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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)