statistics
Statistical analysis methods, hypothesis testing, and probability for data analytics
$ インストール
git clone https://github.com/pluginagentmarketplace/custom-plugin-data-analyst /tmp/custom-plugin-data-analyst && cp -r /tmp/custom-plugin-data-analyst/skills/statistics ~/.claude/skills/custom-plugin-data-analyst// tip: Run this command in your terminal to install the skill
name: statistics description: Statistical analysis methods, hypothesis testing, and probability for data analytics version: "2.0.0" sasmp_version: "2.0.0" bonded_agent: 03-statistical-analysis-expert bond_type: PRIMARY_BOND
Skill Configuration
config: atomic: true retry_enabled: true max_retries: 3 backoff_strategy: exponential numerical_precision: high
Parameter Validation
parameters: skill_level: type: string required: true enum: [beginner, intermediate, advanced] default: beginner focus_area: type: string required: false enum: [descriptive, inferential, probability, regression, experiments, all] default: all tool_preference: type: string required: false enum: [python, r, excel, all] default: python
Observability
observability: logging_level: info metrics: [calculation_accuracy, test_validity, model_fit]
Statistics Skill
Overview
Master statistical concepts and methods essential for data analysis, from descriptive statistics to advanced inferential techniques.
Core Topics
Descriptive Statistics
- Measures of central tendency (mean, median, mode)
- Measures of dispersion (variance, standard deviation, IQR)
- Data distributions and skewness
- Percentiles and quartiles
Inferential Statistics
- Sampling methods and sample size determination
- Confidence intervals
- Hypothesis testing (t-tests, chi-square, ANOVA)
- P-values and statistical significance
Probability
- Basic probability rules
- Probability distributions (normal, binomial, Poisson)
- Bayes' theorem
- Expected value and variance
Regression Analysis
- Linear regression
- Multiple regression
- Logistic regression
- Model validation and diagnostics
Learning Objectives
- Apply descriptive statistics to summarize data
- Conduct hypothesis tests for business decisions
- Build and interpret regression models
- Communicate statistical findings effectively
Error Handling
| Error Type | Cause | Recovery |
|---|---|---|
| Sample too small | Insufficient data | Increase sample or use bootstrap |
| Assumption violated | Data doesn't fit test | Use non-parametric alternative |
| Multicollinearity | Correlated predictors | Remove or combine variables |
| Outliers | Extreme values | Investigate or use robust methods |
| P-hacking | Multiple testing | Apply Bonferroni correction |
Related Skills
- programming (for implementing statistical models)
- visualization (for presenting statistical insights)
- advanced (for machine learning)
Repository
