Data Analysis Workshop II
June 21-25, 2021
1:30 p.m. – 5:00 p.m.
Course Number: 140.614.11
This summer this course will be taught online via Zoom, on the dates and times listed above. Registered students will attend their classes virtually via Zoom, in real time with faculty and other students.
"Dr. McGready is one of the most outstanding professors I have had in my entire educational career, spanning from elementary school to medical and graduate school. He is engaging, interactive, passionate and devoted to teaching. He presents the material in enlightening ways that stimulate further learning and understanding."—Student, 2019
"Dr. McGready is a great instructor, very clear and knowledgeable, and he is always willing to help. I have really enjoyed the class and learned a lot from it."—Student, 2018
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 simple linear regression and correlation, multiple linear regression, and simple and multiple logistic regression. Inclusion of linear splines and interaction terms for both linear and logistic regression modeling will also be covered. Enrollment limited.
Student Evaluation: Student evaluation based on laboratory exercises, an exam, and completion of an independent data analysis project.
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;
interpret the relevant estimates from multiple linear regression.
Prerequisite: 140.611 and 140.612 or equivalent
Grading Options: Letter Grade or Pass/Fail
Course Materials: Students must have a laptop computer with Intercooled Stata 16 or Intercooled 15 installed. Student discounts are available for Intercooled Stata.