distribution-analyzer

What problem does it solve?

This Skill helps users understand the underlying statistical distribution of their data, enabling better modeling, hypothesis testing, and decision-making.

Core Features & Use Cases

• Descriptive Statistics: Provides key metrics like mean, median, standard deviation, skewness, and kurtosis.
• Distribution Fitting: Fits various continuous and discrete distributions (Normal, Log-Normal, Poisson, etc.) to the data and ranks them by goodness-of-fit (AIC, BIC).
• Normality Testing: Performs multiple statistical tests (Shapiro-Wilk, D'Agostino-Pearson, Anderson-Darling, K-S) to assess if data follows a normal distribution.
• Outlier Detection: Identifies outliers using methods like IQR, Z-score, and Median Absolute Deviation (MAD).
• Visualization: Generates diagnostic plots including histograms, Q-Q plots, box plots, and ECDF.
• Bootstrap Confidence Intervals: Calculates CIs for statistics like mean and median.
• Use Case: A data scientist has a dataset and needs to determine the most appropriate statistical model for it, identify any unusual data points, and visualize the data's characteristics.

Quick Start

Analyze the provided dataset to determine the best fitting distribution and visualize its characteristics.