Download SYLLABUS for BST 621 (Statistical Methods 1)

Biostatistics 621: Statistical Methods I
Instructor:
T. Mark Beasley, Ph.D.
RPHB 309-E
205-975-4957
[email protected]
Office Hours for Class:
Beasley
T-Th 12:30-2:00
H. Gao (TA) Wed. 2:00-4:00
[email protected]
Course Website: http://www.soph.uab.edu/Statgenetics/People/MBeasley/Courses/BST621.htm
Prerequisites: This course is the first course in the basic applied statistical methods sequence for the first year
M.S. students in Biostatistics. It may be taken by other graduate students with a background in calculus and
linear (matrix) algebra.
Introduction: The course will offer intermediate-level instruction in the principles of biostatistics and the
analysis of clinical and epidemiological data.
Objectives:
Students will gain a thorough understanding of basic analysis methods, elementary concepts,
statistical models and applications of probability, commonly used sampling distributions, parametric and
nonparametric one and two sample tests, confidence intervals, applications of analysis of two-way contingency
table data, simple linear regression, and simple analysis of variance. Students are taught to conduct the relevant
analysis using current software such as the Statistical Analysis System (SAS).
Competencies:
Students will gain competency to:
1. Understand common statistical models and applications of probability; commonly used sampling
distributions and density functions.
2. Recognize and implement parametric and nonparametric analysis methods for testing hypotheses.
3. Understand and utilize analysis of variance designs with the use of contrasts and multiple comparison
procedures.
4. Recognize and implement statistical methods associated with categorical data.
5. Interpret and communicate results of statistical analyses.
Format: Faculty will present material in didactic sessions; students will be responsible for exercises and
critical readings, and analysis using computer systems.
Credit hours: 3
Texts: W.W. Daniel: Biostatistics – A Foundation for Analysis in the Health Sciences. Wiley
Disability Student Services: Any student with a disability that may need accommodations in order to
successfully complete all requirements for this course should visit the Office of Disability Support Services,
located in Room 516 of the Hill University Center (205-934-4205). This office is responsible for registering
students and ensuring the University’s compliance with Section 504 of the Rehabilitation Act. Once registered,
this office will then inform faculty members of all courses in which the student is enrolled, of the student’s
status, and the specific nature of any accommodations required. Any student requiring such accommodation
should discuss this with the course master and assure that the appropriate correspondence is sent from the Office
of Disability Support Services.
Honor Code: Both the School of Public Health and the Graduate School expect that students will obey the
Honor Code. The Department of Biostatistics strictly observes these guidelines. If the instructions for graded
work indicate that the work is to be done individually, then that is what is expected. Minor violations will be
dealt with on a case by case basis, with the exact repercussions dependent upon the instructor involved and the
nature of the offense. In extreme cases, violations of the Honor Code will be reported to a Graduate School
committee on academic misconduct which is responsible for the investigation of appropriate disciplinary action
regarding dishonest practices. The Graduate School has an extensive and explicit procedure for hearing charges
of academic misconduct, which is detailed in the UAB Graduate Student Handbook.
Syllabus and Proposed Timeline
Weeks 1 - 2
I. Introduction and background
A. Course parameters
1. What is statistics
2. Dealing with uncertainty
3. Universe/parameters versus sample/estimates
B. Types and function of data variables
II. Simple descriptions of data and graphical methods
A. Data frequencies
1. For categorical variables
2. For continuous variables
B. Numerical descriptions of data
1. Measures of central tendency
2. Measures of dispersion
3. Other measures (skewness and kurtosis)
4. Measures of Relationship
C. Simple graphs
1. histogram,
2. frequency polygons,
3. cumulative distribution polygon
4. box and whisker plot
5. scatter diagram
6. line plot
III. Producing simple statistics using computer programs
A. Overview of computer statistical packages
B. Introduction to SAS/JMP (SPSS)
1. Simple data entry
2. Documenting data files
3. Producing simple descriptive statistics and graphs
Homework #1
Weeks 3-4
IV. Probability Concepts
A. Objective vs. Subjective Probability
B. Calculating Probabilities
C. Marginal vs. Conditional Probabilities (AND, OR, AT LEAST Rules)
D. Bayes’ Theorem
V. Introduction to specific distributions
A. Normal distribution
B. The standard normal distribution
C. Binomial distribution
Weeks 5-6
VI. Introduction to Estimation
A. Review of universe/parameter and sample estimate
B. Normal distribution
1. Estimation of the mean (µ)
2. Sampling distribution
a. The t-distribution
b. Standard error
c. Confidence Intervals
3. Estimation of the standard deviation (σ)
C. Binomial distribution
1. Estimation of p
2. Sampling distribution
a. Standard error
b. Confidence Intervals
Homework #2
Weeks 7-9
VII. Introduction to Hypothesis testing
A. Types of errors
B. The one-sample t-test
1. Performing the test
2. Relationship to estimation
3. Statistical power
4. Sample size calculations
C. The paired t-test
1. Introduction and restructuring the problem
2. Relationship to the one-sample t-test
D. The independent t-test
1. Introduction of test parameter
2. Standard error of test parameter (equal variances)
3. Performing the test
4. Power/sample size
E. Nonparametric Methods
1. The sign test
2. The Wilcoxon signed rank test
3. The Wilcoxon rank-sum test
4. Comparison of parametric vs. nonparametric methods
Homework #3
Week 9
MIDTERM EXAMINATION and Preparation
Weeks 11-13
VIII. Introduction to one-way analysis of variance
A. Introduction to the problem — what do we do if there are more than 3 groups?
B. Introduction to sums of squares, and partitioning of error
C. The F statistic and distribution
D. Multiple comparison procedures
IX. Correlation and Simple linear regression
Model description with assumptions
A. Least square criterion and estimates
B. Inference for model parameters
C. Dichotomous Predictors (ANOVA via coding schemes)
Weeks 14 – 15
X. Contingency Table
A. 2x2 table
B. Chi-Square test
1. Motivation and approach
2. The test statistic
3. The Chi-square distribution
4. Assumptions and alternative approaches
a. Odds Ratio
b. Estimation and standard error
C. The rxc table Chi Square test
Homework #4
FINAL EXAMINATION
Course Work and Evaluation
♦ Homework #1 (10%)
♦ Homework #2 (15%)
♦ Homework #3 (15%)
♦ MIDTERM EXAMINATION (20%)
♦ Homework #4 (15%)
♦ FINAL EXAMINATION (Finals Week 25%)
Grading
A = 90% of total points
B = 80% of total points
C = 65% of total points
F < 65% of total points