"The rigorous training provided by the ISP stimulates students' ideas for building powerful AI systems for biomedical informatics."
Ye Ye, student
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 / 3831648
A437 Crabtree Hall, 412 / 624 3110
Office Hours: M, W 3 – 4:30
www.biostat.pitt.edu/wilson.htm
Teaching Assistant:
Candace Wu
Department of Biostatistics, A431 Crabtree
Office Hours: T, Th 3  4:30
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
G23 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.
Courserelated mail will be sent to your “pitt.edu” address only. If you wish your course email 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 researchoriented approach than that provided in the BIOST 2011 (Principles of Statistical Reasoning). The course covers basic probability and 1 and 2sample procedures (point and interval estimation and hypothesis testing) for the normal, binomial, and Poisson distributions. Basic 1 and 2sample 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 2sample 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:
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.
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 1year 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, SPlus, 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 email.
Exams will test material presented in class only. The text presents far more material than could ever be covered in a onesemester 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:
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. Onesample 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. Twosample 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) 2way 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) Signedrank test (§ 9.3) c) Median test d) Rank sum test (§ 9.4)

13 December 
Review

15 December 
Exam 3

Copyright 2009–2018  Send feedback about this site