NishMath

This cluster explores the representation and analysis of data using boxplots and histograms, common tools in statistical analysis. It highlights the potential for these visualizations to mislead if not interpreted carefully. Understanding these limitations is critical in accurately interpreting data, especially in statistical research. It also delves into the theoretical underpinnings of statistical tests and the nature of missing data, a constant challenge in data analysis, and explores bivariate analysis techniques, enhancing the ability to analyze relationships between different variables in a dataset. It also addresses the need for robust regression methods, which can handle outliers better than standard methods.

This cluster focuses on probability and statistical analysis, covering diverse topics such as counting techniques in probability, joint, marginal, and conditional probabilities, and the transformation of distributions. Additionally, it includes a discussion of practical applications of probability distributions, with examples from finance, and the analysis of error in variables models, which is very important in the real world.