Data are often stored in tables or databases, which provide information about size and variable types but reveal little about underlying patterns. This workshop introduces graphical and numerical techniques to uncover data structure and summarize distributions effectively.
We will explore key descriptive statistics—such as measures of central tendency (mean, median), variability (standard deviation, interquartile range) and shape (skewness, kurtosis)—alongside visual tools like histograms and boxplots. A systematic approach for examining relationships between variables will also be discussed.
Understanding these connections is essential for designing analyses that yield meaningful insights. Early exploration of correlations plays a critical role before relating potential predictors to response variables.
By the end of this workshop, participants will be able to:
- Interpret and summarize data distributions using key descriptive statistics (mean, median, standard deviation, interquartile range, skewness, kurtosis).
- Visualize data effectively by creating and analyzing histograms and boxplots to uncover patterns and variability.
- Explore relationships between variables systematically through early correlation analysis to inform meaningful analytical design.
This event is hosted online.