University of Pittsburgh

BIOST 2041 Sample Syllabus

BIOST 2041- Introduction to Statistical Methods I

Fall, 2010

 

Basic Information:

Instructor:                 

John Wilson

                        Department of Biostatistics and NSABP Biostatistical Center

                        350 Sterling (201 N. Craig St., Suite 350), 412 / 383-1648

A-437 Crabtree Hall, 412 / 624- 3110

Office Hours:  M, W 3 – 4:30

                        jww@pitt.edu

                        www.biostat.pitt.edu/wilson.htm 

 

Teaching Assistant:  

Candace Wu

Department of Biostatistics, A-431 Crabtree

Office Hours: T, Th 3 - 4:30

wew25@pitt.edu 

Text:

Fundamentals of Biostatistics

7th edition, 2011

by  Bernard Rosner

 

Supplementary material at www.cengage.com/statistics/rosner .

 

Class Meetings:

Mondays and Wednesdays, 5:30 – 6:50 PM

G-23 Parran Hall

 

First meeting:  30 August 2010

Last meeting:  15 December 2010 (Final Exam)

 

Course web site:       

courseweb.pitt.edu

                                   

                        Requires a “pitt.edu” username to log in.

 

Course-related mail will be sent to your “pitt.edu” address only.  If you wish your course e-mail to be forwarded to another account, open my.pitt.edu or accounts.pitt.edu and set your forwarding address.

 

Prerequisites:

Secondary school (high school) algebra.

 


Course Description:

 

BIOST 2041 is an introductory applied biostatistics course for students needing a more research-oriented approach than that provided in the BIOST 2011 (Principles of Statistical Reasoning). The course covers basic probability and 1- and 2-sample procedures (point and interval estimation and hypothesis testing) for the normal, binomial, and Poisson distributions.  Basic 1- and 2-sample nonparametric tests are also presented.

 

Course Rationale:

 

This course is aimed at public health students and health career professionals who will make use of statistical methods in research projects or in interpreting literature.  In addition to being useful in many research settings, the tools and concepts presented in BIOST 2041 will serve as a prerequisite to BIOST 2042, which is taught in the spring term.  Together, BIOST 2041 and BIOST 2042 introduce students to the statistical methods most widely used in medical and public health research.

 

Course Goals:

 

The overall purpose of this course is to introduce students to the most commonly used statistical procedures used in 1- and 2- sample situations. This broad goal includes 1) use of statistical software to analyze data sets and answer research questions, 2) recognition of situations when these procedures are and are not appropriate, and 3) intuitive understanding of the rationale used in creating the statistical procedures presented.

Specific Course Objectives:

 

The following objectives are phrased in terms of the ASPH competencies for biostatistics.  Applied to BIOST 2041, they should be understood to refer to 1- and 2-sample procedures pertaining to normal, binomial, and Poisson populations.

 

At the conclusion of this course, a student should be able to

 

1.     Describe basic concepts of probability, random variation, and commonly used statistical probability distributions.

2.     Describe preferred methodological alternatives to commonly used statistical procedures when assumptions are not met.

3.     Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions.

4.     Apply descriptive techniques commonly used to summarize public health data.


Specific Course Objectives (continued):

 

5.     Apply common statistical methods for inference.

6.     Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.

7.     Interpret results of statistical analyses found in public health studies.

 

Course Policies:

 

  1. All work submitted on homework and exams must be your own.  For homework, we encourage you to work together to solve the problems.  When you write up the assignment, however, do any necessary computer work and write the answers yourself.  This policy exists for 2 reasons.  First, obviously, we want your grade to represent your own work.  Second, it is important to know how to write up the major features of an analysis and doing so on your own for homework is a good way to get more comfortable with this process.  Violation of this policy will make you subject to disciplinary action (including dismissal) by the GSPH.

 

  1. Please set pagers & cell phones to a silent mode during class.

 

  1. If you are late to class, please sit in the back of the lecture hall.

 

  1. Homework will be due in class on the announced due date.  Late homework will be accepted until we have discussed the assignment in class or I have posted a key on the web.  After that point in time, the homework will not be accepted!

 

5.      If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union (412.648.7890 or TTY 412.383.7355), as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course. A comprehensive description of the services of that office can be obtained at www.drs.pitt.edu.


  1. All students are expected to adhere to the school’s standards of academic honesty. Any work submitted by a student for evaluation must represent his/her own intellectual contribution and efforts. The GSPH policy on academic integrity, which is based on the University policy, is available online at http://www.publichealth.pitt.edu/interior.php?pageID=126.  The policy includes obligations for faculty and students, procedures for adjudicating violations, and other critical information. Please take the time to read this policy

 

Students committing acts of academic dishonesty, including plagiarism, unauthorized collaboration on assignments, cheating on exams, misrepresentation of data, and facilitating dishonesty by others, will receive sanctions appropriate to the violation(s) committed.  Sanctions include, but are not limited to, reduction of a grade for an assignment or a course, failure of a course, and dismissal from GSPH.

 

All student violations of academic integrity must be documented by the appropriate faculty member; this documentation will be kept in a confidential student file maintained by the GSPH Office of Student Affairs.  If a sanction for a violation is agreed upon by the student and instructor, the record of this agreement will be expunged from the student file upon the student’s graduation. If the case is referred to the GSPH Academic Integrity Hearing Board, a record will remain in the student’s permanent file.

 

Statistical Software:

 

The homework assignments will require a statistical package for computations.  The “official” package supported in this course is SAS, which is available on the machines at the computer labs on campus.  In addition, Pitt students, staff, and faculty can obtain a 1-year copy of SAS from the Software Licensing Service in Bellefield Hall.  All statistical programs presented in class will be in SAS.

 


Statistical Software (continued):

 

It is not strictly necessary to use SAS for the homework assignments.  Stata, S-Plus, R, NCSS, SPSS, Systat, and other packages will also perform the required procedures.  Be aware, however, that the course instructor is most familiar with SAS and Stata and can be relied on to answer questions about those packages only.  So, for instance, if you use SPSS, you will have to find another source of information about that package’s syntax, etc.

 

Excel and Instat are not recommended because they do not include all the procedures that will be required in this course, BIOS 2042T, and many research settings.

 

Course Requirements and Grading:

 

There will be 3 exams and approximately 8 homework assignments.   The contribution of each of these assessments toward the final grade will be as follows:

 

1/4       Homework

1/4       Exam 1

1/4       Exam 2

1/4       Exam 3

 

One homework assignment will be dropped from your homework grade.  In other words, if there are 8 assignments, the best 7 will contribute toward your homework grade.  This will give you an opportunity not to turn in an assignment if you or a family member is ill or you have a schedule conflict, such as a professional meeting, that keeps you from attending class on the day an assignment is due.

 

We strongly prefer to receive the homework on paper.  However, if you are unable to attend class when an assignment is due, you may send the assignment via e-mail.

 

Exams will test material presented in class only.  The text presents far more material than could ever be covered in a one-semester class.  Although you should read the sections of the text that pertain to a given unit/class, you are not responsible for material that was not discussed in lecture.  There is some material presented in class that is not in the text.  You are responsible for this material on homework assignments and exams.  In other words, homework assignments and exams will be based on class material only.

 

All “for credit” grades will be letter grades only (A,B,C,D,F).

 


Suggestions for Studying Course Material:

 

  1. Obviously, you should review the lecture notes and attend class.  Although the notes will be posted on the course website prior to class, there are often blank areas that I fill in during class.  In addition, I will often amplify explanations or present new material using blank overheads at the projector.  Therefore, do not assume that having the posted notes tells you everything covered in class.

 

  1. Homework assignments will sometimes consist of 2 parts, one part that I ask you to turn in and another part you do not turn in.  Resist the temptation to skip the problems that won’t be turned in.  Often, they are the only practice I will assign for certain concepts or procedures, for which you will be responsible on an exam.

 

  1. As the course proceeds, I will list appropriate supplemental problems on the course website and I encourage you to look through these problems.  They offer additional practice if you’re feeling uncertain of your understanding of a given topic and the problem answer details can help you see how to solve future problems of the same type.

 

  1. Ask questions!!!!  Many students feel shy about asking questions during or after class. It is easy to feel that a question is “stupid” and not worth asking.  There is no such thing as a stupid question!  I guarantee that if you have a question, someone else in the class is wondering the same thing.  You will help others in the class if you speak up. 


Course Schedule:

 

The dates in the following schedule are targets only.  The course may actually proceed faster or slower depending on the needs of the class. 

 



Approximate

Dates


Topic(s) and Readings.

 (Chapter and section (§) numbers refer to Rosner’s text.)


 

30 August


 

Unit 1.  Course Introduction. (Chapter 1)

 


 

1  – 8 September


 

Unit 2.  Descriptive Statistics

 

     a)  Measures of central tendency and variability (§ 2.1 – 2.4)

     b)  Presentations of distributional shape (§ 2.8)

     c)  Exploration of relationships (§ 2.8)

     d)  Exploring Data Quality

 


 

13 – 15 September


 

Unit 3.  Introduction to Probability

 

     a)  Independent outcomes

          and conditional probability (§ 3.1 – 3.4, 3.6 (to Equation 3.5))

     b) Mutually exclusive outcomes (§ 3.5)

     c) Complimentary outcomes

     d) Applications, including screening (§ 3.7)

 


 

20 – 22 September


 

Unit 4.  Populations, sampling distributions,

     and the normal distribution  (§ 5.1 – 5.5 (to bottom of p. 122),

     6.1 – 6.2)

 


 

27 September

 


 

Exam 1


Course Schedule (continued):

 

 



Approximate

Dates


Topic(s) and Readings.

 (Chapter and section (§) numbers refer to Rosner’s text.)


 

29 September

  - 18 October


 

Unit 5.  One-sample inference for normal populations.

 

     a)  Inference about the mean of a normal population

          (§ 6.5 (to p. 168), 7.1 – 7.4, 7.7)

     b)  Inference about the variance of a normal population

          (§ 6.7, 7.9)

     c)  Assessing assumptions

     d)  Study planning and sample size calculations (§ 7.5 – 7.6)

 


 

20 October 

– 1 November


 

Unit 6.  Two-sample inference for normal populations.

 

     a)  Inference about the means of 2 populations,

          paired samples (§ 8.1 – 8.3)

     b)  Inference about the means of 2 populations,

          independent samples, equal variances (§ 8.4 – 8.5)

     c)  Inference about the variances of 2 populations (§ 8.6)

     d)  Inference about the means of 2 populations,

          unequal variances (§ 8.7)

     e)  Study planning and sample size calculations (§ 8.10)

 


 

3 November


 

Exam 2

 


 

8 – 29 November


 

Unit 7.  Analysis of binomial data

 

     a)  Binomial random variables (§ 4.8 – 4.9)

     b)  Inference about a binomial proportion (§ 6.8, 7.10)

     c)  Inference about 2 or more binomial proportions

          (§ 10.1 – 10.4)

     d)  2-way contingency tables in general (§ 10.6 to p. 397)

     e)  Study planning and sample size calculations (§ 10.5)

 


Course Schedule (continued):

 

 



Approximate

Dates


Topic(s) and Readings.

 (Chapter and section (§) numbers refer to Rosner’s text.)


 

1 - 8 December


 

Unit 9.  Nonparametric 1- and 2- sample procedures.

 

     a) Sign test (§ 9.1 – 9.2)

     b) Signed-rank test (§ 9.3)

     c) Median test

     d) Rank sum test (§ 9.4)

 


 

13 December


 

Review

 


 

15 December


 

Exam 3

 

 

 

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