Applied Statistics II- Winter 2016
Introduction to Probability and Statistics, 14th Edition by Mendenhall, Beaver and Beaver.

#### Meeting Information

Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: MW 2:00 PM - 3:15PM
Email: kpokhrel(at)umich.edu
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

#### Course Objectives

1. Test Hypothesis for the diffrence between two population means
2. Find diffrence between two population meand: paired differences
3. Estimate coefficients of fitted line
4. Fit regression model using Qualitative and quantitative variables
5. Calculate goodness of fit test for categorical data
6. Use distribution free tests: nonparametric tests

#### 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

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.

#### 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. 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.

#### 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%) Monday, February 15 Exam II (20%) Monday, April 04 Mini Project I(10%): Due Feb 24 Mini Project II(10%): Due April 13 Homeworks (15%) TBD Final Exam (25%) Monday, April 25(3:00PM-6:00PM)

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: 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.

￼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

 Date Sections Topics covered Remarks January 6 Review of Confidence Interval January 11 9.3 A large-sample test about population mean Rlab January 13 9.4 Difference between population means January 18 No class MLK DAY January 20 10.3,10.4 Small -sample inference: independent sample test January 25 10.4. 10.5 Small -sample inference: independent and paired-difference test January 27 10.6, 10.7 Comparing two population variances February 1 11.1-11.4 Introduction: Analysis of Variance February 3 11.5 Completely randomized design February 8 11.7 Randomized block design February 10 11.8 ANOVA for a randomized block design February 15 Exam 1 February 17 11.10 Factorial experiment February 22 12.1-12.4 Linear regression February 24 12.5 The coefficient of determination February 29- March 04 Break March 07 12.6 Estimationa and prediction using the fitted line March 09 13.1-13.3 Multiple linear regression March 14 13.4 Polynomial regression model March 16 13.5 Qualitative predictor variables in a regression March 21 13.6 Testing and training sets of regression coefficients March 23 14.1-14.3 Goodness of fit-test March 28 14.4 A two-way classification March 29 14.5 Comparing several multinomial populations April 4 Exam 2 April 6 15.1-15.3 The wilcoxon rank sum test April 11 15.5 The wilcoxon rank sum test: paired experiment April 13 15.6 Kruskal-Wallis test April 18 Review April 25 Final Exam (3:00AM- 6:00 PM)

### Homework

 Description Remarks Ex 9.3 : 9.1, 9.3, 9.4, 9.6, 9.8, 9.15, 9.16 Ex 9.4: 9.18, 9.22, 9.28 Due Jan 25 Ex 9.5: 9.31, 9.35, 9.38, 9.41 9.42, 9.45, 9.50, 9.51 Due Wed Feb 03 Ex9.6: 9.42, 9.46 Ex 10.3: 10.2, 10.3, 10.6 Ex 10.4: 10.18, 10.19, 10.27, 10.30 Ex 10.5: 10.36, 10.41, 10.46 Wed, feb 10th Ex 10.6: 10.49, 10.51, 10.53, 10.55, 10.56 Ex 10.7: 10.58, 10.10.59, 10.61, 10.10.63, 10.66 Exam 1 covers up to section 10.7 Ex 11.5:: 11.1, 11.2, 11.7,11.12, 11.1411.18 Ex11.8:: 11.28, 11.33, 11.36, 11.37,11.4011.43 Ex11.10:: 11.45, 11.49, 11.5011.52 Quiz from exercise 11.5 on Match 07th. Ex 12.4:: 12.1, 12.3, 12.7, 12.9, 12.12, 12.15, Ex12.5:: 12.19, 12.20, 12.23, 12.24, 12.28, 12.30 Ex12.6 :: 12.34, 12.36, 12.38 EX12.7:: 12.40, 12.42, 12.44, 12.45, 12.46 Quiz on march 16th and HW Due March 16th Ex 13.4: 13.1, 13.3, 13.5, 13.10, 13.12, 13.15 ExEx13.5: 13.17, 13.18, 13.20, 13.22, 13.24 this will be a part of exam 2

### R-labs

 Description Remarks review: Hypothesis Testing Data Visualization Measures of Data Normal Distribution Central Limit Theorem Book Data Download

### 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.
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