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    • R Coding and Biostatistics
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    • Team
    • Courses 
      • R Coding and Biostatistics
      • Mathematical Modeling
      • Schedule of Courses
    • Contact
    • Resource Library
Student Portal
info@q4dlab.org

QUANTITATIVE DATA 

FOR DECISION-MAKING

  • Home
  • Team
  • Courses 
    • R Coding and Biostatistics
    • Mathematical Modeling
    • Schedule of Courses
  • Contact
  • Resource Library
  • …  
    • Home
    • Team
    • Courses 
      • R Coding and Biostatistics
      • Mathematical Modeling
      • Schedule of Courses
    • Contact
    • Resource Library
Student Portal
  • 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

    • Performing a multiple linear regression in R

    • Interpreting the coefficients from a linear regression model

    • Describing the distribution of residuals from a regression model

    Review: Subsetting a dataset in R

    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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