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Introduction to the Practice of Statistics Fourth Edition - DOC

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					                        An Introduction to
                Statistical Methods and Analysis

                    TENTATIVE Course Syllabus--Fall 2007

An introduction to the concepts, techniques, and reasoning central to the understanding of data,
this lecture course focuses on the fundamental ideas of statistical analysis used to gain insight
into diverse areas of human interest. The use, abuse and misuse of statistics will be the central
focus of the course. Topics of exploration will include experimental study design, sampling
design, data analysis (visual devices, measures of center, measures of spread, normal
distributions, correlation and regression), sampling distributions and the Central Limit Theorem,
estimation (confidence intervals) and hypothesis testing (z-test, t-test, chi-square, ANOVA).
Applications will be drawn from current events, business, psychology, politics, medicine and
other areas of the life and social sciences. Statistical software will be introduced and used
extensively in this course, but no prior experience is assumed. An invaluable course for students
planning graduate study in the natural or social sciences. Open to any interested student—no
college level mathematical knowledge required.


Goal of Course:        To learn the fundamentals of statistical analyses including data analysis,
                       study design, and basic inference.

Class Meeting:         Lectures: Mondays/Thursdays, 11:05-12:30, Science Building 103
                       Group Conferences: Tuesdays, 9:00-9:50 or 10:00-10:50, Library E1

                       Attendance at lectures and group conferences is mandatory. Group
                       conference will be held weekly. Students will be assigned to one of the two
                       group conferences during the first week of class.

Instructor:            Daniel King (You can call me anything reasonable, but I usually
                       respond best to the name 'Dan')

Phone:                 (914) 395-2424 (office phone with voice mail)
                       (646) 408-0715 (cell phone—please use it responsibly)

E-mail:                dking@slc.edu

Office:                121 Alice Ilchman Science Building (first floor)

Required Text:         Moore, David S., The Basic Practice of Statistics, Fourth Edition.
                       W. H. Freeman & Company Publishers, 2007. ISBN: 0-7167-7478-X
                   This mandatory text is available for purchase from the campus bookstore,
                   but you are welcome to purchase it through any vendor you choose.
                   However, purchase of the 4th edition is crucial—make sure to check the
                   ISBN number of the book you plan to buy and compare it to the one given
                   above.

                   This is the main textbook and source of information for the course. We will
                   cover Chapters 1-11, 14-23, and 25-27. Additional reading of essays,
                   journal articles, newspaper clips is also required. These supplemental
                   documents will be provided to students in class.

Reserve Texts:     Notx, William, Fligner, Michael, and Sorice, Rebecca L., Student Study
                   Guide for Moore's The Basic Practice of Statistics, Fourth Edition. W.H.
                   Freeman & Company Publishers, 2006.

                   This valuable study guide has been placed on reserve at the main desk of the
                   Raushenbush Library. Each section of this study guide begins with an
                   overview of the corresponding section in the main text. Most valuable is the
                   guided assistance provided on selected problems from the text. The authors
                   help students in the process of setting up and thinking about the problems.
                   After using the guided assistance, students can examine the complete
                   solutions which explain exactly how the answer was reached and why it was
                   done that way.

Course Readings:   For each lecture meeting there will be an assigned reading from
                   the course textbook or from a supplemental handout. These readings will
                   form the basis of the course lectures. In advance of each lecture, students
                   are strongly encouraged to complete the associated reading. See ‘Course
                   Topics and Readings’ below for the schedule of readings. The ability to
                   read mathematics successfully (for deep understanding and long term
                   retention) is a skill that requires significant effort to develop. It is also a
                   skill that is often not developed in traditional high school courses. In this
                   course you will have much opportunity and will be given sufficient
                   guidance in developing your mathematical reading skills. Please consult
                   the ‘Suggestions for Effective Reading of Mathematics’ at the end of this
                   syllabus.

Course Exams:      There will be three, 90-minute exams during the term. The coverage and
                   tentative dates for these exams are as follows:

                   Exam 1: Thursday, Oct 11—Data Analysis
                   Exam 2: Thursday, Nov 15—Study Design, Basics of Inference
                   Exam 3: Monday, Dec 17—Inference in Practice

Calculators:       In this course we will focus less on tedious computations and more on the
                   understanding and proper interpretation of statistical calculations.
                   Nevertheless, a calculator with statistical capabilities is a valuable learning
                   tool. Access to a calculator with statistical functionality is required for this
                   course. The Texas Instruments TI-83 or TI-86 is recommended for this
                   course but any calculator with statistical functionality is acceptable. The
                   college owns a few of these calculators that can be borrowed by students.
                       You may already own a calculator that performs some statistical
                       calculations. See me if you need help using your calculator’s statistical
                       functions or need advice on purchasing a calculator for use in this course
                       (and beyond).

Computers:             We will be using the Microsoft Excel spreadsheet program in this course to
                       do some of our more complicated statistical calculations. Access to Excel
                       and instruction on its use will be provided in group conference. No prior
                       computer experience is required.

Conference Work:       Group conferences will be held weekly. Time in group conference will be
                       spent in two ways: 1) reinforcing ideas presented in lecture through hands-
                       on activities conducted in workshop mode and 2) designing and executing a
                       small-scale research project in which students working in small teams will
                       be responsible for choosing a topic, designing an appropriate study,
                       collecting data, analyzing data and submitting a formal report of the results.
                       On the last day of lecture students will submit their final written report and
                       give a brief oral presentation of their findings in class.

Additional Help:       I encourage students who are having difficulty with the course
                       material to meet with me for individualized help. Students are also
                       encouraged to develop and maintain an email dialogue with me so
                       that I may provide timely assistance with smaller-scale questions.

                       Students of this course can also access the free services of the
                       Mathematics Resource Center. More information about these
                       services will be discussed in class.

Evaluations:           At the end of the semester an individual course evaluation and course grade
                       will be given to each student. This evaluation will be based primarily on the
                       results of the three course exams, quality of execution of the research project
                       and class attendance.

Worksheets:            Completion of worksheets is optional in this course. Students wishing to
                       submit a worksheet should do so on the final day of class. Your worksheet
                       should detail the work completed in class and conference, listing topics
                       studied, research conducted, etc. Forms are available from the Office of
                       Student Records in Westlands.

Attendance:            Both lecture and conference attendance is absolutely mandatory. Students
                       who miss more than two classes or conferences (without a documented
                       reason) run the risk of reduced course credit. Number of student absences
                       (or occurrences of significant tardiness) at lecture and group conferences
                       missed will be indicated on the course evaluation. If a session is missed, the
                       student is responsible for obtaining class notes and assignments and is
                       expected to be fully prepared for the next class session.

                             Course Topics and Readings
The following represents a tentative schedule of our activities during the semester and is subject to
revision. The readings listed under each lecture are best read in advance of the lecture.
                              Unit I: Data Analysis
                                    Lecture 1
Lecture reading:
       Moore, BPS: Chapter 1—Picturing Distributions with Graphs

                                   Lecture 2
Lecture reading:
       Moore, BPS: Chapter 2—Describing Distributions with Numbers (Part I)

                                   Lecture 3
Lecture reading:
       Moore, BPS: Chapter 2—Describing Distributions with Numbers (Part II)

                                  Lecture 4
Lecture reading:
       Moore, BPS: Chapter 3—The Normal Distributions

                                    Lecture 5
Lecture reading:
       Moore, BPS: Chapter 27—Statistical Process Control (on CD or on-line)
                                    Lecture 6
Lecture reading:
       Moore, BPS: Chapter 4—Scatterplots and Correlation
                                   Lecture 7
Lecture reading:
       Moore, BPS: Chapter 5—Regression
                                  Lecture 8
Lecture reading:
       Moore, BPS: Chapter 6—Two-Way Tables
                                      Lecture 9
Review: Topics in Data Analysis
                                     Lecture 10
Exam: Topics in Data Analysis (Tentative Date: Thursday, October 11)

             Unit II: Study Design and the Basics of Inference
                                  Lecture 11
Lecture reading:
       Moore, BPS: Chapter 8—Producing Data: Sampling
                                  Lecture 12
Lecture reading:
       Moore, BPS: Chapter 9—Producing Data: Experiments

                                   Lecture 13
Lecture reading:
       Moore, BPS: Chapter 10—Introducing Probability

                                 Lecture 14
Lecture reading:
       Moore, BPS: Chapter 11—Sampling Distributions

                                     Lecture 15
Lecture reading:
       Moore, BPS: Chapter 14—Confidence Intervals: The Basics

                                  Lecture 16
Lecture reading:
       Moore, BPS: Chapter 15—Tests of Significance: The Basics

                                   Lecture 17
Lecture reading:
       Moore, BPS: Chapter 16—Inference in Practice

                                      Lecture 18
Review: Topics in Study Design, Basics of Inference

                                      Lecture 19
Exam: Topics in Study Design, Basics of Inference (Tentative Date: Thursday, Nov. 15)

                           Unit III: Inference in Practice
                                   Lecture 20
Lecture reading:
       Moore, BPS: Chapter 18—Inference about a Population Mean (t-test)

                                 Lecture 21
Lecture reading:
       Moore, BPS: Chapter 19—Two-Sample Problems (Comparing Two Means)

                                   Lecture 22
Lecture reading:
       Moore, BPS: Chapter 20—Inference about a Population Proportion (z-test)

                                 Lecture 23
Lecture reading:
       Moore, BPS: Chapter 21—Comparing Two Proportions

                                 Lecture 24
Lecture reading:
       Moore, BPS: Chapter 23—Two Categorical Variables: The Chi-Square Test

                                  Lecture 25
Lecture reading:
       Moore, BPS: Chapter 25—One-Way Analysis of Variance: Comparing Several Means
       Moore, BPS: Chapter 26—Nonparametric Tests (on cd or online)

                                          Lecture 26
Review: Topics in Inference in Practice
                                       Lecture 27
Exam: Topics in Inference in Practice (Tentative Date: Monday, Dec 17)
                                          Lecture 28
Student Research Study Presentations
              Suggestions for Effective Reading of Mathematics
       1. When confronted with the task of reading a piece of mathematical text, skim the
          entire reading first to discern its general outline and to identify its main points
          and objectives.
2. If necessary, review earlier portions of the textbook (or prior mathematical topics
   studied) to recall forgotten or unfamiliar vocabulary, techniques or theorems
   before attempting a thorough reading of the current text.

3. Don’t rush! Read slow! Mathematical writing is typically dense with ideas.
   Spend as much time as necessary to understand the full intended meaning of each
   of the author’s arguments and examples.

4. Pay particular attention to the precise statement of new definitions and theorems.

5. Do not immediately skip over a portion of the reading that doesn’t make sense in
   the hope that its meaning will become more apparent later. Because of the linear
   nature of mathematical writing in which one topic builds from those that precede
   it, it is very important to fully understand one topic before proceeding to the next.

6. Try to identify the cause of any misunderstanding of the topics being studied.
   Consider all reasonable methods to resolve the misunderstanding. Whenever
   possible discuss difficult portions of the text with a friend, study partner, or study
   group.

7. If all else fails, make sure to mark any portions of the text that remain perplexing
   so that you may raise these issues subsequently in class.

8. Occasionally authors will intentionally leave some details of arguments or
   examples to the reader to complete as an exercises. Authors do this for
   pedagogical reasons and not laziness! As a useful check on your understanding
   of the material, always fill-in in the details omitted by the author.

9. Examples in textbooks often come with a moral. Discern the author’s main point
   in providing the example. Make sure you struggle to understand every aspect of
   the computation, manipulation, or procedure presented in the example.

10. Always keep pencil and paper handy whenever reading mathematical text. It can
    be very helpful to highlight important passages, insert marginal notes to yourself
    (ala Fermat!), and make simple calculations while involved in the reading of the
    text.

				
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