MAT 245 B — Statistics I — Fall 2006 (Writing Intensive)

 

Instructor:                  Jacquelin Dietz

Office:                        SMB 274

Office hours:              1:30 – 2:30 on Monday

                                    1:00 – 2:00 on Tuesday and Thursday

                                    9:00 – 10:00 on Wednesday and Friday   (Other times by appointment)

Phone & e-mail:          760-8234, dietzjac@meredith.edu

 

Class time & place:     MWF 12:00 - 12:50, SMB 263

 

Catalog description:  A general introduction to descriptive and inferential statistics.  Topics include elementary probability, distributions, estimation of population parameters, confidence intervals, hypothesis testing, correlation, and regression.  Students will use statistical analysis technology.

Prerequisite:  MAT 141 or MAT 144 or equivalent level of mathematical maturity.  Credit is not available for both this course and SOC 375.

Textbook:  Introduction to the Practice of Statistics, 5th edition, by David S. Moore and George P. McCabe.  This book is very well-written and full of interesting examples; I think you will enjoy reading it.  I plan to cover Chapters 1 through 6, and perhaps Section 7.1 of Chapter 7, omitting some optional sections.

Course Objectives:  My objectives for this course correspond to the goals stated in the GAISE College Report (http://it.stlawu.edu/~rlock/gaise/) in the section titled “Goals for Students in an Introductory Course: What it Means to be Statistically Educated.”  (GAISE stands for “Guidelines for Assessment and Instruction in Statistics Education.”)  These objectives are included at the end of this syllabus.

Writing Expectations:  This course is a writing intensive course that will carry a “WI” designation on your transcript.  We will use writing in this course in two different (but intertwined) ways: “writing to learn” and “learning to write.”  First, writing about statistical ideas on homework assignments and other writing exercises will help you clarify your thinking about important concepts in statistics and will help both you and me to diagnose misconceptions or confusion you may have about the material. 

Second, you will learn to communicate to others in writing the results of statistical explorations and analyses.  The goal of the mini-project will be to produce a clear and interesting written report that describes questions of interest, statistical strategies used, and conclusions reached.  To help you accomplish that goal, I will provide instruction, feedback on early drafts, and opportunities for revision.

This course will not include direct instruction in basic writing skills, although I will expect you to use correct grammar, spelling, punctuation, and sentence structure.  Assistance with basic writing skills is available through the Learning Center in 122 Jones (see http://www.meredith.edu/learn/).

Fundamental Technology Competencies:  This course will address many of the Fundamental Technology Competencies that are part of the Meredith General Education Program (see http://www.meredith.edu/academics/gened/computercompetency05-06.htm).  In particular, the following competencies will be achieved by successful completion of this course:  Familiarity with a new piece of software and exploring its features (Fathom); familiarity with software and/or technology tools that facilitate the management, analysis, computation, and/or interpretation of quantitative information; and mastery of Blackboard features.

Class Attendance:  You are expected to attend class every day.  If you do miss a class, you are still responsible for any assignments given or announcements made in your absence.  You should arrange to copy another student's notes for any class missed.  Since work will be returned and announcements made at the beginning of class, it is important to arrive on time.  Make sure that your cell phone does not ring audibly during class.  Please participate in classroom discussions.  Class will be livelier and more interesting if everyone participates than if you make me do all the talking!

Blackboard:  This syllabus, homework assignments and solutions, and other helpful information will be available through Blackboard at http://www.courses.meredith.edu.  The site will be updated regularly so check it often.  You must have a Meredith e-mail account to use Blackboard.  If you have never used Blackboard, there is login help at the site.  Please enroll in Blackboard during the first week of the semester. 

Other Technology:  The CD that comes with your textbook contains practice exercises, interactive multiple-choice quizzes, and data sets.  It also contains case studies from the Electronic Encyclopedia of Statistical Examples and Exercises (EESEE) and applets that illustrate concepts from the book in a fun, interactive way.  We will also use some additional applets that are available on the web.  We will make heavy use of the software package Fathom.  Make sure that you have the most recent version of Fathom, Fathom 2, installed on your laptop.  Please see Tech Services during the first week of class if you do not have Fathom or if you need to upgrade to Fathom 2.

Homework:  Homework will be assigned approximately once a week.  The homework assignments will be available through Blackboard.  Don't forget to check that site regularly for assignments and due dates!  Some homework problems will be collected and graded; others will not.  The assignments will indicate clearly which problems are to be turned in.  Graded homework is due at the beginning of class on the day specified.  Late homework will be accepted only when there is a compelling excuse that has been discussed with me.  The graded homework will be worth 60 points (15% of your course grade).  You are expected to complete all of the homework problems, including those that will not be collected. 

Working lots of problems is the best way to learn the material in this course.  Collaboration on homework is permissible (and, in fact, desirable), but you will learn more if you try to do the problems yourself first, before discussing them with others.  The paper you turn in should represent a substantial individual effort; it should not be identical to someone else’s!

Tests: There will be three midterm tests, each worth 60 points.  The tests are tentatively scheduled for the following dates:

                                                Wednesday, September 20

                                                Wednesday, October 11

                                                Monday, November 13

The material from the last three weeks of class will be covered only on the final exam.  It is possible to arrange to take a test early if you have a very good reason for doing so.  Tests will rarely be given late and only in cases where a genuine emergency prevents you from taking the test on the scheduled date.  If such an emergency arises, contact me as soon as possible, preferably before the time of the test.

Mini-Project:  There will be one small project due in mid- November.  In this mini-project, you will explore and summarize a dataset with several variables using graphs and numerical measures.  The mini-project will be worth 40 points (10% of your course grade).

Writing Assignments:  There will be three small writing assignments that will involve writing about statistical concepts.  The writing assignments will be worth a total of 40 points (10% of your course grade).

Final Exam:  There will be a cumulative final exam worth 80 points (20% of your course grade).  The final exam will take place from 1:00 - 4:00 on Wednesday, December 13.

Grading:  Your grade will be based on your total points out of a maximum of 400 points.  360 or more points (90%) will guarantee you an A, 320 or more points (80%), at least a B, 280 or more points (70%), at least a C, and 240 or more points (60%), at least a D.

Homework                    60 points         15%

Tests                            180 points         45%

Mini-project                 40 points         10%

Writing assignments      40 points         10%

Final exam                    80 points         20%

Total                            400 points        100%

 

Honor Code:  

            “We, the Meredith community, are committed to developing and affirming in each student a sense of personal honor and responsibility. Uncompromising honesty and forthrightness are essential elements of this commitment. The Honor System is a method by which individual honors are protected and maintained. Any dishonorable action will be regarded as a violation of this commitment, and corrective action will be taken.

            If I am in violation of the Honor Code, to prevent jeopardizing the Honor System or weakening our system of self-government, I have an obligation to report myself to the proper authorities. If I am aware of a violation of the Honor System by another student, I shall call this matter to the attention of that student as a violation of responsibility to the community.

            In choosing Meredith College, I am accepting the Honor System as a way of life. As a Meredith student, I am responsible for insuring that the Honor System is at all times carried out.”

 

You will be expected to sign your name indicating your adherence to this pledge on every test.

Disability Services:  Reasonable accommodations will be made for students with documented disabilities.  In order to receive accommodations, students must go through the Counseling Center/Disability Services office. Disability Services is located in 106 Carroll Hall and can be reached at 760-8427 or disabilityservices@meredith.edu.  For additional information see the website at http://www.meredith.edu/students/counsel/disability.

Inclement Weather:  If classes are cancelled because of inclement weather, the college will run public announcements on the radio station MIX 101.5 FM and the television station WRAL Channel 5.  There is also a Meredith weather number (919-832-8878) you can call for information.  If the college does not close, I will probably be able to hold class, and you should attend if you can do so safely.


Course Objectives (from the GAISE College Report by Martha Aliaga, George Cobb, Carolyn Cuff, Joan Garfield (Chair), Rob Gould, Robin Lock, Tom Moore, Allan Rossman, Bob Stephenson, Jessica Utts, Paul Velleman, and Jeff Witmer; see http://it.stlawu.edu/~rlock/gaise/):

 

Students should believe and understand why:

  • Data beat anecdotes.
  • Variability is natural and is also predictable and quantifiable.
  • Random sampling allows results of surveys and experiments to be extended to the population from which the sample was taken.
  • Random assignment in experiments allows cause and effect conclusions to be drawn.
  • Association is not causation.
  • Statistical significance does not necessarily imply practical importance, especially for studies with large sample sizes.
  • Finding no statistically significant difference or relationship does not necessarily mean there is no difference or no relationship in the population, especially for studies with small sample sizes.

 

Students should recognize:

  • Common sources of bias in surveys and experiments.
  • How to determine the population to which the results of statistical inference can be extended, if any, based on how the data were collected.
  • How to determine when a cause and effect inference can be drawn from an association, based on how the data were collected.
  • That words such as “normal”, “random” and “correlation” have specific meanings in statistics that may differ from common usage.

 

Students should understand the parts of the process through which statistics works to answer questions, namely:

  • How to obtain or generate data.
  • How to graph the data as a first step in analyzing data, and how to know when that’s enough to answer the question of interest.
  • How to interpret numerical summaries and graphical displays of data - both to answer questions and to check conditions (in order to use statistical procedures correctly).
  • How to make appropriate use of statistical inference.
  • How to communicate the results of a statistical analysis.

 

Students should understand the basic ideas of statistical inference:

  • The concept of a sampling distribution and how it applies to making statistical inferences based on samples of data.
  • The concept of statistical significance including significance levels and p-values.
  • The concept of confidence interval, including the confidence level and margin of error.

 

Finally, students should know:

  • How to interpret statistical results in context.
  • How to critique news stories and journal articles that include statistical information, including identifying what's missing in the presentation and the flaws in the studies or methods used to generate the information.
  • When to call for help from a statistician.