# 140.614.11 DATA ANALYSIS WORKSHOP II

Department:
Term: Summer Inst. term
Credits: 2 credits
Contact: Ayesha Khan
Course Instructor:
Description:

Intended for students with a broad understanding of biostatistical concepts used in public health sciences who seek to develop additional data analysis skills. Emphasizes concepts and illustration of concepts applying a variety of analytic techniques to public health datasets in a computer laboratory using Stata statistical software. In the second workshop (140.614), students will master advanced methods of data analysis including analysis of variance, analysis of covariance, nonparametric methods for comparing groups, multiple linear regression, logistic regression, log-linear regression, and survival analysis. Enrollment limited: students must have a laptop computer with Stata 11.0 installed.

Old Learning Objective:

Upon successfully completing this course, students will be able to: 1) use Stata to visualize relationships between two continuous measures; 2) use Stata to fit simple linear regression models, and interpret relevant estimates from the results; 3) use Stata to fit multiple linear regression models to relate a continous outcome to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit; 4) interpret the relevant estimates from multiple linear regression; 5) use Stata to graph lowess smoothing functions to relate the probability of a dichotomous outcome to a continous predictor; 6) use Stata to fit multiple logistic regression models to relate a dichotomous outcome to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit; 7) interpret the relevant estimates from multiple linear regression.

New Learning Objective(s):
Upon successfully completing this course, students will be able to:
Use STATA to visualize relationships between two continuous measures
Use STATA to fit simple linear regression models, and interpret relevant estimates from the results
Use STATA to fit multiple linear regression models to relate a continous outcome to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit
Interpret the relevant estimates from multiple linear regression
Use STATA to graph lowess smoothing functions to relate the probability of a dichotomous outcome to a continous predictor
Use STATA to fit multiple logistic regression models to relate a dichotomous outcome to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit
Set up cohort study data into STATA survival analysis format
Use STATA to graph Kaplan-Meier curves and perform log-rank tests
use STATA to fit Cox regression models to relate time-to-event data to multiple predictors in one model and to help assess confounding, interaction, and goodness-of-fit
interpret the confounding estimates from Cox regression

Methods of Assessment: Student evaluation based on laboratory exercises, an exam, and completion of an independent data analysis project.
Location: East Baltimore
Class Times:
• Mon 06/17/2013 - Fri 06/21/2013
• Monday 1:30 - 5:00
• Tuesday 1:30 - 5:00
• Wednesday 1:30 - 5:00
• Thursday 1:30 - 5:00
• Friday 1:30 - 5:00
Enrollment Minimum: 10
Enrollment Restriction: 0
Instructor Consent: No consent required
Prerequisite:

140.613

Auditors Allowed: No