Syllabus Entrance
Printer Friendly
Email Syllabus
Education Major Version

MA 305 Probability
McCandless, Peter


Mission Statement: The mission of Park University, an entrepreneurial institution of learning, is to provide access to academic excellence, which will prepare learners to think critically, communicate effectively and engage in lifelong learning while serving a global community.

Vision Statement: Park University will be a renowned international leader in providing innovative educational opportunities for learners within the global society.

Course

MA 305 Probability

Semester

FA 2007 HO

Faculty

McCandless, Peter

Title

Associate Professor of Mathematics

Degrees/Certificates

Ph.D., Curriculum and Instruction with emphasis in math education
M.A., Mathematics
M.A., Educational Research and Psychology

Office Location

Natural Sciences Building, Room 002

Office Hours

Monday, 2:00 - 3:30 p.m.; Tuesday, 1:30 - 3:00 p.m.; Wednesday, 9:00 - 10:00 a.m.; Thursday, 1:30 - 3:30 p.m.

Daytime Phone

(816) 584-6831

Web Page

http://www.petermacshow.com

Semester Dates

August 20, 2007 - December 14, 2007

Class Days

--T-R--

Class Time

11:35 - 12:50 PM

Prerequisites

MA 131 or equivalent

Credit Hours

3


Textbook:

Introduction to Probability. Freund, John E. Dover Publications, Inc. 1973
ISBN: 0-486-67549-1

Additional Resources:

McAfee Memorial Library - Online information, links, electronic databases and the Online catalog. Contact the library for further assistance via email or at 800-270-4347.
Career Counseling - The Career Development Center (CDC) provides services for all stages of career development.  The mission of the CDC is to provide the career planning tools to ensure a lifetime of career success.
Park Helpdesk - If you have forgotten your OPEN ID or Password, or need assistance with your PirateMail account, please email helpdesk@park.edu or call 800-927-3024
Resources for Current Students - A great place to look for all kinds of information http://www.park.edu/Current/.


Course Description:
Essentially a non-calculus approach to the theory and statistical applications of probability. Topics include discrete and continuous random variables, density and distribution functions, probability models, non-parametric statistics. 3:0:3 Prerequisite: MA131 or equivalent.

Educational Philosophy:
My goal in teaching mathematics is three-fold: to make clear mathematical concepts, to help students acquire mathematical skills, and to encourage and inspire them to continue their study of mathematics in a way that supports their goals in life. As the teacher of a course, it is my responsibility to set and maintain the standards of the course - what is to be taught and how students' performance is to be assessed. The goals of the course are specified in a manner that affords me the flexibility to adapt to students' needs: a careful balance must be achieved between the topics to be covered in the course of a semester and the ability of students to learn those topics. The pursuit of this balance is dynamic. I am never totally comfortable with my performance as I continually try to find a better way to achieve the same goals. The learning of mathematics is and has been a humbling experience for me. I have never pushed my mind as hard as in the pursuit of learning this wonderfully challenging subject. It is difficult in words to describe the joy of finally grasping some concept that has long eluded me, or completing a difficult proof. The frustration associated with studying mathematics can be equally severe. As a teacher of mathematics, I rely heavily on this experience. It allows me to empathize with the struggling student, yet to encourage him or her, demanding performance just a little beyond what is often comfortable. It convinces me that many, many students never achieve their potential. For me, teaching this subject embodies four roles that I thoroughly enjoy integrating: coach (the encourager); parent (the demander); friend (the sustainer); and instructor (the clarifier). As a teacher of mathematics, I am challenged to provide the highest quality instruction I can for students from all backgrounds. My ultimate goal for each student is to find the experience of taking a course from me to be enriching in one way or another, regardless of their final grade.  

Learning Outcomes:
  Core Learning Outcomes

  1. Identify and apply properties and operations of set theory
  2. Apply set theory to sample spaces and events
  3. Prove and apply basic theorems of probability
  4. Compute probabilities using combinations or permutations
  5. Apply the binomial theorem and binomial coefficients
  6. Solve problems using discrete random variables and their associated probability functions


Core Assessment:

-Periodic assignments


-Quizzes


-Tests

Link to Class Rubric

Class Assessment:
There will be two tests during the semester and a final exam during the final exams week. There will be several homework assignments during the semester.

Grading:

Homework Assignments collectively constitute 25% of the grade. Test 1 and Test 2 each constitute 25% of the grade, and the final exam constitutes the remaining 25% of the grade. All homework assignments are weighted equally, regardless of the number of questions or points included in the assignment. The lowest homework assignment score will be deleted from the homework average. The final exam score will replace the lower of the Test 1 score or Test 2 score, provided that it is higher than at least one of them. Each homework assignment will be a given a score of 4, 3, 2, 1, or 0.

Late Submission of Course Materials:

Each homework assignment must be turned in on the due date announced. Late homework will not be accepted, except under an extreme situation. Similarly, tests must be taken on the date they are given in class. If the instructor determines that an extreme situation prevented the student from turning in a homework assignment on time or from taking a test, the student may be given additional time; it is not automatic, however. In all such cases, the instructor’s decision on whatever allowance, if any, is to be given, is final.

Classroom Rules of Conduct:

Cellular phones, beepers, and other communication devices are not to be used in class. Disruptive behavior (as deemed by the instructor) such as loud talking, snoring, sleeping, coming to class late, leaving class early (except in emergencies) will not be tolerated. Multiple disruptions over the semester will lead to withdrawal of the student from the class by the instructor.

Course Topic/Dates/Assignments:
We shall attempt to cover all the topics of the book.

Academic Honesty:
Academic integrity is the foundation of the academic community. Because each student has the primary responsibility for being academically honest, students are advised to read and understand all sections of this policy relating to standards of conduct and academic life.   Park University 2007-2008 Undergraduate Catalog Page 85-86

Plagiarism:
Plagiarism involves the use of quotations without quotation marks, the use of quotations without indication of the source, the use of another's idea without acknowledging the source, the submission of a paper, laboratory report, project, or class assignment (any portion of such) prepared by another person, or incorrect paraphrasing. Park University 2007-2008 Undergraduate Catalog Page 85

Attendance Policy:
Instructors are required to maintain attendance records and to report absences via the online attendance reporting system.

  1. The instructor may excuse absences for valid reasons, but missed work must be made up within the semester/term of enrollment.
  2. Work missed through unexcused absences must also be made up within the semester/term of enrollment.
  3. Work missed through unexcused absences must also be made up within the semester/term of enrollment, but unexcused absences may carry further penalties.
  4. In the event of two consecutive weeks of unexcused absences in a semester/term of enrollment, the student will be administratively withdrawn, resulting in a grade of "F".
  5. A "Contract for Incomplete" will not be issued to a student who has unexcused or excessive absences recorded for a course.
  6. Students receiving Military Tuition Assistance or Veterans Administration educational benefits must not exceed three unexcused absences in the semester/term of enrollment. Excessive absences will be reported to the appropriate agency and may result in a monetary penalty to the student.
  7. Report of a "F" grade (attendance or academic) resulting from excessive absence for those students who are receiving financial assistance from agencies not mentioned in item 5 above will be reported to the appropriate agency.

Park University 2007-2008 Undergraduate Catalog Page 87-88

Disability Guidelines:
Park University is committed to meeting the needs of all students that meet the criteria for special assistance. These guidelines are designed to supply directions to students concerning the information necessary to accomplish this goal. It is Park University's policy to comply fully with federal and state law, including Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Act of 1990, regarding students with disabilities. In the case of any inconsistency between these guidelines and federal and/or state law, the provisions of the law will apply. Additional information concerning Park University's policies and procedures related to disability can be found on the Park University web page: http://www.park.edu/disability .



Rubric

CompetencyExceeds Expectation (3)Meets Expectation (2)Does Not Meet Expectation (1)No Evidence (0)
Evaluation                                                                                                                                                                                                                                                 
Outcomes
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Can use calculus to derive properties of continuous probability distributions.
 
Can apply properities of continuous probability distributions
 
Cannot apply properties of continuous probability distributions.
 
Makes no attempt to apply the properties of continuous probability distributions.
 
Synthesis                                                                                                                                                                                                                                                  
Outcomes
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Can use moment-generating functions to derive properties of probability density functions with 100% accuracy.
 
Can use moment-generating functions to derive properties of probability density functions with at least 80%  accuracy.
 
Can use moment-generating functions to derive properties of probability density functions with less than 80%  accuracy.
 
Makes no attempt to use a moment generating function.
 
Analysis                                                                                                                                                                                                                                                   
Outcomes
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Can compute the mean and standard deviation of the binomial probability distribution using sigma-notation with 100% accuracy.
 
Can compute the mean and standard deviation of the binomial distribution using sigma-notation with at least 80% accuracy.
 
Can compute the mean and standard deviation of the binomial distribution using sigma-notation with less than 80% accuracy.
 
Makes no attempt to compute the mean and standard deviation of the binomial probability distribution.
 
Terminology                                                                                                                                                                                                                                                
Outcomes
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Can define such terms as sample space, event, singleton, union, intersection, conditional probability, universal set, null set, independent events, discrete random variable, continuous random variable, joint probability density function with 100% accuracy.
 
Can define such terms as sample space, event, singleton, union, intersection, conditional probability, universal set, null set, independent events, discrete random variable, continuous random variable, joint probability density function with at least 80%  accuracy.
 
Can define such terms as sample space, event, singleton, union, intersection, conditional probability, universal set, null set, independent events, discrete random variable, continuous random variable, joint probability density function with less than 80% accuracy.
 
Makes no attempt to define any of the relevant terms.
 
Concepts                                                                                                                                                                                                                                                   
Outcomes
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Can explain such concepts as the classical approach to probability, the frequency approach to probability, permutations, combinations, mean, standard deviation, variance, random variable, mathematical expectation, and generating function with 100% accuracy.
 
Can explain such concepts as the classical approach to probability, the frequency approach to probability, permutations, combinations, mean, standard deviation, variance, random variable, mathematical expectation, and generating function with at least 80% accuracy.
 
Can explain such concepts as the classical approach to probability, the frequency approach to probability, permutations, combinations, mean, standard deviation, variance, random variable, mathematical expectation, and generating function with less than 80% accuracy.
 
Makes no attempt to define any concept.
 
Application                                                                                                                                                                                                                                                
Outcomes
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Can apply set-theoretic ideas to probability problems with 100% accuracy.
 
Can apply set-theoretic ideas to probability problems with at least 80%  accuracy.
 
Can apply set-theoretic ideas to probability problems with less than 80% accuracy.
 
Makes no attempt to apply set-theoretic ideas to probability problems.
 
Whole Artifact                                                                                                                                                                                                                                             
Outcomes
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Can apply the concept of probability to statistics with clear insight.
 
Can apply the concept of probability to statistics with clear some insight.
 
Cannot make any connection between probability and statistics.
 
Makes no attempt to connect probability and statistics.
 
Component                                                                                                                                                                                                                                                  
Outcomes
                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    
Can apply the binomial theorem and binomial coefficients with 100% accuracy.
 
Can apply the binomial theorem and binomial coefficients with 100% accuracy.
 
Can apply the binomial theorem and binomial coefficients with 100% accuracy.
 
Makes no attempt to apply the binomial theorem or binomial coefficients.
 

Copyright:

This material is protected by copyright and can not be reused without author permission.

Last Updated:7/26/2007 1:26:23 PM