Course Description

Following the background provided in the “Basic Data Analysis Using R” course, this course is a follow-up designed to equip researchers and practitioners with statistical tools for drawing inference from sample data from either an observational or an experimental study. Statistical inference involves estimating unknown population quantities and testing hypotheses about them. A statistical test provides a mechanism for making quantitative decisions about a process or processes. The intent is to determine whether there is enough evidence to “reject” a conjecture or hypothesis about the process. This course will give you a firm grasp of how you can check critical assumptions, perform univariate tests, and establish whether there is a significant difference in the mean of two or more groups. As in the introductory course, the computing environment remains R and RStudio.

Learning Outcomes

At the end of the training, participants will be able to:

• check the normality assumption for a data series
• detect the outliers in a data series
• perform univariate analyses (one-sample t-test, binomial test, chi-square test for goodness-of-fit)
• run the chi-square test for association
• run the independent sample t-test
• run the paired-sample t-test
• execute the one-way analysis of variance
• perform the two-way and three-way analysis of variance

Mode of Delivery

Interactive Virtual (Using Google Meet)