MA120 Basic Concepts of Statistics
for U1T 2008
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Course  MA 120 Basic Concepts of Statistics 
Semester  U1T 2008 DLD 
Faculty  Burns, James D. 
Title  Senior Instructor 
Degrees/Certificates  MS, Systems Engineering BS, Business Administration Six Sigma Green Belt 
Office Location  Internet 
Office Hours  As needed 
Daytime Phone  410 294 7725 
EMail  james.burns@pirate.park.edu 
Class Days  TBA 
Class Time  TBA 
Credit Hours  3 
Textbook:
Required Text: Elementary Statistics, 10th Ed.
Author: Mario F. Triola
Publisher: AddisonWesley
ISBN: 0321522915
Order text at: http://direct.mbsbooks.com/park.htm
An ebook is included with MyMathLab. If you prefer to use it instead of the hardcopy, order the Student Access Kit only at www.mymathlab.com. You will need your instructor's course ID code prior to ordering.


You will also need a calculator. Check with your instructor for specific requirements on type and features. Links in the course Student Instruction Guide are provided for downloading required FREE software for the multimedia presentations of the course.
Textbooks can be purchased through the MBS bookstore
Additional Resources:
If you are purchasing a calculator for this course, you should not purchase a programmable calculator as these are not permitted during the final exam.
McAfee Memorial Library  Online information, links, electronic databases and the Online catalog. Contact the library for further assistance via email or at 8002704347.
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Course Description: A development of certain basic concepts in probability and statistics that is pertinent to most disciplines. Topics include: probability models, parameters, statistics and sampling procedures, hypothesis testing, correlation and regression. 3:0:3
Educational Philosophy:
I believe that students should interact with each other and the instructor to enhance and improve the learning experience. Don't hesitate to ask your peers or me for assistance.
Learning Outcomes:
Core Learning Outcomes
 Compute descriptive statistics for raw data as well as grouped data
 Determine appropriate features of a frequency distribution
 Apply Chebyshev's Theorem
 Distinguish between and provide relevant descriptions of a sample and a population
 Apply the rules of combinatorics
 Differentiate between classical and frequency approaches to probability
 Apply settheoretic ideas to events
 Apply basic rules of probability
 Apply the concepts of specific discrete random variables and probability distributions
 Compute probabilities of a normal distribution
Core Assessment: Description of MA 120 Core Assessment
One problem with multiple parts for each numbered item, except for item #3, which contains four separate problems.
1. Compute the mean, median, mode, and standard deviation for a sample of 8 to 12 data.
2. Compute the mean and standard deviation of a grouped frequency distribution with 4 classes.
3. Compute the probability of four problems from among these kinds or combinations there of:
a. the probability of an event based upon a twodimensional table;
b. the probability of an event that involves using the addition rule;
c. the probability of an event that involves conditional probability;
d. the probability of an event that involves the use of independence of events;
e. the probability of an event based upon permutations and/or combinations;
f. the probability of an event using the multiplication rule; or
g. the probability of an event found by finding the probability of the complementary event.
4. Compute probabilities associated with a binomial random variable associated with a practical situation.
5. Compute probabilities associated with either a standard normal probability distribution or with a nonstandard normal probability distribution.
6. Compute and interpret a confidence interval for a mean and/ or for a proportion.
Link to Class Rubric
Class Assessment:
The assessment of your understanding of the material in this course is done by weekly quizzes, discussion questions, and homework. Additionally, there is an introduction feature located under the Course Home tab that is a graded part of the course, along with a Feedback section in Week 8 in which you make suggestions for improvement for future courses.
The final is part of the core assessment. The final is a departmental exam and it will be provided to the instructor by the department of mathematics. The final is 2 hrs; books, notes, and a nongraphing, nonprogrammable calculator are allowed.
Grading:
The course consists of 2100 possible points assigned as follows:
Week 1: Introduction  75 points (75 points total)
Weeks 1  8 Homework  75 points each week (600 points total)
Weeks 1  8 Weekly Quiz  75 points each week (600 points total)
Weeks 1  7 Discussion/Research topic  50 points each week (350 points total)
Week 8  Feedback to the Instructor  75 points (75 points total)
Week 8  Final Exam  400 points (400 points total)
Late Submission of Course Materials:
I will accept late homework and late quizzes with no penalty if the reasons are acceptable and fully discussed with me. I do not accept late discussion questions, as the point of discussion is lost if the rest of the class has moved on.
Classroom Rules of Conduct:
The classroom is a place where we discuss what we are learning and where we are having difficulty. Some of us will learn at a pace that is not the same as the others. Some of us will find some topics easier than other topics. We are all in this together, and we must all participate to help everyone of us succeed. Thus, I require your participation and a respectful attitude to your fellow students and me.
Course Topic/Dates/Assignments:
This course provides an introduction to the world of statistical analysis. Each week we'll focus on different aspects of the general topic.
In Unit 1 we'll learn what the topic of statistics entails. We'll discuss some ways to collect the needed data for a statistical study. By the end the unit we'll have a view of how the two distinct divisions of statistics, descriptive and inferential, are related.
In Unit 2 we'll discover how to convert pure data into corrupted data, also referred to as ungrouped data into grouped data. Then we will examine some of the many ways data can be visually displayed graphically.
In Unit 3 we will examine ways to describe data by looking at its central tendency, its variation from its center, and how to determine the location of an element within a data set. A method of finding the proportions of variation a data set possesses will also be covered.
In Unit 4 we'll explore the basic concepts of probabilities, the branch of mathematics that allows us to take a sample and make predictions about the population from which it was derived. We'll strive to gain a fundamental understanding of probability through its addition, multiplication and counting rules.
In Unit 5 we combine the probability concepts and the statistical concepts we previously learned to construct discrete probability distributions. Then we'll learn how to find statistics of the distribution. The unit ends with a discussion on a specific discrete probability distribution called the binomial distribution.
In Unit 6 the discussion changes from discrete distributions to continuous random variable distributions. We begin looking at the Normal distribution and then quickly moving on the the Standard Normal distribution. We conclude the unit by learing how the Central Limit Theorem can be applied to sample data sets.
In Unit 7 we move into inferential statistcs. We learn how to use a sample mean to estimate the population mean, and how we can confidently report its value within a specific interval.
In Unit 8 we will examine the basics of hypothesis testing by using onesample procedures for the hypothesis test of the population mean. In addition we will conclude our examination of topics in statistics by discussing the purpose of regression and correlation analysis. First, we'll examine some introductory terms, then focus on simple linear regression analysis and simple linear correlation analysis. During this final week of the course you will also complete the proctored Final Exam and the Course Evaluation.
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 20072008 Undergraduate Catalog Page 8586
Instructors, You should either delete this message and leave blank, or enter additional comments or policies. I have entered mine as an example that you may copy part or all if you wish.
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Academic dishonesty includes committing or the attempt to commit cheating, plagiarism, falsifying academic records, and other acts intentionally designed to provide unfair advantage to the students.
Cheating includes, but is not limited to, intentionally giving or receiving unauthorized aid or notes on examinations, papers, laboratory reports, exercises, projects, or class assignments which are intended to be individually completed. Cheating also includes the unauthorized copying of tests or any other deceit or fraud related to the student's academic conduct.
Falsifying academic records includes, but is not limited to, altering grades or other academic records.
Other acts that constitute academic dishonesty include:
Stealing, manipulating, or interfering with an academic work of another student or faculty member.
Collusion with other students on work to be completed by one student.
Lying to or deceiving a faculty member.
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 20072008 Undergraduate Catalog Page 85
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ALL GRADED WORK FOR THIS COURSE MUST BE YOUR OWN. EVERY QUIZ INSTRUCTION PAGE STATES THAT YOU ARE NOT TO RECEIVE OUTSIDE ASSISTANCE FROM ANYONE OTHER THAN YOUR INSTRUCTOR. To further clarify; classmates, spouses, coworkers, tutors, clergy, librarians, friends, relatives, and pets are included as OUTSIDE ASSISTANCE. PLEASE DO NOT VIOLATE THIS RULE. WARNING: When I repeatedly see identical incorrect answers on quizzes I will be suspicious and will investigate.
When discussion questions request you to answer in your own words, do not copy words from the textbook as your own. State YOUR understanding of the concept, not the understanding of some other person. If you are allowed to quote the textbook, or other sources, you must use proper quotation markings and declare the source including web URL address or book page number from which you copied the text. Not following these rules constitutes plagiarism, and will not be tolerated. (This means you will not earn points for the assignment, and if the plagiarism does not stop immediately you will FAIL the course. Additionally, a report of the incidence will be sent to your permanent academic file.)
Attendance Policy:
Instructors are required to maintain attendance records and to report absences via the online attendance reporting system.
 The instructor may excuse absences for valid reasons, but missed work must be made up within the semester/term of enrollment.
 Work missed through unexcused absences must also be made up within the semester/term of enrollment.
 Work missed through unexcused absences must also be made up within the semester/term of enrollment, but unexcused absences may carry further penalties.
 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".
 A "Contract for Incomplete" will not be issued to a student who has unexcused or excessive absences recorded for a course.
 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.
 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.
ONLINE NOTE: An attendance report of "P" (present) will be recorded for students who have logged in to the Online classroom at least once during each week of the term. Recording of attendance is not equivalent to participation. Participation grades will be assigned by each instructor according to the criteria in the Grading Policy section of the syllabus.
Park University 20072008 Undergraduate Catalog Page 8788
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 .
Instructors, You should either delete this message and leave blank, or enter additional comments or policies.
Rubric
Competency  Exceeds Expectation (3)  Meets Expectation (2)  Does Not Meet Expectation (1)  No Evidence (0) 
Evaluation Outcomes 10  Can perform and interpret a hypothesis test with 100% accuracy.  Can perform and interpret a hypothesis test with at least 80% accuracy.  Can perform and interpret a hypothesis test with less than 80% accuracy.  Makes no attempt to perform a test of hypothesis. 

Synthesis Outcomes 10  Can compute and interpret a confidence interval for a sample mean for small and large samples, and for a proportion with 100% accuracy.  Can compute and interpret a confidence interval for a sample mean for small and large samples, and for a proportion with at least 80% accuracy.  Can compute and interpret a confidence interval for a sample mean for small and large samples, and for a proportion with less than 80% accuracy.  Makes no attempt to compute or interpret a confidence interval. 

Analysis Outcomes 10  Can apply the normal distribution, Central limit theorem, and binomial distribution to practical problems with 100% accuracy.  Can apply the normal distribution, Central limit theorem, and binomial distribution to practical problems with at least 80% accuracy.  Can apply the normal distribution, Central limit theorem, and binomial distribution to practical problems with less than 80% accuracy.  Makes no attempt to apply the normal distribution, Central Limit Theorem, or binomial distribution. 

Terminology Outcomes 4,5,7  Can explain event, simple event, mutually exclusive events, independent events, discrete random variable, continuous random variable, sample, and population with 100% accuracy.  Can explain event, simple event, mutually exclusive events, independent events, discrete random variable, continuous random variable, sample, and population with at least 80% accuracy.  Can explain event, simple event, mutually exclusive events, independent events, discrete random variable, continuous random variable, sample, and population with less than 80% accuracy.  Makes no attempt to explain any of the terms listed. 

Concepts Outcomes 1,6  Can explain mean, median, mode, standard deviation, simple probability, and measures of location with 100% accuracy.  Can explain mean, median, mode, standard deviation, simple probability, and measures of location with at least 80% accuracy.  Can explain mean, median, mode, standard deviation, simple probability, and measures of location with less than 80% accuracy.  Makes no attempt to define any concept. 

Application Outcomes 1,2,3,8,9  Compute probabilities using addition multiplication, and complement rules and conditional probabilities. Compute statistical quantities for raw and grouped data. Compute probabilities using combinatorics, discrete random variables, and continuous random variables. All must be done with 100% accuracy.  Compute probabilities using addition multiplication, and complement rules and conditional probabilities. Compute statistical quantities for raw and grouped data. Compute probabilities using combinatorics, discrete random variables, and continuous random variables. All must be done with at least 80% accuracy.  Compute probabilities using addition multiplication, and complement rules and conditional probabilities. Compute statistical quantities for raw and grouped data. Compute probabilities using combinatorics, discrete random variables, and continuous random variables. All are done with less than 80% accuracy.  Makes no attempt to compute any of the probabilities or statistics listed. 

Whole Artifact Outcomes 7,8  Can apply the concepts of probability and statistics to realworld problems in other disciplines with 100 % accuracy.  Can apply the concepts of probability and statistics to realworld problems in other disciplines with at least 80 % accuracy.  Can apply the concepts of probability and statistics to realworld problems in other disciplines with less than 80% accuracy.  Makes no attempt to apply the concepts to realworld problems. 

Components Outcomes 1  Can use a calculator or other computing device to compute statistics with 100% accuracy.  Can use a calculator or other computing device to compute statistics with at least 80% accuracy.  Can use a calculator or other computing device to compute statistics with less 80% accuracy.  Makes no attempt to use any computing device to compute statistics. 
Copyright:
This material is protected by copyright and can not be reused without author permission.
Last Updated:5/15/2008 8:07:35 PM