데이터 분석 튜토리얼

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.

Learn how to identify relationships between variables using correlation. Discover the different types of correlation coefficients and their applications.

Learn how the probability mass function defines discrete probability distributions. Explore its properties, examples, and differences from probability density functions.

Calculate the average of numeric, logical, and weighted data using R’s built-in mean functions. Understand how to handle missing values and apply the function to vectors and data frames.

Learn what RMSE means in regression analysis, how to calculate it, and when to use it to assess model performance. See Python and R examples with practical interpretations.

From basic concepts to advanced implementation, learn to build effective Sankey diagrams using popular tools. Discover the key components, best practices, and design principles that make flow visualizations compelling and insightful.

Use this method to make better predictions from real-world data. Learn how to minimize errors and find the most reliable trend line.