Applied Statistics I- Winter 2016

Meeting Times:

Email:

Meeting Location: 2046CB

Office: 2087CB

Monday 10:30 AM- 12:00PM

Wednesday 5:00 PM- 6:00PM

Friday 10:30 AM- 12:00PM

and by appointments

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

- 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
- Find confidence interval of a population mean for one sample and two sample data
- Find a set of significantly contributing factors for the subject response variable

- 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.

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 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

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) |

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 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

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 | 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. | Download | |

Salary Data | Download | |

All London 2012 athletes and medal data - Medal Winners | Download |

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