Introduction to Statistics- Fall 2017

Meeting Times:

Email:

Meeting Location: 2046 CB

Office: 2087CB

Monday 12:30 PM-1:30 PM

Tuesday 12:30 PM- 1:30 PM

Thursday 5:00 PM- 5:50 PM

and by appointments.

This course is primarily designed for those who want to apply quantotative resoaing in different areas social and natural sciences. After a completion of this course, students are expected to make and informed inferential analysis. Topics covered include: Frequency distributions, descriptive measures, sampling, and statistical inference, elementary probability theory, linear regression, and use of statistical computer packages to analyze data.

Upon successful completion of the course, the students should be able to:

- Demonstrate understanding of the following statistical concepts: Descriptive statistics, sampling distribution, normal distribution, confidence intervals, hypothesis testing, and linear regression.
- Recognize the use and misuse of statistics in real life situations, in the news, and surveys.
- Visualize the data using statistical techniques.
- Explore the concepts of basic probability theory in decision making processes.

Homework problems from each chapter will be assigned through Mystatlab . Some additional homework problems will periodically be assigned during the lecture. For better in class quiz/exam results you need to master all the homework problems. I strongly encourage you to see me for help if you are unable to solve the assigned problems. 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.

There will be two mid-term exams, and a final exam. To answer the exam questions, you are expected to have clear idea to interpret the numerical outputs of the statistical methods.

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.

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.

Important Dates: | ||

Exam I | Tuesday, October 10 | |

Exam II | Tuesday, November 21 | |

Group project part I (5%): | Due October 31 | |

Group project part II(5%): | Due December 05 | |

Final Exam | Thursday, Dec 14(3:00 PM-6:00PM) |

Your performance is measured by the weighted average of two in class exams, homework, in-class work, group project and a final exam. If you have any grade disputes you need to notify me within a week after grades are posted in canvas.

- (40%)- Two Mid-Term Exams
- (15%) - homework
- (10%) - classwork/quizzes
- (10%) - group project
- (25%)- Final Exam

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 |

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 includiing 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

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

Week | Chapters/Sections | Topics covered | Remarks |

Week 1(Sept 7) | Chapter 1 (sections 1.1, 1.2) | Introduction to Statistics | |

Week 2(Sept 12, 14) | Chapter 1 (sections: 2.1-2.5) | Descriptive Statistics | |

Week 3(Sept 19, 21) | Chapter 3 (sections: 3.1-3.3) | Basic concepts of probability | |

Week 4 (Sept 26, 28) | Chapter 5 (sections: 5.3-5.4) | Central Limit Theorem | |

Week 5 ( Oct 3, 5) | Chapter 6 , review (sections 6.1) | Confidence Intervals | |

Week 6,7 (Oct 10, 12, 19) | Exam I, Chapter 6 (sections 6.2-6.4) | ||

Week 8 ( Oct 24, 26) | Chapter 7(section: 7.1-7.3) | Hypothesis Testing (one Sample) | |

Week 9( Oct 31, Nov 2) | Chapter 8 (section: 8.1-8.3) | Hypothesis testing(Two Samples) | |

Week 10 (Nov 7,9) | Chapter 9 (section: 9.1-9.2) | Correlation and regression | |

Week 11, 12 (Nov 14, 16, 21) | Chapter 9 (section: 9.3); Review ; Exam II | Measures of linear regression | |

Week 13 (Nov 28, 30) | Chapter 9 (section: 9.4) | Multiple Linear Regression | |

Week 14 (Dec 5, 7) | Chapter 4, Chapter 10 (4.2, 10.1) | Binomial Distribution, Goodness of Fit test | |

Week 15(Dec 12) | Chapter 10 (section: 10.2) | Test of Independence |

Section | Selected Problems | Remarks |

2.1 | 7,8, 9, 10,15, 18, 19,21, 24, 25, 28, 33, 35, 40, 47 | |

2.2 | 3, 5, 7, 10, 12, 13, 14, 15, 19, 30, 32, 34 | |

2.3 | 5, 7, 8,10, 12, 14, 17, 18, 19, 22, 24, 29, 32, 35, 38, 40, 41, 42, 44, 47, 49, 52, 55, 58 | |

2.4 | 5, 8,11, 14, 15, 16, 19, 20, 29, 31 | |

2.5 | 1, 3, 5, 7, 10, 13, 14, 21, 30, 35, 36, 39, 40, 42, 44, 48, 50 | |

4.1 | 1, 3, 5, 8, 9, 12, 13, 15, 17, 20, 22, 25, 27 | |

4.2* | 1, 2, 3, 7-10, 11, 14, 15, 16, 18, 20, 27, 30, 32 | |

5.1 | 1, 3, 4, 6, 10, 11, 12, 14, 19, 20, 22, 24, 25, 26, 29, 30, 33, 37,39, 43, 45-50, 51, 54, 56 | |

5.2 | 1, 4, 6, 7, 11, 13, 15, 16, 17, 20 | |

5.3 | 1, 7, 11, 15, 16, 19, 20, 22, 24, 27, 31, 33, 34, 35, 37 | |

5.4 | 1, 7, 9, 10, 13, 15, 18, 22, 24, 25, 27, 31, 33, 35, 37, 38 | |

6.1 | 1, 3, 5, 9, 12, 17, 21, 23, 25, 28, 32, 35, 37, 39, 43, 46, 50, 54 | |

6.2 | 1, 4, 6, 8, 9, 12, 17, 20, 27, 29 | |

6.3 | 1, 2, 4, 6, 11, 13, 15, 18, 20 | |

7.1 | 1, 2, 5, 7, 10, 21, 23, 24, 27, 29, 33, 37, 40, 43, 46, 49, 50, 51 | |

7.2 | 1, 2, 3, 6, 8, 11, 14, 17, 20, 22, 23, 27, 28, 29 | |

7.3 | 1, 3, 6, 8, 9, 13, 15, 16, 18, 21, 25 | |

7.4 | 3,6,8, 10, 11, 12, 14, 25, 16, 17 | |

8.1 | 1, 4, 7, 9, 10, 13, 16, 17, 18, 21 | |

8.2 | 3, 5, 6, 9, 11, 13, 15, 17 | |

8.3 | 3, 4, 6, 9, 11, 13, 19 | |

9.1 | 1-18, 22, 24, 27, 29, 30 | |

9.2 | 7, 8, 9, 11, 12, 17, 19, 20, 22, 24, 30, 31 | |

9.3 | 1, 2, 4, 5, 7, 12, 13, 15, 16, 17, 18 | |

10.1 | 3, 6, 8, 10, 15, 17 | |

10.1 | 7,8, 10, 13, 17, 18, 21, 22 |

Exploratory Data Analysis Wide range of statistical topics are covered in this web page with video lectures and other supplementary materials.