skills.rest
dengineproblem
dengineproblemCOMMUNITY
Explore all skills by this author →
GitHub

distribution-analyzer

Fit continuous and discrete distributions to datasets using scipy.stats and numpy.

Updated Aug 27, 2026
Search Tags:#python#data analysis#hypothesis testing#statistics#scipy#distribution fitting
One-click install
npx skills add https://github.com/dengineproblem/agents-monorepo --skill distribution-analyzer-dengineproblem
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: distribution-analyzer
Source: https://github.com/dengineproblem/agents-monorepo/tree/main/.claude/skills/distribution-analyzer
Command: npx skills add https://github.com/dengineproblem/agents-monorepo --skill distribution-analyzer-dengineproblem

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

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

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, or needs to verify if the data meets the assumptions of a particular statistical test.

Quick Start

Analyze the provided data using the distribution-analyzer skill to determine the best fitting distribution and check for normality.

Frequently Asked Questions about distribution-analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I test if my data follows a normal distribution using Python?▼

Run multiple normality tests like Shapiro-Wilk, D'Agostino-Pearson, Anderson-Darling, and K-S to statistically validate if your dataset follows a normal distribution.

What is the best way to fit statistical distributions to my dataset in Python?▼

Fit various continuous and discrete distributions to your data and rank them by goodness-of-fit metrics like AIC and BIC to identify the most appropriate statistical model for your dataset.

Can I detect outliers in a dataset using scipy and numpy?▼

Yes, you can detect outliers using methods like Interquartile Range (IQR), Z-score, and Median Absolute Deviation (MAD) to identify anomalous values within your numpy arrays.

How do I calculate bootstrap confidence intervals for a median in Python?▼

Calculate bootstrap confidence intervals for statistics like mean and median to estimate the reliability of your sample estimates without assuming a specific underlying data distribution.

What diagnostic plots help with exploratory data analysis for distribution fitting?▼

Generate diagnostic plots including histograms, Q-Q plots, box plots, and empirical cumulative distribution functions (ECDF) using matplotlib to visually assess how well the fitted distributions match your data.

Do I need to install scipy and matplotlib to perform hypothesis testing on my data?▼

Yes, you need scipy.stats and numpy for the statistical computations and hypothesis testing, plus matplotlib to generate the visual diagnostic plots required for comprehensive model selection.

Related Skills

Explore More Data & Analytics Skills→
Nir-BhayNir-Bhay
19

distribution-analyzer

Analyze and model statistical distributions in Python datasets using NumPy, SciPy, and Matplotlib.

Community
Advanced
baratadiegobaratadiego
2

model-validation-and-uncertainty

Compute performance metrics and diagnostic plots for ecological predictive models.

Community
Advanced
jkitchinjkitchin
34

python-plotting

Select and apply Matplotlib, Seaborn, or Plotly for Python visualizations.

Community
Intermediate
diegoscarabellidiegoscarabelli
9

statistical-analysis

Analyze quantitative data with hypothesis testing and Bayesian modeling.

Community
Advanced