, Feb 01) Chapter 11 (sections: 11.1-10.5) The analysis of variance
    Week 5 (Feb 06, 08) Chapter 11, review
    Week 6 (Feb 13, 15) Chapter 12 (sections 12.1-12.3) Linear regression and correlation
    Week 7 (Feb 20, 22) Chapter 12 (12.4-12.8) Linear regression and correlation
    Week 8 ( Feb 27, Mar 01) Spring recess
    Week 9(Mar 06, 08) Chapter 13(13.1-13.4) Multiple regression Analysis
    Week 10 (Mar 13, 15) Chapter 13 (sections: 13.5, 13.8) Multiple regression Analysis
    Week 11 (Mar 20, 22) Chapter 13 Multiple Regression: exam 2, review
    Week 12(Mar 27, 29) Chapter 14 (14.1), Exam II review Exam II, categorical data Analysis
    Week 13 (Apr 3, 5) Chapter 14 (14.2, 14.4) Goodness of fit test
    Week 14 (Apr 10, 12) Chapter 14 (14.5, 14.6) contingency table, chi-square test
    Week 15 (Apr 17, 19) 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
    Camera Data Download
    Cereal Data Download



    Extra Resources


    Bike Data Bike sharing data
    Machine Larning data UCI Machine Learning Repository.
    National Survey National Survey on Drug Use and Health, 2009 (ICPSR 29621)
    Openintro Data An organized Source of Data for class projects.
    DataQuest 18 places to find data sets for data science projects.
    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.
    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
    R for Data Science A comprehensive resource for learning R and Data visualization.
    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.