140.622.01
Statistical Methods in Public Health II
- Location:
- East Baltimore
- Term:
- 2nd term
- Department:
- Biostatistics
- Credits:
- 4 credits
- Academic Year:
- 2017 - 2018
- Instruction Method:
- TBD
- Class Times:
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- Tu Th, 10:30 - 11:50am
- Lab Times:
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Monday, 1:30 - 3:00pm (01)
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Tuesday, 1:30 - 3:00pm (02)
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Wednesday, 1:30 - 3:00pm (03)
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Thursday, 1:30 - 3:00pm (04)
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Friday, 1:30 - 3:00pm (05)
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Monday, 3:30 - 5:00pm (06)
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Tuesday, 3:30 - 5:00pm (07)
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Wednesday, 3:30 - 5:00pm (08)
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Thursday, 3:30 - 5:00pm (09)
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- Auditors Allowed:
- Yes, with instructor consent
- Grading Restriction:
- Letter Grade or Pass/Fail
- Course Instructors:
- Contact:
- Marie Diener-West
- Resources:
- Prerequisite:
- Description:
-
Presents use of likelihood functions, confidence intervals, and hypothesis tests to draw scientific inferences from public health data. Discusses null and alternative hypotheses, Type I and II errors, and power. Develops parametric and non-parametric statistical methods for comparing multiple groups (ANOVA). Also introduces measures of association and simple linear regression. Addresses methods for planning a study, including stratification, balance, sampling strategies, and sample size.
- Learning Objectives:
-
Upon successfully completing this course, students will be able to:
- Distinguish the summary measures of association applicable to retrospective and prospective study designs
- Distinguish between the appropriate regression models for handling different types of public health outcomes
- Recognize the assumptions required in using regression models and performing statistical tests to assess relationships between an outcome and a risk factor
- Perform and interpret a one-way analysis of variance to test for differences in means among three or more populations
- Contrast mean outcomes among pairwise groups using multiple comparisons procedures
- Interpret the correlation coefficient as a measure of the strength of a linear association between a continuous response variable and a continuous predictor variable
- Interpret the coefficients, including interaction coefficients, obtained from either a multiple linear regression or multiple logistic regression analysis
- Calculate the sample size necessary for estimating either a continuous or binary outcome in a single group or difference between two groups
- Calculate the sample size necessary for determining a statistically significant difference in either a continuous or binary outcome for either one group or between two groups
- Use the Stata statistical analysis package to perform regression analyses and sample size estimation
- Methods of Assessment:
Student evaluation based on problem sets and exams.
- Enrollment Restriction:
For MPH, DrPH, "special students" and MHS degree candidates in departments to be determined
- Instructor Consent:
Consent required for some students
- Consent Note:
Consent required for non-PH students
- For consent, contact:
- Special Comments:
One 90-minute lab per week, lab is 140.922. As soon as you register for the course, please also register for one section of 140.922. Course Materials Fee is $40.00.