Biostatistics II (with Advanced R) for Public Health Practitioners and Researchers in Liberia
Congratulations on successfully completing the course. See below for a comprehensive list of skills and concepts covered.
Class 1
• Describing differences and similarities between mathematical and statistical modeling approaches |
• Performing a simple linear regression using a built-in dataset in R • Interpreting the coefficient from a simple linear regression model • Describing the distribution of residuals from a regression model |
Class 2
• Recognizing whether linear regression is appropriate first approach for addressing a research question
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Class 3
• Visually and/or analytically evaluating the assumptions of linear regression • Assessing criteria for potential confounding in a relationship between dependent and independent variables of interest • Recognizing how secondary data analysis may limit full exploration of confounders and communicating such limitations |
Class 4
• Visually and/or analytically determining whether observations are exerting influence on the regression coefficients • Describing why particular observations are more influential than others, in the context of a dataset • Assessing linear regression assumptions for model with and without influential points |
Class 5
• Describing why particular observations are more influential than others, in the context of a dataset
• Assessing linear regression assumptions for model with and without influential points
• Determining which variables should be considered for transformation
• Assessing linear regression assumptions for model before and after a transformation
Classes 6-7
• Re-leveling categorical variables to specify a new reference level • Scaling independent variables when they are on extremely different scales • Visualizing count outcome variables • Determining whether Poisson regression is an appropriate first step • Implementing Poisson regression using the glm function • Identifying and assessing Poisson regression assumptions • Developing a table shell to reflect relevant simple and multiple regression results • Identifying appropriate figures/graphs to support results of regression analysis |
Classes 8-10
• Working on a team multiple regression analysis project • Identifying variables to address a public health hypothesis |
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