Data Analysis Tutorials

Learn the mathematical foundations of z-scores, explore practical calculation methods, and discover applications across statistics and data science.

Learn how to calculate and interpret the likelihood of multiple events occurring simultaneously. Discover practical applications in predictive modeling, risk assessment, and machine learning that solve complex data science challenges.

Learn the mathematical foundations, discover multiple types and their applications, and explore how standard error enhances statistical inference and decision-making.

Explore the concept of central tendency in statistics, including specialized measures such as mean, median, and mode. Learn how to select the most appropriate measure based on your data characteristics and distribution.

Find the most common value in your data. Learn to use MODE() function in Excel for quick statistical insights. Master basic and advanced use cases with clear examples.

Analyze the strength and direction of relationships between variables using built-in Excel tools.

Break down misleading trends to uncover what’s really going on in your data. Learn to identify confounders, segment your analysis, and avoid false conclusions caused by Simpson’s Paradox.

Learn how to create a bar graph in Excel with this beginner-friendly guide. Explore chart types, formatting tips, dynamic features, and troubleshooting techniques.

Learn how to create a bar graph in Excel with this beginner-friendly guide. Explore chart types, formatting tips, dynamic features, and troubleshooting techniques.

Dive deep into the world of p-hacking, where manipulating the p-value is tempting. Learn the different traps of p-hacking, and how to detect and prevent them.

Understand what the coefficient of determination means in regression analysis. Learn how it’s calculated, how to interpret its value, and when to use adjusted R-squared and partial R-squared instead.

Learn when to use Poisson regression, how to interpret results through incidence rate ratios, and implement essential techniques in R.