# MA120 Basic Concepts of Statistics

## for S2T 2006

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 Course MA 120 Basic Concepts of Statistics Semester S2T 2006 DL Faculty Owens, Richard E,, III Title Adjunct Instructor - Mathematics Degrees/Certificates BA: Computer Information SystemsMS: Management Information SystemsPhD [ABD] - OML - emphasis, Communication Office Location online Office Hours 9:00 - 4:00 Daytime Phone 913-534-2475 E-Mail richard.owens@park.edu richard.e.owens@sprint.com Class Days online schedule Class Time online schedule Prerequisites none Credit Hours 3

Textbooks can be purchased though the MBS bookstore

Textbooks can be purchased though the Parkville Bookstore

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:
In the brief time we have together, it is my goal, not that you learn rote formulae and dry facts, but instead, you learn the tools of statistics, what those tools are, when they apply and where to find them when you need them. To achieve this goal, the class is structured around homework and in class interaction with the instructor and other students.

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

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 thereof:

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 non-standard normal probability distribution.

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

Class Assessment:
Progress will be assessed through homework, two graded quizzes and a final proctored exam. In keeping with the instructors philosophy that the goal of education is exposure to tools, these tests are open book, open note and the students may use onsite computers or their own calculators

two quizzes worth 25 points each [12.5% of final grade each] - 25% total.
Proctored final exam worth 50 points - 25% of final grade.
Four homework assignments worth 10 points each - 5% of final grade each - 20% total
6 weeks of in class interaction - defined as one original and two substantive responses to original posts / week. 10 points / week - 30% of final grade

Late Submission of Course Materials:
Late work is penalized one letter grade per day late [a day is defined as beginning at 12:00:01 AM Parkville time. Work will not be accepted more than 4 days late.

Classroom Rules of Conduct:
Profession decorum and respect for the other individuals in the course room. In this class, the golden rule applies, treat others with the respect you wish to be treated.

Course Topic/Dates/Assignments:
During Week 1, we begin to learn the language of statistics and examine some basic sampling and experimental design strategies.  We will also learn strategies for organizing data, and representing data sets in graphs and charts.  Discussion of analysis of graphical representations of data will allow us to classify what type of distribution represents a particular data set.  We will also learn how mistakes in constructing graphs improperly causes data to be misrepresented.  Our online discussions will help you become familiar with the online environment, learn what we hope to achieve from this course, understand my general guidelines and expectations, and assess your interest in the the study of statistics.
During Week 2, we will examine basic calculations used in descriptive statistics including: measures of center, sample mean, and sample standard deviation.  We will also discuss descriptive measures for populations, including the use of samples.  We will continue discussing basic tools for descriptive statistics, including the use of linear regression and linear correlation.
During Week 3, we will learn what probabilities are and learn some of the basic rules of probability.  We will also learn about discrete random probabilities and probability distributions.
During Week 4, we will learn about the normal distribution, areas under the standard normal curve, and working with normally distributed variables.  We will also examine sampling error, the need for sampling distributions and and learn how to apply the sampling distribution of the sampling mean.
During Week 5, we begin an examination of  inferential statistics.  We begin with a discussion of confidence intervals for one population mean.  We will also start to examine the basics of hypothesis testing.
During Week 6, we continue to examine hypothesis testing for one population mean.  We will then turn our attention to the inferential statistics of two population means.
During Week 7, we will examine the inferential statistics of population proportions and analysis of variance using one-way ANOVA.
During Week 8, we will conclude our examination of topics in statistics by learning how to use the Chi-square distribution to determine goodness of fit and to determine if there is an association between two variables.  We will also complete the proctored examination during this week.

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 2005-2006 Undergraduate Catalog Page 85-87
Definitions
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.
• Plagiarism involves the use of quotation 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 assignments (any portion of such) prepared by another person, or incorrect paraphrasing.
• Other acts that constitute academic dishonesty include:
o Stealing, manipulating, or interfering with an academic work of another student or faculty member.
o Collusion with other students on work to be completed by one student.
o Lying to or deceiving a faculty member.
Procedure
In the event of alleged academic dishonesty, an Academic Dishonesty Incident Report will be submitted to an Online Academic Director who will then investigate the charge.  Students who engage in academic dishonesty are subject to a range of disciplinary actions, from a failing grade on the assignment or activity in question to expulsion from Park University.  Park University's academic honesty policy and related procedures can be found in full in the 2004-2005 Park University Undergraduate and Graduate Catalogs.

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 2005-2006 Undergraduate Catalog Page 85-87

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 "WH".
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.
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.

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. Park University is committed to meeting the needs of all learners that meet the criteria for special assistance. These guidelines are designed to supply directions to learners 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 American with Disabilities Act of 1990, regarding learners with disabilities and, to the extent 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

 Competency Exceeds Expectation (3) Meets Expectation (2) Does Not Meet Expectation (1) No Evidence (0) Synthesis                                          Outcomes 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 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. Evaluation                                         Outcomes 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. Terminology                                        Outcomes 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 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 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 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. Component                                          Outcomes 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. M/LL Courses                                       Outcomes