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QUANTITATIVE DATA
FOR DECISION-MAKING
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QUANTITATIVE DATA
FOR DECISION-MAKING
- …
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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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