**QUANTITATIVE DATA **

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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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