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      • Mathematical Modeling
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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 I (with Intermediate 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

    • Characterizing variables as either continuous or categorical

    • Identifying the appropriate function to use to generate descriptive statistics in R

    • Interpreting the results from descriptive statistics

    • Searching for open access articles

    • Documenting observations about public health in the Liberia context

    Class 2

    • Generating bar plots and balloon plots in R

    • Implementing the steps to formally test independence between two variables

    • Calculating the chi-squared test statistic

    • Implementing the chi-squared test in R

    • Recognizing when the Fisher’s exact test is more appropriate

    Classes 3-4

    • Performing the t-test and ANOVA in R

    • Understanding one-tailed versus two-tailed tests

    • Investigating the assumptions of the t-test

    • Visualizing associations between continuous and categorical variables

    • Performing hypothesis testing to arrive at a statistical conclusion

    Class 5

    • Recognizing outliers and their influence on distributions

    • Characterizing distributions of continuous and discrete variables

    • Investigating the assumptions of the t-test

    • Performing the ANOVA and Tukey’s HSD in R

    • Concluding about significant differences in distributions among three or more groups

    Class 6 (Full Example - Correlation Coefficient)

    • Investigating associations between two continuous variables

    • Recognizing nonparametric tests as alternatives when assumptions are not met

    Class 7 (Full Example - T-Test)

    • Replacing variables with data recognized as an updated class

    • Investigating associations between two continuous variables

    • Recognizing nonparametric tests as alternatives when assumptions are not met

    • Using realistic/unclean data to conduct a bi-variable analysis with appropriate checking of assumptions

    Class 8 (Full Example - ANOVA)

    • Using realistic/unclean data to conduct a bi-variable analysis with appropriate checking of assumptions

    • Investigating associations between continuous variables and categorical variables

    • Recognizing nonparametric tests as alternatives when assumptions are not met

    Class 9 (Full Example - Chi-Squared Test)

    • Using realistic/unclean data to conduct a bi-variable analysis with appropriate checking of assumptions

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