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Education Major Version

MA 120 Basic Concepts of Statistics
Marsh, Kimball


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 120 Basic Concepts of Statistics

Semester

U1J 2011 PV

Faculty

Marsh, Kimball

Title

Senior Adjunct Faculty

Degrees/Certificates

MA Mathematics
BA Mathematics

Office Location

Classroom

Office Hours

30 minutes before and after class

E-Mail

kimball.marsh@park.edu

Semester Dates

June 6 to July 30, 2011

Class Days

------S

Class Time

1:00 PM to 5:20 PM

Credit Hours

3


Textbook:

Mario F. Triola, Elementary Statistics, 11/E, Addison Wesley,  2010
ISBN-10: 0321500245   ISBN-13: 9780321500243

Textbooks can be purchased through the MBS bookstore

Textbooks can be purchased through the Parkville Bookstore

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:
A development of certain basic concepts in probability and statistics that are pertinent to most disciplines.  Topics include:  probability models, parameters, statistics and sampling procedures, hypothesis testing, correlation, and regression.  3:0:3

Educational Philosophy:
The instructor's educational philosophy is one of relaxed interactiveness based on lectures, dialogues, and formal examination.

Learning Outcomes:
  Core Learning Outcomes

  1. Compute descriptive statistics for raw data as well as grouped data.
  2. Determine appropriate features of a frequency distribution.
  3. Apply Chebyshev's Theorem.
  4. Distinguish between and provide relevant descriptions of a sample and a population.
  5. Apply the rules of combinatorics.
  6. Differentiate between classical and frequency approaches to probability.
  7. Apply set-theoretic ideas to events.
  8. Apply basic rules of probability.
  9. Apply the concepts of specific discrete random variables and probability distributions.
  10. Compute probabilities of a normal distribution.
  11. Compute confidence intervals of means and percentages.
  12. Perform hypothesis tests involving one population.
  13. Compute regression and correlation of Bi-variate data.


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 two-dimensional 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 non-standard normal probability distribution.

6.         Compute and interpret a confidence interval for a mean and/ or for a proportion.

Link to Class Rubric

Class Assessment:
Students should try to read (if time permits) the topics to be covered in class and be prepared to work examples and ask questions in class.  Your grade is based on 3 tests (the 3rd test is the core assessment which is a  final departmental exam)

Grading:
The final grade will be determined by the following:  Test 1(120 points) and  Test 2 (120 points) and the Test 3 Core Assessment (60 points) = 300 points total.  Test 1 will be over all material covered in the first 3 sessions.  Test 2 and 3 are comprehensive and will be given during the 8th week.   The three tests will be open-notes.  A basic calculator must be acquired for use during the tests and the class.  A "scientific calculator" is recommended (in the price range of $9 to $20).

Classroom Rules of Conduct:
Class participation and questions are encouraged.  Students are expected to come to all classes and to be on time.  Since 8 weeks is a VERY short time to complete a Statistics course, we will cover material at what many students consider a very rapid rate.

Course Topic/Dates/Assignments:

Class Activities

Recommended
Assignments
(Review Exercises) - NOT for a grade

Tests

Meeting - 1

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 will 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.

page 34-35:  #3, 4, 5, 7
 
page 69-70:
#1, 2,5

 

Meeting - 2

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.

page 131:
#1, 6, 8

 

Meeting - 3

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.

page 192-193:
#1, 2, 5, 7, 10, 11, 16

 

Meeting - 4

Test first two hours.  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 distributions called the binomial distribution and the Poisson distribution.

page 237, 238:
#1, 3, 4

2 Hour Test  followed by lecture.

Meeting - 5

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.

page 312:
#2, 3, 5

 

Meeting - 6

In Unit 7 we move into inferential statistics. 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.

page 377, 378:
#1, 3, 6,7

 

Meeting - 7

In Unit 8 we will examine the basics of hypothesis testing by using one-sample 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.

page 446, 447:
#2, 4, 6, 9

 

Meeting - 8

Final Comprehensive test/Core Assessment

No assignment

4 1/2 hour Final Exam

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 students and faculty members are encouraged to take advantage of the University resources available for learning about academic honesty (www.park.edu/current or http://www.park.edu/faculty/).from Park University 2010-2011 Undergraduate Catalog Page 92

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. from Park University 2010-2011 Undergraduate Catalog Page 92-93

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, but unexcused absences may carry further penalties.
  3. 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".
  4. A "Contract for Incomplete" will not be issued to a student who has unexcused or excessive absences recorded for a course.
  5. 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.
  6. 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 2010-2011 Undergraduate Catalog Page 95-96

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
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 real-world problems in other disciplines with 100 % accuracy. Can apply the concepts of probability and statistics to real-world problems in other disciplines with at least 80 % accuracy. Can apply the concepts of probability and statistics to real-world problems in other disciplines with less than 80% accuracy. Makes no attempt to apply the concepts to real-world 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. 

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Last Updated:5/11/2011 8:32:47 PM