STAT/MATH 2501

 

FALL, 2010

 

COURSE WEB SITE

https://moodle.umn.edu/course/view.php?id=11871

http://mnstats.morris.umn.edu//probability/

# OF CREDITS :

4

PREREQUISITE:

MATH. 1101 OR #

DAYS & TIME:

2:15-3:20 MWF

BUILDING & ROOM:

Sci. 3610 & 3550

 

INSTRUCTOR:

Dr. Engin A. Sungur

OFFICE:

1350 SCIENCE

TELEPHONE:

x6325

OFFICE HOURS:

MTWThF, 11-Noon

E-Mail

sungurea@morris.umn.edu

COURSE DESCRIPTION: The course will concentrate on Probability Theory and Statistical Methods. Probability theory; set theory, axiomatic foundations, conditional probability and independence, BayesŐs Rule, random variables. Transformations and expectations; expected values, moments and moment generating functions. Common families of distribution; discrete and continuous distributions. Multiple random variables; joint and marginal distributions, conditional distributions and independence, covariance and correlation, multivariate distributions. Properties of a random sample and central limit theorem. Markov chains, Poison processes.

 

COURSE MATERIAL: Sheldon, M. R., Introduction to Probability Models (tenth edition), Academic Press, 2010

 

COURSE WEB SITE:  The site includes general information about the course, activities and links to the other sites. Students are encouraged to visit the page regularly and make suggestions to the instructor for improvement. Some course activities are located at the UMN Moodle server. There are three ways to access Moodle site:

1.     Via my portal: Go to myU Portal at http://myu.umn.edu, login with your Internet ID, and click on the My Courses tab to see the links to the Moodle sites to which you already have access. Note that the majority of users with UofM Guest IDs are not able to use myU portal at the moment.

2.     Via Moodle server: Go to http://moodle.umn.edu and login there with either your Internet ID or Guest ID. Once logged in, you will be able to see the links to your own sites and also will be able to browse and self-enroll in other sites that are open for public access

3.     Via Course Map: Click on Course News and Announcements and Moodle site.

4.     Via direct method: On your browser just go to the following site:

https://moodle.umn.edu/course/view.php?id=11871

 

EXAMINATIONS: Three midterm examinations and a final exam will be given. Each examination (including the final) will be closed books and notes. But, you will be allowed to use an information sheet. Time table and procedure for the examinations is given below:

EXAM 1

SEPTEMBER 22 (Wednesday)

SCI. 3610 & 3550

2:15-3:20

EXAM 2

OCTOBER 22 (Friday)

SCI. 3610 & 3550

2:15-3:20

EXAM 3

NOVEMBER 22 (Monday)

SCI. 3610 & 3550

2:15-3:20

FINAL

DECEMBER 14 (Tuesday)

SCI. 3610 & 3550

4:00-6:00 pm

 

HOMEWORKS:. Eight homeworks will be assigned. Homework assignments will be given that correspond to each chapter in the text. Due dates will be posted for each assignment on the Moodle course site. Late homework will be penalized 50% of the point value. Students need to download the assignment MS Word template from the course website, type their answers and insert related graphs. All homework assignments are expected to be completed with a word processor in electronic form. The assignments should be uploaded in Moodle course website. Email submissions to the instructor will not be accepted. Solutions will be available on the course Moodle website.

 

 

COURSE GRADE: The weights of homeworks midterm exams and final exam are given below. During the lectures, time to time, questions will be asked. If a student answers one of these questions she/he will get an extra credit (1-10 pts) which will be added to the overall score.

HOMEWORKS  & ONLINE QUIZES:

15%

EXAMS:

60%

FINAL EXAM:

25%

 

A

A-

B+

B

B-

C+

C

C-

D+

D

F

90-100

88-89

86-87

80-85

78-79

76-77

70-75

68-69

66-67

60-65

0-59

S 68-100     N 0-67

 

PIN: To view your progress in the course you need a student PIN. To get your PIN please visit the course Moodle web-site, click on the get your pin button, and follow the instructions.

 

EXAMINATION & HOMEWORK POLICY: Exams will cover the material discussed in the class and the readings in the text. Before the exam, an information sheet will be provided. This information sheet (worksheet) will include (a) place and date of the examination, (b) the detailed topics that will be covered in the examination, (c) the tools that students must bring to the examination (such as statistical tables, calculators etc.). One day before the exam, the topics that will be included in the exam will be reviewed, and important points that should be remembered will be pointed out. Right after the examination, the students will get the solutions. The anticipated grading time of the exams is 1 day.

 

The students should plan on taking the exam on the scheduled date. Illness (Health Service Excuse) or a Chancellor's excuse will be honored as a reason for taking the exam at other than the scheduled date. (Make-ups creates a data which is not independent and identically distributed. As you will learn in this course, lack of these properties creates a big problem on the inference based on such data).

 

GRADING POLICY: The difficulty of the exams will be so arranged that there will be no need for the "normalization" of the scores based on the Gaussian Distribution (known as making a curve). Trends on the scores, attendance to the lectures, class participation etc. will be considered on the determination of the final grades.

 

PLEASE FEEL WELCOME TO SEE ME OUTSIDE OF THE CLASS, ANY TIME, IF YOU HAVE QUESTIONS, PROBLEMS, OR COMMENTS PERTAINING THE COURSE WORK.

 

 

 

The organization of the in-class activities are summarized in the following flowchart. The main components of the organization structure are:

 

SUMMARIES AND OUTLINE: These two components, hopefully, will provide a smooth transition between the topics and lectures. These will answer three basic questions: Where have we been?, Where are we going?, and What have we learned?

 

 

STUDENT EVALUATORS: Class participation and discussion are very important on the learning process. Students are encouraged to ask questions in the class. Questions, comments could help the instructor to set up his/her pace. The input from the students should be constant. If you point out the weaknesses of the instructor, and the problems with the course in general as soon as possible your learning process will be enhanced. To formalize and promote active learning, each in-class activity will be evaluated by the two students. These students will be responsible to point out all the problems that might affect the learning of the rest of the class. For example, the topics that are not clearly covered, pace of the lecture, use of the blackboard, problems with taking notes, etc. Time to time student evaluators will be asked to make a summary of the previous class.

 

EVALUATOR

DATE 1

DATE 2

Armstrong,Douglas Erin

8/25,27,30

10/4,6,8

Biessener,Fiona E

9/1,3

10/11,13,15

Bitker,Guinevere Patrcia Ellen

9/8,10

10/20,22

Bruns,Kyle G

9/13,15,17

10/25,27,29

Caswell,Amanda J

9/20,22,24

11/1,3,5

Chen,Huan

9/27,29, 10/1

11/8,10,12

Ding Sr.,Liexiao

10/4,6,8

11/15,17,19

Fragodt,Daniel

10/11,13,15

11/22,23

Ginader,Timothy S

10/20,22

11/29, 12/1,3

Harstad,Rachel K

10/25,27,29

12/6,8,10

Jiao,Xueyang

8/25,27,30

11/1,3,5

Orth,Jessica Marie

9/1,3

11/8,10,12

Powers,Martin A

8/25,27,30

11/15,17,19

Rach,Daniel M

9/1,3

11/22,24

Riner,Alexander Thomas

9/8,10

11/29, 12/1,3

Robinson,Casey Summers

9/13,15,17

12/6,8,10

Taylor-Hempstead,Kelsey Ariel

8/25,27,30

10/4,6,8

Thebault-Spieker,Jacob Charles

9/1,3

10/11,13,15

Toffle,Nicholas R

9/8,10

10/4,6,8

Vold,Elizabeth Marlo

9/13,15,17

10/11,13,15

Wang,Gang

9/20,22,24

10/20,22

Wang,Qianqian

9/27,29, 10/1

10/25,27,29

Wang,Xuan

10/4,6,8

11/1,3,5

Young,Ryan J

10/11,13,15

11/8,10,12

 

10/20,22

11/15,17,19

 

8/25,27,30

11/22,23

 

9/1,3

11/29, 12/1,3

 

DISABILITIES AND MENTAL HEALTH

As a student you may experience a range of issues that can cause barriers to learning, such as strained relationships, increased anxiety, alcohol/drug problems, feeling down, difficulty concentrating and/or lack of motivation.  These mental health concerns or stressful events may lead to diminished academic performance or reduce your ability to participate in daily activities.  University of Minnesota services are available to assist you with addressing these and other concerns you may be experiencing.  You can learn more about the broad range of confidential mental health services available on campus via http://www.mentalhealth.umn.edu/

 

 

 


TOPIC

TEXT BY ROSS

ASSIGNMENTS

STUDY GUIDE

Introduction & Sample Space and Events

¤ 1.1-1.2

Chapter 1

(pages 15-20)

 

1, 3, 4, 8, 17, 19, 23, 26, 30, 31, 36, 37, 39, 42, 44, 46

¤ I.1-I.2

¤ I.3

Probabilities

¤ 1.3

¤ I.4-I.5

Conditional Probabilities

¤ 1.4

¤ I.5

Independent Events

¤ 1.5

¤ I.5

Bayes' Formula

¤ 1.6

¤ I.5

Random Variables

¤ 2.1

Chapter 2

Part I

(pages 86-90)

1, 2, 3, 6, 7, 8, 10, 12, 13, 14, 20, 21, 27, 31, 33, 34, 35, 38

¤ II.2-II.3

Discrete Random Variables

¤ 2.2

¤ II.1 Discrete Mathematics,

Binomial Theorem,

Geometric Series,

Maclaurin Series

Continuous Random Variables

¤ 2.3

¤ II.1 The Derivative of a Function, Derivatives of the Composite Functions, The Definite Integral, Antidifferentiation, Evaluation of the Integrals, Methods of Integration, Some Special Functions

Expectation of a Random Variable

¤ 2.4

Chapter 2

Part II

(pages 90-92)

39, 40, 43, 46, 47, 48, 50, 51

 

¤ II.1 Evaluation of the Definite Integrals by Using Antiderivatives, Methods of Integration

Jointly Distributed Random Variables

¤ 2.5

Chapter 2

Part III

(pages 92-95)

54, 55, 58, 60, 63, 68, 77

 

¤ II.1 Some Results Involving Multivariate Calculus

Moment Generating Functions

¤ 2.6

¤ II.1 Methods of Integration, Some Results Involving Limits

Limit Theorems

¤ 2.7

¤ II.1 Some Results Involving Limits

Conditional Probability &

Conditional Expectation

Chapter 3

Chapter 3

(pages 173-181)

3, 4, 5, 7, 11, 13, 15, 35, 37, 46

 

¤ II.1

Markov Chains

Chapter 4

Chapter 4

(pages 275-279)

2, 3, 6, 7, 8, 10, 14, 20, 22, 29, 30

¤ I.6 & II.1